diff --git a/assignment1/.gitignore b/assignment1/.gitignore new file mode 100755 index 0000000..11a2c1b --- /dev/null +++ b/assignment1/.gitignore @@ -0,0 +1,7 @@ +*.swp +*.pyc +.env/* +*.ipynb_checkpoints/* + +# gitignore the built release. +assignment3/* diff --git a/assignment1/README.md b/assignment1/README.md new file mode 100755 index 0000000..a453bbe --- /dev/null +++ b/assignment1/README.md @@ -0,0 +1 @@ +Details about this assignment can be found [on the course webpage](http://cs231n.github.io/), under Assignment #1 of Spring 2019. diff --git a/assignment1/collectSubmission.sh b/assignment1/collectSubmission.sh new file mode 100755 index 0000000..0bf68b3 --- /dev/null +++ b/assignment1/collectSubmission.sh @@ -0,0 +1,53 @@ +#!/bin/bash +#NOTE: DO NOT EDIT THIS FILE-- MAY RESULT IN INCOMPLETE SUBMISSIONS + +NOTEBOOKS="knn.ipynb +svm.ipynb +softmax.ipynb +two_layer_net.ipynb +features.ipynb" + +CODE="cs231n/classifiers/k_nearest_neighbor.py +cs231n/classifiers/linear_classifier.py +cs231n/classifiers/linear_svm.py +cs231n/classifiers/softmax.py +cs231n/classifiers/neural_net.py" + +LOCAL_DIR=`pwd` +REMOTE_DIR="cs231n-2019-assignment1" +ASSIGNMENT_NO=1 +ZIP_FILENAME="a1.zip" + +C_R="\e[31m" +C_G="\e[32m" +C_BLD="\e[1m" +C_E="\e[0m" + +FILES="" +for FILE in "${NOTEBOOKS} ${CODE}" +do + if [ ! -f ${F} ]; then + echo -e "${C_R}Required file ${FILE} not found, Exiting.${C_E}" + exit 0 + fi + FILES="${FILES} ${LOCAL_DIR}/${FILE}" +done + +echo -e "${C_BLD}### Zipping file ###${C_E}" +rm -f ${ZIP_FILENAME} +zip -r ${ZIP_FILENAME} . -x "*.git*" "*cs231n/datasets*" "*.ipynb_checkpoints*" "*README.md" "collectSubmission.sh" "*requirements.txt" "*__pycache__*" ".env/*" > assignment_zip.log +echo "" + +echo -e "${C_BLD}### Submitting to myth ###${C_E}" +echo "Type in your Stanford student ID (alphanumeric, *not* the 8-digit ID):" +read -p "Student ID: " SUID +echo "" + +echo -e "${C_BLD}### Copying to ${SUID}@myth.stanford.edu:${REMOTE_DIR} ###${C_E}" +echo -e "${C_G}Note: if myth is under heavy use, this may hang: If this happens, rerun the script.${C_E}" +FILES="${FILES} ${LOCAL_DIR}/${ZIP_FILENAME}" +rsync -avP ${FILES} ${SUID}@myth.stanford.edu:${REMOTE_DIR} +echo "" + +echo -e "${C_BLD}### Running remote submission script from ${SUID}@myth.stanford.edu:${REMOTE_DIR} ###${C_E}" +ssh ${SUID}@myth.stanford.edu "cd ${REMOTE_DIR} && /afs/ir/class/cs231n/grading/submit ${ASSIGNMENT_NO} ${SUID} ${ZIP_FILENAME} && exit" \ No newline at end of file diff --git a/assignment1/cs231n/__init__.py b/assignment1/cs231n/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/assignment1/cs231n/classifiers/__init__.py b/assignment1/cs231n/classifiers/__init__.py new file mode 100755 index 0000000..cef2b58 --- /dev/null +++ b/assignment1/cs231n/classifiers/__init__.py @@ -0,0 +1,2 @@ +from cs231n.classifiers.k_nearest_neighbor import * +from cs231n.classifiers.linear_classifier import * diff --git a/assignment1/cs231n/classifiers/k_nearest_neighbor.py b/assignment1/cs231n/classifiers/k_nearest_neighbor.py new file mode 100755 index 0000000..464317c --- /dev/null +++ b/assignment1/cs231n/classifiers/k_nearest_neighbor.py @@ -0,0 +1,190 @@ +from builtins import range +from builtins import object +import numpy as np +from past.builtins import xrange + + +class KNearestNeighbor(object): + """ a kNN classifier with L2 distance """ + + def __init__(self): + pass + + def train(self, X, y): + """ + Train the classifier. For k-nearest neighbors this is just + memorizing the training data. + + Inputs: + - X: A numpy array of shape (num_train, D) containing the training data + consisting of num_train samples each of dimension D. + - y: A numpy array of shape (N,) containing the training labels, where + y[i] is the label for X[i]. + """ + self.X_train = X + self.y_train = y + + def predict(self, X, k=1, num_loops=0): + """ + Predict labels for test data using this classifier. + + Inputs: + - X: A numpy array of shape (num_test, D) containing test data consisting + of num_test samples each of dimension D. + - k: The number of nearest neighbors that vote for the predicted labels. + - num_loops: Determines which implementation to use to compute distances + between training points and testing points. + + Returns: + - y: A numpy array of shape (num_test,) containing predicted labels for the + test data, where y[i] is the predicted label for the test point X[i]. + """ + if num_loops == 0: + dists = self.compute_distances_no_loops(X) + elif num_loops == 1: + dists = self.compute_distances_one_loop(X) + elif num_loops == 2: + dists = self.compute_distances_two_loops(X) + else: + raise ValueError('Invalid value %d for num_loops' % num_loops) + + return self.predict_labels(dists, k=k) + + def compute_distances_two_loops(self, X): + """ + Compute the distance between each test point in X and each training point + in self.X_train using a nested loop over both the training data and the + test data. + + Inputs: + - X: A numpy array of shape (num_test, D) containing test data. + + Returns: + - dists: A numpy array of shape (num_test, num_train) where dists[i, j] + is the Euclidean distance between the ith test point and the jth training + point. + """ + num_test = X.shape[0] + num_train = self.X_train.shape[0] + dists = np.zeros((num_test, num_train)) + for i in range(num_test): + for j in range(num_train): + ##################################################################### + # TODO: # + # Compute the l2 distance between the ith test point and the jth # + # training point, and store the result in dists[i, j]. You should # + # not use a loop over dimension, nor use np.linalg.norm(). # + ##################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + dists[i][j] = np.sum((X[i] - self.X_train[j])**2) ** 0.5 + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + return dists + + def compute_distances_one_loop(self, X): + """ + Compute the distance between each test point in X and each training point + in self.X_train using a single loop over the test data. + + Input / Output: Same as compute_distances_two_loops + """ + num_test = X.shape[0] + num_train = self.X_train.shape[0] + dists = np.zeros((num_test, num_train)) + for i in range(num_test): + ####################################################################### + # TODO: # + # Compute the l2 distance between the ith test point and all training # + # points, and store the result in dists[i, :]. # + # Do not use np.linalg.norm(). # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i]), axis=1)) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + return dists + + def compute_distances_no_loops(self, X): + """ + Compute the distance between each test point in X and each training point + in self.X_train using no explicit loops. + + Input / Output: Same as compute_distances_two_loops + """ + num_test = X.shape[0] + num_train = self.X_train.shape[0] + dists = np.zeros((num_test, num_train)) + ######################################################################### + # TODO: # + # Compute the l2 distance between all test points and all training # + # points without using any explicit loops, and store the result in # + # dists. # + # # + # You should implement this function using only basic array operations; # + # in particular you should not use functions from scipy, # + # nor use np.linalg.norm(). # + # # + # HINT: Try to formulate the l2 distance using matrix multiplication # + # and two broadcast sums. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + # Euclidian sum = sum(a**2) - 2*a.b + sum(b**2) + + a = np.sum(np.square(X).reshape(X.shape[0], 1, X.shape[1]), axis=2) + + b = np.sum(np.square(self.X_train), axis =1) + + dot_product = 2*(X.dot(self.X_train.T)) + + dists = np.sqrt(a - dot_product + b) + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + return dists + + def predict_labels(self, dists, k=1): + """ + Given a matrix of distances between test points and training points, + predict a label for each test point. + + Inputs: + - dists: A numpy array of shape (num_test, num_train) where dists[i, j] + gives the distance betwen the ith test point and the jth training point. + + Returns: + - y: A numpy array of shape (num_test,) containing predicted labels for the + test data, where y[i] is the predicted label for the test point X[i]. + """ + num_test = dists.shape[0] + y_pred = np.zeros(num_test) + for i in range(num_test): + # A list of length k storing the labels of the k nearest neighbors to + # the ith test point. + closest_y = [] + ######################################################################### + # TODO: # + # Use the distance matrix to find the k nearest neighbors of the ith # + # testing point, and use self.y_train to find the labels of these # + # neighbors. Store these labels in closest_y. # + # Hint: Look up the function numpy.argsort. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + k_nearest_idx = np.argsort(dists[i])[:k] + closest_y = self.y_train[k_nearest_idx] + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ######################################################################### + # TODO: # + # Now that you have found the labels of the k nearest neighbors, you # + # need to find the most common label in the list closest_y of labels. # + # Store this label in y_pred[i]. Break ties by choosing the smaller # + # label. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + y_pred[i] = np.argmax(np.bincount(closest_y)) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return y_pred diff --git a/assignment1/cs231n/classifiers/linear_classifier.py b/assignment1/cs231n/classifiers/linear_classifier.py new file mode 100755 index 0000000..d6e3f26 --- /dev/null +++ b/assignment1/cs231n/classifiers/linear_classifier.py @@ -0,0 +1,142 @@ +from __future__ import print_function + +from builtins import range +from builtins import object +import numpy as np +from cs231n.classifiers.linear_svm import * +from cs231n.classifiers.softmax import * +from past.builtins import xrange + + +class LinearClassifier(object): + + def __init__(self): + self.W = None + + def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100, + batch_size=200, verbose=False): + """ + Train this linear classifier using stochastic gradient descent. + + Inputs: + - X: A numpy array of shape (N, D) containing training data; there are N + training samples each of dimension D. + - y: A numpy array of shape (N,) containing training labels; y[i] = c + means that X[i] has label 0 <= c < C for C classes. + - learning_rate: (float) learning rate for optimization. + - reg: (float) regularization strength. + - num_iters: (integer) number of steps to take when optimizing + - batch_size: (integer) number of training examples to use at each step. + - verbose: (boolean) If true, print progress during optimization. + + Outputs: + A list containing the value of the loss function at each training iteration. + """ + num_train, dim = X.shape + num_classes = np.max(y) + 1 # assume y takes values 0...K-1 where K is number of classes + if self.W is None: + # lazily initialize W + self.W = 0.001 * np.random.randn(dim, num_classes) + + # Run stochastic gradient descent to optimize W + loss_history = [] + j = 0 + for it in range(num_iters): + X_batch = None + y_batch = None + + ######################################################################### + # TODO: # + # Sample batch_size elements from the training data and their # + # corresponding labels to use in this round of gradient descent. # + # Store the data in X_batch and their corresponding labels in # + # y_batch; after sampling X_batch should have shape (batch_size, dim) # + # and y_batch should have shape (batch_size,) # + # # + # Hint: Use np.random.choice to generate indices. Sampling with # + # replacement is faster than sampling without replacement. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + batch_idx = np.random.choice(X.shape[0], batch_size) + X_batch = X[batch_idx] + y_batch = y[batch_idx] + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + # evaluate loss and gradient + loss, grad = self.loss(X_batch, y_batch, reg) + loss_history.append(loss) + + # perform parameter update + ######################################################################### + # TODO: # + # Update the weights using the gradient and the learning rate. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + self.W -= learning_rate * grad + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + if verbose and it % 100 == 0: + print('iteration %d / %d: loss %f' % (it, num_iters, loss)) + + return loss_history + + def predict(self, X): + """ + Use the trained weights of this linear classifier to predict labels for + data points. + + Inputs: + - X: A numpy array of shape (N, D) containing training data; there are N + training samples each of dimension D. + + Returns: + - y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional + array of length N, and each element is an integer giving the predicted + class. + """ + y_pred = np.zeros(X.shape[0]) + ########################################################################### + # TODO: # + # Implement this method. Store the predicted labels in y_pred. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + y_pred = X.dot(self.W) + y_pred = np.argmax(y_pred, axis=1) + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + return y_pred + + def loss(self, X_batch, y_batch, reg): + """ + Compute the loss function and its derivative. + Subclasses will override this. + + Inputs: + - X_batch: A numpy array of shape (N, D) containing a minibatch of N + data points; each point has dimension D. + - y_batch: A numpy array of shape (N,) containing labels for the minibatch. + - reg: (float) regularization strength. + + Returns: A tuple containing: + - loss as a single float + - gradient with respect to self.W; an array of the same shape as W + """ + pass + + +class LinearSVM(LinearClassifier): + """ A subclass that uses the Multiclass SVM loss function """ + + def loss(self, X_batch, y_batch, reg): + return svm_loss_vectorized(self.W, X_batch, y_batch, reg) + + +class Softmax(LinearClassifier): + """ A subclass that uses the Softmax + Cross-entropy loss function """ + + def loss(self, X_batch, y_batch, reg): + return softmax_loss_vectorized(self.W, X_batch, y_batch, reg) diff --git a/assignment1/cs231n/classifiers/linear_svm.py b/assignment1/cs231n/classifiers/linear_svm.py new file mode 100755 index 0000000..3b97992 --- /dev/null +++ b/assignment1/cs231n/classifiers/linear_svm.py @@ -0,0 +1,147 @@ +from builtins import range +import numpy as np +from random import shuffle +from past.builtins import xrange + +def svm_loss_naive(W, X, y, reg): + """ + Structured SVM loss function, naive implementation (with loops). + + Inputs have dimension D, there are C classes, and we operate on minibatches + of N examples. + + Inputs: + - W: A numpy array of shape (D, C) containing weights. + - X: A numpy array of shape (N, D) containing a minibatch of data. + - y: A numpy array of shape (N,) containing training labels; y[i] = c means + that X[i] has label c, where 0 <= c < C. + - reg: (float) regularization strength + + Returns a tuple of: + - loss as single float + - gradient with respect to weights W; an array of same shape as W + """ + dW = np.zeros(W.shape) # initialize the gradient as zero + h = 0.00001 + # compute the loss and the gradient + num_classes = W.shape[1] + num_train = X.shape[0] + loss = 0.0 + for i in range(num_train): + scores = X[i].dot(W) + correct_class_score = scores[y[i]] + + + for j in range(num_classes): + + if j == y[i]: + continue + + margin = scores[j] - correct_class_score + 1 # note delta = 1 + + + if margin > 0: + loss += margin + dW[:, j] += X[i] + dW[:, y[i]] -= X[i] + + dW /= num_train + dW += reg*W + + # Right now the loss is a sum over all training examples, but we want it + # to be an average instead so we divide by num_train. + loss /= num_train + + # Add regularization to the loss. + loss += reg * np.sum(W * W) + + ############################################################################# + # TODO: # + # Compute the gradient of the loss function and store it dW. # + # Rather than first computing the loss and then computing the derivative, # + # it may be simpler to compute the derivative at the same time that the # + # loss is being computed. As a result you may need to modify some of the # + # code above to compute the gradient. # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return loss, dW + + + +def svm_loss_vectorized(W, X, y, reg): + """ + Structured SVM loss function, vectorized implementation. + + Inputs and outputs are the same as svm_loss_naive. + """ + num_train = X.shape[0] + loss = 0.0 + dW = np.zeros(W.shape) # initialize the gradient as zero + + ############################################################################# + # TODO: # + # Implement a vectorized version of the structured SVM loss, storing the # + # result in loss. # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + XW = X.dot(W) + # repeating the the index of the correct score and making the shape + # of the array similar to XW + Y = np.repeat(y, XW.shape[1]).reshape(XW.shape[0], XW.shape[1]) + # getting the correct score + s = XW[np.arange(XW.shape[0])[:,None],Y] + # difference between each elements and the correct score + XW -= s + # adding the margin value + XW += 1 + # removing the negative values + XW = XW.clip(min=0) + # changing the loss of the correct value from 1 to zero + XW[np.arange(XW.shape[0]), y] = 0 + + + loss = np.sum(XW) + + loss /= num_train + + # Add regularization to the loss. + loss += reg * np.sum(W * W) + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + ############################################################################# + # TODO: # + # Implement a vectorized version of the gradient for the structured SVM # + # loss, storing the result in dW. # + # # + # Hint: Instead of computing the gradient from scratch, it may be easier # + # to reuse some of the intermediate values that you used to compute the # + # loss. # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + #XW is equivalent to the margin + binary_XW = XW + + binary_XW[XW>0] = 1 + sum_row = np.sum(binary_XW, axis=1).T + binary_XW[np.arange(num_train), y] = -sum_row + dW = np.dot(X.T, binary_XW) + + # Average + dW /= num_train + + # Regularize + dW += reg*W + + + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return loss, dW diff --git a/assignment1/cs231n/classifiers/neural_net.py b/assignment1/cs231n/classifiers/neural_net.py new file mode 100755 index 0000000..b497b7a --- /dev/null +++ b/assignment1/cs231n/classifiers/neural_net.py @@ -0,0 +1,275 @@ +from __future__ import print_function + +from builtins import range +from builtins import object +import numpy as np +import matplotlib.pyplot as plt +from past.builtins import xrange + +class TwoLayerNet(object): + """ + A two-layer fully-connected neural network. The net has an input dimension of + N, a hidden layer dimension of H, and performs classification over C classes. + We train the network with a softmax loss function and L2 regularization on the + weight matrices. The network uses a ReLU nonlinearity after the first fully + connected layer. + + In other words, the network has the following architecture: + + input - fully connected layer - ReLU - fully connected layer - softmax + + The outputs of the second fully-connected layer are the scores for each class. + """ + + def __init__(self, input_size, hidden_size, output_size, std=1e-4): + """ + Initialize the model. Weights are initialized to small random values and + biases are initialized to zero. Weights and biases are stored in the + variable self.params, which is a dictionary with the following keys: + + W1: First layer weights; has shape (D, H) + b1: First layer biases; has shape (H,) + W2: Second layer weights; has shape (H, C) + b2: Second layer biases; has shape (C,) + + Inputs: + - input_size: The dimension D of the input data. + - hidden_size: The number of neurons H in the hidden layer. + - output_size: The number of classes C. + """ + self.params = {} + self.params['W1'] = std * np.random.randn(input_size, hidden_size) + self.params['b1'] = np.zeros(hidden_size) + self.params['W2'] = std * np.random.randn(hidden_size, output_size) + self.params['b2'] = np.zeros(output_size) + + def loss(self, X, y=None, reg=0.0): + """ + Compute the loss and gradients for a two layer fully connected neural + network. + + Inputs: + - X: Input data of shape (N, D). Each X[i] is a training sample. + - y: Vector of training labels. y[i] is the label for X[i], and each y[i] is + an integer in the range 0 <= y[i] < C. This parameter is optional; if it + is not passed then we only return scores, and if it is passed then we + instead return the loss and gradients. + - reg: Regularization strength. + + Returns: + If y is None, return a matrix scores of shape (N, C) where scores[i, c] is + the score for class c on input X[i]. + + If y is not None, instead return a tuple of: + - loss: Loss (data loss and regularization loss) for this batch of training + samples. + - grads: Dictionary mapping parameter names to gradients of those parameters + with respect to the loss function; has the same keys as self.params. + """ + # Unpack variables from the params dictionary + W1, b1 = self.params['W1'], self.params['b1'] + W2, b2 = self.params['W2'], self.params['b2'] + N, D = X.shape + + # Compute the forward pass + scores = None + ############################################################################# + # TODO: Perform the forward pass, computing the class scores for the input. # + # Store the result in the scores variable, which should be an array of # + # shape (N, C). # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + func1 = X.dot(W1) + b1 + #RELU + Hidden = func1 + Hidden[func1 < 0] = 0 + + scores = Hidden.dot(W2) + b2 + + + + # N = 5 + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + # If the targets are not given then jump out, we're done + if y is None: + return scores + + # Compute the loss + #Softmax + scores -= np.max(scores, axis=1).reshape(scores.shape[0], 1) + scores = np.exp(scores) + scores /= np.sum(scores, axis=1).reshape(scores.shape[0], 1) + softmax = scores + loss = None + ############################################################################# + # TODO: Finish the forward pass, and compute the loss. This should include # + # both the data loss and L2 regularization for W1 and W2. Store the result # + # in the variable loss, which should be a scalar. Use the Softmax # + # classifier loss. # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + loss = np.sum(-np.log(scores[np.arange(N), y])) + + loss /= N + loss += reg * (np.sum(W1**2) + np.sum(W2**2)) + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + # Backward pass: compute gradients + grads = {} + ############################################################################# + # TODO: Compute the backward pass, computing the derivatives of the weights # + # and biases. Store the results in the grads dictionary. For example, # + # grads['W1'] should store the gradient on W1, and be a matrix of same size # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + #Correct way to do derivative of softmax +# correct_scores = softmax[np.arange(N), y] +# dsoftmax = np.multiply(softmax, correct_scores.reshape(N, 1)) +# dsoftmax[range(N), y] = np.multiply(correct_scores, (1-correct_scores)) + dsoftmax = softmax + dsoftmax[np.arange(N) ,y] -= 1 + dsoftmax /= N + + dW2 = (Hidden.T).dot(dsoftmax) + db2 = np.sum(dsoftmax, axis=0) + + dW1 = dsoftmax.dot(W2.T) + dfunc1 = dW1 * (func1>0) + dW1 = X.T.dot(dfunc1) + db1 = dfunc1.sum(axis=0) + + + dW1 += reg * 2 * W1 + dW2 += reg * 2 * W2 + + grads = {'W1':dW1, 'b1':db1, 'W2':dW2, 'b2':db2} + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return loss, grads + + def train(self, X, y, X_val, y_val, + learning_rate=1e-3, learning_rate_decay=0.95, + reg=5e-6, num_iters=100, + batch_size=200, verbose=False): + """ + Train this neural network using stochastic gradient descent. + + Inputs: + - X: A numpy array of shape (N, D) giving training data. + - y: A numpy array f shape (N,) giving training labels; y[i] = c means that + X[i] has label c, where 0 <= c < C. + - X_val: A numpy array of shape (N_val, D) giving validation data. + - y_val: A numpy array of shape (N_val,) giving validation labels. + - learning_rate: Scalar giving learning rate for optimization. + - learning_rate_decay: Scalar giving factor used to decay the learning rate + after each epoch. + - reg: Scalar giving regularization strength. + - num_iters: Number of steps to take when optimizing. + - batch_size: Number of training examples to use per step. + - verbose: boolean; if true print progress during optimization. + """ + num_train = X.shape[0] + iterations_per_epoch = max(num_train / batch_size, 1) + + # Use SGD to optimize the parameters in self.model + loss_history = [] + train_acc_history = [] + val_acc_history = [] + + for it in range(num_iters): + X_batch = None + y_batch = None + + ######################################################################### + # TODO: Create a random minibatch of training data and labels, storing # + # them in X_batch and y_batch respectively. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + idxs = np.random.choice(num_train, batch_size) + X_batch = X[idxs] + y_batch = y[idxs] + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + # Compute loss and gradients using the current minibatch + loss, grads = self.loss(X_batch, y=y_batch, reg=reg) + loss_history.append(loss) + + ######################################################################### + # TODO: Use the gradients in the grads dictionary to update the # + # parameters of the network (stored in the dictionary self.params) # + # using stochastic gradient descent. You'll need to use the gradients # + # stored in the grads dictionary defined above. # + ######################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + self.params['W1'] -= learning_rate * grads['W1'] + self.params['W2'] -= learning_rate * grads['W2'] + self.params['b1'] -= learning_rate * grads['b1'] + self.params['b2'] -= learning_rate * grads['b2'] + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + if verbose and it % 100 == 0: + print('iteration %d / %d: loss %f' % (it, num_iters, loss)) + + # Every epoch, check train and val accuracy and decay learning rate. + if it % iterations_per_epoch == 0: + # Check accuracy + train_acc = (self.predict(X_batch) == y_batch).mean() + val_acc = (self.predict(X_val) == y_val).mean() + train_acc_history.append(train_acc) + val_acc_history.append(val_acc) + + # Decay learning rate + learning_rate *= learning_rate_decay + + return { + 'loss_history': loss_history, + 'train_acc_history': train_acc_history, + 'val_acc_history': val_acc_history, + } + + def predict(self, X): + """ + Use the trained weights of this two-layer network to predict labels for + data points. For each data point we predict scores for each of the C + classes, and assign each data point to the class with the highest score. + + Inputs: + - X: A numpy array of shape (N, D) giving N D-dimensional data points to + classify. + + Returns: + - y_pred: A numpy array of shape (N,) giving predicted labels for each of + the elements of X. For all i, y_pred[i] = c means that X[i] is predicted + to have class c, where 0 <= c < C. + """ + y_pred = None + + ########################################################################### + # TODO: Implement this function; it should be VERY simple! # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + func1 = X.dot(self.params['W1']) + self.params['b1'] + #RELU + Hidden = func1 + Hidden[Hidden < 0] = 0 + + scores = Hidden.dot(self.params['W2']) + self.params['b2'] + + scores -= np.max(scores, axis=1).reshape(scores.shape[0], 1) + scores = np.exp(scores) + scores /= np.sum(scores, axis=1).reshape(scores.shape[0], 1) + + y_pred = np.argmax(scores, axis=1) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return y_pred diff --git a/assignment1/cs231n/classifiers/softmax.py b/assignment1/cs231n/classifiers/softmax.py new file mode 100755 index 0000000..ba681c1 --- /dev/null +++ b/assignment1/cs231n/classifiers/softmax.py @@ -0,0 +1,99 @@ +from builtins import range +import numpy as np +from random import shuffle +from past.builtins import xrange + +def softmax_loss_naive(W, X, y, reg): + """ + Softmax loss function, naive implementation (with loops) + + Inputs have dimension D, there are C classes, and we operate on minibatches + of N examples. + + Inputs: + - W: A numpy array of shape (D, C) containing weights. + - X: A numpy array of shape (N, D) containing a minibatch of data. + - y: A numpy array of shape (N,) containing training labels; y[i] = c means + that X[i] has label c, where 0 <= c < C. + - reg: (float) regularization strength + + Returns a tuple of: + - loss as single float + - gradient with respect to weights W; an array of same shape as W + """ + # Initialize the loss and gradient to zero. + loss = 0.0 + dW = np.zeros_like(W) + num_train = X.shape[0] #500 + dims = X.shape[1] # 3073 + classes = dW.shape[1] #10 + + + ############################################################################# + # TODO: Compute the softmax loss and its gradient using explicit loops. # + # Store the loss in loss and the gradient in dW. If you are not careful # + # here, it is easy to run into numeric instability. Don't forget the # + # regularization! # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + XW = X.dot(W) + XW -= np.max(XW, axis=1).reshape(XW.shape[0], 1) + # for each training examples unnormalized probability result + for i in range(XW.shape[0]): + exp_scores = np.exp(XW[i])/np.sum(np.exp(XW[i])) + loss += -np.log(exp_scores[y[i]]) + for d in range(dims): + for k in range(classes): + if k == y[i]: + dW[d, k] += X.T[d, i] * (exp_scores[k]-1) + else: + dW[d, k] += X.T[d, i] * exp_scores[k] + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + loss /= XW.shape[0] + loss += reg * np.sum(W**2) + + dW /= XW.shape[0] + dW += reg * W + + return loss, dW + + +def softmax_loss_vectorized(W, X, y, reg): + """ + Softmax loss function, vectorized version. + + Inputs and outputs are the same as softmax_loss_naive. + """ + # Initialize the loss and gradient to zero. + loss = 0.0 + dW = np.zeros_like(W) + + ############################################################################# + # TODO: Compute the softmax loss and its gradient using no explicit loops. # + # Store the loss in loss and the gradient in dW. If you are not careful # + # here, it is easy to run into numeric instability. Don't forget the # + # regularization! # + ############################################################################# + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + XW = X.dot(W) # probability scores + # for numeric stability + XW -= np.max(XW, axis=1).reshape(XW.shape[0], 1) + XW = np.exp(XW) + XW /= np.sum(XW, axis=1).reshape(XW.shape[0], 1) + loss = np.sum(-np.log(XW[np.arange(XW.shape[0]), y])) + loss /= XW.shape[0] + loss += reg * np.sum(W**2) + + XW[np.arange(XW.shape[0]), y] -= 1 + dW = np.dot(X.T, XW) + dW /= XW.shape[0] + dW += reg * W + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + return loss, dW diff --git a/assignment1/cs231n/data_utils.py b/assignment1/cs231n/data_utils.py new file mode 100755 index 0000000..7688518 --- /dev/null +++ b/assignment1/cs231n/data_utils.py @@ -0,0 +1,262 @@ +from __future__ import print_function + +from builtins import range +from six.moves import cPickle as pickle +import numpy as np +import os +from scipy.misc import imread +import platform + +def load_pickle(f): + version = platform.python_version_tuple() + if version[0] == '2': + return pickle.load(f) + elif version[0] == '3': + return pickle.load(f, encoding='latin1') + raise ValueError("invalid python version: {}".format(version)) + +def load_CIFAR_batch(filename): + """ load single batch of cifar """ + with open(filename, 'rb') as f: + datadict = load_pickle(f) + X = datadict['data'] + Y = datadict['labels'] + X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float") + Y = np.array(Y) + return X, Y + +def load_CIFAR10(ROOT): + """ load all of cifar """ + xs = [] + ys = [] + for b in range(1,6): + f = os.path.join(ROOT, 'data_batch_%d' % (b, )) + X, Y = load_CIFAR_batch(f) + xs.append(X) + ys.append(Y) + Xtr = np.concatenate(xs) + Ytr = np.concatenate(ys) + del X, Y + Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) + return Xtr, Ytr, Xte, Yte + + +def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, + subtract_mean=True): + """ + Load the CIFAR-10 dataset from disk and perform preprocessing to prepare + it for classifiers. These are the same steps as we used for the SVM, but + condensed to a single function. + """ + # Load the raw CIFAR-10 data + cifar10_dir = 'cs231n/datasets/cifar-10-batches-py' + X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) + + # Subsample the data + mask = list(range(num_training, num_training + num_validation)) + X_val = X_train[mask] + y_val = y_train[mask] + mask = list(range(num_training)) + X_train = X_train[mask] + y_train = y_train[mask] + mask = list(range(num_test)) + X_test = X_test[mask] + y_test = y_test[mask] + + # Normalize the data: subtract the mean image + if subtract_mean: + mean_image = np.mean(X_train, axis=0) + X_train -= mean_image + X_val -= mean_image + X_test -= mean_image + + # Transpose so that channels come first + X_train = X_train.transpose(0, 3, 1, 2).copy() + X_val = X_val.transpose(0, 3, 1, 2).copy() + X_test = X_test.transpose(0, 3, 1, 2).copy() + + # Package data into a dictionary + return { + 'X_train': X_train, 'y_train': y_train, + 'X_val': X_val, 'y_val': y_val, + 'X_test': X_test, 'y_test': y_test, + } + + +def load_tiny_imagenet(path, dtype=np.float32, subtract_mean=True): + """ + Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and + TinyImageNet-200 have the same directory structure, so this can be used + to load any of them. + + Inputs: + - path: String giving path to the directory to load. + - dtype: numpy datatype used to load the data. + - subtract_mean: Whether to subtract the mean training image. + + Returns: A dictionary with the following entries: + - class_names: A list where class_names[i] is a list of strings giving the + WordNet names for class i in the loaded dataset. + - X_train: (N_tr, 3, 64, 64) array of training images + - y_train: (N_tr,) array of training labels + - X_val: (N_val, 3, 64, 64) array of validation images + - y_val: (N_val,) array of validation labels + - X_test: (N_test, 3, 64, 64) array of testing images. + - y_test: (N_test,) array of test labels; if test labels are not available + (such as in student code) then y_test will be None. + - mean_image: (3, 64, 64) array giving mean training image + """ + # First load wnids + with open(os.path.join(path, 'wnids.txt'), 'r') as f: + wnids = [x.strip() for x in f] + + # Map wnids to integer labels + wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)} + + # Use words.txt to get names for each class + with open(os.path.join(path, 'words.txt'), 'r') as f: + wnid_to_words = dict(line.split('\t') for line in f) + for wnid, words in wnid_to_words.items(): + wnid_to_words[wnid] = [w.strip() for w in words.split(',')] + class_names = [wnid_to_words[wnid] for wnid in wnids] + + # Next load training data. + X_train = [] + y_train = [] + for i, wnid in enumerate(wnids): + if (i + 1) % 20 == 0: + print('loading training data for synset %d / %d' + % (i + 1, len(wnids))) + # To figure out the filenames we need to open the boxes file + boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid) + with open(boxes_file, 'r') as f: + filenames = [x.split('\t')[0] for x in f] + num_images = len(filenames) + + X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype) + y_train_block = wnid_to_label[wnid] * \ + np.ones(num_images, dtype=np.int64) + for j, img_file in enumerate(filenames): + img_file = os.path.join(path, 'train', wnid, 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + ## grayscale file + img.shape = (64, 64, 1) + X_train_block[j] = img.transpose(2, 0, 1) + X_train.append(X_train_block) + y_train.append(y_train_block) + + # We need to concatenate all training data + X_train = np.concatenate(X_train, axis=0) + y_train = np.concatenate(y_train, axis=0) + + # Next load validation data + with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f: + img_files = [] + val_wnids = [] + for line in f: + img_file, wnid = line.split('\t')[:2] + img_files.append(img_file) + val_wnids.append(wnid) + num_val = len(img_files) + y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids]) + X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype) + for i, img_file in enumerate(img_files): + img_file = os.path.join(path, 'val', 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + img.shape = (64, 64, 1) + X_val[i] = img.transpose(2, 0, 1) + + # Next load test images + # Students won't have test labels, so we need to iterate over files in the + # images directory. + img_files = os.listdir(os.path.join(path, 'test', 'images')) + X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype) + for i, img_file in enumerate(img_files): + img_file = os.path.join(path, 'test', 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + img.shape = (64, 64, 1) + X_test[i] = img.transpose(2, 0, 1) + + y_test = None + y_test_file = os.path.join(path, 'test', 'test_annotations.txt') + if os.path.isfile(y_test_file): + with open(y_test_file, 'r') as f: + img_file_to_wnid = {} + for line in f: + line = line.split('\t') + img_file_to_wnid[line[0]] = line[1] + y_test = [wnid_to_label[img_file_to_wnid[img_file]] + for img_file in img_files] + y_test = np.array(y_test) + + mean_image = X_train.mean(axis=0) + if subtract_mean: + X_train -= mean_image[None] + X_val -= mean_image[None] + X_test -= mean_image[None] + + return { + 'class_names': class_names, + 'X_train': X_train, + 'y_train': y_train, + 'X_val': X_val, + 'y_val': y_val, + 'X_test': X_test, + 'y_test': y_test, + 'class_names': class_names, + 'mean_image': mean_image, + } + + +def load_models(models_dir): + """ + Load saved models from disk. This will attempt to unpickle all files in a + directory; any files that give errors on unpickling (such as README.txt) + will be skipped. + + Inputs: + - models_dir: String giving the path to a directory containing model files. + Each model file is a pickled dictionary with a 'model' field. + + Returns: + A dictionary mapping model file names to models. + """ + models = {} + for model_file in os.listdir(models_dir): + with open(os.path.join(models_dir, model_file), 'rb') as f: + try: + models[model_file] = load_pickle(f)['model'] + except pickle.UnpicklingError: + continue + return models + + +def load_imagenet_val(num=None): + """Load a handful of validation images from ImageNet. + + Inputs: + - num: Number of images to load (max of 25) + + Returns: + - X: numpy array with shape [num, 224, 224, 3] + - y: numpy array of integer image labels, shape [num] + - class_names: dict mapping integer label to class name + """ + imagenet_fn = 'cs231n/datasets/imagenet_val_25.npz' + if not os.path.isfile(imagenet_fn): + print('file %s not found' % imagenet_fn) + print('Run the following:') + print('cd cs231n/datasets') + print('bash get_imagenet_val.sh') + assert False, 'Need to download imagenet_val_25.npz' + f = np.load(imagenet_fn) + X = f['X'] + y = f['y'] + class_names = f['label_map'].item() + if num is not None: + X = X[:num] + y = y[:num] + return X, y, class_names diff --git a/assignment1/cs231n/datasets/.gitignore b/assignment1/cs231n/datasets/.gitignore new file mode 100755 index 0000000..0232c3a --- /dev/null +++ b/assignment1/cs231n/datasets/.gitignore @@ -0,0 +1,4 @@ +cifar-10-batches-py/* +tiny-imagenet-100-A* +tiny-imagenet-100-B* +tiny-100-A-pretrained/* diff --git a/assignment1/cs231n/datasets/get_datasets.sh b/assignment1/cs231n/datasets/get_datasets.sh new file mode 100755 index 0000000..0dd9362 --- /dev/null +++ b/assignment1/cs231n/datasets/get_datasets.sh @@ -0,0 +1,4 @@ +# Get CIFAR10 +wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz +tar -xzvf cifar-10-python.tar.gz +rm cifar-10-python.tar.gz diff --git a/assignment1/cs231n/features.py b/assignment1/cs231n/features.py new file mode 100755 index 0000000..d396b06 --- /dev/null +++ b/assignment1/cs231n/features.py @@ -0,0 +1,150 @@ +from __future__ import print_function +from builtins import zip +from builtins import range +from past.builtins import xrange + +import matplotlib +import numpy as np +from scipy.ndimage import uniform_filter + + +def extract_features(imgs, feature_fns, verbose=False): + """ + Given pixel data for images and several feature functions that can operate on + single images, apply all feature functions to all images, concatenating the + feature vectors for each image and storing the features for all images in + a single matrix. + + Inputs: + - imgs: N x H X W X C array of pixel data for N images. + - feature_fns: List of k feature functions. The ith feature function should + take as input an H x W x D array and return a (one-dimensional) array of + length F_i. + - verbose: Boolean; if true, print progress. + + Returns: + An array of shape (N, F_1 + ... + F_k) where each column is the concatenation + of all features for a single image. + """ + num_images = imgs.shape[0] + if num_images == 0: + return np.array([]) + + # Use the first image to determine feature dimensions + feature_dims = [] + first_image_features = [] + for feature_fn in feature_fns: + feats = feature_fn(imgs[0].squeeze()) + assert len(feats.shape) == 1, 'Feature functions must be one-dimensional' + feature_dims.append(feats.size) + first_image_features.append(feats) + + # Now that we know the dimensions of the features, we can allocate a single + # big array to store all features as columns. + total_feature_dim = sum(feature_dims) + imgs_features = np.zeros((num_images, total_feature_dim)) + imgs_features[0] = np.hstack(first_image_features).T + + # Extract features for the rest of the images. + for i in range(1, num_images): + idx = 0 + for feature_fn, feature_dim in zip(feature_fns, feature_dims): + next_idx = idx + feature_dim + imgs_features[i, idx:next_idx] = feature_fn(imgs[i].squeeze()) + idx = next_idx + if verbose and i % 1000 == 999: + print('Done extracting features for %d / %d images' % (i+1, num_images)) + + return imgs_features + + +def rgb2gray(rgb): + """Convert RGB image to grayscale + + Parameters: + rgb : RGB image + + Returns: + gray : grayscale image + + """ + return np.dot(rgb[...,:3], [0.299, 0.587, 0.144]) + + +def hog_feature(im): + """Compute Histogram of Gradient (HOG) feature for an image + + Modified from skimage.feature.hog + http://pydoc.net/Python/scikits-image/0.4.2/skimage.feature.hog + + Reference: + Histograms of Oriented Gradients for Human Detection + Navneet Dalal and Bill Triggs, CVPR 2005 + + Parameters: + im : an input grayscale or rgb image + + Returns: + feat: Histogram of Gradient (HOG) feature + + """ + + # convert rgb to grayscale if needed + if im.ndim == 3: + image = rgb2gray(im) + else: + image = np.at_least_2d(im) + + sx, sy = image.shape # image size + orientations = 9 # number of gradient bins + cx, cy = (8, 8) # pixels per cell + + gx = np.zeros(image.shape) + gy = np.zeros(image.shape) + gx[:, :-1] = np.diff(image, n=1, axis=1) # compute gradient on x-direction + gy[:-1, :] = np.diff(image, n=1, axis=0) # compute gradient on y-direction + grad_mag = np.sqrt(gx ** 2 + gy ** 2) # gradient magnitude + grad_ori = np.arctan2(gy, (gx + 1e-15)) * (180 / np.pi) + 90 # gradient orientation + + n_cellsx = int(np.floor(sx / cx)) # number of cells in x + n_cellsy = int(np.floor(sy / cy)) # number of cells in y + # compute orientations integral images + orientation_histogram = np.zeros((n_cellsx, n_cellsy, orientations)) + for i in range(orientations): + # create new integral image for this orientation + # isolate orientations in this range + temp_ori = np.where(grad_ori < 180 / orientations * (i + 1), + grad_ori, 0) + temp_ori = np.where(grad_ori >= 180 / orientations * i, + temp_ori, 0) + # select magnitudes for those orientations + cond2 = temp_ori > 0 + temp_mag = np.where(cond2, grad_mag, 0) + orientation_histogram[:,:,i] = uniform_filter(temp_mag, size=(cx, cy))[round(cx/2)::cx, round(cy/2)::cy].T + + return orientation_histogram.ravel() + + +def color_histogram_hsv(im, nbin=10, xmin=0, xmax=255, normalized=True): + """ + Compute color histogram for an image using hue. + + Inputs: + - im: H x W x C array of pixel data for an RGB image. + - nbin: Number of histogram bins. (default: 10) + - xmin: Minimum pixel value (default: 0) + - xmax: Maximum pixel value (default: 255) + - normalized: Whether to normalize the histogram (default: True) + + Returns: + 1D vector of length nbin giving the color histogram over the hue of the + input image. + """ + ndim = im.ndim + bins = np.linspace(xmin, xmax, nbin+1) + hsv = matplotlib.colors.rgb_to_hsv(im/xmax) * xmax + imhist, bin_edges = np.histogram(hsv[:,:,0], bins=bins, density=normalized) + imhist = imhist * np.diff(bin_edges) + + # return histogram + return imhist diff --git a/assignment1/cs231n/gradient_check.py b/assignment1/cs231n/gradient_check.py new file mode 100755 index 0000000..e1189fc --- /dev/null +++ b/assignment1/cs231n/gradient_check.py @@ -0,0 +1,129 @@ +from __future__ import print_function +from builtins import range +from past.builtins import xrange + +import numpy as np +from random import randrange + +def eval_numerical_gradient(f, x, verbose=True, h=0.00001): + """ + a naive implementation of numerical gradient of f at x + - f should be a function that takes a single argument + - x is the point (numpy array) to evaluate the gradient at + """ + + fx = f(x) # evaluate function value at original point + grad = np.zeros_like(x) + # iterate over all indexes in x + it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) + while not it.finished: + + # evaluate function at x+h + ix = it.multi_index + oldval = x[ix] + x[ix] = oldval + h # increment by h + fxph = f(x) # evalute f(x + h) + x[ix] = oldval - h + fxmh = f(x) # evaluate f(x - h) + x[ix] = oldval # restore + + # compute the partial derivative with centered formula + grad[ix] = (fxph - fxmh) / (2 * h) # the slope + if verbose: + print(ix, grad[ix]) + it.iternext() # step to next dimension + + return grad + + +def eval_numerical_gradient_array(f, x, df, h=1e-5): + """ + Evaluate a numeric gradient for a function that accepts a numpy + array and returns a numpy array. + """ + grad = np.zeros_like(x) + it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) + while not it.finished: + ix = it.multi_index + + oldval = x[ix] + x[ix] = oldval + h + pos = f(x).copy() + x[ix] = oldval - h + neg = f(x).copy() + x[ix] = oldval + + grad[ix] = np.sum((pos - neg) * df) / (2 * h) + it.iternext() + return grad + + +def eval_numerical_gradient_blobs(f, inputs, output, h=1e-5): + """ + Compute numeric gradients for a function that operates on input + and output blobs. + + We assume that f accepts several input blobs as arguments, followed by a + blob where outputs will be written. For example, f might be called like: + + f(x, w, out) + + where x and w are input Blobs, and the result of f will be written to out. + + Inputs: + - f: function + - inputs: tuple of input blobs + - output: output blob + - h: step size + """ + numeric_diffs = [] + for input_blob in inputs: + diff = np.zeros_like(input_blob.diffs) + it = np.nditer(input_blob.vals, flags=['multi_index'], + op_flags=['readwrite']) + while not it.finished: + idx = it.multi_index + orig = input_blob.vals[idx] + + input_blob.vals[idx] = orig + h + f(*(inputs + (output,))) + pos = np.copy(output.vals) + input_blob.vals[idx] = orig - h + f(*(inputs + (output,))) + neg = np.copy(output.vals) + input_blob.vals[idx] = orig + + diff[idx] = np.sum((pos - neg) * output.diffs) / (2.0 * h) + + it.iternext() + numeric_diffs.append(diff) + return numeric_diffs + + +def eval_numerical_gradient_net(net, inputs, output, h=1e-5): + return eval_numerical_gradient_blobs(lambda *args: net.forward(), + inputs, output, h=h) + + +def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5): + """ + sample a few random elements and only return numerical + in this dimensions. + """ + + for i in range(num_checks): + ix = tuple([randrange(m) for m in x.shape]) + + oldval = x[ix] + x[ix] = oldval + h # increment by h + fxph = f(x) # evaluate f(x + h) + x[ix] = oldval - h # increment by h + fxmh = f(x) # evaluate f(x - h) + x[ix] = oldval # reset + + grad_numerical = (fxph - fxmh) / (2 * h) + grad_analytic = analytic_grad[ix] + rel_error = (abs(grad_numerical - grad_analytic) / + (abs(grad_numerical) + abs(grad_analytic))) + print('numerical: %f analytic: %f, relative error: %e' + %(grad_numerical, grad_analytic, rel_error)) diff --git a/assignment1/cs231n/vis_utils.py b/assignment1/cs231n/vis_utils.py new file mode 100755 index 0000000..0aa42c0 --- /dev/null +++ b/assignment1/cs231n/vis_utils.py @@ -0,0 +1,73 @@ +from builtins import range +from past.builtins import xrange + +from math import sqrt, ceil +import numpy as np + +def visualize_grid(Xs, ubound=255.0, padding=1): + """ + Reshape a 4D tensor of image data to a grid for easy visualization. + + Inputs: + - Xs: Data of shape (N, H, W, C) + - ubound: Output grid will have values scaled to the range [0, ubound] + - padding: The number of blank pixels between elements of the grid + """ + (N, H, W, C) = Xs.shape + grid_size = int(ceil(sqrt(N))) + grid_height = H * grid_size + padding * (grid_size - 1) + grid_width = W * grid_size + padding * (grid_size - 1) + grid = np.zeros((grid_height, grid_width, C)) + next_idx = 0 + y0, y1 = 0, H + for y in range(grid_size): + x0, x1 = 0, W + for x in range(grid_size): + if next_idx < N: + img = Xs[next_idx] + low, high = np.min(img), np.max(img) + grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low) + # grid[y0:y1, x0:x1] = Xs[next_idx] + next_idx += 1 + x0 += W + padding + x1 += W + padding + y0 += H + padding + y1 += H + padding + # grid_max = np.max(grid) + # grid_min = np.min(grid) + # grid = ubound * (grid - grid_min) / (grid_max - grid_min) + return grid + +def vis_grid(Xs): + """ visualize a grid of images """ + (N, H, W, C) = Xs.shape + A = int(ceil(sqrt(N))) + G = np.ones((A*H+A, A*W+A, C), Xs.dtype) + G *= np.min(Xs) + n = 0 + for y in range(A): + for x in range(A): + if n < N: + G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = Xs[n,:,:,:] + n += 1 + # normalize to [0,1] + maxg = G.max() + ming = G.min() + G = (G - ming)/(maxg-ming) + return G + +def vis_nn(rows): + """ visualize array of arrays of images """ + N = len(rows) + D = len(rows[0]) + H,W,C = rows[0][0].shape + Xs = rows[0][0] + G = np.ones((N*H+N, D*W+D, C), Xs.dtype) + for y in range(N): + for x in range(D): + G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = rows[y][x] + # normalize to [0,1] + maxg = G.max() + ming = G.min() + G = (G - ming)/(maxg-ming) + return G diff --git a/assignment1/features.ipynb b/assignment1/features.ipynb new file mode 100755 index 0000000..d176173 --- /dev/null +++ b/assignment1/features.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Image features exercise\n", + "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", + "\n", + "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n", + "\n", + "All of your work for this exercise will be done in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "from cs231n.data_utils import load_CIFAR10\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading extenrnal modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## Load data\n", + "Similar to previous exercises, we will load CIFAR-10 data from disk." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "from cs231n.features import color_histogram_hsv, hog_feature\n", + "\n", + "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n", + " # Load the raw CIFAR-10 data\n", + " cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", + "\n", + " # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n", + " try:\n", + " del X_train, y_train\n", + " del X_test, y_test\n", + " print('Clear previously loaded data.')\n", + " except:\n", + " pass\n", + "\n", + " X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", + " \n", + " # Subsample the data\n", + " mask = list(range(num_training, num_training + num_validation))\n", + " X_val = X_train[mask]\n", + " y_val = y_train[mask]\n", + " mask = list(range(num_training))\n", + " X_train = X_train[mask]\n", + " y_train = y_train[mask]\n", + " mask = list(range(num_test))\n", + " X_test = X_test[mask]\n", + " y_test = y_test[mask]\n", + " \n", + " return X_train, y_train, X_val, y_val, X_test, y_test\n", + "\n", + "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## Extract Features\n", + "For each image we will compute a Histogram of Oriented\n", + "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n", + "color space. We form our final feature vector for each image by concatenating\n", + "the HOG and color histogram feature vectors.\n", + "\n", + "Roughly speaking, HOG should capture the texture of the image while ignoring\n", + "color information, and the color histogram represents the color of the input\n", + "image while ignoring texture. As a result, we expect that using both together\n", + "ought to work better than using either alone. Verifying this assumption would\n", + "be a good thing to try for your own interest.\n", + "\n", + "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n", + "image and return a feature vector for that image. The extract_features\n", + "function takes a set of images and a list of feature functions and evaluates\n", + "each feature function on each image, storing the results in a matrix where\n", + "each column is the concatenation of all feature vectors for a single image." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true, + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done extracting features for 1000 / 49000 images\n", + "Done extracting features for 2000 / 49000 images\n", + "Done extracting features for 3000 / 49000 images\n", + "Done extracting features for 4000 / 49000 images\n", + "Done extracting features for 5000 / 49000 images\n", + "Done extracting features for 6000 / 49000 images\n", + "Done extracting features for 7000 / 49000 images\n", + "Done extracting features for 8000 / 49000 images\n", + "Done extracting features for 9000 / 49000 images\n", + "Done extracting features for 10000 / 49000 images\n", + "Done extracting features for 11000 / 49000 images\n", + "Done extracting features for 12000 / 49000 images\n", + "Done extracting features for 13000 / 49000 images\n", + "Done extracting features for 14000 / 49000 images\n", + "Done extracting features for 15000 / 49000 images\n", + "Done extracting features for 16000 / 49000 images\n", + "Done extracting features for 17000 / 49000 images\n", + "Done extracting features for 18000 / 49000 images\n", + "Done extracting features for 19000 / 49000 images\n", + "Done extracting features for 20000 / 49000 images\n", + "Done extracting features for 21000 / 49000 images\n", + "Done extracting features for 22000 / 49000 images\n", + "Done extracting features for 23000 / 49000 images\n", + "Done extracting features for 24000 / 49000 images\n", + "Done extracting features for 25000 / 49000 images\n", + "Done extracting features for 26000 / 49000 images\n", + "Done extracting features for 27000 / 49000 images\n", + "Done extracting features for 28000 / 49000 images\n", + "Done extracting features for 29000 / 49000 images\n", + "Done extracting features for 30000 / 49000 images\n", + "Done extracting features for 31000 / 49000 images\n", + "Done extracting features for 32000 / 49000 images\n", + "Done extracting features for 33000 / 49000 images\n", + "Done extracting features for 34000 / 49000 images\n", + "Done extracting features for 35000 / 49000 images\n", + "Done extracting features for 36000 / 49000 images\n", + "Done extracting features for 37000 / 49000 images\n", + "Done extracting features for 38000 / 49000 images\n", + "Done extracting features for 39000 / 49000 images\n", + "Done extracting features for 40000 / 49000 images\n", + "Done extracting features for 41000 / 49000 images\n", + "Done extracting features for 42000 / 49000 images\n", + "Done extracting features for 43000 / 49000 images\n", + "Done extracting features for 44000 / 49000 images\n", + "Done extracting features for 45000 / 49000 images\n", + "Done extracting features for 46000 / 49000 images\n", + "Done extracting features for 47000 / 49000 images\n", + "Done extracting features for 48000 / 49000 images\n", + "Done extracting features for 49000 / 49000 images\n" + ] + } + ], + "source": [ + "from cs231n.features import *\n", + "\n", + "num_color_bins = 10 # Number of bins in the color histogram\n", + "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n", + "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n", + "X_val_feats = extract_features(X_val, feature_fns)\n", + "X_test_feats = extract_features(X_test, feature_fns)\n", + "\n", + "# Preprocessing: Subtract the mean feature\n", + "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n", + "X_train_feats -= mean_feat\n", + "X_val_feats -= mean_feat\n", + "X_test_feats -= mean_feat\n", + "\n", + "# Preprocessing: Divide by standard deviation. This ensures that each feature\n", + "# has roughly the same scale.\n", + "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n", + "X_train_feats /= std_feat\n", + "X_val_feats /= std_feat\n", + "X_test_feats /= std_feat\n", + "\n", + "# Preprocessing: Add a bias dimension\n", + "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n", + "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n", + "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train SVM on features\n", + "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.095980 val accuracy: 0.108000\n", + "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.121388 val accuracy: 0.121000\n", + "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.119939 val accuracy: 0.113000\n", + "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.088551 val accuracy: 0.101000\n", + "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.126612 val accuracy: 0.123000\n", + "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.092408 val accuracy: 0.098000\n", + "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.098959 val accuracy: 0.104000\n", + "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.135857 val accuracy: 0.139000\n", + "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.370020 val accuracy: 0.393000\n", + "best validation accuracy achieved during cross-validation: 0.393000\n" + ] + } + ], + "source": [ + "# Use the validation set to tune the learning rate and regularization strength\n", + "\n", + "from cs231n.classifiers.linear_classifier import LinearSVM\n", + "\n", + "learning_rates = [1e-9, 1e-8, 1e-7]\n", + "regularization_strengths = [5e4, 5e5, 5e6]\n", + "\n", + "results = {}\n", + "best_val = -1\n", + "best_svm = None\n", + "\n", + "################################################################################\n", + "# TODO: #\n", + "# Use the validation set to set the learning rate and regularization strength. #\n", + "# This should be identical to the validation that you did for the SVM; save #\n", + "# the best trained classifer in best_svm. You might also want to play #\n", + "# with different numbers of bins in the color histogram. If you are careful #\n", + "# you should be able to get accuracy of near 0.44 on the validation set. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "for learn_rate in learning_rates:\n", + " for reg in regularization_strengths:\n", + " svm = LinearSVM()\n", + " svm.train(X_train_feats, y_train, learning_rate=learn_rate, reg=reg, num_iters=100,\n", + " batch_size=200, verbose=False)\n", + " train_acc = np.mean(svm.predict(X_train_feats) == y_train)\n", + " val_acc = np.mean(svm.predict(X_val_feats) == y_val)\n", + " results[(learn_rate, reg)] = (train_acc, val_acc)\n", + " if(val_acc > best_val):\n", + " best_val = val_acc\n", + " best_svm = svm\n", + " \n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "# Print out results.\n", + "for lr, reg in sorted(results):\n", + " train_accuracy, val_accuracy = results[(lr, reg)]\n", + " print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n", + " lr, reg, train_accuracy, val_accuracy))\n", + " \n", + "print('best validation accuracy achieved during cross-validation: %f' % best_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.371\n" + ] + } + ], + "source": [ + "# Evaluate your trained SVM on the test set\n", + "y_test_pred = best_svm.predict(X_test_feats)\n", + "test_accuracy = np.mean(y_test == y_test_pred)\n", + "print(test_accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# An important way to gain intuition about how an algorithm works is to\n", + "# visualize the mistakes that it makes. In this visualization, we show examples\n", + "# of images that are misclassified by our current system. The first column\n", + "# shows images that our system labeled as \"plane\" but whose true label is\n", + "# something other than \"plane\".\n", + "\n", + "examples_per_class = 8\n", + "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "for cls, cls_name in enumerate(classes):\n", + " idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n", + " idxs = np.random.choice(idxs, examples_per_class, replace=True)\n", + " for i, idx in enumerate(idxs):\n", + " plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n", + " plt.imshow(X_test[idx].astype('uint8'))\n", + " plt.axis('off')\n", + " if i == 0:\n", + " plt.title(cls_name)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "### Inline question 1:\n", + "Describe the misclassification results that you see. Do they make sense?\n", + "\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network on image features\n", + "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n", + "\n", + "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# Preprocessing: Remove the bias dimension\n", + "# Make sure to run this cell only ONCE\n", + "print(X_train_feats.shape)\n", + "X_train_feats = X_train_feats[:, :-1]\n", + "X_val_feats = X_val_feats[:, :-1]\n", + "X_test_feats = X_test_feats[:, :-1]\n", + "\n", + "print(X_train_feats.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(0.1, 0.05): 0.416}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511, (0.1, 0.0005): 0.521}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511, (0.1, 0.0005): 0.521, (0.3, 0.05): 0.422}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511, (0.1, 0.0005): 0.521, (0.3, 0.05): 0.422, (0.3, 0.005): 0.518}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511, (0.1, 0.0005): 0.521, (0.3, 0.05): 0.422, (0.3, 0.005): 0.518, (0.3, 0.0005): 0.573}\n", + "{(0.1, 0.05): 0.416, (0.1, 0.005): 0.511, (0.1, 0.0005): 0.521, (0.3, 0.05): 0.422, (0.3, 0.005): 0.518, (0.3, 0.0005): 0.573, (0.5, 0.05): 0.388}\n" + ] + } + ], + "source": [ + "from cs231n.classifiers.neural_net import TwoLayerNet\n", + "\n", + "input_dim = X_train_feats.shape[1]\n", + "hidden_dim = 500\n", + "num_classes = 10\n", + "\n", + "# net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n", + "best_net = None\n", + "\n", + "################################################################################\n", + "# TODO: Train a two-layer neural network on image features. You may want to #\n", + "# cross-validate various parameters as in previous sections. Store your best #\n", + "# model in the best_net variable. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "learning_rates = [1e-1, 0.3, 0.5]\n", + "regularization_strengths = [ 5e-2, 5e-3, 5e-4]\n", + "results = {}\n", + "best_val = -1\n", + "\n", + "for learn_rate in learning_rates:\n", + " for reg in regularization_strengths:\n", + " net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n", + " net.train(X_train_feats, y_train, X_val_feats, y_val,\n", + " num_iters=1200, batch_size=200,\n", + " learning_rate=learn_rate, learning_rate_decay=0.95,\n", + " reg=reg, verbose=False)\n", + " val_acc = (net.predict(X_val_feats) == y_val).mean()\n", + " results[(learn_rate, reg)] = val_acc\n", + " print(results)\n", + " if(best_val < val_acc):\n", + " best_val = val_acc\n", + " best_net = net\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.526\n" + ] + } + ], + "source": [ + "# Run your best neural net classifier on the test set. You should be able\n", + "# to get more than 55% accuracy.\n", + "\n", + "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n", + "print(test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment1/frameworkpython b/assignment1/frameworkpython new file mode 100755 index 0000000..5ed8ebd --- /dev/null +++ b/assignment1/frameworkpython @@ -0,0 +1,15 @@ +#!/bin/bash + +# what real Python executable to use +#PYVER=2.7 +#PATHTOPYTHON=/usr/local/bin/ +#PYTHON=${PATHTOPYTHON}python${PYVER} + +PYTHON=$(which $(readlink .env/bin/python)) # only works with python3 + +# find the root of the virtualenv, it should be the parent of the dir this script is in +ENV=`$PYTHON -c "import os; print(os.path.abspath(os.path.join(os.path.dirname(\"$0\"), '..')))"` + +# now run Python with the virtualenv set as Python's HOME +export PYTHONHOME=$ENV +exec $PYTHON "$@" diff --git a/assignment1/knn.ipynb b/assignment1/knn.ipynb new file mode 100755 index 0000000..3dd7eb9 --- /dev/null +++ b/assignment1/knn.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# k-Nearest Neighbor (kNN) exercise\n", + "\n", + "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", + "\n", + "The kNN classifier consists of two stages:\n", + "\n", + "- During training, the classifier takes the training data and simply remembers it\n", + "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n", + "- The value of k is cross-validated\n", + "\n", + "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# Run some setup code for this notebook.\n", + "\n", + "import random\n", + "import numpy as np\n", + "from cs231n.data_utils import load_CIFAR10\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n", + "# rather than in a new window.\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# Some more magic so that the notebook will reload external python modules;\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (50000, 32, 32, 3)\n", + "Training labels shape: (50000,)\n", + "Test data shape: (10000, 32, 32, 3)\n", + "Test labels shape: (10000,)\n" + ] + } + ], + "source": [ + "# Load the raw CIFAR-10 data.\n", + "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", + "\n", + "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n", + "try:\n", + " del X_train, y_train\n", + " del X_test, y_test\n", + " print('Clear previously loaded data.')\n", + "except:\n", + " pass\n", + "\n", + "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", + "\n", + "# As a sanity check, we print out the size of the training and test data.\n", + "print('Training data shape: ', X_train.shape)\n", + "print('Training labels shape: ', y_train.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize some examples from the dataset.\n", + "# We show a few examples of training images from each class.\n", + "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "num_classes = len(classes)\n", + "samples_per_class = 7\n", + "for y, cls in enumerate(classes):\n", + " idxs = np.flatnonzero(y_train == y)\n", + " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", + " for i, idx in enumerate(idxs):\n", + " plt_idx = i * num_classes + y + 1\n", + " plt.subplot(samples_per_class, num_classes, plt_idx)\n", + " plt.imshow(X_train[idx].astype('uint8'))\n", + " plt.axis('off')\n", + " if i == 0:\n", + " plt.title(cls)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 3072) (500, 3072)\n" + ] + } + ], + "source": [ + "# Subsample the data for more efficient code execution in this exercise\n", + "num_training = 5000\n", + "mask = list(range(num_training))\n", + "X_train = X_train[mask]\n", + "y_train = y_train[mask]\n", + "\n", + "num_test = 500\n", + "mask = list(range(num_test))\n", + "X_test = X_test[mask]\n", + "y_test = y_test[mask]\n", + "\n", + "# Reshape the image data into rows\n", + "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", + "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", + "print(X_train.shape, X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "from cs231n.classifiers import KNearestNeighbor\n", + "\n", + "# Create a kNN classifier instance. \n", + "# Remember that training a kNN classifier is a noop: \n", + "# the Classifier simply remembers the data and does no further processing \n", + "classifier = KNearestNeighbor()\n", + "classifier.train(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n", + "\n", + "1. First we must compute the distances between all test examples and all train examples. \n", + "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n", + "\n", + "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n", + "\n", + "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n", + "\n", + "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(500, 5000)\n" + ] + } + ], + "source": [ + "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n", + "# compute_distances_two_loops.\n", + "\n", + "# Test your implementation:\n", + "dists = classifier.compute_distances_two_loops(X_test)\n", + "print(dists.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# We can visualize the distance matrix: each row is a single test example and\n", + "# its distances to training examples\n", + "plt.imshow(dists, interpolation='none')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 3072)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 1** \n", + "\n", + "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n", + "\n", + "- What in the data is the cause behind the distinctly bright rows?\n", + "- What causes the columns?\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got 137 / 500 correct => accuracy: 0.274000\n" + ] + } + ], + "source": [ + "# Now implement the function predict_labels and run the code below:\n", + "# We use k = 1 (which is Nearest Neighbor).\n", + "y_test_pred = classifier.predict_labels(dists, k=1)\n", + "\n", + "# # Compute and print the fraction of correctly predicted examples\n", + "num_correct = np.sum(y_test_pred == y_test)\n", + "accuracy = float(num_correct) / num_test\n", + "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got 139 / 500 correct => accuracy: 0.278000\n" + ] + } + ], + "source": [ + "y_test_pred = classifier.predict_labels(dists, k=5)\n", + "num_correct = np.sum(y_test_pred == y_test)\n", + "accuracy = float(num_correct) / num_test\n", + "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should expect to see a slightly better performance than with `k = 1`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 2**\n", + "\n", + "We can also use other distance metrics such as L1 distance.\n", + "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n", + "\n", + "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n", + "And the pixel-wise mean $\\mu_{ij}$ across all images is \n", + "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n", + "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n", + "\n", + "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n", + "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n", + "2. Subtracting the per pixel mean $\\mu_{ij}$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n", + "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n", + "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n", + "5. Rotating the coordinate axes of the data.\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$\n", + "\n", + "\n", + "$\\color{blue}{\\textit Your Explanation:}$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "One loop difference was: 0.000000\n", + "Good! The distance matrices are the same\n", + "(500, 5000)\n" + ] + } + ], + "source": [ + "# Now lets speed up distance matrix computation by using partial vectorization\n", + "# with one loop. Implement the function compute_distances_one_loop and run the\n", + "# code below:\n", + "dists_one = classifier.compute_distances_one_loop(X_test)\n", + "\n", + "# To ensure that our vectorized implementation is correct, we make sure that it\n", + "# agrees with the naive implementation. There are many ways to decide whether\n", + "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n", + "# you haven't seen it before, the Frobenius norm of two matrices is the square\n", + "# root of the squared sum of differences of all elements; in other words, reshape\n", + "# the matrices into vectors and compute the Euclidean distance between them.\n", + "difference = np.linalg.norm(dists - dists_one, ord='fro')\n", + "print('One loop difference was: %f' % (difference, ))\n", + "if difference < 0.001:\n", + " print('Good! The distance matrices are the same')\n", + "else:\n", + " print('Uh-oh! The distance matrices are different')\n", + "print(dists_one.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No loop difference was: 0.000000\n", + "Good! The distance matrices are the same\n" + ] + } + ], + "source": [ + "# Now implement the fully vectorized version inside compute_distances_no_loops\n", + "# and run the code\n", + "dists_two = classifier.compute_distances_no_loops(X_test)\n", + "\n", + "# check that the distance matrix agrees with the one we computed before:\n", + "difference = np.linalg.norm(dists - dists_two, ord='fro')\n", + "print('No loop difference was: %f' % (difference, ))\n", + "if difference < 0.001:\n", + " print('Good! The distance matrices are the same')\n", + "else:\n", + " print('Uh-oh! The distance matrices are different')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "# Let's compare how fast the implementations are\n", + "def time_function(f, *args):\n", + " \"\"\"\n", + " Call a function f with args and return the time (in seconds) that it took to execute.\n", + " \"\"\"\n", + " import time\n", + " tic = time.time()\n", + " f(*args)\n", + " toc = time.time()\n", + " return toc - tic\n", + "\n", + "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n", + "print('Two loop version took %f seconds' % two_loop_time)\n", + "\n", + "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n", + "print('One loop version took %f seconds' % one_loop_time)\n", + "\n", + "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n", + "print('No loop version took %f seconds' % no_loop_time)\n", + "\n", + "# You should see significantly faster performance with the fully vectorized implementation!\n", + "\n", + "# NOTE: depending on what machine you're using, \n", + "# you might not see a speedup when you go from two loops to one loop, \n", + "# and might even see a slow-down." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cross-validation\n", + "\n", + "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [], + "source": [ + "num_folds = 5\n", + "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n", + "\n", + "X_train_folds = []\n", + "y_train_folds = []\n", + "################################################################################\n", + "# TODO: #\n", + "# Split up the training data into folds. After splitting, X_train_folds and #\n", + "# y_train_folds should each be lists of length num_folds, where #\n", + "# y_train_folds[i] is the label vector for the points in X_train_folds[i]. #\n", + "# Hint: Look up the numpy array_split function. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "pass\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "# A dictionary holding the accuracies for different values of k that we find\n", + "# when running cross-validation. After running cross-validation,\n", + "# k_to_accuracies[k] should be a list of length num_folds giving the different\n", + "# accuracy values that we found when using that value of k.\n", + "k_to_accuracies = {}\n", + "\n", + "\n", + "################################################################################\n", + "# TODO: #\n", + "# Perform k-fold cross validation to find the best value of k. For each #\n", + "# possible value of k, run the k-nearest-neighbor algorithm num_folds times, #\n", + "# where in each case you use all but one of the folds as training data and the #\n", + "# last fold as a validation set. Store the accuracies for all fold and all #\n", + "# values of k in the k_to_accuracies dictionary. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "pass\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "# Print out the computed accuracies\n", + "for k in sorted(k_to_accuracies):\n", + " for accuracy in k_to_accuracies[k]:\n", + " print('k = %d, accuracy = %f' % (k, accuracy))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "# plot the raw observations\n", + "for k in k_choices:\n", + " accuracies = k_to_accuracies[k]\n", + " plt.scatter([k] * len(accuracies), accuracies)\n", + "\n", + "# plot the trend line with error bars that correspond to standard deviation\n", + "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n", + "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n", + "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n", + "plt.title('Cross-validation on k')\n", + "plt.xlabel('k')\n", + "plt.ylabel('Cross-validation accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Based on the cross-validation results above, choose the best value for k, \n", + "# retrain the classifier using all the training data, and test it on the test\n", + "# data. You should be able to get above 28% accuracy on the test data.\n", + "best_k = 1\n", + "\n", + "classifier = KNearestNeighbor()\n", + "classifier.train(X_train, y_train)\n", + "y_test_pred = classifier.predict(X_test, k=best_k)\n", + "\n", + "# Compute and display the accuracy\n", + "num_correct = np.sum(y_test_pred == y_test)\n", + "accuracy = float(num_correct) / num_test\n", + "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 3**\n", + "\n", + "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n", + "1. The decision boundary of the k-NN classifier is linear.\n", + "2. The training error of a 1-NN will always be lower than that of 5-NN.\n", + "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n", + "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n", + "5. None of the above.\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$\n", + "\n", + "\n", + "$\\color{blue}{\\textit Your Explanation:}$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment1/requirements.txt b/assignment1/requirements.txt new file mode 100755 index 0000000..4d9577c --- /dev/null +++ b/assignment1/requirements.txt @@ -0,0 +1,63 @@ +attrs==19.1.0 +backcall==0.1.0 +bleach==3.1.0 +certifi==2019.3.9 +chardet==3.0.4 +colorama==0.4.1 +cycler==0.10.0 +decorator==4.4.0 +defusedxml==0.5.0 +entrypoints==0.3 +future==0.17.1 +gitdb2==2.0.5 +GitPython==2.1.11 +idna==2.8 +ipykernel==5.1.0 +ipython==7.4.0 +ipython-genutils==0.2.0 +ipywidgets==7.4.2 +jedi==0.13.3 +Jinja2==2.10 +jsonschema==3.0.1 +jupyter==1.0.0 +jupyter-client==5.2.4 +jupyter-console==6.0.0 +jupyter-core==4.4.0 +jupyterlab==0.35.4 +jupyterlab-server==0.2.0 +kiwisolver==1.0.1 +MarkupSafe==1.1.1 +matplotlib==3.0.3 +mistune==0.8.4 +nbconvert==5.4.1 +nbdime==1.0.5 +nbformat==4.4.0 +notebook==5.7.8 +numpy==1.16.2 +pandocfilters==1.4.2 +parso==0.3.4 +pexpect==4.6.0 +pickleshare==0.7.5 +Pillow==6.0.0 +prometheus-client==0.6.0 +prompt-toolkit==2.0.9 +ptyprocess==0.6.0 +Pygments==2.3.1 +pyparsing==2.3.1 +pyrsistent==0.14.11 +python-dateutil==2.8.0 +pyzmq==18.0.1 +qtconsole==4.4.3 +requests==2.21.0 +scipy==1.2.1 +Send2Trash==1.5.0 +six==1.12.0 +smmap2==2.0.5 +terminado==0.8.2 +testpath==0.4.2 +tornado==6.0.2 +traitlets==4.3.2 +urllib3==1.24.1 +wcwidth==0.1.7 +webencodings==0.5.1 +widgetsnbextension==3.4.2 diff --git a/assignment1/softmax.ipynb b/assignment1/softmax.ipynb new file mode 100755 index 0000000..fa43352 --- /dev/null +++ b/assignment1/softmax.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Softmax exercise\n", + "\n", + "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", + "\n", + "This exercise is analogous to the SVM exercise. You will:\n", + "\n", + "- implement a fully-vectorized **loss function** for the Softmax classifier\n", + "- implement the fully-vectorized expression for its **analytic gradient**\n", + "- **check your implementation** with numerical gradient\n", + "- use a validation set to **tune the learning rate and regularization** strength\n", + "- **optimize** the loss function with **SGD**\n", + "- **visualize** the final learned weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "from cs231n.data_utils import load_CIFAR10\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading extenrnal modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape: (49000, 3073)\n", + "Train labels shape: (49000,)\n", + "Validation data shape: (1000, 3073)\n", + "Validation labels shape: (1000,)\n", + "Test data shape: (1000, 3073)\n", + "Test labels shape: (1000,)\n", + "dev data shape: (500, 3073)\n", + "dev labels shape: (500,)\n" + ] + } + ], + "source": [ + "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n", + " \"\"\"\n", + " Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n", + " it for the linear classifier. These are the same steps as we used for the\n", + " SVM, but condensed to a single function. \n", + " \"\"\"\n", + " # Load the raw CIFAR-10 data\n", + " cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", + " \n", + " # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n", + " try:\n", + " del X_train, y_train\n", + " del X_test, y_test\n", + " print('Clear previously loaded data.')\n", + " except:\n", + " pass\n", + "\n", + " X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", + " \n", + " # subsample the data\n", + " mask = list(range(num_training, num_training + num_validation))\n", + " X_val = X_train[mask]\n", + " y_val = y_train[mask]\n", + " mask = list(range(num_training))\n", + " X_train = X_train[mask]\n", + " y_train = y_train[mask]\n", + " mask = list(range(num_test))\n", + " X_test = X_test[mask]\n", + " y_test = y_test[mask]\n", + " mask = np.random.choice(num_training, num_dev, replace=False)\n", + " X_dev = X_train[mask]\n", + " y_dev = y_train[mask]\n", + " \n", + " # Preprocessing: reshape the image data into rows\n", + " X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", + " X_val = np.reshape(X_val, (X_val.shape[0], -1))\n", + " X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", + " X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n", + " \n", + " # Normalize the data: subtract the mean image\n", + " mean_image = np.mean(X_train, axis = 0)\n", + " X_train -= mean_image\n", + " X_val -= mean_image\n", + " X_test -= mean_image\n", + " X_dev -= mean_image\n", + " \n", + " # add bias dimension and transform into columns\n", + " X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n", + " X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n", + " X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n", + " X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n", + " \n", + " return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n", + "\n", + "\n", + "# Invoke the above function to get our data.\n", + "X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n", + "print('Train data shape: ', X_train.shape)\n", + "print('Train labels shape: ', y_train.shape)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Validation labels shape: ', y_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)\n", + "print('dev data shape: ', X_dev.shape)\n", + "print('dev labels shape: ', y_dev.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Softmax Classifier\n", + "\n", + "Your code for this section will all be written inside **cs231n/classifiers/softmax.py**. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 2.399498\n", + "sanity check: 2.302585\n" + ] + } + ], + "source": [ + "# First implement the naive softmax loss function with nested loops.\n", + "# Open the file cs231n/classifiers/softmax.py and implement the\n", + "# softmax_loss_naive function.\n", + "\n", + "from cs231n.classifiers.softmax import softmax_loss_naive\n", + "import time\n", + "\n", + "# Generate a random softmax weight matrix and use it to compute the loss.\n", + "W = np.random.randn(3073, 10) * 0.0001\n", + "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n", + "\n", + "# As a rough sanity check, our loss should be something close to -log(0.1).\n", + "print('loss: %f' % loss)\n", + "print('sanity check: %f' % (-np.log(0.1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 1**\n", + "\n", + "Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$ *Fill this in* \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numerical: 0.249340 analytic: 0.249340, relative error: 1.092407e-08\n", + "numerical: -0.610997 analytic: -0.610997, relative error: 5.103796e-08\n", + "numerical: 2.987402 analytic: 2.987402, relative error: 2.505135e-08\n", + "numerical: 0.449713 analytic: 0.449713, relative error: 1.042769e-07\n", + "numerical: 0.569009 analytic: 0.569009, relative error: 1.667220e-08\n", + "numerical: 0.950371 analytic: 0.950371, relative error: 6.414415e-08\n", + "numerical: 3.744129 analytic: 3.744129, relative error: 5.481094e-09\n", + "numerical: -1.425607 analytic: -1.425607, relative error: 5.618502e-08\n", + "numerical: 1.787923 analytic: 1.787923, relative error: 3.917862e-08\n", + "numerical: 0.207131 analytic: 0.207131, relative error: 8.579092e-08\n", + "numerical: -0.606899 analytic: -0.608941, relative error: 1.679866e-03\n", + "numerical: -1.369106 analytic: -1.364930, relative error: 1.527607e-03\n", + "numerical: 0.309722 analytic: 0.311897, relative error: 3.498688e-03\n", + "numerical: -0.559828 analytic: -0.555615, relative error: 3.776660e-03\n", + "numerical: -0.774006 analytic: -0.770998, relative error: 1.946979e-03\n", + "numerical: -1.185457 analytic: -1.192568, relative error: 2.990602e-03\n", + "numerical: 0.042605 analytic: 0.048969, relative error: 6.949822e-02\n", + "numerical: 1.043033 analytic: 1.043032, relative error: 4.398749e-07\n", + "numerical: 4.914208 analytic: 4.912440, relative error: 1.798456e-04\n", + "numerical: 1.610799 analytic: 1.608024, relative error: 8.619909e-04\n" + ] + } + ], + "source": [ + "# Complete the implementation of softmax_loss_naive and implement a (naive)\n", + "# version of the gradient that uses nested loops.\n", + "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n", + "\n", + "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n", + "# The numeric gradient should be close to the analytic gradient.\n", + "from cs231n.gradient_check import grad_check_sparse\n", + "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n", + "grad_numerical = grad_check_sparse(f, W, grad, 10)\n", + "\n", + "# similar to SVM case, do another gradient check with regularization\n", + "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 5e1)\n", + "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 5e1)[0]\n", + "grad_numerical = grad_check_sparse(f, W, grad, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "naive loss: 2.358067e+00 computed in 25.721544s\n", + "vectorized loss: 2.358067e+00 computed in 0.015808s\n", + "Loss difference: 0.000000\n", + "Gradient difference: 0.000000\n" + ] + } + ], + "source": [ + "# Now that we have a naive implementation of the softmax loss function and its gradient,\n", + "# implement a vectorized version in softmax_loss_vectorized.\n", + "# The two versions should compute the same results, but the vectorized version should be\n", + "# much faster.\n", + "tic = time.time()\n", + "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n", + "\n", + "from cs231n.classifiers.softmax import softmax_loss_vectorized\n", + "tic = time.time()\n", + "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n", + "\n", + "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n", + "# of the gradient.\n", + "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n", + "print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))\n", + "print('Gradient difference: %f' % grad_difference)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.148673 val accuracy: 0.143000\n", + "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.159878 val accuracy: 0.189000\n", + "lr 5.000000e-07 reg 2.500000e+04 train accuracy: 0.261918 val accuracy: 0.275000\n", + "lr 5.000000e-07 reg 5.000000e+04 train accuracy: 0.300735 val accuracy: 0.306000\n", + "best validation accuracy achieved during cross-validation: 0.306000\n" + ] + } + ], + "source": [ + "# Use the validation set to tune hyperparameters (regularization strength and\n", + "# learning rate). You should experiment with different ranges for the learning\n", + "# rates and regularization strengths; if you are careful you should be able to\n", + "# get a classification accuracy of over 0.35 on the validation set.\n", + "from cs231n.classifiers import Softmax\n", + "results = {}\n", + "best_val = -1\n", + "best_softmax = None\n", + "learning_rates = [1e-7, 5e-7]\n", + "regularization_strengths = [2.5e4, 5e4]\n", + "\n", + "################################################################################\n", + "# TODO: #\n", + "# Use the validation set to set the learning rate and regularization strength. #\n", + "# This should be identical to the validation that you did for the SVM; save #\n", + "# the best trained softmax classifer in best_softmax. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "for learn_rate in learning_rates:\n", + " for reg in regularization_strengths:\n", + " svm = Softmax()\n", + " svm.train(X_train, y_train, learning_rate=learn_rate, reg=reg, num_iters=100,\n", + " batch_size=200, verbose=False)\n", + " train_accu = np.mean(svm.predict(X_train) == y_train)\n", + " val_accu = np.mean(svm.predict(X_val) == y_val)\n", + " results[(learn_rate, reg)] = (train_accu, val_accu)\n", + " \n", + " if(val_accu > best_val):\n", + " best_val = val_accu\n", + " best_softmax = svm\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + "# Print out results.\n", + "for lr, reg in sorted(results):\n", + " train_accuracy, val_accuracy = results[(lr, reg)]\n", + " print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n", + " lr, reg, train_accuracy, val_accuracy))\n", + " \n", + "print('best validation accuracy achieved during cross-validation: %f' % best_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "softmax on raw pixels final test set accuracy: 0.317000\n" + ] + } + ], + "source": [ + "# evaluate on test set\n", + "# Evaluate the best softmax on test set\n", + "y_test_pred = best_softmax.predict(X_test)\n", + "test_accuracy = np.mean(y_test == y_test_pred)\n", + "print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 2** - *True or False*\n", + "\n", + "Suppose the overall training loss is defined as the sum of the per-datapoint loss over all training examples. It is possible to add a new datapoint to a training set that would leave the SVM loss unchanged, but this is not the case with the Softmax classifier loss.\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$\n", + "\n", + "\n", + "$\\color{blue}{\\textit Your Explanation:}$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the learned weights for each class\n", + "w = best_softmax.W[:-1,:] # strip out the bias\n", + "w = w.reshape(32, 32, 3, 10)\n", + "\n", + "w_min, w_max = np.min(w), np.max(w)\n", + "\n", + "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i + 1)\n", + " \n", + " # Rescale the weights to be between 0 and 255\n", + " wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n", + " plt.imshow(wimg.astype('uint8'))\n", + " plt.axis('off')\n", + " plt.title(classes[i])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment1/start_ipython_osx.sh b/assignment1/start_ipython_osx.sh new file mode 100755 index 0000000..4815b00 --- /dev/null +++ b/assignment1/start_ipython_osx.sh @@ -0,0 +1,4 @@ +# Assume the virtualenv is called .env + +cp frameworkpython .env/bin +.env/bin/frameworkpython -m IPython notebook diff --git a/assignment1/svm.ipynb b/assignment1/svm.ipynb new file mode 100755 index 0000000..ddb4e72 --- /dev/null +++ b/assignment1/svm.ipynb @@ -0,0 +1,905 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Multiclass Support Vector Machine exercise\n", + "\n", + "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", + "\n", + "In this exercise you will:\n", + " \n", + "- implement a fully-vectorized **loss function** for the SVM\n", + "- implement the fully-vectorized expression for its **analytic gradient**\n", + "- **check your implementation** using numerical gradient\n", + "- use a validation set to **tune the learning rate and regularization** strength\n", + "- **optimize** the loss function with **SGD**\n", + "- **visualize** the final learned weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# Run some setup code for this notebook.\n", + "import random\n", + "import numpy as np\n", + "from cs231n.data_utils import load_CIFAR10\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# This is a bit of magic to make matplotlib figures appear inline in the\n", + "# notebook rather than in a new window.\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# Some more magic so that the notebook will reload external python modules;\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## CIFAR-10 Data Loading and Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clear previously loaded data.\n", + "Training data shape: (50000, 32, 32, 3)\n", + "Training labels shape: (50000,)\n", + "Test data shape: (10000, 32, 32, 3)\n", + "Test labels shape: (10000,)\n" + ] + } + ], + "source": [ + "# Load the raw CIFAR-10 data.\n", + "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", + "\n", + "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n", + "try:\n", + " del X_train, y_train\n", + " del X_test, y_test\n", + " print('Clear previously loaded data.')\n", + "except:\n", + " pass\n", + "\n", + "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", + "\n", + "# As a sanity check, we print out the size of the training and test data.\n", + "print('Training data shape: ', X_train.shape)\n", + "print('Training labels shape: ', y_train.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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NDqvTS1vK8ZHvuoO5uTr/79ePURmM2DeW5/x8g6enZ8mXigwNl1h5eg6lvsb+3RWMynNmts6F1RZDqwGt5Q6HnziHlyuCc47m8iwrLcDRWBz+8+8/TiEIWFps8cEfu7IcnuehRDJj43JQW5St65SL1W6NySZgwPVcRmWMJI63vIe1hqWlBfywQKGcp1ldwighNm3CMGBlpYnneYhAkiT4nkepVKbbbeP7Pq3mGsaAsYLrOjiOkxGJ49ButzHa4CiF42y9IFdKYYwhSRKMMcRxTJIkaK1J05Q0TRGRDXI3xiAieJ6H67oXkXyj0SBN0w3SdhwH3/cplUr4vr+lHL1er69+wVEgYlGiwFqazSZhGL5o4lUbXHExUp2ijcWkGqsNaX/ysVjQlq2oUcTiYHGIIW3ieyUEizUGRxkUPj4ejgtKWS6cOc/47kOIeBmHKR/lWER1wYGoF/Xr5CDib0yMRl9dBuirSuKbyfqyFkyfbzbeIMA6ifXPwYLIpkYRQIFdbzgHrGxYSVdC48jDKFcoKcj7PkVxIUqg0yVnbuRUtUVlbomBI4fx8hVufctd1GoQtb5BqbPM8EjE/NoaX3v4K3yionGSBBMpZroeTt4w6HrEKax1t96i5/UtbyewKCX02gaTQqQ0aWQxidrgCrGC1QZxXNCmrz9BXIXyMn2ZxGASMGJwHAtW0Nps6OdKUJ4il88RdTsk3Q6eI4S+A8ZBiUPaSej2eijXIxe4+G4R3YgwKsdqtUXoOXiuIUlSEINF4yiFi0V5Hkb3ECzeNsR351vH+NIXOiysVaktDuIlBi8MyZfqXJg9z8jEnSzOn+Phh45wevc4hWKOtWZCPhDmF6p8/Ruz5Mplllaa1GvncFXIbD0l50G5FJI0I1ITobchT9d1X9Q/Nx9rLOuWetZXX+hvfVsu06t94bp1C1UAZRxKFQ+zzSSPGHq9iLW1FUYGyrhuSq0d0Y2alItJ37r2qFQqGTH6GaF32m3y+Tw66dBsthBxNyzebrdLvV7HWovWKdpogiDYWo6+/EmSEEURaZpuWNTrxL5O9GmabhwnSYLrZhSj+4T01a9+lXw+zzXXXEM+n+9PLJZcLndZst2M9QnCEYXB4LgKVGb9u65LkiQblv/6vRzH2TjezD/WZpOB6reWUULc66G1IfC2nkxEGULHwfSa6N4y+aCE64dYyTwHOnVxUFhSPEnJqRTHN4h1EK3QsWAVuL5HGHrotYhEJxibGbqOk33YRh/reM1I/HJQkpGW2jwS5OL9rH1bdFNjvGBFOE62BNGblnSXw89+vUJHAq6puLxxWFEtHWQ8n3Amt4/hQ10GjhxlpXmcPzs6Tj4/xB/9zn+kXPI4eXIeV2n2TgwxOJJgnriftx8+iJ8qrsuvsmfSZ9+godhtM98OiUyeW7eQQ/kGryikidBetFgNyrFYBGNU5hrpGWyakYW1YHSKOAprMiVZY1AolJui8g5ocNLM9ZKmCTrVGx35Smi3WygB1+rMYaUCol6ddrsFaUw55xNUcigFrW6HZ6dX2HXgXVBvoVREL+ri5SPSuE3ollGOkKQd4riHB2gdo22K2ebFwL4JeejrZ9BxRKmU48T0PMV8yEToYEuK4YFBjj6rOLlS54lTTUoFh3zJZ+9kkZWlLs+c63BoMsa4KZUBn5nZBktrhhsP7aPga7xQSIwmV95aEN/3+xZT1rfW++26QdHpRdTrdQDy+Tzlcpk0TWk0GgCEYUguDDcIYt0FYCQjIq01xmbW3NZyOLQ7DawIJs0zUCxT9kIGHKHbbOB5Lq5XZGVlhYGBAdrtDtYamq0mAqRJSj5fABRBEGANdLsd0jTF932iKEJEkc/nt5QDMhKMoohWq0UcxxvulPWlv9ZZPzPGbFjiG5azUmit6XQ6HD58mOHhYQYHB7HWbrhS1sl/S6QGrMFgSE2Ca1wc38+0bC3WZCtW2DDvsGIwWOz6hLrJ7RPHKWmiUcbSXljkkUe+wYEbD7L3wNYbjvOhobl8jqh+nna0THFgD45fwg3y+IFD3Eyw9Ki2V1ldOMNYyQFnAV+KeH4RE1usOMRxVufB0iAGTSdK0DqbkIwxuNuM23V8W7ywvW9TbiLo/vfrSr/07A2S37xeBaMsWI21W3eGam6cw9MrnKyGDF13J+Vdh/jSyedZ67X50Ie+l+fvfw4/b3HVPCNTAWcuLDG1dz+lsQP4yiE/UiIKXKpxh+pqRC81NJqG9lCR6QXDzHQPbUH7W+dxJR3oNS3GgtUW5SisgbQriKMwCsQVRIFJDWL6Fe37Vu36pJhaVOjjBB5pnGCtQdCoMMBE9kV+yRfpP9XY1JKmKYnJ3j6UUy5uLsTBp9mJWFqpstqIuLDcYK0R4+QvMFws4uU9WlGNQkERej7tdoyIwhpN360Hxr6ouS6HZx49xnK1TT4MWF5cIWpHeMoliS2VoQHqy6v4tsXBiYCR4UE8N6XZ7mLEJSr7GGBwcJSlapUoiTHaI1dwEVfR7XaIcy6eZ1hZ3nrTZBD4WZyG9VUjSN/AEKVYWl1hYWFhw+Isl8uICNVaDcdxqFQq7Nu7F8/zMzfKhqL78Qyt+v7XrRUSBAFh6OEFAbXaAiNDQ4yOTNDtdok7EX4OoqiD5wrGasRRtNs1Op01sBptDYFbwFhIdUZaiCJOE6xALpcnSVLyua0tz3Wsu0vWP+tkuLH83/T3Zn+5UooLFy7wzDPPcOzYMYrFIuPj49xyyy24rrtB4NtZ4koEsQrEYkVhjMWQWdiO42Jtxg3Zit2S2iyeZNb7Hy9Y6FqnWKNJ4h5JnGJF0bYORpxt2yXt1Um6S9SWT5E6KV4gGCX0DPS6PUzUYKk6y6PPPsy5k09yTWWQd3z/9+F7gtEG189jxAcVEGoyZ4IT4OkuWkcbOtHfju6UdbxoqYrgiLxgfG9yuYh9oQE2X5HttN50DNmMazTbcDjHZubwHZ+Feov993w3Bw4e4otPHKbZ6DE6Ms7BYsw8PmcuLDI9u8SNN9zM1O4b0Sl0k5RGUCLI+ywuP09hsEReW+ildCKXVivh6bk2idGUyluTRa9tsVYQZVCKzAeuBKPBGI2jPRxXoXxBxxqbgtGmTyqCzZgcjMVag9EJjuug0ywoICr7R5xtYiNGU69WWZqbp5t6OGGeSt4SeoIRhy89fpTTM0v0tItSLnnHUD5+mMbIGMZYkmYL5QVMDpdw1AKBCrIAj+ehHEUSpfR/n2BLPHb4LHNrbUrFHCiNF7hoY/A9l7BUQcctDo4WuP7QfiyGCwvnGduVp5X6xFohKqGQL6OqXRxXKJV8klTR6kZ0ml1Gh8s4jkuaRlvKody+m6RPEEplNrX0XSgLS8tEaRaP6bTbLNTreMojTTWFYoG00SGs1pmaGMFVqu8bt5nfVAme62XttQ1pWWNxXEsStcgFOcKgQKU0iLKKSqnCW26+nulTx2jXExrNDsrzmLtwjm67gVIK1ymgjQPK0mjW6XQ6JDrN4kbGUCyV6LQ7G9br1WBdH5sDnetks+l1qheRcq1W4/777+epp56iWq0yOjrKsWPHmJycZGRkZIOwtiNxa20/wQHSNKXb7eIFQ2AAJZj+WwSMtTg2M3ZSk25wsu0HLkUEjEaJRvruv1q9TbFUIu4lJPHWO+9PHD3MSD7BJmuMTUzg+QnWF4xyEKCTVDl85CFq6RLD1wwTr9R57vRTDORH2T95LTk3xfPLKDxyBHi+j1EOSZK+oAvAmm9jEt8OW7lc1oOZFyOzasTErNv1W2Fupcbeyd0EgfDIo4/R7kaEro8UPB760gPcUNbUGymJhYldY9Sba3ztsS9SW2sgyuPgzXfSWZ1h5sxRkqRL6Ie4XsjaWgOHHq5YIp2QRN6WchiTxQEcR+F4Cpw+eRiDIw5pHGNji/gK18uyURzf25iwnKAfUOwTijUpyndQnoNNzEaMYRtXNL1uF3FyJLFB+UUSVeTZmVmWlxbodHucmq+RqByBH1IOfer1ZZaqNeaWlxmbmMB3A86uxDhenkGlCXwPnbrETorr+XTTFGNkWxdfs+fSjWJK5YAktThKMIklN1AkHJqgNT9NoZJnvFRhsb6EG/gUyjlsx1AMhEQc4jSlELokJkY5DkqDYyMKvqLRaNHzcjRbW++3WZ/01t12G/oTS9SLqLfauH6Q+cbDHHnXIecVNoioZ2BmYYlczmVieBgHQSwYDBqDUoK1CtnGfeD7AZ1OB8dxGBubIvBdlhfPMlDO84Yb9xMGionBUXQhJrWQaMv06QXiSNOLOrRbNXKFkKHhCo1GHVCEYUi328VaSxiExL34qi2+i4yrfmxq3fq+FJsJ/vDhwzzyyCM0m00WFxdJkoRCocDAwAD33nvv9m6UPjKfuM0Mnr67Jo5j1haWSLRm3zX7SLVGrKW1uILnuoSD5YutdSRzRWpD0uttBOBMt8nb3vwGavUazjaZ16GXgl6jtnqOQzftIe4uUSmNo5VCpynHjjyCUi2+4wNvo1Nf5vk/e4Cm02Nl9SxL7So3HbyFgpPgkBDaPEZCtHIIcgFKFO1OO5sQr0orrzaJyxUIetNXFwcf7EZneeEEQ7ZmcjZdaEA0oWuIIsHarcnTkYDF5RV812Xm3FnKfkAuyGFJqFWbOLn9hMkyY+ODRJ0OjVgTFvJ06muM7dqLazs8e/jhzELM5fE9Q2oVPRNy1+5Bnj9TI58rMJjbNjsIIbMcJDWQWsRxcVwXk2oOXHOAkdExgnyA8hQrSyucmj6J1gbRZKsUReY7x+CGAcZqjGsxXQNKY7QhTbZO11FYXOUwOjLKifNNnHKOfHGY3uwiq6tVSq7QiTsUQkF0yvjuPVQbqxBrBo1F3DwrtQR9ocGd+100Cq0tjuPiui7K2X6JCnB6tsrkoGL3gE8hcOmaFOVphkdGGJg4wPmV4zgqoNFZwKRNxCYkiRC6AWHgo6yHTnpEUZtmp0XP5FCOz+hAQN7GiCsUcgGdaOs3nXqed1Gf29xn17MtUq3xfI8wn8cqsEnm+202m/i+jxKHtWqN3ROTiLEoQNCZu48XJojt5DDGUCgUqFSGyYVeFviSGkmvzeJcjVazSSGXI041tUabfM7BcV3UgE8vgnqtRr26SKoTSqUB2q0uSZJkmSGuQxiGV02iwAZpX+o2uRTrY3dpaYmvfvWrVKtVkiTh9ttvZ2hoiCRJePLJJ3nzm9/MyMjIZZ70YrwQNM2sk2KxyJlT0/zV5/4cP5/jEz/ywywtLSEIeQONTofhwCGXyzEzc55CoYgX5vH9gGa1Sr1aRzsOidZ0Gw1kfo61tRrVeouJieErylHMCW7aYXQ4x+rCOYaDSQrFcXKlkISYxx/6C+76G2/n/e95O9PTRznx518gmBpDtXo06h0ePfEEY4Pj7B7cw0B5L1HSw7g+pEE/wzFLzfy2JHHpp21d2nmzDv4CcV86gLL0pb61rVK0jlBUMiIXgyUlCBVJbQHRwyh/65/o+8QPfx+//bufxQsUR489Qy9uE3U1BhfH87iw6uLmh/B6C+D7RGnM0Pgwo1PXMLznJs5Mn+GGN38nedWiowfpxEJSX2N+aZ7HuzXeMBRy46130ovr2+rDWouDk7mAlFDOl3nX29/B3/7wR7n5wHUkaUpQLuAGDm4+5OS5szz80EM88pWHePbYs9TbVUyc4hUD0kjj+w7FfI7SwABYQxwbhga3fplfp9HBdzsEfkhrbYbZo8+wWG9iReEFAbHRDFXKjI0OIo6i3kl5z3veCUGADYs05xYplkcJXaiuHUHlW3Q7HaI4pVQqoROQq9hX9u/+7ft4/okZfM9nYbmJMRprDF5pN/mCy5smr8cPchTyOZK2ppcmDAwUQRnOnl0hzOcYHB0il3OIogZ/8egsJ8402Dsc8q67J6mv1rn+wBRfe/L8lnKsZ1RsNiDW4wq+72cRZpvFEGKd4oUBwwOj1Ot1CoUCSZIS9XoMjBxiZm6BocoggwMDuCRYXnAbbOc+iOMelUolCyr2mlRXqihTZWJiEIuH4yvEtdRbq1hxCAoe7bUqYQCe66HjHof2jbB//34++5cP0Y269Ho9XM/F9318xyVxE9rtzrZts55OuE7aSZJsBDWTJHlR8NxaS71e5w/+4A948sknqdfr5POvHmryAAAgAElEQVR57rvvPm677Ta63S6rq6vUajW63Wxi2Q6Pff1r9HpdtDEsrzaZW1jC94Tnjh+j3azz2FPPcmj/Hm6+4RCViRGefuZZdk3tY6AyQH1xicefeAIXoddeQxVGWF1b5bkjzxIboRfHvGG0wt7JIqurXd729Su/4iZHxNOPP0irsURYLmCOH+Oeez1ue9MERw5/nXe+5RC3H9pDZ+4c7bVFQhHWAgcndjKDrChUnSa12Sc49vzjHNh7A6Jd9uy+hZxfJNQuqVLbZy/18eqS+MbckmVgZN+tO0PWz7nUOpEsUCcGxxHOzZzFmIQ9U+W+RWNxBSrlAsurWf5psg1hFIIAVxSOLXDtoRt41zvfyYVzs9RrHfbvmmCpXSaK6/gTg0jaZijXZff+g+QKe1hdWkYnivLIQXSnw9LMWaor59FxG2vAy6fkUBQkpqu39r2uwxiLI1AsFPnoh7+fv/mBDxE4LieOHsNgKYwO4iqL57mUR4d53/vexw3XX8fn/+Lz/Mmf/TGdpJPlzHpkQR8g6rVJsKCEem/rH94xnTaxMkjBUG9UOXv+LLF12LV7N34+Rxh4KN/FDYSCH5IPHdJmnZkza/TcPEmzSeD4DJdLWKPodiOiqIs2QpImpFoTJxajt7b4hm78AHd6DyHaEHUsvThC6QQvKKP8PIXcdTheDkcCbK/LWj3hySPTTO4aJlVlRsYHuOGWUdKOpbqqyQc5/CAicB0KgcIdUAwPOITh1u6DS1MMN7sPlMrSMdvdXpbO14soOIp2u70RkHIcRZparrnmANPHT/D0c89x2623MlgpoMRmaZ9sb4mXyxXK5TLdbpdGo4ovhv17RiiURnju6AUGhorEpkN5qAAWWp0euyZ202k36HQ6eE7A0sIcCs3Y6BCra02M1viuj+/5nDl9loWFBRqN5pZyrGPzylhrTbfbZXFxkcHBwY2Jb31i8jyPkydPcvTo0Q3Lfd2S1lrj+z5jY2MbG5+CIGBsbIxc7soG2JOPPEir3aRUqnB4+jy7BsfYtXcPlcERRkbGuOcdb6VSCLFG0+m0AYsXBojjIAo8ZSnkQwKvhJ8rUPZdbLuFdlwc0dzgWip5zdR2KZedLqER2jbFySUMjJWYnJpkZXmVL37hzxndVWf6yAkeP/IMdjTH2MgU9TgmmVsifu48/lAJfWACN5enS4/HLjzLEEXON+rsmdjLTaU9iHVJrur3cF5lEneISQErLrgqy1ros7cWEO3giALSjQGDUShStKkyPf0052aWuO22t6GsIVUaS4/JoqK9NI8TDKGtg7nsLzG9gJwfMDo4QGVwlGI+x8TwEHG9Q7dleObsGmutGEeF+H4J14fEaCK1l1xhD51oldpqlW7XELebrC6eImpXcawiXwwpuJYoNjQadVpRbUs5NlLXBPww5EMf/DDvu+deZo6fYnFtEccPGBseYWpwP51uk4WFBfxug9TElPIhd955Jw986QFarTZaG7zQyzYNuZZOt023qVGukEbbOcUTEmIc32NhdZFGr4UTFGjHbVa7Na6//WZKlTKtlTonT55GiWLQjrF47gLOwDiBH+AGRdIECvkKnqmRJD3ErOdIa3S6ffaB7wS4pSGsNeTyDcQtYKM2yg2QcIDuWguv4IPrI8qQMx71rsexR6eZ2r+HXB5U0iFqdnGVg8EhFxYYLGgquZBgdIx8MaRY3Hr5nm2geWHFuJ4Tvp46Nz42ztmZC6Rag4UkSWnpFmEYZrshrWV8fIw33nY7vutx9PmjTJ8+w43XXUOhEGyb8rmOcrmUbWKzlrkL57EmYf+BKYZ37aX+2DTPHjvGW++5kdGJcXrNJnFUQ4klH3r0uhYvp1DGZ3b2HHe98708/cwJTk/P4wdFlFI8/fTzpGlGxleDSye2Bx98kM997nN86lOfQmuNiGz412dmZvjCF75AvV7PcrsdhziON9IQIUvFLJVKVCoVBgcH8byt3aDHzh4nHwQ4riJfzqHFEMU93v6Od7J3zxShxEyMDNDppZycPs2NBw8xsmsMDXSriuGhCk6YJ8iPMTo4RjkfctubbiRG4YkiPnaUxdlTjE+NbSnHU08+jmiLyeXxnRx+LLTrdWyhQuXaKYZyHrVOl+VOg/raLO+7/V2c1C3iZhNZrdGpruFUfIKpAq41+IMF2qmiEVVpnFzl0G0DBEEZnXwbWuKJ+BgMYly8xOAAygBi0KKxBGT+7qS/RVnhqpR2c4kTxx/juWe/zp5rbqdSHkbINhcU8x6VHJw5eoZgYhLlGOw2Fp8xhuuvvRGN4szMKp/74uPUOyn1DmgjIAZRHlqFiOfj+RXc0rUsVRtUmxbdbVGrzdBpR8S9FtYkGIQDB/dzKNRMzy3SiRPSXmtbnYiAUsI9b387H77ve1mcPsOx48/Ti3tUxke5+fZbGRgbRlc1UnfpRV0unDlBu9MhVxlmfGyUhaUFMGBTg4ksNtCE5RDTixANRWdry+LRo3OIZ/HLXR45vkRQKGEahoXaHGPjIwSqjI5cRBURv8DZE8eYcjrkLOzZNYaTL+OHZXwr2Ppclj1js910gpB5wmRbEpfkJKa2ghN6rC3MEHVjPEcYGJ4gkBz1pVlGgyJWFDrpEQQOxVKJ2qkL3DYSsms8T7cWE0eaVs+irKUYOuRzPTylGB3bT6+5hDVbd/tsl5/TbxsLxgXR/cwIl2sPHSBOIs7OZK4fnRh2H9hF1OvRaNUZHB7mPe99LzffditRt8vgyAiNdovpc+fZt283YyNDWJ1uWORbaARjMuu1XqsT9dqcnD7D5O49HDi4lwe+dD/veOfthF7A4upZbGoYGh2n064jyqHZbFIslWi3uzRrVcZGh5idWaEXJfR6PUQcfN+9Cjm4yAWklGJlZYUHH3yQ6elpHn30Uer1+kVW+uOPP86xY8cIw5BisYjneTQajSxfvX+vJEkQEXzfJwiCbVcmb7vvPoLEkDTavHX/QYqjY2gNo8ND7N+3lwunjoG2dDoJx488R9xpMzFUplDK0251KPVSOrVFvIEKE1N7We3UGB4og+uhI0N9agqjU0rXXLOlHGfPnmbPtRM0rUH3NI20jXP2OMGEJikKxaDEbNSl6yts1+A6LkqnYDWOIxRttv/D9GKax85ROrSPVhiQDwUTdTl26gh7dh+kWBnftl3gVSZxg0LplIHQYSDn0G11cRwh8F1io4kDRQqYOPOFIsLsuec49tyTzJ4/i+/A1MTUhlUUeC4TIxV0d5liJaCVrFGtrlEsb/1ThlGvR67osFZt0FVTPDfTRjkuYRiilOD5eTzPx8vncRwXP1dh7uRh6ounWJ4/T7exSpJ2SIxBmRhXKTxfUankcR1NoVghcYRmst3gyLanJmnKD3zgg0SNFjMXZrC9HkmnS6u2RqPbYmVpkeXZU7RWl4iVQ726yolT00xdcy1333MPp05Pk5qYNDGYtoaewfVdQs8hNeC/6GcyL8ZfHV+iUFTEZpZ2GtBpKdJUM1YpUvB8Zs/MEBmFKGF0ZAiJppitNth78CZuuf0d1Lodes0qXmeVtLOEH2hIurgS4MTQTkB62/vEtSlmGQNKEYQjaNNBbEwaWUhXKRYHUKJJojZRs4dgWFxYJBfkKPs+nvi0ki6eq4h7EXfcMEa90WFieIChsQkk7eCK3jalznEcrHaz93NISrbHO0Epi6AYHRpkYnSYhYV5/HwBLyhSqZSwDcstt97M9334Q9z6xjcyMjZCrpQnV8iRxA7VZotitcHUrglS7LZZQ81mgzhONnYkBuQ4e2aG8dFjXH/99fytH/goOtY8++QzBKJRjkut3sZxPEoDJYJchdWlZQwuJ48fx+DSiyPm5qvMXpgjlwsxxm47ucILO07Xzz1//jzz8/NMTk7y6U9/ml6vx9DQENVqlUajQaPRYGpqiomJCQYHB1lbW2Nubm5jZydk1vy6n/1KAdLNuPmWNxMtLbOcnKFiXSZGxtFONhE5GEYmpxCTEJgWS/OLPPXIV5jwhaLnEBuHVjvGdxVhscDS0WlSDAf2TVGqlIm0poMh8IIss2sLDI8NMXboZgqDLonqEASD9AolTC5hdHyAca/EiWgW1w8xcx08PyCJE/xiAVUuYuptKk6IjkEtNsjtiknCALExcdLk9MwxVmrL7N93O1fzC5WvKomPSY2kO8/C88f48vxRTJKiDDjiI04BimUGR4a588472Lt3L2fPnuNPPvcf8RxhuDLOG268FZWuceHkg3hOAa1TZo9p8qFDN2ozs9bgxpveRqFQ2FKOaqPBidMnSLWHGbie8uAoQZDHdT2wQr6cIyyVCTwfqwJa1TVOPHY/7fYaJo0RNCIpoRLylQF6cRuIEWORYgUKKc0oomaGtpTDz/tE7Zjved/34HcSnj/yDbqdNu0oYqm2Qv30SWrNGje96VYWpqd57rljvOVtd7BSXeP87Cwnzp7jjre+k7GRcRbqc6QmRaNROLRXu4RKEXghQ5Wtd+T94WPH8JRPIQgYqQjjQx7DxYBm1OOp86vsTgTiLj4JZmyYwUqZW97+bvzCIKfnVnj2ycPkbJeDQz4DS21C5aC6IMoSRjG745CFpQQ/2ZrIHX8/ufJJxGi84ZDiUBnbSzG6h44btKodvvCnj6F1A9doWlGb55ZKjOwe5MZby7jiMVCaIEl7HNRwIO6gKKNyFeI4xfc8gsoApeGVreVwHKxS2fssXB+rLEKAtRolBqNcJnZNcVeuRJgLSXSPhdUmd999N9//0b/J8PgY+UIBpRRjY2O86Y43MT46xhe/eD/FUolSuUyjtopytx5+Whva7TYDAwPcddddPPbYYZJY+OrDT/DAXz7Ax37wB1hbq9JoOeQc2DU5Sn54jCAISNME10s4d36Z+aUaw6U8ew5cw7nZZZ588kkEnzhN8Dz/qtw7th/I1VoTRRFHjx6lUCiwe/duvvzlL2OMYWJignvuuYcjR45Qq9WIoojTp09v7PRUSvHEE08QBMHGlnvHcYiiiG63SxRFW47d49/4GqXhSQZvejPOYI7lqEHJd/GdIq0kpZsatAathE/8zM+w8rEfot5uE4YOs7NzzNcaDPg5aqurzM/NsTa/wPNHjrG0tMBau0OnUcf1feqrVf77n/6frijHgWv38cbv/ADfqC9z7XWT7CoMsWdsF41mnf/0yX/B9/7gDzN9YAqVJsz+6SI66dH2PNxrpqhM7cbvWlKx2Dhi2bTwbUJBW5QRtB+gdI/lxgLFlRPA+68oxzpeVRJvJXBubpaFpQs0e5pCkEPI3iOQ2i42SrGxz7NPP8Y3Hn2IZrMJStAIxUqZxGrmp59CJ12U5FCug58rkC8N0ulETOy7FbE5jNnaxHnm2BkcN2Bi8iDTi2u4o3soDQ7hOC4oh/2HDjJ37iTnL5xleGSM00fup7Z6GtfNI1hEGRzlEPg+3ahOLq+YmNhNMV8gMRZtFRrB2+YdDMZqpiZ38cHv/gDV2QU6acxCfZWFhXka7QZxLyI6eRytNHGrQ6sdsbbapNps0osTAt9HOQrX9/BCB891SL2UXqQxPYtT8ImNZb7Z2FIOVxTWahqtBkkMvajHNZNj5IsVonqTMxeWKOQD8p5iSOVxyuP8xYNfoVgqE+by5HMBjsqhcZiwe/EutJBmjEIRdlIaToLfc/C3CTjrtIaJgTTGSVO0MRitEQy9XszSwgITZU2KZXbZQVHk1Lkad99zLb7RxGmKqz16rS6ha0nrVRzPAeui/Dxpt43TSzHdrTdhZXncFsGhVY8pFUNwXJQDcdrFegkTe3dx0xtvp9tuc/rMce67427ectddFIpF0v5mK8dRDA8P84EPfIBmvcHDDz/EoUMHN14MpbYxxS0W3/dxHQffD5jcNcWFC/O02zHLCys8+OUHeMe73s0NN76FufPTxEmHVqvL4tIKWhuuv/46iqUKw6NjTA2XGRsd4S133sHXHnmW5cUanbiHkijboboNNqcUpmn2JsTZ2VniOCaKouylYUoRRRHFYnHjZVdRFG3EEtat7lqtRr1e33hnS6lUwnEcSqXSljKUW1XmDn+N8wNjjN/9HQyVh4nKLp0opmdr9JKk/6I4Sz5wCSpFdKfLxPgUec9nVxQTDpRRIsStiPm5BYYGyrQaDZ57+mlOHj/BieMniJtb9w+TZvtRur2UXqNHJe+xMjODDQSte3SiDkMHx0iaDcLAx1XCuM4RIegEEqCnY9LAwI27qPmaQWuI6hGmG1Mq5CgUK1RXzmzbLvAqk7j28owfuIWh3XuZPXeOyfGJLE+aFG1T6PYIvJhac5WVpVmiXky5PIDnheRLQ2h8gtwAiXLxvBxhoYJXGKJYGmJXoUKhMoXrFdneieHieSGFQhFJFhl0a+zKxWiTuTZmnznGM8+dpFAcp3ruKRbPPEroZ+9rcFwfxw0JfR9tuowMlzlwYD9DQyOErkO3WiW1ltRY1Dak5Tgu7/+u93HjNQf5wvMnOHn2NLOLczTbTVIdox1Dp9kjd+ECNoV6u83chUViR9No96iIw9rqMvV6lTTWiEffHeRgxOCHHr2uRsdba+SNh/ZRHszTS3qcml6k2uzSO7vAgT0T7Bod5OjpGVY72Sae0phDc7bGuTMX8B3L5PgoA+USec+hNDZKce8bcN0ebgTiOagoRnXn0bNz2M42O1g7j3Jh9iy602NoYBiFAZ0FZ7URlhuWetXQ7Pk8+fwK44OW++66hXvvvp6kmxK3m/RMQpIK/kABMBjj4sQxjnKzXb5W+nsMtmoXJ9vVmABxwlCuSNek1HotdBCz78b93HTdnRSCcVYWV3FyLm98w+0UCnmiKIIkxvW8jIBdl4mJCcTC3XfdxZvveBPTx5/DcZzLZGJdjGazibKGqJXy3PwCRlvS1NBqt4i15bOfe4iFxTYf++hH2LNnkmqjAQLHTi3TiSJOHj/KhZkzrFUXufHAHqrLSzjWZWSkwtz8Mq7jE8cRnr91ts76i6fWX3zVbDZZW1uj3W5TKBSw1uK6LufPn2d1dXWDrKMouihl2FrL5OQk7XabJEkIwxDP8zbSD9vt9pZy5PIFhqMlmk89zbHZGXJ7r8WrjLOw0uDAwSnuettbsxd9KQVJSimfY+iGQ1ngTZXxOknmfvF8ikGOXD5PoVCg02mjleHa22/lxuOnePDPP7+lHIHrQC/Cs5ZTi7O0GlUKOR9VCtBln7VmnbhXJvRcXFcwccRkLkdLCVFg6cRdWvUGsbRoq5S8soRRStpJ0XFKcbzA6mqNzsrslnKs49XNThGF/H/svWmMpdl53/c757z7XWuvrt6me7pn5wxnyBHZlESIdEQzEmUZsRVYip0oDBB9cZwAAfIpSBADQQDLdoIASWBHcBAFtAw4igTJlhQNKZESyRmRnJ0z0zO9d1V17Xe/73qWfHhv1fSQmqr5YNBS0A/Q0923blWfee/7Puec5/yf3z+cI4rnSTqCeO5UDXsRDiECgmzIwnzBcLzNy6++znSaczDMePKpyzz51I+DDTh99pkZJU+BF4HfJAjC2aHKYVfg8Umr3W6BtYzGffJ8yJOPLdAIy5ruZg1fe/lN0qHB5BnT3ntcvHAaox3Xbm4ShS1836PIhyByPv7MTxLHcQ34yXKsNlTG1HjcE3SeH3/6Wf76F3+ObJzy1rV3uLtxl6yYULqKSVEwLHOaYURjPMFNK/qDEdt7+/jtkGmW0W4mDEcD8hm3u26mcgil8DyJkRqtHdUJu+XxtMALPNqdBk8/cZGq0gxHA7Z29hn0JsTtNkU2YTQpWN8+YGG+SyU88jyj2txmNBiytNjGl46D5Uu0H1mlvbCI6yTQbtF+8zWSPxgiJ8eXMXq3XuG7b9whEiEXzluSUGCrEolHFDQJkzZvXt9gah2T0jDdqPi7/9UnabQD8nGK9BRCSpST4Ets6GMqha5KIiXoZY5mu4U5oY9AzSRpZa5ZXexwbkWyMy0YDA6YO9ti5XKX04+extOLJO0F1rc3jzCoxhqM0UwnU0xUH17mVUaSJHzu859nvtvhvbdfnyXxE8I5yqJgOuyTlpYwjIgTnyz3yYqQvHC8/sY7fO4zd+h228zPd9lYv0X/YJfeYMhmbnBUnDl9quatZBmN1jztdhsvCHBGUpQ1i/u4SNOU7e1tBoMB+/v77O7usr29jZTy6ECzKAq2traOZIiH9W3P844AV8YYOp3OUdnk8GuHmvOTkvi6NoyykvOywL/9XfbvvMzNLOJPD0qI50nTMXMLC7TaXTrNDjL0aYYBXhzWuN5SYGxOJQqqSpOmKQcHB+AcJrfcubfF/v4Y5UXHjiP2fFQ+RUxG3DnYZMuXfOyZZzFK0zy7xMb+Nu+9OaAVeKTZmP7+Dvc8g45DJlBr+j1BOKpoNrssNzqM3trE2IC5pQWGg332tjbx8o+mGvq30nZvrSMvSspKI2etsDhBXhl+74WvM5z0sX6Hi489gbgzYH7pCeaWHsNoNaORzUD7WAzmgxyKQ+H5MeH7AUkYMs1r+M2Nm5ucObWK589jTMnuwQFFJpkMNlmZUzx86QLDwZjN7QN8D6bjXZpNn4cvPEaSJFRVVfOVqwpjNFpXVFqjTzhA+/e+9PPMJ23+6GtfY33zNlkxYVrmDPOcotKUxjCuCg6iCS0rsaVmWKQE04oo8AjikHvb2xR5gYoFflIDsJACWxlEKAh8QXBCzfPuzgF3t3fwJDRjn3a7QbfT5eFHH2U6Sdnb36fbbpLKkvF4TBwFVNrS6szhCTDKMNEV/XzEnXQLL5qQVFPW5ArdnX3sN/8ItbdDJY6Xfg53xmT7jqABS50QL7AUeY4nHXHscUo2KQxU1mdhIeHelqZ1roUuJdIZvKSN5/tAwORgh8m0rFvepWBrXLG7N+LJJxsQHJ8+lZI4LEHoMc1TMpMQdQSrq2385ZgsHrGTXcOretzYHHNbp5wZ9Vg9d4reYMDoYIBygqW5OQbCMRj2abeanF5dYTIaooQgCsMPwrH+nNje2mY8GiKcozvfYXFxmf6gx8JimygKOb12kcBzNJsd9nf3WDt/mnYzQBcFe9sHNP2Yjz37BFZoOlGDIowJkw7jaXpEyQsDH2uP/1yuX7/O9773vZlefcTW1hbb29tH9XEhBJ5XCwMOk7RSiiRJWFtbI4oiXnzxRUaj0REBUWt9hNIdDodMp1OMMTz33HMfOo5BNuHatGQvNXzWOebdhEjk3JWC7XTEa1/5Z8TNJjTm8ZIWKmnQ7LYhjJDKQwiFUvVCL89LinTKdDKcweUqbrxzldEo5dzi8WWdfDyhd/sm/f1t/K7h0fOXqaYpmbSI2GeYjememsMzFWUg2bh9E315kcmwz1RINJIg1fgb+1z5/E8yGUyYFGUtlHCanZ11sukQ/4RO68P4twbAOn/+/H1NFbUm148TFlYeIppb5flzD9FsdtHqVZ545jkybbF21nRha9MDgUXMhOY/1J5/THTbXeI4QU4njPuv84e//1XCJCGKAqqqJJuM8ZSPyQfMdS4iEPi+BzZnNByysrzIk08+weLCEuPJlKLIZ1roikobcqOpnEGfMJ7Hzl/iG1//Y178s2+TphNGecr+eEqlDWHkI7XEaMPBYIhKGuAsaVFQugqwpFnK9u4BQkn8MCDxY0QoEFbgJT5BI8JreCdyvIM4AFfzrSelI90v2N3fptsdcOmR06xvTMnSkqTRxGjN3u4eRTFFW4unJMpatvcH3PU0q3On+ebLf8jYaZ5aWeNLaoEf722jTIVwx99uq489xad6N5FByOqFsyhfYnWKqQokAZWyPP7MGYZZyWRYsrK2jMynELUIRBehZhA0XRJGMaI7h5IeWluy7QmPPXqB9nIAJ5QahQRnIQwlvczy6t0BZx4PYdXHtDwmcsxO/00CFrmeFtxRcPH2DZ569ikazYS3X3+Dlh8S+z7C6vqBFJqJ5+jt7ZFEPlXFidD/8WjE9tYuo/GYldVu3a1sS8IgIk4irnz6s3z8mScYbm8ihcWZiiLLuX1rk+2tHo9euMDZs+fZPdgh9EOyrORgNOXO+ibWOeI4wPMUWXZ8x+arr77Ka6+9RhzH5HnO7u4uaZoePW8XLlzg3LlzBEHAqVOnkFISBMGR7nsymdBsNhmNRuzt7R0xZg454t1uF6XUUcPQh4WzFtls8u64x4KpuCA9loKcf6fjc7vMWUn3CTNFvh0ytZapk2xo6BlXS4elxOCojCWwELsK7TRhs0OYhIQb2ywEMb/4yZ88dhyTwYCN996hszDHY6cucvDSO+RJh+KJ8xRZzuWPPU2/2WY63Ge9t8/6928xnzxLW/jkyiMtNMoq9icpo+1dtnp9dOzjG8X+7iba5TTmEtTk+J3JYfxoJYb3sYYPE3jNaa7NHLQXcPbSc7POTgV4fOYznwWnQRQo+QNJ0dWHVu93dd4HIT8mnKtlhlIogrDC2gKTjykqr6YGOokuc4RwLC8vMdft0u8PePSRy1y6dIm5bvdoVVKWJWVZUVUFZZ6jy4K8LOqVxgkz6Vd+7dfZ3LrLNBtybXcPIWtnH1z9+8MPX+LCxYtM04xiMiXaP6D0Stqdbg2qqixnTy/gnKPZbFAW+QzGr2cMa0euS7L8eBMEkxYYZ7A4UAKLw1OK/lTw6mt3Wep08T1HURTs7x4glCJuthns9nDOcWqhTbfdJJTwytsbfOe9XSrP5/t3bvCb7jr/9aUzKCPhBCpb98zP0fnxP0YYAVGEjRJEuIjvHDad0gg1zz5/lnev7TAM1zh3OuS9P/saTkuEciQNQ7t7gebZp2BxhXB+CYHAAx5fK7DpPkrnqPR4/f79HZvdxS53dm6zmISEqxEqsKzYgLbxcZ6gO99G9XPmb/TYe/E1OhfOoXDIho+LFeNhnxtvvsnq/Dx7gY/GEvg+AjAnIIKvX73OzsEAJz085bG19V0839Lttskzy8b6Dq+/9hB/9XPPs3l3nZf+jz/jq998i5v3BgNAqNgAACAASURBVBRVyV/5XMmX/sa/y1NPPMXLL34HjeSf/p//nIPBBFsagkDSajcYDo9PA57n0Ww2aTabTKdT7t27x2QyqWW+Ycgv/MIvHNXBgyCgLEuEEJQz842FhQUuXrzIvXv30FoTxzFCCLIsQ0p51PF5yB//sBBhzPy5i8RhwHfvrvPCMEWnAR2V8oTNWQgDHJrIm9IyAmEd57ya22Mcdb+CdRjryIxDAxmSZK7BaKfPG5HjP/prn+XiuQ/npgDs7vbR20MmgcfVP/omn7j4CO224d2XX+W//Hv/GX/2x3/C+sY2+/v7TA52yfIx3rU3GQ4zoqjJ6N4uWmuWl+e4+huv0mi0OLW0TKEtXhixsbOHcRCceLo3+3w+0rv+DcVJnG8nBOAjnQFXQ2B0CcoLwNkZJ7uGegrAzVrMD9Gsh6wBIY5fAQvhgRP4SoGzGF3gef6spmlZXDqNMQX7u0PSLKPZbOCs5ZlnniKOGvR6/dreylqqqqzLJ1VFqau6tdjUrdUn6V7vbW0wyadMXInvge+HWM9nOh0ilWJxcYnhoEdVVUymGZUn0JViNCmp9B66LNDGYmfJURuN1Yay1Cjp4QUSJT0qffxkUrma/e0AYQVSgMTiBz6hFxIpw4XTC3RaLXrDKddvb2B9SbsxR+D7IB037qyjdMmnH3uCpifIdO2jmFNSjDIiE6LVCbQ856MzC87gxRHSk2A1oihxZYaaZuTjKaEXsLokIO+zuWcoqhJnMpY6DSrdw2/v40cNFOAqg3UaazSqKDCVxp5QAz5y7XGOGMGcFsT7Oaf0MqXISLyQwAaUSKT0CD2PhVaL0Fh2bt/BE9Af9hkMe2TphLWVZXznqIzG8L4m+qT7w2CIGwHWifq8xTm0gV4vY3+vz/7+lDt3b7G3u8ntm3dZv32Pvb5BSsXlRy7yyGOXuH71bU4tzjPJMt65tsH63V084eM8CTjmuk2m45MntUajgRCCOI6x1uL7PsYYnnjiiSM1zqFy5TCBp2mK53nEcXwkHTy0dzush4dhSK/XwxhDFB1fixZS4UUxotEiaYb4VAwV3Mt9NvOShytDV0m6IiCWipiaaFgbxcwSORaNpZrlDjnroZgKzdzCHJ9eO89+dfx9aquU8TAlXFvFOVBS0dvvkQ7GXPv+O/zMF/4qnhcynaS88car/L8v/CsGvS3yXBN4EY04qbuSAx/ZaFAWJXtbW2RVxfzSMoPtLYx1VP5HS88/WnaKdLxvlHz/KsSCqPkhRylaOJzQeKGakd9q2opzrt7rSlnL/eDIrUNKTrRmA9DaIYQ9MqAo8hJCUCrECxp0Ogm93pSiyLh+4wbOWZ5++mOsrq5y9Z33SNP8vp9VzX5ptDaUusZlWnNyE8UgHZNTUeLzt3/5Uxgr+d5L9yjyOfAhbsONq1uURUme12bEWV4yM5jBGYexZgYVq70G7czhREqNbwOUZ0/0UIzD+kpaJ6mqeleDszR8ePjsHA+fXkLrkoP9Hvu9IQ9fvMDW7gHCliy0Wtw7mDDNDOdWF4niBgudLsO8oBEGLMyvMA48tnsl1QmqEAhrRYgo8IXDVgWYEpGXuHKMnRYszrWRnsNzKRQOXWYEgSbLA7xgAeV7GF3gj4cQBDDrlJMiqL0TnYUTdkiHO0SlFMIY1vw2F6sWC8M2vW6C9gOs8tHSwzhL5PtUccnBpM/YWeJWQqAkg709xsMBfuAhtUZRe8weqjlOMuuYFvksKUJWGKQUSKtQysMPGswvLDAajfiTb79GnmuUbBAmmm4c8NTjD9PvDdnb2iMRjkwbvvntVyhSUJ6g2+1S5ilVlRHHx38ucRyTJMkRUfHQ0WdhYYHnnnvu6FD3sJFHSklZlrTb7SNT5IWFBZrNJkmSUJblkdoFOLJ4O6nt3jlBkDQJOvM0+ntMKGmjiVRC3w94cThEZYbASBpCMxcGzFtFVyqksLP+jgKEwCIwFmQYY5VkwzNcXl3mbKvBd8bHs2RW5lqcXVnl++t3+KnPf4mf+emf5Xf+9e9y694dlJBsb+1w4cIl4tixsLjAz33pr/Ebv/G/YywgFUIpirzi7sYWzWabPCsIowjlBVSFZmlxkSzN8NxfWJ74n5fYZoTC2lDpvmqIw6F/oDri6oR+6P5z6OQx+76PgozRWuMpiXWW02unuVXeIM+nlFWBlAMG/U08T7K8vDSTLCmcFbz5xtukaXa0UgNxdEBTluWRaez9qM7j4uGn15hbPc2dm1vEcz7Dg5KwWRDP12YKC/NNbuBmJtCz5CzEDPwlEJ7A6sNWaDWzpKqdTA4NZKuiOHHb/vjFs1TakVWGWxu7ICXddpPL51d47pFzbO8PeO292+wfjAiCgHhBURY5a4sdFpoBG9s51hkKbXn72g3GlWXt7Apnu00SFF/feJd8rBm3TmgjFhXCl3hWYasSV2iUmR1aO4VKFljtBszlhv2Na8TNNr5cRltNpWsVTTK/SBD5mK115Pwijqo2CJAtTGZwosCUx5/6B0EwWy07nBA0wgbnvBWqLcfEAY0A3wkmVclootG5xzB2eDYHoYgCH18I+lvbKOGYVQvrXY7lA07vx0We2nr16UmcsxSFxtqqNj+2juk0ZzLJ8DyPJA5IkgZVs8JYg640zz//aXQ5ZfMg5f/5vT9i/d4uzWaTShdM0ymnTy2QpQXZCbr5JEmIooiiKI7u68XFRc6dO8epU6eOGOWHLfTGGJrNJkop0rS2g2s0GsRx/EPEQ8/zSNOU8XhMmh5fmxcAQqHCENVsE9l6x25kTtODNdEkLSzTwtCrBL0K3jUVQhh8UXu8CpuAEThjqJxhvtEhMoJ9a/ni2TMYpzkYHp/E08kI2RR4ztKKY4q8YG11FWMcr778Kud+Zo3pZEqW5eiy1tRLFZDmExZEPZErz0NrQ1UZJpMpvh/xyCOPcNDrMe4PcEVOUR1fBj26hh/pXX/J4mTD1fpAVErB2toanic46O0dlUi63Q7dbpd2u8O5s2c5/9BZ3r16gzwv8D0PrSuU5yEERytwrc0MVnRY+3cntnefuRTwzCfWeOZjp3n55T/l3q2c1cZpFlcaOAelzukkAbaoEL4iyyqcMYShYvVsl7Dls7Xeo7czOXIwEVKgpIegtqbyw/BEM4Y0zamsIy1LjC1YWVzik08/xpnFLqXOeeXt6+yMSlARvh9ye3OL+SjksUcvs9Rtsz1IaxXNcITxYjJtcFIQBB53bt5jWwuanS6VOv52E0rhJx1UFc66eTUOMEWB0xUiWULFAUoOaUXQaMSEzaV69WfHiEAhwwBblezdu0qSrRJIAcbiZJu8mOB7HtkJ6NXD5KpU/R+pYVhYigHcKCb47ZBEWKZ5SZpJykJTBPXZjLSQZVPcANAWiah3R67mxh+qToWsFVnHhbM1l70qNZ4nCYKINE0ZDacEgc9wMCaM4ro5yTJTaZWcOXOaS5cu8dKLL/P9119jaWWOG+tbsz6IEikFURyilKAoDO6E5rhDJ584jvF9nziOabfbtNvto/copY5W1mmaEgQBaZp+wKJNqRnPZVZbr6p6B3vITX/33XePHQfUnpxSKKKkiTMVpatQThA4RcdaEiVoegZjPaZlyUBXGCMxRtTiCDczjlF1nlhsBgzyAbkQPHN6jdRaWqtrx47i7sYWy4uGsqgo0pRbN24y6g9w2nDnzh1MqdFlhRKS+e4cL734Jywvr9JIOszPL9JuaSbjEa1GwunTZ5mMp4Dk/EMX2NvdY9zr4XsSJf8CUgzvr4nfvwo5yUD5/nifLz7jL9yXoH6Q7/BhUR/CAAKiKOb0mbOsnV470rgaU9HpdPA8j8FwSO/VPs1mkzNn1/A9H12WjKcTRuMxWltAIIVCCIuUCphR704Yx9vfm/DmS1+n2W4xN3+OCw9H9V7EWYTnkTQ8PvdTl2pXbiUwBuIwoNEKkQIqA0rWW9DeQR/rasxnHEVYqxkNh5RlwXBwPNf83a0BOI01Ob/0N36e0yuLBELQH0357ju32BxqEApMxTTXSClZXDrD966vMx2PuLc7xDpHt93hysc/zp+8+Gfs7fXoNJrsOAlZzM5kilk8vjPQlfuIYZ88T4mSGGEF1uZ4wiGCCE9ZbHpAtrdOy6sQZoRX+ujpEOOquuZclgynI16/fUBwL6chFb4E4QtUlOCJgP4J5NVD44d6FyaQYcAtC0b4NNwyu9cMu4Gg7TV4Mp5j3+/hlQYrHE5ZqDLGBynisHw0azByWJxw2NpR78TSX5qNCcMaIJVlGYPBmE63QzU7MHROcLDfByRSOpaXFzDGsL6+yXvv3iQMI6aFZXBzmzAIMaZAi5wg9FhcXKHIB0gRcv786WPHcdhNmabpkdNQp9NhaWnpyEAjSRLOnz9/5L+5vb09G2Nd/9/e3ubMmTP0ej1u3rzJ0tLSUYml1Wpx4cIFHn300WPHkeUTjLYIFUCcgC0JpQBvCt6EyJf42iHzgqzImUsUkYFy1iRlrcUaqIxBmxpUJQXovMKTkic7C3x3muJdfuTYcYxMgFcIRrnFIplORtx+7z2Ertjb2uB//Ef/gE5nnizLAUOajYjjiKIs2LqzyXhcIwicgPU7G1jjkMrj7u11RsM+P/8zX2R1eRE/+Gjep/+/XImfPB9YEAopBcZWRzV1qBPo3FyNQx2NRkfO4IeoTClqxw0z4yIj6lWVUALpZO3MoSTKKaQ9frt8b2uTJEwYD0Yc7B5grcFTgiiMGacpnoJZ4QSp6okv9iXKUzhk7d8ITPICozXWaspSE0UhceDV43PuRM9R5wTCwEp3iUfOPERVFuzu9Xjn3fe4d3cTqeuygucFBF7NFOn3xjP1gUCoEJdPKAY9Hr90liwdURpLIwlY6TbYKCoG+ZhWeTzDZdTf4MZrbzAd5TS6XcIgRgoP69Ja8pfMYXVK3t8mMyk4gRcNcFWBrqqablhWTE3B1p7EipRG4NPwExrNEM9IMJb9wfHb1PfLZSCVwvMlnoFQeggp8I2hKC1Na2kWI0Iknifqaz37GULVKqfaP8JibX0AJu5jcp90sNlsNrG2XnQgJMqTVEVB4AdYa6iMptls4Achg8EBeZ6hhCIMInC10XJeOlzmaCUhnoAw9inLsi5/oAjCCK2PN2Q4deoURVEwHA6ZTCasrq4eKVPCMDxiil+5coV+v0+WZTz99NNH+Nl2u83169f5yle+ghCCp59++oiTLoRgaWmJMAxPbPYROJQncdrUDTNJm6rQIAvwPdQMWhZLgRcFOGPxiopcObRvsTPRQaFrnfpCN6CoxiAqVjstSlsxROGfIHVEBmztHmAkrKyuorOMrXvrjPoH5KZgsn/A3Vu3DxtaQIGnxOxsqi7DOmeRnqDb7tBqtmm2WjQbLTyluLe9xf7+Hlr/RayJu8P/hfoDue+W59hqtvvhLx8ZTIj3f8bRayeUD5586nGAetUsqqMb0vMUURSilM9kMp6hSCW+79NqtWaI0voEPk1Tsqx2SjlqS640eZ6TF/XB5+bm8W2zBwdj+mJMEHj4QV0H95QgDDLKUhOHIdbVJsq+qicdSovDYS1EgY8TjjTLicII5SyBFChn8YSjtBqjDXl+/EN68flPcvOtt7Fxgz986RWyPGPvoEdvMKAyFqdq93dPSrSt5aGTSYF1ru7GQ6KlhxOC3/v2y6R5iVAKfTDAWcdQW0wYok5gY/zub/8hr3zjbayt8bVRHOL5ce0jWtVlJF2VGFPVjPcix1MBSiq0cSAUxlUY5ygrgRWWJCiJVYEfeORVjjWWqjpZgipkfVCHEBiYbW0tvnG0FIRSEViLZyqQNaHTzcomCA5Pd2YPcq2pOtRGu8MD6BN2as5KyrJC65IgatJs1tfBaU0SRRBExM0GaZYTF3WSxtYWcJ6ShFGA7o3wfIkUFt+fKTUMDPqj2q7MByuOTxaHyNg4jmm1Wjz00ENHde0sy2g2mywuLpIkCQsLC2RZxu7uLnNzcyRJQhiG9Pt9Ll++TFmWRxLDQ0ztYanlpM5RHBirZzsahcHhRY0aJKYURld1m7tWYOqSZm1IW/vlOuFhFVgFvhS0W212dndwDs512kx1hegu0W4cv9iQViBljbZ44YU/oOH76DLj1MoiuSvpRA2k8PD9gKLM8DxHEjcIgxr8JVX9DCEFupp9LYrwVG2WvH+wz2QyIU0/Iuf9ozbIPIgH8SAexIP4ixcnQ54fxIN4EA/iQfyFjQdJ/EE8iAfxIP4Sx4Mk/iAexIN4EH+J40ESfxAP4kE8iL/E8SNVp/zLr7zhjNFY644kXIfcE+eY0c3e13q/f+bqQFg+IDuxCussQkiUUDO6Yc2plVLyS19+7kMlCH/zb/2ii6MOzz77LPfubbK0tMTVq+/y9NMfY2NjgyiKmJub44033qTfG/H0M09z9epVnv34s9y9e5dKZ5xaW+Jb3/o2C902ukrZ720T+G3WVs8dgbGqquKPv/7Ch47jl/7233FJ0KXMcv7mv/9LtJurlKXD6RzpGQ4mZ7CmJAwUo8Etvv2nv8mt69/h+R/7JJ/4xDO8ffX7vPrm6/hhk7/y2V/g01ee58Kls2ijmZ/v/OA/96Hj+F//4a84ZwXOKUCgTV6jdbVGeYooiHD2g5K4199ZZzytyIqcxfkGn/3xTxD5HmmaY7QlTXOCJCIvDHlxF2enWBfx3/73v/uh4/hv/v5/7KypqLJdrCtIhGZVhURBxJZrs957C+UWKEuYW15lb3+C7wcsdpao0pyG38QUNUb29vY1FlbmKE2PzXsDsjRnfmWZX/yFv8vK4iqfvnLlQ8fx93/1V1x/OsGXinI0oaw0pTZU1jFJcxrtiLTMCaRksdEi1Zqx0diyIgkT8jxDUDsmrc4tUKQZ0jpKXVFZi1OC6TQFHP/i17/xoeP46SvPOKsEc40meVagTQlIAilJIg/fiwlVSK4zlroNKq1Z3+sTB36tnlEKbQzjdEIYhIzTlMAL6cYxvq8YpYMjl6Ff+70XP3Qcr7921Vlbt/9bK/GVQAmOlDZSSqSQFGnKZDyhu7iAVPIDapyjECCFPGoCOrRoO4xHn3rkQ8fxH/z1LzvPUzSbTQbTDKdC4kChbIG1BWdW5+mNckIVYKuKOElQXkh/dEAQCsKggRAxZWlQSlFVBdN0n6XFU4RBzCjr44SHUor/4R/9dx86jp/7O8+73d0+YRzy2nfvoKTEWkdrMapxBIstlA3o74zxA8Xy0iK5TimsQwmFlRbpwHchYaJYXGhx7vwiS/NzaM/x9Re+QzpOyVLHe69tnCiY/pEmca2rWrs8i1qyV//5fvunWo5Vv37491pK+L7GFjdr+HEWi619EY80uMfrb6WUjMcjdnd36ff7KKXo93sMBgPSNOWtt9460oZXOgM0Wue0WjEITRD4rKyskiQJw8EAqEinKSkVzjrKsmAynZ5ov4VTVOVhmz4z3bVCeT5J0mZ315C0QsLA0Fy+zJVP/SzXr73ExsY9vvDTX+Qzn1pm2J+yvbvNwe4e3/ve60gfzpxZO2JWHMZxzVT9gz7OCaTwaLXbMz28oJzxQ6yDoixn/GtRd+KZktWVJYyztJseRlcMpym+5+NJSavZwCmYTlJ85aOtwvOOb17YO7hOO2nhnGY0yYjbCc6HUuSM8wlVZfEDTVlorl27Tmk8FhrL3L53D2EVnUZOFHh02h1WkmUO1nsgfeblIv6SYr/sc/P2W9y6doNPX7nyoeOYjiYgHGEYEEQJJJJCGw76Q3RRUWYxASE+AqMF1mgwmjyvMLpCWPAp8eOYXOdoNO0koZxqPN9jXGRYCUEQHns9iizDeYqp9MAyo1NqvCimEhGB54giiaLBNK8nXg+LrUqM56OdIy1yxKy3wSGojCFpJGhdzqBvikbSPHYctVXdYaOdOyKR1nr6Qzm0xZrD5+6Dybtulz9ctNXyPKUURmscDmNM/bo4vjBgrUYp/6j/oeavOJw1RFGAlBLP92nEDSajEc45ihkOo9FM8AOfPKuw1iBkvTA8zBla6/peF6o2nDkmFlfm2DsokaI4mqw85WFmsmCjDVVeEscRzuoawmYE6TTFVIYg8YliDxWFTKZ9qmHB5js9xsMJn7zyBCoIeOa5M3zErvsffbPP+/zw9xPL0Yc968asLyxHLj2HpMIf/BlSKZy1CHfIEv9onZ/z8wtcv36nNowVgq3tbfIs49XXXpvNqpbBcEiz1SSKfd67dhVtSl55/XtMJhNwjjffrPB9n+1+H6gnp7IqMUZzeBNzws3grMTOJiel/Nk+w6CtoyoNAQWRr1lcilEiYnH5Kf7v32xwsN/n3au3+MIXfpZHHr7LxYcepjG3yuqpNeI4+sCEdv/1/bBoNVqUZVXvhrTFGA8pI3CSPK1wIqMqC6pDWmNZcWZthUZ7gc2tLTwJSlr8SOJ5M3ch4dBOEIUBk0mJrwKkPB5wFEUCz5dMUkPkBxgBO1WOJ72ape0k1hSEXpOiLJimGXKYYktN0oiI5lsszM0RxTGeFJTZBOMsXhDTajXJBhO+890X6CTHo0adMURRiLQ1k8YCvlC04gQlPXKtafktpFMkUYelZkSj3WTj3hb3tndJogDlauxBpnOEgHE+YZSOaM91aQYJgYrQJ4G4bIVwAqQg8CuixGPl1DxVCTu7JYlQSM9QZJrRYAjOEqra+1VKD1vNdqqeYjga4UcRRV4wSVPCwKMsNZ4H5oT7QzqLcwZhA3xpZgzRQ/eVOplKIeumMamOYGx11zIIWzfXWQnpzAlIGkvYjKlMbU4xnIyJT8IyCEdZ5khZd9VaJ1BS0YgbWJuDqxnthy5CWmuCqHHEflHKQ0qDHxxOFpJGkuAphbHUAC7pn5hDttennDu9hBfC1bcPKIucwPMpxyUmL5EyIpABpc5ROIa9IasXHsK4PfLRlEhKiqpAVBI8j0JriomP31zi5tv75J0e71UpYXS84fth/EiTuBSungEPk9yMM1LPwve98dC5x1mkFEdJ+/4Qom6NdrPV5uFq4cT2RGBtZZlHHn4UqUtaUV2WiaKItDCk2nFz/RpzcYP+3gFualGm4rknnyUOYhYWFgijFkmjS1qO2LnxHrnVNOe7OGXZ3d3ht37ntyldRXnCVCqFoqoq4lBRVQZPatJsSlkYpnLCc889zNlLS/iRxIkCR8Avf/k/4Xd+63d4973bPP30hMsP/xhLp2POPXmGwGvgSku/3yeMApyrYUMn3ZRZXtQdgJVmkk2wAsIgJE4SkiggjrswY4wLUT8oo37O3c179Pd2OLV0mf29HpKQnd0DjNG0OgnLSwsIC7IyjAYjEMc3cxQ9SyoHVHZCJ4wZT3IcEmsM07ygxLC/PeDZ0/MkcwFvHlS0uzHt5QadVkgUBXihT6U1caNB2O5w684dVpdCpPK5vPox9tK3mRbHu0L0JxPMaEQSx9isoNvu4KmATqtNoAoW587y0NIKcTjHx57/HKrSfPWf/xo/9sRPUT3f5PWbbzBM7xBIx62DdQoc43FK6PlgDQbHZJyTTo+/Hn/ri59lY7fP7b19vvRzH2c6KVk51SRLp3zta28wHEkG04rBNGNaVITS4/LpBXw/ICssaa4ZHRSk0wkgCTyfRjugNBXpeIpAEoUJg97xWAZPSoyVHBxsEISOVnOVqtTs7m1x49a7nD17Gl+FTIcp3bl5WrbNeDjht//Vb9BoCc4sPsrifIPxWPNP/pd/iktTvvwrv8zZxy/wzpuvcOGhy9zdHPDqq9/l137yUx86DucMQRjQaidMC4V0kkA64kgxHI5qtkteL6LiOKq7IoVjPBySlxOajS4eIU5YhFJY51C+h5s19TmpqIz5QHnnz4tPX3mEb3zjda7d3EZ60G22WVlqc9AbsbC0RFVo/FCSVz6yEPzn/+F/wfr2PVZb8yy0mygvJC0ztIF//L/9T/hnNEMq1t99Bz9Q/NQXvsAjH4v5yZ/dO3YcR5/PR3rXv6Fwzt5X8nD3vf7+Svz++EHK24clo6M2ZlcDh05KWqYweInCoRCeD0bjed6stVpyZvUcuixZPdPhsac/Q6vVIkwiKq1RSlIKSakzxGTM9rCH8BSXzzxJEPt86sev8M2XvsX6+s0TxyFEiRTgeyFal1ReXpectCVoBiycalCUJX6S4GyAtZZP/8QV3r76Fjs7Q3qjHs0wwKKJVIDTlkpriqJEV4Yw8vhz211/IJrdBtY6jHGk06xmkNi6s8/zFMx+dwbKqmIyGfP9N27Q6x+QJDFxkDCejLDSsLnXQypFvyhQUUw7atKKQ2K/jT7B2ceYkjStiMKaBJjmFdkkJww9jLH0RzmJr8ikYJT5JH6bTjum1QhoNVs1vtgZmo1O3b3rHHGSMMkLgixntd3FDGNu3No+dhxe6KOzHOl7REFAlCSMx1OcFVSV4flnrtDf2eGgN6JIcwb3NiHPKIcjwkaXh85c5t33Bghd4XJJ3AggcnVHb1khHPhByErrh84tPhBLrQTfC9gZ9imnE3bW91mau0QzEHzy6XP84Te2GecF2hlQHkVREfke2tZQtmmakmY5xjnSNCPXmm63jR/HaF3N+Ps1+/K4+NOX/gWKiv2dXfZ7Q1aW1nDasN/bZzIds3l7HmcDiipnZeUUK9un2Ly1zjtvfosw8Xnbvsvu1oSd3QOmB33WVud58Vvf4ve/9q+JIsM0G7G+eY+dwfqx47DWp8gtVWyZ5BqcZXG+QTuAqah3Hb5wKKFRPrPSS8nKfIcyEjjjEZSSytcIoRCz0ozGIZXAoVDKnfjcrl8fsLM1QXmW9vwcQtTlmdWFBYIkYi8d4IRk7uwiZ/Upere26E2GXP3+u0zHAxq+YjmQvPLKWzRPzWNUwZXPX8Jqx90b2wz2p/R2u/zu/1XwpQ93qzuKHy1PXEjczIDg/deOS8xw/2Hm/SWCH/p+MfMOdx+BYlgMaSWnaLZXyaqSZuCRjXq02l1U5Xj9lTfAkzz3/KeYWzpFPvlrXgAAIABJREFUUZQMdYn0FEpJclMRKctga5Pli+fwfJ/Owhxnzp9hv79Pq9tiftQ8kePtnEEKD6U8qqqcsSYK8rxEyIJ//D//Q4QQ/MRnP8uVK1doNBJWVlZ57InH2dr+FkWV0YxCysIgREClK7IsJwhCnHV1DRV3Imw/n6Y4aqNpa0xdq7SOUTHEuiGuqNDGkFeGNK8YjCek6QSsodloIKwj8gIIvHpFrysKDevbuyzPGRZCjedLYnX8OLzEoqcFQjUhUwQ6YKonLMyF9PoVXqEIGz472wWjoSCKE9rNmE6rSRg1yaqC/X6PotC0W23yIschmOvOUZTVzE/Sp98/fgXs+wLnPMJQIa1gkk6ZZhnOQrs9h8QHr0F3ocNgcICpcs6cPUfSaLB8+hSPrz3Hs09+jDxN+erXf5N72zdJpSAPDaWu6EZNCq3xTqgB1wx5wSAdEagQT9ZGJkkSsna6Tbuxy2CgcNbgSR/ngXG6RlqIin42obQVjSimPxqTTy1SBoQE5FlOt5EAluwEdsoLX/1dvLB2F9IDzd5m7ULlrCaOPEb7BQ5BEsds3Oxz89qrtcl0s8HeaMwr33+T/kGB50kaYcAoL3j36k2k71hebXNn86263BOc0GbuZosKQAiL0RVKOpqNCL8fIFCUeQmhIgh8rDNkxrA4N0fuWcpK4TC1GZgTGF0wno7xwwBfyNrJ4AQCCMDWzR0maYbRhkxmLK206e0OmWt3WFiIWL9bEApBvt5j8enneOXWdb7x0rfo9/qYKscYyxef/wRr589z+cpTfPXtr9KZi/hP/94X+fV/8sdMhmO28w3ORw8dP5BZ/GjLKfJ9coq13FfHrr9+mLTFDAwusDgrZ8oUeJ+PMiuY35fUjyp0hwehx8R4ss7bbx9gZYu0knhCEFLiVEBuBHlW8uQnPk7UbdFP+/W/IQKq0lA6jZMWbXK2tjfwoxUuXrzEO9few/hw4+Z1FhbmObvYRp1Q46sqTSA9gsDHmIo8p7ZAO9hh7917/MEL/xKE5YWvfY2f+Imf4Mtf/jJnT59GCg9jNJUuMNaQZhZtHFobpIQkibDOsr99gLGG1dVVGo0Pd3gf9Ac1j1xIpPJQQiI8WSN7raOyUJWGSkNlJFEyR2ehy3Q0otFuUeQ5RVFQFYKFxS4OSakNRWXY3Nlj/qEAKSEMjr8ed9YHLHcb+J5gPK1wVPSmOZUt6USn6DYTVOixcdMnThKkNCwtLBJFEUGU0FZztBoN0nRM6AeEfkwUWJphSOrAkwJFE50d73ZfVhlCShz1AautwNqatZOXBd3WImGyyP7eHpvrd2k4zTtvfZ+l02e48MlPMb+0zMLyKtIqrr34R6x2VgmabW4M97i2u8lco0PpasXLcZFXMMoyhmnK/PxyXZuPJF4YI6qKZz9+mnfe3ubm5gRhJVIqhpOSuXbCNB1yMBrTCCMaSU3Q01ZQFRZROcIAlKrBbVl1PE/8vbeHLKw06M4FtJTFVeNZKUagREpZgi48IuHwDKTTMZ0goTRw+2aPg/0xgV9zgApdUU0q4kBx+fQCRTnm6jspwkFVHj+Z1Pjo+vOJlKRyDindjBbqqHRVE5mEwIq6Xp6VFX4Y4CnLTpGC5wiaATpXyFyTpRNMt43v+XXpjvvSzYfE1Y0NAl+RG49yWmCmBQ0/Jol9PGmZW2yQ5VP6gwmb+5ukzqMz12Q6GEAS4Iceuzrlp06tMdUw3UvZv31AJ2mydmaR1uICn/7Sj/HeN149fiCz+BHXxOFouSwOU+37CVeI95Nwva62CBcAxX3JG46S+Ae/HVPPDCeOQylDZcZUqaGRzFFJRTNq4YRHIgM+9skrhN0mpRAI6de+gEJSZRlZVT8M/mjIeH+XZDniD174fbLc8OaNd5kOBzx74TKbt24i48Vjx2GMwQk3U4BoMJAXKRv31nGiwjqQ0tLv9/it3/ot9vb2+NV/8KtMxlOm0yl5NqVstAn8mDRPyacZUeRjnabXH2AspNN8Bjv68MR1em1tdh0lQkqkqhN6bdIhmKaaaZYzmqZ4k5S8LAlDReVnTPOUQRoQeD6g6HZbnDn7ELfv3mNja4t0ktKK1khCiecdr8aYliWDDIyrWO1e5J2Nm+xPNO/0h1w+Nce5s/PsbqcoL6ot17IpSZIgpMT3FGEU0ohCRl59oHrh3FnSImN1cYnheIyUPpEXEvvHq2QC36esNMPhEF06dGUJg4iiLPG8hHZ7HqEdzUIzSEdsbd7lzp07rJ1/CIEjyzOslOhRSf/GbeRoj5WHWnx89QLGOca6VjX46vgl30Gvz9beAQf39ii04dEnHiXN+gjh0Yg9Ljxs6HTP4r1suHmzh3YxOwdTPBnSO5igs7qkpXVJu9mgKEom4wl7RjO32GFn0Gd+wSOOjp/U2t2AVjvEFxX7+xVx2wdbMU5LfN+j0A5PeBS6JE81SSQYFJZpaigrixCqvtcBp+tE6aoKVVaMpim7g5IkFEThCW0rwlJVOdILCD2J8EBIh7YlyrMEATRbIaWxFFWJ7/tUumCp1WC4f4+3v/dthIHHH38cF85TFhOktPi+Igh98sp+AHP9YeFFPtkkRQqJ9BxFYYmigLgVE3gSXYxBhyRxi7jRZTAdcO7cMnvbewSx4Itf/Dy37twmHe9z40aJF4RYqfBEE2Hg2S+ucvvVu3zvpevHX4/D8Xykd/0bCinrLTt8sAwi70Nz1tu0mkAmEHh+Bk6iq/dX2LUm/IcfACXliW4pAI6ztDstlB+ymHj0xjmr3QgDjHPNnVu38X3/CL3Z6XRonTqNs+BTu4IMv/Mm+sYu3xmW+Drj/HKXM5kmKxx3Xnmdgyzlyc88c/xAhMOgCRsJzhnS/4+5N42xNDvv+37nnHe/+619632ZjTM95IyGpCiJlGRSHpkhLTuRaChAAhgxkCDwByNAEiBfEwQ2kC8xFMAQEkNybFmOY8PRzojiIooccoaz9zK9VHV3Vddy6+733c85+fDe7iGlsIofjIEPUGg0UCgcvMtznvf//Jc0pdc7YGVphc2tc3zvrbfoHd9H2AxhFd/61nf5zGc/RS1s0q53ebS3z/LSImEQkOZ9Mq2pqQ694yPSdMawn3Dm7NnKJvWEtf1grwp7zkumSUaa64ouZqvuM2pE82AAS54V1We+qGLupCxJyyGNRkCnWWd1eY3+0RFFHOMjaXcaSC0QpSVLT7Ya/fmzP4trCs5cPE9vOGRr/SqDyYAiGxEGTd5/5z4XNrYYD/ocjwZ0awFpOqMsLdNpgue7dDpd/LDBcDzAcwJmx0eMPA+lqhCPbqvOmVNM/3Uh8f0mo2lMEHgoEigtW8sX+MLPf5mD4yMQLq4paTiKB4MevcmMr/359xiJgEGcUPM9jnZuYkYD9g8PeP3hARtLTeotn0NfMNYFySmufY1mwPlzL3Ll0nmG4wnOkaa70EJYjbUxUagJVn2+9OpT3Lk34vf+6B3C2iJuXXDu/DIvPH+e7Z199vb7bCw0EVjivMXt/WPe33nAxa0lgrKix520Qr9OOhVoXNbWlhhOM4yxLHYkjhTMkpK8tLRbdbIw5rg3QhUGv6dhkiOlwnNdpJQsd+sICprKp+Y7ZP0ckSfUGyHZKdmWVloMUIvq/MwrT2GVhxIWV2qe/tgm165dA6H42je+z82bt+l0Ip575gztacK5lTa//j/+d9x//T3eff8hbw4GnFtokJ5dYTzLUJ6DNQXScTgtRHDUn9LuhozHCbX5l2smCnYO96j1asxyycVn2pxZ/wQ2qSOPJ2xtdLn9jk9jocnVq1c5TI5481aPMx4cJCVHDwfUa/vklPzOP/wWaaL4Ce3EP2JMHPEhd1qIH8KgxBMuaYWwSLAGnc147/p3WeissbZxCSuq5OoPQSv7I3/98Ql6Gj9by4K4mGGKDK9Q6FwSyCWefu55/Hqbne1dsumMOJwwnVUJAjLNEbnGzQ2T3gFiesg0OWBxUuI7isn2A96+t8NgcIyq+7jtbmU3edL1ECCUQDnO3HO6oilKo8njnBdfeIWv/vEjPKfk7PmLhOEyN7a/S5IW6GzM22++w+VLFwi9Be7fvcPS6iaj8YCjowO6C02imsfR0SM2Nk42/d/c3KQsDXlWkGuNeUwDRWINlS1tf8h4PK1YNMrBDV0cz8dRssoGLEom0wlR2MAYyXQ6I88yKDVZHmB0xek9ab3w7AVcBcrzadQcMp0zjQXPf/xZXBTpOGM9MqRCEtdbuDol6+1zPMuZJDmfePllfN9nPJ2itaUUGm1KkjQh8EMc4VALa7indOLHwyFRvUWt3mIyGFDMclxZ4+KFZ3G9kNlwjDGK4dEBce8Rk8mEBIeN1U1+8M4t7u1sc25rDZMnzI4OMGWGkC5ZVuJMoOY2SKQlO6XhaNV9PMewvtJl+2CHVn0Rndao1T10qXFkgFU+2kxYW2tw6cIyyvi0Wj6DQUqeTajXAtZWVyjSKe1mm8leDyEk9SjCc0OOen2CU2Y3a80WjWYdazRHBzHWuPi+otNYQkiXuq3CMDqtFrN4QsO3ZHfvY/SAVtgmViUCXQ2egZVum929Pm6/xsLiKotLEuVkjKenBEdLie8HWOPyg7feQOcprhI0mhHNToOXPv4CYLh8YYtG6GBNymK3TrixStSqUZqM7OZtRKp56ew6zbDO3YbDTPch8NFpFax82nury5JRf4ZRkizVuG7B85fPI2ol01GGc+DSXdhE+RvoZIrvOzzq5TheQOiF7G4fIrSl7YcshQE7YUSuCx496rG60uJoP6fZruOfYhH8eH2kRVw9kRxU8/An8MlcGCCswdoq8EA5mv/3W3/Kv/6/f5sv//JX2Dx3hVKbCmYxBiv/6gBTWDClQJ+CiQf1JSg1oevTtLB6/hwXz5wncpvE44IFDUZ66FoLXa/jSoEejRDljKOdHXp3buAEBY2GB4MD0tIyLTRJHmOlJWqv4jkO7dbJIopAukgrCVWVS1gUBWVR4ihBkk95/uIVrkcdIq/BZ3/qZ1lcf4ZHv7XLdDLBAne27/AHf/THrG3+HcbxmKg+oVVvs7S0SLvd5Lh3jyRJ0Lpkbe3HF/Ja6FefvKVlGk8pTIHWllkcM5nGPNztkWUl2ApuEdJ5wgXO8xzXE3Nc33J0POS4N2Q0miCVxFcGhMRgSU+hXPb3H5InBdKv0Wp7CEfy4P4+CsP59S4LnQ7FdB8rBSvdFvGk5OBolyioEXTbRLWQ2WzC8dEBnhcgsIRBQK3WQAqBcl1KDfX6yb7m0g0YTWJU0KDUFqMVVy4/z0J3k7t3dog8xXA84/7tD2A2pMhiLn7sKV7+zGf4xp99GyUEi90lzpy/wJ/8P7+DEDmFsSR5iXSgMBDnyRPf+R/7fDgGZAGU3Lt5l+UFj7of0Qzq+KpNYYcUWmMl+KHkuWfOcOut+zTra1y/vkPNdag3O+RFynJ7CVsKsjhnMp7Q7XQo0oKiyCjkye/L+fObWFUFnliRYI1D6Pu0pOTw0SHbE5dYg3s/QRtNrkt+JiuZBIJGprgfFyidY9DkvkdQi1hdTXGcklZtkbAeEScThHNa0bKsrK7w4nPPs7RY4gpLqx4RRiFBWKNR65LlBetrLs1GDWEzSpNjjUPqWKaeYPkTz3J3Zxd3uUVrYY3yYJ9SG9KsqOjKSiJPEaVZBMI4LKzUON4Z8srLF/ibX3yGIpc8fNhnMGjw4GjAe9/7Dg1Po8qCT116kS/96vNYbWg4Ab/+0k/zaHuXb/YH5I8OcaxGlA7pyKKsQBjNKWfrk/URd+If9tDi/49pYkFYjVKw++geX/vG77O+doaXPv4pqgRDgzESYwViXvz/yt//CZRfme5Tsw5Obji//gyR1+Tw7g7xbEZZljTLHNdXc4aHJdU5oj9hcHebnTvXKSPFaFyyUG+zvz9glqUYV6G8iku9Pxyx6NfZf3T/5OthJEpIHOE+CVm2c3VqXmZsLq3w3NmnEMUERzuEXovP/fRf5613XuPoeBdjDT94622++qdn+JnPvsDeg/uM6kM67TaO41CrhYzHQ/qDkwdX9+/vIKikw47j0GzVKEx1PY0BrSv61fwGodH4yqnYAHmGNV6V3i4kQjiUpaUoDI5UaAuuFxDOB3InrXu371FmGs+PiJeXWd9ardJp4gwB3L5/n42GoBY1CSIfU+aE6ZBu06O2tEiexsRpRdP0g4BSl4BEW4dZKZBGY/KMdutkap/n+4xnI3r9YzYXF3n+ky/y/DOf5u03r/PmG69x4dwaDx4+YnCwz2a3SbtdsWMynWCVptNt0qg3uHDlKZa3zrB9422UNUjXJRea/dkQfEVwChbtSPA8SZqXtOs14smUtJmQZxY/7KLtEEeUaO3gOpK1lRpvxyn1oEkzajEZTeguhmCnOMrj4GiANhZHShwBYi6W4ZSOT5gcnRmODnNee+cmoOjWQtqzFD0reNOExFhc4SFkFUD+khpzL4k5SA2JEUQyoi0zFiNJtxGwurAEGJLZhFJDVAthdvJzKqSkKBJG4x6HvUcoa6iHHp7j4blVzFtRltTaDdqtOq1GgIgLPF1F4lmpqJ3Z5NrWBrERKMcnGBwR+S5xPEU4lfjIcPKA9dlPrNBZaGBK+C9efZVzq89zbmWTeq3LwqeWGN/f5tbdh+ycfcCj430urpzh1ZdeocxjUjzSZMbNhzvcHg94760fkCcJth4wLTKaQYguc1Qhye3J4rgnz8lP9Fv/npaQ4om68nFKDszxcWvn6TEaqad865t/yOHhQ/7ef/bfsNRdZWO5wWDYYzyrAntLbZ+EEgshEFLyWNp5Gi5+fPc7qPYqly+9Qjkd0e/3MUWKlAKFoOkKIlWAKYlLwVhbDj74gPHuHsqCozwGoxnbRwfkRqMCH6EESIUQinqrQ73VxQtPVlxlWoPjo+cq0aIoSOIEG4DWIFXI1asv8MHb35+zPVLi8ZgL5zcZz/ZIc1hd3eT+7hFFXhJPxxz3j0mSVZ5pL6CUJI6nNBrNE/dhtUYqQRQGdNsdXNfhqD9CosgzXSn6BChH4DiqGiYVBa7rzJVzpgqJFpLpbEqc5lihsLailJbGkj3udE5Yg94RFoWXpuSFJcsBLXGki7Eux/0xy7U6O496vJOUdJyEp89tUa+3yG3KqHdAEi5BuExhLc1A4rkeiRYcTTLqoY9blpSnfBFM4jF+KDEmocwTtMnYfbTN/d0bHAy3OX7/PsPRGFFolpYimqsrxLHlg3dukAzGSCnY3r9H7WYdv15jYf0MS4FHo6kQXgYywWjw3JMpl8J1sdYQ+SGXL51jWvTJdEZqEjzRQggP5WZ4wseYlCgyCNdn0u/z8sfOs7PfJ6w3SOIhcVYwnM3wI592HpDlCQvdReLpGOcU+PH2+7u4jsPt+wN27g1p1CTdNcPdoyFHuSJzDUhJacoqmlBZvpbmpMrlkARch8TkrDmWK/UmhTFIBwLPx3MUsZYcHAxZ+at+Pz+ypBSMxwM+uP0ehR4iraYe+gSuT7u5xHA4ZHtnh+F0TBC4XDi3xrWrT2F6QxqLDahFTKea+lKHpl8jlYJLa0v4RnN0POE4teTIyoPphPW3fvWnWHTXaKZXuPzsC5w7dx6/3mA2OMDEh7zxF+/wcNin19uh2Wzz8O47fH3/Ptd3D4mLimrZWGnzzvY93HhMmZW0Fz0W2zWs0RhdpXHV104XLsJHXMSdeV6kkLLy4RCC0hq0sFVxL2P6x7t87Y/+T16/8Qbt1hLPbl4m2btOZ+0iB+Mb/MN/9L+yeeElvvK3/y7GGpSY80YtMA8fPU15v+TX+a/+/n/Lw2+/w7/9J/+MRafBCg4136fVarLwH3+OpZdepr65hawHbH/nG/S+/ge0lyMGieWN3i7DPCZ0FAZFgSW3Ja5Xw+Dw6c99nuXVLYJG58R9xFkCyiNOC/RgyGwWk+YxCMVoEHPQOqZ9dYtr5xZ4+PCQJI3Z7T/g1u0b5EWK40kcNeP6299m5b/8u0gbose7bGws0Dt+QJqXBFGIG5x8orfbnarLyQsKrRnPYuIspTcYczyYogyEnouQFS3NdR1SU1IWGY6SSCNwhM/q5gaLS8tYA8PBiH6vRzzpkRYpOtcIcfI+Qs8FqfCdAJMeE++NCMuS40mT/lGCKqaksSTLY/rHU8bSYb9/ExU0uLi+RLR6ntwGIAylKYhnE8KwXsnCpUShSdMZjjqZJXN0dIwQkjAMuT854O7df4ejPPKsyjDV5CRZRsMPubf3kIvnn2FpdRktHrFxdu1J3Nho1Gd9fZ2rV57myvnLeA68ff37HL/xxxiradRPhtviJCOwYI2hs9xkc6FDLlKMk5CmLq32FqXJKLVkNHxE7/CQzbVF0tkBawsrvPziWR4cTMgWOjx8uM9St8WSlXQ7DXYHU7Z3d1hot6k3T4aXTDvlaKwZZROWVhw+/tIao70xs4eaHIesyAmkYMWZITR42iOSBl2WtB1L02qUlFDCIMsZjx5xvJuSFZp21+XpzbNk5pjj9ORy5DiSsBayurHOQnMTJQW+57DQ7hB61YHYaATkVhDWQlwF+3mGdmGUJLiOR+/okE1Vkk0eMC4yTJmx0mpzYfUcX/vBDklqKE+BMe7dTDHLfXaz3+U3/5d/xPU3U2qRh68MxirqRqMCF+M0eNDr03I9RFBno1tHCMHw6Bj6D2l2FygfPWR1s0WzFZImKZN4gm81f2PV8LtHJ38RPLkuP9Fv/XtaSkgeZ2JW4hKLIySOtbimpL93g9neBzzVDXEvXWFvavn93/sdOnWHN9/9Dnd3t1laOcMv/bUvI6lCbOFHFZ+aOW5+wmrJZfb/4l3C5AHfPLiLrC8TjEb8wsVFXli5xOW/+SrUumQYsuERH/zuvyafzYi14e3RgBu6ILWGC9Yl1TlGClASYRRJqRFKkhY54SnYvC5LrNZkacp0NiNJEqaTmDzTFEVKfWAYugLpgBvA/sFNvvil/4jvf3eLWx/cJAgFezsfYEvDV//k69TrNQwxH3ywjZQlYVRHWMFkdLKs2lLiKI/mYovZNCHLkirLsijmNgYWi8FRDo5TpUf6vovnKiI/4OzWGS5eukiz08Dzgrk5mSXLC5J4zKS/zWw8QJenULccpzoIy5JcF0xmM4RUmEJQloqNhsZ1FJHvs9pM8D2fOLcU1pIWBR1hSNCU1oAukFJQlJo8TfF0jmNctCNpddon7iPwa2hjUNJF4hJEglk8obSW0kqU5+KryiuktBrl+nhBQKPVZBqP8TwXz/FxXRelKnZP6YMKI2Lp4rqSNM2J4/jEfRS5xnUtnuuhTYGQLlFYw5EuQleCpFrYIE4TwMVoSSMK0UpjbMlkfIguBPV6AykO6XQaFEnKOJlQ5JVZV6GrYfZJS8kMx9VEDclT5zfYWolIDoa40sHDITMGbWGmBUoISgpm1pKUkpnRxEqzKCQF4CxoLl7o0o1THvuuLHYblHZK2Dj5kL90bo3xeIy10Jp7BEVRRKfVIZnO2Nvb42t/9mfcuf+A/mhEt9Pl+eefJ45jarWIdrtDWeTsPtirIDUhsBKEV6ACzbmNRfTDHpPs5C+1Wx9sMztc5vjRlM2tVS6+3EaUMaqE3jDm/v19lgjwzp/l8y/9HKEoWHrxFab9Edd/8AZm+SwbF85gnJDtwYT7KqEsY2axpD8YkYxS/vd+TKH+A+zE4YdMr56wVMBIQalL/s0f/C794RGB9Dhz8Xme3erS6qzS7S4jfMXS5Y+z2F2l21z/kYTuJ6ZZgCPmBjwnrOGDB3z9N/8ZF1uGVxbOcFRoHjGhu7jAU9dWyIc9zMNDzHTK+PV38N58H1LDveE+90TBxHUx1pKlFlEYpO9ivAqXFdqwvXef59qLmFNejiTNUCrh+PiI496YPC+o1SLSeMh4mJPEx3Q6DeJsSjHNkEpx9ZmrvPqFV7n23DXCSPHBzRv8+Z//Oe+8/RaXLl0hL1KMKWl1HOLpDF2cDi+1Wk2m0xmHhwf4Xsjy4gLaQqlhOEpRSqCUQMq5YEtYmo2ItZVVzmxtsbSwWHlViIoiarStOvbIp9VcJQgkTnDAsH984j4c5VRijrKywXVdF2stsyTFsz2maYzxc1zPx0qHJMswOCjHI9cGGw9oeTUOciCbMHMEZZyQ5BlKSQwu9WaXKDoZ5iqKqkv2PB+0wuiCIPRRNZcslQwnPYwF3/WJs5xJHNNqSjw/wA8CpJA4joPF4rhVAHZmCsoypVSQ5xmOck7lI/t+ZdokUIyHx9TbSzieh8SjLDVpmiKEQ5pmSAnNZoPho0kFNUpn7tKncd3gQ6c+qXClpBEGxKGPLUsm4+mJ+9jsLOMsSi4uWM6srdNqKUa7MTfMDI8ChIO2lp5Vle2FNQgtEQhcIWipEkeWDEtLx/U5fjCkdFwunW0hnIJOIJHdGl54ctPz7IU1EOu0223azZDJZILv+9RrNVypaLfbbG5ucTQa0e8Pmc1mHPePK6fRKKLIc2aTKf3DPr1wCMA0rRwrz507zwvXrtFobHA8Phmb79Y97t96wCc/82muXWpzcfMCvfs9EtXg7oMH2H/1exjXIR73yf0VVutNPri9TStQdJcXmRnFxIm4c/smut2izFNe/uSzvP3ODUaxYuwGWDPE5P8BFvEfnkPKOQ5ssBgJ1vf55q3bpMbyy5//FT79C18iUhGeqxCEWKERUmPKEqN/FFO3c8xWzOUp8pROvD84JHlkmHoOwdIKP9UIabZeYXGUs//1u7i938KdFah4yuGDmziNh4xmOYPA4eXP/QIzFN9/7dvk2ZCmGxE7ArfTwV3ukvR6vH/jBlcvP3fq9SgLTZalHPePkLgoYWjUI/LUocxdiplGNj3yqYXCIi1cf/cNzm5NWF/bIs2mtNotFpdXiCcTrC7Y3d1lefUZxuOYTqsSrZxm6KOUT7vlE/gRk/GkGvAaA8YQ+j4GjVKVJjafzw6uXrnI1sYmURjhKoXEIqWLlA7VwjwsAAAgAElEQVTISiQEGq0Nvtek2VXI02T3buV1gS0q9pJU5EVWmUZlMwrl4pWaqO7h+QHj4ZA8z3FCsDpClAXNok9mFIdZhtYuUjm4fojn+jh+BEKSJCe/pKYo8JRD5PsUaY5yXLqtOpPJFCfQzGKXLM7Iy5K8MDw6OmR9+TKKgGbYZjIbk1HZzs6SBD+sY3MHKw15FuM4dfJ49kRC/mOvh3JRymE6mZEVOYP9CQvUkaFLns9A5BRFjqak1CmuB0VpiDOYpppON6JelwyGBcZaZnGCJxyM1rRrAVkakZeGND+58/yVv/Y5oqhGNisQjkurFjA+zPnjP9sBDBJJObeHFvIxz1ogKACL40giqSHTfO/WMTLwKGzOYr3Ll179GFILpHcGfQp68MzFDeIkxXUdZnmOMYZ3332XO7fvsLq0jO/7RLUa9VaHlaUl1OoqaRJz8fx5ojDCcRyyWhNdQqfTodlskJYJeZETRRH9w4eMJ2Nq7c0T9xHYgLZfsOQ5rDhLlFnA2nPPE7kdmrU2Dxtf5cARlA3FwyxnkMYMjg85u77IuaUOr733HtIsstGUXI9r9D+oWGCfePE81hNsv/UWaVbOHU5PXx+52KeyHBZPmCSOBc9AaQR/+5d/jYVOh5c/8dNEbp2stJQ2QViD1OqJwlOo6sF5TE/80L72cRE/eWX1DnvmAQdENFYbmMMDopEhcV1G/Qnr2S2WV5YY7t9nNH7EmS9e5k+++Trp5jrnzl+iXWtT5jG3//SbHJaG/jQGUbIzHJJnKd2FJY6P+yx0V0/cx8LCImEYohxFqAJcz8XzffxOC29um4kULHYv0wglRhccjI948OAdsqTPufNbdBZrnLu4xZvffY0H9+8SZxPeeP11Ll/dpNHqUGLITnlJj3sD6vU6IKsghKRSZbpSEIU+YSNgMOiTpilh6HHmzAYXz5/HdRycuVrSdRwElT+1lPPHykKJwHNDcCS+OoW6JQRZMsMajdUlpQVrSjxpKfOUwncotUaIysPGcSXTaYLBovOA/Z7h9v6Q7kIXv7aCF/o4suoSlVIkSVKpXLOTPTrKoqgOB8dBYDFaE89isjTF2mqoq5SDKTRWw97BI649befMBtDakBY5DdcnyzLyoiTLMmp+iBIO9VqNFIvnngwfCCnRpSFNU+q1OqPejCLrI70pfr1k0Q0qb2xlkaqCvZTrYWVAnAvC3FCrNRkMB2BtxdOXlarZEYa65xPbgjQ92RK3FUQIGSI8iZCaPJnwcO+IzOYsLjRRo5y1jQWmZcxxLwXh0A4tC3WfRrfBcBjjpRM+efE8z1w7x6Wza6R5xrd/cJvlhbMsNBoIYSnMyV+utcileqJgYfUsCwtdut0uzUYT33FJ05Qszxke9xn0+oRhyN1bt1laWqK3f4gQgkajiRWCokjYuZ8ynk3wg4ClpSX6R7sUZc7K5snFU1ifROT8/uuv83/9mz/hzLkzPP+pszg0+eUvfBn/wlmChwc8OjxCuh5HiWB5dZ0iOUa7NVbX28hGnb37IzI0WxdW0KVkOI7xXEOuDUjBT1jDP2I4RTwWz1eDTFlZ9SCAUCl+/UtfQVhLVhg04EmL0m7V1CmLtpViywqJRGAALKh5EIR47GB4ivR+4dxz3LjzgCMz44uffJnp91/j7tFNlnKPlaklFoLc8zg4HDM4djj4tzextRabLz5HvdagGda5dOkyt7/zGtuHBxghkInBTSXSwkKjRZ7n85CHH7/W15apRzUCz6dei56krHiOg6sktZoPQiKliytzch3jDjRGSzzPItBcvrDBxQtn8bHs7N7h7NlVkiRjOpuS5CHjOCVJTi5aUeShbUHg+zRaTdBh1QVbgb1/QOb4TGcOZeny9FNPc/nyGUK38l1WsvqRP8QKkqJi6mvAyMrmwNWcOlAUrkSbgiJJMMagvAhBZSdqdMW8kI5PkuUUWiNkZXTkBj7aaPaODnnjwYyLF5/iZ3/mWaRTQRZxnDIe9un1Dyvaoz65aHW7C7iuSxzHhJ6PLkryPGc6neL7PkVRoISLO0+lqSx8Z+RJRlbkWEeAsRhpSMuU3uCINMs4H15iZWWL1978E1r1Flaf/LmcFRqpJI7vEfgOnquIJzFv3voBm09tsNq5gJAGNwgoRI1cJ3hRgpxCnsFoULC0qGmELq6ryMqCTOfUmnXyvGSp22IcZ6TlydfD9SOwCuE5+J7Paz+4wR/9xft0uhH/6X/yOf6Pf/5n/Pf/9ZcRNuetN+8S+CEbW226kU+02OB/+6d/TD7w+Qd//yt0Gw66mGEw3Lq1y/bekO65anokvZMPtUmczH37C0JdkiQpnU6XT3/qU0hTMdUQAoNEz9PD8jwnDEP6/T6+7+MHPoXO8V0Xg+B4MEYIyfLSMvWoThCFePWT2Vxx5lAIxZ07d7EzweazcDAZUMQTfuvf/QabH3uKO0dDlBKYaR+pG7SaIc2GxyyJmemA8cGQR/1HtBuKW68f8+1vvI8QFmUr5blAcwra9mR9pEXctQLJnAo4f4AF4KJRWGRelXRlKvWl0AZ3Xpi1tfMiDcaCnnfdKIGYx7L9JIEQABtPfYxXm03GNz/g5j/9F5RSoII29xs1Ho37vBof8OmbmtF0wEE8pj8s2et0WFtYolWvU6qqu9yejnB9WQlJHInCRQlFYaqYuatXr564j6eubuFZh8iNcEMH13PxfR9jNHpuj2s1kAmKQlCUgnZjiXqjg3JClBNgc8nxwYALF59lZXOVWTokSWLG4yHHxzOEgFl8stw9n8woiwIbBGxduIQnFUWWUBooly3v7u5z9swGly5cYG11qSrcc5/yx4XbWI3V+Yf3SoiKKYTFlbaCR04RUezHms7CKuXgkNBxyfMUiyIIBLXlc0xzg5WC5tJZLj6/yfB4j527t5hNY/bHKY2oyeaiy7vvv8e5reUq1YYqEaYsCurtNlHUpXvKF5KUldHSxvo60/EEPffqcZXzpEMPvQBTGHSuGUxGFBhmeUZ/2OeDO28TFyNqtRBrIXAjOtEqQnqsb22RlSlx5qHEya+fF4QkSYJyXXQJge/gNyWvXHuaaZlw9+4O7cUmNVnDCoN0BJtXOqhIcLQ3RfkNBsNjOq0Wq6td+oMxeW7oHfWp15skaUZUD1k8zaqiKNC2QAUuf/iNt/jHv/mH/Oe/9sv80mev4QUORRrzP/3Gv+QzP32NX/3yz9Gt1zGiRGG5fmefncMR/8M/+ArLaxHWKBxRw+iSIKyxt9Pj2oVK3aq8k99fm1QxcL5RzHoD4uMhvudROgob+KBUdZ8yi2uqZ/IXf+7n0dZUBm15QaEsajJDCME4CNjQleCqyDWEC/RHM+7cOdmquFSSW9evs7xcY/Vii8XViI2Vc6x2NzmeDnjq8vN8/XvvUh6P0WHEeFrwxrvXObO5zLoYk5QOsaqxsbSMcV2S+AZFklFrKJABYeBgS4X4CbIR4KPmidsK14XHlO4KVpHWzB3mwBiLmttdafH4d+YJIZVNIVDl9D3ut+Xcb4W/5FP+49ZMl3gLK9Q/UefW3Zu00pxu3dKJNc9ah013gcwPSCnRWLIs40GaEyUFjnRIteYvvv1tyrLEcVSVMSglwgocx2FjfYN2u8Pdu3eBn/+x+yjLEiEkmSkrLjSiEmO4DsqrDiVhLFJqHAPkEhU0kU5EkpeksxHffu0HbN/d4+MvfhIjNUZYZrMErQWD4yFh5OK7J7+kZ7e2iMcTmvXmk8GcBXSWUW/U0GWJ5yqWFlsoaVFzKKuK5/rwxTNGV4fsvAt6fH+xFmEMQp78UFqnw/5wwHKzw6R3iFAKJ/AqfYHrYrIE6TcI28u4fsjqxkWscDk4PGQca2azlMHsNmfWF4mzMY4T4rkuWZ7iBRHLyxdYXFzDO8WUwnMqyADdwnFdhDYURSVwsgICF0LfxW9GJEmGLjRJFhM16lgWkNsuolRMpmPCsEWcFjRqRZWXqhw0HrlycE8p4sboCtIRgjRJwGpcBdJa6o5H0K2RlikyzbDMI8eUotkO2Xt4wHRmSCYjfFeidYHnVeyiMPSRUpCWBRjIT3EPzLOs8qbXhqP9AZ/8+GU++8mLWJ1QppKv/I3P0A49fuOff43LW0t89qUrGM9Bui4PHj5ibanN1nKbcpaQa41fWnr9Ibc/eMAXPnsNnebYssCeEhoyk7NKUOYILCVGG5zSgVLiElIWJVlRMMwzSmsrBbTVlMaQJDFJkmIwXNlPGXR9BsolyTNyazEa2u4iRV6SnQK33bl5iyiM0Jnm2rXnKYIZhZwxnPVpdWo4xQRdCnSSE6Q1JrOU4WBA2dtl2grZHaWcvbpFY+k8uU4psoQ4Kbjw9AIHuzGlBiVB5z+ZZPMj9hM3zB1TsLIq6AILcq7etAKBRs0LtsCibVWclag6eImYV/OqIBhrqDT4c8XmT4AjmVxTGonwO7wzHiPzMe3Coyt9FvCJ/A65NDwoM27ojN1kwnGtQXHjA9punYPRgPffv46S8kc8w6UQ+J5Hq9WiKAp2dnZO3Ifn+XiOj+MGRFEbpeYHghIYU1TJRWi0nWHnysPp2LB3cJesLMiLksksoZSSnQcPECql2VWUZcl43GchrBFPYuQpdzmeTKkHEfUgRDpupbKMAgqrWagtVOpYUylplajgL2M/zFoE5jmhtgqPmOPI1lqkqlgK1ppTO4tGs8FMgmw1MIMRyXSG53t4gQNxgl9vsztM6L39JlcuXmCxs0irvUgYRWR5gdawshgiFcySFEcZpHTpLCywuLSBcn2SLEU6J78cge/ju5KyLCgKjSkrQ7AwCLEmRykzv+92HoVX8mh/m7ObV1he3qDTXmO8O0I6HlmW48hqKGwp6fcPnrBjxCkPa5Zmlc3BDzUnxlTzoCzNWG2dITU5aRGTFwXWasazAZ32EgvdNv3DKVmaMRgOn+RSWmuIahFYgZWC0mhms5PZKdJVJEmK47vc293nmSubhJ6l1AVKKsrC8OpnX+Du/R7b9/cx185X843Ccu/eHp956VnqoUvvaFK5bPqK+kKbZ66s0K5JCDxkqVH65OvxL974Ko7j4joOOD5y7jhYlppSz9PAgEwASlVdu1B4Us3N9yD0fYQ2aF9S5CVtP8KLIrCSo0djDvZ7p9p2xHEGumCSGIKaJp4FIDwmsxHjeEI4TvnY8+f48+MJk7hHWAsZDTTTWcHC6iLXnnmaUa8PNuX+7Tu8/MoZ9jYDhF9y9ZkFHtwV7Nzt84lXtk7cx+P1EfPEP6QWPi7S4ocMaR87q8x7baoYN80T90J+2JN8/o+Z+5OLU73cnyxTllgM5XhEiqXwQ4bacE/nIHK+ufc9grnsPLOCQgjCQjC9dZNHd24zSxKKLMdzPZT6MBTWBTzHw22tUCpB/96tE/fhuk7lmua5IEMsAm0hTzXjSUqj3sAag7QBUoVkRUZWlOTFiDBq4fgliTmiswK7D7ZZX1tBCgdrS/zQImXOeDY+lTfvWIs3V186UoDVVaCrH+I3WgRhQJqkaGOwAowtwFQBEI+TlCoreANGVvxyoNQaZR+nmdtTv5LaC6tEjSXKMmfjmU9x67WvYqUAN5h39x55mRAnR9y+Z6uXWScYnVOWOa4b0O00qoImfFoLaywtb9JsdqpuUwhskRFPTubNu65LkRtc1+Xh3iGecGjWaihHUaQW15FMxjFaZ0ipCHyH48EeK8trrC6tc+7sFdIiZhb3SNIpeZ6xv7/P2fUBYVThymWREJ4SlJykCUo5uK77xLa2LCzCghQuxwcjSgFu4GILgzYFsqyRTTWBqnxWGqsR4/EQoytHyqLM8TyHLC2wSlCW5lQ3RTf0q0MZRX88Y2N9FTeoUY4Txr0+XuQjfAenTPHxKtX0vNve7R3zhV/8JGVu8V0X4XroMscNHD7/89cIpUQqCaWosgNOWJ7yybMcRzgIJMr1CAOPwPEI8arD1/NpeTUiP0RKiS8VgeNhjGY6nZHYgtCfEXoaJ5kyPDgkTTNGoynvXr9Ne2mZlbWT4TYVaNZXVtnZOeaNt26xurJJ1qwjM5d7O3cILz/F4prP8aBHbg9Z2TjLcDSmsIYNF7pLHvsHKdLRrG40eOHFJQ72QwrrkucTXrx2jn/529/j/OWTrawfr48WE6cKBjYIEB8m/FS4pUHbCjaxVoCosi8f24/POYQ8Kejz7qT69JdI+UNGWqdh49IymQ25v32XwHXwrcTauW8JoBGk8sO9ebJKAcliTakUnuPiRcG8KJUop4JTmrUOC8sbhL7P0aPbhNnJvGhtq68PpEteVrnKjvJJdYpxA0ZxgHBrdFYWqNdDlv2Q3Tvv02hXUvckS/HcmDT3ufHePQwG6S0gnerPDocj4mSGf4pnSatew/U8hFJQasoiYdAf0V1aAaloNpv0ekfk6eN0nAI5f+HKonjCyxcCSmtJ4njukV4Z9T9OFrKcEgzsBPiuJD18yPLFj3Nw7zqlTlBBHUcXTHKB7/lgSwaDAf3+Id1WrYIJVCX+MpbqxY5COkurtNqLRGEd38s42H+I0AW6PJliWJYlcZKSZVUYts6rwaZOSjJTPuFdC1EVfGMkQhjyYkyc9Dl//hy94SGD20d4vofwXcrEMBgc0W4HWGNRWNQpniV5lhNEDlprQv+xFNmh1JBry3ivj6aiZvqBR5alxGnB7uyAIkuI/CarqyskcUqSTpBSVfYJSmJsZVSVl7oKCD5hzXoj4nhK4Cqmo5R4FLN//5AHuz3u3tnmS7/0SYSQfPqlq3g1HyEtejrh7s4Oju+y0K1TAioM0EZT5jnojIbvkc+GJINiHgp8cjn6W0//NOPxhFariRu0cZTCcapkLFtS5dWGIYEXkOcls+mU+7t7TNOY/mDA7sOH7B3sM4yH9Ho9ptOCIp+iywIhPMLOBuuXl8nFyYfrhQsrrHfXuHfvANeLcHyfSdkncn3qjYjSUZzpLLPUbTJOBQuL69RfaHJ43KPVqJPmhtW1Js16RJnM2L57yLCf0lz22bk95eKFBb7wxaeJTp6vPlkfLcXQ6MqhS/DEhlYIBzGXzhtb/WCYF3OqTltUBVzOMXILCFUlzyAqTxZj5tJ9LU7NSs6ynHfeeZfe4SN8358zZHQFA8xHwn9ZiOG6IVEYEUVRxZ0uNaPhkHZ7iVqthu8HgCUgp737XZYdQe3cyXdhPHaQ0iFPFWOm3Ll3nXo9Yn3zKr2jgpVNjfIMuC5RQ7G27LBzr+CD23s4VrDU7nBh9QqjJOHXvvI03/n2t5iMS9pdnyhscePmNpb8VB+CIAwRsmJyFHFCoXOiRh2vFlJqzQvPPc2bP0h5sL3Nc889i9ESbSrmzeMgDiEEnucxnSXkWUaz1aqSkIqiGn5aiznFls0YWFxe5OjRPfyoQdhe42j3BuvPfII8zylGAxpBncm4z3Q8rL6oLCjhUqu38fyQKnGmSjaylKTDB/T2LdLxqNfqVRr7KSTUSNYpjGA6mrHYaRM0fZKsJE5zAgm5Seh0m0zGswo/FS6zfMo7779OXuQ8d+UTrHcWeIDAlmU1/GsYdofbjG4d4WKx0iNPT35Q63WftCxQ2oFGDVMWGFFgXShMNWj1EERBDSEFUeRDcoyUEaLVoLSGD+5uk5YaN2wwyxKSrKBZjwgbAfev38KXDhuLSyfu48HOA8DiKsHf++KLkA/YuX5MoRTNuuL1v3gbHEGRxgg/5Hj7kPMXNlmvh3zhhavs7+yxvtwgK3XFIXccwqBW2Tb4NaTv4yqHQp98uL7+xnsUZUFRFIymCUmSEMcx4+mU0Szh0f4jZnFMUubkZQVHKlyElU90JEICUuNZiRKKet3jzFqXS2cvs/ypX8cEDTznZD3D8z/X5N1vPuL8uXNsXdxi784hLpq6t8Bu7yGXr1zk/TuH/PW/8yvs7rxHeiBIpWaxu8X+/oR0lIE1PH/1PPvvJdztz0hyTTieslDf4t3XdylVwFOLCyfu4/H6aMU+GCqIe45hP2GUCISq/FSUBWQFvZhCoOfDv8fE98flyNgKgy7Lgr2H9zGmcs1b7CzjnnIT8ixlPByQJUkFz9jHME9FlVPzLr/CpKvlKIXruHiui0AipCWs1wijEClBFwmtQHK2G7DWsiTagVPEHMYoBA5ZYhmND9m5v027XccLI0otKbM6Ujkc3TsgObSs1C/z+c//HK36Cr39Hq4Q5KVFyZBSF1y+9BRJ3Gc8fYjrC5TyGAwHP4GvuXjyUyXaOxjfq+wELNSigOeee4Y4jjGlRaAw5BhjMXNurxSSJI2J47kTJA2sNZUzo53jladgnoISKRzCKOLg0Q611hIP77zDbDrGC5sEjkOt0aiySak42UWuaYR1MII0ySi1YTSagivxXB/lRtWswnXnaUV/1cL4Ly+LwXEVXuAxK3OStMQVHmEQkKcFuKqC+cQ8VR1JXqSsLq7QajUII4+NM6vUboakWUFpDFmZISRMZ/ZJeoxzSgec5xmO4yNthd8qqvNYSoWrLPVGnTRJMbrEVfMEqsBDlxkGS54k5FlKmhVMy5ywUR1io9EYV3p0ooDQ81hdONmG4OmPX6MsK797E48pZ1NwHZxmHVPE2MyifJd8FlNYRTqb0VpcQM9S1jcDdM1HWReVpEglsa5EY5F63gCooJrkuSd/mfzP//ifzGcvhnL+7+N3VAuD53nVXElIpKioyUKDfPz8GYPE4klL5EqWl7qcP7/FSt2h5VrMzT9CugHKDYBf/LH7WFle4WhjBgtNzp5bJKpropZl9/Yx0/GMRsfBlQP2+3t02jUK5fDe9RmOMoxnIyw1Wus1JklKsFhHJYb79x7wyWee5pWfeoXf/s3f4cWXn6XT+snK80eLiUsHVwoscu5aOJf9zCuz47io+f+llJSOnheBij9ZBUdUN0TP/2OtIUkSyjKfu8KJU0UURZHiC8gVlV+0rQ4XHEkpwNEVHGExT7pYXzm4suKnKxxwHWqBh6cSWjWX1W6T1ZpDIAw2Cvn/2HvTIMvO877v95793H3tvWcfzGCAAUCQgEmQI+6ULFmSaSlOFDmWP0QulV0pW6VSkrI+uPItqUpiVuI4ssWUVZFKoihLIhUririIBEQsJAACmAEGsy+9d999Ofvy5sO5t6cBYW5DURViV82/qqv7nu6+97nnnvO8z/ss/78mFKJD+m/39rbI58oIDF7+4XO4bp/tnZiNrQ1Mw+LxJz7BidNF8rbNzevXuH71dX7u53+cXv82o2BIEIWEIYhEY3l5kfyxefq9iL23how9H0M3mJtbYDyeXbhSFYGiCFKywnMUR6i2hZQp2USepFopUyoWsoGtJM1+JyUyTTPpLSkJo5B+t0ecJFSrlewcCokQ2fTnYRTBceTQam+jGHlcx6VYbaAaeZIgwK4XCZ0ehpYRHp06dhLLtmntbtB3fUQagwqKrpMmAlM38BINTdEgdiFVSVMboav43uz+/ZiIVIkx8yatQR/dMKgVTNyRBzGoZta2qKiATMjnc6Sxjx+47OxssrJ0lHHQp1Qr4O85WeQfSZIkQVXVLKUgU3KHaI4qInNIqqoxHruZio2QWQeRqk5YPCWuOyZfKOA6DrapoWoqMk2RcUIpl0c3UrbWdhi4DvliAQVBkiSsNKrZnII+O9hIwhCpZNQLo3GAqhhYVgEhddIkwbRVHN9DMfKUi2XypUwzM7UMIlVDk4LIj5CKimFZKIYKkQ+qTpykRE6ArgkUffaOcehH+11tqqoiVC2TfCSjhBAT3nqZZNU1KSVqmgUYhq6j2wa2blC0DAjHVHIGWugT9GMiAwqhg26Yh7JL5rQxH/nQEfa2NdzwDpViGUX3WFnUcAY1uhsDHjqxiFVW2VhzMBcEt/64R64C1oJFEoaEseCVl1/n8aeOo21LHj5/hNWFRRKZkl+q0Bo5uO77q/KJ99OS9wAP8AAP8AD/YeJ9akc8wAM8wAM8wH+IeODEH+ABHuAB/iPGAyf+AA/wAA/wHzEeOPEHeIAHeID/iPFBi0LIaf/1dFgnSRI8z2M4HDIYDBBCsLKyss8XHkXRO1rggH3ejukXgONkyh6+79NsNjlx4sR9S7uO48jpyPj0dd79/FO817GDjw9+P9hjPv1qNpv3tePatWsyiqL98xDHcTb0lCSEYbj/nNPfT5kRJZJkYn82dhzjux5xHJMkyf6xMAz3n/PXfu3X7l/q9juSJIUULl18jVMPP4RdKpDxHChIBUadPtdeeJWVE8dYOHeGe+t/xkt5/150OZnPkiBUIH9fO576wpNSsZSMm1xAuVmnIA0G3oBCM4+dM1mdP4Yeq2zu3cFJhywfW8FWy/TaHcaDPmbJxE0caswz0nw67oiiaTFu9UgDAzVWwA/52le+fV87/s7PfVEe/Fyn15nveuysb7Gzt0MSSkSqkStp5As1uj2BoSXMNUt8/jMXCIMIRdH42te+ThAFCE1kXB5RTKNWpFRpcOz4WX7rt758Xzv+6T/6adnvdDB1jUb1DBubLYSmcmftNq12n0LRJpez0XSTtbtrJKFPwdbx4pjhcEwSZxMQQtHojYdoho6mqtlHIuWE1jd7ec8L7mvH5bd/Qyaxl+nQasmEbkEhTlIU6WGYBTr9hN4w4xYa3F2nWc9ROlJH4KMp2XvPKDYMBrsRvW6Am8S03Bb9YYSCjWUU+Ge/+uv3teOf/843ZBpncyD6hCN/OnSFBFVVUTUl4+hRUogTPF8nIQaRvONe11WDVGZUEXIyiyLTFJlkHPH//S9+4b52/K2f/8+kEALbztEfDFEUhYVmk4Jl02jUObKyjJ2zsW0bzTR56MgJ9EqF229fYWF+juMnTkx8RXZfC5kSTFqu11/9Ad//736NM80GBpJnfufrh7aofODKPtObI45jut0unucxdWRT5w33HOH0Z7jnUNM0zRj+Js81Ho/Z29vbf3yY7NVBO5Ik2ReoePfvDj6+XxfPQWm4gz+/mxzqsP9ND7TqxXFMFEX4vhyvw9QAACAASURBVH9PCHryN1LKTPlm8rdJmpImCVGUHZs68anw8tSxz0KSJKRSkjgel57/Pjsb63zyC5/HKOWRwM7GNi/+yTewBj4VO0966iTo06k2sc9Z895naLIIIg5tMRx1e1i1PPmCTb1RJ6fmOVqY565/F72ckM9VcdIui5VFfvrJn+O7r/4xhWadNPTxO100TcF3xkgjpVTI43b7+M4QGYekUlLMF3AH/YynfQamn1t2zu8t8Pl8nuOnT+JHAYP+CFUYKKqCrmmcPNpA0wVh6LLX7WBqOqqi8pM/9eOYpkmv30fVVDRV4/XXX0ao5qGKS7pYIApcluYXGA9HfOxvfJSd1h6u57C70yZv2bjOGN2ISOMQRQHXD4hliiIEwWQB14xsShMhkWRtodmHksKEDnoWOu27JHFEKmNKBYMUQZCAFyaEvuT17/4Faze22WsNKORMzuYNzv2tZ/D6Y6QREyOI04zr/IXvXeP6xdsU8yXsYp43bu0wGDioaUq5YMOv3t+ONEkz1aI0m+eAyQI7aVnOWoIVhNRRkoxHX2BPpwb325UVRSEKPVRVmcwwTD4HOaE0OORz+ZV/8ssIRVCwbX7z3/xrkJJipchoPOL8w6f4zCc/R5IkGUeOItm5eYv/8X/4V3zo0fOcPXsaz3P2dVjTNOMUEmn2up5I+V11j1rSQrfgmZmWZPjAnfjU0ezs7JCmKbZtU6/X99VnDka0B6PjKd79uNVq7fffahP+6CA4fKx6Kp57cHWeOlX1wCr/Xq978G+nNr/7Pb4fHHx9dcKGmKbp/nsRQuD7PtGEi0TTMn4TKbKfD0bampZxbBx05NP3Ov3/WZAC0ihmyS5z9eWLxEOXwnwdL4lorW0yvL3J2YVVHGdMnEq0dzPwTfv4Zzw/zO5H1nWdnG1jmgalWom4HzNfaWLaOjveHcbOmHmrzEeeeIpj9UfYbt1iQ3Zxo4B8vUS+XqTd6yPyCr29LuPhgFK5yHxtnt3RJv1WK5MyO4Qd7qASkqLcc+ISEIrG6XNnabc67G23s6nRNOH4kTm2dreJ04gr12/gjoakYcjqkVWeeOwJcoZFsVjAsmxUVWXkOGxubs20I283cEY3GPRCeq025dImR44f49q1KyzMzeO5Dp7rosiUcslmMBiRyJQwDEAoLCws0G63idIYRcnIr5I46+/Prr1sLNo8ZK7C9VxSIRCaQrsXMho5DEZjwjhi6BV46RtvMq9rLKkSw/f5wifOoyVD/JEgMlS8QGIX5qmWVvj1f/1vWawU+cLnz2DkIo4tlkjnyhiagjbbDOIkQaYCIRQSmZJKiS5EtgxJiaZMhvQSiRARpq4ShxM2JkWgyHvBWraTBRD7vmCKw9que50OiqoSmS6PnDuXaX1Wi2y1dvBJuLOzia4bNAyVGz98iZf/8KugFTl9+hTt1i6O67C6eoRarUEUxRMG14ymW7Vt7pRKXFuU+LnZcyZTfKBO3PczgYJ8Ps/q6uq+wz6YMpj+DPci4KlTPOhwoyjCdV0KhQK7u7v4vk+v1+PMmTPUarWZduRyuWwb865F4mAENn39g48PYpYTv9+xd8NxM/EDRSikMptsjOKIcMJ9DGDbOWw7i7ijJCaK4wnjY4quaeiqQZKkBKFPFGbMhlEUEicZVef74pKRoAlBZzxiu79Hfa7Kxvo666+8SpCmLNQKmLbBKztrPLzc4FFVJWORVDMGSSH2syvphPPg3VF39mi2Ey8vNLFtA12HgdejUC7Rjdp84sM/glb6PC9sfYd2e8Dv/D9fZWWuSiW/xJ1br2FZFqaZY2llCVsp0vK61B6uIfo5xk5Eo7DEI596jGcvPgfbHZLW7EX26u3rHD9xBlMBXdH2ozdgQi+gM+gPCcIQQ7Hw3IDX33qZwXBEkqaMRiNsVee/+tVf4YVv/Dn/4l9+iWa1Qb3W5OTJhxgOR/SHY3b32jPt+N7z36ZarhL4gka9wZ1bt3jrrct4gU+jUuX23S6BF6DIEClSKtUSd9e30BSB5/m4jp/ZLWTG4idAphJ9MtyjKga6omBZs7lCLt/qEzkulqbQrFQoFgyqc3NoqkHgexz7Bz/BwrLg0mu36b61hb56lKtqG2RKIXeC+uJpNncS/uIbt+kMPQZDj+G/f54vfvHH6UYu43gbPdXRk9lDWCBIJxR50ZRBU2aMmer+blpBxh7NRkyhXOTqHRdIJhOnCkmS0fsqypS0TWb3XRRjGDoyPlyMYW5+nl6vx8jz+MTnv4CmaZhqpoql6zq2puGGAVoYcVSE1M9VMX/yV3jj0muEMiCNU27evkmumGfQHbMwt0BKglRTzFIBYaXEqko5P3uSdooP1IlPI29N04jfNc14MGVxUG5t6sDfKyJXFIUgCKhWq1y8eJFKpUK5XD7UjoP6nO+2YZq2mD5+r/dw0N775cani1Eul7uvHUGQbfM1VUUomaOYpok0VdtPmcg02/BqQhALZeKcmdA+phNOGQVV09BlNoGaynT/f98PJBBGEUESEQYxCIGmqICCDGO8JEa3cziBTxTH6LrxnpH3wQncvyqc4QglMmkcW8KRHovH5yCnU1yusqIt8Y1rARrQrNcpV+dIgxRTLZJGIUIVbG3t0F7fI5hMUQZBgG4YdIMBu1d2CYMILdXxndnptsT1cUZjcvVqRoHMvWvjXg7ZnXwuklSROI4zWXgllmkyX6jRKFZZrDSpFEsIRTIc9YmiEIEgSROq1epMO5aWlnDcEISCl4T4SYQbBHTaXWQ1IoxjEiEQukkc+KSej6lnt7Rh3FswEwGk2fbd0FQMPdvFFUsFkjQ9NP24YCgY+QqGKsDSGDpDesOUKFQZjEYYap3Oxjq5uSJWNMee32M3SVlZmUfLn2RtV7K1PeY7zz6HqlgIkbLdGvDNb7/E2UcXWTmRQ1Gh2Zx97+4NRkiZ0Q5ICZrQUJVsB6spAjMAIQwkCYuqTRjqDPpj0jRLo2QEexl9tE+MoSgoUpDEGeVGEESkUjkkuQRRElOt1TAMHdIEkQpShUwPQCbEUiEiG/uOqk02XZ2Plmv8yOPPoFkmpm2iGyoqEJpjZNwncEckoyHppbfQEgUjlsSj2ZPWU3ygTjw5UEg5mOOdft0vL30wLzw9PnV4QRBgWRbz8/PMz89nDvAQrpAwDPcXh/spjt8vjXPw+DRCe69C6LtTLu+FfD5/zw4Zv+O9SXkvV62IFOI+/c5dNK2CbpSJEpVUpKQinCgB3VtQMv4TA39CMZocElpMhRwUIVjb2iRXzKOpBssLi8Rxwqi7g50rsDcYMK8yUex5b0/9ftWV3gsZI6Vk5LmcOHuEXM5C2LAz3ODu9Wvs3d5AMwULK/MMB12G60MiEaAqksdOPMbJ5Uf47vhZ3rpzkcANMFUdTc8RBjH92CXyIoKxd6jSkeZF9FodGhNOkYPplWlgkdUrEqIkQmgKkKIJhTAMmJ+f54hlku7c4kRBcLSWY2PooCoKu3tbKGqmUGOYsyNgXTc5emyVxPcYDvYo5GzSJGSopYxGA0zLIJYxURSTLxQYj50sMhUKpUqNMAxwXRcFBdPM0nOqkAiZYBkapqYRxumEzvn+MK0yI8+jO+oz8B3Go4DAFxTzDer1I4zDiF47xVTGrNTm6FBGzR/FKJ2h7Zgkisb62g2uXHoDXY9BSFw34tadXep5lTOnSgy9EYEz+zq9fXcNVVVQFDVTBFNAVxUydgyBqkw4U5KAuVwDYZS4vr5NOiFjkjJFxlEm7KhnPOOkGadPdi9nOrGH3beWYaIqCoamY+kmuq6haSqaltEuq6qGKkOk0LkT6Lyo1LigJ1BMUN0B7HVwB0O6boT22gv0O3so7R6u63DRb0EoqQmL9NDlJMMHrHYv/5LTvF9EO3XcGxsbjMdjlpeXyeVy78hfh2GIqqqYpsmRI0dYW1tjeXl5n/r0fpguHtOFAN7pfN/Lgb+Xoz5o77u/vx9Mn1dOo+dJBJ8kCaTZ8UF/wPbmNdzBJcb961iFoywuP4VpzZOgkaopcRoRx3L/f+MkJo6jezuC9yPWp2SyarphYZsWmlDRSbB1gZK3COMIBclwMEQRUybAe/TA03d9GHf5TEiJbhpIRXDtylUWnHmWTi1yZ+sGbsfB8lPqK4vEdkxeMzi2vMJ4p0scJEg34dTcCX6gv8rcfI31m+uYBQuUMY1ajUq1RLjbI0zIjs+AHkqc4ZihM6ZZrP6la2LaUZVdQypxHJEzNAxLhySBKCEQATvtNndu3sILfNI0xjA12u1dKtUqiqIwGMzmNc/n8iAzcV9v2MHO6Vh6kb2dNRQtRxKHNKolojAg9ANIYmSaYuZsyuUyw+EAx3FQyXLHADnbRNOz3VzeziNdn9whVMVf/96bdIcj4kwbhHp1mQ9/6BM8/fTHOXlklbs3LvF7X/ktrq/dpD9X4HhunpXTn8BJVRQLksDhjVdfQTp9PvWjZxCq4JVXrtFtj3nzrU2eulBHJHlGndm85p7vZkV0RUFXUlRbYFgmaRgRxinCzKHgo6UuJ5ZP0RkFqFpIkAhGXoQS+9jCR9cUvDiHm0zu5Qm5nkzAUAzSQ+7hOzdvoaoKuqphW2ZWw9I1lAnne4ogCMdIvUCQFGgef5rNr/x7Gs//OaEzJAiGbAUev73X4aMXaoTFhPGjOZxGntsDA+83BhAErJ4+OtOOKT5QJ65pB1IE7/o6iIPHL1++jJSScrm8n5qY/i6OY2zbJgxDtra2iOP4UG5kAMMw3jMnPsUspz3r2PT4Qec8C+/ehdzrLEmQSfb+DMOgVMox2NlBCTcYdFKkKHPyVI040YgShRQFTRVEMgYS4igiiiPSqbZgcEiuUWQdCtVmnYKdp2TlyNsmzqCLJOXIkUXWN/Y4Ulth9fgJNNVAyAk3MEwq/PevH7xfqAgMRSNnmEip0NvtU66WaR5tkJ9XsW7FSD+BRCeJUlIDTp84w+3bd3jz2hsovoKWizi1tErn2SGJFxFIjzhnUSnUaDkxBTOHac92WlXLyAit+kPUSh0UBSHlhGApJYwiPM/H0k0swyAKE1IRYWtFApmJVAxlGaN+jK56h0GkZCIocYSCipTZ9eE6s3cEQmg4josmcwxHIzxfUKuXAZV8qcL2jbdYnG+gkJBEHjnbZDhKMFWJRoSlQd5UMXWVWrVMoVjE98dIJKpiIIRGzjbI52bvCG5u7ZJtGDU+9rEvcvToIxw5cgo/tbm7PuRL/9O/IfBSLl3c5Fn3Mv/FPzrHoqFNOmMUFJEQOT2aBY0zJ23GrsenfuRh1u563L7T59rtdVQBC/OzqVdlmhAnCYoQaHoKMtsWKiJGJSb2PAxd4/hineWFBvONkCtXr/H65esUijVWmiU+98xTtPp9/vi5myiqSZpOSNomdMlJGh4ahvyz/+a/Rdd1yqUiYeBnEn6qoN6oUygUQNcYOV1OnnuK0x/6NNVyk8u7r+LN38Jp6IznCmwmRV7+DvD3n8IxAzoKJJqBsebSvfEC3FHpvNWBLx1iDB80n7ii7Lf5HOylfrdjnzrYacFS0zS2t7epVCoYhrEfSQ+HQ65du8alS5fodrtcuHCBubm5QyPx+zlvmAaWmQTZ1AVLKSfdFfccr5D3/uC9ovH348Q7nc5+P7iqqvtq96qiIkVEmGrYhk5s6ziRzTg6ScFK8fvXWb8BRx66gC7yJImGr7ooMpO8MwwdZdLZoikq4hBNSciiS8u2ePjscQwdiqUq5dpHkELl1t1NlF7EW1euolUriDRBKhriYIT/13DeU8gwIRm5xIZGkEhUXWP7Zout9Q5pHPEZvcKtN7YoN4+wfLxJclTlCx//eV5560W2x+toQmew3kH1Lc4sn2Z7vIldSgn0AGU8RJQsRJiQ02bbWrWrHF2soJRyJBPVqDSMifwQNANSBUuzKZo5zp45xZ07N+l1u5yeUznxaINK8zxf/Hv/mOtr1/l7P/O/8OV/+3/w9T/5Ywxdww1iwiCetC7Ovj58P0TVLbzA5+1b11F0G+X2JguNeW7cvMb5xx8m8j3arV0Wl+YwdJM49MlZgjjo0ijkODK3hJUziZMY33dYmK8wdn3SWFAsVOkPu0TR7EXeLpgUijo5u0Sn5aLlBpSbHhd/+AK/+b/9z3jDPrquYZoW841Fvv2Hf8RjZx5n+ehRoiAg8tr81JPHWPlckz0xophvYNljihWVhx7VKVfKSJlm6aAZyOczMQ6ZJtiqhChlNJAIBdTQJeneZShjXrg45o3XXsYyDZ79sz/FMHSqi0f5u//4v0SLBrz+w+/zodXjvH35etZD70WgKKBqpIqGbuVn2jEadLOUiZAMnTHC0FBVFTcMUTUVoVnYJ4uU55vcvHwRRaYcPX8U5bM/z63yJomSECA5+dOncXsSX4BMYwQRSSlBSNBiQTKYvTOZ4gMvbM6Kwg9Gc9M+8mmkLaXE9/19B3337l2uXr1Kp9NBCEGxWOS5555jZWWFxcXFmXYc7J2e9nPv93YfcN/vcPTKgZ/vefeZO4nDsM9/fKCnfP+8pAZC+ChqzF5rm3a7jyJiAsBQdYIgJI4DUDPpqXcXVP9KkFk+PElSWnt7nDi+ynA0wgliHD/g//6z7+ANhswvztPb2+X1l1/myU98CtJkkkj56ztwyLayitDwAz/j7UbghAPscg7Nj7AWytgpNPQ8cRiyeWuLK0uX2e5s0OruIhoJteUFhhtDuuEOOTNH1+0QhCG2VsL1umge1MzZYh2qofDhjz6Dnjf43uW30S2bO7fusL2+nQ0sCYFMJKqi0O/32d3dBSlZaRT5zFNz1Jc+ROSP+Mrv/S7m3xV8/qlT9NeWub7d5+aOh4jCjLv9kF1joVjIzoHbQ4YhnjSw9IRBd5uirYAhKNg1vHBInHh44wA7X6BU0vdTPqkQeH42JJaJeptkKf4E3VRIE1DV2Yt8o6yj6RGG5tDd3UMRCqNGkR88/yxREBDH0X5B3ncc0ihg/cabzC/N4QvBN//823Re+wE/+WNP4idDCkWTdNxD1zTsks3Y75PL6ywdm92NYVs2yoTDXselUDKJ40yz19QNTpz8OL70uPXWRdR8hXK5gK4rxEFEa+0aX/7yvyRXzuH7IePtZzmSM8mLkGs7QzS7QKobUGnSOPr47OtD1cjn8ywvLzO+cX3SZDC55yW4tYj6fzpHwx5Tfc3k7faYh1UfbZTjjnDZzXUgsok7AZd/9zqmqVD56BHsJ5qkdohIE4SqEL/P++oDz4m/l4M7WOScPp7mvn3fx3EcisUirusSBAG3bt3ipZdeolarYRgGvV6PK1eu8Morr7C6usqZM2dm2pFJOqn3bzFUxDtyu5lTzQYjMp7i7EdFVfd3De9VID3MmeZyuf0unYPtlVJCmmQFtDTxGAx7BF6KbiRIzSJOVeI4JYp9VGHt58Onjjy7mOS9idRDcuJSShQpGA1G7O10kXGm9uN5W7TbbR5/6BSFQgHHGdNqtfjWv/tDTN3i7OPnUU09U2Pabx+8lx3/q8KSGiKViCiiZNsMHAdFMfF7AadtSAcdqqpFuZbnWneDt+9cplZZwJFDbly+Qb/U5uipUyijCEURKImKGuj4HZfN4Rpx4iI9PXO6M/DI40d59EPnee2112i1WizYK6RpShhGWTubqiFSQalUolqtZYFG6tHvjPF6NQa5bZZXHwWpoasu1uAaX3i4RBwH3G0PsW0bESqEh/DNG4bFaJR1ZKwslGiNE0qFAnPlAk7oIBQNx/NoLjTY3dli7I+w9AKqalCtlvbrSlLGjMcjbDsbfDEnO7M4DsnlNJJDSiaNso5pK1kBL6qwvXmbnarFrStvIdMYRVEnPOlZ/UUXCWkyot3dIRI52q02veGAm+0u0g5wYw/DVPDcENuwcJ04W2TH/Zl2qIo60elVKJo2BUtjHMSouo4V+xRMgeImFEgo5kxyukazUsYNYvKWQc6yGY1G6AJ8XSeq1Om4fez5CmaukA3glBYwzdktylJKms0mGxvrWbeMqiDEvWBw7qhBtOgiWrf5O6uP83oxohePKG/YeIMWfbGHOtbpv7rL5tfugvSxXr3Lh//5T8C8yRc+++lMyON9zpt84E783V0m7/799LjrupmSzGSE/MaNG1y8eJF2u02n0yGKIqrVKrlcjmazyWc+8xlM08zkmobDmSmVgznr6YDNwY6WII5wHXdf8FdVVaqV6jv80sG0z8HnhPt3vLzX+5063TiOMwX5JMlEFxKFMIkQUUQUSDJTItxEpZQrUKlUSdIIdzwgDCRREk6U12Ume3TgPR4Wl0t5byGt1xuomkKr1SKKIhqNBkGcsL29hWVZ5PN5dvba/Oav/+987FM/wo/91E+Sq1QmlfS/XkQ+N1+ieaKJUCJ2b3dQANswQA05VrJIIwtNwJazQzscYqY2G9euUz1SR0Yq/baD51zFji36wz7Lc0d4/MjjpInC9sYulzdHpKZJZbE4047qXBVVSLrtPq1eh4WVJfL5PEJVmDRbUyzk+fQnL/CpT3yCr/9RlT/+9tfZGIV844095nsGT5c2uPD0M9y9u4bR26BqaBhKpq1Zr9YI44jNne2ZdthWDlXRScnhVizi1KNRLSGjLCVZry1QsA3yhQTDUlHUbQY9FzdQmS8UaDQa7O7uopBM4g9JmgpMTUcIwdgdoekpxdzs1r560aRYVlA1BT3N09qM2Lp7k/GwQxIn5HMmSRQSRhG6aaIZCogY3xmSOF2WbVh+aIlYDgkDScUykTJCqJIoGnP86DF6nQG7O4c4cVVFSSWKqmIaElVJQcYMey5jZ5P41ltEnsewvUfVLrG7fhd3MMIoFCjmS8zV5tE9ndAfk2/OYZx+lJSYRVFEphJbpMhUx0lmX8dJkjAejbNulok/E5M5AkVR2LvjUPyjAX/a3aNveZxcWMXMVXAdh+GLPUqpwd76JmnfI5ezSCIT57JL55vXePQ/f5JnfvZn2Nre4cU335xpxxQfeE78vaYZ390jDtlgUJIk3Lhxg729PTY3NxmPx8RxvM+Tcvz4cX72Z3+WT37ykxw/fpwLFy7sR9iz8G4ulukE6V6rhRcGSKBULJAzLZAZL8ufP/vdfYWcQqHA4+fPc/zY8fdMY7y7B/5+iKKQIAjJhtIBMiceBgFpKollTE7TyOdKaJpFHI8xrBx5u0zOsjB0SKWCgoGS3KMkSNJMh9Dz3CyqTw8bM8/+ppDLsbqyMulnzjpcgiAATWdhcRHXcXBdF8PQcUYjXn72OUQQ8Lm//UUKc/PZeSDNtAz/P+TIGwt5Fk4t0NreQMYRplAoGhpzjSI1obK9Jqk3TIbBEC+JsDDQAwWv7eB0fcrlMnGksNcbkjM1ev0OT3/saRaXj/LVP/h3qLk8wcgjl5992Q9GIa7rIXUNj6zNc2FhgTCIWN/YhjDmkxeeYXlhDlNKji0tkzPy3Ol02e6P+OUf+0+ozq3SH0coWpGNdpvvX7rEjZ0BURTjOQ7Lq6u4vjfTDlXVsSwNP8wK9qbmEUcuu7ttpCb46Mox6tU8O7vXyOeLLK0IXO8WY2+IE4zRxyoJEXEYkZukJRWhk6aCMAypV6uMvQG6Pvt8nJyvE8UegfSp2FusLjXoOw4JCUms8fnPnyLyPDa3tnGDAN0w2d1YQ9fyFLQic3kLa2kBtR6zu9nGVipIVUfNKXT6XXrbO9RL9qGyivf8hEAR4DoDCFPcrQ1qpseNt94gihNG/QFRCq1WlyjwMPM5dre3ESoYJYvQTdAViMchdjlHz3VJEkmYhjTLNnMrKzPtAEG71cey7SzNmgCTNt0kSfGvjjilNAm1HNsPH2fgFDHfvMbxI6s8ffajBG7IV1+6TrfXoVCr4A48jFRn77Udhk+O0USMpsHW+vohdmT4YNXudZ0gCN5RyJyOiQM899xzbG5usrOzQ6/XIwxDBoMBmqZRqVR45JFHWF1dpdVqUSgUGI/HPPbYYywsLCCEYHl5+X1HwVMnPnJc/skv/1Ne+P5L/Nzf/wWscoWg2+c3f+PL/K//4ksousbv//5XqS40aCzMMdecY5zG/Pbv/R6XX7/El770Jebmm+j6vf7SgxQCs6CITFpQSvCDkCiMiKKMA0MVCXff/D7j1m0MVaOYK7LX6mLWiiyt1Kg16viUsy6WKLhXV0wlQRDgug57e1lhWB7ixBWZ0WgYtsVadxdbkeTzKqQafgQ9x6WoCBRdpe+OCIMEEQsMdF557iVu3bjN3/6Fn+f4w2dIhY5yIBklM7alLL2jwKxLbu36JleubrC0PEe9Xsdf3yTt+hhJhTCXo7ig4PR7tG5t0UlTKpbNzi3Jxt42vaGLUxqilyzCJObkuSUeOn+MO+2rfPMv/pS16+ukmk3ZrhMks3PRf/bSJVpagR/79Of43q23MwV1Veex84/y+CPnuPD0x3ji0fP4nT79docnzj/Of3301/jUhQvkDAtTN3ACn2PnzrGz3eY7r17jKy+s0ahUWDpyio8+8RRCV7EK9x8EA2g0GzjjAN2Eva5PpVojCF3yZYNez+fFF17kb/7NH2Vx4SGgQJpuceohiAKPIBhz5fptVKFCJDh39jynTi0zGIxxXY80SXBGAxShIw9RFu+1Wxh6kZGvo7LH8sI8Za/OQ0dPsLu7xkPnFQQGT5fOEAUJo6HL2bOPc/7xH+Vb33qOUbyHaQrCKKF2NEfgRuxu7FLMayw0qqzvOVzvtTgse5AmCQJIJAwHIwwlJogcCg2VkR9SPH8WU1Wyqdx8AcPSOcoqpBD6DiN3RH9rj8RPiNUW7bW3ODpXxHXHlBeOstYOmD93hoefeGymHeVadSK6LFB1DV3TEaqCaVnous6nP/VxBD7jWKNeW0bLldle7/Gd3/8jCjmDo6ur9Mcjqs05Tj50ildffRkZJMgfjpHaOrVfeIreaMRy/fDBRfj/IRI/WMA7OI25vb3N888/T6/Xo1Qq0el08DyPU6dOAbC5ucnex7KWWAAAIABJREFU3h6tVms/Cn/qqadYXFxESkmv18OyrEkE6tFs3l/B++CAzle/8nt89zvPopsGt2/e4uqNm+gyQDdT7LJBtdHks5/7DG9fvUo5X8TSDTRF49FHzpMGMTdv36JcLmGaxl+Jf+EgMgbDBCEkqpC44yF7W7dZf/sHJE6LRnMRDZM01cnli+g5E6nbJGEeTU9AiXEcf0Iklu0INE2jWCwiBCTxbO4UwWR8WUpAwVSgYpkU7Dx73RFrvTZ7d/u0+z1SIVmqzrO3cwtVzwqzvW6XF7/7XRaPrGAUK7wjsyJ534kWTyaURY7YD2m7Y0ajMcNRQDQy6esdPvLkWfScRavfp6KoNJebRP2AZqGGEqjUyw20ikV32OHjn/4RBsM2L734PN4ogVBHaAJ3OETM7qjjdruLePNtnnnkI/zDn/k5bq/dZbfbwY98yrbNhz/0OKHj0draplIs0Wnt0Ol0iaKUgT9GQ8HOa6hpTGdnB0vTOHXiGCePHef8uUfZvrOG2/cPFdJut3exrDLdXodao4mhC1xngAH4rsvlN39ItVbj+JEjKEKhWCiTSg9VCDRVx7HG6KqKN/RxHI9Bf8SJ02fY2Nyk12mRklAomvT7w5l2qKpCv98jkCpCSdDyOrlSnU9+5rNs71wiTHZJIp2+G1Gy84R+lIl/90OqzTlqzTphvEciwemPaPe6LC8uoOIS6QM0oWCJPLc2OzPtSJNovx4VJgpnjp6hXrEIwxGu57K2vUV/0CF/bJ5cLk/N0lldOImpKxi6Sm9vj29+/f+itb6OJCFVDVpDB00kdHttjp58BKVUYH3jxkw7Vo+tYhgapmns87aouo6m6xiGTru9i5QBsVqiuiBJw4ycrlgpY+kCz3FIwohytYLre8gwQsaSburTIyaeEAL6h7UGT/CBE2BNIUQmdrqxscH6+jpvvPEGtm2TTrgnhBC4rsvi4iLf+ta32N7exrZtCoUCx44d45d+6ZdYWloiDEOGw+Gk+p6N8x82sQn3pi037qzxY5/9HPliATOXY7le489++8vUVZU3v/cdPvTRZzh35jSPn38MFLFfFE3jhLOnTk8Wlja6rlIul7Ft+x1FSnPGVJ7neZNdCehKSmdvl9b2OoNOC2ewjSU9fFUwcsZYlonnJ5QrdezCIoG0CAlJJ3s5KbMIPGOFzKY4FUWlVCoxHHYPPR/TVs/V5RUUZ4iReORVwVI5TxzPc3NvgF0rUCpaSCl5Owrx4wgRxxSNHFfeuszN69d55ImPTMu/2Xl+n1NnAGmYIiwNUHADnzRKqRSKSEUwMhXarQ5L8xUMRVDXLGpSo3lkDvOh0wQheFGMS0inXOTVZ1+jvdcjkRJTs5FKQhjBqNtFHqKqXm7UyRWLPP/m6/zcT/4EhUKB2u4ujjvm9PETpH7MqN3DtFXCxKW1u8HrFy/xuc99jmalhuc4xFFMv99jt90ijmNOHTlOHCdESDwirHIO056dPhgOe/S7Q/baW+SsAoW8Scm2GY+HdAYuaSS4ef0KyytVwsjBNC2q5SbjfofWsI2hF0mTGElAsZQjjD3yBRvLMsnlckSBhRQJtcrsrpBqtYrvtwnCCKnEJCIETaM6V6dSXSENXdzQZGu9i1d0mJ8vIFNBf9DHylc5cuJhbtzoEAYSYhPDMNhqdTD0kNSV1PU5hp02iTv7c1En21ZFVTHzFo9/6Cy1Uo7RcESr02MYCfR8hcB10YSCUCBVLAqVMo35JstHTiFTlSuvvQamRqlUotmok89b6HaBPSdlMBxTnp89ZFMsl1hZXeDI6gp3b91lc3NzMjUtGI8DbEtDRh7CygMCXddIkpiEFMPM4bnZ0JKqaxQrZU6cPs2dO2tEcQSBw3A4JEliokMmaaf4wJ34NAp2XZfLly/zta99jTt37mBZFuVymRMnTtBsNnEch/n5eSzL4sKFC1y/fp1Lly5hmialUgnHcej3+5RKJSqVCv1+f5/V7+CY9Cy0Wi0uXLhAlGZFxVy1jEwi1r72RzQNkxf/zz9g68pt/sGv/iqtfo/A8/dHrsuVyv7rbG9vUa/X8DyPXC5HrVZ7Xznhvb09hBCYhsHWnaus3bxGEoxJfIc0GlCxVTSrSrs/QnptVM3EsHNIUSNKVBKcrDMkzSZP8/k8uq7j+y5xHBEnCcPBiOvXrr+v8yGl5PxHPszGlTdh3CZJQgxDYXWuTpprcvXuBrdu3sQwNKSmZLkgRRDGMTKK2dnY5PwTT01Gv9/53O/nctSFznA8xtAs8uUielmhZNro9TwUNfzBiCQ0Obq8xE7LoZwKGjmLWq2G64VEqUAvWNzZEnzv0hUCJ8Wu6ySTvn5DMWiWK6SHUOJ+7GNPZ+1smsqfPPdtmtU6BTvHwydOc+rkKW6vrZMEAfOVPKPhAFUVrK3dZXt3h4VmEyEEnW6P0XBIu9sjSmLq9TqvvvZD3DDg7MOn7lsfOgiVlOGoz97OFsv1ApudPUoFk1q1zKmTR7i9vYdUJb3BNoap4LQHzFfmGHUHaOgszDXp9XuEvk+Q+GyubWDlc/hejGloFPI54iQ69H7pdHrkCmUIA/JWQKA0GKUlHKfFcsnlzmZMoWyyMlciDEYUyylxlBL4W+jmMvWFY1y89E1iTyfJheiagiFsRiPBrWsdnnkipZDLMTd71gdnPAKy7rJzD63yyMNLpFFMFIYoikKpWKFSbZIEIXEYoRsqlmIxGAxxfB9d1aktHeEjlSajMEDXdAzDQNF0vDBhb3eHJLVpj2cHHv1Wm6JlcmPsIdOEYa+HohrohkYYuXzypz6LvnGZcbtFVwPV0FidX+DjTz7C5sYdfvC9l0gQBGFMrVZhoVFjbXMLBYU0VkBGCBnjjGfXTKb4wJ34tO3tlVde4YUXXsD3/X1H3G63ieMY0zQpFot4nsf6+jpnzpzh1KlTnDp1ina7TRiGXLt2jYyYPYvONU2j0+lgmub7mtoUQtDr9djc3cHK2zieh+Y7qAI+/hM/jd/uYHsuS2fPMnLHDEdD+r3+hPpV4voeqqJiGAahbTEY9PiDP/hDnnzySX7xF//hflpjFkqlCkno4Qx6XLvyGqqMkbFPGI2RcUAYqyiqxCCm43n0hgmKnqc9GGHm7IwICEFCOiH5SVBVBcPQSNMQZzCg2+2wujo7spBKilSzNsrU1Li1t0dFkdQsm2q5yHe/+zw3tzqEUjDo9zLKX8Vk5AXYSoJuaKAbbN5dA0W8Jwtcxi83Gzo6bhLguSGqqSJliqokKHqAoqsYGpTzFuX5Gpbt4PS7NOcaaFo2FZikksgfsVTI8cTRo+y2+3Rln0holJsl1nd2KekG/iHppWLOQgCaIgiEZOw6nFw5yqmjxwldjx++dZEjy8vkTAUrX+T8E09S+PPvcvfuHZ760JMgBBffeIMTJ05y+/Ztbt++xeLqIo1GlVqjuj+wdthCXy4XWLt7G1UI3PGAOPLw3QBXT5lrlOl7AaqlMRp0WFleYDTqMo5BSWMs3UIVCrlcESkC2r09xv6QO2s38ZyEerVM0Taolit4hxRYXTcgHMW0un3OnKwilQTTENTKVeT4FmkS4LguxUIdpVTC0ENUdcxO6zXK9YiCrlBrqISuYLvro2oWqYwwdJ3FhQaOMyD0J9S4MxDHIUmaEIUKx1bnMNUUz49RhEBVlUx4IoVSvoAnXTzfJRUp49HehOrZQFVUklSimyZoGmahiGHlEX6AaXQZ+A7bOzsz7fjBS9/n5e8LFEVgGCZSpiiqgWmbzM1XOH30JBXFYdu/zW4Q4kcBwWDAGzt3GTk9kjQlSVOGwxFREJDLFybNABKZZvoGum7gHkLUNsUH3mI4VepptVr4vs9gMODchJP34Ycf5qGHHiIIAjY3NxkOh1QqFdI0ZWNjg+XlZU6cOEG/3ydJEvr9PsPhkEKhwOnTp/d5xd+PE4csF2zkLKI0AU2hWa2Rt3PoT0larRbVMECvlvjan/wJoR/z8KOP4Touum6Qej66qhInCVEccfmtN7lz586EC1wlSQ4fYizaFhEh127fIA0coijAd8fIJEYBRkgsA2yrhAjGSBngOAFC1zB0E0UzUFBIJuRZUzre4bCP73kMBn18PyNkmvm5KBKppKBCsVqiPr/AzdfewK9WSXWbuYVVzj/xNDt7bbZ2W7R7PV5+8yr9bsBqs0S+0kSYNv1uD1BIRfqXHbbImOJmQRUqC0uLBCLBDUZYuopp6XhegNsf81BjAVtV2draoDOWVIsF7GKB4aBLztaIAg+v38dUVI7VClQKCnd9lfYoQJASRT6pnFSSZ0BJExRFoAoFW7V57Mw5Hj71EHGacPHqZRYWF6nX6liWhWWaWKbFx555hp2dHYaDAcVikVwuj2kahFHIcDhgRV3kQ08+htRUkjjd576fhWG/h5QR+ZyNjNvUawWSyCVNfUqFOSzToFAtYFmCUXtA4rpUGk3ydgN0m1BqeGHI0aPH2Ny8wZmzJxh0AuI4pN/vcmTxLLZhIw45H6aZR8dEqCZxKgj8EYG/Q766TGvk0JgrEsoxSRTijSWmoZBvmLS765TKY27c2mJnc5ticZ6aXWLkJWzu7JIr2YxGAxpWneHIo1ie3Z+dpNniqyiC1aUGxCmaNEmSAUmcEMdRxioocjQaVdrdhL2tLY4da1AtlIljhUTRCJOUSt7C9wMs0+TqlStcvnyZxeUVTh9d4MypYzPtWFhanNihoqoGqqIgVB2hQrla4fW3blJ2HZwoTywhr6hcunadG1ffZGl5Dk0oSJnijMZ02m1IUpI0QUl8bBFg2RXc3THtTm+mHVN8oE586mDDMOTTn/40zzzzDIPBIOMhKJffEZk89thj79hyTlv5PM+j3W7vp1fy+Tznzp2j3+9z/PjxfcWMw5CmktffuMjbb7+FbhhZ58xozHxznjCMMQ2bMEz5wXMvsr5xl/n5Js1mg8fOP0ESS4SmkMoEwzTZ2thgc2OLD3/k6f0F5P0o+2xtrnPx1e8z7LbotQbEcUKuUGL5+BGOHjsGaYSQCSqSzedeoFCM0WREmngEnkTTTaIUtrZ32NzcpFqtsri4yPHVZRSZ8uJLL9Fr7fF2t3XI2ZCoiorQBLqmsLi4wJUfXuL5H75JZzDiwt/4CN949nk2tnYRmoGpC37hpz6Faee5dH2N9c0tnNEWleEISSZ5pSh/9RZDo2HSPFZi2BoReBrVRo18XuMzjz0EoU7Ud9nrbLPUqPOxC+dodbqMdq6TL9u01rfJ50qUizYYFrK7SaWgs6rX6QuDb167gRcFJAno8ezrw1B1VprznDv1EDkdeu6Y515+lp4bkETy/2XvzWIsS9L7vl/E2c/db97ca6+u7qrqnu6e6elZOEORQ5rbNDmWKBISpQdDtmHBfrBgGrZhP9iyHvwimH6gYQuGIAKCbdryQmu4iDTJmeGQs3Cme3rfas2q3DPvfu/ZT0T44WRWV5PszDIgDIdAfUBWZlZlZUbec+I7EV/8v9+f586dIY1jbkezD6z0aj7T4Yh/+du/xY989nNcvX6dTrPNwlqPzy//KIHvI4/lr6aSoJ52nyZpTJGldJpd8nyO6xnanQ6mUCTDPul4iGu7TPdmtOoeLzz/MUaDTWq1OoHv0u0ska0vcWevz/nmAq7jc+ZSj1t3bxPNplCk7G3fx3JOPumdzwq0EURpxMFhwmKnQ1BP0fmc/f0x9YZEOIpoHmMKgyMXMCkcbu3RbY3pBB0CawHXquOJjP2tPoFdo9tp0Ww0yWYzzp1bYjQ7+WBTiKPdi9REM03fznjn5i0m0YTd3V3q9Sae55GlQ/Z2R/S6Lf7Lf/Bv03RtpOWgqDCzRVmwu3GXIAh469ZNDuo2v/iln0RIwRu37vIn3/kW//5P/eRHjqO3uoJj25U6xZa4jkO9VcNxbWzL4e79LTKjcYIV6qI6nHRdl8WFdXw3JI0nCDQ6Lxjs7DMfjrEtm1I0eHdzwN/+O38fJSyE+2iL0e9rEj+2uDr2f3y401Ephed5D7aZxwdtDytJpJQ0Gg2azSYXLlwAPrBqC4Lgz7WwnxRSSl599VW++affBKoJVa/V6S0ssrW1xUsvvUQQBEymUyxLEtZ8JpMBe/vbZFlVQy/KEt/3SeIY23Ee6MUnk/kRj0LQ6300M9pyQ85ffor30pTSOCyfucD6uYu0Oh2MpUhmERiFUCWzKKbdWcQSJUiBJS0c18EUCscx1Q4mTZnNptjkhJ7N889dYzYfcmtj49TX4wPui6DX63H92lXQmuHbb9NtN3nxxU+S/Mk3mYzHrCz06NVrOF5AtxUyjxs0ax7TNEWXCikkwnwA3UcIzCmcEIDh9oh5f8xya5F4OEE1G3i1Bu/t71KPW1w+08EVAa5jmI12WVlo8c5+xOF4Sn9vyPKqTy20Ody+y8JCjVJZDIYz3j/c5d6dbZJhig5siuDk+8NgCOt1eouLbNx9nzvbm2RGgeXh42Ebw9beLvt5jGXb1WIDQ7PdIlMFk2jO+soqBkOjUcfS5YPGsONFxqO4P2VZjmNXvBBV5KQoEksyHkxQQpLFU3zTptXxyNIJ92+/iSMUpAXjdBfvQkqtWWe961PUVjBOyN2NPaLZjG6nhdZFxcHWJ6sg/CBgOJxhCUOzFuA4Hrb2qNWXMGENlafozMJxLbA1SWLwrH2uXLyAFGBbNvGoQBQjZDuk3WmjU8XuYYoxisH2DNurs7528o4xjmOUUjiOwzvvvs/qSo/3bt5A2KC0ohkE5NOE/fv3ef/GDRQFbpmwstDlwvmLuK7P1vYWxhg23r7BeDxmq3/I9157C+k4lJYkbPcQpyCCbaNxpcC2LbwwQFgOjuXiSIPvCGqWJDQGZQksPIQd8CMvvgBFJTr4k298o4KWociSCF0WoBSO5VS1dT/EOWLOP0p832vix4muLMsH9mSWZT3wg6x4vNYDK7KH/x98kGyOv+5Y5XL8vY4nyklRNcBolpaWiOcReV413Az7A/Z299na2uLcufO8+OKnmIzH1Oo+WZoTxynTyQwhbCynmrxJkvD6668zm8wetOO+9ebbD8b5+R/+zEeOo96s49hr9AeH2K5X4QKOyiKh62DZFRclLzVxmrIY+uRagDJkWY6UNkYbeq0GruuTeoKD/X1mJkPUfFxX8vGPP8Pe9sap1+X4wSeljTEwnQzxHcPTT57BEgVJXlALHFp+mzO9Gh3PxfZdrp1fIUtmbG7FCGMwSiEdC0O13QSJMWWlRT4lkeezkq7fxVUuoRNwuN8ny1L29vbxC4fl/+AnabldVheW0JTEcczhWLOz3+fc2jp704R7b7zN6tIC7ZVzfO/9G9zZ3WdvGjGf50zimBoOi92TOzalkOwd7vPK228wnoyYpTnClliA5UhCP6BWr1MO5iAMylQJ2nFs0jTjzRvvsnZmnbDRYKHVITrcw3E+8IQ9vq9POzNxXY9ebxmJYrA3JSVHGEOt1SFXinYak81HRNOSZrNOM2yzttQlS3ImYogqIpJZhh/06C4tMlc2lnVIvebTCAOEELQWushTdgRZmVHqEt9zwClJzQDHXsLzfHqdVQY7N9Fo2q0WeW7Y2puzZzbpdBsUB7d4+omP89xzT3NwuMGdezGj0RTlKWrLAbaU1II1JnlE0T/5YXL84CtLxatvvMX9zTpKCkbjPv3+Id9NvsPocEhZFIRhSLvd5FuvvUpveYHXb91kcDBga2uL/uEh6UFEmqakusTkJcL38VeXUVFMaJ/MkmnJAktrJsOEu3dipO3i2RZXLq3i1j3mdoZlCizp4UpwsojB5l3KbMYsSiizlMBzyYqCOE0o53Flm1cqMqPREqTWyEeUKf+lSgwf9pN0XZfNzc0PJefgCBl6XIY5/vs/WzI5TuLH3/e0yXHr1m2MMVy7do3bN28ihGRxcZGf+umfptlqs7e3x9LSMvv7e2xsbLB+ZhUhLO7e2aZ/OKMW1rHcauVRr9fZ2NggS1LOnz/H6uoqOzu71Q7iFHmdlKCOlAtnzp4lDDyKNAFTsbuNEUhpo7FwHA/XkszjHD/wEQIsaYNRxOmcwHWoBy5pPWA0ntGu11BlQbPZ5HOf/dTJF8Mcj0cibIkCsjzHd13yzGEyS5jEGZ5r0w58ejUfxwEtNcPh4Oh0XhJ6IUIemb9ahoPDA3b3dnnm+jNHY+XEsrjAYjicMRvPqXebZJQM9yfIwsVZqbGRTrneWqG2dgHHtpiPRtwbvEtp+TTX1pgPDyj9kLOXr/Frv/l7bI5GxLnCdl3aQcjaE2fIpwnx/GRdtBCCKEu5vXkf2/ewPL8ChCGYFin70xHatcm1Qit55CxT3Y/SdRgnEXd3t2jW6nzy+Y8z/863mCYRlmU9gLk9ym5xf++AVn0Bxy5xbIHWAqWhLDWuLWjUamRZShjWyPKcUhl6i2tMplPcWsB0NsbSim7DJZ7PKISLLjIunj+HY1U0QOGGpyqHZtEEBRTaxrMd/LagVAm2ZXFx/RLb928wi+dsHfYJgy6OHZJHMfd3dknSCTrRnF1rk6iEtfOLJFnMPDfYukar5ZCWCXkkSMuTF1/Vw88ghEWa5aRZSYFiNBoym81I8hQdSJx6iPR9lCVQQpAUJVky4bA/ZJaklEi8Wg1j21XJMisobQfbDTHSIi1OXgHXSXGlS24y7t25h7As6o7N5aU6RpRkIkPrHEs5aBz8epM/+urvgzHEpSJKcpQy2CUUSpHmOWVWdQanSYTnSCwjEKc0YT14XR7pq/41xvFK/HhLeSw3rNyhwwf/nmUZx8YPWZZV7IqjxH+8kmk0GpRlSa1WIwzDR9KHA9y8dfvB9rYoNHE858qVq6yvnwMpSLOC8WTK7t4eUZxycDCk01nGdnyMActxqNVC/MCj1WzyxS/+LFopgjCk2eqQ5tWK4lSe+JF1WhAEuK4LiAeu9Y7t4Dg2ZVkgEFy/fp1arYZWiizNHrxGx04zURThHtX2i0KRpgWuayjLnPVzFx/5+ijADlx6S4vM9w3pbEpcaEaTKcJopM6pOU1ibdgfTLi7O2I8r+SMWllYjocxJUJYvPv6a/yj/+of8kM/9EP87M/8m3z6c58D76OvkRGQa4WRknkUITwb13YJGoJgPeSt97awzkvmI4PUEA+HbE9GLK13eWPjPUps7GaTWMPUgvXz54hHM1oLHWb7AzLLoG11OhBMVs8b13PQsnJW14CRgsJo7uzvYiy3ut+MoSwKbMcFUXXwaWPY29lhOWxw7sw618ZP8sqbb1T+kKZyWjo+4zkp4jhhNt6m2bCwpKBWb+K6AcPhkCKZsLi8QrvWw60Z5tEQY+fcvHcD129iDOTa5dLZS3hhnXu33meelni2JAwC0AX1WgvhtzDqZExEo10jjnLGozmhaDIzgmQW88TqkBeeucav/8Y/5+zFc5jxmLIoCHxFKXLC0McKJIPpDOkrUlvSbSqW11qEc8PBYMrBQcwzz6/j2jbx9BQHKimRQiCkhbBskMeLvfDBbtqyK1BXdS4jGfVHJPOIIs0pshLP8RA1wWC8g7Qrdx4lwHNDPDfAcmyEe3I5xZU2Emg3mjiWQNg2vh8wHM+YjEekhSIvC5LyHtJ9jS9ceZL9/gBL2OQYqmeEhWVAaE1oCbSjq8N0y6Ee1Ahcj/AHsSZujGE2m6G1pigKoij6kDv7sXrl+POiKB4498RxTK1We4C8PN5aJUklj1JKMRgMKMuSdrvN2traR45jMpk9+Ln//f/wT6qaZlmijroWl5ZXH9Ti//pf/3ngw+TF41X/8ST8EDdFPYqYroosz0mSmLzIj3grGltw1CCk8X0fpRzq9QaNZpM4jqubVVT1siRJqIx7PaT8YGejNRSFRpUlw1lMdgrKxRi7sqwyoNOE+PCQfDzh3q07SNumLHcRKkWnMWXmcK/sM9vuc+veLtO4wHZdAt+BUvGlT3+SdqsFpmA6nfLM+fNsv/06/+P7r/Pbv/kE/+i/+6cfOQ7f93Bsi3arztq5VULPp3+4z3AwQG4mqPMO3375HWqej1Q2DDOMpTmMMwpTsPveJpYDkyTj+e6TlHZOuHyOdw+2CZZ6EKVoo7AaJ5cPjKysvlRZVnZg1tHuzlRw0FFesS1cWQHHbCEruzDbfrDQGIwHfPft7/EF/zNc6a2w8GKdb7z9GjPXRsTpA9zySbF+4SIHe5t4ds7ihU+TRDPi+QzbFyyfOYMxhnqtRqFLFpaW8GwLI0tKXSKEy7PPfxrH8dnYuE1W2riOy8ULLfIsY2lxkfW1ZQbDPu+9c3IfwXffHLJ9OCfJUmxzyGwSEXo248E2X/+WR7PZYTaZ0ur4LC7WqRmP+/0JDgq33WE0ybg33Gep2+XO5oxoOMNte1x9epHZeMp7rw2xbI/0FKNkLQ1lnqOVZiZ80jRiNh0S5zFZnqJKjSVtfKdio0RZZVw9GRuK0oCUlGWlPPI6S1VZV2tk6OI4PkK4SOlgnZIWX9/Yx6iCPFd4touxJLFSvLWxg0DjySOmilAEvmBxuYfjuNhCUndtXNfH9Vx8B9aXFlnqNvFsg0JjOzah7yKFoCh+AN3uj29a27aZTqdHmmt9tHqsauLH713XJQxDptMpRVHgeR69Xu/PcUmKooI9HZsw60cwfi1LhdYfEAiNMVi2jSVEtaI82g1UNfYPkK7HxMNjEwf4IKF/2NTiw4bLHxVGa5TSeK6HOnLzERIEEqMM0gLb9jHa4DoG5bpHRsQGRHXpKvs1hUAiBBRFwjyaIyyHPIsojYBTeNHVWKvf5au/+VsMN+8TDSfMJ0N6iz0srcC3EdKj3u5i5wVhnNLyLPI4Ik4zClFDCGgv9uh2u3QWumitKyMPz6O92OGJq9dOHEeWpZR5ZTbr+h7Nus+ZM2do1NsUKIrFgN7lFvPBiIPtPrVOl8WlLpHOsHyLhWdaKA3d3hL+JGdSRKi8pKldbmzdxwtCsjSlzE4BgnG043OcBw/zhw0/Hj5/OX4kPEC/AAAgAElEQVSrHp7VLlEIQY5hkubcub/FmaVV0izDSIES4Lgu+sh1/aR44vwKgUyZjfZYWb3IcLCL5zmUZUir2UYI6C4skOYZnu9Sr/ns3LtNkqQsL7cYjCcU2YjJaEyt5lEUGb1uk/F4gutaDA/3uH/vHlly8nx58709lNC06j4fe7qLKds0ajUcx+C7DlLaHAwOaHaXyQqLw+0D7Habva1tnLlmNIjII4OeRfi2Q7fRY1zOmAyn5PMSbRSNhqThnGzGMJ9OcWTlkrS/P6nklibH9iyktCkosC2btChIy2oBWKrK+rDU1dKqclUSFPpoJyQkeVEZJHu+dwR+O/n+8AIPaSwCX+PWauSmMnJwLAspLRzLwZJg2aZqBOvvceXcMt12g9B18X0fz7VoBDau5+JYFohK8Wb7Lo50q47NH8QkniQJcRzjHE2Oh1UoWZbhed4DlUoYhrRaLer1+ocYK8cT5niyVHyQD0wdHnhUnhC2bWGMfAhJW2lPEeKBv97DWNljqeNfVG8/Lgk9vBrX+gND3ZMizdKj8djowqCPrNUsKbBtF2QGRqDNB56jrVYbrTOyPCFNEvKiQEoLpTSgybKI6XxMUkIYBpV5q32aUfIRHhjASJxaE7Sh12rRDGtMJlOeePISYbvJvbt3GW7u0Ftc5eraWV7otJBeiPAbNJotnrjyBJ4fYNsutmUjrcoQWNgSIU/epoojz04jQRqBEJLZPOKJK1fR0rAZ7+MZi4WVdc4snkPNFL1Wg1E0YqYTOqsrqFzgO3WGcouNl+8hlcDkBV4hwDLUG02m2cku4sfXHKoWb/NQ+UMI8UBF9bDS5EMLAsuq7NyQvH1vg63DQ2bxnETlSI4NfU8/uxnsbqCzCdF8ws2b38O2NKPBLhhNHFVt9gBFOUPKAmd5iXfeeoXe4jK+oxmUuxjlIoxCypwsj4jjFpgSrTI27txkNh4jT3mYrJ9p0em5tOs1ejULBERRilYeO3sjVjqLhEGLwbhgFs1oiJDJNGFlbY28zFkI20wO5zTaLebjOaPxBKsT0G20cEOb0tmj1QyZT0529tGqRAgbXWoCP8D4EqUyjDCkSQrGotRUhh2i4rVbtg0KbGEjpY1lVddSobFsBwQ46uhjc7Lr13H0um0cabAE+DUPZQwqy+k2W0f5qMT1nIppb1mgJVcurdJtBNiyOjgXApwjDrvjuyhhELaDsCwcnAdl5EcJ8f/bBeZxPI7H8Tgexw9MPFrx9nE8jsfxOB7HD2Q8TuKP43E8jsfxVzgeJ/HH8Tgex+P4KxyPk/jjeByP43H8FY7vqzrlP/61d415SNr3cBhAiYcNYQQYG8toJBpLpeR7b1EO73GuplkVAzbNCqO1z1GES0itjtwdq7bAX/l3nv7II+Zf/of/tXFsG8dxsG0PbSpNdVmW6CNfyWO1ycMdoseHwJULvUId4QOOFTFKVY0kxwoXKSX/y//0Tz5yHL//rX9ssizmxuaMxdoCe9MhllNQCwKG2YxL7YtspW/TDttsHU6JRcGF3hLKNNgY3CHLbhPlATVvgflAcnfvgOeuKSDirRsZeZHQcrt0uy3++X/6ux85ji/+3V80x/p7iWB47z4918MJPe7s7LB+7gKFKhkMBmhtQECjHuK7Dq7jcO7MOu1OE9ez+NbXvk40mRK4HtKF55+/TjYbkuYFzW6P/+a//fWPHMev/uqvGCENCws9xuMxRZnSbDRJk5JZPKO1YKGLEqiaPUbjiKJICIIGpZDUggaTeIRfc1j0e1hoijxhOotZW7tMUWRkWQoI/t6/9e9+5Dj+8T/4G2Z3cMBe/5CVlRXmkyn9wRSFfCAvhONOYQtbShZ8izBwmc5mKGFRq9ep1UMmkwlB4LO4uEij0UApxc2bNyoTAa351X/xvY8cx7/38z9hlldDhFsiu00s2Saeh7x/4320jtELdZaWu+xu38IJSywVke6OWFle4lLvArv7A/rMubh8jukgwrc15y5c5Ct/+DKe02Nvc4OXvvgFvvD5n+VTL33pI8exuLBghFHUwoD5bIpS1T3QarUwWnMwHKHKEtuSXLv6JMNBH2GHWGjSeM65M2vYlsXS8jJLqyvUGjWEtJjPcl5++VWcWodnnn2ed95+iz/66u985Dh+5XdeMVkpmaWKUSKrphlV4MgKuKZVSQVzkxil0LokKzXqWOF1rD7hWO0FQlQqJK0NlTxFosqC/+2Xf+ojx/G7b22bwPNo1APCYo7teAjLpqZcHODbN77K3a27fOoLP0aoLBwtGWuXuid5+/f/mBe+8GPM4xF3b75OlhVEWU7YauOES9RbZ0nKEq0MYPPzP3T21Nbe73Ozz0OM7Ydc2CsnGOAhSmhlHawq7oYQqHRMfPOrOLNNRDfAciM83SdsXSLyWpRYH/g5niIRmo0n2EfeeLZdaa+10ShVHjl0fFjS+PDbsU746AseyM4eljg+DOw6KRxLE9YaPLW2SC1ooWWDe4MbjPQ2rg2eL/HKFvNiTj1sIeQU2zPo3KZdzylkSN3PyUjoDyNQgt3+kP2tKXnh4dZtFCmuWDlxHLaQCF35qAkMgefz1KXLnLt4FvGdP2VzdxsvDIjnM+Ikw3YcJqMJvusS+C7xPKHRDDFkRPM5T1+9RpnlpCbDsm1s20EUf/HD++EIgvoDvkqaZoRhwLA/BCOxXItZHCGUwZYOjiOwbEFSlozjMcICoyOi+YgicSnmEyw0uc5p9noc7u/h1aprncQnS7ekrDpFLWkDldzTsm30kf7fsiwEVQchR1LSi+sX8B2Lm+ldEm0QlkRaFo5doYk9z6vsA/MCgVXJzE6BbdabPXZ2YopU89QnzxIVc/YONqnEiyFOBg3ZYGA66HSO7wk6fo1z9TWur6yw6vt88/0tmmqBWxu7PHW1zf3b2+jCpRSSJFHcvHGf56+fLLk0RpDnJe22B8IiK9LKtSqrtNhJoVhdWeHSpYucWVulf3jA+7e2+MTzzzHY20aogqvXrrJ2bo0grKERlEpz4/YNSuHz4gufIU5TWt2lE8fx05+6Ql4KhtOEzX7MYDSjPy1IVUXhRIYVq1tpSgABtmVVbcimur+NqXTjiEoqqc1RXhLHTXugTul2rzuAzjCFRAsLJQRaOAhh4ZsSO5mQbt1i83selnBpdRZJ3RbW0hLakuisoMwS8jQiywqU0dy9f4vWgsJrrGFLgRES8Yjp+ftLMTxaJwMfpoo8PLkf+tAYBUJhhMTVET+ymuN0HPrTOdPCwTExljAo4VTrb6NPh3gDu9vb2Fa1Encc7wPyoQBkZehsyWoSCsBIiTjSkUtEBaZ5SDd83Bz0sGYcTk/iF1eusLt7yEp3iahMwc/onamxO91Hhm0OrBsEjUUmWUCjXuIVFkZqGq33qNcXCWyb7975PW7d0MyTENvucLg7JY5qdDqC5bUET7e4unCyKYQjLSaTCbfv3KEW1ri4vMKFixd49rlrhK2Qf/bPfo3BTp8ozpjFGbkyGDS+6+B7Ljs7W3SaDVqBw5d+9mfpLnTZ3NwkVymea5NhsGzrVFd1iUWWpUzLCb2lZSwEBzu7vP/eW1z/xMdJ87RC5mJwMoMiIS1njIdzlhorjHf6FFHG/cMtpCvxXAdLQH5ng0985jPkhaZRa9Fpf7T/KoASLo5Tx7MjtHFpdBqEfgNpDFpKbMchdDyunL/IubUVWu06rU6DLIl5bnubu+/dYHh4QDJP2C48SluSl5q0FCSFINXqqM/h5Pvj6U/8KMPxDpQD2n5IuHSW4fYbmMKh60u+eH2Ng9mIdsPD9QOefeYib717ixtvbLLx8l2uX17n5z52jeks5upLL/Hm229y/959aoHHKB3TXFjj8tUXGMUnd0paboDrBfSHUxw3xBUeRVHghi3Onj3Lj710FV0qBJokjsgKweLiIkVeENbrbG7c5atf/wb90RC/1uKFT36adqeHsVs4YYHnNxiNY86fu3TiON7f2sd2Q5SxqAUeNd9jbQVKBEmaszuckeYlaVaghY3AQxoXy5jKNIVqt64NFWVTq6PkrtEGCkOFTjgFQ3Cx2SFwJK4UbM8i7DgmF4rYt0jLIY3XXuELh0Pk/BUOXTiseUxiQeMLPwE6A5Mxmx7SXahzcNBnMh4zONzl5p0Nbty6wRe/+Eug5aOkMuAvCYD1KNr0B6tzqm2OEAIhXWzp0PA0WRIzc9pkVkAhXCxzslvLw5GlGdo+6szEIKs2yQdlEykEHHeGSlmVSAAhqwYUjAYtEOZ4t/DB6vthuNFpv+fO7i7zOKUdXGYv+jZx6SBdGykcojjHiCkLXgNpJDPVB3LyWZ2syJD2nIanWO0FFGmHd6IhnYWEc2cabLzroqTNlfMGlWhqp5hkHHefpknCwf4+6WTCai0AWTLPIoQxpFFEnlflIqOPTCSEATTGKMoiB1uQpQkbGxtEaYwfuogj9oglrVMfamk6Q0pI04R5kbHU7DHuj1hZXUWrAlGCcASlKXFtgYfEpkWZ5eTzIUmWMU8TRvMhjusThiFlnuK6Fmk25drVzxJ4IbPJyStPy7ao1Wp4nsdsNiOOE4QGWzioMmGlZXP18lmagcvXv/IVFlYW+YmXfgYsB9/zCRxJGc1IM4WQdYxdsV4sDZSGw4PK3eW0ePvN15Cm5OmnL7B9b4enu8vYVoYjc+q2xMynLLshl8538XyLclxy1uly8ZML6EIdecEKlhbavPqd77B+ZhmKBdylFV6/u83+fErNa7PQbp44Dt8PcGwLz/XwAp/RdMr6ufM8/fTTWFJSZBUeFlXiWC47W7u88PGn8X2PV1+9RZJlBEGIsAJ+4ie+RJopNjf7zKKcNM+Jo5j11VV2t++ffH8UldNKriST0ZwiS8m1Ia8AgORIlPDRrosjNbYlsExInmVobRBCoQVo40CpMaXCkRau7YGETFecIjiF865AmIz5fEgLQ1BMcGot5FzC/j75YUxYOgyMoCEk0zTlzv6I2Z2bFIkFMkNKRZbEmLJgNh6RzaaYXPHOm/f47Kf+DZYWV/9Ch6y/KL7v7JSTCG7i2Mq6+gxJ1fJsGYllSoSQSMtGklFmKREGbexqhW8ezccROHKFrzo1zVHyfrit/uFk8wDRqnXVZSePErb+4PfRRy28Wj8a2Og4DobbpFgMh6+zk91FygUs0yAIWiTTKcNJhN3cxpM1KDRm3mK+vY7j1Uh7r9P0Zlw7u8a5zmXee/81rp2/wNUrNtdXJPe2C65eXCKbh5jpyW33lmUhj+iRaRSxmUTcaNZ58uI5cpUxnU4rR6MgpNAG53i7Z8CUCksY5tMJjvLJJhFxHuM2PIJaSBJNsYRGYpCn1A+WlldJ05hiXCLQjIYDdnfvc/6JNZr1JkK2MKYkSfvEwz2ycUQ8LymVpihLSjRRnlLoEsdoAt9DWTatdpPF3hrbWzv4nocqTr4+s+kUx6k6gtMkqeBXxmAjIS+5+qkXefq5jyGlJDMGP/AI2wvIMic+3Ic8pteqUc4LilghC0WSJgjbIUo100lKlqVVOeaEuHBmlcmoT7sespFEbG7epUxj1rsNlmsOB7Gh25TUtKLMDdIyLPYaoA15klJkObI0+NLl0pkAx0m48NwTDJTNt19+jXqjiystmvbJ12V5eY3dnU0c20YbeOaZj9HtdsnzHNdz2b6/SafdRpU5vufiOR62KTncGzAeDkFINBaLi2fw/Ab7B7t4XoOsmJPnBagSW8BsMjlxHLcPc7ScUZSKUBhCR+JKSakr5lGhDaUqKEuF41SU0TxNCUNwvTqeX3VRZnlBPItx7ADXdnCsih2k8nnFvj9l/t6P5jTMjBvf+wafqdnoeKdy9nG6FFFGdyUgPoxwtaSdKBayDN+22ZtPiSLN4XCPyWRIf3+vunezjOmwTyGgSPuMhkMWe8vo0+o6R/F9T+In/b04+kMAGIHBqg4rtcAyCtuCPM9IsgxleRghquSgNeYI/PooifyDJG4wkj+HuH24tv2g1l1WWy9z3KqvQSt9xG1RR28flFWAU2vAg/mUiYI8HzBNSxYaKWXpQymxpU+ee0yjCNtLYbJCefMi0VyiMgdv6WkOrQNqrqIbdghdiZ42cPQqY+dtrj7VxrYKdkYZXeuU7bJt47huBd7SmrwsmcURezu72HZV7w/DGkGjjpnHRGmKRuJYNqHvEXoe0XwMtmA2mpDrDLfhEMdz8niOV+YYXbHFTwqDpFZvYnsOW5sbbG7vMptN8L0LrK40mUUJOleMdg64+ebbuNIjLkE6DoaqDl2xbxQCU/ErHIdGvU293qVbr5GlCfPpyayQG7fu0Go1K0SoPirRCYUyReXeUq+T2w5r587y0tPXKZMYwgbSKFQ0xtcxw4MDhu/dwzYKZTJ2Bn3MYESWQV5Uzuin+WS8+OKnSJM5t268BlLy6ptvMIsSnv/ERfI8493DiM8tNWjUNEqJI0yDwrYthBKYXJPHKaVruPbkEtP+mDweYBKbz15/klduT7EtCE9+xjOMC5bPXWY86HP96et4vs9kOq4up+tweLiDIWNnZ5Nus8nVSxf42je/hlEwnMxZXllDOCHt9nneePl7nD1/kWmUcvb8efIsYzYe4Nlw686tE8ex1GqQFQpjBMt1i14zoBF4WEdnE1qXFHlBluekSpIZi/5oQl5qjKzOe4q8RJYlzVpVwy5LxTxOyPKS7Kg8qk5hp2wdDgizAbV6k2bPgygnjxPq59bonD2PHV9i9NYGzdhBmJzpG29g7+3gjkf4SjK4f5tpGlFGY7LhiCUrYDvNGcZTpsM+/++/+r8wxUs8eeXpky/MUfyl8cQ/Kv5s2qsODcHokjSJEHm1gpGWg+P5FEJgtEHIR9x78OEkjvzgcPI4eR/XuT8EOyoKpKp8Is3RAYg2/IVJ/FFX4k4zouPY7M5TtNLc74+peYKa00NSoJQkTxUmrSO2zmISm3oA46Qg2XXZTNZplAW1yyVxlvPuxh4vPrdGkRUoSzItchbb63RPQVpqrXBch1q9Xv3OlmQ6n7O9v4/W1aQIwxDH9XC9klkcozAoJSuT60YdKTVFmbCzv49XcwnLJr7vkKURUisKw6mMjiiO8DyberNGzffYuneHwXDIfDJnd+sWSk5JJgUbNzYYD+bYNUMGOEDg2AilsRDU/QDfdRAP7YzarQX68wGB57G01DtxHKN5jnFKHM/DCq2Kc0HF41DG4mvffZl3bt/gcy9+nGeufQzXcmk1HTK7RvOJZwi6DaZvvU6xMcQZp+RKEasEnRfosgKY5WV2Km/+1v0dyizmzfdusrCwjAgbrHRWyKMpk9GA99+/z6o7IW9IVlbO0+l6JLMIbRQqL6syoChRRmC5Dn6jTno4ZLK9w4vPfprvvPVtprMJtebJTBvH8VheWWdlaZlzT1zkYNgnGuasLq/QrDdYXFzADzzarSaXL15gsLtPo7XM3t4Ba2cuk2Qll888gS495rMBk8mIRrtHliVMRn0KR3Dm7BqBc/KOYMF3yDwHISXdlks9dHCsqlznOjahG1Ycfq0pFMzjnKW2RZyXzBNNlGiSQpIpyTwuyUtFoaAsBQUOmbAAgRInl2bf+vZ3+dHnr/LExWfoPLlc5QfLgaJEaMncUnQ+/0mk5SOsnPbn/hrlv/wyRTGllc3QyZh2rYbYj+DeLTR12oVkr4CVzjq33/0G51c6fOrZZ04cx3H8wCXxh9Ulhg8OGCzbENQcilSQzVJK7VH4Nkincs4rczjFkeM4kiSpQP62jZVlWJY8Wo3LBw72QgrqjZBmo07gV87htm0/8FTMj6SI0TyiUOWDLffDb6fF+csDXn3fQ5VNHNkkIyKaz8mdSh41Hoxpzq8T7a2gyhm+E7N9Zw/PcxgMDilUyR//4QxvOSd8ShO5MS+/dpOrT10hmkdMUgfL7FBfOoWmWDlMsLK+xruvv17VDi3B7b1dFtpthJAEQUC718PxfUaTKdKyUaUizVK83gLnz59nMBqwPR5izQXtM4vM+5ukswG218atNZGneDkiNYqC2xtv89YrbzKZTDlz7gx7B33eePNdep1lKAp0KWktL4G0kNMYkZf4tcqYWKsSu95E6RyBpNvrgFTsH2yyNzpA4lBmJS/9zM999OtR5ETTEY5nI93qIV4qg6Qq+UVlyr35hEwZvM4KT12+iAgauFYDlMKtGdrtPog3mU5H5HqOlDlSC3QpabaatNqtU1I4lHabe1sDTG2F1968Qa1Zx/N8otiw1HT40t96hnSSEcU5+9vb6DgkTcfEsUDYAc2OT3PBxrg+mRXi1m0WlE2zscj9vTusLC/z9T99hbVenUs/8tMfOY6nLq7T7TSwhOCdN1/l/KXznOn10JkizWOKzFCzbT73/Gf5rd/+DZAFP/zjf4tZHBOnBdPJDJRDtxOiyxbzPEVkMw637lHzLO6OhqyfOUdt5dyJr8ftaYm0XbQxbM5ToMJQG62xhKBIZgwO9wkDnyKeMDrcZjjqc+HiZTw/pCg1tdYiTn0BJV2EI3AcsA14RlNTGUJKTvNi+Obv/N9868tzziw2+YWr67hpgptnBKsdWFsjWPJo1S8T1uu4jZCa57P4d36J5cDiKQnjzPCHX/5/6K5fYX3pGSazgk9du8ri1Qvcu3+X/+y/+M/57ne+xY9/4afh2ZMPe+EHMYk/pBTnuEgiJMYo0jRGFzmWbTMd55Q1ENJCWhJbSvJHLKgc28Adc8mPyygVzlVSFopavUar3aLdbrK00ANtODw8pL+/V5lFRBFlWVKv1wlqdfKH9OGn1dSOI84Utimw5Jw0s8iKEnSO7R5hTXWDw4050d5NbEfTbrdYWF4lihMGo7cwpmQ46NNt1ag5NVYX6ziOIJ8FBAH00xlKKvqTk0/bjRAYIaqHmiVxXRsvDMmPNMCdTpe19XVyrdk9ODi6NAal9QNsa7PVRRlNfzBCWDCPIiDCcwT1RhNle6cqh/Z2D8myiLAOB/0+pTY0Gy2kZXH12hKqUJg8q6RZChzXwz4y0jBCk5c5WZpgY7HYq+N5FTI1zTOG/V0EggsXLyJPue1roUcQeEjHQstKT2wEWEJioektdFi/cIUXPvvDXL32MWqOg5F+JVujBAmlKtBGHZWjHGxbYGFhSgudpJXRxClZvF5vsLZ+Di+w2Lm7SZaXWEkGvmC4P2I5LFnrNOi1a8SxZHdrk96CSyW6yChzi3q7zrwEoW3KrEDaNlk2pN1ziN+dkuY5v/cHf8zf/uX/5CPHMZ+O2d/dJQgDuq7D/qvvsLJ+HuN6DKYReV5w52CDZ69f5ezSIpfPLfPqG6/QW10lbDSJ98ZMRxnR1KfebpNkKWkas721SWAb2r0u7773LtuDkymGbmhj2UdUSY5mujJIY1PkGe++/x6b9zcwSpFOJ4wPDsiznLdevoFlWfiBz4Wrz3DlE5+i26sUSseihOq9PKJWnlxOafkWN27cY6n7JI1OhzBxuffd73D48rcZGxeaAjXzadYtrKBG0OzQvXiFRq/OpSefxA07LIVNvv4nX2dwWHDj5j0+89c+x99Y/gX+91//F1iWzXAwZjY7+YzgOH4Ak/ifTcSVAgI00gJhVbpc2/EoTVVX1EqjTflAUXJaPOzX+TAf3HMrLa/neXiew87ODjdv3KDTaPKLf/MXuHPnDjdu3iRJkgeoyCxL6QqJGwQkcQxHmvNHqc1rZeO6BRQ5ZV5HGkFRlmRpii99KDym0wmT/oiwHrK8tMCP/PhP8e1vf5tGo87m5h2KMqW71KbXUKy2NcP5lGLYp70sabRqlCphNDlZF12pbkRlwGxbLCx06PYWCFwflMGkEWCIojl5kVfccqVwLEmn06ZUJePRiKLMcTyPUuVEUUyjbnAcieO4CLva3ZwU6+uraG1oNDzeeeVtrHYHtEYrgR1YLK4sksdzBocl0/EMIy1azQaz2ZgsjShKRRTHdJsdktkY1w4RysUXkv7eLmeeeJIyLwmD8MRxBPUA3/cwQF5k1S1oGbAlQjh84Se/xCde+Cyt9iJCayQlRoNRMULFGJ0wi2OSXCFkiS0ktuUjpYWWBufIhkycksVHoyFZWdJsdWn3FjE6ZzaOGI4P8L06b26W7O3cYa0pWFu6QN2ymM2mlKVDGNi4lqAoKjGVSiPKckoSp1iWQ3+3z3PXLtE+jLn1/smmEDs7u8RJRpymPL96hmIwRLWWMHWLyXyCLQW273Ln/gYXVpZ48exZ/uDVP6C31uXwcJM8HeNJC1MYstRHqZJElUjLYWV9md5Si/vbY3r1k3ninl1i2eJDajDXtklmEa/96be4d+cmuijIspRGrcHy2hpRFB+dtVX3+P7BHuG926wsLRwJGCTGaLSWqKPLoU/ZI9lCUeYpSZZw/cd/Cjno02i2ufna64iDGZO0j2UcrCxFaomxAg4279DfyRlu3EArm3xpifdu3OVwIrhxZ4OLH7uOUYY7t+8QxwnaaNLs5LObB+N5pK/61xYVkF0gsCgr8b2QlEKihcDSx67oBoRBixIjJCiB1BJVgC4kZQ7TVNE818MLbUxD4jh1Nob5I+nE4cOJvNVqceHCBS5dukSn0+YrX/kK3YUupcop8pzbd+5w//59fu7nfo5ut8trr73GbDZjb28PY2AyHrPoegSez6A/OJLsnV5O0UqRZIYsL1FFWhlGa02ZpxgPHBe6F1zyoY0lNKur6zTqAXEyIawFhIGPY9lIJIuhS5YUZLlFTU3oH3hc7loMYs3u3vjkcZiqtcro6uA2iRKieczCmQU822XSnyPtAsdVWKLEOiq/UKZoXfG+V1eWuP3eLfbn0B8ckOUlHVx0oSpLO0tWzPYTotWqc9gf8tr3qsno10LOnj3P5v1dprOIpZVl7Ead6WyEkccyUIVnCwQ2w9EQaXs88dRV3nv1j/DDJoYalrHJkxhRlpRpyvTkc14838N2nKPdkHMkkVS4joVluZxfXqPjh+goqia8ayO0hjKmnHJfNFcAACAASURBVB9STPbJ4xiDhevYVX+BZYElq7Kg+fAB+EdFf3BIfzbHRrHQ7bC3fYfxwTZ+w8JdWGBWWJT9AW0hmehDOvWAw2JWWbC5gjKbk0cGt1YnjQbMkj2G4wJXLpLGPotLBpYabN0/uczVaLU5d2mR3b19pnmBdG1ubN5jYX2dJ568zPtvzKgvtpjlc55ZXWbw2uus9BaJZhOe+/jHefrKFd5+5TWm4ylz7VBr1IlmUw73+qxduEQa9dnf32e1ebLUca3lP3D24qjlSRWG7755g/H+FiaZYlSJLHKiqKTeWUDlmrIoUKWqTBaiEeNv7nN+scGzzz1bOSwVJYVSlEi0NhSnqJcKVWC7Dnc37uGsnad79hKrT13HuvgEq6nm/t1XaIgOlh1jWx6psWisthgPt9BZyvhwgu0JLl26xJKqs7a2ynPPPoNr2YRBnbK/z3DY58tf/g3+3t/9pRPHAt/vJG7kkdpEExZ9PEsSl4LEcjGuhzBBNSkERy4oJRZVc4dbQqlC8qxgOk+ZewGfffGvUVtoMZgfsjeaIVhFG1PpvE8I50g3bVkWn//857l8+TL1ep04jphHMwbDQxaXFrhy5Qrra+u8IV/la1/7Gi+++CIvvfQS58+f59VXX8V1Xe7cuYPjeBh9wBNPXMaUmn6/X/2gU54nSWrw7RLfKRDSQ1oGFwdVJORFSbfbwdYOZm0dV0G31cRxJdH0AC0MvcVlBocjxn1DMrfIPIGNQLiCtCjZ2OuTFQGT5PQHSlXvhSAIKYuc+XReJXUDq2uL/M1f/BmC0OP//D++zO/+q28StNoM9u/x5JWz/If/0d+nEYb8r//0f+bw1fvkucJ3PWwhKPMMtK7avE4ZRpbHRNGQd997g5Vu6/9j701jNLvS+77fOeeu7/6+tVf1vnJtks3h7NJoZrTPSPKMlCAaW5KlOIk/JIgAx19iREYiwHaMeElsffEH20gCBAqiyShao8y+iEPOkMOd7LW6u/aqd9/ues7Jh/tWk4zMqgkQMBLQD0A0wK5GP33vuc95znP+C0994IPMtZY5ezHi1q1r7HX2IU/p9nokacooSnDrPmXfQfoB5y89zKlzF3nkkcfYvPUSOJKgUUFPNYEMuHPzLc5deIyF5TNHP4t3mG47jlNcbougcDX3S8wtrhVdNgLPd0AV3aHCFrZdwwHRaAhG4Hnh/ZFgcRKxODOfTymPfiBZFhPnOb61VKRhoRbSFwlL9fMkMqQ2P8+NW9cJywK/rmjMlQgGPjq2+C5YBaljCQJJf7/NTjvh1KVLDHpDlpaq9CcHyMSHYzgWS8urrJw4iRtWuLN+k8uPXGFhYYETqytcv36D0yfXSEouQifILEHuHzDKAj7/mZ/lc7/0eRZrddZqLb78Z3/G+q0tKuUyjpScv/woYa3B7s5NwlIA9uhy5EUjXM8prPBcF2NhOI1pb98jkIZo0MHovDCPQNHv91lfv02aZuiZPIbFUK2WefQ/+w9ZqxbNVp4rksySGPA8lyQ5+sPd73UIghJ5FvO9F19k2u2xUKux2+2B79PNerSnMf7yHNk0xi9XiLHsRlMCafnzN17hxHLE/OpFGKQ0Ty4QDQ+olwPWVtd49fobSCF47bWXjszjMN7XIp4qgdAJLjGfWtqmluyzu7HBmbMn6MYTbsuTdAcOWswR1teYa81xsSEJWoZm+TI1fYnnvv4S3/+ffh/lh7z4yrPE05w/+sNvMokCfvq3/jtw3WN74HqlfN+hpdPt8sRTTxKWy5y9cJ5SGPCZzxaXXuPJiG67w6Xz55mvV1lZmccLAp5++goPXzqH4/l888vfZNgfMkgmCM9laa7F9/qDwhT3mM3kzKJA5E16gwnlMKU/BKM0udaE0gPpM9iDTjthba3GtTsvceP2Olt3XiZLIyaRoT9IaNXLjFNFMjLUGh6yFjFKUqJ2gJETFupHjw90EtNrd+i2O5xcW8MVhfvI9evXmWu2OH9yjevXbvPTP/thPvGJp3j5+6+S24AsqGBiy/XX15lECYPMpdvpMBl0MWmdUmmRaRoTjYc4jk9ZHr3cNjfuESdTfvYzP00aZ0RZym67y3g0xiuVOb/4MNNxn7UT89y4foP2wZT5Rp3HP/hRguocxgpyLdja67Fy6iL94ZSwsoxTdyinCetv/oCDrbeY2gnwC++Zh++XitOULW5lhLU8enqJJy+fpVTyGN38ChPlUCrXyIIKqlQlaC4XF8RSob0ao0RihYfvV1BK3neNMkbjqqAQlTjm7mStamk4gkZJcWFxjiSt84OGS+wusz2YsrI0j/rgozz3vRf4xhu3+PWffYaPXznP8889z6jfo1at0yxnuGJKlircykXeev2AciBZOl+jG+e89tYNGo2j6e54PpnWBKHPE1eexPMERmju7twh0hHlekDLaTH1YsS4Q9/x+Vf/9L8lmD9NnEne3LrN6+1bsLbATzzxGEo6heaMF+I4Llc//EFyrZHimCKuNQFucZI3xQnSl4K15UVG/S6N1lzhWzuN6HU6TAYjdK6RQiBdDykFS6fO8fBTH+RLX30RJV8qTqFCIAX4jpxdbGo++9hvvGceP/nZTzLuDciTjP/yP/1PmE4TBIpGtcSo3ebHzs9RrS6zYRULlQavvfwKJ0+f4LOf/UlGgy7ztkXvzhY6siSey06vT5rD3/qNv8l//Vt/j4/+6Af5B//wvzn2eRzG+4sTh8Kz0uSsnF5FdTKyUQeV9ChnY05KRaANNhhz+vQ8J89XWLSC3M+wKNJRRiwExvOoOj57b1wndSrMnThPrbRYHPF/CO2UMPALk9lSyPbWFuvr6zTmWsRxzMLcHCdPnKRUKaFNjtaa8+fP88yTj7O1u83+3i6D/Q63Xn2dKEnRToDjOqhc4oY+k9GY1lyLTrd7LEMxjXNqnk/JzemODaEvSVEIR5DlkJoEvylxfUk1LBMGEa8+/xVsnFCt1PBdC1rSWvCZTg25ycg9oFwhH8VEPQfVgDg9uli09/YZ9PukcUwlDPFn3Wc7KxA4yqvz6uub7Ox9ieloArbEeDRC55a9/S5f/sq30MIB4eA4inqtMtMScvB8nzxLEDOBsqOiXKowP7/A8tIave6I4XgfDOR5xjSa4JcchA8mzVlabVIOanhKYoTHcDLCcRziaYLOLUFQwp2kuNLD9UPCUgU/LDGZjJHp0bNGzy2/y4pPCsHCymUW1y6CHmHSDn5QQjg+blDB9UPSpCAIuUqihSEDhOPgWXGfh3A4Qjn0mhXHAMUvnAxQTo1eZ4/hOKdabaAt9PoDfL+CKwytRouw1OLmteu8/MYWH33qNE65QjodY82IVMfotIVfWcIdJJxabmF0zHQy5O5un8FUo0R8ZB4f+OiT6LyQCvBESOj55PmEaiXE5DnRNGWuMY+xfcz3nye/coXvvvQCDz3t4lbK9LptyCWXzjyM74UIobAWvMAlTuJixm0s1h59AX9jqz07RdsZxNeQpjlIl5W1k7h+iel0SpZlLK6cZjgsjMWNtbiOU5D7XI9BlLFUL+EFhb2dnnELYq2RKLQ5Oo+Nu2+ilMtwMMZ4LjbOONjv4pU8KqsLvLoX4Q92UVUfT8HCQoO5uTJbOxt02h0O+l2apQq+0YhqnROtRa69eZNOv4/rCn70Ix9i/Zd+gWOmbffj/4eLTYmxgoXLT6EPFtjaH3Knu8l40Me1U/BLrJxY4sL5Cm49Jot8Ui2RU7j2g5e4/uqrxNEQJ05IJxnTqsfyI1dxTl4AKWe02aPj0MuzXKmQW8n21hau6xJNpvS7XZIkodlq4jiSyWTMC889x8riAvVmizs37/LnX/0W3/ji/8HcwgKf+pVfRirFnLuEFpa4VOWgO2AwHB7biedaYnBxpCDPXIQyOI5Bpwkmg9SLceuC1VPLlJUDOZw7O8dBe8QwKnDX1YpLoxUwnvQIShLXK2Gli4PHeBRToYJpBEfmsX7rJlmS4kgJeYZTr9/Hx8dJTCYE5fICvcGYcV8wjS25MRgEe+0ejcGUucVlrIU0SaiUqxjpEKU50lhsluIh4BitkEq1jFIuve4A13PQZsJ4NKRcLtPrt+n0eijX4EoHr1zDHfUoleuE5TqaHIGgUZ8nyQzTg7tYnTOdTqh4PnGasLR2gkn3gLla88g8DjkDdiaO5HoezeVVEickSSY0ast4pRrSCZlogcp96vVyoZo4GdPvdIkn48I7lbdVDw81et5prn1UnFs5QbfbYbM35sTJZcajiChOGU8sc80FlMmQJkOIYtO/s9EmyQyq1CCdpkjfJxGSOzd2uLR6gkHnLuHKIrnJQJb41gtvYYI5lppHn9Sq1VXSNCn8b12PkyeWadZ9Lp07Rfdgn92DhAsPncf07/DnX/86F68+zb+5dYOzT+cM+wes37hJvz1CUGegp/h+iLXgeIIkS3CUYjw+GpkCsNMd3FcdLDxlLRhDNJ2SpDluEBJKiacNTiYwbhWtD71QZ7N0pXCCECskcZoVsGFjMUaAcLCoYwES9doct2/dYX+/TRCUSeMMx1PU6zWiKKKXpEidc7C7xW13mw889ji7w4jNH7xRiKAJH6+1QD/NyfoTDBnTKObVV17hQx95hmq1wdraKba2No99JvA+F3FlLViFIcAGdRprPol4npevbSL0lEZjGbe6hFp8lDuDEqPdMZ3RPjLS2OvP037jG+xvHSCiCZF2ifxF5MJJWhceJW/M88MS713Xvd8Z+Y7LoNun9liVTOdMJhNu3LhBpVqhVAq4d+cOX/nKV0iShJ/7+Z/jxu3bdPsDnrh6lV/4pc+z8uTjIATTOGJ7d4d72R2m0fRYJ3OAUF1ilI1AFRcqrpUI6SDzAE+VwM3IsoywlWH6Gc3aPFV/iueFBIOcbm+CECmpHZHnEms94mnEyuopAr9KqZXiphXEMQJHg14PozVKSELfx/N8hLWEYUg8jZikA0r1VYRpsN/ZoTcaIB2FFpLucMxBd0hj6QTjQRclBfMLC1TqIVY5CCnJ0hjfGIQ8+uPY2trEdQICv0q1YRlNd8iykCw1OErx+KNPIx2XXveASsnllb2vsLB6Ai8IGQwmlIISWSILB/MkYWXtBFeeeBIjJTdvXccNSziOz/SYgiHEoZM9BexMSrZ2d3j9jVc5f2oJIVo4vqJSaVAp13ACH+UkZMMOk/YuSXcPGxeaM8Vm8Lb64Tvn7ceRwtIkx2SGpeWTnDi5ys0b6yRZjuNVCihsMsUkA1ym1EoeJ1dXmPTH7OxPiWNFOauxc6fPzVsdHj9tCMsh4ygGR5AJxZ29Ia2VBVbV0fwKk1miSUKSJOTeiNHQ8ulP/BTKpuhI4vinmMQTov1d+u0Of7j1LS791GeRXomS7xP6Iev9dSqL81gpyR1ZnNSUhzYWpCEV+bGYhCQpgAvCFtDjQwExK4u1mGpDZgrmLwI81yW1aTHOwhaX647EdwWBLETcpLEIo8m1IclNgdQ6ZnNt1uZYWdS88uKblP2Ahx66TPWqSzRJee75l3A8lyhKGWYW6StuH3QIZiO6QBcCXdl+F4tlvN3lxKnTNJotrt28ySc+9Qn29rpcOP8QQXAMr2IW72sRd4wgtxKNRzqJMYwZ9boMu31cTzBsNQjkCfbvBExuJojMMixrWklEeGObSqfNggdJReFV5uiuPo5ZeghdaiKt90OrfoVhSJqmxfF7knDQ7dI+OODSQ5eRSjGeTEjimE6nw8bGBlJKXnjhRU6dO8/uwQETAb/8a1/Ar5b41re/TZJm9Id9Bv1+Qf3ONZ53PPGoFpyiIzaI0ilJBq5SuMKQGo9KWCN22mhrSPwRzbkFQr/M3e0JveGYLBfUG1XCik8Sdglzn8XSHCdKJ3k0vIpYNJi5jGFvQjdpH5lHlqZYbdDibdSO47pUq1UqpTIb99YxGuqVOZqtFtV6DeU4RJGDHwS4rsewP6C9u8vy0iKNRgM/cAnLHq5j6exvkef5sQzFubkWOpdgXPb3N9G5nclxGmr1GtVSlSwz+E4VJQXCSqTn0Ru0qZbrRNMx02nKYNgjiSIee+KDLK6epNvvsHbiJLvb9wBFo1o/Mo+CxS3uF988z3njtVeQaMgGXDr9SRSCPElxPY1wDTafoMd9sv4+dtzGMykSt5CsFaJgGf87NHmOfC95hCalPlclM5b9Tp8k03glH51nVMou23e3qXmaJy6vcXatwdbmDt/53jVyIbmx2efW3QPmGxWyfEi5EjJODKNpzmZvmx//6c9y0I/Y3d86Mo/NzW8ymUzIsoyS75BOS/S6Z0mmA7r7BxBeQTqCbn+IOz9P1B5QzsD3KgwH+3Q721Rrmq39V7FItDYYDUFYJU0LzkYyI90dFSaJEFIWp0Az448IQZJlpFl2H+0jBSgpcKTFSJDCIgRIaSmXZuNPYfB9H9d1yfMcbSzaWkbjMUl6NCT3A1c/zN5qm/Vbm3z32T+n1+vQrAWU/BJJqmlHMZnOUa6DV2+R+yU22kOiKEbPmN1CFdhPkwhee+MGiysL/Npv/E38oMQf/9EX+fo3/pRPfvrDx64ReL+laK1C6gwlNc/+n39Mun+TV195EyqLxH4Zzvw4Q1Uhix2sUCghiamR6oyGp1ls1klIcMoQOQpdq5OU6mRCoYTBGjkzkzi6WFSrFbKs6HL7vT77u7t846tf5876HZZXljl58hTGaqbjMUkUgwVXubz4/RfIMVgl2Oi32b2xR2+/z+rKKlFUzN+iJEYqiee5x36oc7UGd/f2ieIJQjjk0kXoHF+AFhN0LjG5QbtT/HkXBnDQGTGJJiB9as0mYb2MaTgsuotcWHmMsrNEvm0oKZhEOfWgSSWoHpmHEhItCvRIMvs3uE6FhYUFatUq12/cpLO7R3jCY3V1EaUeR0gYDgdIqaiUS0wGfZQArCaLp1RKDXw3wKOMzlLyLD2WRBEnGb5XQRtBs3GCbs+l2VqmXA5JYk17fw8EZGlOPJ3Q7XR5NPCZmozRuM1wOAIUly+f5oWDdbrjEV/+xpdJ4ymNRh1UAXcs+UfjkYW0SGmxxnKo96JtjnQVnf6E8dTgKIMkJnMnSKXx9IBsdEA26SF0UmCnpYNFFMVJimJtzgSW1Exw7KjQuaXaWuL169fp9oZcv76NFSHT0ZD9gzaLZ+aKTr1RodJSNLwhNzcjtjoJWkkORglJ7lPXUG02iLI27YOYXhrSixVOWTPq7dLrHQ1BvXIF8twnzxU6gVop4O69Z4kmI4QRVKsXMIlLffUUP/Ebv87Ju5v8yQtvEPgfJvWhUo+p1TO0W0CMtYY8N2gzns3Hiw7bcY7+XqqBIs010yQlRxXVGoPOMqzWKCySQyE6WRhBUBRwbTQSS6pztBAIbYmzHEtx5+E6ilIQUA6DY8l600nEV7/6dZI4I81yBoOY4WCI1aCUgwxdrMnRRjCZJrhuyihOufr0B7h37x63b9/GyASspawqDMdTOOjyla9+jRdfepFonLG1ucud9b+E4xQjLEIYpIRommO8ReTlVeLqaVK/hTT6/sxL2AQjJOQClU5ptXJ0OSWZJpRKTb70pTvsVjSXPlGldbFF6CmsLbRNjhurnDixVuy+WrPYWuLR9BGiKKLT6fDczZv8bxubgKFSKdNqtQrt5HqD9WvXmJubA+BP/uBPuPbWNT7/+c/R6bTZ39vj9vo63W6Xp59++l0OMO8V33r9De7sjkiTGirp40ivkElVksBX5NMxlZLHOBNE7jbN6hW8ygLjVBKEIeNJyuMLj/KRD/8oN194i+56xProLeI4Js0yHrt6lZMnLpJn0ZF5PHzpMteuXcNicZXCdxx0njEaDJgMhiwtLDAaj9nd3sRVgvPnznHj5nVOnjgJwObmJo5SNGsNtjevUw4XaNSahIFg0BviKEXoKUR+9AWa54XEkaFeqzIej0hjnyQwBL6DEJZ2+xZuEJKmGeNOn2mSs7e1w9Mf+wSD4Qjf71Mq++xtb3Dm4iX80KPWbKJtQq+/TZwahtMpSh09Ay6FNbDFRTFkgC0wEbZYn7e373Lxko9XKWP9YiwSjYfkaYwSRVEoKUNdxAxVFS1VoVGvAKNBFvPZLDsa2teYW0D4Zc5fDHj+f/lDxlEhBbu+/ia3797j2gs5zzxxibOnT3L3xg3urB+Qlxt87JPPcOPaHUadiGrDp9EoszkKmdSvMr+4wFvPvcTO+IAgjVg8dYHPf+E/OjKPjzx5usDMOw7WpFibIuUEITwQLpvde+Q9j1vdfVr1En/wp7/PE1efYNS7jdZtfv6vPUPg7uLmxez5cAwiZGHQcFgz5TF3JotzDbI8BwST6RQpJK7nF248xrC1e0Cn2yfLNbmx5LpgZx8+Z2st0ygGqVCzy3uYmUHMkjCiIAAdFcN+m1/7G/8BX/vaN/j+y8/x0Q99nF/961/g3/7rf4PWhrdu3yb0C9nsyXDAsNvh3/vc5/B9n2ooKPuW4XTEvXubNJfn8R0PqzW372ywnMQ8dvkCFy48xMOXnzoyj8N4n4u4QcrCGOLNvktYWiZv1EmcBrH1qHCIGhD3BcWVBWVSpI2wViNNTsWzLK8sYucvsnLqDE6l/K6D+nEdcK1Wu2+r1qgWCyDPMk6cWGMymbC+vk6n06Hb7bC/v8eFCxdpt9vkeWFRliQJruvS7rT537/0JYajEf1BnzTLWFpawnVdPM87di5+dy/DiADlZlQbNUyc4LqSfqxxcoMQIdIqlHWIs+IE4LklwlJCvdHk9TfeYtLT7K932N7exOZQby0QZxmDwZBuf4izuUEWH13Erc0xJsf3PaSAyXiINRbf83CExA98BJbpdMLt2zeRErCa8XhAHBUU6majgcDwxBNXaNQDymUfISVGa6Tj4bkux4lS5FlGudRkYX6BO3du4fsBlXIFrCAIJa6fopSLkIY0TXC9gNE0YfPuBkurq4zHA27dKgwZrj7+NPW5FaRTIcl6tLsbXDz/JMlBTLvfOzqPvGAJG2tmGiwaYwopWkc5zJcr7N28zcGtO6ycPs/iyiqlmYyD47qEYUg1jKgkGQNjsMIr9Oh1jjA5uZ5JGB9zYkzyDETO3kGPoFRjOB0SZyl5nhNFEa1yjW5/xIc+tEwUpex1htw8aPNWZ5ednTYmMlw6fYqT5x/m0Q//DAepx1tv3aM/nDAaDTj50HlWVleKWfMR4SiLESDIixGTdcCI2Vw6pxakDMpDLpczhG5zYhmWFuuE7oig4tDZSVk7UUPbgokoZ+Ywh2qgrhtijCU9ZlMb9HrU61Xmmg0qJ5fJ0pRxFJFmhkxb1Ik1DIIkM6S5pd3pv4tUdUgRwhbwzsNL5vvWikIhpFM4/xwRb77xKnfW19nf20NIy+bGFr/7u19ke3cfhGGuUePO+l1Cz0cYgzCGLB6TxSNOrCzjO5YPf/Rj/PN/8TsIR5Cb4j7q3sYmlVqJvYNtfN8nPxok8/b7+eF+7P+b0Ha2CLAMKhcYzGaFwkBIUhTi+wu7wOcqK3BsQiAzlA2RNsWkfR576jLLp34UGi0KT5G3O/Djbv1LpdL9Im7yYkZlfYfQ+JTKAWHoEycxo+GYTqdTjF36fdI0pd/vkyTJfar+5uYmjudSrdWo1+ssLS1RqVQIguDYIh6aCrkwaC/FYCmVHdREkduIySCh2lCEjkQ5DqvBSapynnq9geM5DEcTcm3Y3NjGsx7t/TbzC4uM8xy/VuVEfQ7Pc9nd3CDqd47MY2dnG99zC4RONCHPUzzHJXNcqpUKjpKUSyUcpdjd2eGVl1/mqaeu0D5o0+v1kEoSTyeEruIDV69SKfu4rqTTGxD1BnR3NjBCHStyXwpKhclAFnH+3FlK5TL93gC/HLCyssjm1mssr6yxv9/G90ssLa9x4fIj3Lx2Del6+H7I2topxoMJ0nVw/OKSVI8jmvVVJD6Vaov0mI/0ypWrWDsz+yDDWkOWpeg0xceyXGtwc/sGfhAWo698SpLH6FyTJhFKamoVj6CxzHz5PHfbB0yjIWiB0ArHFqSg8THvZTSe8oPXX+HG+ha+38APfaJ0gjYpJ06cZNjZJyhV2ds7YH55kVPxWaL1XYJwDk/uMJ4OOXvxYa488yPYcJFpf5+tjQ1Ip5xZbrE410BZTb9zcGQeSVJcDmptCkerGeJDKokxKWkyxZgJqys+rpPxa3/rw/QHJawd0WwWJhyOMkjhFJK5FKcVjcEIyLVGa3OsvrrROfFkwlhBKCpgNCVHEng+dza2WN/pMpykJNqQ5QV08P952rFCYM3bdouHJ3Ihi2bOGj1Dvrx3fOiZD7C9vUO1HLK2ME/nYIfuwT7TOCIzmlazjnQdjOOSpSkoh0mUMhj0WF5d4yd+6id47PEn+eKX/oCDTpcss/i+T7lcYTyKeOWV1zl/7jKFr9zx8b534giLRpCpcnF8txkeGQpN/q7j1AyjayzSxLgiJU0MOrcYaTDVKk59jkQ4FPvrD6ebAtwvrsYYtDVIqWY6KgCKsBTg+S6BH1Kr1ej3+/T7fabTKfV6cSl2yOZrNpuUymUqtSq1Wg3f92k0GvcRMEeFkAppNSVRIlNglaVaLxGEMcNxihYWm1oqbkDTrxF1plghKZfrtLtD0lQznU4YDfuM44SWcovLQAnNepnexh1A43hHPxujc3zPxfNcptG0EBjKcjIRo4Sl23HwPB/HcfB9lyRJiaMpjpJUyiVG4zF7uztIq5mMI2yuWV1d4ezZReabLfa37mGEg3KOlsRt1ueYTCaEoaLfywpZUVtcElfKTS6cfYqd3S5Yn/m5JbajLbLMkiYJB/v7nL94kXykKZfmOej32e6NOX/uYUxqKQfL9HsDwnKVysLR9O6PfvRjxRxVm/u/psmUNBqTjLps721SaVVZO3mKRquCtQk6SXCkQkhFlKZE8RQVVHj88mXOP3KBV996mc17WwjpzZid6lg47LPfe4Pb9zapNVtoIwkDnyAwlEoOjWaNaa/DXndEaafD8Hd4xAAAIABJREFUed/HUT6PnD7J+taQ5aXTlKslfuHnPsPlRx4nFy4bt26zfus6YejRqHjM+S6jKGanf/RM3M4McJWSRFGEUgrH8wqETQbDbobvlkHnWBVhTYQnKwhpIU9wZx6uhzLP1tqiiaKwRSvsLo8u4ACuycmjjFGekI9HVCpllOMRJWOktohMk04nGFuYUOjZKOWdkE7lughRYMwxvAMhVBjLGJOTH3MiyHPLqVNnEELwN77wBTzf5+yZM/zjf/JPEVKR5RmT6T3coESU5TiOw2gS8fEf/QSXLl/ghRe+x7PPv0gcp0xGI6bTGD8o4XkOUsDWvRuUS1UeffShY58JvN9kH2FnuoSSQE9mxzGFkc7MoeedD0/MJFQM0uRkecQwT8EJMMonqlwgUSFm5nuJfdvQ7bg4JFyYmVv9YedujC0WlVQ4UiGli+t7hOUSzbm5t0kJFJ2EchSBHyBm3bJUBU5dOc59VMJRIZRCWIUWBYVbui461/hemfm5MllqiUYRda+JsnWS1NDt9anXG6RZjnKKo7sfOFRqdSr1FnGac7C3zbS9QyXwZhIERxdPaYrb/NDziaJoJoqvcaRkNBwBklKphJTF5Y8jJZ12m0q5jJSCbqdDvz/AWqhWa1idoYTCdVyWV9d49MrTDNOcZq11ZB61agnXEQhraDZrtA96zM0v4gQBt+5s0Kwt46oUYSbs7G/jBgorDBceukAUZ3Q7BwRBSLlSodlsMI01vfYBSkh8r4QQlihNaBzDHFWz3xYz7phSAqUFFs0ginhz/TYrK4ssyJQ4HmLThNDGOI5LWJ2nPzWYpIBXTvr7hHMVApsSigKnnBsBXkBr5cKRefzg5evU5xqEpQrdXkTge5g8pdmo43oVzp85g1Ca1998k+l0SKu+iHRccBWt+QWeeOIqTzz1DKPRiDffeo3vfuebJGlGELg06hWySQ8lPOwxtHsh5AxvXWjAHKp/FuQ9AcbHam+m0hgjjINOFZVKhUG7iyvLmFRiZaG9f9gFY2fyzrnBcdxjO+BaGLCwuEAQhNy9e5ednX2SNGM0nuJ4hY5QFqfEaU6CJJ0V6UPvWyGKE7uQllxr8ixDyEJ+2pKT66KCHHdXcfPeFmEQEpZCTp+/VCDRhOTy5Yfp9frMz8+xubFN4LsYHbK4uEiSRHz4Qx8iCFx+59nv0OuOiKOMXq9XGMHbnPXbfQ4OqqwuzlOpVAqtlx8i3tciLnMFVswuD+7/35mLjwDjzDC6RW9tpKKsh+zZJrv8DGq1jBNU0Cpk6rQwonANN7NaaWYIi+OOZQcHB/fVC9M0fZdtnLXiL4xjjDEoR3HovfcurC8WqzWZNYh8dlTM3v79o6JvRmRZisVS8poooUBmaGmI04xMjHDqEidw8WyNm29+n067yw9efoVOr0ejXsNxPSqVCjfv7qLlBtNBl9BXxElMabEFjouwxxzLcoNUiizOyA04QuG7LjZLEdZg8pQ0KqCZwkCexFx96uN87KMf4+WXX+XenQ1GoynW7fMjn/wUd29dQ5gMYQsNiytPPsPB7gGufzTutTseMo2GOMpl2BuQ5xnOyOAmBQEoT5YolRyyVFGtl8jJAYsXllhZm2Nvd483Xn+N1dVVbK6ol0ooJUmShDBUuG6FtZXlYy+clZox/FD3OzXh1wlqdVonznHm0hP0dzfpbmyxObqF52jOrC6C45GqCs2LF1lpzqOVi6uBtMOHHr7I9Tzh9rW3yPQEFdbIxNEw1B/55I+xe9BlHKUYm7N+d51GpcLli4+RZBAqCXGXlflz3Njp04mGBJUypy4+wtUnnuTpZz5IBrxx7Q2++Lv/Iw89coXeZEqcTDhzdg2Vjvjad57ljTt7R+bhOj6e55LnGsfxC+18U6wZozXzZ4a4qsbG1l1OnWqi3BKOySjNO7h1B4WLEFOk8WYEKkscxyjXQwiJ5/nkuTnWSDsIi9n5wcEBrVaL0WhEuzdAeSW0kdzZ2sNKF+tIpuMpWa7fBT0srBcVQqjCpWv2nWtj0BaMdACBOcYoeW8QM9nqMJ1O2d3eIMtmiqaTKY61vPrSa/iOx3y9zOawS3tnkx7w23//t1g7scyvfuGXqdfmCtYvAuFYMpNhDzccrXAc91j00mG8vxBDId82FBbOrFi/8wfU27s0BYJI46JqSziVwo0lFZJceOTCQ8G75E3FbCx+XPGM4/j+i31nJz4zsf8L8e/y3TwUzJVyBh+b5fFORt5xkescoSQKixIWKcFahdU5ykhc4aKZUglaMHQZDIckacra2gmiJMXzAhzHxUoPx/UKp/FojE4tgedhpUOl0WI0OZpmbozFiGIG7LoOjVodE8VkOVijCWaY98l4RBAExNGURqNGo9Xiox//ON997jl2dnaYjEd4YYmTZ86xu3EHTE4aRYxHQ+IkmilUvndESc40jpEiY3Fxgb29TXZ37+E5PrVqg0rZR1tL1hcIWWw0c3OLLCzOgTUIioJQqVSI4pQ8zyiVq0gpybLsPh652+0emYeavT+d5/eP/0Upl0gMrucg/RJzpy9xrvlBvLCQrlWOg7WFFHGW5whrwJWkE5juH7BalfSdMXujMXG/e6yeeAFXdRmNh+zubPDsd77GRz7wYRZWTrPYbGDSPsSW7YMOKqwwNzePIzJGvTatZh2FRmcJ3/rmV9nZ7xKWtnDLFcKwQhTnXFhdpV5vMegfLUVrjCFJUrIsIzcaK4s1LmeY5xdfvo2wZXzHsLjSIp6Mef6FHc6er+E4GaHXZHHeQeUJSjoYY2bdq8LY4jtIU00UHV08zewEXSqFCCEplyv4o4idgy65BYMgTlKiNCfLcrI8JwxDoiiaOXCZ4iLVUTjKJZsVeGt04dIlCskOcwzffW9vn83NrYLFmhS+AnEcE4+GSKOLsZdfZq5R5QNPfYazZ86wu7PH/PwcZ86coFavYLVCa4uxmjzVGFvwKATgeh5SqmNVLg/j/R2nzGzMgOIIcwjpPvTYlALzjiIOlhQHiywU3/IEIR1yFBoQaIo2vBil3O+ajoEqvbOIv9OJx8xU+w7jnZvBfR9Q8Xaxvr/hSHH/NPD/pohPBxO8sILjQ5QNEbmDq8qgBYHyGWdjPFOjoU4xmmYYDAtLKzSaDYbDMa7nEE3H5NqgHMmg38UkUyqhS5wYpkmGm2nml1aPzMMYTW5B6QJGVg4DxlGEznNc3yEMA9IkZTSdFu9Qa5QsBJ2q9ToPP/QQr738EjvdPjv7Hc6fXmM86DLqthmPhnS7XaRQx3Zawhgef/gK1giUMHieS6/bpRRUcByP1twCr7/5OvEk4uy5SzieQhCgc+h1OzQaDabTmFKpQn+wg+c7SCGoVqszw4tC2a52jORpAQdUMwVDgTUghIOwCgE4JYdWY4EZARkri8s+TTGCkUg8JXGsRUqBX6+zu54Rd0Y4c6eprixhcjuDy713dEYRvf6EcZSgsJRLZV554w2k4/EjP/IpLp8/g07H3G2PKJVCmtUyu/duEJTK+K4kTyOee/bbPP/d5zh94WEcN2Q8ilhozdOoVFlcXuPKEx/g9vboyDzu308pSSkofF/jOCUIik76y19+mXgCT1y5xCTO2d3e5Hvf36FS1bRaPp/+1M/guIK6C4FXeIy6jsUK0NqidVEPjnOZt0IwGI4QUs74DCnN+UUy4TOOErq7XfqDEVGag5Roo5lOC/6GnXmlOkoQOArPlYyznCxPSfWsE58xjI/jMyTxFGE1lXJIZ3BQGMtYw8Jcg8W5Bk899RSrK8vUK8W9X61a5YlHC26FNinaZMX4dDYBtlA4R0kFAvJco3V67FjnMMQPYyP2IB7Eg3gQD+IvZ/xwLeODeBAP4kE8iL+U8aCIP4gH8SAexF/heFDEH8SDeBAP4q9wPCjiD+JBPIgH8Vc4HhTxB/EgHsSD+Csc7yvE8F/93c/ZfObIYaX6C3A9JVVBIniHNZaV4l0iNofuKO/EkxtjsNqQJsl95/bf/Jd//J5g8b/7m79qHd9HW0N1oQXaELgerpBIx2c4idnc3ERKiVdySLKYpx9/AozBZhlxFBFNI2rVKuV6kzfffJP5uTnGkwlRnjONLAVLUvLf/5N/9p55XHvx9y0z38VJluFIULLQljHG4rrefbiiIxRSKmJd2Fkdgoy1NljLfcfuOE2I0wSQLLSW8ZSL1ponPvqL75nHv/37v26t1RT+0T5Yi1ASpEecJLjZmMD3CEsOOwf7mEzQrHs4bgklfbIsp1L1yfOYV17toFyf+YUQT0K9USFOYxwkaZzwy7/9P79nHkvnT1sDeIFHHhVqOFIpfM/FmAKLW3ANCoMFYwyOsGjAdwSn58oMO1v4JcvS4iL7A8nOUOA4CiEMWqj7VPo7r7z6nnm88Ef/yHqOIokT4tGEwPdJZrBUYy3CLWzn/DBAOR4bG1ukoykmL4hH0vdJsozpdIrr+AyHQ8KggrWCPNckaU4Ux5SqFf72f/Uv33ud/se/YkMFyskZi5yT5x7j//rTbxCN2pxenuMjDz0DMsQpSXSSkAxTpCOot5Y4ubrEjddfodlaZWFphdF4zGgyRmOIRz3ceEBeLiOUT7NW5ed+8++8Zx6dqbZa6xnNvtDhN9YWUDxTfJMWi7aFaJiQKaPRlGZ9CSFByGxmRSdmuPt38zEcC6BBGOqlxnvm8e//9d++77ZtcApiiM2BgscheLumWCHeFjM99EotVtSM51Ewr4WUhdCbEhiTIgQIofjXv/Ob75nHf/5f/D3rSYXrFUbLXppikwFuPCLGpXrqcUqLZ2jOrVCqzTOOJrz4nT8mGR+QZgnKUSg0whhSM5MEoDBq/ms/+5PcuL3Hwvkfw5OWX/35y8cKz7+/ZB8pkbZ4mXbmeHJYlGFGqmEmBWkt2mi0yVBS4Xne/Z89pP3CDL9tbQHQtxZ5yMI5Ko8gwIjipRvXZb+9S8XxWWy2GA2mXLuxzng8RjmK2ExYXVsmzQvxH6lctMhAFnbiw1FEfzBBKJ8811TmF1k8NU+WGcwxNGJLwRrNtQZtCpaqNRht0cZiTYpyCmwywuA4M+Gg2b/RznDIBovNLJNJxjTNisLn+2hhyaw5lrxQ2F3llMohWWJIkhi/WkXLkNsbdzjb9HGVM1P3KzRjwjBESI88gzhKCUsK5UjmF0soJ0BKcJziA8l0BtLH5Ecf/KqVCtZaXNelXK8SBAU+PU6m5HlakLuEwPcDpJTkeY4SltyCIwwmzxiPJjiui8lTpPQwRmOsi6tc0jwrlAnzY96LMQgcBKL482amvGcpNMaNQQLCFGxdz3FIMo2wAoyBmVBUYQum8P1g5hjzduNxiFs/Ks6dPIdMp5RDh2+/+T0maUyz1qCzvcFd26birdNqzjOnqlT9gLBVYmGhTobD0olV0ixh0o+I4gjhCNq9Dq1mg+X5eYY7I9ywjpAerjzavu+d8d1nv0Oz2eDy5ct4bqGnU2gQ6RmfQ9Ed7NLu3sX3HUpBs5CjEIU2u5it23fx8Wa+uMfKVMx0CMEihcHe10MsRgrF3zFjXotC5uMQh13IYIAo9CQ53Ays0SRRRmwtxmS8/dPvHY2KSxoXGuaeG2IqLnGlRJCs0SpJBrtvER3cpO+UCEo1Mp0hknGhlz7TdDBaFbrm4lBTBkLfoVYvUQ7BkSmVsPJDvZP3VwDL2FknBXKmX1Hs4gXAvSDraITQOI6L43qEjvcuGytjC1nQPCt0hY3RGF3oARfaJxZ7jNHppD8ldDwC5XB64NDQddI0Z74WktiE5fOXGI1GZGlGMpngpB4Ht7dw6hXUfBNVq5MLl2//4GXOnb7Avc0DKvVFPvXpn+TSo48RJSm5zgrG3lHPI8/QVmOEwfMONzOFsTkYizZgMgMCtGPJtMZTEmkFaa5J05xcayyW3/5nX8StzjMZDBgc7HHhwgp/+9d/DmtTkvRoKdp6vYJyHIIwpNsd4/kBvu/iLZ7Dqaxw/dU/Z8HJubTYRCnLoN9BxxavojBS4lTrjLSH64RU6m0Cz8cYyOIJ0+kUYw2usFSqR5sxNBtNfvJTn+ZXfvkLrC2fxPc9BIJpPOG1N17mH/6jf0iaJQShIM8zrLWUgzoaxXTYJ2pvUC1JpNWUA4/GSouVRxyiaUoU5USJjzaGJD5a1zyaJMSThChOmA4mOCJBxznRaIJRcPVTPw7Ardde5mB3n057gI6mnDpzgrAk0CbBaF2QhKzCUUXHlmWWPIM8M0jpIo5xM9/tbSHjlLnqPCW/zFd//48Ytcd89ANXUI4kiSO6nS1cf4Ezjz9GvTzH/nCXaBrz/A/2cHKB65WoVqqs39sgSrqEqsb+9g7j/pi9zk3m55bpHUOOM6ZgVCIcvvFnX+O5b3+DS5fPMJxM8bwq//h/+Bco10XrlNQMub35Mtv713A8yYXTH0EA2iZoLdGxJc8zhLQ4jofreigLrlDoY1jmakYOtFik0Pdr8dt2emZm5HEojSF4p0eMuF+7iy7cHCpeW1tsyKrYg48j6730g2dx3ZBKdY4nHj3H1Kziigan1Qbh2gLOY1fppwm9nR2S8QA7TWhJg4vG2sILNvdcxni0hwPiZIKOIkwa8Xv/a4/G/Ao2WMJf/EsogCVn4u3WWsxMUxlRFPZcFx2YcgXKkTOBeIvU5r5k5KG2QPEfFEX8UPfZkGtd7LbHFM/N/pBWWOWULygPImphBeMoqm4Fx/jY7gATe4wQxA5MpwkHkzZpp4PreowsbG/vMRynbGxtsbu1zad/+ic4cWqNVqnEVEkq1XnS7GidZmMNyMJZRwiJEhLDTNZNQpbo2RFQECeGLDVMxjlpoplOc+K46Bz2OvcYJ5LFhsRmEQtzNXq9MS++coMzJ6u43tGduMABK0nTFNd1UGImcGQNy6fPcO/ea/TSMdfvtalXPca5xXEtCgVuQGVuDSecw/EbbL/2NaScKUM6zkzoCDxXodTRHc6Pf+pj/OLnPsNk2ObGcMLK6jLt9gHTZILjSZ5++gp31m9Rq0mk7+K4AY3aIq16na31mzz/7U38skPgScCwdqrF3MUlut0BWWZ58/UdBIrJ9Oj1MeknGAGj8YRRe8BkMGbnboe9nQOqC1V+8e/8A+5ev8611/+Qu2/dZHO7w3wrxPV9mkstvEDgi+J9TSd65uJu0dqSZ4Y8nqC8APKjhcm6SZ96GDAREfEkxreGk5fOsjRXp9vd50NPPgqZZeXMKU5fuMD+Xh+hFI7vUip7dHf3GUzGxDblxNk1uh7EOiK1GryQ0/MNcivY3rh7ZB5ai5m9mebF577L6ZVVklGMznKsyvm9L/4eTz39IVZX1oh1zEF3H4Nlr73LqZWscFKSGcJVeCgc7WKFASvJc0vm2qKa5pajPKisFUg0Zc8Qljx6kxRjwBFgUIWmSyFxNeu44e3xiSi69qKsAKBmu4C93+EL5A9xIti6+xY1v0zz8lUuXLhKWJuj1mgQ9xYJa1UunD3JeBIzHYyZTMYcdNu89dYbDDr7KEfA5l3c6ZT5E6fo5op7b11DTUdMtWZre4OrTz5JN7xGrbp8ZB6H8f7qiWuNNoV9kuZtGrvre4T/N3NvFmNZdp3pffvM5547x415zIzIeaiskVUssjiLpCRabEpqSS3LouVWGy2joX40DBiwYcOQ3Va3ZAuG0TC60RaE1qymmpJJSsUqkpU1T5mVY0RmxjzciDtPZ97bDzeypAZcN/VgFHoB+RBARsbKG+esvfa//vX/meHVTBPqP7jMfKj7LSUiThCJRFNDDEwJha4ztMFSkiQ9FmNRo0/SVjckbcc8efY07ngeI5/HcDOEvZAkTZmanmb7+m2qaR874+LaHsHODnMnp6nf38IsllganyFtBNSPqlw+d4GppXl2G4foanhVzmYc3Ef5bA7vgMMZgJRIIYhTSRAm9AcJ9aMQJXWCQDIIYpJjfQmVDOGj4aGl8fbNByRJzKDbJuh3yVbGCCKfsaJFkvikj1CpkxIG/gBJjJTD3DVNp904xFY69b0dnOIYW/2QwX6bZv2ImXLA+UtjFCuzFMaXcQtjKGGyHqcEIsEyBGlyjKMqCWmCYYz+vXz9J7/AD17897z79tv8wi/+Y9546we8++7bJColUZKLZ8/yzNNXODi4j561sZwM7VqN5fkcU5fmaR0u8Mbb22RND6kkTlaxuFxiaaWMEENFRk2YoIoj86ge1NFMnSiOCQYDdjY2uXVzhzjWmDYNttce8P5rr7OxtoYmNGzPQwKNRoed7QNyY1nQI5aX57lzt06vr4hSSZj4hKFPtxVhWgo3Gv36VfcOcE4ucFTbQ0Qpn3rqKSamJ2nUDqlMTjBWLpDVLcLA56WXXiSXHyOTy9IPe6SGAbaOihPubdzjyakymqOzduceIjGZn1rEsiy64YCp6emReQRBD6Fi7t75gAvnltGFxVtvvouVcViZmOa73/5LXv7+D/mxH/8yl59cIuMZ+M2hK1IqY5JYIA1jKEWdxkRRCAI0YSClwJUpoSHQH3Ez0VSErYVMFxymJ3Os7tYBnXLGIZAGW4c1NDHspPOujVKKbpCgjn+OJoaQD4Ih9MXfwLGI4REwbJlGH/L7ey38jOS5z0zwo9ffYe+wRm/QI0liZqcnuXJqGjSdhZl5Ll15gpXTJ7h8+QIvfv9lGoeH9I0DZK9JdmIMx9co2R62YVIN+hwdHvHOj17nRJonP3FxZB4P42OGU+SHjiZCEx/i3Lo5lIeEFHXcoYvjUzJ5qC+hhp6HUsrjwdvDU/UhRgaWoX3orzcq7EhnTGhMzU0x/vlnaYcRidCR/ZDAH9DSbdq9FrqtcemZT+LqHvqNV8k5KVONDo0wRHk2m2lM3nDJ2R49P8auGDSFxIoj9FYLz3bxMh8tvxr6KWEiSSJJrRYQhpIgkARRShgq/ONOPIoFqVRoKkWI4TBPqBRdgzBJqLW6NDst0jCDazt0BgFzUxnGKy7tbpPoEc4+/f6AdrtNNu+hiEkTjQSBP6gjdQPTztDp+HSCGCl1lFGkMnOSaiPEK+t4bh7bMEmimChOiUSEaZgIQ0cwFDsKwgj1iJdja/0ef/yH/xYhFRv3V/n+S99jff0+qVREieTs8ilOnzqPZgk+uPs+j10o0t4+pH0gOLO0yOVzp6nWdkmDYCh0ZFlMT03gZTMopTh7vs7+bp3csZjaR8Xe/hHFUgHD0NBTMBBIJcCw2T+ocX91nb29I7pBjN8bEGFSdPIEoWRz4wD7yMLOWGhaHt0eY21rhyBxGcQhfmrQ7BVB6uj6aFXHp85cpCdjwjGbLBm+9JnPsV3b49r198h6Oe7v7jHm5SgUSxzU6jjZIn7gE0UBfZGSyWdA6Oj9Drdv3KSQy+Dlsty7s8H87An6aUqhVEGko6U3as1VTGXyw+//Nb1+l7ev3eRwv8b09DT1ZoPGYQ0vF/Lnf/xvuX17htMXJ7Bygk6vRhi18OwcQqV0GnUmy0V6zSZJkjJemWSveoAzN4UuEzRt9PMxV9FQicIzU+J+m9lSBtfSmChkSTWHyG8Mm0LT4MziBP1ByJur+0PBLiFByL95AsXflq8e+hwMi/mjBfTOXXgWlQjevP4+jbaGZnlEMkXoEMQhb774JyDgwrkLeHkPx3TZWr9PsVTh9KkV+ienee93/4SVU2d4/Y1v81Nf+yniJGB7Z4s33nubdt9ncekkSTRauO5hfKxFPDoWaNd1Hctx/wZ7EimKIVSiS4FK5Yfqhg8Lv5ISqVLQQNN0tGMBnSEGdjzQPL4WyUcwJ/dW13ji7DkqnsuN9QcUJqfJlYugW2SzOewQVk6c4b1bN3l3dYvZpdOcf+4zRN194oMdjMYRu/UaMh1QwGDtnQ/YjPqc/MQTLJ9Y5LNPPEW+UMJ5hG71O+/WGQTDg2vgp6QMNZalEscFLz0e3Qyvgg9VGjlWA1QI9o8OCOMIx9Ro1GosnjyF4xo88/gKQRCTJpA8Yq4Zf+jubeJldCxDZxBElIol8lNT3PCKdHd3MXUXL5NhbH6CUrFAFIToaUzit4g7HWSU0up0yVdMLNscQmVRiGM79MIB0SP0kV975QfoKiGbyXDj2lt0Goc4JoRBgkBj7fYaq6v3ufT0JT7xzCex0xqXT1Q42Nmml3fodtr8o3/4q7z43e+xsbGJjDW2N/YxTZPx8QmKhRyhrwj90c9Hs9lD1000oQiqVQylmF+YZnV9D12HS089R258mnqrzo333iEOE/p+OGQYxAl6P0BpBe7cW+MLP/MZ9nqKnX0I0jyRpkj0CcIgot/pjMxDTwXtgwbLzz7Fg5230ZWg0aiRLRSYnZqnMjOBo5sctZvky2MMwhA7Y6PrgjgJafe6uHYO07bx211mp8axTIdBdJfdo0MWJ8ewNEmnN/qQ39h5k7BhsnrzHldffZV+GpGvFGj7bdrtNppSNKpHFAtjvPKdd3jvNY0v/ewzTM1Ao7mNpRlEgz4qSantt1hcWODoqEGnvoerK77zB3/G5yvzOPU6/MOf/8g8Tkx6yNgkl3EZ9PpYekoa9VGxwnEVj52cIE1STNNguuxSJUGmCYYI0RSkun7c9B134cfv1UM4RT0s4o8YbBqmRXPQI0k7pDKH5zlYKMIowrRMgsDH0qHbrpGkCZuHB7QbNbLlCtgaupchTFKMapuZSoWFk4sUxktMVGeYPr3Awb0d5scLNNujJYI/zOfv9Lf+f4p8vvDhkMEUEnWsc50mikQpokRhkA5LsBjicMIwPyzJuhh23EqBSmNQKUrTkWpY9FM5LOraI/5bX/30Ei+4OS4PFCdrFvfW7rDtd1i6cJ6x0yep6j4nLyzTTgO++9a7rK5tkitUKE8UmJ+cJStCWq/+FeNHHRaXTzFhZ/jh6+/yp1evkpub4ZXLlynmC8zPz/Hf/No//cg89qsDUhRCGJiGIokkiBTTtFASbBUeW4TpoFJSYSCVRqIEdx+scu3mO1i2yczcPH4s8fI+YRjRqe8wXnl1Zf4KAAAgAElEQVQC27TQRB4ndUd+Hp1QounO0GA2SogGCaGeRWpZGntVFmenKRVy5PMeYT8mTSS5vIM7XiSXc0F1h7eJahUZttBkmTAMhzRAhjCa6zqkSTgyj/31O0wUXDzHoXlwH8+IMNyURMQIqbG3vUqQphwe3Ofs2QucW5rA6Vexkh5Xr/6Iva7Ou3e2aB1ssri4SNDJcLDmYDs2t95Y5/JTz1Fetui0Rw8293frtA8bmCj6rc7Qod00cDMa3/iZn+U//6VfYmxhhX/86/+Ur/+nIf/mf/tnbN18H8epEIQpSahoRn22ewYH3zriybNnuXihzY/ea3J91UTobeI0JuwejswjlgaPn38STzk8/XM/TzcJKU/OUJ6exdANun7Ibr3F/MIic46Dlkq2q4esLK/Q6XbZPWpgaAa5bBZPN3nwYJ3tvX0iofjj7/4FP/XURaQpaAwSfmlEHm+++ypZy0X3mvz6//ANMm5uaA1475CX//Id5ionEMD2zi4aCTmzyFvfvgtSEn7J4FPPR6ycP4ORcdASyc7WLkoqZuYmeOO1N/n+qy/zVq3DRUfnV0cU8bJloWwL2zYxNZ0kDukrgyABGfRwNcUgHmDoDqgMQtfRDAM97qLHASpTAGEeDzOHGDtKHXtqyg8noI8abD5Ye4t2EFOZnKSYLaL6NXZ2t7AzRWbL55iYXEJP+kxPzeEPUmp9xZuvvoV38y56p8tyMU8vq3j9z7/FlmcTdc8R3dni6TSmNZ5n/8vP47a6JOX/CNkpKHkMlXDMIhlebyLNAiGwTYku5dAx8yHP829rdwv9w7Mz1Rle7VGQSnQZoKnk2Hx29LVsfLJAe6fB6itvcubxZ3ny1AJzrkaiG8jNXbq3V7nZixFFj4pnctQ5olNv0bkTUQt9xuq7zPkhZ9wSaRpTKhXJzE6ylobsRyHSD7m+/j731u+NLOLoFppKkZJjulmIVII4GbqGSqGhGTrDBlYbsh0MxbWb77H2YJVypczE5AxRHGPrGvL4yprGEXEiUUpi6BqaNhqbX3uwMexWSwWWFicxDJNYZHBzBXK2SfNgDy+bBRHSrrcI/IjZqRyaLqke7dPotbANhxgfjRRNgDj+/WoCoihCw8DQR78cjmWipwkZx8BIdXTNQvMjEhVDIom0BM80cPSEtRt32L51m889keXoqE6tEVEbWDQaVTwtwvcDakd9mv0jxicmUDLH0sITXL36GlubO/D3PjqPfj9AGZCzTRA6cSKRKsazHQa9Pv/JT36FwMqCqROmOu5YhYVTyziGIOj3GVr8ZVCWy719m/3tD/jiZ3O88MlTbGzfpC1NpEoRYvSsQqWAZWBYOroG9UGPo0YLxzbIeA4ZJ0NH63FUb1D0suRdF8Ow6fZ8pIJeZ4CWcTE0jYFKyXgZPC/DIOojheJ2rcbj588SidGdeLvTpjCrs3DWZa+6wfLcCmNOGW9pker5Bhu32ggFGcfFdkzCKCbsJXTabf7yL19iYnyGlXPnONw/II5CTiytYOgmzWaV3d1dfuzLn+LqG2/ye1ev8qsj8tANDXRFkoTopk0qUwwRY+kaMhrQTeUwVwSGoWGZAo0ETUh0kZLCcMeCoUHzUIKa4ddKfkj1U4+4yTumQ6E0z8XHH+PMmQu4pkmkhnK24+Uc5fzXuXf/NlcuXGTh5Gms7T3kj30JMfD50bf+Hb3btyHoDz1Zr1xkr9nkcH8TYxASNBw29wqYpk5peXlkHg/j4x1sxgFCG/KIlNCO2T4KW8QYukCTMbFmIY/5pELTUJp5DCNoQ4L/Q66tLo61mx9yzIdduqYEmhjNVToaxDiYdLa3mBxA/ugE0889RpSzSGXK2t0HfLC5yfwnHueTF8/z6os/5EzoYLW6WFGXXAImDjJO8Q928FObGX2M4sQUTV1nxwjZCQfsbozutDieo+tCIRQYmiCWQ8aNQJEYJkhISVFSYOgad9ducGftFhnPxXUcojDEth2UlHgZl/mpcxRyGrZjEsvkmOUzOhKhkDJit1bn1k6VqalxPvvCRWIZIVXM9NwSnmsjVYBhOPQ6HcYnK7Q7NRCSrOmBCFFWOJwpC4GII0zTJJUS03EI/PCRfHXHzWImPTIZHS3UEbqOLhxiDaIkQFMpumaTzRikKiH0+6xv95BRxO52jciZJIoVupbQ9336zTYyALdQZHZmhs3dB1RrVbb29kbm0Q8idFujlPewVIrtGeSyOZyMi6EppmYq2JNz2J7FWGGcb/5X/4R3XvpzekcHHG5tEXb7pEFCmYTIzNELSqzvtXlhSTFTTvB7JYKohaaNZi9tHGyz09phfnKKcjlLxbW512zSiH0q4wUsr0Qxl6HbH7DZPkIXAsfJ0hkMb7PjpRw6kq2dKrv7VTw3g2c7FBfL3N/Yxs2X8DIFipWJkXm0D7ocaRr5vMWd67vIlsXJGQPTsDi5vMD113+EYzl4jsXR4RGDOCSIFcGgT5pEbO/u8eorb/AHf/T7PPXcs6yc3GVmYpzzF0+zsrJCGgU8qJQ4nBo9qxD6kLggEEgVI7ShRniSxgR+Hyk1BMOFrGazTqPVQ8gYJVLQFYaM0JSGYxo0j8kBw39YgJIYSIIgwNBHs4YmxifIF2c4feoUj505Q1g/RKQxhUKGkmcR6Rr2iVnG05jeB++TNOt8afkUG7v7vNxrYUU+Sa9HXaV41UPe+b9/lxO6RqJppIbF96SOyrjM7R7yU7/4n43MBT7uweaHlDsBmjH0qgS0NCHUNDLlKZzcFLaXx7KsIeXQdtE1begkYphDPFwIdMsefv8xHzTRLISmD6l6j1iiWG/1qTV8omaVE7bNqattcveqMDeBe2qe537xa5wNA3RhMjO/xJybI3rpGq6ZxU+zpIMeYb9HQowbtRGkJJ0YGbbIZ0ucWVkimJrn/tEjMC2ZDAeWGsRRgqFppEqRIlFKECVDLvxD2tMgHHDz7ns4roGuC3rdFoNeF8/zMLJFLl24yPkzp4nCPdADwiRF1/5mIeKjYmmhNKTDBbBxsEOnl3DpsQZLJxaptzqkuiBbHGMQdNk4eJczp0/iZDxqtQP8IARVw8saDEKfMEqGkNexfynHpriaMD7cMv2osDM5VHhIztUBQRwqPNck0WEQK8JgaAdiyBTHTHD1lEQmuI7J1GSejVoPxzKwNBuFIONlcCsV0KDT77J2/w6mraM9guoYxSmxAbbrousMIS9LY3J+mvLEGCdOzJKdnsct5ymXx+i5gnNPXaG6cZ8wHLDb7tKoNznoQlyMQVj0Wj2C9hYy2ENPF8h7GvnC6Dyq9Rq5jEV2/gRpFJDJe0yN5dnaaR1jzBLPy1PUBYkGSRxTyLr004h2s8X03AKDbh/XLVBv3Kdv+2RtE9MKCYKQieI4cSpR5uiba84oMZlfwbQ1Ts3OMlWZACUI45gkTsnlM8xOTVHbP2R8rEK11WBzbwNkgmFKbt29zdbmBm+/cY1//92XqZSL/P1vfI2vJz/BK69cJUXQ3a3y5PzkyDw0TUco0HSDREaYmkYgjjeWE4khYKycp98dsPnOGt1uF5FZwlYRl05PkPFyBIEi8EPefND8kFP+EF8xdIFQ0dChfkT02m3qRwOKxRyTRwPeefEvGBNQjEPGXYv4xByNnMlBJ8I/7OBHXeJyha32gNzuAYEWMTZRouSNYSGYCRNyfoeBpuEVx2mFHaxGg6b9dyvPHy+cIiXiuKjEMh0OMHUDvTBJafYEkxc/heN5OJaFebxWHcnhhqbQBIEMaXc7GLpBEsY4tkscR+RyefxuG8e0aDabxHHMUyPS2N6tYfsp+XKB1QWPtBpTae+Tb1QpPdimfGGRibMrxIaBJgxOP3eF3bpPZ3WTtCvAdrCzeUyZEHYjzDDB7A/I+T4yTjm4nTA1VuQgfTRlSh47FyVSoQmBhiRJjzfYGD6g6CZh7HP33jUME0zbOJ4PKKLQJ7ENlmZmObV8GpmGWOYxp16qYYfxiKLlGYoUC8tyOXs2i25mkBiEUUrGsknjNuHA5e7tu0Ok6tjkdmHhBBk3jxSS3b0t2od1hKajaQJdM4ZbtTIlCSWW4R1jjx8dluvAwGBxfgpp5vng7iqpHwx565pBFIUkSR9NGthmimZIpuansQXkynlqvXt0GhI76yLQmJiYxhmbRClFGETUHjQoFscYK5VG5pGkCj+MCeKYUjFHlCY4+RzZiRKlqQqGpoj7bWzXJRoY9Hp1xmYnkDJk9fYqtV5Aww/Zbnfx9R3K6LgqJuekLM057KzWyZcrjGVGU/s6zRYzpZNkjAxevki1U0PTNYLBgKznMD+exUKQaiatdoiumZhKocshlNYd+BwdNWm0+qxv7DA7W2Zm+gT7B02CKGZja5diOYffHG1X9/Tjz/LFr3+TIIxw9RzrG5tIXXHi3Em23nmJ915dRzMVtmdTzo/R63VIkgAQVI+OuLW6xi//ws+xt7vDnb/+IY16jz/4o+/x4g/eodWoYXkW5xamOPfCaONohM7+wT6maaBp4Fj28BZ7bICcyhSlDDbW71FqNBjTFbfTBidmMjy2XKZYzNHtBGxv9+k0qkRJ+jcMOV0jVSmbe9tY1mjWUN7LoTIZgvo+d9fu0DFcTiyeRnvze+gdhSrb2NLAqUdEey1UXuEHMUG3h0x9pO+TzxZZ/Pwz/MVf/AnjjodvSRLDxst6LIYhGZHyYO3m6M/jOD7WIu5LHaXAShVCs6nGKfaJJczSEpnpBWTk0z28z/b2LrXDJpHQiDTBYDDA9wMMCb1OF00I+uFwfTZOQtIkATG8CikUSmp8/ce/8ZF5JP0ES+kI2yJducTtay9zKePRMCNWZZ+z/+fvYUcaViGPfmEB4/Q8k19+hpmvPE/c7tPf26d+/RaD1Q1SdYYgp3AmTDIpWNLncGeL/eo9nOLogaJUx+vIQpACfqIwdYGpKRI5HGjquiCSfVYfvE27uYshfYJ2B0sXKKnQnTwrpy9y8ewpBq1tlhcnyLgzhKFPEPZJ0vB4Hfmjw3My9HshKk2YnZvD9YpYZp7uIGRqaoY3fvhXkKRcuXiKucMylp1hYuYcuVye/qDPoN+hMp7BtKZR/RS/12QgQ/SMg6YEec+iPwiIotHsFJX0+NznP8PTzz3HVttkq53hwepdRNTCsSAMW9RrB8xPr/DY5TNUphfYbDoUbMhZKfVeytYPV4lCQRCk7O1W0ToJ9XptSDE8fYb9rX36/f7IPJIYOpHO6naLbKdNsTDBlZPznDt9isp4EeFKihMFnHwOwzTwpqcJBOjWGL72DtfWdmkdHWFYghdWXsO2BK5jsH8/5qlzE1x70KBXM9jYuzYyD8sQHDWaVMM+Xq1Lxs1x9f03KRfKzM4vc7i7SYxkvVqlPQjwCiVOnzqJ1gNnfIJUQbN7RELMlcdOs7S0SKfTodas4xgab92/QyADPnlpNB/56YtnGSuNoVkOX/3MT+AYLpEK+ZlvfpNpL8DL9KkeSTRD8fr19wmCGA2Fdsyx2t7c4n/6zf+D3f09fvxzl1mcn+bOvR2u3bxNo9XiU8+eoTTmMnlidCf+P//O77B7DIUJKbl44TyPXT7Ha2++zf7WBpdPzvL8p55iejrLk184TyFJuPHiNV544XHOrywShopKJeH0yhy/8t//Sx7OLxXDZmcs7zGIgmHzMyJkElOrH6AlHsVBD3TF7Zs1lgtjyFwRe26e2bJFbjHDzLMl9nTB+flpinfW2PjWn2JOjVOcWaB/eETFyFIwxihMLJPmSphLk0RXX+IXfvW/4Nbm1sg8HsbHWsRNQyI1DWU43L53yL1mm5NumStTAtc/onf3Ho3796g22ry7c0BbCLJuhqOjGp1Oj7Hx0lBgRwhUog07Qo2h8I4whj51uo4wHuGaPZ4jq0z0MOXV+/fZ2VujOj3FZ6ZnKQ1Cbjl1iqUypahD8fUbJDfX+as3rvPEj32O6SfOkp85RXa6SLtcYPM7rzJQEl/ECGnQtUx6ChKh4Uej2RiSIfddkA41HpQiSdKhcbLS6IR9jmrbdPtHdFuHhP3+0FHbtKmMTXDq7Hmc3Dgnl8/Qbu4xNZZnZmqaJA5xDBtTM/GjLql6RB66JBIpfhJgiQCVDkhTm9RXxN0Cj3/iqzz59DOkMsQr7ZCkKbnxGZI4RRkKYaWkekwiBnilCWQjxESSSIWh63S7HYTukj5CDuHs8jzzi4scdX3+5b/5NnfXapgCCqZFMeeSpgdEYUK+XKYyPcdhK+Uvvn+bsYzg2UuLnFi+wvjtLlHgo+sGSSzRU4nnZYnjmOreIWk6FEwaFYHsIZRD0onoBDF7+x30NKWUMVFnlphcmSeNfQK/i6u5aMIjjUKqmzXu33pA2IsoZjJMVjIUdIWBIm63kBkTTJPnz0WknmB/Z/QhPz0zjubmaRBgP7jFkxev8Mmnn6LV67C2vsPta+/jFfI0O22UEEzOLKAZGmkSkfMK7FePSKME13Lo1Dusx5s0O01s0+SZJx7jpdffY3Nzm7OzS6Ofj1QhdJ1QSTq9Nq+9f5X5xTnWrr/HoAyLkxnWH+zRHQzYP6ziZTLDYfTxrsdBo4Ftd5mezPDLv/hlPNdgfnGCRJlsbB7yuRceZ7xUxDJH3xi3qgfEAkxdJ40VNx7cZ31vl0arS04X/NTnnmRxYQrbloxNlijlylTeukvGtHBs7Xihx8LRhoJo6Pqx9yaAwLRtLCQZb7Q8hONaLK5McuHcaXbW3qc8NoOhu5DVSCdmeOLTXySfLeFVynTTCP3eA95cvc3EzASWZTA5fYJLX/wy12+8h+HlWPzZr7P45GPEnoutG/yrV15m/doHlDP/EbJTxsdzxGhIaxzh6ywXi0yFIeL2WzSCFunGfXQ3h2ZZTM9mmcsVyTguZ1amSJMUz9QR6qF6X49EKjp+Sqsf0+5rxLFCN0wMe/RgYrZQxBI6rm6ytr/JYNrjXSsgEl2eHh+j3PTpD2okeY9AWNx4sMeffXDE1a01vvHTf49PPv4EZqVI+QufZPvGOlGvSyqM4UalCW9XN7EqBeLRtWKo6yAVQj5UJVQfHlK6Jnn/2qvs7N0njQNsXVHKZVmYn+fZFz7P8pmzZDI5jqp12u0acS8mchSGbqEDpgYyiUhT41jV8KOjFykaAfixxB50h0s74YCZ8TJHW3dZuPgcB3t7aEKj3e7S7bcpjk2hZIJpCvyBZNAfEPh9Uk1HGSa6rogGPumxSFf6t7j/HxWTExMcHdW4+8E697Ya6FaeJAnwcjrnzkyxvbtDGKaYToVXr1W5vX5IqIoc9RRv397nmSvLXDh/huvXrqPpDpquo1J1LEY1ZDVI9XDF+qOjMD/BzoMj9EQwV8iyv13l7o0+03md5YVxkjDAdV0016Efx2i6wrIM2u06ewebxGrAVDnLwmSWUtbEcyx0LYOhg97rc7KSMHkxxwfO6EPNmxyjOD7L+s46T6+corm+zsnzF7mzFbO+3qE1kPSTHlGUcOWJyywvLdHtd4k1kIM+1YMDgiCkfljHH8Q4bsJnP/splubnadY6vPHOTXRNw3VHF61Gq0u908cHzj92Cdf2sB2DnJMyN1FmwThBr29yZ62OYVh0+zU+88wl4iTGMAS5nMvMbIlLF06Qyyik7HH+zCy7ByG1ep+VE3NUxkpINZr6KZUiVYqc6ZKIAXEa0uuHOKaGYxkEtUPMiQKOruM328QhyGgASQIygnQoXKcEWFoEwkHTh4NRXQxZYbowmZ4YDXNVKmVSo8jeYY1mqnPi5Fk8L08UNgkmJjn77HNEYUyiFGrQ4aDT4Td++7f48lOPM3X6NN7kDGe/9GWyTz3F4W//JuWzKyR+yOGt63T3amiWoHNYpXJ29Gbxw/hYi3i5lMWXoJdmmPIVQWsP0T+gXu2jo1i4cBYzm8EQKRXdxBUmg2McNU1T/LhPGPnEcYwf9Dmo9ah3TTL5OVJH4GZchBBE4WjK1GSuOBR8chyMfAa1NI+VpoSDPu8kAz5TzpHrx6jWgBYRH9T2GXvmAvVeh3/3r/8vxt59nsUTi8TjZbyffJ7pNKHT8zFSwUnN5F//4NvkdIHljqb2STn8gzZc+9WFQKCjaRrV6iZ3V6/huR6lXAVNMzh74TIZ20QojXb9iPW1+zQabVwzi6Ysjo5a9Hpdcp6DSoezB5UOud+jotaU1HomaBp5X+LkTEg0up0+kxWX7dt/yf1Y0W4OaNYaWI5D0TUwbQeZpvh9HxH0UP06BztrODKgMD3FoNMjiiLKFY9WJyB9hKpjq5Pwzu33WG8oEjwsw0DTDBxPo9/vYLllpNrh/esbBPYE7TSDoXfAKtOTJnfXN2g1D7AsHSkMDMMiHdrRDxkNgqE6pBrd8T3/ta/yr/733yPp9DkznePkVAlXxIi4w4O7H5CZHCONodscEMiAXFHSqu0RHazy+Ikii5kTFD2LomeThD4aIUngE6UpVq6PDGf51h+s88HNNr/xzz86j9LCPI3DNvT6TNkOupvjnTdfxS4UiKOIiel55memCSOf5z/9KVTi0zjcpx4FtPYbKCkxLAPbtjhqNlk4OcviyVnG83mOdg5JhSDneeSyjyji4RGvvvddotii1wkplsssLc4yaPdpHCRUDIOT8yXe/WCLeqvLynyZb/70Z1GGQNcFGc8ZSgmHEa2Gj0wSdCNFk202Nu7j5U/yua9+ger++sg8bB0SKXF1wVOXz2GboOuKbCZDGvps7BzQbDZxMhZuJoPleSzPzWPrBjLVELGGEhKpKS5U8tiJQNc0gkjgS8FBHKJpNq1abWQepXIJKzfD919+iZnxMt3Nu7iuR97OU3FDXvrN30RPI/JLZxl77Cneeesamm3yyo0P+Ec//4uk/T7NvQ3mMi4ZTXLzT79NGEY0Vci7Wxvo87PI5ZNkL18emcfD+FiLuNJ1NKEYtPZob98EPUSKhEBFFPJ5qraJoSJ0TeEIiVQRcSyI44gwDAiDHmEwIE4SBn5K68CnF6bk8yaVnMXKyjzBoEdtZ/SJ7mYyAGi6xrhXgIxE1xRpnMePfd5tdlkSgtmugeUrZCo58/hl0maDW6+9zCvXXsNttXD7IH7tZ9GlRnN3j169jR4mnDh5EjPvsVd/BMVQcbxsMKROGZpA1wTVw03efPeHPH3l0ywtniTjuISRwvVySBlx9bW3mZqoYBg2aw/u88wTz2PpBoNBn2atQc6ZJA594tAnjUJIRxfxZjsglh624yKlwtLB83R0I6Xba5ARA7Q0wrBjxuZsaq0eH7z1fQr5Ero5FO9qNQOazSO0QRfd1pECrFyRJOiTKOh0+tj2aMnT9b0WW1WfXpgnFUMFC93QSZTOtdu7aO44meIU9zcfsHBujIKXZeXkIoOBol2vIzRBvVlDAlIcUzUV6Jjo2EQEoGnHkMpHx4PdKkGaogtBGEtKmQwL4xaTYx5rd25w6Kc8Jz20TJlw0CfuNdi+9Tb1/S3GVcDspINmCkIVI5wM3W6PMB0KtFmpwfqmzrVrNVq90SyIa++9T9KPuDg/izAEbtaierBJsJmSzxRpxyH5fIliaRHTcOj7PiiNVr3J6eUVZCq5tbrG5OIku/sNnnv+WTL5HNdv3eL6B3cJkph+FNB/RNPT7daohhGb95vcvnGHK4+dwu83yBbyyESiuTaTEyU0PaFaP+DiyllEqkiVQCUJvgoIwhCJIBApMkpBhJSKJhnX5OUfvsdTn3yKRjNgaUQen3j8Em+9/wEKny8/c4pge4PUj1icGSPodVnrHiDaXaKOAmGSn55lYXwW66hGJ/VJQoWtm0gt5SuzY4huGz+I8U2do8jgoNdEuAXiYPRNbRAl2EJSynuYKKxuA+toC6PaAy/PUbNBsTBO+nRENZPjwc4Wc5US6zs7NAYJhbbPX/+v/5xZz6VnS97euIM2OUY70fjh9hZfnV9hIAxC+QhZx+P4eIu4YaJLhZ0E6KqD3xkwkIrOIKbd7A6xR0PD0BSOoWOZgk7fJ4oi4jgmkUPmQJLE+IOYbpDS8lNiw2J8fJzV2w38fhsrHf1y6Lo2vN5Lha1rCMNAWJA6BiI26ZiwWmvTjWKmUwPyLp944Xm8fkT15n3ebx0xYSlOZkt0v/sau60GuweH9DsDaklAtpynFQ2oNUZP/U0BUjuWmtUMhIC1B9e5+saLWFaOJx/7xBBuMQSWlZIkEtO0mJhaIFco4dpZxtoBnUGPcqFCGkrq9S6T05MEUqMbG/Qig+QRG5uIoaSv328j8h5h1MeyU0rlMjoW0peEYUjY76MbkolKkWb7ECMdkKoEYZkIkWFmtsKDQUC56OGoBC+rYZRLtDtdTFNgW6Nfju3GAbqXIfYTTNNBk0MYJEwS6t0BU0uLLF84xQfXXsfUE372Gz/BxYsrtNoDmo0GB+u3WL15bairIwSlcoYgDYgjReBL3CyUxgpUq6N54u9fvYbW75IxXO7sBzhaB9fMMOaleHaWrdX7WMb3KE9U6DXqdPa2MSzYrR4SB32urMwwWclR7XRppgYDzWO7O8C2s9TaNu/c3SWMBhiMfk5VEqPigLDT5QfvXSeTy9Id9OkcNoA68xefZmnlFJMTY5iGQKUJRAGzk4qM7XDz5k0M3WBvv8rlKxe5cOE81eYBYQL3NrdJlKTT73Nz9e7IPGzN4Kje5aDaYHK8xNRkmbnZEjJIqFYfMOEV0DTFk48ts7Z5gNIUYZASJCGWpRP6MYYNujnc51BaitASKuMFlk/O8frrr/L6KyvIoM0Tz/zkR+YxNznODVPgAWp7l2h1FUtG1HcfYGmCC55NLpfFsGw02yFbzOH4XexahOqboEz6Scre+h1mLKilMUEcoVKFbVicW1mg3k/RGF081x5scM7yeOH5p9g+OEIT0O84iFIOI1PAPHsaqzJNy3DZWb+Dp0m0jMumktT7Lc6cOcHq4RYv7e7x0tEuEsGMHM7FpgtFJhcqPHFmBfsRWjIP42Mt4ro+5Oiapu0zGg8AACAASURBVMmXXvgsSkEqUyJihEx48fU7uCY8deUCuhiqFUoxhEi04zV8dazynqqUVFrYToUggLt31kh0DTUpSB6hFz0U5AdNEyjDQNc1TB1A4BomedslzRZpzwbsRQFJw2XczTL92AlO732Fv/qt36H6g7eIlc4bqU9VxNREinJsBi2f3lqMlnWxc5mRecRKR+lDetPdmzdYXV+l3m7y9LM/Q7ZU4c5RG6Gb6CiUSkhSSZjqxLHFYSMkSEOkPsbG6gFOKQEpudNa5cSTL4Cj8+3/5w9Zu3ePOIr4B9/86DymJifZOfI5ONhnbipDotm0g5S9W/eQcYgpA4JBD9cQTE6MoyuPftLDEiaVsWk0GZGkbbKex+OffJbOQDJIwHFshKlhl3yyziHmIx7KZz/9WfIWvPydv+bKJz+H53j4/QZ3b77Ppz/x8/zcN3+ZrKvxR7//e/yL/+VfULYsTs8tk8zFDMKQq70eTz79PM1Ok2s3N/ncmQXmF0uMjY1x6cIlIj9F0yVJ0huZx1HNR5kuia5R0gc8eaLC0kwBaYBQCSWzT3J4izQtkrUV+QXBD360z+pul1Ipy3gj5aC2R6YwyZurVe5s7NMPJZWJKSzbotvRKXrzxMboIj69VCHuBlx77wFuapDNWbgkLJYrzC+f5Rv/5Nc4bBzxYHWN5tERU5MTwyUbP6YtO5y/fInbt+7z/Xdf57/+b3+dfrvO6z98natX36Zaa4NUKF2wsbs9Mg/dttjd3qckxqictvnEE8+wv7vHG6++woVTOc5dmkdoGtMzk3xwd4Pls0uc+8QSiZJYlo5tW3TbbTzXxbItdN0kUn2CWPFgs8srr68StHc4NT0aA76zts7E9AI769v8xrdewRYSUxoYCCxNw3J6ZKwOtmuT0W1ct4lxvNkZRBF+FBLGCXEcsttOSdJkqG5oWRgZk9qDJrpu4zijZ2r/5a/+Coe1Jkf1OidPLQ33NBwPS3NppgNK1TbaCy8w2Nri9quv4iV95scmuJGt8vu/+7v8oQmmaVIuFfncZ19gYXGOleUlJsbGsEyTOE5Ik+FA+e8SHy9PnIfuHAKVpB+u4NsmaKZBnEKpkCHjZVBJiFCSFPP4+0BDHWuKJ6QqIUkiBr0W+7sHVFc3accQSEXkjy4WURiiGB4qUqUYSidOQQcM28QWAl0zERbEro2nhVR3t/ByNmP9gEyaEEqfinTIB9DUJcWCi5nNUosEfhig2xa6OfphQAiUhEQqNg8P0NwcUXWb1bvXKM+dpFiYQGiKRCriKBg62dhZNAmmiEkTkyQKMd0MadjDsnO0mnV6rQaf+tSnGHztKzSbbarV0UtHTsYijVt0Wh38YApND0llgGubjI2NYWsx4aCPkAnK8uglDppnITMZ+rpO4kMaghQhtmihp+AJDeX7JImDZtnkJxcfuUn701/7FVrVB3R3OnzlS58l62ZpNffJGjpf+NLP4Vpl0mjAF7/0Vb7zre/w7W99m8efeIJB0KM4PsEXv/QTPP3UFerNQ/67//G3ePmlV7Eswfz8DEiDiycep3F0SPVwl/nZj84jDXySNCZOJVdOz7IwlScOBwyClFzWZWJqDJRib79KoZynVM5Tniphd2IOGi0qeRtHS9H7LXYOWvipgbQsBsqkN4hINIdIDFlao8KwdLScB5aDFyUIv8uTVy6yvbbFtWvX+Tlbx83YrJxe5tW9HdburXLuwnm6wYDDoyPubNxjf6eGPwi48cF1wnqD+xt7DAYBYRQPmxgpKRdHF8+jWp3aVoeVSonFqUkOdna5fu0GjVaH7sCi79dIVAbDctC0FKEkSvgkUpKGEEY6SRoziDT6YQBIBkGdVNr4vTYz0xViqeONjcbmQ6kTJgqvOIZnjKElCYaSOJaBYxhktYisa2E5BpmMSzZfIIpTKmNjFEvDVfY0SvCjmD/9wV2SKB06jWkCX9gYVo/W0RHNTnNkHg+29+kOAvqDmJ3DIyzLpjxWYWl6Cc202EtjDtfu8sZrr/NgfZ2TxRyJk8UwbcqlIhOTRRYWpzl9ZpknL13Bti1Mw0A79k0YavkPYcq/S3y8cArDogXiWHJ2qKWC1FCawZ0bWwymx7i0nKKSoQaKVJ3jsi/QlI6SijRJ6ccd4tgnjny0tM940cDoxwwSSfAITYrhCSzRNBulhiYTUgiUaaKnKVITaIaOlihchg421z94B83SeNA4pJWk3O11sM2EODXxZTQUVep2kKaLbuvops4jahZKSXShiKMI0auihT1OlV2c9JBCO+Xk4hQIjRhBHLtDD02lk0pFEA2wLZtYAYTEcYBKExxNcrC5hfUZjScvX8ay/kNnpP+vKJbHcJ0Glq3RaPWPVRSToQMNPuWiQ5gadDp9/KhPLq+Ymj+JMG0CZaHnSiRallYU4O/sYeqKjK1/6IlZKldQ9iTSzI/Mw5IxnpNjbvEUnpdHFw5hLJg9sUKmkMMfDLBNDcdw+Pl/8Pf5wz/8M377d/4ZO7uHPHblWT7z6ecRosNfv/hdRKpodHrYuQzt25so9Vf4nw/IOB6ZzOii5emKjkxIBGRy3tAiEIiihHavy/zyLEmi6DV8br+1zsLsBJWlKSYGPt17ffxExzQtXDeHMFtIHQaJpH5wiFQCSx+jnyiMR2jaTGbHeXB/C890KTgxF0+fwnMyeIUcBd0m9H1sy8HSDE4sn+L1q1ept9uEkcJyszQ2Nri/s4NpmLz56luoIKTRD+mpBM3VMCOJJUzOnhyt0dFp+wTNAeaUzvbOPrvV62zv7nNUO2RqVqGsJaS0/l/23jPG0iy97/ud8Kabb+Xq6hynJ6edzVxyueQyaGXZFERKsmgTliWZFgQI+kAIhmmB/qCPBkSDMiHZskAbkphEcgN3lxTJ5Sx3Nkzv5Ome6VzVXbnuvXXjm845/nBu9+yuOVVDfRgsgX6AmWkUquee+4bnPOd5/oFQRSwdaePI6Q8HlNailNcxEVoxsQV5lhOFEU4FpDmUOKr1hL39lL3h4MB1CN0gxNBYanKsmaDKAiUKkjjyAhaTIbVqjFQQx5okqdIbl0T1OrVGlcIIClFiCCmjKoVW93NQIRTVICYMq5j8YB7B/iij0xswmkzo7+8zHA7pfuMSERqjDMWgzzDL6XV7zM7NspicIw80y0sznH7qYc6dP8nK0TmazRqxjD3JT0qQ8r53grMc+t7ei/c1iWdZSZb5ncZ7F3pvR2yOMYLZqkYXQ269/cZU2tfrZjt3TyrSeMVC50jTHDuldhdFTpkZxuPUK/EdAtZ3GKQSWFuipmgQKaX3r7QWq7zhqtQaLS1aSnrdDl/80hd549Yt7pYpI5fRlxDX6xRRggogCWNUFBNL52Feh1mcmtI7+yjN2WaCLaFWa4Cz5HmOLkegYoIgRgWh95NE0esP2Zh0mGmHJJWYCEmnmyMDCGSF9dXbbG1vM8rs1GhDUD+gtRPVljhxzjIpYWtjm153zOxck9bxZeJWnZsbtxgPB0gpadWq1BsRUSDQukLpKigdkDRqFMUEJyJGkz6b65tYa6hW67jIENgcXR48QOv3dkgnKSoMEUQklTpZYanUaqzdvcXs3IxnbqYFTz/zDK++9jpvX7vK25ffYn9vyGh/izC27O7uce7cSfTddfYGY6RVrN7epj2/QKvRZKZxMGOzGPe9Jr1QXL+zQfX4DHW8ZV6ZOYzwx++gXmNj/ybdbJ2LzTZrd3bY30+5aXo04wpzJiJHoat1xESAVQihSYVBiQJnDy420l5GM26wPrlDH5iUIaPtPmGSMFOts7d6k/biMqOsoChyTp44waWvv8T83BKD0YC97pB0NGF+dpHNjR2acUiZFQyGA+q1hJX2HO3GDEePHOwg024scvZ4yPLyPF97+zJrq7e5duM2WZ6y253j1qZhd2+DJAwxhOzsTejsa5yASlWBKQhQWOcorSZLBdZF9IYTwkYbM3LsD1J2ewc/H8NxRlEUZEqgjUWYAoFFSW/VJ60jHHnlT6UAMWaUQ31jTL2iyY0kNwVZXrK1P8FZiZC+N2CnNpHOaeQhxUaWTdeRZuxu7zAYDNjf75GPx5TW3Pc48I5kBTv9AcnskB//1Ic5vXKUSrWK1gIpQaDwlozgpq5lXpjLHYaEvR/vbztFgFR+bUrL6XeVSCRF6fjUD38IaSdEgbrvdi/dOzq/uCkFXwgmk5yyMOSF1zrIi4x4PCLLMvL8EHU4Z3DOeweq6ZHWOTfVLrcY601xpZIgHFJBe6bJzm6XuWqD4PwFSCfkWAKpiWNNNQyIg5BAKxaSkHF+D5N84Er8ZRFQWzzDuLNOifCO98qSj3uosO6dfxAIabyxrS0IiyH7e7fRrXlmWm2iYAUVxJRlwX6ny6VLLzMsrb8+Rcl//1//9LuuIkeSNFtcfOwJBuNvYbKM+eUlao0mZenY6/U5eeI4YRihS+8d2t9fo3Tr1FpL1IJ5nAyQOqA5u0iZzSH1PN1uj96opD5zlFC+40T+brG5sU4QBCwtLRBHNRyW3Z0tlo6sUK/VSRKBNSk6CEHknD1zlmxU0NnocPTIHAvtCludLj/8yZ/g2PEjXL15k9//8vNsre+jZMzzz3+bp558jNn2wUJLUbVx3+h6bXsfieP80hz1IKbb2WNza0CrXSOdpET1GmXQ4BsvbzAcB6i4TTd1GOUY7vQotad6504T1RKUCinMZOqUfnCs3Vxn5dgKSRLSqM7y6tUtTrYrdDqbZPOG0d4OtihxSYV0PCQIJC+98iqzM5vkecr2+hpuktLVPfb7E9o6IrCgi4If/vinKHcmLB87BvZgYbIkbJGIXUbjHvmkz1IzxB5pc2t9m62NAf/Xv/1TnFVYDPu9kuFowurq5xECmq2EPJ8wvzRLtV5lNBx7cVJpGKc5e50cLTRX34K5+Q8duA6pFNI5rJPsDkrs9D0WwkwdsgJkPm3XSvwMKYe4PyHSYIWcislZRrkXmfPvlsNN0Uxe9vng+7IwN8N4PAJTEGpNqBTVSkwUSrTzFpQIX4hl6ZidXoePnP4Qjz10klBo3FSNVatgamTuIbDOman4nZjaNH4ftlNqtRpC5j4ZCemNja1F4xXrFqQAV3oipvKtE2HcfaEan3idlzaVATgojcGUJZaM8XhEOplg0oOPIcamKKUpipyKTKau8RJjjHePVwKhJLkpUcKilKTIc+q1iIvRHHZlltz4il9rR5pl9EdDknrVV/YOoiK8vxG9W5RInPFOIs2j52keOUc56mOyEWU6YNIfEiUWNxkglSZ3XpSpGkrOHDnqHzgJ2WRAVKl6w+UowpaWP/jCZ7m6vk5ZFAglD0ziToB1mkqjwqd+/NNYYyltRiQdFaW5daNKd2efDzx9gWazQp6V/M7vfZn5hUVaQyjGbyOFh27OHz9NUm2j21Was1WECDBmhCsyikMGzsdPLNPtdlFK89obz1OrV9jaWafdmqd1pEqv+wZJEjAegxAlH/roE1QqMT/7cz/FjZtvo1TAR9vHWDpyjI3N61w8d5pHzz3O5Su3+f0vf5GXXr3MC19/kQ8++zT/9Bd+6V3XMaouIKUiso5iMuTaoOBqZ4goLZGCt/ausDzXQAtHfz+g2+sQJiE6CkhijbGGiSzZKgM2uwKnmwRRSWtWMTcXk41LZuZahIfY9/V7KW9f/TM+/elP8uUvfA5TSFaWL7JVGl6+9Aannl5lrjXhzvZdXn3jNdLxhLTheOn6y3zgxFn+mx/7NJfXbvH1K2/xwUef5Imzp4gCTZqOeOqpC3zj29fBQFI9GL20fXeHzs0rmPGEv/uTH+XYfEJjYZGt/SFXb95mbSiQriCMY7714qtcvnKdxz/4EQ8Y0JJAK/9eO8fCQoTJDdUoYOnIAn/4h1/h0SeeYGN9m73ewTMkJxQ6kF42NvRJO0DeVzJVwbRFaw3KCe+/GQh0oKgnMUL5k5C0llwMKaZ64s55FVFjDBbjVQ8PiGceP8uzT5wlL0pefPUyO7s7lHlBLYiZmW1x7uwJmrUqgdYEYYBSnvxmnQEkZjoPzIpiagdnpt/PJ25jpxvLoUWgj/cXnSIFQRwildfQZqrnK63CCRABOKuQyrc3yqlAjTG+jeKPSKAzUNL/XIgQaw1CJtSbVSaTlMnwkHaKExSF+a7/CiGnVf7UJRuBcwJn/cNgymkrR/veviZEO9AOAhlgraM0ILVCIbGCQ4WnmApe+YugQUHYaGOzGB1GlBNNHIcUJkcHmiSOSNMMpEZpjdRepjdLU3+sxIKzSB2jbJ/1tZtoKQmDg2+zc76l45wlSRKMsWT5tF9fGk6fO8fKkUVmWxXGo5Q7m3e4cPYkM+0ZWq0W/cGAwXDAcDDg+ttXiJOI9swM84uniWtNtnbHZGmOdodUfJWEwaBPHEe88OUvkWYZ25sTrl25yzNPbTK71Kc1U6VZPUJeTghDzdrmDR5++iSzR2bY20npDfp0Lr/G7GyTfFISqoKnn36M1nzMr/yfv0ZuDF974dLBt6Xa9ixP5xCqMpVGcF7Mq8xZz/bZ3ZgQSKYIj4hKWENqTVmUFEaQE5KSQCXyEE4Zstcp6fcmSFlnc6vAuoPRKat375LmOa+8cZljJ06S9ceMMsuRo8exacHqrVtUzlXZ2+4QqZj13Q1MaKhXW3T7Y3ppxsBkuCLnY88+SbNaI5CSiobZMCJXkiSKqVUPbh8srDT5e3//Z7h2a5uP/sBTFONtdC0haMZ0+32u7G4iygk1YHFhhma7yfxsc6pNUgIGLSOcdd7FMtBoBd3eFg9dPI4OLEtHmoeqSyLEtPJWaMAZhxXOK7QIgcsl1uHfZQHWCdLcIEso0zFOTh0drWGcOqydOv06AO8JC/7kfVBI6XNFHEV8+Jkn6O33yCYplTCk2WzQbDXuV/Neo1yiA6+TZI1FCt/7NsaQTxUTy9IXr943wCfx99gSf5/bKWVJNrY46SgLh1JeOlaAT+g53Hendl7gygnrsaVANrVSslaisNMK3esHWyx54bA2IAwPPoYoFWGM9VW3nX6O858tpXd+x00vpggwxXRSPE12YvowWWMpncUgSJI6jmlFXxqktb4dc0CI+yYZFus0OIcCD3kKGkgFUjtiFwKO0pQ4Z8gzgzYWm+XIKca7VBpkiZMhUgbUq1X+27/xg9SrCbVDKi14x80kyzIP4TQGIUGrgLnFRawQ7HVHGKtJmos0G3Va9TpxHIMOKVEEcYXe7gaT3haiHBKqAFuOiIXFhDHj8SEDZ5vSbLfQusaJUyv8+q//HttbGUqscuXa2zz3wbN88IPPUK2GTLIhZZnR6/fp9ka0mvNUq4rRoODW9WscWVlm8cQKFV2lsNCarXHx/Au8dOkNsuxgso+VAVh/Lwj9Zi4VCGGxZYmLYnJnKaYyCQ4Y6wAQWOEgEDgCnNAIXeD7owGiTPzpLSxx0h1KOhrnE6wT3Li1ysc/9Bxrw2sMC8tSu84PPPMUa9ub6LPnmGvVWVle5uTKUV548yXihQb1IKa5sMCiKqkISehKyiJDqIjuOGM4HjA/N8cbb76FPgRSZ4OEM48/RuvEgJEClzQZ48XpqDYIog4qkBTOMr+04HvLJseWDiRYvGaSEApncqRUpLlBSEMQJeRZjtTy0HZbacqpJY83R/cnef9MeeE7hTXl/c3AGMekcCihMFJOK2wDTlAWFodFTI2TPWCi9O/2IckzDMP7BsuCgrl2C9G2aOklo60xxHFCHMc+WZeOvMi+o/0j7s/hLA5jLKYsoDQoFXh5aiEQ8rChmg/xXkv2B/EgHsSDeBDff/HeOucP4kE8iAfxIL4v40ESfxAP4kE8iL/E8SCJP4gH8SAexF/ieJDEH8SDeBAP4i9xvK/olH/+u9edUhKlNFEU4IzBGYMQjv3+gKMnToL0U2ohpo73eIKPsxZh/RTaGktWZhhrKEuDLQ2m8HAd4xzWOX7xr19419Hur/7abzuhJH/6J1/mP33xCzhjvdOH8HoqzhSEOmD55DJEluWjK6ytrXn/y9LDI52FdDJmd3ObpJqgo4iytERRTKXWINAVmo1Z/tN//J13Xcf483/TGeumKFcx/eedcMI7/DgryFyJU1BkOWFSRTmFdBInLVBOma1eQ1z8Oc41tc/8+3ddx/Ef+Q3nvgf+56THylsJqlQUEpwd03ApBkfmGmgsJRrx50AH732YtRZTlFOCh+buV/7Wu67jX/zix53AO7eIOCIMIoQTHt+rNVFQoRiPEEJjrCMzKaMiQzpLWhjSUqJlQCXRjMdDbGnJswlRtcL+oKAooSwFC+2E//mff+ld1/Gv/+9/43q9HtZa6vU6e3t7zMzMYK2lKAv2e/ssLS+RVCrcvXuXSqVCEAS8/MrLDCdjlNb32cT3UAjGGMbjMUorsizDloZ6pcZv/r//7l3X8ZXf/Gduq7eOK3NquWYUC/YmGUvJPLXWHGXQJx0NeWX9GqPhPkvtNq/euMnS0nESmTCL4HiiCcIKIjH08332RhMqtWUqlUW+9e1XObqwRK1R5b/7+V9513X8qz++5gwRwrnv4p8I4WB6752nPVJaR+k8YkoK7iNOSuet2qy1cA8WaCxKSKrBvXce/t4Pn3vXddQ//DEnAOtKrDEEOvJSzmUJxuKCqSxHbsCAMBahvVfvPbP1Iss8fDn1doSuyHHWYUuDltqbrUvINq4dBA1x90iH//lhAUeWWdbXN2nNNGk1Gogp5nGn2+Hy9av8wHMfOvRD3nfGppAeOpMNB4TKUQ0DVtdWGaUFJ06fmd5Mn8T9m+8xkxgxheJ52NI9rKYSFiclQjtcCQKJPIR2XxQFCo2zkGWewSWlpV6vIaWk2xmjmglSBxiXsd/3ErllWmCKAmMtRV54yNTUaGDU2ycIYrQMcDYnDGqMB/sHrkNJwTvPwf//UGSnGjPWeZglUnPj7oRaawYpBVnWR4uSOFQsLSRIcpQFcYjpwfeGc3b6Mvq/54TfpJxwgMRQUtqQtp7wcz9+gs3tXX7jawrrSgTZn7t2T1YAa43/nwnBYbZoOlBT4kOBljFSCjQCHTsQhjBwJM2EQAVeXImAZp6ALSiswxgHFoJQM4kN1hiKUhJGITMVjbESISOi6GCSjVKKIAi4fv06S0tLZFlGo9FgPB7jnGNjY4NWyxuL7OzssLCwQBRFJEnCKE0xU/q0J4/JqRGvTyZFUdBsND0BuTwYN2/MLrOViFpllstv3cCEFYqiYGPvNvO2JG8IuqMUU22QjQdEQYVBL2MwuoOWCc+eukBYWWFtawNmDZudDrOtOTq7AyZByNEjJzD9Me35g23AhLAIDALInPd29eblEufklOmMhwnfu//TomhqJO/hfAKEEgTSEktHqAJCpakEFiXvwXzfPYKkMtWIN8gy94zGQKFk6CGhxvn3RTmcsZi8JOsPoSwwngOJA6RQKCmn3JQAKSTCOgpjQEjkIZ60wKFEnHdL8vd+7u69C0KAFIwmExr1ur/Gec6Vq1e4u7UGHMxihfc5iWspkUCZpdSigGzU5/b6KrV6g8XlFcIpm+deIkcKnLfp454WgbG+zrTKMyOF9MnCWYeW2v9ZHnyBjfGmrlEUgwwQzjI31yKOA3Z3d/0GIjWmhO3tHfTePo1mQpYWTCYTgsBXU0VaIJVESDmtCgTGOLSShFp79tIBYd0U0O+B8j573rvvzhsbFEVBYQTjUrC+3uUPv73H+u4mVjqyrEsxKaknCf/Lz/8QrTAFa7Acop7458b3nAKcw1mDsAKFwZVw7pjip3+wyu5Gyr//SooOJFC+C03Z37h7uFj/k4M3F4fzWhbWIIQnQQRBSC2CIBCYYjKlLEMYKEo7ZRtahVSKMs8wpUEoQRJ4Jm5RhgRBgKgJxqlBSI07ZB3GGNrtNgsLC2xtbXHkyJGpRk+BUgqtNaUpKcuSPM9xzhGGIVr76+4JHoIoiiiKAjk9XUopCYLAJ3UpDzWODlVBmikG45wrO+ss1o9z+uRR3EaPuKIZVWIGe0Pu7PVoBiFqMOHR2aPc2t5lbrHNZJzz+RcuoaOYiytn6E022Ny9w/5qhyfOP8rjH3+C4WqP4BD9/TDUlCgwltJ5b1jrvDesF2/6zrvukPcYiN/xDCjhUNLzKAIpiJUg1BINSOFJNocZ+All8b72gtJ6bRaRFzhjwHrXn7IoEfZeolaESQyl8rokRYmzFmcKrPIiddZ6fwKJRN5j0L4H2LWU3124lGWJcw6t9X3tk+/9nXtxjxkqtfI+uxLysmCcpgRKsdPrsrGzxWB4sGTyvXh/BbA6e6STMe2ZJoO0RCnF8dPnEEIhBIz292jPzN9P2hiLM8brkGuNkQJlBUb6B0Q6gXAOZWEwGXP3xi200tNkcfpd15HmGTrR6DDmwqOPQGFYu/kWnb0xQZAQhQGNap161Gatt0Z/d59mWGW+vcS22aXIcrKhw9lyKg+giOKEMi/pbPXQQlIJ5nDmELIP4juc6MV351EBwjoIY7Z6hq+9sc1XX77N1y7vMCkcURhR5gWDiWAyGnD7zu/xiz//gzxzro4pJn+ho969alkIgXYB48AhnEaVjkIZdOH40Ye3+Cc/s0jYfZVK9zofmF/i5e0FVOhfKx/f/fDf24zt9Dse9pIa50iiCoKEohgQaIVwJflwTOFK8iIniCsklTmisE2kY2yZEQSS0nmtdSktSqRUkwAjFHnucKXFSEkUxpTOYMuD6f9ZljEYDIjjmDNnTiMk9AZ7mOmz+MgTD2GNJ+tcuHAB5xw7Ozt0O3uYbEIQekKYoMRhyMc5ZeHFyITwG31WGor84Er8lZdvML+0wuKxhKefOM96t8vz336Fztouj585R/t4g+tv3aLT2eHRZ89wZmWJIOhy5NRZXnnzBp3O25w8N4ttaHbGfW6+vUWI4JnHnmGuXmVrc5Vi1CVS0YHr0OSE5Mikgtnr0Kg0cSbDKEUgBKG2SCzCSd9CAUoMlnc28Hs8aOcMsX2gPAAAIABJREFUwk01kWyJFQ5h9NSe8OBNzWSpfycE6CRGqMDLU5cFtjTYLENJDcYhEF7Z05WgBFZIhA5RSoEVmLygzHPv2esEKIGZDN+r5tQ74aVaGI76bG3vsHLkKGura0gpuXDh/H2mdxAEWOcYT8Zsb2+y291mYWmZtdUdXnr5FZxwPProRUKlef7FP2M8GLEwc7Aw2Tv3532MshjT2VllZ31Ca2aGLBtxF4lAYfIJ5x/7IGVaoHWIs+AJS15nJUoS4koFrT3L05TGm0Q4z9rq73eRRmBtjjyE6WSzHOlianEFHUes3701VdyrEoV1sjwjDCIq1Sq1egOdSayTWAPNehMTOfIhlBbSvCQrSiqtgCBIwIzZ3ehQryyQHMKE+87w3+V7fiYFw0nJK1fX+YMXrrO2Z2g2I9R4AkUOZUnoSlIBL1wb8n989mX+2d99jpWa9on5PYY1FvBJ3OoUWcbEdkSrklEGAX/1acPf+cFFmuUGne1tchfzM59qEHzTcummBWkRQmHtvb6+u5/A5VRe871EGIaEYeLVAgvfkpIICmOxFASJmmqw+1lKYSxJFPgXUjjSAqJAEAUaoTybzpB5zXpVwRQGZwv0IeJTL7744v3KOwgCdCCR2rN7tdZTVq4lz0vK0njXqakOtNbCu1NN++Jpmk4TuD8R5Hk+bbXYQyvxahDRagW05wwvvnaZ21tdZo6c5uizj7GcNLg53mFjvI9KIvbSCWkQEoQV8nHBJ559ju6wQxZNGAuH6Q64uLyILDLickx3q8Px+dMMtEEnh7QGelt0N9eYn53n1T/7JhfOPYJzDhPHKG1oJxFJoKlqMEKSO8XEhYipW5VzDqREKnmfpa2EQpQlpkwxgHFTpuxBobxWkABMVgAlxksBoqQmqYRY47ClRWtFno3JuwPPgFTqftPPSIuKtHcThykD26FSgSsz32M/LPwRE4DheEB/3COsBKRlwZ31bTp7Hfb7ExBQrVSZW5zBIegP+9xeu8Hu3hqd4R7LC+cQArY7u1x++zXCMGK3u4UrJ/R672EdvM9JPGzMcLRSQzhDo9lE4I8/zjoUlv3eiFpFEsYWMz2GR4HG2JKyGJGOM5JqhTCOEKWlGE8Y9QcUZY7NMxAOnEUfIn4gi5xJp8tgb487124TOE210qQwXso2jqpeC1oEjLOcwhjySUlzZZbhaMhoPEEYRSSr9Pf3kQhiFyAIcNbR7+0zXhgTqPp7vzhuuqXfS4JYHA2+9uYd/p/PvsLmQFDIkDiwtGoJpnCkAsJEY0VGXgZ8+dJNHjrZ5B985nGq9DHT1sOhH40/BjsHhYOFypj/4tEJ52obmEJx/nSB3o3o7PXZH+0gF89z/HiVZ7fXGNt5XlvNUULjHycNuKle++FU6u+MMAAwCKmI4gpKAKbEBRHOapROEU7jrEHLglGao12IdJLMGUprqCtwhaNwkqLMyLIUKSMC4bUrpLAIcfBj3+v10Nr/TpqmlCZHh75PboxhMvGmz2XhZRMCHTC/MO8Ttytx1vjh5VQfg2lbzEsw+4GntY4iP2RGoAXjvMPWoMvOcADCEbiMhXpIO44Yjmd56mzkxcfsCFlGvPrqN1haWIA0g0aFuDXP/u1bPLW0QF4JSAdjwiAgrM5wc2uL1Rs7YAV/84B1RFuvc+ePfp8b+12MC7m79i3CIKZx4jxqpklZq1EGATKR5MYxzBw7YwVCoZS/jqUz3mM0DPzPdUgSSJQtQTkG4wxUAjz5ruuQKvCbh/WuX0xbfg4obQFS4ZzAOJBCYqyBUCOzEqbgBz8Y9fMfgUBIeV9tVAR6OoY7XGHSga/iBdzdvsvmzjpSRWRrm/zxnz7PaJTy4uXbKK3ROiBJ/PNTlClZ3kfLHjP9LptbHUbZLnnZZWtnxPZWl9pMlf1BH+2+Dz02G/UmoVJEYegt0oQiChVOGPJJTqC6JCqgdJAL57URjPBytUJi0pz9SUYQhbgiJ5ukFHmBNTlVHTBSEwKp0YdolozLMYmtkJUp/Z0N6rUmrdkWtfoSKyvHefzRx1lcXCTPc06eOkGvv0eeZkRxhW73GuNRAU4RRZZWs4XJDaGIETKgVvGyoEpp3CHu7t8Z7p1U6sWWcHQGJZfe6nBrK8MIRdKM2dvdZ3auRaADRhTUG94xZH88wTnLn718ix975gRPnQiZGPee+nvfOccprWEmEXz0Yk7txp9QrGbYykPsVCzpYB/rmmTr28zHR/jg6QorF1bY/e0ud3ZKFNIPSJ314kJOIWV4v9I5fB0W4y2CCLVgMh54DYkwIQhiinxEGFSQKiBNx6STnJwRzVaDIq8gZE4YeqemUTHAWUuoE4wNmZSCzIK1ApEefEpRWmCdRz6laYaQEIpwOkwvqdUDSlOSZ44sK5BaECYSaw1FlvlBNI4wCn2yLgwIhxCWIBREUcJkkt9HdrxbvHLzDqdq81Q6gq1dwWxznuFeSlEpUUGFY7oClYjJeMxircWsaPPMY09R2hRjUm731mm1NMVOh71xyvUbN2i15jh34SK1xUVuvPQiI73AZO9gM4aEIe3Y0ajNY+yEbHAXMcgZXd7Azcxy6sMfxmlNllt6vX0Qmvm4SZaXYBxhGOGcpbAFxbhEKA0onAxIS4sRYLIRobLAT73rOoIoxknAQTYYgDOYPEc4SyAV1Xqd/cEAoQICqZFRTBGE2MLg8pJilGIKPxC1Rel1U8y9gtprmGgdIQ5xXAIYZRPKMufO9hovv/4aRWnp9keMU8Nb168zO3eE5twxwjjGlCXdfoeqDKnX6+QDw+7ubcbliCK/zbA/QMgKx1pLLC7Nsbp+i7ff2qIa9Q9dB7zf7ZTeNkZIbBgRSE2gA+5s3eLa1dd47rlP0ohjJBAISSgEZtpScQjEtL9SGIPLUvIsQ1iHcl4ISwlBLQr87x9yfB+kKQsrx6nNtCjyMfsDx8XHH+NjH/u4r7BMzmuvvUytXuPI8iKf/OQP8LUXL3H9+g3SsiAtS1QYEsQSbQXO5eQG6tUqJ0+cIAwhjhXV6sEem98d4rv+bZFcu7PN6vomSRyhoxCnJFGk77eelZS4IiPWAt2s+upXSdISDFP5Tw7Xlr83VwVH4CK2OjnfuhnwtF0g2/0mRxtPIDSgKmxnVT73xa/zkz9kOXHhUWYWQz74UJvV9XWCMPAG1E6AsFgnEO99H/MDKen8H0oPIxNRZQqXkQgRA4rSgClziiJHRSGuUsWWdfb7t2lVtB8yWknpvNplZiSZ8Ud6gaLMD+6JV+oRprQUuWA8SdFojJFQGMIoYOXoDOC/p3OCSZpTFA5jDTrQ0/GGYDL2Q3DwiJdKNaRajUgnFhgwOcQoeS8MWYwa3F3bYmvT8czDT/DcI+fJOyNuX93gq5e+yt1ujzOnTvHYj/wIrahFPdjFhiEFIfORY7i3ixwMcSrgxKlzkLQZ6ippLvmdr14iCmPiQ3rz+xaGMubRhx/hhef/CNfv0tYeDZKPdrkZ5LikSthoEcSRN0NJu5jSoHVIOrondgcJJcopkAF5DpMU7tzdJlQZjeTg9kG234EwQIaBb28ZhXIRVhQ8Gc7SW27Tm4w94EMqokaFfjpGhg5ZsQTVCJMX3j0nK/yzaZxP8mUB1iPB1CGqn2vdLrfXrrOxeYdvv/ItevsTTpy6QKffpzcY0huNkOGAs+0WC0uL1GoJ3/zmi9Rm2pw8vsitGyO2Nr26opMZaMeJ4+cxRcybl9/iW5cuURQGKQ5Gt92L9zWJp/s9hBTYQCPiOiqUXHrhi7z1+otcOHuBZPkYQllCoUBIhJYo6V2grZVYZymlx4NbBZYSQ45zBbYsiSkpSnOoOlyZluAC5tvL/JNf+F85ffokd7c3yXNDnMR86Uu/xeXX3oDcMBwOWFie5x//T/+UpFZh+fgK/cGQna0dImf52EeeQwWCIAoZ9IZUgpD+aMjdtXVqleZ7vjYWicQhXYkVmoyQP31rl24Gp08eQ9icsbE0K8EUq26YbUbEWmMdqLhCUVoKZblyt89DR2tUZOlr+78QnjVlT8G/+ONFFuVneHTpCf7HziuYyYS+qGJkh7/+4x9AHn2MzahNRbW4dfsKFi9hW9oBWkdoGXtYJPmhqnD3r4GVYARSKvr7HZJaA3TFb+xSgaz74VQQosOcstzmTy5tsbG9RyNJ+eCTc0TRIoWVBLJFEFiCIGVxDrJJzl6/YFQ4gujg52M06mIt5LmhUg2J4wRrHZNJSlkKrl/1ju5SW5IoJhQBX/itz3Pq+Ek+9uEnWViYxQrJYJLy4muvc/LMGa8IGVisTZFKMb/Y5Hux+d8b13c2ePU/3mGm0uCXf+GXCCZjdu++RVCUFPvb/Fef+Qna9QaysJSTjLu717DWUgtnKSzMJieYn0tIZ3p8/it/xO6oR/OsZVzuYm5mPHrqDC+/foNPfOJgGFs/V9zey1i8u41K5tnbWKVWK1EqQLuCbPUKm+sdwiAkaTQJazVko05elBS5d3dK+wMGg320mdCu1wiigKTRoN5e5OHFJv1+zsL8woHr+MgnPsba5ia7WzuM0wIpA0QCgdV8I5zwifZDlMcc1+/cZmIN2hrMoAcosryYAiUsOIG0Hhlmp1hyGSWQFVBm2PzgzfWX/vdfplaLeP31V1DCkI0KSllnYkEGCXv7++xs91jvdTl79izLSwsoFeAmgrW3drAmY3HhDDou2e1v0FhIWO++ydqtASqMWTiZUBSOudn3lj/eX49NZ1HC24VZY3E48rzABglXrr7BkeXjCO6RWCROKbTz/pdCaj8RNyVKgKbAUiK1R7BYYf3AAg7tw37oqccImw1On1hERXV+5/OfZb+fI4zkzLEFBr0Oo8mAMI6wITjpq7BPfORjXHr1ZW6sruIkjPb7fOu1b5OmI5xwxHHMkZUjXHjoAjPLs1TkwVP/7453WilCwCQrub65j5EhSgqqSUwsFHu9PlLAeJJRr9YItJeu3O3tE0YJBsWtuzv0R7NU6oeB+u7fmfvVurFVRGbRYsSOlXz5zil+6qM1jh/ZJO8VVOMapt4iqFQ4dnSB9uIMf/VHz9L5ra/x7GPHWVg5xm9+8S32OgWQ4JxFinLadzxkgCYUpVEoAtJCELkArROEGWPKnDCOUCrEOEktqSBNjWeee45f/df/gSd/6AznTy8hRYkIIoQZk6cTJIZslJNPxliX4FRMfgg+u9Go4axgMs4Jo5AgkAjpaM8072vPWyyFzZhvz7I8t8z622/zAx/5MBfPLrOzs8P6bpfFpZM0atuMBhnVZshwNCIvxsRRgyDUhw58f/4zf5vXv/0WjajChaPH2F5bxS2tgBIE2x2OLi6w3+lSZjmmKKk16sy1ZymdYJTm7K1vsHHtCkkccP6RD1Lp7GHCCaEQ/NEb3yAvSmaDhE8/+dyB67h6+TICR5REXFm9jetPmK01qSYRo96YKBTU6jGpKRkXQ7q7faJhBS01UVihWqthZA6iZJCmNCqKFo6ZULC1scZ4MKS0kLdmD1zHEyvHWG61kA8/wvNf/RpZljMajxhLgZoIWrWEM8efIdaCSZ5zcnGJS+Mh+4MhQRAQVAKfd4wjG4yYnvF9dS68/Z4trO91HxD7vS7pOCGdGBaXayRhhtYlo05OaRRzc7NsrG3z6MOPszC/QLtZZX5ulpX5BZr1Grv9EWt3blOrWyp7i2TFCNEecfvWy8wuxBR6kUqSMDfz3mZq7zNO3CEpEQiUsmik1/Z18PqLX+VHP/FXPKpC+paIyS0lfqdMJ0NKl+N0iNIRIsumvVyBlIJACUzhdcUPi4XFZUw/Q3Z7uGDCB46dZ+gkTgcsLbW4df0xkkqdssjZ3NwkjGLOnznHTL3N2eOnuXHzJlEQcOKhR2jVK2xv3WVnb5Ptzg4vvfoSt/Zuc3RukXNH3x3m+L3xzlhz+gAJya2NDkbWKXSBjDTVpEKnN20XWYjjmLwwFAYGgwlVERDpkLtbHTZ2uizVZ/4Cn++wAiQ53mBEoKUgCDS//HzMP/jJh3jofMY4g6FRVOst6o05oiDmr33qBM8+FKExHFlewIwz/uVv3ETrCkaUKONfDHdIEleBosgMENBszVCpVJDSQzidg0CH6ECTl55Eo8OYi6ce4djxRdJ0hJZQWIPSFkuKUN4FqtPZZzwaQiPBhTG5OXgdsQZnAkQoCQKLkF7H3ZUeA55lGc4J5maW+MCjH0JYwVNPPsrKSpvu3i5313e4fHubjW9cYdDvIYOQhx9/zPu6Go95L4ucPD34RHCiMUc506W7s8Pe9l1q7SbVhQYbm3eZnW0ji4K5VoP1u3fp7few3R1a2YQ765vIIGRhfoFsFHJ3e4symqCTGpFOWLt2i8JWeOz4HLGKONs+2BSiGO5wcmWG2XbIwpkTrE/6vH1nxOycQRvDidlZhM4Z9br3B+TgESdCKeIkwVElLQzdXp/OaEAcGs60TzM3v8Kv/dFLnDx3FhUcrHv/t37iJ2nWGgRCcevH/grd3j7ffO1VNvpDXr16jf/hb/w0F46fZHNnm1E6YaE9w//2r/4NL63e5PW336SwhXfVMf5Zt9agpILC+h65cCgZHjrLqlQt4+GIMMqpJgrjQkb7fYa9km53zHxN8fiTj3PuoYd57ZXXGY9rdHq7vHn5NZSAm6vrrK3eodmIKZ0gHaXMz9ZIwipzixW21ncZpxmZ/j40StbS2595qxyBLSRRGDFJx+xefx3pCooiQwQBQRiiHMy1K2zv7/G7n/1dlhYXaMyvkFRaLDUqHlRvDNJZHIZg6vxTHnJ+z8eG19+8TpQbTs7Ps9Ju4yJFfXEOnST8w3/0jz08bDBmd3eHTneX7Z1d9jr7PP7UEySViKLMOXPiLLFS5HnKYNhjr7fH2sYd/sOXfovXN9/k9rUb/MOf+0fv6dpIz8/E4pmotVAylwjW9sdkUZ2yLHDZmEluMAZUEJCmKTvdEXaSU1clpSnI04ytXsbVzQlPnzksbU7D+Opfgu9HC6YUfnC25O0NzZ+9WvLIX0sQvQ4qr2OMoNPtIVRIICUrbU2WFmzeXSPU7j7D7zvPAoeeClyJdSkSjdYWU07QKvZi/8LD+rQqCJXAlCV5CVHe45GLF+htXmGcC5Ca8WiMwVIPZjB5h7QcMSwTXArCFnRHB7uZ1xqAs1SNRzsIQqKwyuzsLO12iyIv2NzY5MjiEU4cWeDa29e5fXuVaiWmUqmw3huw1+/hBDTaTYSDwBS0WzWMlehAgJVE9mBI3fN/8gdMNvd54uGLXLt2mZVTF6g0a7Tbsyy0Wrg8I6zXGYyGpOmEMk+ZDHroIMIay+2NO6zt7LK/dYeVhWW0UtxcHXHr6jZlvcLuepeHTi1RTg7uvT739MN0t4bkQvCpH/8UOxdO8oVf/yxv3tzi5FLdWwPakFCOSXMHKEZpQa2qkdKR5TnOBczUGmyaHUb7I+4Yx/JgyHxNs98fsbrZ5djDBw8Ub95cpVqtMNdo8OFHHgHgw48/xmAw5MXXX+W5iw8zGY1YmZnhpZe+zd3ekE88+SSXrl2nyHNMNkZYUEIT6xjrHHle+oIljsEUOCz2EHTKwkLAVpGyMF+lWa1yZ3OVne27bHcsceUovaJDoBMuXbrE9as3mG1XOXF8gbX1VZaXFxHScPnyK0CBKRz1pEXjsScIqpr+tmBp9ih5OkTzfYhOCURBoAOkEkTaoxikEmxvrFFxJZXY0+hRBVrD3vYWRxaOcvX6y7x15UU216pU24s89cRzyJkTuMIhygJnvc9mlqZTpuHBqUsHFtFISNHIM0fojEeMuruE/S6hLWgXKdHcPEWlwtEjy1w8f47N7h44QTWp8NyTTxHFISDJJykOR3OmxYkTZ3j4/OP0hxO+9e1vUk8Od9S5F/f0Tqzw4/fAZfzEhx/m3/7BG+RliXEKV+akuWcNSmsZ7O+TBCFnjswRU7BZBuQ2ZH9/wms39/jpjy/7IeMhmdyVBj+GENgpXt053+4STlEGNVZ3S2SsmW23kMOIsnDs7u2QjofM16q4ooNDEMdHaNRyFBZb5FNtl2mL65A2l3MOpQRKwmA4QAhoyYg4quOkwtqSySgliiKs04RBTJ4OePKxx5kcX6BerxBXY/Iio1KpY23C+t2XqTYcIszZGRb0+7tkh2DojfXHY60kUdhkrrXMuVMXuXjxYWbnZimLgo07d8hHQ7QW5GmHpeUlhuOS7e4eg2FGoiTalqxurHPk6ArGpTRaM1OquofCYg+uxDfTLueOzVOWOcdPXGR3r0eRpsy2a4RKEImAzBqqrSaD4QApFUEYMDTw0ts3eXtjh86oz9F2zFOPP0mtucytnausj/eozy6zOkzprW4x+eo3eO7dLViZn28xGRru9AwXjxiWj83z4U9+nM/95mfJrWQ8mpDmjklu2U8tQRShtfHPkMkZj4YIHaGEJS1hJo7JJ7n3Uy0y0JIr129z9PyFA6/Hy2u3mG00mOvXuHD2DDhvpdashDx59ChKCX7lV3+FK1feIgoCTp2+wEuvX2F9dZszxy5gTE6v2yEbD6lEVchCwoZmMBgzmqSYKdNShQe3QavVnNk5wdamoL87ZrY5Rzo03Lx9k/Gk4PSTp3n7ynVUuEW30+XG5S5XL1eoNKscP7GMNAGBllSqTSjHLC60mD/SZmd7h7U3tym04fSpk5hDevP34n1N4tJOUM6gUGgMqZWs3bnNaPsun/nZn2Vr923S8YQPfPQjfPELX+TS177Op37kk7zy8utsrd/i6tZtjhw5wfmlJi90L7N96xbFqGB9u8vs0jE+8KFP+mr8kKzVHfbQIkUi+Oxv/ztqjVlWjp8iy6CzO6G/b4jKHi0BM6FAqIy4UUNFmttf/xbtZp3Zc+dpLi9TjQQLSwsIC6PhGJulfPrT/yUrS+eZ7O38Z18rB3z84XleeLWKTeY4c+oY/UGf22vfRsuA2ZkqjaRNWRQ0AotJUxrSEbTnWM3GXLtxC62fpCjfjRb/HffFWE8VD/TU/fsdCyklFKUzOFGiqTBTr+AUrHVyQLCfpmxev0Jv9RJnzjxLeNzx8OkTHDta5fp6Rkg4hTkebvwqhCQMYoJAE1caaA0ycEyyITJMqFc8rtjZEWFYRRCAyDm6cJTK8eNUkipRpcp42Gc06HCrs08va7MYFTTCMaXMaTQ8muSguHDyKMb4oVy7cYZ6tUm9EmLzMZO+5HOf+xy93h7PPHGRuXad00darK82ePPKbe5ubJHogKcfeYRQCuartzBxQphUGadjdFTQ2euR51Ac0tZ55EPP0sgsg90BtUbA9tome92MbL9CrZGQFwGDwR5xGHD8xDlu3lojihWDTo+//7f/Di6s8LVvvchXLn2Tf/n/sfeeMZpl6X3f74Qb31ypqzqH6cmzsxN2Z7m7FLmMkiBZwRQUABMGLAEyKAOGaRsGCEmQPhmGJEswYMiGSQO2BZkUbK1MLkSKFJdcbZ7Z6Zmd0NM9nau7ctWbbzzBH251cxWmaiTZAxLo/7cqVHeduu+9z33Oc/7hK79JPstJkja91QHX714nN46waGGio1+uobKsnuyzcSBwswOcc1x+8jTPv/o01956j8k0YlYUfPPWAbPK0esUXFzp4oyhyMcktaPf65DGIe9tjri0nHJ6ISabTJloT9LrkR3s8ttf+wZ/7Yh13N5Yp5te5tSJVUxRkhUF7TjhxvVbtAc9Jrs7/MX/8M8QxBGDU6tYFNt7Q/7hb77Ot954lzff+B5Pn36Ov/7zf5HTKwuMp3N2dne5dvMm3379e/zjr/xzrDX4ycGR16OczminKWahxbd+9y6hr1DO0RGSvJpy9e13oHJ4OefP/uRPM0gD7u894PsfXuPKd75FbS2RSlCuTSsNmR3s8/03vs7506skEXz73Qds3h7iODrO8CE+2U48UAjZOKGpMCDbH1NVOT/2pR/nT/7xP87m7g6fefllvv3Gd/jelTf5/Be+yJf/8a9xb2fIbDIlKyqGwwM++PBDvvXOdyn2NnGVpygLku4JLj/7MkkrOVatGErJZH+Pvb090naHjY0HJEnE5SeeZKmfkk1yVKDQYUCgA+raIOcVfj4jEJJiOiMfjfgX3/46w/0Rl568zA/9oR+hrkp8nRH2FllaWmZqyn+v69WKJYNeB5f0MbUnjlt0u23StMXach9fFzjVpxVH3Lt9Ax8mTIZjpBAM+n2stYeF8zjPkgbONhTFhnLocdYfmmBBL9X0ui2EmTfsk8CD1/gaZDRALVymCHpErmA6zqjmAAFeGIR/KLo+GnVlMKbGhhaloyZXtfI42xhtBTLEmzm1GZNVU5K4RyBz8uE+8yjh3NmE8WSP2XhEXY+58eFb7G1v0316kSB0BFoR6wB9jLdOEgVEURucZm97gi09C4MBW1ub3L9/nwf373Pm3Br9pQ5KelqtgCcvn2ZheYnRvGS4O0RIzWg6Q7ZaqCilFXXotUPSjqOTtKgri3dHv0xmwxFLnQEnT56kqiuWT57ESEFV5EzKkk6vz5Wv/QYrC8tI1SNIuqQtyekgoh6NyJmyu7fL7Qc7ZFbz0suvUFYFD8ZDLIa03WKw2ObC5dNHfzAWVK0YBBnS09DwtODkmZO89fr3MdZjnWdqIEfhsopZXhHJEIEjcBZTl+xMD9CtNjd3h2R1TBQGiGBOu3OStDVnesyY6+f+gz9NL0k5uXqCRMU8uPUh22VG1OnT6y8yWFpELFkaU6VmGHFyeYHPvPgEsYA/8tqLPP/sJV5+9gK2Llhsd3n52af5sR/6HH/mT/wxxpOS3/jq7yD10Z14IlPKsqaVeIRT6CAhn48a0VFRcTCZ44qCSV7z/tUzLHcSnn7+SYyD71+7SlkXzGcjDoo9Ll9cpd/rE8cJ09GMDIeiopgfMCuOfpk8xCdaxMfzGfP5jDiK6XcHHOzv8MzTF/npn/hR3nr7LbrtNm+99Rb/6z/43znYPWCh3eW9q++RtPukaYKWy2iNLP8PAAAgAElEQVQJ2XTIYn8BGUcMegt0un1OnjpDLDOUc8e6wynvWO51GG5vsbB6hr39fT784B26rQClFEnU2KAG0hJpgW7F+BNtpB3gqQml4Fvvv8mV119nsb/KtWvXySvDT/30T9FZ6FJYQTtJ2J4eLaI4DtY7Or0OJuowHB4QxTHnz59nNp8Dkk63x6yCYWGogi5lWWGtJ4oj+v30UZjrsUXcOVCHI5R/xX3NO5C+5OTSEkpWCOkehUsbY5rT/qDNpZe+RLeTsLOb8TvffcD+gUepEHx+eNB1/DrKqmxEXdYQxh0eKjuMmYOXuKhNUTqKqia0jd1CFBRUdoLMMrq9mPX1dZJQk8+nTPYfELhGnn0orsOaGnkMPzvPakzt0Tqm3R7Q7QzY3Rny4Ycfsr29zbPPPkueFXgnGgOuTkWnO2delxC2SOKIuqjRccRQCqQM8c4zz+ZYUSGVp9VKkPbomecTZy+wff0mqwshk8mEg71dLjz3KcIwIq9r9h5sUBnD/QcP8GLK5ec+xaATUM6mmCJDRDFboz0qrbh48hyDdp9rd6+ztbdLUZZcPnOOVz7zPMIfbbQ0Hu7hbZ9WKFHKIGUjiHrmyYt8cPE8W8NtKmeZlRVIjVcBB/MC8LQjQRA7JvOcuNPhtc9f4tf/6W9STmraBxlogW9LWolkNMuOXMelM2dBQJI0tNPiw7f42t/7O8SDFfTyCZaff5l0YZHW4jLpYIFWt0d3cYHLizEnP/ckC/0BUahxVU5ZlxRlwZ27Q3Z3d7h18wa3rr6Ltw6pjj6rCAQYapRytGSKo8YowcVnLrL94ICtByW5lOgkYmt0wOLSRcIw4eTKGjdv3sIUFd04wMSKbjtCWMv+wR5rq6ssDvqoNMSLEPTyket4iE+0iN+6fZOd7R2MNTx16WmCUHJ6bZkHmxvkeY6rCr77ve8y3NtDScXrb3wXg6PIp0Sh5szZJ3nq/Bm2N7b46c//MNLWPP30s5w+eaphs+BRYdIoxY7A5GAf5Qyh8BSVwVrD5uYm1nq8t+znEwKhCIQEJSCUdHSE1iG1cKQ65M4H1xj0B1hhabUCvvG7v8XFc2fpthK2dnbZ2dokER/fv+Rfg4cKSdrpUakWtqqZz2dYLzDWY7xvvJvrit3dIZPpnFB5PAFpOmBhofWxVJJw6B1x2I8/dGD7ve8LNJZuLKnrKbNZRln1sTbAW4HTgsHiIk+cOYULBD//t9/h+oOSXMcgSpQJ8JjDmfgxStq8pJW28EpRmwopAsIgQYoKtEIITRB1MU5QG0tWCiIEtjaM5xOm2ZjvfOdNPv3skzhbEcmA85dO45jgkDjrD50Ij74eeVZSyIpAe6Yjw+2b97l95zbj8YQnnrhEFCbcW99md2dOL+kRR31arYIVGTOfV0z9DKdCAhHSKh115RrJv1RYWyOVRQYRUh79+EVhC5IO3eUT2MkW77/9HaZlyWc+/yW8qdCUXH7qOXa39zlz5gLbW5usrLyAEiEH929z9d5tvn/9fca155SxbK1vsjueMs5z8qriqScu8fbb73Pnzi3+/t/86HWUVUWWD+kmIUJ6yrLZYcZhi1dfeo6bV3KG4wnPPX2RsiwZ7R0wmU4p8xmtQKGVZu3CZc4/+yTDEXyz1WN7POPW5ojVxZTVtSUmkzE7+3tHXo97WzuAR7pt0n6IMDWdOkOsf0B17S3u/MY/xqoQGbeQnTai08K1elQq4oP9IblQnFpcYqHTY384Yns25/rogDt7O4y39imCHmGrzzGuHWTFGEuN85b5dExuS9KFiP7ygDI37GxsILXisy+/AkZyf2eXi+fX6PW6fOlHvkCn26Usc+ZVxq1rVymzjLOnlzhz9hy9wQLjURcvVEN7/Bj4RIt4HAWcOXMSAfyTX/snKOEw1YzaCeJQk02HGJoHNM8KnJGcPP0MClhYCDn/xGn+2B/+Kb76W7/N5199CWEdcbtFVedEQcDmxn26vSXkMafL+zs7bG9ucP/eOk+unedgb4fbN29w5c236A8GRELS7XUI44gwitA+YFIVIHIqa5kJycFwn6WVVS5cvsT777zN3oMN3n37bZYXlrh39yYCy6kTS/8WV+cH3AwPfR2stHhi9sdTklAx3S6RGtJWSuUMG3tDhntDjPONLUAaYQ7N+NNWFy80eMNxHbCQsrH49OAO6YUcFnOBoVYe7Ud0nCSb75ONhqjWM+A9SRxydm0ZKXPQKe98MMTGywTaY40B1KGj3/FwCIyXaKGp6wIpHYGKUTKisZFwJEkLqWLKsqaq58RBSD6vuHf7GruTMW9c+YB8fMDSSoItM5Z7jRR7sNRBCg24Yy2Ca1PicQRhwvbuCImi3epRVxDqlCQNybMpG/c28JVhYdAl6fQo3BSVVaRJhAmAICLYO8AYg3EV7VZAu9el1YoxzjCZHt15fvjuO5w5d4m1U2e5/p1/ztXb17lyb5Pl8+c4c+oMxYbl7Ik1Flo9xqMZVVVQVBlL/S5hNmB3OqLVaiHHc67fv8uphT65NxSm5sVXPs2VD95jfzQjTI++TysXkLZjet2Ynd0NqrJEa01ZlATa0+u0EQL+0Jd+nF6vx87GFjdu3KGuKvrdNmdOrrF24QTEGoVlqd/n/t4MFafErZTTp1cZ7e2zvXX0GdLV9VtkWcZ8OqPtBaIIqP70zzLf2mTn+k3U3hbj7QfExYRVZehVOYHx2KTDa2fOMKwqAi84EYe8/s1vcvVgyANgRJNDIHxJZQ4QwTHeS7HFOUE2LxBhiawECsFsNmEyH6KEYGl5hScuXORgb8KVN97kpRfPc3ZplZaJUFLS6UjCmWGx32Hx7ApR4kGWxGGB7ne4eu0a5cccx36iRXx5sYv3oJXkpU+/2Jgb1Rl372/y4YcfsHZqkbIyPP3UM0ihGA0nZLVgOh6itODc2dMURcGJk6eoRYnUip3ZHhs726RJi53tHaL9fYxx/Fk++ri93UpwK4ugJLs338XP9jm92OPW1bcweOrZDB3F6DAhSVp0Oz1Wz50habXotfrUYcD23h7Ly6tk8wylQsbTKbPpPsuDHsLZxtujPFre/YP4wa5ZCAn+cKwgEsKoYjjdYjabsrTSR4aS9Y1dhsMZoZAM+n2UysiyeUNU9Jal5ZNMizGdEI6dR3uPEI3woSncP2DE5SGN4MlzA3y+QyQdESWy1cwFw1DhvWeSWaSZ8+lnVvje9QqtFTgJwv7Abz+GNaSb7lRJTV0VBIE+9BiXGFdhXY6wEqUC0jTGznLqcsbCoM/qiYDa1ix0oMr3uHjxZbLJAaPRiNvrm7z4SpvFQQTWYo7ZodS+Jo4SlNKsnlhhZ3uXMFQoJZnOZiRpSLsTEWgwpmZ9Y4MgjRu5faAp84JJllFWrum4Jhm1HXLy7BK21kxHhtJmzI85YF2/eZsTy2ucWFnhN3f2WB/P2ClmPJgcsHbuNINum4Ven+3NHa6/d5XtyZhLly+RBB18KyZIE4x31HXF7sEQo2tG1YxPv/IiTzz1BP/nL//fPHH+mYapcQSk7hAmDmtzqrIkTRJ0EDCfz8lMwc27GwwWu4Qx9BcSBr3zPPvCk+RlialrojDE4ii9I+0ErKwuYK7dIascu8MZz0WapUGXfuvoWfQf/uznUFISBhHZbIalCYMp5zk3b95gff0Oo50dZG145tx51gYD+svLBK2YVm9AUZVkZUYcRtxYWObKr3wZd7CPnE4aN3NXILw51m6otjml8WRlzuLplIOdnOH+Hlk+pd8ZcL8oObG2hqwzFloRg8GAsi44mOxhatOQB0zNfDbFOtMweeoplZFgK0qryfI58mN4uMAnrtjsUdUVrXafP/5HnkQpRavV4Vd/9Su88/2r/L2/+0vsDHcYdFt0ojZSKP67v/Xfciff586tOygRstBf4eSFS7Q7LazxBNYThgvs7e/R7y8xnc5J20eLF9YWFllud7l46ixxqLFfeI0yLynrmiCKyGZzitkcpCera3LjebDxgPn6AdvjN8myGWFmuHn1A86du8D+1g7Xr77LnRvvEAVtlpeXeO2VVzm9erSM+KMvVDN5uPJOzWxeM893EFXFwkKPd+/doBhntMM2K70FpDSMDnY4ffosWRY1CSresj2cs7FfcWk1RR9jYFLXBq0b0ZS0DfceGt8ai+QXfuYkrz1Vsp8XKNEiTSuilRW2d3YJpUQ5wa1bNxCho90KsTY/TC166MjY2Lgep1AMgwBvClzZ0Bu1FtQmxzoYDXfxaUza6YFoghi63R5aGabj66wsVqwOVnjlhYskUYgXLexSCydTgt6A9Qe7xEmCMQ53jC2DTNpo3WNl6TxPn22xvhE1lsX397l1c52NjW1Orp3Euoz9gwwhQ1QNN2/dYVZOCOOIvDQcjGaMxiU7m1u88tJ54lSR5xnOpARhSDs62lvn06/8GM88cYFs+x5vfPddNkrJNIr52f/qr/PCqXP87f/sz/Ptr3+TyWTGcDQhDdvk+xt849a7fO3td/jW7fvct4bZrGJl6SR1JBmcP83m7hZbVz/kT730KnEQ006PZkEoJWgrhXCWtdUVqrrCGMPCoM/SwiJv/ovvkmWOam744L1rSAlJmmKto9vtAp6iLImTFLTns699mjffvcZTLz2PqXKK6Yy0m3LhqTNHruNv/E//I++++y4P7t/n13/5V1jr9gh0CJ0eaysnEF/44r9y9OJo1A/NN0OgC+A9/+Xf+Ju89KM/yl/9hb/KlTevoFWESDoIqTjOObmYzcirjCqfsXK2x+CEYzZN2NrIWH9wk8JWhEqx1gICxd0TS0wPZmyHB4jDQBCAb3/7CqPhiM+++iLCGUbDKSrYpJIjRuOM6ezj6a0/0SL+3Tc/QABBEBDHTVxVFAZceeMN5lnOl7/y2+ggJI4DwiBC64jCxDiXkmdbjIYZtz7c5cRajV/WSB3ghMBYTau7Rj+QrFj36CJ9FDyeKDjsDpzFKYmMQlpBQBTHRN2EwC013tVSUdeGUytLuLqmsjXeWybjER+uP+DNb36d8c4dXnvlDHHkKOaeF57/PJcuXebEMV4QP4jfO0ts5tCBkjwoLHOlqEjIM8Vwr6AYe1LdoteKGHQ1xni6p07yxKXzFHnGLJuiojZLq6e5cffrnF06TxAefTOkaZN2ZF3j4eKdO3wQBLWZ8cVXnyeffY/aQitpEyYFSskmtktqvKkp8wPCsMfNO7tYpygr1+woEEg8jcXsMVS2UOJqhxQOKUQzP5YCpSPSJAEn8U6itEIoQZp0yPKSIAjwTlDkNdZ7QucRSoDQaKVZWe5RVRnOOMq8JNBH3x9Sa4SCMJYsrnTY2HNkxYTVUwsgPSoQ9AZtlJPs7e0znO7T6gmsEk2+ZG3Y3d/n4GCM0gmrawOWVxcQssK4irousUjCY3zNXSQZTveRRcjP/syf5uqtW3z99m3GdZc31m/zO995m8VOh+lkxJkzp1ldWmB/NuEb793gn33/OtHSMstBgPP77Ng5qRxg742YbG4TVIZWfJeo2+ZLX7pw9DqqjFoIyjwnSDWmbhKNNJrae+J2n2w8o/bQX1pGCKiKGWGgcLZmPq/xSOqqIo4jhBB0Oy1efPFpmM4JdMDS8hJBcnQn/ov/2y8eMq7gL/38f84XP/ca/9HP/DmKrOD/+JVf5k/+0T+KcZavfu13mGYZ1hnOnTrDeDqhk7Z57aVXOH/qNMI53v/gKn/37/wtrrz5PYTQeKURcQhBeGx6vDOWQAZEQYipHM6UhKGkv6DxPiSf1Vjh2BvNQCuU1rz+3SuoUBJFEUVRYJ0nTlpsbA35za++fugvZLhwuce5SwmtfsR89vtQsTlYbhGpCCUUZVVhTROX9NSTFzl1epnNjR1wAqFpioNrltjrLlKvGlr9VW7f2+ftD94nifsgFbU7zM7zHq0UgWoClv+Tn/0TH70QLXF1w8pQQqHCgMJkpHGMDEOi0KEKi9YxMoko8ozkxAmUriltk5kYqCnPPH+W/WGIswU6LIgiw3xiCNQKUbvTONp9bPxAgfNNsNXWNMepBHzM3u4O0/09TvZCrDPE2qJVxfLiIp12n0B6ZBRQl2BwrJw8x/b9r+NEgDiGjfFf/6efZTieM5oUlGUT7ltVFQhBEgni2DMdB+iwQ6USklQ2uwXv8MpR+BpjLLEz/NjnTvKZUmHqQ1/wMEYBYSRJWkdnW3osYaiQ3qNVE2whpCeIApRs43IHXiGEQkqBNZ5ZVhKFjiiIEDpCyBALVNUMTIGsQ5wxDHodpPBoodHHnRHgcaLA64wHe/eYVVPGsym6GFOrmsILsrpmkIacv3SGeH/EWx/cwAlFVmeEBCwt93G+RGvF4lKfMFVkhaM2migVKN2Il47C4sUOs9mM2WTIk5cuILxn9fxpLg+3eef776H6HZ545klOX7rI/t6QXhTwu7/zDd7/8C42CNmfThks9nniybPcm82pipLRzk7TMMQxC6trXDjXJkmOHvv92tfeIqKmLCuCWD/ySO/GEaEQZFZgTE1ERSdMAUuBJi8qPPJwTNdEmE0mFcPRiDiNOLm6SCY8tTF0u23kMc9LUTcxblqFfP2t7/O9mzf45rvvYaqaK+98n1//9tcBWF+/D1qBUujaUZUlSirWTpzg1MoJ7HzGe6+/yYN790EEeKkPRW4GkAh9zHNrFMZabC2wM4HSGqk1SVIjFiLkuRQtPA929rDO4oMVytISq4A8M3iniFsdlk+ssrE15GBcIVB0uhFxp0Pa1XhbkyZHPy8P8YkW8bXliM9/5vMs9pea9DVr6bRbhxl7DilTPFCUWZP+ITVZnmHqirKuMU5RG8jLGVlhybKcLCvIipyiqhiPptR1RXXMLFrHKbWskE5h5gW1q6iVg0jhbEmnk1JYg4o8ra6nlQqqMiVOE5yRVGVFXjgWFhLiTsr+QYlzFVES0EoDWgQUxjA+hgf8UWhirDyRbjGbF2zeukcq5nz+R5a5cGKV19++iUq66CSh02ox6HWYz+d02in5fNjchCogrwTD8Yx04eiP+cUnKnQQU9WqUYl5UCpBCIkUgjwbYXWLIF7A6gTV7WGJUE6D8dTe4oUkEDV/+c++wubeNmVZ45xoMpK9QgfHd54gqK1AiIAAhwoCcJZiPkaqkN5gCetqpDJEcXNI6b1EqxDvLVEcUVtBZWqUkDilKcopdemoSkukBEEYo+QxYx0lUMJz/8E62XyO1FCTNUU5DindhO3hGCEW6UchWZ2TtDTuUHHa6UR0Oh2ipCAIQ+q6QipDHDcy/jCMkEowz472i7534xotmXL2xBkGqyu89Z3Xee/a+yw9eZIvXj7F6sXz/MbrbzA92KedRLz8xEUyIfCRph33wVq09LRbEWstxXRSM9kOkFpx4sQKuIqYits37x+5jrc+uId0NcYLhPQY07CNtGsCic93BS+sLTCZFNRuSr/fIUli2m2NNQbrHFVtH8Xb7eztsbi8SCsOINEY1+zOS3P0WCfuLIMQ6CgCHHVZ8bVvfReEJ0kirt9ZJ59lBEoThAEqENSVpSgMWns2doZs7BxQzCbMdvbxQTNzbrIwBTpq4bRG6GN2SLWiLDwqSGi3Y2TqmduSIIxwSRtdlAQyJVZt5llGVdXgJEVuSdOUS5cvsrB6Eq803cEKpqrY390jL4cEgUZqSW0LhP99KPaZHWxj8glzb0kDRTaf0YtO4RHk+Yy15QikoxdJAhUghcIOejgUeIFzhmk+YTxxCBXSa3VxtSOvC2rnaCyBPceFvQtREoYOrQQychjZ0LlkpSkrjbOeKIlRgaKsCoT1ICPmmaPOM2xVU9QCZIhUgiRNyEtQYYo1lsp6jG0sc/+dIEAqQWxhuL/HpeWAn/jSp3nhqRhdh2gKbmwZ4oU1Ou02GqhrjXGWeVaQDDo8WL/L/a0DdoZtTi4c7Q53MGnYPd4aqjJvvCRUkweJtyQ6ZOHEUygpcF4SpAt459kfZwjl2Llxl9sf3uaVl58gGQ8pStOMEmSAUgFISUiAOfaAtbF/VYFCWIO0IISjrkpk0EYMNLK0KAlRGKCk5sTyEt6XlOUYJRxBGJCVzTiqdgpkjRKWKBBU5QwddZHB0R1O2MTXsnewT1GXxGmMENDtdijyElNbrHHc39tjdzrBWseJ1T67u7u0Oy16vaaDbndDBoMBk8mYbi9kNqsJwxYHB4fF+xjK5Zd/7XV2NvY4tbLAX/6P/xTLp1dYPtjmyvsfEKUR//T1X+Lu3gEqCTh7boH1+Q5hK6IKQkztKac5S2GLpbjP7tY9hJe01pZRicKImpNLguXlAFsdbQ/RCTxJFBIlbTZ3R2gdPCp8tfWkgWFpaYlf+9rboAL6/Q4XzyzR67ZptVporYnjgDQJEQrub9xnZXUNLQ1CS5Z6y0gpCI/pxKuyRIcB1tYN40lJkrCLEB4dQKCiRpyXl5iqos5zVk+fpbYW5zxSCkxtSNUS1SCnms3hUBAnpMSroLGllUdTg12d4ipHoALCRFHVObEKmoAbr3GtAFNr0taAqLXE9vV7GCew1mDdnOlszvlun3Z/QLfbYtDrYauS+xu3+fDWm8giJ5KGWnw8YsQnm+yTnuDKlfcJQ00rbqGk4ua93cNi4bie3KU0FuMMYagPD8cOHQ2tJYkTpFbM8wytQkIdNEHJusnZy/Mcj0KHIXzhhY9cRyuGclohat/wMeeGA1fhjCFC4WRKXZToQBAkKVophpMpodYkYYgQklaU4KVCOEMnhkRJjDVoIdicT8F7WseIBvxDxzcBj948AsBRViV/4UcD2vGTdBOFtBWiBicLfvwzFwnf3efOpMZ5h9QBtfWMZ3PWzj1BFAfcfv9NchPx/nrjv9E+Yh2JFmgFQgd02ilCNEKeJE0IgxAhPNPSH840JXaeURvD2cuXqCvH2pnn+NSLX6QoCvK6ZqG3zMriGkqppgvBP8qrPAp1kRHIAGdLnFTUpmyix9IUFcaU+ZRW3EHKgPksR0mDUvMmdNhW1NM5QRDhvGBWFSjVwnmHVgcsdDVFHTIvKmp7dBEfFjlCSVqdLqdVRFmU1HiqYQZSMM/ndLpdtFJ4b1AK8skBrUBinWGezZnncwSwf7CHVAoDGO8R0rKw0mc8GhEfE1D8rasfMh7XvHvvAFX/Qz51YYFAzXnlhVPc25tzttK8/MKnuPDkBU6c7nH16nu8cesup37oSba+e42B7FNWhq/99ht88Ydf5tfffoO4t0JfKU4tBJy5HBO290mPqRW/8Ff+AnUxxguBFzCbzcjznG63T1kaRDlnYaHLC1/6fLOTLnJ2RgX7ownjrLGBfbA9Q6K5ff0uZ889xXMvXWZjZ4jUbXxpoTCoYxK5bJlhC48UzchDRyEEFikVpnbEnZhANlx26UOUUDy4ew9r3SFZQCCFxDuLswIZt4AmVMZ7j6MCA84c3WycXfvCYVKTR4eeIp8zz6aU5RwReNprbaxT7I8Lbt+4x2h/h/5Ss2PUKmE02uOffuX/YmFpgFSO2kxYWEoIg4gwTEiT0wS6pNf9eOX5Ey3i33vzHYKw+ZVaaoJQHx5chdi6JowCvFQY02Qa1lWNOuR8B1KCd1SmbvLndNgcfhnTcLl1k+dnTWMV+hf+/J/6yHVUVpEZicstnbSFswVIgRWWeZUznVqwBlEpYhxKQlaVzPOcadhMVAMPxjmMgEhKEt30b5WtkUIym89RydEUISlEIxGGHxiJ/14xP7tgwBmE148OCRGKUHvWVlrc3tljPzMkcQdDQBQrtI4IA02vm7K7XrE3MsyLo7dl8yxrjH+0Jk2SZqusNcY68DXWNCHKSoUUpScv88aESwicFxiTP8rUVErjnMdaj5TNNtl7j9YBwTEdMAiMdY/ORMJQYk1FVTvSWDcRqr5GKoVGU1U1VVk3h4RR0KjoDo220mShCbf2B/Q6GlOU7B9MQbTR0dE+zfM8wzoHrTaTuiT0AmEMoVZktkIIj7emUffGCbPJlEAqpJLIUOOAaWkIwgDpoN1p442jKkp0oKnLikAr1DHb9pdefYVr1+9wZvUUS4stTpw/xc7OTYxMqeqcIvVkYkZejxgd5Fw8eZ7vfbDBsJixO8uIXIDGkwWCxV7Cp55YpdtbIxSaXhIy3J0ySFLcdHj0p1JXdOMQB1S5odNdgC7U1lArTbiQEgURkUrw3lOWGWdPLNJvhRhriaMIxCJIyUI/Zp7X7O+NmM4mLC+GxKEmiqJHeZwfBSU03thDY7YaU9XU0uFFQ8utcw1C4HBoFSCdQEmJ8GCNBeuROkTJBZzmsAP3TTCEEBArhAelj1Fsho3Fs1KKKFYEugQxQcoJSnq0jsjzkhNBm3bc5sypFaQaYY1lPrPUpaA0Bc5XdLotgqBFlCjSpEOa9EhaKdgAjnmZPMQnWsQ/9annkKJ50AOtmyxD20Q4OedwvqlpUfQwbNcjUY8c9ZCNesw4S543WYaz+RxrDEJIZrMZeNDHdMCImLAV4WSNMBVxqpHW4IoSJyUqCBqKs1QIB1VVkeiIWtSURQlIfKCQzuCEpPKWyDcyeeEFQRDRThzHHXMb2/zN/3KEmn/0tfSH6e7C4zF44ZEmBlWz1A9JNWxuz5iEnmdfeIUw1ty5cQOlGhFO5RXrOyPWt/Y5ytn8YSK9FKLZzfyA9F4rTaBDpGgOdLVWCNlE0kkpsc41Xt+HXbaUkrqum0Bba5oO5zAl/jiKofMNPx7nUR6U0oTR4SGmlchAYmyGr1UzpqERJ0lAS0GnlaJ0SFkbpAgwrkTLZm67szumrGMCZfHm6DFXoAMirRiORrQ7C5RlzXA4JEgiLCAjjclLdJhgq5psNiOSmlaa4msLzhEhkcbT7iQoD+PxBFs3LxkBtFtt7DGE5M/90GepfUkoBD/y0z/BZLjF1beHnJIJL778Gl8YJKwuLlDOp7iyoiwVrz79FL/6tX+BkpbV02ssdDvcvL3JyeUlkm7N9tY+dz8ccurZl1gdLODG64L+ACgAACAASURBVLjh0cUi0CFaOWSgUdIxHA6bvFEgaccoa7G5oEigKEqsDJjPSgIVEwWSbD7H2ppOt8XKahfnJGncY2NnB1dk1FlFpHooeZzrp0Yqibc13jtwHmssUimEamLWpGpe6M67R6QH+5AyGyiscEhXgQTnzeEO+FCMZg0O8P7o+8NxGFIjNM4roiRG6Ih2p4/EU5RTlA4QThEoTSuVFKXEWUijirI04FtEcYDzJUJKrIVApig07ThgMqpZ3xkdcz0aiI8rzX6Mx3iMx3iM3384jhL5GI/xGI/xGL+P8biIP8ZjPMZj/AHG4yL+GI/xGI/xBxiPi/hjPMZjPMYfYHyi7BSODQr7/xQfeeR+782v+Xk2xRiYDqds7+zgnCVNWywtn6DVamOtQenGoW82zZiMM6AxibKuanI9C8P25hY7Ozvc29jgYDwmz3OGowOQHmMN37j64Ueu44efO+sTDcYBAaRLLZYHc54/3aZftom7A9Y/WKd2MK8tlRzz1IstxnsFezuSzZFmqzB4CXFmSFohYSroBwE9BZ2wRClFYRV/7R/d+Mh1/NL//N97rTVSSoqiwJiaOA6RErQO6HZW0CrCS0dd5dRFznQ6p9NfIAgj8ixjb3eHKAjopCkbGxt4Ken0enjvqaq6UaIBf+mv/BcfuY6ffOlzPqsFqQ6oXIk1JZ965ikunDnDJJvztW9/D6kEaZSAh6wsiKKYpX4XU+c4LxFhRDGbIZSmKitKUwGeQAjSJEUqQa+T8Iu/+v989Dp+6kc9QBiGWGswxlCWJdY0CuMkVIc8YR6pWrXUjUhLyEP3G9HQQlWjYXDO/Us+7Q/xD3751z5yHb/8v/wP3nvPZDJBa00UxSwvLzGdTllfXyeOY4wxj+7XurYsLCwefoaG2pR02m3iOObg4IBut4/WAXmeM51OQQhm0ylpmvJz/81f+8h1fPnLX/ZRFOG9JwgCtNaH/kcxzjnyPH/0s0opgiDAWntIS1WPvlcUBZubm9R1/Uj1OZ/P2d7eJk1TtNb83M/93Eeuo5oPPXictBgrkUpS5DV1bWl1QqxXTMsC4TQgiLUkDjzb45L3HxSMygrdhPoRKEnla+raU9UNc2VeGtpa8OqlHk+fO/WR6/jSn3vG17UhSSIunHuKhf4araTD2snniDpP0ArPIL1s3DclWAUBNW98/Ze4+tZXaLcVTglcBW4WIfHUypOJjKKcMrwf0bNT0n7CV/7Zu8fyDD/pIv7vgH9T3f94/MmPQhRFGFshcGzcu89oNKLdbjPKSvK85NMvvYRzAR7PbDbjwYMHbG7sIqXi/PlzJGmIqStQTaGeZ3OybEZtCoJQMhj08MKRzY/2i758asDm5jZZ5RllntjXdNsJs6Jgb32HzuAiJ8+cZG9rh26rTV5Bvp+xvOoI+yV3vuOoy5Talrz8/NNIHVIYz/6DB2RVQd2FWFtms6MThrw/DIDwnjAMCbRCYBr+nhcMJwdMpjl5nXNieRHpLFVdURYl3ivCqE23a8myGXGa0Oq0yIoCIcE7T5rGaK3w9miKoTEOKRR5XRJHGq0caRxyZnWZG3fmeNFQGIXw1Lbm8uWL3Lx5h8FgQFEErK9v4nVOKwopK0tdWapqzvlzp9FC4Kqa0XjG9vxodcvDImuMOQzJAJBYWzWUNS9QvuHBP7o/f4CWGSQpnXaHuirJi/xfC+fwvongkMcER3e7Xay11HXNgwcP6PV6GNMUwH6/jzGGdruNlJIsM0RR1NA+D+XtUdSiLEucc/R6Pfr9AWnaYnNzk4ODA5aWlmilKUVx9PWI4xit9SHfXzf3SBA8+nvDMHwkEHv498Xx72kkHv7bH/z7k8MQ8bt377K1tcW5c+eOFYMVRUlR1NTeNm6AWqK1Bm+pshKnNNQWC+yOc6Yl9GNFECiSQFA61fDGnSOp68b0DUMraESFE6eYGsvdUcbT5z56HUmawjwny3LGw3WEHeK6K4Rxl7BO4dRJQpfjvUO6gHqaM5sdkN3fRE4kwwOLjgIC6QmsAOEp6oJaWFLdJhQhL64usvMxk8H+ABTxf1UC+/HyGo9CXVmkDBiNdimyAltbpuMpYRRxMJnw/AsvNHznquLGjRvcuX2PyThjMpkynU749EsvNOoyKdg92Gf3YJ8wjghMSZblTfSW8Lj6aJFNL3TkrYCkmzI7KCjzjPGwwJzs014L+eq3bvP8hQUWRQV1xkq/y+50yjiuoBXSXQoYF4oT/SUuPXGOKO3iVZud5VVuvPcuu5N9upGnlx6l14QkSVBKUhQl0PCqcSCVAq+wXpAXhvXtMVJ1OLk8wLuG+1vWGbF2dBdahLFEhyFrJ08xzaYY0whiyrLxlbDVMZa4xhFEAU5KhJAkUQS2ZrHfYb+dghAo2fDGX331FXQUcvv2XTY2Nnjl5U9RVY71rW16Kyusb9xneWlAKCNWBi1cXePTkHmWH6vIAx4VJKU0UiriWLKyvMjZM2fxQrB+52YT9SYavrQII6IoJAwjLj/1DIN+n4O9ba5f+4CyLBte9Q+kJkkhGq7zEdja2kIphdaaXq9HHMfkeU6SJBhjGAwG9Ho9ZtMpzjnm85w8zx+9fOoapBSUZUm320VrTVVVj8Rc8/kcKSWLi0fbMjxEoxNoSkae58Rx3AiekgR3qKp+qMyN4xjv/aO1OOcwxjz6P5IkYT6fNzucQ+n7w8L+Uagrh7UCYyVKN4VXRQFB3Ox+nBIo1WZnb848h1IIxgXIqkYISSfUKKWwpm48fWQAaGxV01ae5ViRCdiZHv25eOcb3YqQTCZzEqUZ+jFZeZPlLizo81TTffb3dhiOhuyuX6eejyhHm+Rjz9ZwwspyD91pzNmgJkSQRC0CA1lZ4rKaMBl8rM/l92URf3izN92h4mG34w9Ns6R0KKX+pW3pvw3W1x/Qaqf81m99FUzTFSVJAnlBWRc8ePCAhYUF3rzyJrdu3iLPS/Ksxnt488qbzLMJL730IvN5zoPtLbKqxHuDkA5jC5wLCJTC1kcncxyMh5xabuOQpFIyzwKCUjKbBEydYCPXTK9t8ZnTLc4ttzgo54zzlOpuh4UzF/jcqyf44R/qEIYp0uaHwqCKS0+e5OyFZX71H/0T5mVBII5+meTFDCk98/mUNOnR7nRx3mGtJ4hbfOmHf5x2p0tZ5dy+fRd8QOeV17jy9re4dvsDZnlGC82JTp9ZGDJYWsLXFo1Co5BBSDmfUxRH70wcnqLIiKKI2lRUteX7H9zg9OopmkRmaLcCPv+Zz/Lm22+zNx4TddoczDO+8b03+LHPvcanLp8jbbfZ2tlgXs6pXMVT50+jXMFOYamF56mLRyfZmNrT6y4hhODZ554kiELSNAVnCZOEcxcvk03nTcEKQ7yAMAiRUhKGIXGSNIVCCF5qJxjrmY5nbG9tMhodoJQ8FEAdr+jVWtNs3ROklBhjCIPwsCs3jA9HeGVZsrm5ybPPPoe1hhMnTjAY9Llz5w51XbK7u8twOCaKYuI4Io5jgiCgrmsODo4O5HXOPRqNQNN5l2XJ9vY2Fy9eZGtri5WVFdrt9iPlrrX20chEKfVod/DwmZ1Op8xmM1qtFoPBgOl0eujH8tHQicRHqrEr1ppZadnZLgBB2pZUlaGwhlDB6eWAKAo4GFsq5zDaYUtPTpP9qaShn2oKESGJEa7JJlhScSPTPwp1hbSKjtL8zIXnCYRBaonyGXFxjVerTZbiGZyPES7HPKeojMbZi8hyxt//6k1+91ZOOaopENTWEOsANbOQJNQLLa5JyXg6PnodD6/Lx/qp/5/xbxIcPazPUj5MmTn8WjT+FP8+uPrBddI0Zj7LqOpmSyqUAhzZJGe4fwAIDg72cd6SFwXGCaIoIghCtra2GR6MmOc51tQ4U1PbiqLMyYvi0PRogjmmE58UhiVv6SeK/kpCVqYMs4LIaYT3rHQDXBYwmQdMOgGTMmM8l5w6u8LSygm0TxDGE7cVZSbx1hEECuEFSdJmcbFPPd6lPiYEYT6bowPxqGvK85yiyBmNJyilefHTLxFFjQ2ClhprBc7Ay59+DY/n7votljp9YqGYjMZMxiPqw45PqSYnM9QpUfeYBBkJde3w3qGkaKyGTc3t9XucP3MK6T1ri4tUswmzyRgVKMLQszBYIptOuXLlTf7YT/4ES0tLxFHKtJix1muRRDGzccbdB7uPFLBHodtdoNvpU1UFyyvLVFWJpBmPOSRVbfFeECct4jRBBhot5KPGw1oPIiBOOjg7JwlTut0FdKCZZ1MeRscc14Ls7h38v+S9WYxl2XWm9+0zn3Pne+PGHBlDjpXFYk1kcSpOoqipLVITJaDtRssPfnAPb34wBNiwGzBswX7zowHbje6GegAa3RAkNimJKlIsslhkzTlUzpGRkZEx3fnMw95+OBGXRVq8QbYNmYIXkEhEIvJi45xz11nrX//6f7rdLvV6gzgOMHQdjSZRGJNnirwIqFTrVCtVdKGzurrEZNzHMAyiaEJRSPJCYtkGg/4Q1xNTOMowBLoukFI/O3l+QJq1/P+C4+NjkiTh8ePHeJ7H9vY2m5ubU5mF6ZbkSQfywW3d0wp9PB4zGAyIomj6YpgVqdTphzmTuDTsRkmk0JAyJ401DKmhCYUmFVJqpKlEmApHaCgpyLUUPQiRhoefCVxTwzVN8rxAFgWmYxIlsnRDmRH+JCJNFKnl8vV3nhAlMWGmQFgYlsc5T7G995D7gU3LLnCTCXNmqaWeKo0OMWYakRseaV6gpMRQYHsVEl2ha4IoCsnOMI6e3p+f6rf+X44fH/D89RV1uQK+++iAo8M+e3t7bD98yMOH9/mdr3yJT3/60//RlfiDBztM/AnbDx6ytrFBIQviOMC2LYpCsrO9w+Mn+0RxiGYI4iwhCAucLD9pocb0ewPGwYQo9MmSEClK5UKlFGmWMYkiijOSp9Q0gkyn4yo0rUAaGisLVTRDMjye0LV0Ko0Fwt6A3cMJfpFRqS2xvHEe07LQlAlSEk5GVCtVkjgGJTF0i0IqNq9sce/dIZ4xW2hJAVmWnXyJfDzHQxeCOAx4sr/PN/78T/GqNbIM0lRiWTaeW+HGjRsE4YClxTauZaEjSNOUYlRgGgaO65aDPlGusnPG/TIMDSH0Ej/PM4SpoQTsHx2xtbkJeU7T8wgnk1LmVpmcX16mM9fhB2++zUGvz41b9/j18xdLbROl6La7UCgmk5A40VBKMopmr1UblslR7wl+MEDmzyHygmAypkjLyjv0IxzLwnVdvEoF3TTQRZncsiwjyzLSLMe0XfA1TMvCECa2ZZbGdydJ86znt1qtoRT4foCSOa35Fo6TMR6PUCrDEBqGLqZVbtWrkaYZ1VoVAURRjGUb2LZgcXGB8SQkzws8z0XTdMIwxHW9M9U2LavUvNE0bZrEi6JgMpkghGBjYwNd17l16xZXr16d/s7p759W5XmeY1nWNIlH0Q/hn7NeJAC9QJJJDdMwKQqNQmkoShMYXZbSC1VbUXccDF1D0zM6WjnkzLKM0VAnsCpICX5RIFSBritkUboXXVlw6fk5/hlaQ8f7GlIqqlWDYwvivLy3utApdJdISl590OfbPZd6tYLqjfnF81Uutg3+w7VjlrptkjzCrTokQUBW5OXzk6ccTyZ4rkeSJGTZz6FRslQKTYjpw5sr8IOAwA/wfZ+79+6x+2Sf7Z1HjPo98nDCa9duE/sRRSZBabi2ycraEp/+zGdPQRZ+OhveH8bW1nm2t7e5c+fPWVxZKfG6LMMwdNIso8hzJnEEQmGaFr1en53dQzqtDt12CyUVR0dHPNrb4/GjHVr1GpbrUJy0naPRSRV+BhfHNMCPC6xuhfGkj6abLM1XaM7VuRGGeEch5JJGwybKctxah2defJ7u4iKSDFUokBJDB6kyNEMhUOQyRaKzsLbK/sP72Gd0LiU7wKJWq7DzcIc0TlBFQaXiYWhw/dpbxElCGGa4Xo1Wq83hQY/J2GdjfZWluXlMvRQG63TaaEIjL0pmi5QFaRqhVI52ho34XKeDbRuMxiFpWGA7FqkG4yAor60hqHou/dEIpZloSmNrdY1337tGMI7QdKfUi87K58wzDbrtJkiJplsofGxHoz43G2t0vQpxPAEhKfICQzeouBVSLSl1eZTCNI3pwM+0LISk1CcTAiUVaS5xvSqjnoZpWAipOD4+pJDFVOb3LKqW53kMh0Nc16VZq2ObJkEwoCjGPNrdpt1awnMdQMJJYhwNjwiDEX4QUW9UkGTs7T3m2ec+ghFr6JrOYDDENEs/0zAMpzDJT4pTFgwwhWAAVlZWyPOcw8NDms0mQRBw/fp1Njc3cRxnmrw/+HdRFCRJQhAEjEYjhCit+7IsYzicrRWiZIFF+bJPNUFcAEpHAzShME1oNywadqk5hLAADUXOMErYf9QjkRaGnhMNY4RS1DoGaBaGLqjYBp4pSq/LGeGPM0xTx6ybNGoulTjENUqcPNfBs02GiWJnWKClGTZVdvQqi7rivcBhflEnlQVFHJdVfJrgpwlGFCANjVTEyDwnO8OD9TT+ZitxATdvvc/rr7/OW+9cww8iwihiNByetFZ9EqkIkwRD5tQs8DUbXRgUSoLUqDc63L5998c++EeM9c6MubkOg8EA13WmrAyp1LTNPb91nrBIuXf/NgJBGMYcHh7hOR5SNjBMk87cHLfu3Wc8CVlaWCJXBVJy8qec4g8Hs9XhXnrmKXa3H3HU77OyNMd4PKHimLQbLqurTYpCcTgeYxlNFrsbbF19htbcPIYuyPIEKXN0IXAsi+HoCNMs7chKa2KNVqPLxUsXeXL31sxz7O7ukmWluXG1UsfSNeI4ws8LkrzAadgYloZVlG47j3f3SDNFo9XCD0J6gzHr66v4/gDHM9E1k6KgTNpKEcWSvMixzpAaFSInCHpEkaLVqFGt2vRGE5JMsfdkD80WmJoCmWKSsbq4xI1b1xlOJiDLZBBEAVGWowQszs9xbmWeJEw46E8oshSn2WbQmz31784vAJI8T+kPB9hWKW5l2Q7VVhuhMgRMB3LiRGEzy7KTp7Cc6eimiW6WieTgYI/Do0N0rbQl1HX9r4URPxiu42AvLJRsEAG9o33uPbjO/sEDDFNxuP+EONwkjjIQGs1mnQfb74DQ0IXDZOIymvi8e/0W+/1jNlY3WV5cIc8TbLscSsZ/DXvmx2M0GmGddB7D4ZB79+6xtbXF0tISh4eHbG9vT4evBwcH3Llzh0uXLsHJNTrtTqSUhGFIHMfToWapfKlPk/ys8GyFY2jUKxZKQZBJRlFOkpZmHJ5TgCbINdAVCKWdiOdBtWLTrMD72yNGUQ5JjiwElWoV01F0nPJZ1XUN7wx1yfqchmFouFULTSuLFz9TJIVE2IKaAD3NMQuLil1HJgnhJMJuWxSFwNNyDJWSpRnNZhUztzFMnZVGi0IWtJfmsAyT7IyO8TT+ZpO4gqWlJW7cuIFtWSwtL1Or1Wg2mlSqVf77f/IHqLRAt0sucLvhcnTsE4cTDKWoezWyyRG3bkU/1pn/bJX46QPsuSU3VaHQNQ3TtLBtG8d1iYIcpTSyNMOyHJaX12g06ti2jW1pLC8ts7SygWE4NFtNDg6foAkT04CLFy4wPNpHP6Md2r9zm7X5GnPnLvDg2jW8mk0Up9y584ThOGBrY5FL3kW6q5dx6y3SQiOVAs+pkqQ2RRGWQveFwnIrFFlCEIYYlk29WSdODSzTZeLPfpm8f/M2vu+j6zr/8B/+l1y4eIkwSfj6n/05m6urXLz6NF61xihISGKJrgzuXLvB450H3Lx5g/v3t/nt3/4SzYZHksQomWMYBknik+elYUAQBEh5Ru1ZRHz0+Q/z3o3bPDkYEkcu7c4ctWab+zuP+Z1PfoqD3YfIImNhvsHdnTu05pYZjXMyqWNYBlGaMxoN+ZVPv8T97fv86V9+kyiXCN2m6rUYHo1wK7NfJvVGDc3Q2Ng6z+33vo/r2pxb22R1/TymZbL3aIdcbrO8usa59U3qtXrpKOUHHB4dcPfePUzb5vLlKwilce/WLfb3n2CZLpmK0UTZlebF7OuRpGmpoOk4vPGDv+Dx7m3yIkbTCkxLZ3VtiTDcJssLHNvjxrVbLK/VGA19Dp4csbzcxjIkzz5ziX4a87Vv/DsW5uZY6C6yfu4CS4vncRznTIrhV7/6VVZWVvj0pz/NYDCg3W6ztLTEZDKZcsWTJGFubo52u/0j1MNTGKVWq01piEVR8PjxYw4ODgjDkI2NDZRSZ2PzlB3e0M/R9bJiatk6lYaJpQuEoXGKvEvK4uz9W7s82hvQ68X0xmNq1QpGlvHsc+dITZtq1aBiQ80xGCYxmlC4lsUsC2vDgHASMN9aQ7k6ptdmfanDfLvDeCypaI85ShW+MGl4Tfae7GJsLuE1BIg9ilqDX//dz5O0u5yrmEhDR+Y5kUoYxGNELEiFOpO99MPr8jcYQgjq9Tp/8Ad/gOdVymk1P0zB/+R/+AMMTZCGCZnKmWg5Ks/QKStOW5cUCvI8IwwjXNcpuZia9jNBKu6JZrbjOGRZhibcKU5pOTaFlMiiKPWNTQ2FwD2Z5p8uL6RZBkLH9arohoXQdHTNoBCSildhqKDiznYzl6bLOCnwJiFbm5v0ghGPjwOCBNaWV9l78ojWgokz6aGERLcqWFYVoWnYlkMQ+qSZLAeHtkN0wiEuFCRJUuLYlSqrqyszz3Hx4gUuX77ExYsX+eIXv4jl2Dx4+JD2XINK1WYSBISxZK6zxHEw4P2bt1hfXSUMh9iPHBxD59133uNTn3qJOE4xNBPT1DBNE8uyyLKMarV6pl50lha89db7TIKyorJMFyEVnuWyuXGFQW+IU28yGo4ZjnqYhsPw+Ig0kmydv0yz3WCh22RlfoE/++N/yyROcJtVlFKM/RQ9T6hWbZZW2jPPYVqChUoLEGxduIA/OuZg7wFFkbB0bpNvfOMVCiX5yEsfpTu/WD5/mkacJGxvb/Pqq69SSEmz2WQ8HOJHKSvr55GyYHB8UDJUDINWYzb1s16rMBikjMcjGs0mrdaHODx8zPV3r3HlylXefafsFDe31rBswepmk+PjAb2jHoZVDn0f3LnP0SAiNTooVcEPY7K9bTQUVy48R6/fx9Bnf3eklPT7faSUVKtVer0epmnSapWwlKZpVCqVKWMmTdMptfTo6IgoiqieLB0FQQCUFM4wDCmKgiiKME1zCtn8pCiEwDY1PFPH0GTJFdd1dE2V/pjKms5dTl8Klu1hWznzLZtuq04U+QwPxty69gDpeLgVh26zxvq5Dm6zQhBHJKnCm8F29CwLp2Kw1O5Qa7UpioIgzLkb9LCsOuQJubAQCGr4tA3B+dUuVW3Isx2Hlm7xb//iTTZe+AxJN6XWNLFNhSwyKrnOOC8QtoE/mm3fdxp/44NNgaBRq09/PjU5BqhUasRFTKpisigltwQql2gIalUP29JJckWhFDs7u1y+fOGHH/IzFONpEqEjaTWrJ8stFnmhCPOCw2DCO+/fAqnoDUbopollmyS9HrKwMUwN23EI4wQpFfV6HRuFpetkJ9t5lusihI5lzh4oDlNFIgROr099vkWudHaOJ2xc+BBL51aIwiOkkKgiZjI8Am1Ec/F8aSCtNBAKTdcwTIskDEFoZdvvepi2TVGk1OtVHp+hrvD7v//3efrpq9Tr9dLJPMl579oN4igsvyxFQeD7HO69x8MHOxw8OSQeH6MZCq/icPRknyf7uziOxRe/8BmSKMKxHaI4JkkSwiAgCsMzBzWubXAw9LHrDcwwLXWfE4mWjWg1V1lfXCWa9KlWW7h2jaP+iKU1wcg3uXL1RWxdZ3N1njfeeptMF5ieS71eQ09CNCqMJk+otxt4s0kyOJaOrpcJYml1Fb9isXPvFocHD6k06ozGYxzX5vi4RxRFFLIAAb1Bn+PjHig1HRa61QaVegvTNInCsKQkagbVavXM5yNLkxO3G8mFi1c5OtwGobhl3iHPJJ986ZNMxgH1hsuTg22Ojh9huRUUFm6lAcLi0oVN5sYB9x9GXH7mKTzP4srFLW5dv00UjDFNA36Kim84HLK9vU2/32dlZYXRaISUkk6ng+d5bG1tYVkWgxMIMQxDwjDk4OBg+v1OkmTKU19aWqLX66GUYjgcMjc3m/YJ4JiSqq1j6mVBKNBBlVh56eYjpnMoCUhZcDiUmLaLzCT9SUQSSzQNlNDIwpA4UVjoiLU6dVfHsiqMz3CZv3hhla2lDVTikCaCSTQhGAwwnQrdxQqiSNBMkzlP8EI7pCIEn50PWMwj/vHLC4yilP9154C0e8TGQODqgqLlYS3WKXQd9+ICg+GQiR/NPMdp/H/DTjn5+7QKlyc3eWX5HO8f3sQ1PeJUEQYxmjLxvAqNZhNNFbi6zbGf8/bbb/PUlYsUiLNID/+36Pd66Cg2VpdxGy1MNJIsJU0KguGE//3b38HzqniGQRoEOJbFhYubOLZDtdbA86ooUXpaaifOIaZunLgL6ViWg2G6qLOm3OOQpaU2cZxy7c5DDqOY9vI6Fy6eBxLcioPnuagiIU0ChOaAkliWRRTG6Fp5BlHuHZVbckpRbzYpZOkEL7KU0XA23/TFF58nCEMePNhmYX6Z927eYWe3T73RQaDQlSyxdqNA8zTmFzsM+31u3b7JxB+hTpL8n/zJ1/lH/+i/wHMMilQSBjFhGLC/f8BoNDqzXY6ygnanzsp6l9t3YtIsYnWuyeqaxbVr77Cw8Ktkhs6j+49YWlzlwrl1PvR8hycDQRBb6Krg5s23efW1N6nN2YhCI8k18kIg9IyNp5bQZUJ7frbDkCoUCIVUBQgN123QaHfxxz2KNKHRqILQmJubo16vl5WkYdHudGi2WywsLtJstVhbW2M08cuVe6kwLRsl6wjDLo2cz8CA5Qnds9PpYNsVDKOOpoW88OJHiUOftZV5Vl5cpdc/pu7aOOzxuPcIw6jxBH9MHQAAIABJREFU8OGINMp49pk1kniX5y93SItj1pc+xDNXnuf88iX+7C+/weLqxTM3R/f39xkMBjx+/JiNjQ3W19e5fv06w+GQlZUVut0u7Xab4XBYvtROhpej0WgqAXC63HNacZ9y3qvV6hTOOdvboFyN14TC0hWmVhYxQlOoTGEapZGMVJI8yznuTwgmA0QukXFCFgalU1QSYbg2iAK9iFCh5GjfxjYFlaqDfkZa/MxzHydNhlR3HpAXddx6xuqFRrmgVonRxyFpnFMUsOFoeMYhlYMAr25QM1wEDoWR8fr9a5xbXkRJC6od2s019osJr33/OmkRYzk/h84+H4wfnc2Xbja/+ZtfYe/Dj8j8iD/8X/5H2osNugsL2JZOXmQYukaaJGRZwZtvvsFv/daXsSyTnxUTz7KMLM9ZXV4hSMASGkIVaJZJFsX0/TH9KKRluzS9Ck99+Fl+7Yu/DEowHE4IgohcwtblKwTBmHQ0RhgGWlpqMlQcG8O0UNbsZFGrVWm3q7gU3H9QkOsmL64t0fFMEgnVSgPLrmAIiV4ay2NZTomLGgUUGroGRZFg6QrLqaB0l0wW5EWGZRrkuqBeOwPWyTO+8+3vcnf7EZ/67C/x6utv8/jxI7Y2F0vPxHSMH+X0+0OSJKRWa7K7s88nXv5FhoMj7t26iWWOefG5ZxkNAtY+dIk0jmm3BFIWbGyulxTGdPZLLS1SFufanFtpk2sWReTTXdR55sU1BqOIHIvuyhZ5Jhkc7hNPfG4ZI+bWrtA5t4wpJP3eLjkp8/N19vcH+H7MXKdJvW2QVSyyyES3Z28GCmEgRKklY1s2uW6xsLxBnkakccJHX3oehcGVp56mkAX9fp9KpUaj2eTjn/wEly5dwnYcms0mcZyUbveGQcVwQdPRDZuiKDDPYIWEYXDCu5YoDCyniZdmNOoNwrDHjWvvkMURUpYFULdRZ/5YJ6PKXjunWTVACm68+z6///f+Lg8e32N/d5vH3V2yTPLW22/ycn2BxcWFmecYDAbTzcrDw0OGwyHHx8f0+336/f6U7jsej6cMkzAMGQ6HFEWB7/sIIXBdt7T9O4FckiTBdV0cx8FxnDOpjqoQZEqRqhxLgKkVuCZYeklNRUmEUGiAqevohoXj2MRRTiBTLLeG3z9kFMfohY5p6FR0iaYpZJYwOdgnHujQnJ95jqapYxeK5zoSQz+gaiss6xBMHWVa+L0EmaQEQYSMIZEZoargFwKzUEQpmLqDYThEAuR8E63pUbUd/AfbfOff/QVblzdY//CM3f8PxN8sJv4Tfjqlnv3ul38dKCfam1fXuX//Pnfu3ONrX/sa3W6XSqWCaVq0mvCtV77FN//y2/zSL//Cz3yOLElQukDPoRgMsSs1bN1AlxpX3Tkezq3wxV/4RT72+U+zcfkSda/Ja99+HQmcf/oChm5x7+59qlnKsx/5MJNejxtvvcXN997h0cP7eI6DU6niWbPb5ZZrEQx6pEIQpBK3WUeaVYJcw6k0WLn0PIVSyCwiiwJcq4JmlRxfhEAvbBQFhi54cG+X9a1LVGo1/GCCVDm6LkjiEfXa7GShK41f+cIXuHf/Abdv32B9zqFpz5HGYwzdQWUSIyswJyFiErC3f8xTVzd4763XODzYx/M85hda9AeHBOGIPI+RMkVoGpouMERJN9SN2S/bT37yEkFusTuJCOSYRstgabnD4Pgun/+FBvv927hOi49/7mn84dMkkcKpWBiWSRjHnN+6wmd/eZnGuUU0/RovpFWGxzF5Kqh3bPalyfEQJvHsjsCruei6jpQFSkhM08CtdpmM9jk66vHcxz+HUhq6YTIcDIiikKpXY25xGWHZLKyskCYJcRojVUa9UW4ymqYJSuBYzZM19dlte6vVxvd9kiRlHDxCFhqON48hJBWvw9raPOPJIcPBEW+/+gbPXL7IcM/jt7/yG4wnjxn7PVxvjlp1hd1JwM17IzbX1/nu26+Q5hZ/50t/j+5c80yNHyhxccuyWF5e5vbt29OqWSnFa6+9RpZlPHjwgDt37mCa5jRxh2FIkiT4vs/58+e5evUqcRzj+z6TyYROp0MURYxGI6rV2TOCbkOAsEkym36QkKGT5wpyiRIKr8hL31dZkGeS42HKZDBBKEgjSSQVo77PzoPHRBONKxcX0RyXXipZqtZYvbyG0ECc0REMi4Tefsx39+CX6wWGZaPpNrJSYNsZ54VW8vvDCEfz6NZN3n0c0HEdnr86h14ULHVXGGtVHqUZN773Pb7gGHzbCRg3BS//6ssoTefO20/OvC/wc7Kx+eMhhOBLX/oShmHw3e++xvb2NgsLCywvL7O4uIBlGfzT//Nf8L3vvc4XvvD5M5PDj4csZMlZl5LlVEcLJyR5hm3anM9dvvKf/V0uv/gcFze3sHWL8XDCoDdiZX2ZPM+IwpgoCgknEXGco+kmCysb9AY+fhiT5oosL3DPqLRaFZNMFqR5zrm5GrkO7ZrDeDjAcBfpLKycDILqpNYE162i0EDT0BCYFkRxgGFotNtz2LaDlApV0obRdIMsiYiCs9Z3S0jm0sUNLmydI0lj4iTm4cOH3Lx5nTgJMXWDmqczGMQUWcQPvvcaw16Pc6uLoCn2D47Y24t5/fW3eObpZ7CdCnmRIWWBY1klfeyMSlxzqhSTjDTPubDWoGrrNB0D4oxwlDGO9hFWxP5BgUyWsawGD/eG9Ic9sjzj/Ts7rKyuYnoNDsMEGaUYNgjH5Ikfo3sWhtDJ5WwYI47jEwEpG8uyTgbnCserkMsj4iDEcjzCMGQwGHD9+nVc2+FTn/kc3YUFhKGd0OoklUoFgZjSCmUB4mRj1LRmCz55XtlB+b5PnmWkSY6mwPAcdMujP/SxrTkuXGiQJRETP2Dj/BKmlZcdqhJ8/WvfYfvgMfbCOb7wa7/E7r1tgknCYBiQJAWuU8GfBLOfjqJASjmtnsXJrsdpIh8MBty/f5+9vT1u3rxJlmXTFfvThR9d17l58yZXr16dfs6pto46mSGcNdjUKH1aTa3A0SV5FmHbOqZW0mo9S0PTdZKkYDCMMGQGSUCWgkxCXCGYrxuE3QYX1tvUKwaJ0rFNjSiMKLIUoQl0Y3ZaLNSE4fYOdw9DbgsDR88hS5C6zVol4R88JfFa81yev0SrNWK18JFunYVWBaMQGLjE4S6D0TFXX3oax73AOJH4O30KCtp1mywXXJqbTUg4jZ/bJH56c5955hn+8A//J+bm5mi1WlQqHkEQ8M//2b/knXfeod/v053/6QR8TuP0oQmGYxb7GVEYUfcczJrOyCsB5kl/hFzMGScJd249YOfBDoiC+YUlkjjFH405fHLM/Tv3yeMIKQU5Juc2r3B/ewffj2h0ZrMgGq5JLjQiJcj8nEqjSt3RiZJyWOPWWqSSkmOtQDcsMgQlCK6QQqAJnTTOqNUb5Yo1GpqmI6ROtdriSZxQq8ye5BUyR+VlVSilRNM1qtUqV65cYTga8PDhfeI4AAqkypAyp9Fo0W62qTgmo9GA4WDClac+xPs37/Hqt76PV61x9/49jo8Ome+0eerKZS5dnmXXDLYnqcqCPIxZnO/gjwMCWa4rj0OFaVtl9XuUYAqJ4zmkeUhvnPLssx/mmeeeptaoUWt5/G//6i9JM4uq7aHpBYUIqFQt3AyCM9aZT5+/Uv7VBiHIkoRqY475xZRgPCDLCkzb4c6dO8RRhEwzth/cY67bJUvlyTOW0Gw2EICDc6JBwnSLUanZL5PBYHAibBWg6zqddq28To6FYRpAld7RHo2aSbPe4MneES99fI1RfMQ4jFGGi+E2WV7Tee/+IYPBY1ZWqtR8nXq1wPMMgiCkUqmdeT2EEFOJ2TiOp+wPTdNI05QwDPE8b1qBb25uAj9kidi2zWQyIQzDaeU9Ho+J45g0TX8qdsrhMCLLFEorZRn0QmEonSzJyDKB2TRQSiKFwGlWmatpvHFjBz9IS7Pr0YgiC6m7LrpjEBQGmunS8DJUFhOFKZZtoKRAn9FEp+EIi5S26bJnNRkGAUJVOW/n1D2dJDvgo5e2+Nj8BpviB8wHFv5cAz8BhI2oV/jY1TV23j7CsFw8w2OSKfpBBqYiPDhE00zq7mxtndP4uUzi8MOV5Gq1ygsvPD/9d6XUydqwxuHhIbdv3/2Zk7gscnQlqDY7RP0J0XyTfKGDtTRP3dJI44x4L+RmcJcojrl77x5vvvEWpuERDks1tjjKodB4eOshjm0TxTEyBc9po3U1HLtBRZvdLlctHWU7BOOQVHe5sr6JH5QT8/rCPEIrq5lcKmQu0chQpkOa5SerzRqaZiBVyZmdjHwaVr3kYyuoN7rkqaSIZ2tSnGpbnGpAf1DrYm1tjbfffgMpC4KoXNrY3X3E4vImzVoDlefsPjrAceqgdBzH5a/+6lssryxj2RbLi/PIPKXIE8QZSavb0ZhvuhwcBaVMp+lxOE7odh0spQhDHU1WEbKNU+kghMmVp57iYy9/Et3Q6c53qbUqGLbBQnODOwd3yKIQy5FU6xYgabQq6ObsdtmyLAzDOBGfKpdUkjjBdhqsnXN4543vsrR2mbplTxkXuYzxJ2PSJEXTS+VApeR0Q/n0+qJKKMcw9HJwOiNOl2SyLGN9fX2aLDVNI4pidN0gSyV7e4e0G136w4x7j/ZoNjoc7A/QLYOoGPHo0S6FP8YR55FZzOOdBzTby9gWpGlKvV6feY5KpTK9JlEUYdv29PoYhjFd0mk0GiwvL5NlGa1W60RJMZvi6ZqmMRqNSJKE4XCIrusMh8OpUJbv+zPP0Y8LlCp52uPjCbZuUxWi5IQXBkkgT3TcBajyHqyvtUmTmEe7T9g/jgijmPbSOpmiZLcIQafdZm2tidR1okQitAx7xntNx8TqzhH3x4g0QaUZlfYcg+iAbJhidkI+1tgjjvvMGyNc06TnLfA//+vv4PsBOYKDwqFWX+PR7hNefOFZiiSnW+/Q6DZwKKUaeof7M6/HafxcJvHTN/9pfFBrpaySSorWcDjmjTfe4FMvf+xn+nzL0BFCp9aap7G8RbPRQPeqpJkiCiJkFjCYJDx5fI+RPyBOYxrtBvuH+9zffjCVzQwCn/xJjmmZpUaI1E5YBDl1xwR5hiaFLhhMfAajkF/9yj9gcLjNm299jziI+LCu4zbn0cjJspzQ94kF2DWbrCg1KCzLRlLKpaIKXLeC0Aws2yZLFZbp0Wl32T24PfMccRz/tUJFWZZR8bxSzCvPEJqF4zgsr6zQ6/Vp1xtcu3adIAh4+bOf4wdv/IArF5a5uHWOza0NbNuk6rksLcxjW+aZ7IPET4gnEWtzC0xGY2xRI0ozjo8mLFRbHD1RaM02jlEnLzSU0DCMAk1PsS2PyXBMEE2otaq8ePVzTI4HGEZMlkW4woJCozAkZ6Bc6EZZhZcMiuJEolfguFVkIfBHQ+RShq5bSCnwxxMs0yBLU+IoQKqCLE9pt9to2qnAVIHQJEJpU00dXZv99ZvrdPCDgDiOKYoC13XxfZ84jjEMHdO0sSyX+3evUX/mKl5lDq/aIYhzHu8fsnVpmVqn4Pn5ixRpSOKn3H30kP2DY1x7Htu0EZp59qakV+qr+L5PFEUYRglrnOL8UkqGwyFhGDI/Pz896wdX7vM8J45j9vf36Xa7pGnK1tYWg8Gg7IqDYKrR8pPC1ssNzCxXHOwHuBWJVzGxbIFwTTRRUphLlcgC09AxJURpQrftkmdtjnoJiTIwXBfXraDpJhgGoZLossTDdTUb5soViHqHo2RAnAelMUZ4zKjQ6Uc6d3uCy84OluagJzaFrfHVV+/xxzcDpAEaOXERUPd3abYqPH1xi+MnBzTaHZbXllBRydbRz4RBy/i5TOI/Hj8qFFSK9M/PzxMEEdev32A8mlBvzG4Jf+TzkOi2Q245FGsdkljhHw4Y7Q/pDUNGY58w8pmEAx49eUiURoRJAEojzVJ0TadWqzEajUreqyZwTYfNtQusrWzwYOc+rYrN+hkdwjgM6Ic5i+ev8vVvv8I3vvUqaZZTt20e9WOO45xW3cPxauw82CYOIzqtORY7DWzboLuyjq4JilwSJRlupYHmeORyQpzkpP4hlVqLVM7+cqRpOm3xT8WN1In+cyELbNtl/+AQw3JAd8nSjCKXHB4eEYQ+Fy6eRxeSNA6wHY2PvvQCS4tLZFlOkWekaYZQPxR++kkxHGWQu8SBjmk7RElMq2lRpIJwkKNShyhI0SuSKIswcgttcowqRljNDtKskAUGKk559wePccw55ldyBv0jhr0RFzaX6I98sjMkYKGEPLIsoygUeV7QbLYwTAM/jBGo0uEoTalU6siiQDg2rutSZAnDyYh2p02l4qHpeinFoMqXbZHLMskohTxjY3MwHOJ5HsvLy9NK1nXdKabs2DU67XmSeJkkgd2dQ9Y3N/DDPu/fuMvBwS6f/8IL5FGFg6O7vPrKO0gl6HQX2d7pcfVDDnGcli4/M+JUh/wUSjk1pCivT6mHMh6Pp9CKZVnTzuMU6z51R7pz5w5zc3NTFcNT9cLRaESj0Zh5DlMvF+/6vTFRnuHkBlkY4dkWuq5OFn1USR00dGzTYHNzgZ3djEd7AbphU294mJZJjk2cFXiagaYbGCc69YUqFwpnhbIstg/6ZJrD8dEjDNNEmxxjVxo4usZX9102Fguapga6QDMMXr2+T2Vuns58HcvQ8ScBBwf7uE6HmufhrZ8jz3QczSBQstQ/r8++HtP781P91s9VlGL6//xf/NP/6E/orqzx6lf/jGF+j5cGV8k0nZ3HT0jCgMeP7vHg8TGt+So7O4/x/QjLsRgHQ07FtpRSFPsF4sSuTSmwTRspMnr+Ef6kh2OaZOnazHOsXX2WWij5wfu3ef84IhUmmSrox5L9R2Pe+qM/Q9cFWAZSQhrnOCpnsWHz8Ref4elcY3FhiTAY0p2bwzIELjGjg0NEcsi//A9/xAvPf5w4mf1URlE01XwOw5BCSoIw5sbN9zk67vHamzc4OuqxuLTG+voSupYQ+Hv4k2M2zp8jCHwePbjLh86v83u/9dt0uh00DRzHQNftKZwgz8A882qTu/f7vLv3kKeeO0dmxBjjhOhwQLu5hCg8TNvBrddZ2bzAxqXz1Bow7B8TTwJUOmFweMBw0KPttPCMJYQfcX5hg9rlCm+99w53HhzSPuPLIXPItZJzHAQ+9XodpSSTyZggSBC2xnh0TFYo/MkxxokY1mQy4fHjx5zbWqfT6ZzohhTTIkRKiWO7JElEmqZE0WxsPk3TUo9FCBqNBkIIarUa9XodKSW3b90nSxLqzS2Gfki3vcWTR08YDo9YW1okTwWjJx4/eP0Wv/47n+D17z2m1mhRqdb56NVnGY7GoPgRF56/9nqcCLtBqaPS6XSmPO/T+YEQgrt3707hllqtNu3wgGm1f3h4yNe+9jVc12V3d5dut4tt21QqlTMpholM2X7Y57g3wVNg4tDrDzHdNo6SFDLBqVRAlYs+sSy4tr3P48djBqOCWqNFo2FxNBjy59++iW3rPLXVYRA10O7ZfOIjm+Xeij77OR2nAa5r02povPyZ38W2PV7587/iypUtbEvwvW9+k//j3YhzTQ0/DKhWGtzqp3z2c89y/vwmd+7eYzQaUat7VHTFeDSkVu/iD0foPfDDcfns5LOJAKfxtzCJ/1Cj+DR+Vkna9fOXsJy/4p3vfIe/euWb6LrA0XUubqyR5QEfeeklPvnZj/Knf/wnfOsvv0WhKzzHAqWhpEQqia47U1zaNAyiKOSZD1/lV375P+G/+W//a5rVNucvPDXzHN99/U0yZXL58gY7R9e5vNJGpAmjIGF3mODZHu25Nl6rzpP9QwI9JA5i9scpr75xjUf7B1y5sIXr6DSrLqYHx8cBb37/PRqujyFyCiWoN2d3BKfsg9NqKc0yRqMRD7bvs/NoF6kKhCYYDnw07UmpHRH5NBp1xqMxda/O8uIc7YYDMmd35zG2Y1KrVaca0fV6A9OYXYnv7hwidA/DlaTDPpsr80T6BL2xiqXVScYGCoESAqHrmLZNpVFBtx30Rdi7fZPhYIjnVjANi6rdAq2NmdsUvslC/SmMtS716mzqZxynxHGKaZZWZKcqfkWeYVsem1tXmfR6aOS023UMzaDVblGr1ajVa9SqNUCdUAi1qSflqV77D23MZsMYuqZhnMAVSZJQqZRSFc1mk4cPH9LtzuH7Pv1+n82L53FNg5vvfxPbc/Ef7YKwcKsN0OHe3Yc0mnX8IOPy5S1ss4Zu2wwGfSpnDL6hXJM3zdKfMwgCKpUKeZ5PWTe6rk8Te4nZ/3Db8FRn/fR30hNNmNMq/VSv/CxYp3fsc9Qb0aob+L4kLCQqKmimGhgFjmWcPMsaQi/VJO/vjElzi7E/4Pb2e/SPDtg5HKCwKBTs71pUdMELzzzDp146R16okrY4I2zbJooPefvtd9jd3aYoBIEfcv/eNTRdsLBymX///jskkwHCcGjUYrT5VT7z+V/g+GCXdquOoQv8MOH44JCvfv0VBDb93pgsD4iSEWmaYRkW//i/+u/OvDd/K5M4/OyJ+4Ph1Wo8deUpvv7KtxgkIboqEElMMOohbJOXv/i7bG5dZGN9k9fd1zEsA2EIkIKiyE8MY62ToU4psZokKcvLy5xbW6VRa/CJj32CT7786ZnnODg4pEDn8uUVPv2hTcZBjKkkkzjDfnhAu9ZgYXGe63fvcq5Z46O/+DKvfv8GWZZiazlxFPDgwTabm8tkiU9h6ly/eYfvfO91Fjoml9ermF6d5fXzM8+RpumPDKGSJEJTGR+6coG5Vp3D4x6jcYAwGuiGTa+3z6VLl+i0u7QbHSqWRa2is7bcxp+Meevt61iOxfzCPOPRGNdzuXr1Kq3m7AFaOvFpdE0st4k6PiRVPUItwXDmmIxDbHOu1KNG0T8+4OEdAfIcrVYNioSde/c53Nun3mhQq5fqgpppkBY5lrSY716kXV9l3D+YeQ554iVZmmiWMwLdMNGNDN0yWVk9T8+w8KOIVtOjWZ+jkDm27dDpdBFCw/fLAWS1WsMwjGkSk4VEFjlKSUxzNswlPuCXebpo0+l0CIKAwWCAodtT3RLTNFleXeHd9zQKKajVOywsdBlPhmxubXJ02Ofpp19ACJu57gJCGUwmEZVKhWazOfMcp5AJMNVMP4VX8jyn2+1iWRZBEEx1UJaWlqYWbeUzlUxZNqdStvDjLl6zk+fNd+7gxwlJYLG6sIKuSyaR5OHDQ9ptg9WVBVJ5smlbCNIo54/+zZ+iaSaVepW08Ln19pukmaRTrRJlIXeHQwyh84uf+ShhkqOdrPLPCqXg3Poil68ccXgwYTIOmPgBSumYpsN43GOUSHohGDYERs4nXlglSQIe3H9Aq90my3JkXuDW67z25puMR3Fp4SxSDK2K53l052ffl9P4W5vE/59FwcufepmbN+7xz/79vykNBkybWNPRMUnSgHDiU63VqNYb1Os1XM/DNAwUZXvZ65W6Gc8/9yKu5/HGG28wGkyIgpD//D/9+3zm5c/SqM/G6a9srVAYLlXHhDSh0BX+2KdTb/KxqxssdutEYci2HPPSlas8fa7DM5d+k0QJPMcgGh2UfoWeSXC0S+yP2d+5zcpyAw2JlAZhrtFpz97IQypkUVCcaKlLqVACmnWXqr2IZdkc929Tr5hUqg6m2ebXf+33aDbrDHsHWFpBd66FbRtoQmN5eZ7BcMz+k30KWbC0PE+16p5ZeZ5ba6BbDvd3Dthq1Bn0fZxOg7BfoGKoOBqGpmMoSeaP2XsQEo37VGseRRKw//ARcRCSpSm246EbBoYAXdNJkwTDTgl9H/8MGQLTtDAdG8M0SKMQRSmVmhcSTVcYlkutvYgRRdRqTbI05/p7b3PhwmUM2yMIS5nger0x7RpP5w1pWho1BEGCdcYyWLlwJKdJ7lRA6uDgAIHAqrpYdmlafHBwgGOb1JuLQM784hqVikMc59RqLkqt0unOMRiOMHQbQzeRsiQKnLV2H5wMV7MsK7XNT5L+qQ75qXv9KZVQCMHi4uL0309fYqezl9OK/fT/nwpk2fbs61GYDpbh4FbqjMIUpTKCccjeXg+vWePRsUlKgQyHpVqQNGhYKee3Wtx/1CcdTqiZkIsMzzWpNBbodFaYn1vi2Nf5/ruHpLIgSQuubG3+xHOkSYquGzz/0Qsc9wKe7B3Q7/k8vHdEFMZohw85tzLPxvoSMisoFDgqo3/whFq9Qa1WY/vBQ9I05dKz51i9VGfvUY8oylhZmSOXASsry+jW2R0S/P80iYdhiFLwm7/xJfaGh7zyzVdQQvDlL/8Gi4vLFHmGpetoAn7vK7/D5StX8DwXXSuV2aIo4l/963/F66+/zm9++cvous7+3hMODw54srvLh69+CNfQSc/YhKvaOlq1RpGnhGHEYW/C4VGfymDC1tYa822bwMn5yDOXMIqIb3/j67zwkU8yt7LO/HybJ3GPuYVFhCl45zvfwEGxWClYnmuSFzZ5mlFpdnHM2RifcaImKXUD27RKIRZNkhalUqNTW2DjUoUiHLO+1kGIeW5de4+trVUWFxp4roVj21Nu9fkLm9x/8IjhcEiWp6ytrWAYZ2uFdBpVJhOJa7kEYY85zyT2J8hYw9BtTEPgOTZVx0YWBVLmDA/2GR1KsiRk1BtgAlLkJeyVFyihUZCAXpDGJrLIMM6gp+iGgToZxHqee6K7nUyTqWFaWLYHaDQaNcbjEcsrq1RrVdIkRtM1arU69XodXQP5AU1tOIUN1Jnd5Cl9z/dLXL5SqZx4ZQ5YXV2l0WhwdHSErhvousbe3h7t9gqFSjEMSVHk1BsOlUqNNMkxLQfPgyxNiUL/xG/TRZxhR5ZlGZpWqlKemiA7jjOddezu7mLb9nT4espyKmmW6gT/j6bsMsMoDYtPmS6n+uJnmVNkqU2cKcbjiPGJDrqfJOT6/9XevfY2Ut1JvJXhAAAIt0lEQVRxHP/OzJmbx7c4dpK9sRdY2LLAqrSlPGhFpaqvgUp9C31VqC+halWEVFWqRJF4UJXCll1gd7Mb4sRO7LE99znn9MEk5lGTPsTifF5AZEf2z3Nm/pc2em4z/vZTytUSTYbjtXn99Xv84r3foFsRqn+ESlZc2/k113YGTGyL7bDV1JjbNnUFR6cnTJYFzw5ifv/+e//zdSilUDIHIRld77JzvY/M4Y+zv/PVl0co6VAdHtNtR/Q6EWGrRZJVPPzyEaPRDo8eP+boeMIqyTg4fE4lCyopkZSMp8dUVc3R8QL1f44T+UGGeFVXxPGSk9Nj7t99mSvDAWmaUiZLvnn4BVgWi2kzQlMpyeGzJziOOLuNIvCDgAc/usfP3nqLR59/hlaK9979OZ7nI7OUZHbK4dkDnouMbt3m+fEMR3lcv3qFfndA68FrBJ7D4SRmb7jLX//xT9qhTy8MsFWP03hKJmM++MMHPH06JeoE9Adt+pTcvdln0AJ0wvNlxttvv8Ozf32MePnWha/Ddd31w6XzLSt5njMupyzKlPd/+zssy2Hy7DEuK1zfxnU6WHaJshSW7a+P2UpJwiDg/v37WFazlbyqmi+pvqRT8uiLAqs/JPICOlVIPs+QdoCsfeb5CctlSSUt5GhI1GlORzJfMjk+Jp7PCTyHKs9RhYWwm2XEVZoifA8RWMi0wLUDnMHepZ+R8/BpB/46vD2vqY/O0pSD5/uMhgMOxwfM53Puv/E2QRgStkKE8CjLsrmKrkuqsqmTdoTAFg5pmuO67qWbjuL5gjRN2dvbo85zMhXTFT7d69fxO22WyxW+H56tFWzm5VS1xvcjtK7Y6vc4PT2lKBb0eh3i2YLn+y+YTqfcv/86yWrFKkku3QV7vsxBa81oNCLLMmzbZjabMZlMuHHjBkop4jhmMpngui6Hh4fr6pQ0TVmtVlRVxWKxWC9TzvN8XRlz3sF5kT99+DE7oyE7Vzo8G59wOJkjdIlVV9RlyTIvOVkmlLUmzVLEnz/h6q07fP34IXU8xrNhtHuF7Ruv8KtfvsuJVXBrt08ndHkynRJEHq06IxQX16v7rktFc4EzHk9YrZakScr21S7jyYLFImWRxxzPYoTrYmm4un2NvetDjuOcqlS8GM+ZTr7FDvrsjnrceGmb/lYH13OwW4K6qrjkmmftBxnivu8zHLmMdra5++o9lFbrI6+sm047rfX6Cue7W3Xn8yK+q1237aYmVSlAW9hYqKrCwmpGlF4gT2Ii38bVNbvDLr5YgVVjW4rpdEI7epMsS8gTib0FvV6XV+/fYXz8DT95cIu7d65TlCXPX4y5ttPDFYLJdMr2oM3e3h4fffghfhixPbh44JPjCITjNBvua4ljN007gR9yerLPJ3/7iFs3bxG4Dko5JFmB7xVYdo1lNztFtdJAs2fU83y0ligNUlZIdd4GfvHNRiuPqRIJasHujXvki5JFARJFludoJyBeLFBAkOb4vk/LKlnFMb12hKVkU9JY1FR1c/TPVwVFVoNlIRBYnoNwL64Dhqa6wfOCdbt582CvCV0hHKLIZx6fUteavd1ruL6H73tY6HVXptaKLE2bk53nYQFFVeK6HnVdoS7pI2iFEa7rU9ea7cE2Ti0pspw8K6ntlDyp8QOf1SpBSsXsdMbeno+UTdt7kq5wvWa+SJJkKCkZbW/jCQFKr6/zLqsKOZ9/YlnWepFDv99nOBwSRRH7+/sA66XHRVGwv7+/rqw5XwZxfhI5L9/UWq/nzZ+XuF5kvjxEWglPjjUH42OklhRFRV3W6CoHJbG9EGmBtlKkcvn24AmuI5BeDyEUZS05mXzL6fELtrZ2+PQ/X9PtRSwLxcu+i4tz6fISpdXZ+4J+p40vHFpBSBRqwiAkT2uSJG3er6UIAkE+L9F28wNWViVhS/Hgx7f56bu36UQuke9jO5qyzFmVNDNgLvlRO/eDDPGmbrVCuA6+2zx8sR0brTRVVdM+a3et5XedkZ7bLPqVstmMXVbNB9+ybKqqpK5VcwTXGttxkJYGefFxKF/O6G0N6bQCHj/6nHbUoyxTWlGL4WjA0WSMZSu6nR77B2Nu3g6JTybE0yN8y+GdB68yi5dsd9u4xZRW0GarHeLYJU+nRwwHLUZ7I9rtix+gyUpi6WYQmXDc9Q/XnZsvsTMaktclykoZn8RkWUocxwRBcLayzGexWJx18Tm8eHFAFEUEbkC30yPLM/I8Y2dnlyC8+HVc2d0m3PLww5DQhmmm8bSL4whiL8R2HPKyROQFlnARjk1WLul1OrhCsEpWVJWi2+msHwgGrYDFbI6Siq1WuxnIdckSBFs4za0frRHCxbJskiRp1jZiI+uUqsxRWnDlajOKtd/r4grBfBHTEi5h4FHXBVLVuIGP4wjKqqIsC3zfOxtbfPH/YzKd0u12CQKfoigpkhTHtvGCAMtxqYQmP5vZ3mq16HS66/GuYRhyOpuSpgm+76OUYtAfUBXVelmx63lwNjf9Isvlcl0u6Hne+gr6fB64UoowbC4UBoPB+u9HUbS+7QJNU9n5bZbz1v2qqgiC4NJGH4CHX/6bSpbYjkdVJHieQxBt4wBVmYBoUeZL6jqnrjKE8HGFxxtvvslW6HM8PWC4N+KVu6/x6NFznh59RTybcfulG9iuzV8++wpHcOn3RalmLZ9wbAaDEMtq4Wgb13HRuqmKqWWN1lCqkkKWiNrFsRSOI6grjdIW0gHXq1ESVkWFcC2kaqYyBiJEyv9vPZt1+QxfwzAM4/vq4sJdwzAM43vNhLhhGMYGMyFuGIaxwUyIG4ZhbDAT4oZhGBvMhLhhGMYGMyFuGIaxwUyIG4ZhbDAT4oZhGBvMhLhhGMYGMyFuGIaxwUyIG4ZhbDAT4oZhGBvMhLhhGMYGMyFuGIaxwUyIG4ZhbDAT4oZhGBvMhLhhGMYGMyFuGIaxwUyIG4ZhbDAT4oZhGBvMhLhhGMYGMyFuGIaxwf4LOW2XZO6W5DEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize some examples from the dataset.\n", + "# We show a few examples of training images from each class.\n", + "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "num_classes = len(classes)\n", + "samples_per_class = 7\n", + "for y, cls in enumerate(classes):\n", + " idxs = np.flatnonzero(y_train == y)\n", + " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", + " for i, idx in enumerate(idxs):\n", + " plt_idx = i * num_classes + y + 1\n", + " plt.subplot(samples_per_class, num_classes, plt_idx)\n", + " plt.imshow(X_train[idx].astype('uint8'))\n", + " plt.axis('off')\n", + " if i == 0:\n", + " plt.title(cls)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape: (49000, 32, 32, 3)\n", + "Train labels shape: (49000,)\n", + "Validation data shape: (1000, 32, 32, 3)\n", + "Validation labels shape: (1000,)\n", + "Test data shape: (1000, 32, 32, 3)\n", + "Test labels shape: (1000,)\n" + ] + } + ], + "source": [ + "# Split the data into train, val, and test sets. In addition we will\n", + "# create a small development set as a subset of the training data;\n", + "# we can use this for development so our code runs faster.\n", + "num_training = 49000\n", + "num_validation = 1000\n", + "num_test = 1000\n", + "num_dev = 500\n", + "\n", + "# Our validation set will be num_validation points from the original\n", + "# training set.\n", + "mask = range(num_training, num_training + num_validation)\n", + "X_val = X_train[mask]\n", + "y_val = y_train[mask]\n", + "\n", + "# Our training set will be the first num_train points from the original\n", + "# training set.\n", + "mask = range(num_training)\n", + "X_train = X_train[mask]\n", + "y_train = y_train[mask]\n", + "\n", + "# We will also make a development set, which is a small subset of\n", + "# the training set.\n", + "mask = np.random.choice(num_training, num_dev, replace=False)\n", + "X_dev = X_train[mask]\n", + "y_dev = y_train[mask]\n", + "\n", + "# We use the first num_test points of the original test set as our\n", + "# test set.\n", + "mask = range(num_test)\n", + "X_test = X_test[mask]\n", + "y_test = y_test[mask]\n", + "\n", + "print('Train data shape: ', X_train.shape)\n", + "print('Train labels shape: ', y_train.shape)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Validation labels shape: ', y_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (49000, 3072)\n", + "Validation data shape: (1000, 3072)\n", + "Test data shape: (1000, 3072)\n", + "dev data shape: (500, 3072)\n" + ] + } + ], + "source": [ + "# Preprocessing: reshape the image data into rows\n", + "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", + "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n", + "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", + "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n", + "\n", + "# As a sanity check, print out the shapes of the data\n", + "print('Training data shape: ', X_train.shape)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('dev data shape: ', X_dev.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n", + " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n" + ] + } + ], + "source": [ + "# Preprocessing: subtract the mean image\n", + "# first: compute the image mean based on the training data\n", + "mean_image = np.mean(X_train, axis=0)\n", + "print(mean_image[:10]) # print a few of the elements\n", + "plt.figure(figsize=(4,4))\n", + "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n", + "plt.show()\n", + "\n", + "# second: subtract the mean image from train and test data\n", + "X_train -= mean_image\n", + "X_val -= mean_image\n", + "X_test -= mean_image\n", + "X_dev -= mean_image\n", + "\n", + "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n", + "# only has to worry about optimizing a single weight matrix W.\n", + "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n", + "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n", + "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n", + "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n", + "\n", + "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SVM Classifier\n", + "\n", + "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n", + "\n", + "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 9.081178\n", + "[-12.88323008 -5.88185139 -13.33413429 18.45178335 26.10210743\n", + " 31.93883796 15.87429722 0.14230975 -14.5133769 -45.89674306]\n" + ] + } + ], + "source": [ + "# Evaluate the naive implementation of the loss we provided for you:\n", + "from cs231n.classifiers.linear_svm import svm_loss_naive\n", + "import time\n", + "\n", + "# generate a random SVM weight matrix of small numbers\n", + "W = np.random.randn(3073, 10) * 0.0001 \n", + "\n", + "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n", + "print('loss: %f' % (loss, ))\n", + "print(grad[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n", + "\n", + "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numerical: 29.592835 analytic: 29.592835, relative error: 6.567989e-12\n", + "numerical: 23.081379 analytic: 23.155573, relative error: 1.604628e-03\n", + "numerical: 0.920969 analytic: 0.920969, relative error: 1.698419e-10\n", + "numerical: 20.183962 analytic: 20.118679, relative error: 1.619820e-03\n", + "numerical: -0.430144 analytic: -0.430144, relative error: 8.740187e-10\n", + "numerical: 40.510177 analytic: 40.507118, relative error: 3.775556e-05\n", + "numerical: -4.703669 analytic: -4.703669, relative error: 7.458722e-11\n", + "numerical: -7.301623 analytic: -7.234730, relative error: 4.601771e-03\n", + "numerical: -7.035636 analytic: -6.967020, relative error: 4.900172e-03\n", + "numerical: 25.212677 analytic: 25.226805, relative error: 2.800897e-04\n", + "numerical: 2.537665 analytic: 2.476550, relative error: 1.218826e-02\n", + "numerical: -15.051878 analytic: -15.046054, relative error: 1.934862e-04\n", + "numerical: -14.840320 analytic: -14.921552, relative error: 2.729416e-03\n", + "numerical: -5.023521 analytic: -5.027193, relative error: 3.653688e-04\n", + "numerical: -16.207956 analytic: -16.202156, relative error: 1.789502e-04\n", + "numerical: -4.238452 analytic: -4.245232, relative error: 7.991655e-04\n", + "numerical: -31.253534 analytic: -31.256671, relative error: 5.019788e-05\n", + "numerical: 22.412497 analytic: 22.351310, relative error: 1.366891e-03\n", + "numerical: -20.292381 analytic: -20.296137, relative error: 9.253194e-05\n", + "numerical: -10.687445 analytic: -10.685528, relative error: 8.967390e-05\n" + ] + } + ], + "source": [ + "# Once you've implemented the gradient, recompute it with the code below\n", + "# and gradient check it with the function we provided for you\n", + "\n", + "# Compute the loss and its gradient at W.\n", + "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n", + "\n", + "# Numerically compute the gradient along several randomly chosen dimensions, and\n", + "# compare them with your analytically computed gradient. The numbers should match\n", + "# almost exactly along all dimensions.\n", + "from cs231n.gradient_check import grad_check_sparse\n", + "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n", + "grad_numerical = grad_check_sparse(f, W, grad)\n", + "\n", + "# do the gradient check once again with regularization turned on\n", + "# you didn't forget the regularization gradient did you?\n", + "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n", + "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n", + "grad_numerical = grad_check_sparse(f, W, grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question 1**\n", + "\n", + "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$ *fill this in.* \n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Naive loss: 9.081178e+00 computed in 0.193623s\n", + "Vectorized loss: 9.081178e+00 computed in 0.039667s\n", + "difference: -0.000000\n" + ] + } + ], + "source": [ + "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n", + "# we will implement the gradient in a moment.\n", + "tic = time.time()\n", + "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n", + "\n", + "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n", + "tic = time.time()\n", + "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n", + "\n", + "# The losses should match but your vectorized implementation should be much faster.\n", + "print('difference: %f' % (loss_naive - loss_vectorized))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Naive loss and gradient: computed in 0.187759s\n", + "Vectorized loss and gradient: computed in 0.005469s\n", + "difference: 0.000000\n" + ] + } + ], + "source": [ + "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n", + "# of the loss function in a vectorized way.\n", + "\n", + "# The naive implementation and the vectorized implementation should match, but\n", + "# the vectorized version should still be much faster.\n", + "tic = time.time()\n", + "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n", + "\n", + "tic = time.time()\n", + "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n", + "toc = time.time()\n", + "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n", + "\n", + "# The loss is a single number, so it is easy to compare the values computed\n", + "# by the two implementations. The gradient on the other hand is a matrix, so\n", + "# we use the Frobenius norm to compare them.\n", + "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n", + "print('difference: %f' % difference)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stochastic Gradient Descent\n", + "\n", + "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(49000, 3073)\n", + "iteration 0 / 1500: loss 786.117018\n", + "iteration 100 / 1500: loss 466.292801\n", + "iteration 200 / 1500: loss 282.968399\n", + "iteration 300 / 1500: loss 172.498729\n", + "iteration 400 / 1500: loss 105.914370\n", + "iteration 500 / 1500: loss 65.926524\n", + "iteration 600 / 1500: loss 41.614109\n", + "iteration 700 / 1500: loss 26.987115\n", + "iteration 800 / 1500: loss 18.918239\n", + "iteration 900 / 1500: loss 13.588392\n", + "iteration 1000 / 1500: loss 10.215357\n", + "iteration 1100 / 1500: loss 8.777470\n", + "iteration 1200 / 1500: loss 7.302617\n", + "iteration 1300 / 1500: loss 7.059345\n", + "iteration 1400 / 1500: loss 5.556112\n", + "That took 11.125399s\n" + ] + } + ], + "source": [ + "# In the file linear_classifier.py, implement SGD in the function\n", + "# LinearClassifier.train() and then run it with the code below.\n", + "from cs231n.classifiers import LinearSVM\n", + "svm = LinearSVM()\n", + "tic = time.time()\n", + "\n", + "print(X_train.shape)\n", + "\n", + "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n", + " num_iters=1500, verbose=True)\n", + "\n", + "toc = time.time()\n", + "print('That took %fs' % (toc - tic))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# A useful debugging strategy is to plot the loss as a function of\n", + "# iteration number:\n", + "plt.plot(loss_hist)\n", + "plt.xlabel('Iteration number')\n", + "plt.ylabel('Loss value')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training accuracy: 0.378796\n", + "validation accuracy: 0.383000\n" + ] + } + ], + "source": [ + "# Write the LinearSVM.predict function and evaluate the performance on both the\n", + "# training and validation set\n", + "y_train_pred = svm.predict(X_train)\n", + "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n", + "y_val_pred = svm.predict(X_val)\n", + "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iteration 0 / 1500: loss 800.556530\n", + "iteration 100 / 1500: loss 479.515704\n", + "iteration 200 / 1500: loss 290.522495\n", + "iteration 300 / 1500: loss 175.828077\n", + "iteration 400 / 1500: loss 108.696712\n", + "iteration 500 / 1500: loss 67.520591\n", + "iteration 600 / 1500: loss 43.037045\n", + "iteration 700 / 1500: loss 28.059113\n", + "iteration 800 / 1500: loss 19.138352\n", + "iteration 900 / 1500: loss 13.667597\n", + "iteration 1000 / 1500: loss 10.349017\n", + "iteration 1100 / 1500: loss 8.271575\n", + "iteration 1200 / 1500: loss 6.770222\n", + "iteration 1300 / 1500: loss 6.724322\n", + "iteration 1400 / 1500: loss 5.993306\n", + "iteration 0 / 1500: loss 1564.218828\n", + "iteration 100 / 1500: loss 569.369754\n", + "iteration 200 / 1500: loss 211.037076\n", + "iteration 300 / 1500: loss 80.368855\n", + "iteration 400 / 1500: loss 33.112110\n", + "iteration 500 / 1500: loss 15.815026\n", + "iteration 600 / 1500: loss 9.028691\n", + "iteration 700 / 1500: loss 7.157834\n", + "iteration 800 / 1500: loss 6.302182\n", + "iteration 900 / 1500: loss 5.768550\n", + "iteration 1000 / 1500: loss 5.702092\n", + "iteration 1100 / 1500: loss 5.991851\n", + "iteration 1200 / 1500: loss 5.702318\n", + "iteration 1300 / 1500: loss 6.116359\n", + "iteration 1400 / 1500: loss 5.732748\n", + "iteration 0 / 1500: loss 795.903572\n", + "iteration 100 / 1500: loss 1744.428717\n", + "iteration 200 / 1500: loss 1340.084727\n", + "iteration 300 / 1500: loss 1303.798698\n", + "iteration 400 / 1500: loss 1616.601681\n", + "iteration 500 / 1500: loss 1456.645442\n", + "iteration 600 / 1500: loss 1647.045969\n", + "iteration 700 / 1500: loss 1227.954558\n", + "iteration 800 / 1500: loss 1189.783506\n", + "iteration 900 / 1500: loss 1253.891214\n", + "iteration 1000 / 1500: loss 1756.667714\n", + "iteration 1100 / 1500: loss 1119.350295\n", + "iteration 1200 / 1500: loss 1268.640702\n", + "iteration 1300 / 1500: loss 1407.120294\n", + "iteration 1400 / 1500: loss 1452.982674\n", + "iteration 0 / 1500: loss 1556.018775\n", + "iteration 100 / 1500: loss 690200370895158074242275434124321751040.000000\n", + "iteration 200 / 1500: loss 114084593548603107439024087675236607919710437885811738714328525952711131136.000000\n", + "iteration 300 / 1500: loss 18857269619066902234161799676452823441213160565626297447981758433876062335440858297334610878713935369972219904.000000\n", + "iteration 400 / 1500: loss 3116955641645774519323843826960302395004889399387000150269054413359124374261900082797755217243555466453280658727734725442727338611773712552165376.000000\n", + "iteration 500 / 1500: loss 515207804112001874716860545131432834308327738255568209277479412785132176052211219680350918456823818827301373406978624538765451118025623462716917979274598343843702153489451273158656.000000\n", + "iteration 600 / 1500: loss 85159723761021240398946657999740406080942606976642592888541062456676761690246121853610818088511142151551770231420757506591648603581527928473867373476730650607014234255598506305601404316769779482704439762155374379008.000000\n", + "iteration 700 / 1500: loss 14076220300181790234446280145891793195367472922234824670459103848383267884966739534862578067574735981323245015195392058532007073147171238915059277795977607236723142489140215241340418249517476857361982998645380567975022455564644405996138368468814659584.000000\n", + "iteration 800 / 1500: loss 2326686480281201462339290176544803662625173091104138294144178379854199907456068516650027640459132602811902028413274716633484029013892647156448011103346218655395748860169112533225945549936080319883043923751124323426115566394830880508000482859663906831779336331325689843451934034966347776.000000\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kalkidanfekadu/Desktop/CS231/assignment1/cs231n/classifiers/linear_svm.py:113: RuntimeWarning: overflow encountered in double_scalars\n", + " loss += reg * np.sum(W * W)\n", + "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:83: RuntimeWarning: overflow encountered in reduce\n", + " return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n", + "/Users/kalkidanfekadu/Desktop/CS231/assignment1/cs231n/classifiers/linear_svm.py:113: RuntimeWarning: overflow encountered in multiply\n", + " loss += reg * np.sum(W * W)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iteration 900 / 1500: loss inf\n", + "iteration 1000 / 1500: loss inf\n", + "iteration 1100 / 1500: loss inf\n", + "iteration 1200 / 1500: loss inf\n", + "iteration 1300 / 1500: loss inf\n", + "iteration 1400 / 1500: loss inf\n", + "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.383796 val accuracy: 0.379000\n", + "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.371061 val accuracy: 0.393000\n", + "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.136612 val accuracy: 0.148000\n", + "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.107551 val accuracy: 0.121000\n", + "best validation accuracy achieved during cross-validation: 0.393000\n" + ] + } + ], + "source": [ + "# Use the validation set to tune hyperparameters (regularization strength and\n", + "# learning rate). You should experiment with different ranges for the learning\n", + "# rates and regularization strengths; if you are careful you should be able to\n", + "# get a classification accuracy of about 0.39 on the validation set.\n", + "\n", + "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n", + "# This may be caused by extreme values, and is not a bug.\n", + "\n", + "learning_rates = [1e-7, 5e-5]\n", + "regularization_strengths = [2.5e4, 5e4]\n", + "\n", + "# results is dictionary mapping tuples of the form\n", + "# (learning_rate, regularization_strength) to tuples of the form\n", + "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n", + "# of data points that are correctly classified.\n", + "results = {}\n", + "best_val = -1 # The highest validation accuracy that we have seen so far.\n", + "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n", + "\n", + "################################################################################\n", + "# TODO: #\n", + "# Write code that chooses the best hyperparameters by tuning on the validation #\n", + "# set. For each combination of hyperparameters, train a linear SVM on the #\n", + "# training set, compute its accuracy on the training and validation sets, and #\n", + "# store these numbers in the results dictionary. In addition, store the best #\n", + "# validation accuracy in best_val and the LinearSVM object that achieves this #\n", + "# accuracy in best_svm. #\n", + "# #\n", + "# Hint: You should use a small value for num_iters as you develop your #\n", + "# validation code so that the SVMs don't take much time to train; once you are #\n", + "# confident that your validation code works, you should rerun the validation #\n", + "# code with a larger value for num_iters. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "for learn_rate in learning_rates:\n", + " for reg in regularization_strengths:\n", + " svm = LinearSVM()\n", + " svm.train(X_train, y_train, learning_rate=learn_rate,\n", + " reg=reg,num_iters=1500, verbose=True)\n", + " y_train_pred = svm.predict(X_train)\n", + " train_accu = np.mean(y_train == y_train_pred)\n", + " y_val_pred = svm.predict(X_val)\n", + " val_accu = np.mean(y_val == y_val_pred)\n", + " results[(learn_rate, reg)] = (train_accu, val_accu)\n", + " if(val_accu > best_val):\n", + " best_val = val_accu\n", + " best_svm = svm\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + "# Print out results.\n", + "for lr, reg in sorted(results):\n", + " train_accuracy, val_accuracy = results[(lr, reg)]\n", + " print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n", + " lr, reg, train_accuracy, val_accuracy))\n", + " \n", + "print('best validation accuracy achieved during cross-validation: %f' % best_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the cross-validation results\n", + "import math\n", + "x_scatter = [math.log10(x[0]) for x in results]\n", + "y_scatter = [math.log10(x[1]) for x in results]\n", + "\n", + "# plot training accuracy\n", + "marker_size = 100\n", + "colors = [results[x][0] for x in results]\n", + "plt.subplot(2, 1, 1)\n", + "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n", + "plt.colorbar()\n", + "plt.xlabel('log learning rate')\n", + "plt.ylabel('log regularization strength')\n", + "plt.title('CIFAR-10 training accuracy')\n", + "\n", + "# plot validation accuracy\n", + "colors = [results[x][1] for x in results] # default size of markers is 20\n", + "plt.subplot(2, 1, 2)\n", + "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n", + "plt.colorbar()\n", + "plt.xlabel('log learning rate')\n", + "plt.ylabel('log regularization strength')\n", + "plt.title('CIFAR-10 validation accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "linear SVM on raw pixels final test set accuracy: 0.368000\n" + ] + } + ], + "source": [ + "# Evaluate the best svm on test set\n", + "y_test_pred = best_svm.predict(X_test)\n", + "test_accuracy = np.mean(y_test == y_test_pred)\n", + "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the learned weights for each class.\n", + "# Depending on your choice of learning rate and regularization strength, these may\n", + "# or may not be nice to look at.\n", + "w = best_svm.W[:-1,:] # strip out the bias\n", + "w = w.reshape(32, 32, 3, 10)\n", + "w_min, w_max = np.min(w), np.max(w)\n", + "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "for i in range(10):\n", + " plt.subplot(2, 5, i + 1)\n", + " \n", + " # Rescale the weights to be between 0 and 255\n", + " wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n", + " plt.imshow(wimg.astype('uint8'))\n", + " plt.axis('off')\n", + " plt.title(classes[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment1/two_layer_net.ipynb b/assignment1/two_layer_net.ipynb new file mode 100755 index 0000000..e1b61e0 --- /dev/null +++ b/assignment1/two_layer_net.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Implementing a Neural Network\n", + "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# A bit of setup\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from cs231n.classifiers.neural_net import TwoLayerNet\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# Create a small net and some toy data to check your implementations.\n", + "# Note that we set the random seed for repeatable experiments.\n", + "\n", + "input_size = 4\n", + "hidden_size = 10\n", + "num_classes = 3\n", + "num_inputs = 5\n", + "\n", + "def init_toy_model():\n", + " np.random.seed(0)\n", + " return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n", + "\n", + "def init_toy_data():\n", + " np.random.seed(1)\n", + " X = 10 * np.random.randn(num_inputs, input_size)\n", + " y = np.array([0, 1, 2, 2, 1])\n", + " return X, y\n", + "\n", + "net = init_toy_model()\n", + "X, y = init_toy_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forward pass: compute scores\n", + "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n", + "\n", + "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your scores:\n", + "[[-0.81233741 -1.27654624 -0.70335995]\n", + " [-0.17129677 -1.18803311 -0.47310444]\n", + " [-0.51590475 -1.01354314 -0.8504215 ]\n", + " [-0.15419291 -0.48629638 -0.52901952]\n", + " [-0.00618733 -0.12435261 -0.15226949]]\n", + "\n", + "correct scores:\n", + "[[-0.81233741 -1.27654624 -0.70335995]\n", + " [-0.17129677 -1.18803311 -0.47310444]\n", + " [-0.51590475 -1.01354314 -0.8504215 ]\n", + " [-0.15419291 -0.48629638 -0.52901952]\n", + " [-0.00618733 -0.12435261 -0.15226949]]\n", + "\n", + "Difference between your scores and correct scores:\n", + "3.6802720496109664e-08\n" + ] + } + ], + "source": [ + "scores = net.loss(X)\n", + "print('Your scores:')\n", + "print(scores)\n", + "print()\n", + "print('correct scores:')\n", + "correct_scores = np.asarray([\n", + " [-0.81233741, -1.27654624, -0.70335995],\n", + " [-0.17129677, -1.18803311, -0.47310444],\n", + " [-0.51590475, -1.01354314, -0.8504215 ],\n", + " [-0.15419291, -0.48629638, -0.52901952],\n", + " [-0.00618733, -0.12435261, -0.15226949]])\n", + "print(correct_scores)\n", + "print()\n", + "\n", + "# The difference should be very small. We get < 1e-7\n", + "print('Difference between your scores and correct scores:')\n", + "print(np.sum(np.abs(scores - correct_scores)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forward pass: compute loss\n", + "In the same function, implement the second part that computes the data and regularization loss." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference between your loss and correct loss:\n", + "1.7985612998927536e-13\n" + ] + } + ], + "source": [ + "loss, _ = net.loss(X, y, reg=0.05)\n", + "correct_loss = 1.30378789133\n", + "\n", + "# should be very small, we get < 1e-12\n", + "print('Difference between your loss and correct loss:')\n", + "print(np.sum(np.abs(loss - correct_loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Backward pass\n", + "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 max relative error: 3.561318e-09\n", + "b1 max relative error: 1.555470e-09\n", + "W2 max relative error: 3.440708e-09\n", + "b2 max relative error: 3.865091e-11\n" + ] + } + ], + "source": [ + "from cs231n.gradient_check import eval_numerical_gradient\n", + "\n", + "# Use numeric gradient checking to check your implementation of the backward pass.\n", + "# If your implementation is correct, the difference between the numeric and\n", + "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n", + "\n", + "loss, grads = net.loss(X, y, reg=0.05)\n", + "\n", + "# these should all be less than 1e-8 or so\n", + "for param_name in grads:\n", + " f = lambda W: net.loss(X, y, reg=0.05)[0]\n", + " param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n", + " print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train the network\n", + "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n", + "\n", + "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final training loss: 0.017149607938732093\n" + ] + }, + { + "data": { + "image/png": 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siYjVwCXANcDvAH/XyOAW06n9HewenmSiVDn2zmZmK1Q9SWFrRNwwvZAOm/28iLgZKDQsskW2yT2QzMzqSgr7Jf2BpFPTx+8DQ5KyQLXB8S2aJ+9VcFIws9ZVT1J4DbAB+CJwLbApXZclmZVtRTh1pluqG5vNrHXVMyDePmCuSXV2LGw4zdPXkae7mHP1kZm1tHpmXjtT0lWSvirpxulHPW8u6WJJ90raIenKOfZ5paS7Jd0l6Z+O9wQWiiROXe3RUs2stdUzn8I/Ax8GPsLhk+3MK21z+CDwImAQuEXSdRFxd80+W4A/BJ4bEUOS1h5P8Avt1P5O7n58uJkhmJk1VT1JoRwRHzqB974Q2BERDwBI+gxwGXB3zT7/GfhgRAwBRMSeEzjOgtnY38FX795FpRpkMx5C28xaTz0Nzf8q6XcknSypf/pRx+vWAztrlgfTdbXOBM6U9G1JN0u6uM64G+LU1R2UKsHjBz1fs5m1pnpKCq9Lf76jZl098ynM9lU7jljOAVuA55P0cPoPSedExIHD3ki6ArgCYNOmTXWEfGKmeyA98sQYG1Z1NOw4ZmZLVT29j050XoVBYGPN8gbgsVn2uTkdR+lBSfeSJIlbjojhKuAqgK1btx6ZWBbM9A1sD+8f4z816iBmZkvYfNNxviAibpT0itm2R8S/HOO9bwG2SDoNeBR4Ncn9DbW+CFwOfELSGpLqpAfqDX6hndzbTj4rd0s1s5Y1X0nhZ4EbgV+aZVsA8yaFiChLejNwA8mNbh+LiLskvQfYFhHXpdt+QdLdJD2b3hERT5zAeSyIbEas72tnp5OCmbWo+abj/OP05xtO9M0j4nrg+iPWvavmeQBvSx9Lwsb+DicFM2tZ9cynUAB+Gdhcu39EvKdxYTXPpv4Orv/h480Ow8ysKerpfXQtcBC4FZhsbDjNt6m/g6GxEsMTJXqK+WaHY2a2qOpJChsioqn3Dyym6fmad+4f4xmn9DY5GjOzxVXPzWvfkfSTDY9kidhYkxTMzFpNPSWFi4DXS3qQpPpIJG3E5zY0siaZTgrulmpmraiepHBJw6NYQnrb8/S259m530NdmFnrme/mtZ6IGAZGFjGeJWFTf4dLCmbWkuYrKfwT8FKSXkfB4WMZ1TP20bK1qb+DezyEtpm1oPluXntp+vNExz5atjb2d/Dvd+/2ENpm1nLqaVNA0iqSgeqK0+si4puNCqrZNvV3MFWpsnt4glP62psdjpnZoqnnjuY3AW8hGeX0duDZwHeBFzQ2tObZ2J8kgkf2jzkpmFlLqec+hbcAzwIejoifA84H9jY0qibb5HsVzKxF1ZMUJiJiApJxkCLiR8BZjQ2ruU7paycjJwUzaz31tCkMSuojmfvg3yUNcfRkOStKPpvhlL52d0s1s5ZTz8xrL0+f/omkrwO9wFcaGtUS4HsVzKwVzVt9JCkj6c7p5Yi4KSKui4ipxofWXBtXdfCI72o2sxYzb1KIiCpwh6RNixTPkrFpdQf7Dk0yNlVudihmZoumnjaFk4G7JH0fGJ1eGRGXNiyqJWB6YLzBoXHOXNfd5GjMzBZHPUnh3Q2PYgma7pb6yBNjTgpm1jLqSQoviYg/qF0h6X3ATY0JaWmYTgoPu7HZzFpIPfcpvGiWdSt+OO1VHckQ2g/uO9TsUMzMFs18Q2f/NvA7wOmSttds6ga+3ejAmk0SZwx0cv+e0WPvbGa2Qhxr6OwvA38OXFmzfiQi9jc0qiXijIEubrpvRY/oYWZ2mDmrjyLiYEQ8FBGXR8TDNY+WSAgAZ6ztYs/IJMMTpWaHYma2KOppUzhhki6WdK+kHZKunGe/X5EUkrY2Mp7jdcZAFwAP7HUVkpm1hoYlBUlZ4IMkjdJnA5dLOnuW/bqB3wO+16hYTtQZA50A3L/Hjc1m1hoaWVK4ENgREQ+kw2J8Brhslv3+FPgLYKKBsZyQjf0d5LPi/r1OCmbWGhqZFNYDO2uWB9N1MySdD2yMiC/N90aSrpC0TdK2vXsXr+E3n81w6upOJwUzaxmNTAqzTW4cMxulDPBXwNuP9UYRcVVEbI2IrQMDAwsY4rGdMdDJ/W5TMLMW0cikMAhsrFnewOHzMHQD5wDfkPQQyTSf1y3FxuaHnxilVKk2OxQzs4ZrZFK4Bdgi6TRJbcCrgeumN6ZdXtdExOaI2AzcDFwaEdsaGNNxO2Ogi1IlPAubmbWEhiWFiCgDbwZuAO4BromIuyS9R9KyGWH1jLVJt9Qd7oFkZi2gngHxTlhEXA9cf8S6d82x7/MbGcuJOn26W6rbFcysBTT05rWVoKeYZ213wT2QzKwlOCnU4YyBLicFM2sJTgp1OGNtJ/fvOUREHHtnM7NlzEmhDmcMdDE8UWbfoalmh2Jm1lBOCnWYHhjPVUhmttI5KdRhuluqk4KZrXROCnU4uadIR1uW+3aNNDsUM7OGclKoQyYjzjmll+2PHmx2KGZmDeWkUKfzNvZy12PDTJU9BpKZrVxOCnU6d0MfU+Uq9+12FZKZrVxOCnU6b0MfAHcMHmhyJGZmjeOkUKeN/e2s6shzx04nBTNbuZwU6iSJczf0sX3Qjc1mtnI5KRyH8zb0ct/uEcamys0OxcysIZwUjsN5G/uoBtz56HCzQzEzawgnheNwbtrYvN2NzWa2QjkpHIeB7gKn9Ba5w+0KZrZCOSkcp/M29rkHkpmtWE4Kx+ncDX08sn+MoVEPo21mK4+TwnE6b0Mv4JvYzGxlclI4Tuds6EWCO3a6XcHMVh4nhePUU8xz7vpevnDbIOWKB8czs5XFSeEE/O7PPY2HnhjjC7c92uxQzMwWVEOTgqSLJd0raYekK2fZ/jZJd0vaLulrkk5tZDwL5UVnr+Oc9T184MYfU3JpwcxWkIYlBUlZ4IPAJcDZwOWSzj5it9uArRFxLvA54C8aFc9CksTbXnQmO/eP8/lbB5sdjpnZgmlkSeFCYEdEPBARU8BngMtqd4iIr0fEWLp4M7ChgfEsqJ87ay3nbezjb27c4Yl3zGzFaGRSWA/srFkeTNfN5Y3Al2fbIOkKSdskbdu7d+8ChnjipksLjx4Y57Pbdh77BWZmy0Ajk4JmWRez7ii9FtgKvH+27RFxVURsjYitAwMDCxjiU/O8LWt45sY+Pv7tB4mY9dTMzJaVRiaFQWBjzfIG4LEjd5L0QuB/AJdGxGQD41lwknjNT2/igb2jbHt4qNnhmJk9ZY1MCrcAWySdJqkNeDVwXe0Oks4H/p4kIexpYCwN84s/eTKdbVk+e4urkMxs+WtYUoiIMvBm4AbgHuCaiLhL0nskXZru9n6gC/hnSbdLum6Ot1uyOgs5Ln3mKfzb9scZmSg1Oxwzs6ck18g3j4jrgeuPWPeumucvbOTxF8srt27k6u/v5EvbH+fyCzc1OxwzsxPmO5oXwDM39nHmui4+4yokM1vmnBQWgCReuXUjd+w8wL27RpodjpnZCXNSWCCvuGAD+az49PcebnYoZmYnzElhgfR3tvGK8zfwye8+zDW+mc3MlqmGNjS3mndf9gweOzjOlZ/fTjGf5dLzTml2SGZmx8UlhQVUzGe56te3snVzP2/97O189a5dzQ7JzOy4OCkssPa2LB97/bM4Z30vb/3s7Tx2YLzZIZmZ1c1JoQG6Cjn+9vLzqQa884t3elwkM1s2nBQaZGN/B2//hTP52o/28K/bH292OGZmdXFSaKA3PPc0ztvQy7uvu4uh0almh2NmdkxOCg2UzYj3/vK5HBwv8c5r76RSdTWSmS1tTgoN9vSTe/hvL9zCl7Y/zus+9n32u8RgZkuYk8IiePMLtvC+X/5Jvv/Qfn7pb77F7TsPNDskM7NZ+ea1RfKqZ23i6Sf38Nuf+gEv++C3OX1NJ887c4DnnzXARU9bQy7r/Gxmzafl1l1y69atsW3btmaHccKGRqf4wm2P8s0f7+XmB55golRlbXeBl1+wnldu3cgZA13NDtHMViBJt0bE1mPu56TQPBOlCjfdt5d/3jbI1+/dQ6UavOKC9bzjxWdxcm97s8MzsxXESWGZ2TMywUe/9SAf/9ZDZDJwxc+czht/5nR62/PNDs3MVgAnhWVq5/4x3veVH/Gl7Y/TXcjx2uecym8+9zS6izmGxqYYmSizcVUH7W3ZZodqZsuIk8Iyd+ejB/nQTfdz/Q+Tu6FrL1M2I85a1815G/t43pY1PO/MAToL7jNgZnNzUlgh7t97iGtvf4xCLsOqjjY6C1l27DnE7TsPcPvOA4xMlGnLZfiZp63h9IFO2ttydLZlyWUzCJBgdVeBLWu7OG1NJ8W8SxhmrajepOCvl0vcGQNdvO1FZ866rVypcstDQ3z17l3c+KM9fOf+JxgvVeZ8r4ySMZlOX9PJaWu66CrmGBqdYv/oFBKcua6bs07q5uTeIuNTFUanyuQyGc7f1Ed30W0bZq3AJYUVploNxksVypUgCKoBu4cn+PGeQ+zYPcL9+0Z5YO8oD+47xGS5Sl97nlWdbUyVqwwOzT7MdzYjzt3QyzNO6WFotMTjB8d5YnSKXEa05bIUchl62vP0tufpbc+lP5PHQHeBdT1F1vUUETBRrjI+VZkZOVZK5rjOSGQEGYm2XIZcRuSyGfJZkc2ItmwGSYfFVakGEeF7PMzq4JJCi8pkdFT7Qn9nG08/ueewddVqECQf+NNGJ8v8eM8h9gxP0FnI0VnIcWiizM0PPMF37t/Htbc9xkB3gZP7ipy3qo9KBFPlKhOlCgfHS+zcP8aBsSmGJ8oNGeepkMtQzGfJCMamKkyWq0hwSm87G/vbGeguMlmqMF6qMFmqks2IXDZJOKVKlclylXI1WNPZxsl9RU7qKZLJiHIlqFSD7mKONV0F+jvbmCxX2T86yf7REp2FLBtWtbO+r4NDkyXueXyEe3eNMDJRor0tR0dblo62LMV88sgKpipVJktVqgGdhSwdbTnyWTFeqjA6WWGiVEFpEsxlRX9HG6u7CvQUc+w9NMljB8bZdXASCdpyGfIZMVmpMjaZnF8xn6G3PU9PMX9YEh6ZLDM4NM7g0BilSpXOQo6uthx9HXnWdBUY6C5QyGUZnigxPF6iVA16ikkiz2czPDE6xb6RScamyqzrKXJKXztrugocmixxcLzEockKnW1ZetvztLdlGRotsWdkgn2HJmnLZegq5OksZMllnkzUxXwmiaOQo1SpMjxeZniiRESyrZhPvli0pQ8hJsvJ9a1Ug+RPVJSrVYZGSxwcn+LQZIVsJvn95bMZ2vNZ2tNrUKkm13OyXGHfoUn2DE8yNFZiTVfbzHWcLFcYGisxNDZFPit6inl62vN0F3N0F/N0FXJMlCrsOjjB7uEJxksVMkq+oLTns/R2JL/vXEYcHC9xYKxEpRqs7SlwUk+R7mKe4YkSB9LOITD95QdymeTLTj6bIZ9LnhdyWboLOTI1/48RwchkmT3Dk+wZnmDX8ASnD3TxzI19C/6/VauhSUHSxcBfA1ngIxHx3iO2F4BPAj8FPAG8KiIeamRMlqj945vWWcjN+gd30ZY1wFl1v3dEcGiyzIGxEnsPTbI7/ceSkn+oQj5DNiMiINL9I5Jv/pUIypWgVKlSqiQf4sk/eJXJUvJhWg3oaEs+BKrVYHBonEf2j/HDwQMU0w+HQi5DpRpMlCtUq0FbLkMxnyEj8djBCW59ZIgDY6UT/v11F3L0deYZn6owNpV8UC90obsz7WE2ValSqiTn0NmWpT2fZaJc5eB4ac7kO52kDk2WmSpXFzawZSiXEeUlPiClxExyH52scGBs6qiY33TRacs3KUjKAh8EXgQMArdIui4i7q7Z7Y3AUEQ8TdKrgfcBr2pUTLY4JNFdzNNdzLOxv6PZ4cxpIm1/yWWS0sTIRJl9o5M8cWiKYj5Df2cbqzraGJ0ss3NonEcPjNOez/L0k7tZ39d+WHVWxHTiqlKJoJDLUMglVV5jU2XGpipMlat0tGXpLOQo5JJv0pVqUKoE+8eSb+jDEyUGuguc0tdOT007TkQcVX0WEYxOJaW04fHkm3xnW44Nq9rp68jP7D9VrnJgfIp9I1PsOzTJZLlKTzFHT3uefDb5pntwvMRUOVjT1caargIdbVl2DU/w2IEJnhidpDstkXQVsoxOVhieKDFi4cAGAAAHsUlEQVQ6Waavo411PUXWdLVRriRfBkYmylTTDBmR/J5Hp5L1bdkMPe3Jt3EJJktJSXOyXGWqXGWyXCGAYu7oLw9ZiVUdefo62ugq5KhGUK4G5WpSJTk+VWGiXCGbSasfM2J1V4G1PQW6CzkOjpcYTK9jMZ+lv6ONvo58UnqZKDM8XkrjLzEyUaaQz3JST1Ki7CgkX0AqEYylv/ODYyXK1aCvPU9vR56MYPfwJLsOTnBoskxfWproLuYQSqpzq1CuJkl++ovPVCWYTEvbQ2NTDI+X6SxkWdWR/P2t7XmyCvaknmID/yMSDWtTkPQc4E8i4sXp8h8CRMSf1+xzQ7rPdyXlgF3AQMwTlNsUzMyOX71tCo1soVsP7KxZHkzXzbpPRJSBg8DqI99I0hWStknatnfv3gaFa2ZmjUwKR1daJ6XA492HiLgqIrZGxNaBgYEFCc7MzI7WyKQwCGysWd4APDbXPmn1US+wv4ExmZnZPBqZFG4Btkg6TVIb8GrguiP2uQ54Xfr8V4Ab52tPMDOzxmpY76OIKEt6M3ADSZfUj0XEXZLeA2yLiOuAjwL/KGkHSQnh1Y2Kx8zMjq2h9ylExPXA9Uese1fN8wngVxsZg5mZ1c/jA5iZ2QwnBTMzm7HsBsSTtBd4+ARfvgbYt4DhLBeteN6teM7QmufdiucMx3/ep0bEMfv0L7uk8FRI2lbPHX0rTSuedyueM7TmebfiOUPjztvVR2ZmNsNJwczMZrRaUriq2QE0SSuedyueM7TmebfiOUODzrul2hTMzGx+rVZSMDOzeTgpmJnZjJZJCpIulnSvpB2Srmx2PI0gaaOkr0u6R9Jdkt6Sru+X9O+Sfpz+XNXsWBtBUlbSbZK+lC6fJul76Xl/Nh2YccWQ1Cfpc5J+lF7z57TCtZb01vTv+05JV0sqrsRrLeljkvZIurNm3azXV4kPpJ9v2yVdcKLHbYmkUDM16CXA2cDlks5ublQNUQbeHhFPB54N/G56nlcCX4uILcDX0uWV6C3APTXL7wP+Kj3vIZLpX1eSvwa+EhE/AZxHcu4r+lpLWg/8HrA1Is4hGWxzeirflXatPwFcfMS6ua7vJcCW9HEF8KETPWhLJAXgQmBHRDwQEVPAZ4DLmhzTgouIxyPiB+nzEZIPifUk5/oP6W7/ALysORE2jqQNwC8CH0mXBbwA+Fy6y4o6b0k9wPNIRhomIqYi4gAtcK1JBvJsT+dg6QAeZwVe64j4JkfPLzPX9b0M+GQkbgb6JJ18IsdtlaRQz9SgK4qkzcD5wPeAdRHxOCSJA1jbvMga5v8Cvw9U0+XVwIF0mldYedf8dGAv8PG0yuwjkjpZ4dc6Ih4F/g/wCEkyOAjcysq+1rXmur4L9hnXKkmhrmk/VwpJXcDngf8WEcPNjqfRJL0U2BMRt9aunmXXlXTNc8AFwIci4nxglBVWVTSbtA79MuA04BSgk6Tq5Egr6VrXY8H+3lslKdQzNeiKIClPkhA+HRH/kq7ePV2UTH/uaVZ8DfJc4FJJD5FUDb6ApOTQl1YxwMq75oPAYER8L13+HEmSWOnX+oXAgxGxNyJKwL8A/4mVfa1rzXV9F+wzrlWSQj1Tgy57aT36R4F7IuIvazbVTnv6OuDaxY6tkSLiDyNiQ0RsJrm2N0bErwFfJ5nmFVbYeUfELmCnpLPSVT8P3M0Kv9Yk1UbPltSR/r1Pn/eKvdZHmOv6Xgf8RtoL6dnAwelqpuPVMnc0S3oJybfH6alB/1eTQ1pwki4C/gP4IU/Wrf8RSbvCNcAmkn+qX42IIxuwVgRJzwf+e0S8VNLpJCWHfuA24LURMdnM+BaSpGeSNKy3AQ8AbyD5oreir7WkdwOvIultdxvwJpL68xV1rSVdDTyfZIjs3cAfA19kluubJsi/JemtNAa8ISK2ndBxWyUpmJnZsbVK9ZGZmdXBScHMzGY4KZiZ2QwnBTMzm+GkYGZmM5wUrGVJ+k76c7Ok1yzwe//RbMcyW+rcJdVaXu29DcfxmmxEVObZfigiuhYiPrPF5JKCtSxJh9Kn7wV+RtLt6Vj9WUnvl3RLOjb9b6X7Pz+dr+KfSG4QRNIXJd2aju9/RbruvSSjeN4u6dO1x0rvOH1/OhfADyW9qua9v1EzP8Kn0xuSzBZV7ti7mK14V1JTUkg/3A9GxLMkFYBvS/pquu+FwDkR8WC6/JvpHaXtwC2SPh8RV0p6c0Q8c5ZjvQJ4Jsn8B2vS13wz3XY+8AySMWu+TTKm07cW/nTN5uaSgtnRfoFkHJnbSYYIWU0yeQnA92sSAsDvSboDuJlkQLItzO8i4OqIqETEbuAm4Fk17z0YEVXgdmDzgpyN2XFwScHsaAL+a0TccNjKpO1h9IjlFwLPiYgxSd8AinW891xqx+qp4P9PawKXFMxgBOiuWb4B+O10GHIknZlOYHOkXmAoTQg/QTIF6rTS9OuP8E3gVWm7xQDJ7GnfX5CzMFsA/iZiBtuBcloN9AmSuY83Az9IG3v3Mvv0jl8B/ouk7cC9JFVI064Ctkv6QTqM97QvAM8B7iCZBOX3I2JXmlTMms5dUs3MbIarj8zMbIaTgpmZzXBSMDOzGU4KZmY2w0nBzMxmOCmYmdkMJwUzM5vx/wFMsT6g+AdSAAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "net = init_toy_model()\n", + "stats = net.train(X, y, X, y,\n", + " learning_rate=1e-1, reg=5e-6,\n", + " num_iters=100, verbose=False)\n", + "\n", + "print('Final training loss: ', stats['loss_history'][-1])\n", + "\n", + "# plot the loss history\n", + "plt.plot(stats['loss_history'])\n", + "plt.xlabel('iteration')\n", + "plt.ylabel('training loss')\n", + "plt.title('Training Loss history')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load the data\n", + "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape: (49000, 3072)\n", + "Train labels shape: (49000,)\n", + "Validation data shape: (1000, 3072)\n", + "Validation labels shape: (1000,)\n", + "Test data shape: (1000, 3072)\n", + "Test labels shape: (1000,)\n" + ] + } + ], + "source": [ + "from cs231n.data_utils import load_CIFAR10\n", + "\n", + "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n", + " \"\"\"\n", + " Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n", + " it for the two-layer neural net classifier. These are the same steps as\n", + " we used for the SVM, but condensed to a single function. \n", + " \"\"\"\n", + " # Load the raw CIFAR-10 data\n", + " cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", + " \n", + " # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n", + " try:\n", + " del X_train, y_train\n", + " del X_test, y_test\n", + " print('Clear previously loaded data.')\n", + " except:\n", + " pass\n", + "\n", + " X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", + " \n", + " # Subsample the data\n", + " mask = list(range(num_training, num_training + num_validation))\n", + " X_val = X_train[mask]\n", + " y_val = y_train[mask]\n", + " mask = list(range(num_training))\n", + " X_train = X_train[mask]\n", + " y_train = y_train[mask]\n", + " mask = list(range(num_test))\n", + " X_test = X_test[mask]\n", + " y_test = y_test[mask]\n", + "\n", + " # Normalize the data: subtract the mean image\n", + " mean_image = np.mean(X_train, axis=0)\n", + " X_train -= mean_image\n", + " X_val -= mean_image\n", + " X_test -= mean_image\n", + "\n", + " # Reshape data to rows\n", + " X_train = X_train.reshape(num_training, -1)\n", + " X_val = X_val.reshape(num_validation, -1)\n", + " X_test = X_test.reshape(num_test, -1)\n", + "\n", + " return X_train, y_train, X_val, y_val, X_test, y_test\n", + "\n", + "\n", + "# Invoke the above function to get our data.\n", + "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n", + "print('Train data shape: ', X_train.shape)\n", + "print('Train labels shape: ', y_train.shape)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Validation labels shape: ', y_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train a network\n", + "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iteration 0 / 1200: loss 2.302966\n", + "iteration 100 / 1200: loss 2.302603\n", + "iteration 200 / 1200: loss 2.299025\n", + "iteration 300 / 1200: loss 2.271828\n", + "iteration 400 / 1200: loss 2.162173\n", + "iteration 500 / 1200: loss 2.115962\n", + "iteration 600 / 1200: loss 2.093821\n", + "iteration 700 / 1200: loss 2.038090\n", + "iteration 800 / 1200: loss 2.044906\n", + "iteration 900 / 1200: loss 1.931607\n", + "iteration 1000 / 1200: loss 1.945000\n", + "iteration 1100 / 1200: loss 1.895230\n", + "Validation accuracy: 0.311\n" + ] + } + ], + "source": [ + "input_size = 32 * 32 * 3\n", + "hidden_size = 50\n", + "num_classes = 10\n", + "net = TwoLayerNet(input_size, hidden_size, num_classes)\n", + "\n", + "# Train the network\n", + "stats = net.train(X_train, y_train, X_val, y_val,\n", + " num_iters=1200, batch_size=200,\n", + " learning_rate=1e-4, learning_rate_decay=0.95,\n", + " reg=0.25, verbose=True)\n", + "\n", + "# Predict on the validation set\n", + "val_acc = (net.predict(X_val) == y_val).mean()\n", + "print('Validation accuracy: ', val_acc)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Debug the training\n", + "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n", + "\n", + "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n", + "\n", + "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the loss function and train / validation accuracies\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(stats['loss_history'])\n", + "plt.title('Loss history')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Loss')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(stats['train_acc_history'], label='train')\n", + "plt.plot(stats['val_acc_history'], label='val')\n", + "plt.title('Classification accuracy history')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Classification accuracy')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from cs231n.vis_utils import visualize_grid\n", + "\n", + "# Visualize the weights of the network\n", + "\n", + "def show_net_weights(net):\n", + " W1 = net.params['W1']\n", + " W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n", + " plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n", + " plt.gca().axis('off')\n", + " plt.show()\n", + "\n", + "show_net_weights(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tune your hyperparameters\n", + "\n", + "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n", + "\n", + "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n", + "\n", + "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n", + "\n", + "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Explain your hyperparameter tuning process below.**\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "code" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(0.01, 0.025): 0.087}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47, (0.001, 0.05): 0.477}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47, (0.001, 0.05): 0.477, (0.001, 0.1): 0.469}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47, (0.001, 0.05): 0.477, (0.001, 0.1): 0.469, (1e-05, 0.025): 0.176}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47, (0.001, 0.05): 0.477, (0.001, 0.1): 0.469, (1e-05, 0.025): 0.176, (1e-05, 0.05): 0.206}\n", + "{(0.01, 0.025): 0.087, (0.01, 0.05): 0.087, (0.01, 0.1): 0.087, (0.001, 0.025): 0.47, (0.001, 0.05): 0.477, (0.001, 0.1): 0.469, (1e-05, 0.025): 0.176, (1e-05, 0.05): 0.206, (1e-05, 0.1): 0.201}\n" + ] + } + ], + "source": [ + "best_net = None # store the best model into this \n", + "\n", + "#################################################################################\n", + "# TODO: Tune hyperparameters using the validation set. Store your best trained #\n", + "# model in best_net. #\n", + "# #\n", + "# To help debug your network, it may help to use visualizations similar to the #\n", + "# ones we used above; these visualizations will have significant qualitative #\n", + "# differences from the ones we saw above for the poorly tuned network. #\n", + "# #\n", + "# Tweaking hyperparameters by hand can be fun, but you might find it useful to #\n", + "# write code to sweep through possible combinations of hyperparameters #\n", + "# automatically like we did on the previous exercises. #\n", + "#################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "regularization_strengths = [2.5e-2, 5e-2, 10e-2]\n", + "learning_rates = [1e-2, 1e-3, 1e-5]\n", + "results = {}\n", + "best_val = -1\n", + "\n", + "for learn_rate in learning_rates:\n", + " for reg in regularization_strengths:\n", + " net = TwoLayerNet(input_size, hidden_size, num_classes)\n", + " net.train(X_train, y_train, X_val, y_val,\n", + " num_iters=1200, batch_size=200,\n", + " learning_rate=learn_rate, learning_rate_decay=0.95,\n", + " reg=reg, verbose=False)\n", + " val_acc = (net.predict(X_val) == y_val).mean()\n", + " results[(learn_rate, reg)] = val_acc\n", + " print(results)\n", + " if(best_val < val_acc):\n", + " best_val = val_acc\n", + " best_net = net\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the weights of the best network\n", + "show_net_weights(best_net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run on the test set\n", + "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.484\n" + ] + } + ], + "source": [ + "test_acc = (best_net.predict(X_test) == y_test).mean()\n", + "print('Test accuracy: ', test_acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "**Inline Question**\n", + "\n", + "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n", + "\n", + "1. Train on a larger dataset.\n", + "2. Add more hidden units.\n", + "3. Increase the regularization strength.\n", + "4. None of the above.\n", + "\n", + "$\\color{blue}{\\textit Your Answer:}$\n", + "\n", + "$\\color{blue}{\\textit Your Explanation:}$\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment2/.gitignore b/assignment2/.gitignore new file mode 100755 index 0000000..a5c5231 --- /dev/null +++ b/assignment2/.gitignore @@ -0,0 +1,7 @@ +*.swp +*.pyc +.env/* +*/.ipynb_checkpoints/* + +# gitignore the built release. +assignment3/* diff --git a/assignment2/.ipynb_checkpoints/BatchNormalization-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/BatchNormalization-checkpoint.ipynb new file mode 100755 index 0000000..b86811b --- /dev/null +++ b/assignment2/.ipynb_checkpoints/BatchNormalization-checkpoint.ipynb @@ -0,0 +1,960 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Batch Normalization\n", + "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n", + "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n", + "\n", + "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n", + "\n", + "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n", + "\n", + "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n", + "\n", + "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", + "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", + "\n", + "def print_mean_std(x,axis=0):\n", + " print(' means: ', x.mean(axis=axis))\n", + " print(' stds: ', x.std(axis=axis))\n", + " print() " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: forward\n", + "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n", + "\n", + "Referencing the paper linked to above in [1] may be helpful!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before batch normalization:\n", + " means: [ -2.3814598 -13.18038246 1.91780462]\n", + " stds: [27.18502186 34.21455511 37.68611762]\n", + "\n", + "After batch normalization (gamma=1, beta=0)\n", + " means: [5.10702591e-17 6.21724894e-17 3.98986399e-17]\n", + " stds: [0.99999963 0.99999971 0.99999973]\n", + "\n", + "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n", + " means: [11. 12. 13.]\n", + " stds: [0.99999963 1.99999942 2.9999992 ]\n", + "\n" + ] + } + ], + "source": [ + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after batch normalization \n", + "\n", + "# Simulate the forward pass for a two-layer network\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 = 200, 50, 60, 3\n", + "X = np.random.randn(N, D1)\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "\n", + "print('Before batch normalization:')\n", + "print_mean_std(a,axis=0)\n", + "\n", + "gamma = np.ones((D3,))\n", + "beta = np.zeros((D3,))\n", + "# Means should be close to zero and stds close to one\n", + "print('After batch normalization (gamma=1, beta=0)')\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=0)\n", + "\n", + "gamma = np.asarray([1.0, 2.0, 3.0])\n", + "beta = np.asarray([11.0, 12.0, 13.0])\n", + "# Now means should be close to beta and stds close to gamma\n", + "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After batch normalization (test-time):\n", + " means: [-0.03927353 -0.04349151 -0.10452686]\n", + " stds: [1.01531399 1.01238345 0.97819961]\n", + "\n" + ] + } + ], + "source": [ + "# Check the test-time forward pass by running the training-time\n", + "# forward pass many times to warm up the running averages, and then\n", + "# checking the means and variances of activations after a test-time\n", + "# forward pass.\n", + "\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 = 200, 50, 60, 3\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "gamma = np.ones(D3)\n", + "beta = np.zeros(D3)\n", + "\n", + "for t in range(50):\n", + " X = np.random.randn(N, D1)\n", + " a = np.maximum(0, X.dot(W1)).dot(W2)\n", + " batchnorm_forward(a, gamma, beta, bn_param)\n", + "\n", + "bn_param['mode'] = 'test'\n", + "X = np.random.randn(N, D1)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n", + "\n", + "# Means should be close to zero and stds close to one, but will be\n", + "# noisier than training-time forward passes.\n", + "print('After batch normalization (test-time):')\n", + "print_mean_std(a_norm,axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: backward\n", + "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n", + "\n", + "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n", + "\n", + "Once you have finished, run the following to numerically check your backward pass." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx error: 2.4825083150038806e-05\n", + "dgamma error: 5.9824277839050605e-12\n", + "dbeta error: 2.8795057655839487e-12\n" + ] + } + ], + "source": [ + "# Gradient check batchnorm backward pass\n", + "np.random.seed(231)\n", + "N, D = 4, 5\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n", + "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", + "\n", + "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", + "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n", + "#You should expect to see relative errors between 1e-13 and 1e-8\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: alternative backward\n", + "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n", + "\n", + "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too! \n", + "\n", + "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n", + "\n", + "we first calculate the mean $\\mu$ and variance $v$.\n", + "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$ and normalized data $Y$.\n", + "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n", + "\n", + "\\begin{align}\n", + "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k & v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n", + "& \\sigma=\\sqrt{v+\\epsilon} & y_i=\\frac{x_i-\\mu}{\\sigma}\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n", + "\n", + "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n", + "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n", + "$\\frac{\\partial \\sigma}{\\partial v}$, \n", + "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n", + "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n", + "\n", + "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n", + "\n", + "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n", + "\n", + "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx difference: 0.0\n", + "dgamma difference: 0.0\n", + "dbeta difference: 0.0\n", + "speedup: 1.33x\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D = 100, 500\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", + "\n", + "t1 = time.time()\n", + "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n", + "t2 = time.time()\n", + "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n", + "t3 = time.time()\n", + "\n", + "print('dx difference: ', rel_error(dx1, dx2))\n", + "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n", + "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n", + "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fully Connected Nets with Batch Normalization\n", + "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n", + "\n", + "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n", + "\n", + "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running check with reg = 0\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "[{'mode': 'train'}, {'mode': 'train'}]\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "Initial loss: 2.1631431253070756\n", + "W1 relative error: 6.03e-01\n", + "W2 relative error: 3.33e-01\n", + "W3 relative error: 3.74e-10\n", + "b1 relative error: 2.22e-03\n", + "b2 relative error: 2.22e-03\n", + "b3 relative error: 1.18e-10\n", + "beta1 relative error: 3.34e-01\n", + "beta2 relative error: 1.84e-09\n", + "gamma1 relative error: 3.34e-01\n", + "gamma2 relative error: 2.90e-09\n", + "\n", + "Running check with reg = 3.14\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "[{'mode': 'train'}, {'mode': 'train'}]\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "dict_keys(['W1', 'b1', 'gamma1', 'beta1', 'W2', 'b2', 'gamma2', 'beta2', 'W3', 'b3'])\n", + "Initial loss: 6.992501965152032\n", + "W1 relative error: 2.30e-03\n", + "W2 relative error: 1.00e+00\n", + "W3 relative error: 1.14e-08\n", + "b1 relative error: 4.44e-03\n", + "b2 relative error: 2.85e-08\n", + "b3 relative error: 2.01e-10\n", + "beta1 relative error: 3.33e-01\n", + "beta2 relative error: 5.72e-09\n", + "gamma1 relative error: 3.33e-01\n", + "gamma2 relative error: 4.08e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "# You should expect losses between 1e-4~1e-10 for W, \n", + "# losses between 1e-08~1e-10 for b,\n", + "# and losses between 1e-08~1e-09 for beta and gammas.\n", + "for reg in [0, 3.14]:\n", + " print('Running check with reg = ', reg)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " reg=reg, weight_scale=5e-2, dtype=np.float64,\n", + " normalization='batchnorm')\n", + "\n", + " loss, grads = model.loss(X, y) \n", + " print('Initial loss: ', loss)\n", + "\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", + " if reg == 0: print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batchnorm for deep networks\n", + "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(231)\n", + "# Try training a very deep net with batchnorm\n", + "hidden_dims = [100, 100, 100, 100, 100]\n", + "\n", + "num_train = 1000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "weight_scale = 2e-2\n", + "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", + "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + "\n", + "print('Solver with batch norm:')\n", + "bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=True,print_every=20)\n", + "bn_solver.train()\n", + "\n", + "print('\\nSolver without batch norm:')\n", + "solver = Solver(model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=True, print_every=20)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n", + " \"\"\"utility function for plotting training history\"\"\"\n", + " plt.title(title)\n", + " plt.xlabel(label)\n", + " bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n", + " bl_plot = plot_fn(baseline)\n", + " num_bn = len(bn_plots)\n", + " for i in range(num_bn):\n", + " label='with_norm'\n", + " if labels is not None:\n", + " label += str(labels[i])\n", + " plt.plot(bn_plots[i], bn_marker, label=label)\n", + " label='baseline'\n", + " if labels is not None:\n", + " label += str(labels[0])\n", + " plt.plot(bl_plot, bl_marker, label=label)\n", + " plt.legend(loc='lower center', ncol=num_bn+1) \n", + "\n", + " \n", + "plt.subplot(3, 1, 1)\n", + "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n", + " lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n", + "plt.subplot(3, 1, 2)\n", + "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n", + " lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n", + "plt.subplot(3, 1, 3)\n", + "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n", + " lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batch normalization and initialization\n", + "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n", + "\n", + "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "np.random.seed(231)\n", + "# Try training a very deep net with batchnorm\n", + "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n", + "num_train = 1000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "bn_solvers_ws = {}\n", + "solvers_ws = {}\n", + "weight_scales = np.logspace(-4, 0, num=20)\n", + "for i, weight_scale in enumerate(weight_scales):\n", + " print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n", + " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", + " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + "\n", + " bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=False, print_every=200)\n", + " bn_solver.train()\n", + " bn_solvers_ws[weight_scale] = bn_solver\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=False, print_every=200)\n", + " solver.train()\n", + " solvers_ws[weight_scale] = solver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "# Plot results of weight scale experiment\n", + "best_train_accs, bn_best_train_accs = [], []\n", + "best_val_accs, bn_best_val_accs = [], []\n", + "final_train_loss, bn_final_train_loss = [], []\n", + "\n", + "for ws in weight_scales:\n", + " best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n", + " bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n", + " \n", + " best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n", + " bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n", + " \n", + " final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n", + " bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n", + " \n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Best val accuracy vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Best val accuracy')\n", + "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n", + "plt.legend(ncol=2, loc='lower right')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Best train accuracy vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Best training accuracy')\n", + "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n", + "plt.legend()\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Final training loss vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Final training loss')\n", + "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n", + "plt.legend()\n", + "plt.gca().set_ylim(1.0, 3.5)\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1:\n", + "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batch normalization and batch size\n", + "We will now run a small experiment to study the interaction of batch normalization and batch size.\n", + "\n", + "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def run_batchsize_experiments(normalization_mode):\n", + " np.random.seed(231)\n", + " # Try training a very deep net with batchnorm\n", + " hidden_dims = [100, 100, 100, 100, 100]\n", + " num_train = 1000\n", + " small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + " }\n", + " n_epochs=10\n", + " weight_scale = 2e-2\n", + " batch_sizes = [5,10,50]\n", + " lr = 10**(-3.5)\n", + " solver_bsize = batch_sizes[0]\n", + "\n", + " print('No normalization: batch size = ',solver_bsize)\n", + " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + " solver = Solver(model, small_data,\n", + " num_epochs=n_epochs, batch_size=solver_bsize,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': lr,\n", + " },\n", + " verbose=False)\n", + " solver.train()\n", + " \n", + " bn_solvers = []\n", + " for i in range(len(batch_sizes)):\n", + " b_size=batch_sizes[i]\n", + " print('Normalization: batch size = ',b_size)\n", + " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n", + " bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=n_epochs, batch_size=b_size,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': lr,\n", + " },\n", + " verbose=False)\n", + " bn_solver.train()\n", + " bn_solvers.append(bn_solver)\n", + " \n", + " return bn_solvers, solver, batch_sizes\n", + "\n", + "batch_sizes = [5,10,50]\n", + "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.subplot(2, 1, 1)\n", + "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", + " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "plt.subplot(2, 1, 2)\n", + "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", + " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "\n", + "plt.gcf().set_size_inches(15, 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2:\n", + "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization\n", + "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n", + "\n", + "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n", + "\n", + "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n", + "\n", + "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n", + "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1. \n", + "3. Subtracting the mean image of the dataset from each image in the dataset.\n", + "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization: Implementation\n", + "\n", + "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n", + "\n", + "Here's what you need to do:\n", + "\n", + "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n", + "\n", + "Run the cell below to check your results.\n", + "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n", + "\n", + "Run the second cell below to check your results.\n", + "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n", + "\n", + "Run the third cell below to run the batch size experiment on layer normalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after layer normalization \n", + "\n", + "# Simulate the forward pass for a two-layer network\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 =4, 50, 60, 3\n", + "X = np.random.randn(N, D1)\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "\n", + "print('Before layer normalization:')\n", + "print_mean_std(a,axis=1)\n", + "\n", + "gamma = np.ones(D3)\n", + "beta = np.zeros(D3)\n", + "# Means should be close to zero and stds close to one\n", + "print('After layer normalization (gamma=1, beta=0)')\n", + "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=1)\n", + "\n", + "gamma = np.asarray([3.0,3.0,3.0])\n", + "beta = np.asarray([5.0,5.0,5.0])\n", + "# Now means should be close to beta and stds close to gamma\n", + "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n", + "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gradient check batchnorm backward pass\n", + "np.random.seed(231)\n", + "N, D = 4, 5\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "ln_param = {}\n", + "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n", + "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n", + "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", + "\n", + "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n", + "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n", + "\n", + "#You should expect to see relative errors between 1e-12 and 1e-8\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization and batch size\n", + "\n", + "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", + " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "plt.subplot(2, 1, 2)\n", + "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", + " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "\n", + "plt.gcf().set_size_inches(15, 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 4:\n", + "When is layer normalization likely to not work well, and why?\n", + "\n", + "1. Using it in a very deep network\n", + "2. Having a very small dimension of features\n", + "3. Having a high regularization term\n", + "\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/.ipynb_checkpoints/ConvolutionalNetworks-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/ConvolutionalNetworks-checkpoint.ipynb new file mode 100755 index 0000000..2169f14 --- /dev/null +++ b/assignment2/.ipynb_checkpoints/ConvolutionalNetworks-checkpoint.ipynb @@ -0,0 +1,1251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Convolutional Networks\n", + "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n", + "\n", + "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# As usual, a bit of setup\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.cnn import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n", + "from cs231n.layers import *\n", + "from cs231n.fast_layers import *\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution: Naive forward pass\n", + "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n", + "\n", + "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n", + "\n", + "You can test your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_forward_naive\n", + "difference: 2.2121476417505994e-08\n" + ] + } + ], + "source": [ + "x_shape = (2, 3, 4, 4)\n", + "w_shape = (3, 3, 4, 4)\n", + "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n", + "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n", + "b = np.linspace(-0.1, 0.2, num=3)\n", + "\n", + "conv_param = {'stride': 2, 'pad': 1}\n", + "out, _ = conv_forward_naive(x, w, b, conv_param)\n", + "correct_out = np.array([[[[-0.08759809, -0.10987781],\n", + " [-0.18387192, -0.2109216 ]],\n", + " [[ 0.21027089, 0.21661097],\n", + " [ 0.22847626, 0.23004637]],\n", + " [[ 0.50813986, 0.54309974],\n", + " [ 0.64082444, 0.67101435]]],\n", + " [[[-0.98053589, -1.03143541],\n", + " [-1.19128892, -1.24695841]],\n", + " [[ 0.69108355, 0.66880383],\n", + " [ 0.59480972, 0.56776003]],\n", + " [[ 2.36270298, 2.36904306],\n", + " [ 2.38090835, 2.38247847]]]])\n", + "\n", + "# Compare your output to ours; difference should be around e-8\n", + "print('Testing conv_forward_naive')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aside: Image processing via convolutions\n", + "\n", + "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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XEAzj+7PMoDPhKsrMc6YoiqjVaoXGVWb+ZW3e9cE0RmibfaX27JjfNM24UqmQpin1er2wboKxFJ6wHLIso1arMRqNCvjIt1KcIHNMwGciZYGRJEnBIKcx32kLHewYu/f69XPMzmn3zhoql10WBI7hbrcF4DR5R35/uL/9NvnjIiIkSVL0nZsX054r17usQPjCxf/fJzOxVvTEfAEKy85XLNz/02Cm8nj7c3szSMhve7mdYOfWZpBWmfwyyuvj+dB1IQAsNJAPOhoVGIJQFVBNpVIB45mEJkGpECEgyVKkGtmJL6nXQVcyCjcJRAuaXBM2hjiyWrLUIU0EjJDJEAhRYtAyhkaMDsamYBqTZUFRvopCUJDpESIZmBBE55NpiOTdbdsQkmWGNBnXqxBsxmK3qVZIzijLZIxgjM7hoBwlMhkSjBAxBMqgjWAkswIqS8dM1mgrFLQmyxLSbJjXeduGFOAKRuUW7jRNyidfmyprPeVnnMAof9xzcWzNPh/e8iEAv8wsy6hUKgWTcOa3Dy84BWMzYeTemSTJBNNx73XKgM+E/PLcNf93X8N1wsrV39c6fVjEfbTWE0JvO8iHT/Qm87PMAH3m6PrQCajy/X4ZZavPLxOYOk98pWla3ct+H8eAXbk+Y3cfJ0inMeqCrzyL+ezuLwsDn5n7bXD95UNAZQvohdB1IQBMZLVmURlCjDYQqACN6yBVpDcTpRCpWg3WGAgsM4lMgFHj5ojnELadNmYgFu640iknZoybG13NBwqMBy0pBK1zTN6476DNGBMemWE+WBqtffwuBaxD1+gqWZahK2MBACAEViPXYEw2iTuasbacGRADJtOYHNvKcm1+DJup3LRMqJi4KCvTfbJMSEYZQ5NidIYKhegazIZpC9jv8820Jp9Jlhe+P65lweDa6Pepv+D9MnwmUl50YBdlGYpyz0+DDXxm5OrvP5um6RUaqQ9vlbU6XwiVYaCyAPDxf2dFOG27DGdsF02zYqZd3+yeqwmmqzFVvy/9Z8rfy4zbWWrue7/fn3iPD1m5ezarR7ltW81tfy74jN/vI9834P52zvxrQdeFAMCEOU4eYB3BpctbDEAQBBiLcWz5inIHT9MqlFIonUc2mGCsKTIeLPFeMzGQpMVvVVO12H4miPKwbaNyi8BJfFVg7+4WpRwk4xiNmzTWHwAWrx9p6wwWDTp/t2VYUtwnKLTOyDKNy52ntSZLA5LEMAohSBRRFJCmhn53e00Ap/mUHV0+4yw7+9z3MtN15N8/DU/fzF/gLIEyU3F1nKYpO9inTGXB4de1rFj4kI4TKJstZp/BO4HlM3pfE/Wx8LIzPUmSIrJqsza8EJrm+JyAKvP+9y2Fze7diqZd3+zZ8u/lOm4lSKbh/dO06zIfcgJ7s7I30/rL9S8LDX8ulpUcHx7dDsvuuhAASjnGPzYHRQRkUhKWTUmlFKm2MIiIYPCgBiYXuSPb2dO1zswYHA5i9FgjU6EXVqnHTMZnJooc1kAIVG6OAoghCnMnowkAhTYpgYydoxPMjCx30IL2nLv2M+6HSAuiDQrJHeEGrW3SwjIzdZqh1SYs3p9lhkEAURowHKYkCdQb2zsdfBPa/e1juD6VF4evrU27p/zsNG1xM2ZQ1qJ98p3D5fsdzuv7Dvx6+ePorrn/HfTj12caI3X4ePm+slB0vznrACggFcf4gyAgSZKp79kumsa83Hf///L92ykU/N/dOvUd+zCprE2r9zQFwlEZbvLLdNenCQf/uw+XTVj8nnXi18EpAH6dyvSS8QG4xkVBSCKmCGiMxKAk97BzpWYHEOR3G0WBsYsIYhSiDDbN+1hDEhGMGmO+gYy1U/IB0lojoRUiWmsSNBLkMdhiTTYDGJ9pmTFM5aAai9FbxzII4gSD5DH4UGh1Ywamchk0xpudRqBwk1BQheWjEVwUgmDEXjfa9oEVYhBIbN8VhGQmJU2stRGNrL9lOAqp9Lc3DNS1r7wYp8ECm+GZZW192oIoR4yU4RnfavC/+5q1DzWVtbPNBM+0upaVjWnwkd8ffnQLTDoEfQvGb5crI01TKpXKBHzk5spoNCq02+0O792MCYvIFbHs09ruC7iyhbAVrORf2wxquVrEzDRYyi93Kyze/32aElge62nzpsy//L4oQ1XlevtllJXh50vXhwBQIdioR4zxTHFlMCITxzvZBeMsBYXDZJz2bX8XRBQCxbO2w8JcmOTaiQjKk/ZlrcVpfMZYYSCiCEIPy/MHYCKWn4lyivpN0RTdwJejOsqT4YqJKE6w6Yl6Z7nvQ5TCaI1SYn0l2k0aCzmpQAi0Qip2Y5zGbDtcUF5k/uIva/ibWQSbLRj/Hb725DTtssbswzO+me1/d/+XmYDfhmn+gmlMaRpjKNffh5+mkS+QHPP0NUe/TWULKoqiqde2g4bD4RXvn1bnzcawfG+5jGn94cqYZik8G8GwWTBBeX2Vmeo0AeJDf1tBT5tBYNPWtD+PpwkUt3FxWt1fCF0XAgDmEKxjUwFkubaPzp2+AibXYowNtQQswzfOaatx7gMZBzqiJESbFEHZhw1QaJIWdipP1LKmqbLcGjAajb+ovMUceAJAT8e1i3u9Z6dpd/4zExizzzQlQwW2QzTjeGYlauwDEDc5DRJZ4WqM3bMgkjuYEghCQ0xIve6flPfCyWf47rs/wTczbbfSvMpM3P1dhkemLaQyXl+2CKZZmNO++89txnB82gp+8LVmP7rHr4v7e5r/wgkxF63k18GFOF4LJ3C5T8sKzTThMw0mKY/DtLHfyhfg/+1r9mXBWp4v06wGv9xnAzWVy55m2Zbx+2nPOhKZDG31lUB/bmwH43d0XQgApUIkCMkQRGdF4yMVIIX2bdA6RQVgcPiYfX4couWYuaDVGEOD3Lpw9oABl55BE3kQUOZZG9Zpai2GAKMUmegCcgLQMtZysmwcKkjgnHmC0lm+EclMwD7ajCdaphNUYBeSYhIznmBy7sUiKAyibDirZBHWEsAK0ixDBQajnTk+flopBZmxUVcGTAUiDIESeuH2Rho4+MfWYVIbn2YdlGEi38HmFsVmuL6jrayFct3cYioLff/5MjP2/3cbzGAy8sdRGcZxv10xrnm7XR84K6RsAZS/l2Esxyxcn8dxPBFquV3kLJEydl0WBOV2u78LZaWkofsadXlzXpkh+mU7QThtZ/JmVoZ7n2Pam/kOyv4e/xm/XWVtf5og8YXAVvP4asqPU6ZeMj6AQM0SuBBONRYAmBzmMQ62AIxB4ZnaZkjg+LfJIIdqJA/7dFJCZ72JdxqTQe5foJik4w41OhxPVgwY1+HR2AMfZBRROvm/FnLxBlCC/EO+M5f8fR6jMb5WnDMD5ZzCZvxdjyenTUCRQzomHUNCOiByIZSBKurqFs4Yg7YQGcpO4jTZHkzRpzAMJ5ygPgP3Ga3Dvf3ffCinDHv4z5d3ApcXWJm2wlddHaYtYlcPn/n4QsTBZz5c4/re383pU7m9fv2mMbQ4jov7XNk+M3blOIbo/ADbDQE54eQY+TRLt8ysy31Z1sjLVB4n1/++IHBWj+9DKc8xf59ImXG7664O0+ri3lfeoDhNYXBl+H3g3+t+KwuZrWia5bJZnz0fui4EgAprqJxpFR0qgsHu2LW/aVKTopRNzFZoFtiEbcYYm1RNKYyIhX3ERgYZYzDEE1JTm8QuMj0Ot1PGmwSmktfBOV0gzVKMzpmLUWTpOIVDKAajjXXOpoMxfCOaJHX1dZobaKML0z0kxmT5/Xo8kbW23oox008Rcf4Ou0/BaI2ItoJStN38lcNb/mTzF5DW+XWEzCQoBWEk2+4D8BPyOa3X39QC4wXja1DlheU/U8bWfQEDY028rD36GrWvQfvMw9XZj6P3F68PVYhIEVRQhjWc9eLuneb78Bn8NOvCleM/5y/8smXlP+P6I47joo7bSS49RpmuhDmvJD/U1Ye/yo7VaYzT7wsnEIEJwVB+92YKQ9nCdPf4Fp8vLNzffhLAcvnTLBb3uy80yxaMI3/sNhOQzwZyfC50XQiASmiZeKAmOzZQNgI/UwZMRKAqSKpBec4SPY6rFmMTyBljCovCYHJmayeLCiEzGegKxoBoQxjHJJkh0wOUTmxopY7GGo700MQkWZWKChgNUqqVOgOgFgvpYINROKIiIVESkKkIsKGcYvIJl0dtuMkTKkOSJNRrc3S7nbGzOR9TbQyofALkeL3oNGd6LhOm9XNoIESjtP3uhIQ2OW6YZRidovK+QRmM0RhlqJmYYaAZZCnI9kaLOEbsh0T6Cx3GEUFlDHUzxusvPl8rd4LOD7X0sfWyRlqGm6rVKr1er9Cq6/U66+vrE4vUZxplp70j35rxM5yWaRqzLMNS/jVfSLp+KDNTX0PdrH7bRdPq74+Nr3mX+9B/bjNMvTzOvhLjBLT7+GPrmK3N1msTHA6HQ9I0pVarEccx9XqdIAiKUNnyPHPllWGsNE0nrDCfypCWMaawwFybfGHgyvX7xrdmy+NfhsAcb3qhdF0IgGmSTCmbF8iIDfFUEuZRLVar9hfL2Mnlm0l5OWI3Y+FjhyZC5VBLGghaVUnSlEqlTjoaovPduiJCWAmJwv0cPnoLSarZd/Ag+/Ye4NFHn2T/0gznzz3NU0+dYDAakg46DGSIMS4fEZDDOzqzsI7ObN3jOAYzYtBPCYJWPphSAP32efux7SSvsyHNvCRq5FgsGdoY+3FMRFFsKAuUKXaiahmHGmoCtNEYk8D2WZa2n0sTfKu//YWyWQifP+n9+32YwX8mjmOGwyGzs7NsbGwAkyGjlUqFQ4cOoZTiyJEjzM3NcerUKRYXF7l06RJPPvkknU5nIrePv8PXF1bu3XEcMxgMgDGTLm/Ymab5+dDBZs4+H+/3+8jBIV8t2kpz9e8ppyXfjHGW2+kLfxgLen+Xc7fbRWtNo9GYYIgbGxt0Oh3Onz8PQLVaLTLCnjt3jnq9TrVapdVqFckKnYXqW6W+kHdj6Pt8XLvLMFeZSbv6O6iprBCU+8xvc1nou3r4c/+F0nUhADY3UUPcli5j3KCIC7MvFiRcGQqm1HhPgDEGk2nI4+Wjqs3VsrS0xO49B+kNhkRxlTgKGHbbmExTr9iwqyNHjnDrra9iZn6OqBITMyBNNN/4mlcwTDMeefwxXn/vvTz26ClWVi+wvHyWlZVVkrSHJiEMAjLPRJVQCMKQYZqiqUAQo3UVm0PIQj4OuhKjrc8j1+gx2grB/LrWbg+AxuROcttPeX/lm8KUiL1GBgLaTzcggMkwOrP7CrZ5XH3mOA0z9e/1GWpZe4dJS6BsIosI1WqV0WjEwsICBw4coN/vE8cxi4uLPPXUU0Vyum63y+23387CwgKHDx+mWq0WC+61r30tIsLTTz/Na17zGh544AHW1tY4d+4c7Xb7CmjAzS9nOTit39V7WkoLxyjCEuzp4/l+f5Udwr7F5Lffx4l9CGI7MWO/77fC8ctOft+6m3avS3I3jVz7B4MBq6urgO37fr9faNp+f1erVe64444iDbgTIFEU0e12abfbnD17ln6/T61WY/fu3czMzExkr3X1NsYU88NnxD6k6o9Veex88qHJMlzkCw2/HH9++Jl1/f9fCF0XAsBIiNYZoswYppCxozTKcwFprW2CuCLRm7GaK5bhZSoo/AP2rBODGAhF6GE3mimEmVqDPTfs4ciRIxw9cgNRXKVSb9CoVmi329RqNaqR9RlEUURUifOBAWPqRHGu7ekRr7njFaACjt1yM4YhFy+u8uVHn+D8+WW++IUvMRquEgaCMqDCiCxLSJIRSgUo5eK6TQ6P5H4MA6I1Jihj1sqDiDRGpwjWNxFbDzki43h/Ak1gLMMXiVDKMZdRsdjS1NYlkBQl1e0f2y2Yvr/gy5EuvlO0yA7rCQJfE3bCol6vs3fvXo4ePcqxY8eKA2Cq1Sr33HMP1WrVJhbELsZarTbxbt9EP3r0KEopbr75ZtI05fTp0zz66KOcP3+eRx99lMFgUCgflUqF4XA4FbopwxS+ojINuhIRb9d2WoRy+lCag0j80ECnnfpCxu0E3u5kcGVLy6fNtP1pUTG+87XcHvd7v9+n3+/TbrfJsoz5+fkCillaWqLT6VCpVIp2x3FMtVrWD5BbAAAgAElEQVSdUBTGc90mfmu1Whw4cIAsy1hbW6PT6dDv92k0GszPzxeM1j8bwm9P2Vfm90d5jpap7LPx+61sAbg6lKlsSb8Qui4EAOQNFT2RwsEnX/OaJE8jLGLrAfJwu9Rqu/VKlUoUc9ONR7jlhv3cdtttNg11vUFcqWFybbnRaNjyjKe1Go3bPwCZ1boBE8REyvogGnGANgEHdi8wU2/S6XTZ05zh+Fe+wMrKCiJCMhoSRiE+1uImaMGAcgGgRJEWUUkO7pnEXVUQ5AIOAnHawnjyaLG9GYUhmU4K4SBE4wUSVTBayIIhUTR8jqO2NfnaTNmsv9pzjvyF7F93pvjs7CzNZpObb76Z/fv3c/ToUWq1GmEYTuC1MzMzwGQEi6Oyk87Xzt0pYDfffDOHDh1ifX2dG264gb/9279lbW2tOEWsHALqW6a+UChr7b7G6KjsZPTxbdcXvtDw4SD3LqeluvMQrhX57SkLdEfPJoKlHCnV7/fZ2NgooJ56vc7c3FxxOI8jt16nOWbLgt2dD+F+11pTrVaZnZ0tBEGSJCwsLBRpzP0+fzZ9MO3aNPIFheujzd43LSnd5vzwudF1IQDsgOQwCeNFqMzkPeXvWmswVhqLiIVachxdmyGowDL5MOTwwQPcevQmjhw5wt7FXeNzAwK7f8AYTaDGnezSSIiAMjbfcubymIsgRghUwLCzSiUUkgxEpVRUhcp8k8X5Fgf37ubvvPFuHvjKI3zxwUc5v3yGTmcdBMpjVwzsxL5nx6ic1uClznUOKwNRoJAsj6X3BABG2U1fBgKPyRivY5VRGCXE8ZWHYLxQ2kwb9mnTcfV+8/92C7lSqRBFEQcPHuTmm2/myJEj7N69+wqIyGew/vt9TazM/JVSRTpnh607zbFer7O4uMirX/1qHnnkET7/+c+zvLxMv9+fyvS26puyM6+M7fpCs9wHfjvKjlCfSfiww3ZRmelvxQD99mxFjoEPh0M2NjYKv8vc3BzVapU4jgsfS9ka9Nvtj7V7t69NOwHg/GHOmqpWq/T7fS5cuMDjjz/O4uIiS0tLwCQ8CVeP0y+3+2p+EL8PytedEC/7zjaD3p4rXTcCoNDk87kqCKI8Lc3LtqnTfDETYJS36STfOazThLm5OVSSsHdmhjtvfyW3Hnsl1XqLar1GKgotGbERBqOEamgQnaKDMa4XqQgTWOaotSIb9QlFgwgSxDDKMMNLyGhAhnUmB8Yg9TjH8SGuhiyGDb7u7ldwy6EF7vvSw3z5yfOsrrRJ9QY6G6JIUF4OI/KoH41hMgkGiIwXWyB2M5k2dvevL0AKTDnvM8SCacY5hxk7vEQZklE2EbO/nVReOJuZr06rd8ysvKAc3DM7O4vWmvn5eW6//XaOHTtGrVYrdjG7BT8cDgtm4Ri6w+n9VBG+89Rh+C7SxIeZfFggCAL27NlDo9FgaWmJ++67j3PnzrGysgKMs376yeJ8WKPcfr+97l4fD57Wj5s96z7Xch+AT35dni1D8gWcG+9ut8vq6iqj0aiAvubm5gp4xz3jEt2VlQkf63f/+w571ycuQsiNq/M9OIWi0WgwGo24dOkSTzzxBEEQEMcxrVaL2dnZQhCV2zzNwVtO7e23vfybr3yUadpel5ccBFQmvyOsI3PMJFXkx+t7sAgBZBkhhlpvwFvf+lZ2Le6i0axRq1cIIiv5K/WaZdKjjEoQoLMEhYFE5wnXMrLAniGglEKbiEAgGQxQUYQajRBtwzh1koAYRAWko5QgTiEYop0Tx9hIhSgM+TvNJkdu2uDCSpvHnnqc06dOMOi2i/qX4YKys8ePDHAtzln7mFFMaJSTibcKbUhPRiFM0zyuBU3TwP32lmGUMjlc/K1vfSsLCws0m01qtRpRFBVhfu559xuMmYOf39/1hXMiulwrjvm7e6MoKphSmSk3Gg0OHTpEo9Hg0qVLrKyscPLkSZ544glGo/EekbKjt3wIypXjeuVO2mlOXhhDWmXG4Xwjrt7bSVv5AJ7Ns34ZLlzz8uXLBdOem5uj2WwWz0RRNJE3343BtFPX/H7yj610c7wcnupCk/3+j+OYPXv2sGvXrmIOOV9M2fkO0zV9/3ff/+PfU7YKpo3TNDj0ucCpV6PrQgD45rDaVLKNF16GBpXjp15OnRChGge88qabeeMddzG3e4G43oAwIpCMMI5ACQGGZDQkBiQdgc5IhgMkd/wGIgSZ3S8QRBGaFBUFpJ0h9SAkHfXBpOjRCKNTgkBBOkKP+ijdgjSw7TBindZincmtSLN3VjEaRrSaC8w01xn1B2TpOGxwcnCn7zYEsCeeOYtgnELY4CcNG5fnNB6RsSMZQBu7MWw7tQpH03DZaeQzY7++jnG6uPwbb7yRu+++u8BoXX/FcTzBEGEcMuk0RscQnfnvM1GnETqt32cs/k7RMkOBsdNxfn6ejY0NWq0WrVaLy5cvT2iijjFNs26mWQTldBC+M9zd4zuRp8EPZafqtaJpAqHsxPctPHd/mqYMBgPW1tZoNBo0m80Cp/fL8C0wHyefBv/4io4fPOCPpQ91lrOTusypvg/J7ScYDAZUq9ViH8E0JrxZX5fXVxlCK/82rW/9dpTve750XQgAH6czelKaO7IMzw5yvVErBgQ9xkAbjSYH9uzi619/L625FlFcIYwispEmlRGokCCK0VkKOqXfHxIoA1mKoDHJCEPu5c+xc4IAFUYw7BMFQjLsI9kIGFrGroR+b4NqIyYZDakkA0ajpNilaKoNlLKLv15RKBOig0V6ukY6GoJO6XYvMxgMJibJNE1jgoyN+rH9Nz6oRnmMxoWBOiQpUE7DnsynopTNtbTdNDGuZvq4umtKqeJM3uFwWFgmdlwb3HTTTdxzzz3Mzs4W5rpbrJILWNfuXq830XdOCy7j4X4G0NFoNBHZ4xi2gwnchiIXYeJbAvV6vSg7iqJiz0Gv12M0Gk1lUD6VMW2//8oWQNlqcM+Xyb3vWggAnyE5YVUWQn7yP9ePrq5OeKZpyurq6gTz9zV1B9e5e31FIcuywuLznaT+nBsOh1QqlSugMDdvHJTk/D3GGCqVStFng8GgcBRrrRkOh3Q6nYlnn42PxffRuD5wVO6zsmXs7/guO8pfMj6ACWeYmcSJtdhPaOzGKq1ThhvWaVsJFL0spVWpsm9xnhv3zXPrzbfQqDWJiCGFTOcnYSWaNO0R1QzJaGAPbc+GdsIpg0iASUcYJRhVJUtGFl6y+SEI0oxAQrJ0wGg0JMAQxgplIsKwT5ZCmI7I2pegMQOZIkkUOmkjgbIHvwch9XqNvWFCfKjOQuMYjy0s8sDx++j3NcgIiUJMkk4ssjHTsDt47QWb6tli+uMUdaJNkeLaYPMBaT3uXxVYjR/HhA3YNNmaKIq3fVzd/76z0rXLXzhZlhURH44JVCoV9uzZw/79+4u4bqfZumfdcXki48PG/Y+735XpmKh/kIpSqmDU7ghFH08eDod0u91il69TPhwTiKKo+O78EU888QRf+tKXGA6HxTv8BewLA6cdu36ZJgx86wiY0G5962eag9YpI9tNvjbq6lQec78uDmZL05SNjQ2GwyELCwtUKpUJxgsUloDbV1GpVCbONQjDkH6/X7y37Fdw5TjfTzkpnu9HcPUqQzVOyUjTlHq9XrzDjybynbGb4fuuPxxtpuGXx3sag99uYX5dCIDM6DyBs3eohzEUOq5Mdo7TYtM0I45jAlLm6yFLs3PU48gew5gM8rQIdlCtl7+LTgZEShgOu8w0GqTpOO+IZBlBHJH2h/Z84kyjR4ZIUkCRjhJUvlgrlZgkNQTGbjrLshGKIcP2OlWVkiUaollAkWUmd9qmMMqIo5i5SOhWMhZn6xy58SYeevhLJKmFLALG5xT4GtY05mDbN3aiigjkvwUyPtbSty4m8gOJPYNZq8lUwttBfp3dhPYXqn/feFwndzy62Gy3WMsOMRe94dJsJIm1vsrJ1KIoYjAYFL+HYVg4fMtal58/x5XR7XZpNBoTwsUJIid44jjGGFNsPjtw4AAnTpwoHI8wqT3747pZlIg/roWfp4RZu999aMOHirYb2vMZVpnpl+enf/auHy5bq9VshF4cT0S5+LmdNjY2rvDr+MLfT4fhC0hf+DsGXbbCYAwtOWE6HA6L8XbOflc3ra0vr9vt2jXq7XKelkF0K1hnWl86mrZW/DKm+R5eCF0fAsDD6WzEZT7RAzU+/D2bTHVLnuOnrgyvuuVGXnnDXvbuPkAYR2QY9GhQLDxrlreLiafJCIDl82epz8zaiaU11TBPkaA1USTo1FokOk0YJZo4qpPpYa6tCIGQH1OgUPmmtGpg0GsrBItN0kpEJW4RhKCHAyLdAxHSLAOJaNYi9i1ErK4sUKs1SDqDiX4pm/0wyUDKzCIIAnSR/G7y/jJkUCxebaA4P2F7yWek/iT2BRCME7j5VKlUuPXWW23Y7t69xQYu56Adj+tGMa6Oqa6vrxcpABzm6zRxH0dP05Rut8vMzExxn+8Mdn3vtMi1tTV2795d7A1wp225BesYdbPZJMsydu3axfLyMu12e6JtVxvXcprfcuI3/35fEPjas+8AvxbkC61p0IZ/zWfu/X6/sKYajcYEjOPKcXOk1WoV8F2v15uAiPz3OE3d708njF29XH+7vnWWnoPrHOTjJ4tzmr4ryxhDq9UqFIetqHx9s/s3g3SmWTRlYfKSgYDQQy+HjRTaX0pQpIIgtP9rMozJCExEFMLN++Y4sqdJo6Ywpk+WJujUasCZ0US1JoPhCFJDJVKY4ZAUTRAI1XqFIE0ZEWB0QhhGRBlUSEkSRTYc2Pz8qWHYXiGaXSAIQjJtkCAkUJBqm2k0S0dkaYAmI662CIKAWnOWrGo3IEk1JRv1CPpd1GidUKdEegD9Pqa3zmDQtc7qPHOoAdAZokIr0MQQFIFPFgoKEDKxO4TBHQhj2Tki+W7hHCbwFoOvfQb5QTdRYEC2/wBxZ3qXGUYZpvF/D8OQI0eOsG/fPur1erE4fWFSrVaLJF9xHE/gvE7LdE5dp8mNRqMCy3dMcjAYFBvHfE3aWSEuAgig1WoVO4h9+Mcdwu4zseFwWOxiLTvDfUelTz7OW8aJy9BZ+Tf/b5/ZbbdVB5M7YX0n62Z4uIN9XD+6DV3ummPszoJz0I6DAavVKs1mszjo3vWTH9Lr/ndzYDQaFQn+/P7wtXUnNJx1Wa1WJ+Ai3zdQ7ls3p/xxupovwKcyBLoVjFT+fTvpuhAAkazZ3DaiEYnQmcXfjIwdSf6Ri43WHK84+kr2L+yiJilV6RNpTTroohGMikhTmzYi6Q6pN5qouML66nlMMiQarRCaEfOLB0ijCsHMXrTESKrRDElGAyJRdNc3qM/PEkQRM/UYnXYxukkUBKSDEVRCwjCi2+sxGI4QUyUeDuh3LrBQjUAnSJZgggitQiSeYbU3oGIilE6pKMO+lkIf2cWTJ1ucW10h0eM0Dc6Jq42Nekq9Q2TI3CQaO4ttX3nREdhzCLQxRbisqIDUZYvEJpEzuZ9lu6ECR26RlTHO8m/1ep2jR48WmLC/mF27nVPWbd6pVqu02+0CK3Zwn0sB4TRGh+UChalfqVRoNpuFIHFhkz727Kyl0WjE+vp6gVX7cEMURXQ6nYIxR1HE/Pw8+/bt48yZM6ytrV0BM01j9mVIx3/HtD4trGbPhzbt3u1mHv47ns3eEWMMa2trXLp0iXq9zsLCwgTs44Sss+pcCK8TqH6uJqUUg8GgGAM/4seVNRwOi7w/vqXsQ6HufzdPgEI58C2WNE3p9/uFxed8VcYYZmZmNg1dngaH+X1XFhbT4B5/bvj+oufS91ej60IABMEwb6gGejZ3ThBggup4MXgQRWaEjY1LzN9wE7PVjLRziUGvi0R5pseoijYK0YpKrZXv0gyJVEBv0Ge0fg6T9uzB8c2YmeYMQVinvbJGtQpRKAw668w2ZuknCcnQUBdhfW2V1qJ1BoXVqt1Ra4RWqwXK0Es0Fy+ukrWfYe3pB9n7hgqD2iEW9h0ksJyZ5uwcJDWCbpv+ap90OKJZFV7/2tfyF5/+BIPBhtfmSXIpkEy+sUtrbVm4p+k5bVtrjc7TaxsociRlmUZb5wjG2LBRFdhzCLYbLvA1fB+L9rUff0EYY6NobrnlFqIoot/vFxFB5TJd6KZjyMPhkLW1NZRSNBoN0jQtFnan00Fru3ms0+kUQqVSqRRa+sLCQlGeiDAzM0OWZQwGg2LR93o91tbWuPXWW4vIFVef+fn5wjnsrJW9e/dy77338slPfrJgIs+mj/2MpY58TduRD6O5v929QPG+MqT0QqmchtqRL2h8xhtFEYcPH2bv3r0T0TxBEBTC2PkCgGLnr4P7HCP3oTzXH75z3UF1jUaDtbW1YvxrtVohUJIkKQRNs9mkXq/TbDbRWtPr2UOjOp3OBKzk2uKw/ziO2djYKKxHf167cSgz66spP+75Mqzm0JByRN12rdXrQgCgeihFvgnLOVY0Ip7DTwkYu2NYBim9lac49bhw9OBh0BswGtBsGiSI6A+6iEoZDAydzjqN2V1IOiJZv4AaXmY0GGJGXTrJwywefAXJSpukqVhtrxOtDVjaZxNFbWy0GQCN+jxpZZbaUo0oDIEhab+Lqs9ganUCpYl7fdrddc6cfITLq08wkoMcGD5GY/djzN77BoazN1BpVFE6JjWaqFqnOb+PpNqimo4ImwEXVu7i+AP30+v17OJWmY3S0Rr0OHAnPwyMDKvZG52hTT5JBLL8WE30aIyzoyDNk8PpJO9LAGOFQmrIku1PKTwtR055Yfia/jPPPEOtVmNhYaHQ9pvN5kSmTYfRu2fa7TbD4ZDRaES/36fT6VCtVllfX6dWqxVaXa/XKxzBjpm6qJ16vV5ogG6PQaVSKfD7++67j9FoRK1W4+zZs+zdu5fbb7+dVqtVCA2nrc7OzjIajWi1WszMzNBut/n85z9Pr9ebgCEcrlvW/hxj9/cd+Bq+z/idJVF2Fvt497VIFV2uxzSflLvHxd8vLi7S7XbZ2Ngo2lfk3sox916vVwjaZrOJUorV1VXiOKZSqdBoNBgOh8Uacam3HabvhIYT7m5nL4yVo0qlUgiTVqtFt9tlMBhwyy23sLi4SJqm3H///UX/+cECbjwqlQorKyvFpjUHC/nzeprg9QW7659pwmBa6Kg/5s5P8kLpuhAAosYOHFEGpWyK4wntUEAyITQRu2ebHNrVYiEa0l4+TWs2Jg5jBp1VtIRE9Rl63Zj6zC5UpUUQVlnrnkGGXczlZbQosjSln6ZcfOYRWqpOrbaLuFoj0iGZVkhcZ6bRoJKOUBLSbq+xcNNRGPRI+0PCahPqNahW0SYjbtWY3b2bW195J3/5H5/mqV6HP/ngezi40OK/+ZHvJVm8nZu+6R+gGrsQFTMYtAnCECSg0+kyHwW87lU3cWnlFKdOnaQ/2MByeoocRcZEY9Nfcu1fZ4RjXm4TauTJ64Sx81BrGyJqgEhNTjYbqZSS6s62jqubxH5KBN8ScBPZMba5uTmWlpYwxnDp0iWazWbh6BWRIuJnZmamiPTpdDoMBgPW19eLMtvtNmfOnAEoIk0c843jmNnZWdbX14uw00OHDtFutwvt0ceWm80maZqytLTE8ePHOX/+PCdOnODWW2/lne98JwcPHuSuu+4qBJuLdBkOhwVkdOedd3Lp0iVOnDhRYNiuf3whUMb0y9rgNI3fFxrlrKkiUmi+20l+OKZPZYblhLUTwO12u0jZ4drvhHq73WZjY6OAXdxpZq1Wi2q1WuR/cmU5zd+de+wnjfMzqDoLw0X7uL7pdrskScKZM2doNpu88Y1vZHZ2tnAE33rrrZw9e5bV1dViU5hTPJzCcPbs2cIimXaanh/l9WzIH7ut/AnPxddwNbo+BID4O1annRtrG1ytVqmZmMVogfnGIpVanTgdEpmMQbdLZhSDNCHrZczsXiIJhwRBTBYqTFghnltCDwesnnuSjdVlzj1zksNHbmNf5QJhsB9dCwmjCojCqJhBb8AwGxCoEXGWQndIGtcIZmKyBAJtwCh7bGNjlppE9FYuYxb28Dvv/zOeYR88vs7v3fdr/Og3H+Z/OvQqwgNHiGYW0MMuWo9QWYLSGSsP34eqNtjonac/ugDBEJcfyXaBQUlJ4hsDJptgFna7nPMWj3egBoFg0PYgePGxYmtVaYZs94lgPkPwMdvyPbVarYBuWq1WEbcuYnPEAEXs+MLCwsTzSqmCSZ87d45er8fy8jKDwYA4jouIHd9h6zZ0OXjEaf0ORnDvVkoVWSFvuukmHnroIT784Q8TxzFf+tKX+PCHP8x73vMeDh48SLPZLDYdAYX18swzzxRCzGHXZZoWBw5bp1zwtUenHfoRQdeSrhba6K47i8sxcGcNOmbpoDwHx/n5nNrtNu12m7W1NY4ePVr4ARwjL8N+zWYTYwydTodGo8FgMGDXrl3FHg63b8PBQmDn1Pr6Omtra/zhH/4hH/nIR7jnnnv47u/+bt785jcXew8cDOmETrvdRinF4cOHOXfuHI1Go3AY++T7aTYTAmUryl8v/j3+/5vNl+dD14UAINdoRRnQXpZENKAQCagEMVXV5NX7jjBbqxCqEBl2yZIe7Y0BQsjy6jqzrRnquw4RZhWGwwq11iK7FnaxdKhCJe2xOrNI2l/m7IkLXNroIaee4sLlNkdv18zv2cfBV72a3iijUQ8IJUV0jTQdEdQarJ17iOrMHtTCAipLodICnaGN3cEcVGL23HUPP3DLnTzduov/eKZBv3OZzqf+Pe//5HEWfvm/5gd+8B8T3/YGgjxnEcMBo16b3kiotB/lx7/xXv7g4z0evPg0go2GsP1hN2zZ7hIylR8OowySeSa4URRzR1lmJFpbWyDMoy6UbxlYIRbFhjDafgFQDvksT2K3MI4cOUK9XifLskJLdhk2nQN33759BbThcvvv2mUzuz7zzDN0Oh0uXrzIxYsXMcawsbFRJJA7evToRKSJ0xDjOObcuXPUajUOHDhQOHVdPR1WfPfddxf7AN72trfxwQ9+kI985CP89m//NocPH+Ztb3tboX1qbfPYO8jo4sWLvOMd7+B3f/d3C40Xrkzq5vqivF/Cj+kv3z/NwVgeg+0WCpuV54+tc+y62H0XdeWYqIP0fGbuYuzdGMzPz1+xB6ZSqRSho2tra0WUmIsycvs96vU6vV4PERuWu2fPHrTWRcpnJxxqtRoXLlzg7Nmz/Omf/ikXLlzgi1/8Ih/60Ie44447OHDgAKdOnSoix5zvxwn0IAh4+OGH2bdvXxGq7DP0aWOy2XV/LP2NgeX73XraDsvuuhAAtoGWyW0mKUMqHJpdYt+uvYQqwOge3XaHfr+NISFNBNIh6WiEhBUGErNr/w3E87shjkk6K6wsn2T19CmeePQsDz7xNP1hl5WLlzi4v8+hznmqBxcYdldQqkpPa5qzTeJshDaGSpBQW1wAKiSXLxFkKRtrl2jtOYBIjBp1IAwZoaHe4mfe/XZ+4LLwne/+70kMvOqeN/Lnn76fu5f+gDcs1snqN9LvdTGjPhsXHmX9cofdlQHZ6c/yD7/tbfwvf/JhLgxOAr4z2JqbdnOcQZTds4CAKEWgNss2Oc6ZEoZeFARg0RmB1BDG2ysAtmJOjsm5Yxn37dtXMH0XyQFcoVm5FMELCwsFXOAiSM6ePctXvvIV1tbWipj9paUlZmZmCibjTgzzsen5+XniOKbb7XLx4kW01hw8eJAgCOh2u4X2euzYMX72Z3+W48eP87GPfYwsy2i1WvzxH/8x8/PzvPnNby6cjBsbG5w8eZJ2u02lUuH8+fO8853v5IMf/GDRlnJ8/7T+ckJ02rj6G8L886anaaLbSWXBM40cs3TzzqXvcLCVw+OdBu/glZmZGWq1GjMzM0XbVlZWipPAnEXnHLTOQnMH8rjwTGeBOavBQUH1er2IMhKxGw1vvPFGDh8+zPve9z5mZma44YYbyLKMkydPcvToUXbv3s3KykrhCO50OmRZxurqKq1Wq6ijCyRwfbQZc/f7cau/y/OhbOG9ZDaCieX8sMlmJKUUqqJo1Cusb6xRUzG9/ln6vXVMokmSPjoLmV08SK05Sxo1qc4vkaAYXD5HREL/wjLxsMPx//cveeSJ8yyvzdBZT9i/EDBIQipxk1rUYtTboDEbEDZnyIKIwPSR0ZpNQd3YRxZpgrgGlQotRvRXz5BmCdWZXajUEBNC0iUxdZYaQ77w73+B7/6Rf8rjjx6nNX830dwRnvibj3P4rjcjErO2MeLc0xd58sQDvO62G9lFhKLLu779Hfzmh397IiHYZFiQKfrLdp+beN5mIePwRBcFandb4zmdtE4AZX0vwfZixVsxHhfm5sL+ut0ulUqFwWBQRO04/No52vr9Pvv37yeO4yLZmjPhP/3pT7OyssJgMODixYuFE9bFd1+4cIGZmZkiwsQJGwf5OKtg165dRT0c43LCyNHdd9/NRz/6Ud71rnexsrLC7OwsKysrHD9+nKNHjxaO6X6/z+nTp7ntttsQEQ4cOMBb3vIWPvGJT0yM67RImnFY7+Tu383Ix7d9q+FaQEKbJSPz/RFa28N0nCPXheK6OPtut1swfQfbuNz/Li/QzMxMAds4oe5y8bjcTX5uplarVezPcBsEnQVpjGFubo5Go0G1Wi0Om/F3ad9zzz0cP36c+++/n9e85jU89NBD3H///dx9993Mzs4Wc1Qpxfz8PI1Gg42NDQ4dOsRjjz2GiLC4uFgItM36x++7rZzo02i7HfrXhQAwRrCZM8fn/ZJbA6kOiNOQ3bUFGlEEgx5UM0wa0l4fsrq6zpFD+zHKHvSchg2ai4eoRsLp06do1Sqk7XM8+bnPMFw7zxfv+yve//lTmF7IbfuPkeoOS0uzhM0Z5pb2UG3tIhN7RGLQOUf78lka9VkyadJJNmjNHaC7vkFvbXyBfygAACAASURBVJ2LZx7k0sXLhGFMENXZu/9GmvMtFpYOkAwukHXWiFstPvS//yJ//dmHefhv/orbXns3B+44RvfCac6eOUf78hrty21On3ycuLdB6zU3EcdPcfDYt/CqW17Hw49/gaHpYjP+eJABAVpn1i8Q+Fv/PbhF5fBLfsyMMSaPH808bNIdMwjC9ueM8RmRz9Sctra4uDixYced+9rr9di3b1+Riz0MQ/bv30+1WuWZZ56hXq+zsbHBAw88QLvd5nOf+xyf+cxnGA6HLC0tFc7d2dlZZmZm2Lt3b7E5aGNjg+XlZWZmZgoootVq0ev1aLfbPP3001y8eLE4RezAgQPs2bOniEHvdrvMzc3x8Y9/nI9+9KM89NBDvO1tb2P37t1cvHiR5eVlLl26xFNPPcUjjzxCp9MpEtkdO3aMc+fO8cgjjxSLuawpluPWfdosasSHSTbbD7CdtJkvw/kjHJS3uro6sXHOOeOdZg42rXa9Xi8cumtra+zdu5cTJ04UETbO8rt8+XIRAeNCMMMwpF6vIyJFGK6LFgImDoJ3zugwDFlcXCyc/64ub3/72/nyl7/MX/zFX3Ds2DEWFhY4fvw4e/bs4ZZbbikivtzZD2B9kwcPHmR1dZVut1v4PHyIxik8PpUhnGl9WvYD+d9fMjuBXedkejKZFyagEdc5Mr+fG+f3U2NI1Khz7vzT9LoDMDH1VpNUZ2Sk9KhSiaqsn3+SZ5KApQOHWD39GO3TD3L3q27iB//R7/HFsymHD389c3/3v+Tzf/TL9FN4Xf0mbnjlKzHGkAy69jCWtIuKa6iZg4xMRpKCDmY5c+YCH/nYp3ni1DLV6hzoCt2NDdLqBWrhWRZmQ77l61/Dwd0VWpUKZjiC4WXuObaPV7zi+6lVA0xUo37wNfRPLdPptvnSo4+y1htgwiYPP/UVbjy8n2S4ztff+kboDPjyM19mqDIC3/SWK3ODWLIZlBys5mgz87IcWXItxnaa5uPw+/n5+cKUv3TpEmtrawDF4nZao9P6B4MBu3fvZjQacebMGRqNBp/61Kf4xCc+QRAEvO51r+P++++n2Wxy5513cueddwJWc6pUKly8eJEgCNi1a1dxqPiuXbs4ceIEjz76KCdOnGB5ebnwFywuLvLggw9y4MABjh07xnA4ZN++fWxsbKCU4pu/+Zu55557CshiaWmJEydO0G63eeihhwqo4uLFi9xxxx0YY7jrrrtYX1/n1KlTE7lyNuszR5vBQC4O3nc2+uVtR7hg+Z3T6gYUO3md4HZ1cpv1nFXm4LV6vU6r1SocugsLC+zdu5eHH36YN7zhDXzhC1/ggQce4JFHHuGmm27izjvv5MSJE8zOzrK0tFSE6zqry0WLuTBR56DvdDqcPXuWJEmK2H+AQ4cOsba2xuXLlxkOhywvL7N7927e8Y53sLy8TLPZLJi7E2bOB7GxscH+/fuLc8RdiKsTZFezvtw4+8pRGfaZVoYb45dOGKgIWZZeufNJCxEBaW9Eplbp6sskJuTILV/Hiace4cRTj5JlXZq1mOb8PL3OOkYLAxJUbZ4LZ56mf+EkZx79Av/6D/+E46f77J9Z4OLyeU488Rjv/vF38Dcf/hh/7++/nlkJkTBk2N8gjiKSsEW92qIWZ2TpgGSYsLox4Nd+5/08vTIkjedYPfEEq2sr1GoVKvEelMR0e6v8+ac/z49+5718+xtfS7LYotGMqBAR9rv0w3mS2f1EpBw6cpS1CxdJjfD0mWc4um8P+3YFnHjyy9x661uZGa3zyv37aQ8us9zp0ButUnTSZumbjT0hQQS0Sa65JrgVlTVVf6I7s30wGBShfQcOHGAwGHDmzJkCd9+9eze9Xq8QBE5QXLp0iYcffphPf/rT3HfffczNzRWL+1d+5Vf41V/9VSqVCufOneMVr3hFwRwctOAcy1prnnnmGU6dOsVnPvMZTp8+zfz8PE899VSRcthlrVxcXOR7vud7mJmZKQ4Pd45Jl7LAaaOVSoXV1dVCm3V7HG6//XYuX77M4cOHCxjCPzxms/HyFaNy2oerhRpeCwFfFlBubKMoKlJ2OwF+4sSJgrF3Op3itDaXg8k5+3fv3o0xhgcffJB777232DvxTd/0TfzUT/0UDz74IJ/73Oe48cYbC4w/jmPOnz/PaDQqggicw9ZZdS5c9NChQyRJwuOPP87x48eZm5tjbm6ON73pTRhjOH36dOHIBbjhhhsACkHl0oUPh0Pm5+fZtWsXly9fZmlpieXlZRYWFtjY2CggLWf1TPMFOMbvC4HyNTeuTpj4DuDtoutCAIyhiACbDlphtEGUkI4MlTpkyfr/z92bBVt2Xvd9vz2dvc8833nqCUB3Y2gSgCiQBGiCkQyJcipWTMZlZ3CoPKWsRKmKHvLkPOQhVXpQVaRybFF2bKsiKbYcOYhliSICkQRFTATQQKO70fPtvtO555553mdPeTi9vt73opsa2FCh9FXdusM595x99rf3Gv7rv/4Ld9BmONE4c8YkMd6b4Yyk8HydyWBMIm2i4+NPfExTI2iM8Lp7VE+c481/8QqZKMApGAwOmpiv/1N+52Ka//i5p7GCgJFpEfX66I4DyQJJw6Pf2WYw6JHLVmkNPP79a+f53jsfUV5YYtKvU55bxayeRTdznHryc1QqBZo719k8/yf8L//k/+GJJ5/mVHKCkQogWcTQTbTJlKDTJIw8xpFJa9AnkZmn04i40xgxGU4Ixm0e6R6gF9OUvQxPLq5wRdthuzNl7I8ItIAo+niBcHYe7xV8dV2Es4407WAAPtxlWT0o4nwY+woflzSIR/bT6VThtYKVC1QThiHdbldpx/T7fXRdp9PpUK/Xeeqpp/jVX/1VLMuiUCgQRRHf+973+PDDD/niF7/IyZMnWVtb4+DgQFE68/k8mqbRbreVntDe3h6/+Zu/SaPRUEXcUqnET/zET7CxsUEqlaLX6/HP/tk/41d+5Vf4tV/7NUXplEKnQAnCDGk0GszPz3Pt2jVarRadTodCocCpU6dIJpNUKhXW19e5c+cOvV7vY9Ot4s5SVrzoG9/zuLH4i1BJf5x1v9eUiFSMrhzPysoKtm2r7C6VSildJunSNQxDMWtWV1e5ceMGQRAoiuW3v/1tdF1X0F+lUsE0TXq9HvPz82pUaLzJbmNjg8lkQq1Wo9lssru7y/b2Nuvr6zz//PO4rks6nebtt9/m5MmTLC0tARxiJJmmycLCguoPEFG6fr9PKpVSmUsymVTQkud5CgqK01YfdP5+1GMClcrPD3t9KhyARDYysSqKNDRNRw91so6NE/r0mx0if8TYtSj4NRbSBnv5At54hO0YRFFA4I456A+IsHCmSUzXpVG/yR/+fxdwPZ1sxmDSH+IHQ/TA58W1U/x3f+dnqeSTHGzdZGFpnYSVo31wDXs85KA7IJFy+MP/8Cr24qP8+jf/BYZu4QYhj519gj99f5NEaYXb13/IW++8wfpShf3rPySatimkV/jVf/Iv+V9/8e+RdxLgZ+hN+tjJLJP+AU4ux9L6Kc4+63Oj/scYVorzH97i6z/3N+iNuzTrV0ksPk0mlWQ9XyLja4zHLntBiEeAxuRjEUO8kBRG0Yzhfx+8OAxCoruCeApR+ivMFKRAKEVc4fgLX99xZhIgqVRKReDtdls5DLlBf+u3fksZPhnoLQyQv/t3/y66rnNwcKAad6Io4p133iGfz1Or1cjlcrRaLX7zN39TUf2EFnju3DkuXrzI97//fTKZDBcuXFA35je/+U2+8Y1vsL+/z8bGhoI7ut0u6XSaJ554gn6/z5UrV7Asi6tXr/Liiy+yvb1Nt9vFsixFOwW4efOmgqPicwoetB5kCD5JKO9BK36ssheine/7vjKUAtcJD1+eE6e4ptNpJRfRarVUM57MCr5+/Trz8/MUi0VlhB3HUc8XTL3VarGwsMC1a9eU4U6lUpw6dYrnn3/+EDVVZCbEYW9vb6usSo5PBsNMJhNarZaqJUiQ0uv1qFarShwulUodGiEqny8eEMXX/foA7vf4J7E+FQ7g8ImZKVtqmo4RRSSCEMZD/PEANI9HHnkCLZWFRIlGf4tw0GBuIUutts18ZQnbNNEjD7/bYXd7i9df/zat1ow6Ng58BiOdjBvxj37xv6FcNDAdn6lpUSnkmLhDuv0amVSZphcyvzLHeNhlfuNRfucPv0/kT9hr7BOZNjdvbeLoCUb7l1lOOoz2uly/M8GY9gndPkH+Dh9q63zr9Vt85dkycyfWyS2uEHkhkXl3oIhts/HEo/zkUOdb//pfsrm3w0QbMQoTeEGTqNdDNyCdTRJ5Ps+dOM6lVp1LO3eYxi6uQxHDXTE43TQguDcgO37RGYYxUwqNZtnBJ7mv92O4SFQjEV8URTzyyCM4jkMul1P8fYBbt24xNzenHMZwOGR3d5fXXnuNWq2mYJ3BYIBt2/z9v//3mZubU9iy3OyC68v4xkwmw+XLl/ne977H1atX6ff7Skoa4Pbt28oAxLtZU6kUL7/8Muvr6zz77LNomqZ0axKJBJPJhHw+z3PPPUen0+GNN97g5s2bvPDCC6qhSJrUstksCwsLZDIZdnd32dzcvK80hJyzeJR/NKuCwwYknkk87HV0T+NwBtxT2ZRegMFgoGY6xK9Zofym02l0fTa4ptvtcuPGDZLJJDCLxq9evcpwOOTEiRNKxkH2O5VK0el0FEwjBdlCoUC73T407lO0oKIoUiwigVdM0+TOnTtqmpzruurzSHAyNzeHYRgcHBwoiKlSqTAajRiPx0j/gvyvfHb5LD+qgetoN/hflTP/VDgAP4ylOHqIFliYOjhYFHSLaHxApA0xzAqLC6uYkUnz4AKmtkQDg2vXN1lfXMSdDhl2XRJhn0FzD90p8PRTz3PmZJ/KpRaukebxxx/nc2ePsboyT6t5QDJp446naEkTPZgwdX0mpovmjXnnzTd560/fxM+f5LuvfQ/bSmFpGp1OnciIcIMQw7AYhRqeFmEaBtHUB92g4If06jU+GDh8OXeC8aiPk0ljJLPo0RhtOiXSdCzD5LOffYyf+k//Ab/3r/4x3//BZb76k6e4frXGiZ+00ENwA59cOY3RCJg4aXrZAlu9CC/yCfUIPQoOXTBHjUTEzMzfi2q8j+HIn0QGcAh20u5p1YjGuqhwCp6q6zrb29tKDvjq1aucOHGC6XRKp9NRN18Yhpw8eZKVlRV1c508eZKf//mfV5CR0DfT6TT9fl8xiSqVCnfu3OFP/uRPWFxc5Ac/+IE6jn6/ryJwaegKw5kk8T1YbWZM2u02+/v7rK2tqZqCFHQFqnj++ed56aWX+J3f+R3Onz/PCy+8wPb2NsvLyyozE+iiUChQLBYPRbJHobmj0aTs8dEC4idtPI6yW2QJZCJdu4LtFwoFRZUVI9fr9chkMhQKBeU8rl27Rq1WY2FhAc/zaDQa9Pt9SqXSoW7rMAzJZDIkk0mVdS0tLSnGUDabpdFoKKMs57Ferx9yQEEQMB6PVWNhNptVNNNsNqtmAQuEI0Xs4XDI/v4+juMwNzenoFiRBBdY1rIsVVP4s2i596OFyu+fpFP4dDgA/x79MAzAwCAVRcynUmQ0j2k4Jgw0cvksrXadheIGlWoRvdnnxvVbfOHco3R7LYwQLCOg0doll59nYe0Uy2aa0PU4/uiEUaBTXV6lVJ0j8oYsri7SOtilmM9w7aPbPPb4ZyhlK4yjOt39Tb7wk8/z+//3H/D+D3c5ODhgeX6OMHDJpBxGk8kMRQ/D2TEnIAw1dMsio03pdAaw+BhbXYMwcwzMPnoiRac/IZdJY3U7aH4ItkUy6fOFrzzPW2++wtUP3sH/fJbbd25w7isJ2l0fJ5fF6zWx01kWDYPdTou6buITohMQaTrhXVloLYp33QbKOIRRhKbrs7kLkY6m35u8BHfHRD7kdT+aWyKRIJ1Oq/RbVCClU1SKqdvb2zz++OM0Gg3CcDbwpdlsEoYhJ06cUL0Dp0+fRtd1Tp8+TRAEVCoV1akpejwiOtbr9bh+/ToXL14kl8vx27/92+zs7GDb9iEdeKEdimGKG3/hr+/u7vI3/+bfZDQaqWYxqRXI/+fzeV588UW+//3vc+XKFZ5++mlu3rzJmTNnVA1iMplg2zbFYpFms0mn07nvTIAHLaFcxkX34s78k6gBHK1XiPEDlJT2dDpVWc5oNKLZbCpjP51OKRaLqmAbBAHb29tqhu/Ozo4SeROoSGQiRCbEsixVU9A0ja2tLaW4Oj8/r4y3dAYfPQfxjnCZDvdHf/RHPP7440ynUxVESBAgTsD3fVXU7/f7qhFNaKwyg0I+t5yHPw99M471S+Z+dLJdfD2Mff3khUP+HCsMCoRBAW+aJQiS+JFDwcxTNgOm/QOsyCGdypNOp6lUSnS8EfNLX2C/U2M8nnBwsE8YTekPG7QHDTS7SGH1cfTCBkZhncrJczz6xRc584W/wcLjzzLVbcY+jNwh/fY+nV6f3NwKmmVysPdDah9dYhjpdCjy7Ev/GSuPPcuzL3wFO5cjCiYk7QQE9yI90zTRwwBdh8XlFZyFU/z3/9t/4LmvfJ1Jd58JOtd29ogmLinThOkE23Tp9w9m+jtByCNPnOO/+IV/SH6uzASD/XqL98//kMia1RwMPWLqh9iWw0oujy5qoH5IiEmIiRdoEJnqK84aibTZgJwgConQCSONMNKIMNXXJ7K34b2RfIDC9dvttoJP8vm8isgWFhZoNpuMx2MajcaMmut5tFotfN/n7NmzrK6uUq1WOX36NM899xznzp2jXC6Tz+dVV3Cz2VRFu2PHjlGv17l+/TqmafIzP/Mz3L59Wyl3ChwR14mRJQZ2ZWUFz/P45V/+ZZ555hnVSyB8dRlLCbMiqPDKz5w5w9e+9jVlyK5evcqNGzdIJBIqcpSu2UKhoAxN3Ej8WRnaUcVVMR6fRGFfjieepQjenc1mAVRPh23bSmJ5PB5TLBZVZByHiWQwvLCHkskkxWKRarVKsVikUqkoqrDv+0ynU27fvs10OmV5eVn1EcAsC3nvvfeo1+vqXMj5jJ8TyfJarRZ/8Ad/wG/8xm/wcz/3c1QqFebn5xmNRsqJp1IpxUBLJBLKMF+5coVWq4Vt22oCnDQ2ivOJzzI+Wvg9asCPMnx+VHH4Ya1PRQaAnp5d5AboWkTCMMilXCxCrFwRt9/GTDgzHY/xiEk7ZPH0YxCYOLrGYOJxbfM2WTMily1TWVohNJJYtkNufoVIt7AsG6YRg/6YwXBM1TLp13bwIo1McZ7uqMtH518jnXLQi2uk9Sw3btfI56qkM7cYHVg0Rxb9qUF73EbTIiwtCfhomo6mz6KFg70t8tVH+Bf//P9gUL/B3LGzvH/+El84W6Hr+qSSGgQew8DF1C0m3TZmukShaPH5F77I4//vk1y7fp0n1rNMD87D6jGIkrijDoY1m2GczZTAu45rONhej8C/22CFBiIPAUR3G7s0IsIwuD9EEAozCB52PBCPngX6kYipWCwyHo+xLItms8nCwgLtdpuFhQUajYYa+r23t4emaYr3LZj+ysqKoveJHMT+/r5yLmJgcrkc169fZ3t7m3w+r3RhfvqnfxrTNJlMJrz//vsMBgNlpMWYiRDdTBp8wJNPPsl3v/tdDMNQctA3btxgdXX10GAaOb/JZJLFxUVeeuklvvWtb9HtdjFNkxs3bihjKAYyimbKoxJBxlk08nh8347uZfy5Rw3/w4b34jUKMXTJZFJBIPl8niAIGI1Gqp9DoDepy4iMh2gjyV5KgVXXdTU2cjAYKJlnmbC2urpKMplkb29PQTjlcpmtrS2Wlpbodrv0ej0lCCjQnry3QDUnTpzgZ3/2Z9nY2OD3f//3aTQauK6rmD/dblf1KQhJAGBtbQ3Lstjf32dlZUVdy/l8/lC3slyv4nzi1M+jKz4HIg77xOckx7n/f220gBJmPnZCNMp6C6fXxnAidCPEdceMA4NSOcdoGjBXWSSfK1Pb3aPZqTNx+2TTaRILc2hOiSiRIQxHtJq7tDoHOKkcppWiPwnwArANDY82iQTkUhV2bl4h4SQJfJN0YY2dtksqZTIIZhfhk099hsTCkHq9ztLjn2e/1qRSnqe9d5XRYIuUqeNrEYYBjpNgtPc+2c4tvvaf/0MmkcOw26HessmkPHxXp9dvYmZT2JrGwe4OhWUNO1/CyWf4mZ/7L/k//+n/zJ3aLk9ubNC8c53CygnM/DyWNyVtazhGxHpllVqtTqhl8aMZbTYMQxKmfQ8K0O/p6GiBdy8C0g+LSslzPPfhOgCJZuPsEEm7ZR6v7/usrKwo2YdKpaJknuPyzZJp+b5Ps9lkMBiQzWZJpVIKK56fn8dxHA4ODmbKsckkg8GAWq3G8vKy0vnf29vjM5/5DJubm0ynU27evKlkCPL5PFtbW4pyGu8FuHDhAlEUcfz4cX7qp36KixcvEkURy8vL6qZPJpOqRiDQVqVS4Wtf+xq/9Vu/xaOPPqpYItKBKphxqVQim83SbrcPRdZwf6kAyU7iv9/v509iyZ6KRr+maarzNopm2jxCkxT11Wq1qozfcDhUUgy6PlPzlGwMUEZTKLySneXzeSXv3ev1VFF9MBhQKpV45pln2NraUlG7BANBEKgBMHFJim63y6VLl7hw4QLtdpvjx48rQy/OWWpEcUfleR5zc3PMzc0pjSGhg4rUhTCU5Nw4jnNIbfZHYfvxx+OMovvVhH6c9amAgOCesUiFY1LBARZTbE3DMjTCKMCy0uimRq5QJJGy2Lt9nc8+c5puv8doPEXTUwwmBp1xhJ0pYCeypBJJdD/ECjzGvRaWEVAspdhYX8Z124yHHfb3b+FOdvGGB9jJBK5m8OS5J6jX60wmE44dO8afvvrH6P6E1YUKve1blBIT2tvn0UdNMraBH3gQWJhaEt/VSC49zqkv/wP+3b/+tzxx4hgasLx2gldfe4eLH1zH9S0m4xmzxAS6e7uMGg1wfVL5DBPfx8dks1an29nF7XXxx3dVEcMpFj6PH9/AJEvozRH4eQI/D1GJKExBlIYoTRQU1JcbZZmSY0oOghyRnyXys4RhjjDM4fsZovDhSkEIvhp3BAIHCVacz+eVMZeoKgxDGo0GzWZTFY8lGhTWj+/7asygGOFyucz+/j71ep3hcMitW7c4ODhQXcRSpN3a2uKb3/wmL730Evv7+8zNzakmrsuXL9NutxXrRwy6RG9f+MIX+OpXv8oTTzyhoj6JQAeDAd1uF9d1aTab7O/vMxqNVMZQr9dVz8Abb7xBt9s9xNaxbZszZ84ofZp45Hg/4x//+Si8Id8FEnuYS+o2YvwBxeYSbB1QjVrFYpFUKkW321Xyyrquk8vl1MwH13U5ODhQzl2GvoRhqJ4v14EohgrrKpvN8thjj9Futzl//ryqAQiWXyqVeOyxx9SQIck+5BjfffddXnvtNUqlEteuXVPMI4But6smvAnnX7JZQNVshHwAsyBA6lxS0xKnKFG+OO94JieZrOxlfJayNL7Fvx6Gk/9UZAAAoa5jhy65hMsckDZ1gihkMJjgaRb+xGelPM/lix8wv3SKqH2H6vxxbNPBslNMAg2jMI+eTGNEEWjTGd6taxh2ipX5AqNpQCKVQQ8GuIMOjVaPTDqJZaaYuB6e1mX/2kWmU535SpXK8TVe+85r/E//4y/yP/yj/53HVlfodns8/pW/g3lwlX/z734P26kSTBpEloelRZz+/Ne58MYbbP/wOyytLFHJ6OxsX+V3/vl7PPMTn8PJmAynHlm7gOEkyegBd+5s4gU+Tn6eQjrL8spJOu2LvPbG9/lvn3qSUW8Pa0HHH/Vx0g7TKEElZeAkqoyCFhoJtLvaSSGzBrrZugcHaaE+GxAfRQTcbZgJQ4hmKqwQEGiZh76vkvJKw5Rw/IV2OZlMKBaLvP3226ysrADw2GOPcfPmTdWoNT8/T7lcVgZHbhYZHhOGIfPz8yoiFJqfsFLiDkRw3S9/+cv8yq/8Ctvb2zzyyCN88MEHPP300/ytv/W3+Oijj7h69SpXrlyhVCrRaDT4hV/4BV555RU13/eVV17BcRw+97nPcePGDU6cOMFgMGBhYUFFkDs7OxwcHFCpVMhmszz33HP0+31efvllvvGNb3BwcMDa2ppiKQEsLCwoKeN449eDOOXy+eXvcdZLvOP6YS7Bz2UfBPaKa/5LhiNQnhjubrershZxcNKcJ7UYgcaERbS+vq7gD3GoMkMgm80qh/OVr3yF3d1dDMNQTWCvvvoqp0+fZmtri1qtRiqVYmtri8997nMAVKtVXnjhBSaTiWoEsyyLxcVF0uk0W1tbKnOwbfvQgJ1kMkmpVGJnZwfXdTEMg3w+fy+jvhs4eJ6niAbxPo+jFN6jReD4uh/t92GsT4UD8DSLZOixnAzYSLj4fR/dTBIGPp1uj0kwxcnZtLoDWo0GTz39E9yuX6RQKhOGDoaZw0jkSaUyzFXn6fbaJIwsI3fAysoJBuMhmfIjlDIlNE2jdvlPmU6GeNMJ/dAnCgfMzS0wDVwC32fr5hUmXo/2uIGhe/zjX/tVXvz8c7z51lu89B/9J3z3T75FZxziaEVOnnoGO50ncvd5+7VXSQ4bPLmUQvc9NK/Fd//4W2RLOX76b/wE6aSDbup3RchmzSdur47vuYSBT6t5QGdUpz/tY+g2HRfevnCRE0urZG2DQDPwTB3NTGOZNn4AViJFFNybExARUw8lQkGGoY92d4oYWjhjAUV3Z0sCuhFh6A9/HoBt21QqFcXQgFnR8uDgQE18ksHb5XIZ13VZXl4mk8kwmUxYXFykWCwymUyUQZhOp1QqFcW8WFpaotPpKAx+NBqpdvzl5WX29/dxXZdKpaJw+PPnz1MsFnnppZfY2dnh61//Ot/+9re5du0ajUaDwWDAuXPnOHbsGC+//DIvv/yy0oL/8MMPyefzJJNJfv7nQvK16QAAIABJREFUf55isciFCxfIZDJ0Oh2lhSOUyF6vR6PRYHd3l42NDXRd58033+SRRx6hUCgogynGXiLn+zVZHf39aJ+H/D1uTD6JGkDcCcQzDjF6EgGHYajorYVCQbGeRLohzn8Xto4YSdGKkmhZICbJKEWOezwe89577/Hmm28qvr84w69//etMp1Pq9Tqf/exnlbzIO++8o1hJW1tbuK5LrVajUCioqL7RaFAqlSgUCjQaDdWDIHslVNdkMsnq6irtdpsomjU3St+KHG+87iAw19F9if9+tAb0MWr3fZzEX2Z9KhyArcFcIuBMesR0NMLJlWbYoGkS6gaGZmHpOq3aLuP23kzJMVvkvSu30a0EERq6adDvjbE3bFKGjzedUC2VyaRnKfzcwgq9bptpZw+312Y6Hqn0uFKeYzAY0B60yOVKzM/lSOWXmfg2YdFkfmmf4bDLyrENrl96iy9+6QsEkwGtOyXGozbLxxb599/aJNR0zEmDQiZFvz/A0Hy6jRpf+OLn2NvbY9BtkSuVmV+o0OvuYVrZWWSjQeBNsRIONhYr86t88PaH5JMOw1GbdOY4geeTsmdzbTOlItNxQKRZBARoRiySOLSl9+R5LdNR0XGozS4uQzPv0UTDEEP/OAvmx1mGYSg54GKxqDRaZFSiYME7OzvUajVWV1fxfZ/z588zHo+pVqsMBgPG4zGlUknRLQU2KhQKLCwsKDEv3/c5ODhQLIxcLqf44PPz87RaLT788EM+//nP8+yzz9Lr9ZRMwJ07dwB46qmn+Oijj2i1WqyurvL666+zsLCgoCzf9zFNE8uyqNfr/O7v/i5nzpxhY2NDzaBNJBLs7u6SyWQU3dMwDAqFAvV6nU6nw97enoKRBD+en5+n2WwqWuePog4eNSj3MxjAoTkBD2s5jqMor8PhUElRx6m8IgUtEhCSHQguLnWVuJ5//LPOzc0pVpAwxoBDeP5gMGA0GqnGO2kUbDabLC4u8s477/DWW2+prutaraY6eQXOKRQKiixQr9eVXMcHH3yAruuUy2X29vZUZicTzKQmJce0tbWlOoOlRiFRv8CYkkEc3U9xgPF6jjiQo93BD5vZ9alwAFlrwqp2G9N1SJaqM0peMkWn1UWzk7jdBkl7ymi8y3DU4cr773P81BnarWu0e3UwHKpLG8zNV2YFtHREaXEFH42RF/Do4+do728x7mzTun0Dzx1hJZIQdilVFvCCAPQxhWyBEDh/4YdEpkNl8RS6mUEbj6jOV7h65QpPnP0M737wBjknRaO2S7PVZ3uvw9kTx/H9CdNoRuvMZbKcPHOGQb/D7etX8Qn4r/7rb9A4uMEHF36I41gktA42EyzHptaooxt9+oMuw94exWKekhPyxlsfsJrP4ayUOJ5x0XSLyB+zP7aIjAAztAmNe1DATAouBC3ED0RADAKCe7LRGoTR7DuacbdHwJwNH36IS24AYV8MBgMAhesOBgN1k2cyGW7fvs3S0pIa2NHpdFhdXSWbzTIYDBRElEgkOHbsmOL2d7tddnZ2iKLZkHeZGyxYaqlUot1us729zblz55S2fKfTUVoxAj9cuHCBa9euUSwWFUTTarU4d+4c2WyWWq1GpVLh5s2bfOlLXyKVSvH0008zHA7pdDpcv36dQqGAZVmMx2MmkwnNZlNNpxoOh5RKJWq1GvV6/VChWYqZMjglzraJT8YS6CRuHO7XRSpR+ieRAXQ6HZWpiOiZ8PglMt7f30fXdUWPlMg4CAKl5S+GPz52MZPJKGPaaDQUn96yLDXJKwgCisUiMKOdvvrqq4xGI86dO8dkMuH111/n1KlTqugrs6OXlpYOTSIbDAZkMhl6vR6FQkFJjIvkxNtvv021WlWjPaXgC/ccYbvdplwuMxwOGY1GVKvVQ8PrpfFRGhx1/d5AdzH68f2O/z1+zuPfH9b6VDiApbBGNhqTLlXwiHAnY8aj4Wzc4nSMOx7QMoZkMwXaOzXS3SbVaoXFhSXy2RylQgHbSqBpAbl8joyj4YawvnIS27ZpHOwTuG1G3Qa6mcAfjIlCi1y2jOYPSNs27jRkHAb0ekMWl47jkeCDC1e5eWeXv/31v8f+Xh1j3Gdv8ypb1y7B1CdfWuWRR04yGA8Z9JsMB10u3xrzpc8+ybQ/4vjGOm6wzrUbH/HZpz/DtY9u0Gnd5vnnPstw2CebSeG5I7rtFqPRiMGozTvf+UOGnW3SdoSnW5iZLPu1TRZSRUZUKM8t0+73uLUd4OsWug5R4KvoXtOMmLpD/CKKzxcOHzR756EvuaFl+Ip0zAZBoJg+qVSK69evc/r0aarVKsvLy5imSalUUo4tPkx8fn5eQQZ7e3sqEhTxrWq1qm4gwzDY2dlhcXGRarXK6uoqly9fZmtri/X1dVXo/b3f+z0ymQzZbJZz585x6dIl1tbW2N7eVrWD27dvc+zYMU6cOEGhUCCZTLK7u8vW1hbb29usra2RSqXUXABAFTffeustOp2OgnZyuRw3b95E13VOnDihMqPt7e3D/Rt3jXlc8lnOKxwewgIf15yJQzQPc08lsxAOvBg5eT9xBsKDFwcmw1mk6A4o6EfopNlsllwuR7/fVwZUCr8iyQxw584d5SxPnz6NpmncuHGDp556ShWnNzc3WVxcVKyuwWCgxAZXVlbUDGnJ6mq1mururdfrfPWrX2V3d5fhcKgYTXIsMrc4DGeKpp1ORzG/DMNQj8G9Xo14x6/8Hu8SPhrxH93ruAN4GI79U+EANLdJwjFwp0MmfoBOyHg8pNtu0e4fEPkTgmmPYnaFVtahXClR298llyughRGhH2ChE0YBk8mIdGmB/GIZjxAjmDANWhzceB/bcsiWVvCDfTIZB0P3GIwH+NOARn3II4+fYBq2cD0dJ5NkZW2Zpz//Od5/94d0uy6pZJ4Qnc888yybV66xur7E1vYeUz/EKpWZumNW11cpLB1n9+Ytvv2dP+XRs2dxknm2t/bRmOJP22zduYGlG3Q6uxiGRehG9LoDXN8laaewtASalSBRPs7YbjHwDnB0sPNFPDtLozvhdrdLQBrL8/EMZtRONIKQmSAQR2fGivHQCcN4JHEvAvkkioVCpxPDX6vVVDFP02aQVi6XwzRN2u02t27dUlGkdINK9JfJZKhUKqrI22w2OTg4wLZtBa9Ix/Dc3ByXLl2iWq3y6KOP0mg0VE9Ar9fjzJkzNBoNzp8/T6FQ4Omnn6ZcLvODH/yAarXK3Nycwq1ff/11pQWzs7OjbnTpBt3b21OD6R3H4fbt22iaprqY5bNKTUQGzQBqWIplWbRaLcVqUvfGka7e+/38o3oCPokV17cRvR85TlH5TKfTikEl14DAZ9lsVkFYwuaRbEXUP3d2dtQ8Btd1VQaRTCbVGMnTp0+rATEHBwesr6/zpS99iRs3bqjZvydOnGBra0uxdoSRY9s2w+GQcrlMqVSi0+ngui4bGxvq+Or1OhcuXFDic0Jlls5hXZ8NuJHrIZ/Pq6wnLkshGkfxkZhwz+DLubgXxGmHHMOD+jr++tQAQo1p5GGMRjM4xk7jeh0mgYuTSNKdTGDaZ9Abgh/Q2LxDJlGiOQrIFisEuIwmLXodA8Md0EjpWKkCUaJNd9zBHfWxDAO302TQOGDsT7BTRYaTKbl0BTORwfPvMA1cbm1usrR8EstOUS45XL/4PqEf0Bh2KBUWKBXKXLp5jSg0WTt1kly2yJ3+mBvXP8L1pmzuXWH0ygHFTJrnvvgVfvj++6S1gEceW+faR5t89uxJuvs3CMwEljmbwtXvubRDF6/XxI9GeITYlo3tQ3n5Se40r/PFlAmGQ7Pf5t1rO9SGqziEYEjqf/fC0qTxaqaqqht3B4bMRoLdRX1m0hAwa7zTNAiCcOY8HuIKgkAZaIn2gEMc/+FwqArAg8GAU6dOKWMtWP5gMDjUii/6KsIEGY1G1Ot1VVfo9/uYpsnx48dVlN1qtRTWPjc3R7PZZG9vT4nSVatV9vf3WV1d5dy5c/zxH/8x6+vrfP/738c0Td58802SySRnz54lm80qXZhyuUyz2eTRRx9VtFQxFsIY2t7eVpCHSBlkMhkFf4RhyM7ODh999JGin8LhCF4MghgL+f2o0b9fs9/DrgHIHAR5X2FzyeeWWQDSuCX6/6VSidFopAy6ZAyTyYRkMolt2ypqlutlOp2qOoPo85w4cYLhcMidO3eUET1+/DitVkvJSCwsLFAqlZSs9PLyMvV6HcMwWFpaUgNpGo2GqiWJjHWn02E0Gik21s7ODqlUSjm1g4MDPM8jk8moDFbgsIWFBSUWNzc3x3Q65eDggHQ6fcjwy4pndHHmVjzaP2rwH2ZW96lwAJ1Jh3A6ImONyGRKTKcunju+26k5o4UNR7OC08jzmV+uslmvsdkLcGyL0XCCN53QbddIBFk6jSylypDhcALekChw8cYDhr19hpMx+XyVbEpjZ3uT0Etx0N4lm3qU7Z0aKyur7O3XmI5aVOfnMHyP4WDK8cUNEskS23t1Gh2P8uIKd+7c5uKlC2zut5hMOkRMcKc6t3cHDJIOqXdf587mFR5dX0V38yQTJgc7O3g5k3I+TX/So9btYefWSSQs2p02w1GbhYUqveEQbA9fC2H+Kf5gN+Azuss7dZut8Wk0vX8PFxQyT6QdGhQjN75h3FMGPbruXUf6QzcUwtoRw5VKpZT+j2C+cnOPx2OOHTtGq9VSzT+Sbne7XdWKv7i4qCR+ZeygMG9k5msURYxGI7a2thRuv7a2xq1bt0gkEty4cYNSqUQqleLFF19kc3OTQqFAv9/n9u3bPPvss3S7XS5cuKC4+zAzRu+//76KPj3PY21tjWvXrpHL5RTLpdFo0G63lXpkHN6Qwq3UK/b29rBtW4ngHW0QijuAo/sKf7ZoWJwR9LCWHL9E9CK3PBwODzkpYQNJYVSuBymGTiYTLMtSaq/SuSsYvRR1JYAwTVMxrSQQyGazBEHA1atXVQYhhvq9995TNNGrV6+ytram+hRkpjRAs9mk1+uRz+eVM5PXle5wKebu7++rIr3AjkIBFbVZoanu7u4qaYsH9WLEjX0cArqfk4DD2cBfm4lgw8kUS/fIaDqDfo9MeY7hoMs0hp8RuozdEXvNFonEHsurp1m2HS59+BZaFGDpGlrgU6/dobZzna2tSzxyYh3bDAinE/qtXQr5PKFmMnFH3Lh2B8ecEngOlfwq/WGLUU/DSeV4+vFj3Nza51//23/HCy+8hGM3Gfda6Amb7dvXsfQM+7u3ePGpn+H/evl3CTExDY0o0gh8Dc3SmLgDLl96j6Gp85OlHNtX3mNxqcKkC8eff5Z+b5e+H5HKL+KZNt1WCz0KiZgSYbKxsYHrGQxxGWsZeqT4kz0bLwJT66Bjo3M3CowNiCdGA41HDLMpYR+XDtb12SUQjyYf1hoMBkRRRKVSUYU46QSWJh5pCmu32/i+z5kzZ1SzkDBEpGt0b2/vEHYv2YHo6tRqNVVgXFtbw7ZtLMtSYmQbGxv0ej0++ugjnn76aW7dusX29jb9fp/XX3+dfD6P4zj8+q//ukrZ48VV+Z5Op/nggw/48pe/rNg+b7zxBqdOnQJmN+bKygpRFFGr1cjn8yq6rFarCgcX+ufVq1eVsb+fVEC8APigPToK+8ixxoeeP6wlmYu8tmREcX0boYQmEgmSyST9fp/pdKrgIMdxlGEXGCWRSKhej16vp45bnic1H4EWNU1TzYKJRELRgk3T5ObNm5w8eVKRAkRnSqQjNE07NGDddV1lsKXelMvl1HMsy2Jvb49Wq0UulzvU8SskhWazSSKRUPUNyXCOjoeMZ3ZHnfuD+P9H14+6Fv4i61PRCdyeZIgCGAez1K++u8VkPMOMk06OhOWgYaOFNsWsRXfqokc+c6UcnjfGsWd0u/6kiWakYOozn50wbt2kvXeVQWubwXhAu9Nj3B8wnnRod+oEvk6puEhAhJPRWV9Zolqd4+adHQYjn8989jnavS4nH30SK5Xh1o2bVCuLJI0Be4Mt/uVv/ytMEtiagR5GGJqOFoEZghbp2Hc/T61bp7KYpVJyKGU0rl/5iN29Bp1uk/LycdK5R9FCn+mkzXw+O7topy6RngKryMTMMyLFINCZBBoBFkGk4YfM9ED1UH1poaG+4gXCQNPwmc0BizAII50IAx8NH41Ae/iXgrBaRFNHNNzF2QhDSChycuOdOnVK0QrjcgES9fV6PXZ3d9nf31eYqwyKkejRsiwcx2E6nbK0tKQw+StXrpDL5Xj99ddZXV2l0Wjw4YcfsrGxQbfbpdFoKLgmrnopEbbgvzKKUNd1Vbjc3NxUBen5+XlKpZJ6XiqVUjh0qVRSkaKci6Osn/g62uV7v/T/ftAP3OvGfphLIDZh68g+C61aHJwYyZ2dnUM0SCEB1Ot1Go0GvV7vENe/0+koumc6nVa02V6vRy6XU41jMDPS2WxWzeZtNpu0221Onz6N7/tsbW2pDOzKlStqcLvATHIscV0pYWbVajWlKrq3t4dlWUp6JO7E5LoW2DJu8ONSH7LiBIX4nh6twcm9Ee8GP+pkf9z1qcgApoVV9vpjot4ORPNomk4ml0W3NCYTDyth0Bu4TNw+vV6PjZNPEGIQabOmEnc6JpvNkteOYWi7OOUJB4MI0/DIpNM4lgXTiNHUZ2PtGH13iJl28fQkd7ZvYiWz6EaG0LRwfZ+5uQqZ8YDt7Qa13TrZXInlhTI3rl/FnR6w1+7Rbk7xtDHTyEPj3vBmLQL0CFM3sKKIJ3sug90ddsZd8lYC3+viDzpUltcoFUukNJ3lx09wdXKD3v4H+FGAk87QG7v44QA9Xzx0rtQFEMZ0QSIdogiiu/XfuyuKNCJmX/H1SRYI40skcdvtNtlsVkVuYqQBZTD6/T5LS0uHWBMi4ywqjO12W+nliyNZXl5WVM8oitja2jrEHRe8WtL5hYUFoihif3+fixcvEoYhlUqFZrPJtWvXGA6Hii64t7f3sU5aSfUty1LNbBt3p4KJYuja2hoAy8vLdDodarWaohuKQbmf0mNcLuMvs+KO6pNcMrBnfn7+Y3i1QCVi9DqdDsvLy7iuq4y8sH5kv6WBT2YywD0Kq+j9C7tGHLD0ScggmNFoxPHjx1XdQaic5XIZXdfpdrsqwIhnROJ4xdhqmqZ6M0qlEpPJREGOQhV1HIdarab0fYRCHFeTjY+mPNqYd5T5I+vonIUfRfn8a1UD8HOreE6axq1/z8RrYiVscsUS7nSA70/RDUALCcIR2UyOa1feo784pTxdwh0NyOfmMIgYhpfQJjCdVJj2umSyKTLZEps7e6wtL5HQI9wQ6vUGBiFa6OOFPv5kSiaXZdBrE919vFAukLR1Hjt+nNe++wqZbJFSPk+j2WVzdwvfM0H3sI17bBoTDQMNE50cOrkoBMNnNbPEu9t3OAgiVqppCuUMi3aafK6CbgRYYYfHHn+WfvMWvd1Ld1PlFJZdwk0k6fsm3vSe0Q6CAC2Kp42q3ZdQjw2H0YxZK5huoIf3Lq7DHaX3/vawDYfgvzKFyfM8qtUqYRiqG2s4HJLJZAjDkPfff1/h78LUmE6nhxgiEi1ms1nS6bSK1qVWII5BaIiFQoFWq0W73VaQk0ANMjBeIrogCFhaWuLOnTuqkCfnJs7YEAdQrVbZ3NxE0zRVfzh16pS62TVN45lnniGKIi5fvqyiXzEaAnXF9/Xokvc+uh7EDJGfH8QkehhLHJkoqAKq9hJvLNQ0jXK5rKQi5BoQaE8yADGqcQE1cSTlcllli8Ph8FAWqWmagtekZiTn5ezZs9RqNaQzVyCko9TL+BInBCgnMBgM1DWXz+cP1XLkdUXzR5RRxZnEKZ/xFX/v+F6J03jQ+os898+7PhUOwLCSTM0TeMXHiJofoHkpnHSGUX+AO/ZAcyGyGA465FIGrWaD8ahBu2+TSyXRmWLbKfREnlw6hTeZsFCeI+noBJ7P2uIJpmiEgc6w1uKgdotmt4edyPHk6VNEkQbBgFZ3QlLTMc0pm7drhLpBfzfiqXOP886772Ki8eZ7F8k6WQoZg0FrTKjPjGxGsyEES7ewiSgYNvmETT6fpexZrD71ef6oscnzK8usnT1G0czNuPCDNtHePu5wRLWyhNfbx4umWJaDnjYYBQGzwS46mir6HJYIjtTvMuJRMOtIRTWhphPetQOHm4jixuEhN5nchQnG47FSVhRBMKFyAsrwbm1tKeMpwm+SZosW/MbGhjISYghgJsq1vb2t6g5PPvkk0+mUVqvFZDKhWq2q4mytVlNZibBU3n33XSzLUloyYnglYhNnkEwmSaVS5HI5bNvmscceYzwe88QTTyg+v0S5vV6P7e1tdR6kGBrXtb9fxK72NdbQFV/xzt/4/8SN79HXe5hL2C/1ep2FhQXl0OAeQ0gUNGWvC4WCqhmIbHQYhqyvryvYTM4RoJhCk8mEXC6npKWlSCtRv+u6CqsXYy0F5bm5OZV5SPNYGIYqqLifGJt0q/u+r6QiFhcX78q3GKrWIQ2KUpAWvR9x8gJZxvdM9kuuJcmIjj7nfoX/ozWhhyEFDZ8SBwAanm5gVJ7kwNNZGV6i3ayBETKe9AmmXTwjCYHPdGxQKtrc3r7OsumgRR6mYZNJJdATJpE/ZWWxTBQOMHUDI2mg6xG5zAKDbo3e6IAonGKbKdwBtNtDLNMklytRySeZehFrG09gbu1TPxhSXR3SqW2zlsvTqDeoahB4Hr43oWyXSThJ0E1CLyLlpDF1i6SZwBx7VJJZFvMVqqkUU1MjEe2TW15HN9J4BvhTF63fw7V22N3aQguH6EaGhD7FTFg4mEwNHV2LiHQf+Murdf5VwT5HVyKRUBTLMAxpt9tkMhmGwyG9Xk8N906lUuTzeV599VUF6wj3X4qAMjZScPdMJqOgIYn+hC0Uf59cLkev1yOVSinKn+DBiUSCy5cvk8/nFTTgOM6hKF2wX4EH5ubmmJ+fV8f8ne98h9XVVfL5vNK6EZikXq8rzBxQnH9N05Qx+XH2Ju4o/iqXSG1vbm6q0ZzS6xE3dLKP8lh8IDvMIu1MZiZCGJc9lgK+nEMZ7SmF+vF4rK6LZrNJsVjEMAyVYQrTTCBDXdcVlAP3iqiisikRdTwzE6hKusYdxzmU6cBhyQaZQSHf4wY9Hr0fpegepfIezfqOOgP5v4exPhUOwI8siDwiaxFjJU9n2yftbeOGEbqdw/VGJCId3TJw3Saa5eCYMGjt4CQ0MmkLXfMJ3SlJ22bQrVMu5dENDU23GE3AN6YEmk6Ihu5U2Lm9Rb8fkJ1fJpfRaVy5Stq2STk2W3v7XLl2Hc+dMpgY5HyHjG8zbHqsO0tMjRT9qUsiu4hpJUA3GQ7H5JwcKStJMl0g6UPVyZJ0HCqVMqYNT/U75PJlTs9lcBIG9dYubthh2vUxow6D4QjLMok0Dd1K45pZpoEDmoVFxDQeFQb3ikbxlDteTArD4L4G5n5/+ySMiLymaZpq0pfg8ZI6i2a8QDnHjh2j0WiQTCaVEZabVNM0SqWSwpelUUw+dy6X486dO2ra14kTJwjDe2qTk8mEW7duKYE2YXbouq6eI5Go3Ngy0FxkLWTwuMwxTiaTmKZJoVBQ4whFj0iyHWHDSDFSDE98z+LRfnxfj0aIwMei/ziH/H578LD3Vmo58/PzqvdBIvw49VgomQK/VSoVRd81DEMxZuKGWTIowzBU1C7ZUjqdVhLP8SxoYWFBUUxFRmQ4HCrjLwXUODQnxxi/fsR5CUxnmibFYpFcLqeoyJKlSGObSFtId/DRrt1DTLz70HSP/ny/2kC8fnC///tx1qfCAchlG0Y+mpVivPR5jMYFTK9HaHtY2OjhgFBrAR5uz2BjY47bBw2chIGluRi+hq8ZGOjYhoM77jNyA5K5CiEaacvCSVaZTkaM+7s88shxLl66xrvnr9Ibuzz2xDGOlRM06nt850LIWnWOK/tbJAMNPZFgw86SLOUxEvMMfY1KIcnUdkjnSkwCjXIhwgghk3DIp3I4uklyPOD04knmCgWypTxffPRx3u6/w2j8LolJgYQOY1djPK4TTKeYuoEX+CSsFJGRpTs16XsJJp6GYUUwddGN2Vxfi5mBj6KIkHsppanHsMG7F+BsILweA/xDhflrvlxMn0zhUIydRPDSBKZpGplMRvHkpVu4Wq3SbrdVO71EycLHbrVaCoOXZiLHccjlcjSbTdbX19WwkAsXLnD27Flu3rxJOp3m8uXLOI5Ds9lUfPBTp04pDDp+84qshBQcAYrFoqprLC0tkcvlqFar/NIv/ZIqOgsEIAPm40ZCnJ7ATiKRIBFo3Ijfz2mLUTmK94vRkD2NM4s+iaKwnDtRem02m/T7fdUNLX0BrusyHA6xLIulpSXVEZ1IJNRrSGaQSqWUXIhcE/IcoXzKeZSB7JIJBEFAs9lUU9wSiYRS9Iw7JekwlyK8QHvyPMlU5Fxev36dTCaj6KRRFKngRBxTnMEjrxmn8j7I8Mt7HH08Xjw+WmCHj0NBP+76VDgAWRLx6FaacelR7EEDNBc9YRP164SjCRlrmYXVJHvNJkkDIiwMQ0PXQgzdw3VHuOMIx06TzpdIJW36gwnudIAZ2Dh2nnypy3Dos7hcJArHXLttcvGDJluFgHprSH2apzP22O+nSGPjdrq89Nmn6Q+zkK4yNRKMQwNMG9POoNspRm6ffrvL2twK2WSRQjrLnG1SziY4ubSCbunge/ztR1b5Nx+9zbBg0hoP8PwRka/Pon4zQSKyZxcQM3586IZEkeCJOmEw0/gM0Ig0XeH/8XUv6nhwVHjvpP/VyALFm8Ek1RdjKPzwVCqlone5MeTmlaKjGPtcLqcGhwjmKl3B6+vrtFottra2FL9f9Pll+HoqlWI6nbK4uKiYJPFpZZLuSwRZLBbVNKpUKkW5XFYTrnRd5+zZs7zyyitkMhnFeZcVb/KR45THJfIS+nOEAAAgAElEQVQVPPpBvG9ZkhHcb18flAU87CWGKt7s1e12Vf0lDEM1kSuKIlUPkc8oxl2cvDQC6rquRkJKUV4cSRAEyoEAqoPbcRyCIGBubk4pcMaLwUK9lUBEjsH3fSXYByiISGSsYUZHFgfkeZ6Sfo5r+schm7hxPorvx9f9egLkZ/ku5+5o9H/0f3/c9alyADAzbCE6kV1GS8wKPZlkmVA3YFJnOuwywWTj1Fmu3bh1t6DkYdo6YRgQBgEJ3cAyYDIaMHFDNNOhMr/EdOqTziToD3RKc8uEuo03mHL6eI/9Vp2JX2L7Lvd4HHbxtAnDYISORjE9TyLMYSRyBIkUgT0TCEuk81hOhrFexlrUSaCTd7Lk8wVWCmmCoI9l6jgJA83zMfIV9PEBVsYiEVj4JAiJ8IOIMArJpXNkHIcAm/bQJww1QNJdm+guqTMQI0+EFpN9DmJsH7RYxBDdm88rchGAkoT4pFac9qbrulJ5FHkAMYSSGczNzSlaoODHcZxWdITE8M/Pzyt4oNfrUS6X6XQ6CgeWGa+3bt1SjBQxONIrIKJm4khEmkJqDfv7+8zPzytHIBCERI9S5JXiZJxLLsXGXC6nHIqwUeCw7vuDjL8Yk/gNf79eAXnuUdjnYUNA8UIl3NMzksKvKLkKPCYMH4n84+qhcl1I1C8BAaCcspzneG2h1+spkT4Z/iOfW4rFw+HwUFYmXH/JAlzXVVmoZAryPoZhkMvlcByH+fl5PM+jVqspJySOJK7SKhDSnxd2O4r9P2jv4wy9h72vnwoH4KOhR6DfFTPWtRnM4es5Epkc4yBAT04IchEF8xbT0GPnoEPCzgAj/CBi7EXkM3mi0MPAI9RCwmCKHmpUCwX80ZRMyqHVbmA5Vbp9D99Lo6dT6IM2a+k815p10lZEs31AZFmYnoavQ8syyHkhdjJHOV9FT6boazp+oGGXygSWg6FZhEkdbTplZXEBO4K0bmCUF+nc2WelWIKeC706Bxe3Sa26DJw0ftph6pkYhk3GydK/e1OkHYu0bXHghoz9mRHVorvRHzDT+rlLCYtm6I4WQXAoKIhlCBLpaxpRREwL6B5sEDzk4DGe3h5tc5ebWyIqacKRAiCgGBsyNEaiIckgZLB8KpWi1Wqp1BxQ0gTFYpFGo0E+n1fdpXKTy3MzmQy5XE5FnFEUkU6nFbXv7Nmz+L7P0tKSYpVsbGywublJuVxWUeTVq1dZWlpSWLQUkIUJBfcKzIIhy3mSdTSqi0MJD7rh487jKOzzSdR2jka28rvQayWTGg6HqmgqjCsxvkEQHFIEFbxcsi/JxKQ5TM6DaZqHJDWCIFDwjHzeePAg0FFcsE7TNOWMJSARRpkweaQ2AXD58mVFLhBYSt5LMtn4uZbzIQNzftQ+xJ3+UXjnL+JM/rLrU+EA4uuBBc3UGmZoEOk+1mgH1+1jYGBaDpZjEUQwdiMMbdbZ6jg2jp0gCGbNQCEDuu06uXSaTGTRizxSocZ40iRVSRF6GuvWrHnM0QMarguhgxv6RJbB/HyViZcmpycIIgMzmQQniZHNo6XT2E6K0WRIazjAG4wI/IBMMcfk5h4nn3gU/4OrmI5LNLXR5mz2pvuYyeMwLYDhoxsOIRb5dAIritCikCiI0IOZgTciHtitGzIz7n+R1pC/CqjgQUsudBH4koh8NBoxmUyUporc9GIwPM8jlUopIy0pfJxhIrK/gOKQS1qv67qSjhAdmmq1quAK0bZfXV1VzBOpB8hNLRLHe3t7rK+vs7W1dUiBVG5YwZQFghAVyQd18t5vSZT9FzUAD0sm4Ee9PjxYpkDgnUwmo6JigVzEqArVUuATEcETsTxhXcl1Ko+HYUi32z1U1JWeDzm3knWIlHdcn0f6FuKdyfHIX4w/zK7Tg4MDNc0ul8spZxIfeRnP4iTAidNA48b86HkThtL9+j2OOvJ4PeDPggr/vOtT4QA0TZuNMQlCiNGbZhFpRKRBMgrQwwnhuEXg+ZhGgoSRwEll8YKQievhhzqGFqHjYqdmHaeRrtHstTENl263zSidJJUskDATZPIJyBVwvSnt9pBSNoljZ7DoknFDDtow/P/Ze5MYy7L0vu93hju9OYacqqqza2Czu8hukhYFCmp62EqLXhiwpQVhgJQhwivC8FIAaQigDS4M7wjQ4IKEQWohGLABLWjTgAgQMCm4RavJttVUd3VV9VCVVZmVkTG86Q7nHC/u+06cuBlZPVQknXC9D3iIF+/dd4czfMP/m7Ydrgv86+17/IOXfoK8OcLNZzRHC86Wa6b37nJed4TgOJ5OWbgWm2eMp2OODhZMZofQbLGqo3YFhTri6M1/H/PRCd1oxFlzQZ7dZjxdkNkxpQ1oFWi8woWKYAMZCu0cziWTnsI4BHamUz+OiVNwSP13V83I1Jy/6XmVawwdl6lmI1qbUio2G5eM4VRjFAhJNENpwCGlGcRkn0wmMalHqo3+7M/+LH/5l38ZSw8/fvwY7z3/7J/9M3791389WhX37t3DWsv9+/dRSsXwv/fff5+XXnqJjz76iPv3718pSiaY9a1btzg7O4tVK+Ve0nIAEo0kjEqExXXMAS4tgMu5u35eU+0//f3zmNf0msP7TS0+uX4qFFMGmjI/GSNhniLw5SVQmpwDiB3YJpMJJycnsdqqRHVJ9VfxIYiVp5SKVoREFgERKhLhVFUVBwcHvPPOO9GJvVwur0T4SL6CWH1DQSBjkDrv0/0gENLHWXcyrrJebtIqeCFqASk0aEMYhGkF1TO3oMDpQDO+BZO7hKIiy8fozMb+r6enp4TOY03BeHKHNozofAZKcXp6AkBmLL5pOX38IeNyih0dE/QBhKpvDl+VPHz8kKPjBT/zxmv84ufH/NxLC15+acx/83/8z2TZksO7YyYfdZi85f6br1J6x6LKKZoOta0Zh8C43uA2NR/827+mKz14cNZQjA1f+7f/GnV8j0ot8V2gqj7D5OAeJp9QlSOcLjltMt67sHx/bVluG7puyzb04xCE0WtDQOF8oPcRBLzvq3sqZbis/9+TWBH9S2MxWAwBTUCDMrgbXg0x0igxva8jSY4SXD2EwMVFX/bjww8/ZLvdkud5bPIhjOOjjz6K2l0IIZZ8lhhy0Sil+crLL7/M5z//ee7evctrr70WewvIPQBPVW/cbDYYY2K10Pv377NarWI4omiLf/iHf8iruyQ1pfo68NPpNPoZlFKxzo00JRGGNhyv1IEowiHFgocMV5iLvIYx4jdt7YmmLUx56PwUbHyY9CUhtIvFgsPDw9jvWaJzJJxyu93G5vHps8iYiPV2eHgYo4IODw9jtq9Ykq+88kr0EYiTV9aXRGFJLgoQfRaSPCj34L3n3XffjZFFEqIskJeQKCoyDul8pIIwFWZpmGo6h0N/j/jB0uNvgl4ICyCloUdcqCHHWE1jZ+R2TtecUeYV1uagG7T1bLbnGK9pljVaW7wPeNdRjSY8OTtnXBVURY73sK2XVOWU1iiy6SHbM4sJLfePJmwvPmKzqnG6ZXy44Fv/5yl+XvIf/U//LV/7r36PbnxAeeowdxSHiznfPznl+PYRxigefG/JyIMuC6av3qc7v8DmGaFrYXzAn//VW7zxH/wEpj7Fnzm2dsyoGuOdo+0azmvHulWsugwfIChDwMNA6qeM4+O0Afmu85dMJISAl9/rvxkoaGgFpCQalGhewlRFMxSYRzaybAjR8ASv996zXC6ZTqcAEeeVWHGpSyQ+B8kB+K3f+i1+67d+K/ZylcbgSimOj49jRcmiKGJ4Z9pW8OjoiPF4zHw+57XXXou/letLBEka7/4sh246rx+3ya/DjYXBXFdr6CYpjUx5lq9hu91G5i+huuLgTZ2mEm0lCVjC6ELoczAkXFieR8ZQBMViseDevXucn59HC1EpxXK55OLigr/39/4ef/zHfxwZr5TchkuBK9FLEto6m80i5PQXf/EXHB8fR1gyTWpL81SEwafMOYVrBGaS8bvOanqWpSx00xFA8IJYAOmgDQcgHqNLWj3CV8d0eoJTUNcbjM0oqjFFNWI2P8YHS9PC47MLms5xtlpTty3alDStomlBacvy4oR6/RgVtqhuzagA31ywWT5mdbHErUD7EYeHb2CqnPlKs1k3fOU3foVptmL+xTegUJjDKccv3+L04UPazZq7924RlGc+KulCA5st7eqCpd/CuODf+0//PsXoHlszI88LxtWIthUTuSUvNG1nONsoLjZbXAAXFF6bWKIgZeSpY/VZ2rb3Hhc8aIUnRKtKmP9Nm5XpdZ8FbQjJopaSzkqpGHsvGuN8Po+MVCpFSqiltTbCBFr3VUHT6pLSsvDJkyex0qdAPRJhtF6v+cf/+B/z/vvv89prrzEajZhOpxweHrJcLmNp34uLCxaLRUxoEr9FVVX80i/9UuwClUJV6X1st9vY5lCYhTCHdD6HIYY/zPimAmFoAdw0DDSE7+BphiRlnWezGaPRiKqqYiXPxWJxJdzz0aNHMVpLsq6lBIhE7Qg0I5U/33jjDV5//XW2221suCNwn+QGvPPOO3zta1/jK1/5SvRFpDkk8pkwdgkMEOE+nU55/fXXo1AQC1T8ScL4U4hSlIKU0oihNGrourlNLT4ZW/nds+bgk9ALZwEIhRBQSXKSDQGvAkZbVG4JXUfb1GzPT7B50b/ClM5t0b5lMjEoFSio2DYN+BZr+vOdLS+YForNxrE5vcDomlFluXt3zGh8nwcP1kzGhhZPoxyfvTXjZBs4r9d8bdLx93/9P+GP/vm3qaoRZx8+Zv7yK7y26ggaXOiY/+yb1O+fkgeFzQyrZkl5MIVpwSb30Hqq2THr5iG1thSmgtCCcoTQMCrnVJ1ls93uqnsGvPN4nh0Nki6aIfNXSkWmH9hFAO1KBqmb5fkfS9cxizQMULSr1DSX0ME0Ykc2rUR7yOa4uLiIcJJkHVdVxXg8joxELIGyLLl16xYffPABAIvFgn/6T/8p/+Jf/IvI/KXtYVVV1HUdLQ0gZvpKI/GiKNhsNjHaJY0MEaYuTUakAJwwnB8UBpieJx23obD/QcL2eZEwP2F8cm1hkhLuKbX6JVxU/D0iAMVCg6tW4Xa7jU1XlOrLei+XS5bLJYeHh8zn8yu1oUTwhhBiW8df+IVf4M///M+vwEGyrpqmiUlsUrtIYv0FypOINIkaEoVEwpHhMqcjpRSSS+fkuvdy7NAnl2Ys37R/58UQAFr1LU1CIASF2kW7OH+ZHOP1LgTSaJSrCZ1HeYUxHVUxxeQTnDeUWlGpEb6bsK3X2GKG1hD8FqMgM4qMluBr1t6Rz+eUmWe9OWO0bfnut99lMRsRXEfoRmTFhs/envKqHfNkWdPVKx7yhP/wH7zKv/wf/g3l/ACMJrx+hEJjN1vIS4r1BRTHoBxmXbAJHSfn32Ez+pCXQsf310tONzWbrmU+AW0UXdfjpOMqwynHk8bjXQCl0NrS+a4P61SgfUisJnE27cIbY+z/JR6pMTFcKCiF9gHlw5Xy0YSbqS8i9KwIhuECT7MexakqJrc4bYEY9y1leIWxigARx55zLjpfpaPUt7/97fh7YdZvvvkmL7/8MmdnZzx58oTDw0P+0T/6R/zO7/wOd+7coes67t27xze/+U1ef/11NptN7BMMRMff+fl5ZOrr9Tpm+opjM8WNheGIQEvDPGUshoIyDVu9zkJIhb9kFqf0vIWBzGt6XdGE8zyP0Jpo1qJBC4wj9ycav/QVluigNClKSkwcHh7GDmwPHjygaZpYFE6cusLY33rrLW7fvs3P//zP89WvfjVGjEkUkMCLMsZiIYzH49jsZblcAlyJakox/DTZLx3zH3XsU+F5HbyWfnYTMN8LAQFpHzABbKLx94vbxJcj4F1DtznDqoYyr9C2wGYZzXZLqFfkhSHPLVrl1J2j85pt27Kqa+pOoU1J0AU+FHhVos0IzIyOMY4RZIaf+uLrfObVO9Tbhru3F5SF46dev8XhyPF3fuZV7i0q5nNN9XrB7/zhf00xW0CpOO/WMDYwLahZ4ZZLKD1nmzMmtmSyOuXt6i02736Vb7/1b/jOoyfMju4xPTjEUVNUOfPFIdaUjJTnziRjWo0wSqN3zlup86kTuCBlnENnoaZ/4Z/WLtMF+jzgn/Qaz9Jc5Rjv+7o+orELkxAcWZxtElon2pjgwcJs5NmHZrhSii996Ut8/vOfj8k/0megrmt+7ud+ji9/+cuxAumv/MqvRAZeVRU/8zM/Q9u2sROUaH1FUfDRRx/RdR0fffQRH3zwAR988EGEPeR5pE5QnufM5/OIeQ/HZohND+c1PU6YVTqPopmmz/43aQkMSe5ReuymMK+E3UoUjzRTKYoizo/kTMhvBG45PT3l/fff5+tf/zrvvvtudCyLz+Dg4IDJZMLt27fjuvhX/+pfMZlMeP311yOkJJaazKUoEeJglnLQcm3pLHdxccFms4m9I9I9OHTsf9zYDCndL6lgT+Gi66z8T0IvhgXwLNrVr/E+kAfwwWMLjemmqBBo/RN83ZJb6Noa5dfkRYkzmlXrGVUlnevrkeQ2Y7m5oCoKgrJU2QhtQAfo2o6iHKFK0N0Z2/MPeP2zb1BWM7JxwdtvvcMsq2D1iKOp5uXZZ3l08RH/+9mf8F+89U3cG/cpA3gcepRTHIxovv4ttu2WuRmzfu/7/K/bP8OOa+b1Ex4GOLz9xZ1vIseUBbaaktkK1XXMLLQrjzFrMm1od+Gsml3CFzBcYpcL4lK7zpLvuxBuTGv4pCT3kVoBEoWjlIoameD7orVLFq2Y61JnRkoASNRPmjCWOogfPnzIq6++Gk32x48f8/LLL7PZbOi6jvv37/O9732P+XzOL//yL/N7v/d7QN/YZjQacXx8zHe/+12Oj485Pz/n9PQ0Hv/o0SMAbt++HSOHptNpjEgSyCN1Hl8HGQxpKCzTz9J4/+F4Pk9KwxqH9yBzK5qxPKPMb13XMW5fQkIlM1x8gXKsWBAybmIdCjQnvzk5ObkCn4g1tlqtYu5ICIE/+7M/4ytf+UosEy4F5+S+xVl97969mAMARItEYKk08knyPOR9WqpjCNekWn0q5IfHXCfEn9fcvhACQGEISuGCR6kep1AKVPB43WPWtVfkKLomo+kcumnIVIEdj3GupcMTlktC0TGZHuH9gm1d98IjN6xW55RZQdtpitJQu4DyiiozFFVOs92w2taUpsDOXoLOQVYyNoHXfuIVptWIVa0Z5ZqODpcd0j1+yK/83n9M+cGc3/7v/jnt4wu0VmTvL8nrGv/uN/izi7/iL97/KqPXc45XxzC2zM2UtVaU0yk2L9mul2hvUR7m5Yjgzrl3VPATG8W3tg1tyHBO4bms4292mcB9VdAkgS4o9M6wc7tcgaB6QRodjbtQ0QC7QnGBEDwh3IxWEed1YK6mMNBwQYspLiF+kmATQohZtBJVI5EkUvtHioTleR4dckVRxIbd0jDk8PAQIJr+x8fHscTwBx98wMnJCbdu3YrdpX7t136N3/iN3+Dg4ICmaXj77bd59OhRLIP8ve99j5/8yZ+MtYQkBl3KVgsUEUJgNBrhveeVV16J7SvFQSxjlY5TqvldFyDxcYEA183BTVIK5V3Xb1jKY4imnWrIMkaCs6cMNU2gEosvzTKWeZbxKMsyMmYpDCjjCpe9GyT5zHvPH/3RH/EP/+E/JITAd77zHU5PT6OT+vbt21xcXPD222/z4MEDoO9fIRnNSqkYSiprVJ4jhBDLXVzHxJ8Ff6bfpXsk3SvpMTdNL4QA8KrX7tvgMcmiVh76MHdNrlq0a8lVh9npv84runCZdq3QtA07OKGX5jrssg9HYzarJW7dYjaWW7du9ZmZoeuFj7IE1+KMxaucTm8IrcPiyTMN3TnUNdYEfOeYFhkcvsQ2bMnvBH75f/kl9FqT155XQs70WOPKJQe3blPetrRnW554y/frmsnhnNGxwaNBaWaLQ0IAa3qNKNcjVHD8/Bv3KLKcf/PuKUvXXdEonAbvAxiFibWCdATRrovSSBNYhK6LGrrx+R1oOSmlTAwuHYdyj4K5itNOImwkq1TCC1erVSzfK9ZA+kxp0pXUAVKq7ygl+HGa/i8OyN/93d+Nm1osB2stt2/fZj6f8+DBg+gYTvMCBLZKQwDl91/4whcoioK33norlkcYktz7dZBZ+jcVECnzeJ70wziZRaMW7V1q5EuYZ3rfAv2kviIR9GIFDNdt6jdI/SpVVcXAAAktlfdN07DZbPjt3/5tXn31VV577TXu3LnD+fk53/jGN/jTP/3TeO7U7yCKRwiXfY6lhLnMjVidUrrkWVr7s5h/+r/c/1DID+fg/zdO4KB6Lb9P/NK7AmcKE9SuoFkgbxy+PsOvv4NyHVlu8d7iXJ8QZWxGZUd0vtcqMlvQdh0WFTdjVVVsV0t0ZtnUDQUKSy9ojC3oXEsXLFZrlHOcN0uUdlSuhrCmMIrp3dssVzVtW2GWH6HCiEYpXg6ayd0py+YRizKjKCt8XXC+fIgpZgRbUi8dxfyYYAu0zRmNJ2RFSdfU5EVJVY3xBeiNY5xZmtDwyqtzvv3wGzTbJ3SJyyZwmc/brwPRrLsfyMyvi0N+XgLgWYwi/Vw2JhC1PHF4KqVi1cgQQjS5lVKRaYgmmMbbTyaTKw1KUqesxIOLyS4VPiU6aDQaxRo2kmAk9WZS7UwqVqZ1YqTiqdyvhDOKc1Z62t66dYvvfOc78bmfRUNH8MeRMM7nKczlnoaOffl86LwU7D+1aAQrH5aJSOPnxbKQjG/5XDD71PEsAkSeP60tJYJH5l/8DuKvSbF2mb8UupJ6VfK5nE9KSqTCV3JWpFCdjImMBVz2TB468NM9mArx1J8wFO43YRW8EALAs4v8cLqHM1DooGhtAN+hvaNRDZaOIjsk008I9S49OldYM0KpwCoEcttnhrbBg9EYD6Oy6jdrF1CmwFoTnUDtaklRZFSjAqUVmW7pti3L1TnO1YzGFbqYsjk7ZXHnDsorxqMpHZrx9FZ0UnoPWe6x/pi8LGjbQEODYY6yM6azu6y2nkBGUZVoOhSBLCtA5xijcK5FbwJd07KsHWfbmnVbUo1m6E1H2K4uGWfoncHee7pwGR1iJKKKnbkpmuIujTj43Qb2z9Y6b4qGWtCwPIBohEDE6QUjF61PNomY3SneLCF5wizSOi8nJyeRsXvvIxy0Wq1iETDpDfCZz3wmOh8lyiiEwOc+9zm891cyReu6vpLgtFgsor8idVhK9FLK8AQSkoSoxWJB27YxL0DGJh27CO0NHOnXORqvg4CeBw0TnYSG8532PIBLaEgspjSpL30eicRJryUwTwghVv9M2y8KHJX2YxCmLL8XgTC8dsr4hSFLJVFh6uLLkeeUKCVRTsTSWywWsVrtUNGS3w6Zevr5cEyH57hpKOiFEADppoYdLu37wm5hF8liDOTWomsFyjAal7Suo3EdnVcEAsZm6CzD5BbX9QsnxoqrgM4MVvdS/ez8oi8/UBQY02+yzFi6tsF1gjuO0JR8ePaEUX6Ldm2ZZJamc3jfcjgZYfSuuYVSODJQGavas151QIYjMCnGnK07RtUBR7fu4q3eFb324BrKcopRAbxDNxs8gcZ56lDwf3/3Pb7zeENb16BN7wUGUjewVru2dATg0rGolEZrdWXDDjW3lOHcNKWmbHqt1JyXjSdMTrIw0+xK0frShh0Sry2bVBitbFaJOffexwqVQrJpz87OYgG5i4sLgPg7ifOW7GS5dzH7pY5M27bcu3cvMv30ucVqSBm2POs777zDw4cPI7R1HaWRTSnskjo8r4PXhpriD+Ns/lHoOggo1Wbl+cUPI7V80pIPAslICG96v2IZyHcyH6vV6oqTWOAeWc9plrWsn+G5UkavtY7Wmdy3fDebzSKTTwWYwHlpIplSffKitP5MfVhC1wmD62goXFNL4bp5+KT0QggAoVTTEe02OI8CrA+EZo3251ha3LambRt8PiazGcaAV4qmdbimZjybRshgs9nQbS/rjDdNDxPVm23fSMZkER7KjcG5gNagVcZm02BtgcomaGVpFKzqPu38ZNWbn5nKuFg25GVgU5/TeJhNDzm/OGFezrl4csbLrx5iMGS+o6hGdB5819Jut4SuwxYZrq1ZLle0nccUFVAxPVhwSE57vuLJpsFLvf8rc6+5CgoNP4dhrdC/qYiR67Sd9NqiTclCF+04tQLgMnpE8NU8zyPcI8xdGIrEdItWLqF6qaNUjpEqnQLRiMNYcg1STDqt7ihdp46Ojq4wBHk+KV8hjmmxPIR5SfLSarWK3/0o9HGbfzi+N20VyL0OyyALE05bK8rnqeYu9yja+1D7lnNKBU+BZAROW6/XMUIsva5o4uLHEeEjPoAUZpLPUqYv1qSUKJdgARFaEoGU4v7ii0rzCuR+RfCkYyb3K2Pwg/bhs6ytH8YP88PQiyEAvCZ4g9KOEDQBcMFjjaL1GuU3hG5F0T5Gh4aAozMZNlswGk1jGFlZlri2NzsvVut+wgNom+F1hc77bkHVaEK7XRGC60s75wUXyyWjPMP5LcqA8jltE3BOUU4OMJmh6RpcV2CKBXW9IbSBk7MzppMDgjKsNx3GFGS7tjZZUWHLiuPpnDKv8EHjlUapiuniMDIN7z3Ndsnq9CNW2zOKYow2kNkK0ziWZytWdUPwvi/YphTK7bBE/K4hTB855dDgQ98bIEgWcF/1we/+dwqUD/R2yG4RKfA/Gg/6kSmFfGRDCfOXlzjyRJOSmHvZWFJ6QZiwbD5p/n56ehoZEBBjvdMaM/JecFqJRhEBIvcoFodASJvNhvl8ztnZGePxmNu3b0fN9OjoKDIB8R8AsTyBMC3ZyFLOIoV/nuXAHTr8ho7gH+T4fZaF8ePSsyK5hGkLNCJJWbI/U7xenPJyDsHZxfKSPA8g+mAEy0+twaGFJvcn50+zg9OIpJT5phhnDIEAACAASURBVOtPFEYRYHIdUUokGzgNRJBzSViq5HyIYEnH7OPG8Tor4Vlzd1MW+wshAKJzLfSZriI5ne+7XQW3Je/OKJUnMyN0UVCUM4IqQfWbOVOarfOApRwd4brzGFZmjKEsxr0maCz1ZkteVlhtsJlG6Q6tc7rOoVAoMnzoM2qV1vQBqJayKKmbDa4DrUrQLdPZIXle9jH9SrHebJhUU9oGRtUchaVtA1pbxuMF+WhC5xRd58jzAqU03q/YrM/YrM9xweG8x3jPee1QWdVbJ01DGwKBncOKy4XTqRAtguBtX+JHgaG7dAqGQejgbtzld8/LIhgyKyHR2oRpCD58cHAQmbdo0Gmt+K7rYk1/YwyHh4cRb12v1xHzl2SrFHqRDS5x4qLBiYAQ2EK6PskGFce0QEnz+Txe7+7duxEbTksihBCiv+Ds7AwgaqDipJSM5PQenzVmqRafClK5rsyrPIdQ6tC8KRpaFOm6SvF4ce7L3AoTT+9RGKjAfimsJ9aSWA9ijaVNZ+SzFMZJ4aiURLjA5ZyKtSfPlNb5T/0M8/k8zo8Il5OTkyiEhOmnEUlDp/hwrGR+0vuLMPjAefws2O2T0gshAGCHU6peH0094sF1FMpT5obSTCirGUZXeOVpfNtnD/uAyRTeQpmV0AV08AR32Qja4QiqBW2pxhOsgXZbR+x4NCpYbi7IrMK5QOcVne/LUgRv8E5RFDmr9QVql2LlcQQPmTJ4Oow2KGOxpsQRKIsJloDNKvK8oAswLkuKYkI5KmjbLXXj6Qg0QVHNDsiN6YVGyDm7aPjw8RnL9RbNTmMIH6+mBwWd932HNfU0073CXPrBvvz/hmlo0g/fi7aolLrSHSqNfJHPpIyCWEzC6CWsE3onsIQdAjEJ6Pz8PGpkaUVO2Wxpo5JUi0sZTFovRhzQ4gOQPsWi3UpimeQgSEN7OY/0yBXmAj8cTi/M4lkbP2XAz9MZfEWRUJfli+XaIYT4/HCpwQ+jhsR5LmMgsfYCxUjEjWD3qTYPV30kqS9AhIzMYao8AFHhSB3MUr46jS4S6yJtAAN9zSnJNxELQQIOpGFQmhV8XfROKrhS4XOdHycNCU3n4CbohRAAPiiUAe8vHR+9ltphlEd5T2Y0eTElsxm+a1E4tIeQWXSAwlhGqs/sazVUsynNesXF8gnedUzKMVZrnM2xGbSbFUVhIDisyahrh0bRtZ7NtiV0YacJaPAdyhc02x4fNlqjtaVpO/K8ZBe3RPCa8WiBtjlaNWTKkmWG0WzeM//JFGNztDG4LjAZz+jQdL7l4PAOTdPRdVuC82gM5/WS2gUuGgdtjcfuBJLHa32pve+YUj94HUqD875vsRkjhXaMQymCb/EElO5bcUbGccM8Y6jJDq0MCeWUCI+0RaJoVMI8BIqRcE3p+yup+0AsNDb8X+u++Nd2u42MQTZnGp8ukJJzLkYUyaYXBiTQkWx4yR4VbdW5y8YjwJUsVmEGDx48iH0sUsdlysDkXMMwS9Fwh07h9O9Q+71pYZBeOxUG6XXFvyJ+nBTLl/GeTCaEEKLAlHEXZUDmGnjKaSw+mxTSgavFBVOLQpSCFE5LGfHQihK/kCgcqWNZQk5F0y/LkhBCtExTLT7NJ7gumicds7RoXLoWhlaDzP+P6je6jl4IATBcsCJ9g/MEa9g2mlZl+NDRNiu0CtjMkucjtlqTFTlt3eDbC6wtybMJjgAWmuYU3dU0OgPvGVkNeY5vtmjVO5tXqwt8cGyXa/LC9kza9lqhzXO2naMclwQFquvYbGryHLoWjHaUxZi28+R5iev6nIbF/Dar1ZJidoe68Sxmc3yzwdnetHWhjyPPRxNsOcWjsUWJzcZ9RFDouKcr3jtdsmocF0vHtm2ubP4hAxhuStcl7ebUpUahsPSZv8RyoCleeZPzOnwv9y6bVjTz1HGbavnCkAUSEOEgGpgcK0JEms1772PUiODG4ghMY9AlCkQ2qIyXnFOidIzpm4yL8/bo6CjmG4jQEZ9CqvUDVxrOe++5e/cujx8/pq7r2BzmB81r+lnq8LxurNNgConIuel5vU6oyLwO7+U6B2hRFFEwprWb6rrm4uIirgWxLFJtOGWqaXRXqtmn0GJ6r2llWbkfrXWcMwkYkPDutESJrL/NZoPWOioJkpkuSoWUqkifNx2zZ+3b9H36u1RxSufyJub1hRAAKaUL1ihoO4fSOU23ZdVckLlz8lzTOYtSHl2MCcGRB4UqDntcmQbjPE4pivFhDwNpRdiuWZ6fMcr6fqXb1XLXSi6nazuyakxQjs1mxaQoaNqOslLUTUcXAs4FrM3pukBme3NUYbA2pyiAYKjrDbbK6ULG/OgIarhz/xWcycgKizYFnSqwRsWIBd005HlJWYxQ45ymbsmYcHjk+dLn7nO6/DbnF1fr/Q8p4p8DX1PckFc+v0y51/r5Np1+FmMSEvMauBYCkg2fnk9rTVVVMZ5c8NiyLDk6OmK9XnNycnIlFE+uLYxcGLdE+ljbd5cbj8exA5kIFYk/L8syhnau12tu374d+xiI0BGc//T0lKqqooUgGasSSfLGG29wcnJyJVrkOow3xbVT+kHa3/OEgK6zPoZaq2D1Mr7iWBUHvDhVxaEv8JxYRcJEi6KIAlxq+ojPR3xIcDWDXGCZ1Mkq15DxThmyQDgSSCD3IOtAMrpFUIsFIL4OsTgE3hO/QOrDSP/KPcHVxLCh1Ta0GNL1MPQt/Lj0QggAl4Yv6oAPnr7qsQE8Wjma0KDXZ3TtQ2qdo+2MfKIZZSWT+YLOg2tW+LbGbRVKZ2hdUoz6BJ+Li0cYrZmOZ2jt2GzWtE3HfHG4ayCSEdAU+Qjvc2rXUlRTjC6wyrHZNpQmoKx0HQqUWQ7a0NFv6ovzFWWZU9gxwXsKu8AWHZu64fDeXUhCwpTKwWiqIkeFXSRQu8XajCLP0QHaFgqbMS4txmhU52ICV+8P2C0sLrHQXrPfbU6SheR3L8D5FqUDSvsrLgXf3WwY0FBzHUIVsmmkNo4xJlbclMYgkrAnZYOFiZdlGUv1CiMfjUbxWNHmRBOsqiq2ckyLxWmtYz0ZCdlM2/xJCWlJ3Oq6jsPDw5goNB6Po1WR+jPE4SkMS44TbFv6FEhlSWHoQ0GQmv+pkzllainEJu9TnPymHfzp/V2XvCTHSL188QnIPcJlETU5ToRn2lRHHKoyluK/EWdruqYuFRp95ZgUGoLrS59IHokoAQcHB1d8EOlzyj0J45bxlc9msxlALGt9Hckcpvcl93Qd3CNO53T9D+fhx6X/70tDcjWKASxKZXiv8fRJXx0KMHSujxjIyzEHB8ccL25T5YF2cwHdZXOGyWRCbjMUjqIIKFWTW01R5eTjjDb00T3leETrAw4FxpKVFY3zeKXxQaFNRt05QNN1HtAYnRG8wnWBoAw6r1Da0HQejGE6P4hMZLPZ0CkNGlbLJwTXkOWWvKzIqwVluaCrdYxxB3D1Bt+2BGWoJhXVKOPW4YRMbZ9iDPJyviXgCDy94IcLLP0LxA5hLjz/vIDrHMGyeVIGIE04QriM/hCmLxq3aJbSc/f4+DhqmBLdIzCTUn2zEdEaRcA452LEDxAjP4ArGqNAVbLJT09PuX37doxcEW1XNF65Dwl9FO1W8OPRaMR8PufWrVtPaXHCxIev4TEp7g1Pz+11x9w0Da8l70XLFx9LGv55cHDAbDaLUVwyPgLPiSBOhWm6RuT/Ick50rj7j3t2YaKpD8haGxv6yHpLzx1CiPct8yLhyGmmsTj54ek1D1cztlMnemqVXNmjP8D5/0nohRAAV7LmdiULjLYonYGxBKXxUiZCGVbNBY9OPuD7332b77/77zh99ADf1LEX7HK5pNluwHdkxhP8mjzTKANtaMmKnMZ12Dxjtd5SVmN8UNgsx2Y5nfOYvKDzAeehbRxGZzRNhzEZWVbQdR6T5fgALoCxGc6DMr0AEqbVbLZ06zXu4gIdPLmxjCYztm5L027QvosaqveePFO70MQcoysW82NGecWsHF27KACMUYTgdq+rQkIYyrM2g9eKjoB7TojB0ImZZk+mzyG469nZGe+//z7vvfceJycnMX7eGBOTxCQWW7BjEbaCCZdlycXFRcT308Yygj1L5JAw+fR8Kf4qDOrw8DAyBK0177//fvQvyHNJG0hhFmk8eDp3qdNYcgaexeyjkB+EiqZwQPq5HJ8mpT0PkuvLWAkMIuM3xO2FsYuwGPp2xGEvzFUSuGQfyboZPrNYeHIPaaRRamUMrZQUXpS1IGG74rweQjJpGKtEKklROBEk0pA+LQ2dUrqm0s+u86sMne3PWiOfhF4MCMj1zV9CCCh9+cAuaAhgg0GpDVYZMpNTB40LLWhYHL6E1paL5SlhCaCxNmPVKcbVrkZPlnF62qf92119/YODQx49erTDkyecn20Yj5PYeuexUqHQtXhXs9msOTw6Ji89y9UGk5UoB6Ut0eWIkbdoDeP5MW3rGI9ndPUS5/oF/f3vvM10ccTB7Zew2a58g+lLXeR5js4zNJBlOV5r8irHdRl/+2//HJ1v+eBff4u6azE0uJD1OQtK4X0QdAdDX0JV+dCnhyl2xfWSRUQCJSXQ0E1HAaURF/L/0HQXMxou67VIFrCY8efn5/E3UrBNQghFYxfzfTqdxsiRqqq4uLiIZrkIiRRm8b5vJD+ZTCiKIlYVFc1O7k+iWkQYjcdjzs7OYpbvYrHgzp07UZkRCyTP8xg+KlCDRD794i/+Is45Pvrooyua/7MEvdDwuOvw4+ep+cs15VqpRizXTRO0RICJhjwej5nP59GRL8wUiJbwaDSKwlVCc0ejUcTh4TLhS4REGnIpn18HjwERp5e1lzZ2ESVCGtUIo09hunQdpxZknuexV8R0Oo3C8Flhvqk1cB1jHypL1/kLPgm9EAIgpeCvSuxLRmFQJsMWU8p8hHO+d8Ruzmi8RdmCycEtQlBsNx3jcUFmwJjeIbdY9NBMoHfkPnr0CKUtOs9pfGA0m4M1NNsanRe0dUdwiizLKSuNzTLOz9coU+DCFge4XblqmxVok2FMRlN7ytEYNg3j6Yxz3deH32636NMPMK6lWz2hUzmjyRSdV2iV0bdjNIynE5xXGFPg275Zje867t+5zXzyPT580tA6j83NADrbMdhwycx1SDDl8LQzyjmH4flVAh1GYKTamZAwCmm2IRq9wGJa66gl9/kao+g7EOcdEFP3Q+ijNMSJKBh+WsMndcgKvJAWFRN8V+LDxfSXMFBp+nLv3r2I4S8WCz788MMII6TRIOIrCCHEMgPyeu211/jmN7/J6elpPPbj6OM2vjC55+kAhqua6TCiZhiZkkI8UiwthMCTJ08ipJZGYInwF2hOmJ0I/bTSpzDW1P8ijuEUthnyErhMypP5TpWJlOHLWkgd2KlDVpzKcm1jDPP5PGZ4yxpLf/OssbyOhoL9pumFgICuSjSdvC6/1+0FuXIQHHW9oetqLpZneDTleMb48A7OWwKW0XgWNTUJC5S66845agfFeEY+mjKZHrDe1ORlyXrT0jmFDwbnL1/oHB802uQobWlajzYZ2vSwD9qw2dSMRxOUMqAtymacL1dUs0M6lTGaH3HnM59j6zXbTUdoNviuw++as2htGVUTOucJypAXI0I1wk7GqExTt1sMnjLLUcpEkzOFULz3l5VAk7H7OG3wpk3KlIZmbqr1C1acNtiQz8/Pz6PzT5q4iCYtloE4xtLriMUgDmEpGyHfSy130RjT7mHCCIRhpBtchI60ehSGkWUZr7zyCgcHBzGxq21blstlhDRE45c8A8k8TiNf5PNhyGQKb/yg8U2Zww/Cvz8pfVwEUhp7L/64w8NDZrMZ0+k01lsSK0meO4WQJGIILvMvxJcw1LjhaUxdFAmZy3SNy70PMXetNZPJBCBGJj169Ij1eh2FSioIgCtCSykVrcG6rqmqKioCcr1UIfo4rT+lVAimit5N0QtjAcQBDg6UIbArDeE947CiUA5lLM41hLBLyMhzgq4oxxOyImfVOrTOaLuOwvQlj4PKyYsc73sn0modKEqNNQWEDhMcCounjy03WUnddNShZqx3TMlanPeUxYhOZ7hsgqLDO4NzDcYoimAYT0b44Ai63/TbZU1R9rXlz0/PGM1nzDrIC4VD4ZRmPBoTsoysKNjUW6rxFGtzWtdS5BWYHDtd8JlXP4f+6jfYuC2ZVrE3gFK9z6RPRev7KexGFPROK1SKTnmC9xjUFWvAIZvg+QiCNO0/jahIN7hSKsZSp061NEtUfpdiq2lZYXHyCjyTksTyS7TJMBJFNHVZg9L/VzBoESIiTB89esSdO3fI85zlcsmdO3d49OhRDA+UiB8RVIIrC3QgUEGWZbz88ssRCrkummZIaYRMStf5DuT4mxYGaWZqqknL/xLtlNZYgn6eBOKRZC9pDymUlnSWuU9hvmHG7hAKu46xpnBNmn0ux6RJWqJYSL0fGee0XITATeLbEH+FrJeu63jy5AnQtxMVhUOE0Y86H6kFc9NC4MWwAOjLPvtAxK29oq/nrzXatVjoE8NUhrIFthihs5JicYtOlzy5WGNcS2hWlEZhgqdp+sJunW/RNsMFKEfjOBl1XdO0nnI8oXOBvBxTVmNMXlC3DmUz8tGYoBQ2L1DG0rqw0/4tZBmmKMBmNL4FbRhN5mgMeVEyOzxic7Zkc77CGMvm4pT1eknbebJihM1L2s6jtQU00+k8am+y2FPc+Ge+8NOUNqdzFhcgKN07yBV0u/4HKXXeiU/96TFPMFvR2p4HDRNvhIaaGFyFEETbPz8/j0xDIJkUBxasNy3DK445KRchv4VLZiLROuIoFg09HfehgzKEvrWjMBS5j+VyecXxK+eQyJbpdHqlrIHcZ5ZlzOdz3nzzzfgc14UFpkIQfnDZiOflMBSSNTq0NGQdpfcnTvvtdntF8xc4RRyrwkDF1yIas0T2idWUKg/iGxhCPMNnlvkQYZhGd8n4iuAXX5AED8gakt/KNSQ3JG03ma5JqUWVKj1CYqHIGpFnTx3ow4CEocVyU/P6QggAp/r2KF4bXPB03tN6h+sC+AbdbdChRmvIyhJTTqlmR5CNuDg7Y3n2Ed3qCU8+esDZySN8s6bdbpiOK3SmcSbQeciKCm3zuHm11n0IqM3QeU5ZjaldH845mS5ovSJgKEcTgjEU1RivTN9APs8JKiMrKlrvMdbS+UCWjfFeUTceXZaEZsl2e0pWBtrNikzv+h970CannE7RypLZgvVqe6UmDlx2GhqPx7z5uZeZloEWB6ovlud86JPctKL1l6YpAFrFEM9h5IQcly6om0gtf2puk/jpIYNLTeuqqpjNZhEqqOua09PTyGDPzs7ihpPubmk9FjHbBXpIo2IkEUsYsoQZlmUZP0uxYPlM/AXioxAGd+fOHT788MPIgNbrdRQw6TOJgJXEM9GQ0xoxxhhee+21pzRWuVbqIJUxHCYBDcMw4en6/DdJKdMSknGXa8paSgulSbjuZDJhPp9HyEWc7eKLkecNIcQkrGFUkVxTtO50/IbC7zqtWQRxqnzIOMpaknpGqaCT+U2DFeQ5U8hO7jn1L8nnKYyV0vC79JghhHpTFsALAQFp5QmE3gOsLYSAVWCUw3iPLQy6hkwbimKCyTOcC/jOM53M2Wwek2eGzE6jc61uG9hq8mpElRX4rk8i6Zo1Hk0xnlGfn+C7Dtc5fFCQF+RB4bdbpuMJPjh0pihsznrbkZcTrCkpR3POeERWFlgFVmlao/Eqx1mF0hXGKIzSFNMj1ucP+zjx6Swyn9FkjiksBE01ntG5Td/DQFtQCqMNbVtfmejFbM5Pf+4nWX7t31G3Na4PkuqbvIfQt9Ckt54CYJ0kmyiUvtTYAg5rCpzXWFysMLo4+MaNzqswH1m8qfY21GyFqcimE2ghjc5YLpeRKYtDVWi5XAKXTseUKYoDUTTuNOpIfAZSc0gYQ5pQJJaHmPIABwcHnJ2dcffu3egAFMxbspQF0ppMJvH6AnHIeay13L17lzfffJO/+qu/iooJXArPFMYR5jNk7sPEKrjEnW9asKfwy9BnIpq0wCoiPMXKOj09jT4RiZISZikhmUqpKJzleWR8U4syFQzp3KRRM6nPJxX2Mi6iYMg8n5+fxxIPkruRWnda65jnI8JZhJZYEmkp6LSzWBp9ltJ185OGtKYCPBX+Inw+CannZfrvaU972tOeXmx6ISCgPe1pT3va09887QXAnva0pz19SmkvAPa0pz3t6VNKewGwpz3taU+fUtoLgD3taU97+pTSXgDsaU972tOnlPYCYE972tOePqW0FwB72tOe9vQppb0A2NOe9rSnTyntBcCe9rSnPX1KaS8A9rSnPe3pU0p7AbCnPe1pT59S2guAPe1pT3v6lNJeAOxpT3va06eU9gJgT3va054+pbQXAHva05729CmlvQDY0572tKdPKe0FwJ72tKc9fUppLwD2tKc97elTSi9EU/j/8j//z0IIrm8qHSDgCR7wLjZRDr5v9u3ahqZpaDpH3TQsN1u22y3nyzM6D3UHdTAELD4oiqKg6zoObt3BBUORl3gU49GUPC8p8hFZlpFZvWsE3u4aSBsUGW23pigzFiPLFz57l8r2jbxVVhCMxSpQoeHi4ow/+t/+mHXtYXKXvKyYHdzFkaOUxlpDUE8/u1IqNrq21sbG2lprjO6/l5c04XbtiuA8dB22qcmaNbprKNsN1m0w7RoDWF+TuzUoh/cdwdUoH/rfOnA4XPC4LtC4Dhfgv//a+9fc5Y9Hv/qrvxrknoHY3FoaZst75xxd19G2LW3bsl6vaZqG7XbLxcVFbL6dNkaXeT0+PiaEEBtkS0P2oihiM3KtdWwm772PzdLzPGc0GnH//v3YEF4aj0vj8/Pzc37/93+fg4MDqqpiPp8zGo0IIcTjpEl3OqdAbAIuDeW11vElcyokzc+libk8b/qKeyH5K+Mn7+Wv956u62ID+T/5kz+5sXn9J//knwQZH3muIaWfp03NhWQc5Li0qXyWZZyenpLnOWVZ0nXdlYbqsqbkWdPG6dKMvq7rp+ZSfuuci2vs+Pg4NoqXJvZyHblmep3rSK49/H54b8Pv0/+ftYbk+jI+w7H8zd/8zU80ry+EADDGAP0D4gNg8MqjtMbvhEAIFnSHshbvwdPSOUemDa0x/SZzDqM0xnu64NCA0QVBaZ48PmE0nuNbRzka4ZzrGY9reoa+Y7qT6YK62WBNDlgWB2OenD7i8OCIybhC7wQRIaB8QBmFtTmj0YS/8wt/l3/5J39Ks1lirKart5hRDkqBVoQd80mZhgiAy3EgHqNVuCIAQDZ/gVcdwQeC0qAygvI0eELwQOiv5QK+CzhavO/wzqHwKKcIDgIB5zxt5wlKwdP79Abm9XIRp4wwZWjpMSEErLU45+LmFaYNl5tNxuTx48dMJhO6rqOqqsgQRaDKfYxGo3hN+W3XdRweHlJVFVmWxe9TBjAajXjzzTd577332Gw2VFUVP7+OiaUb2VqL9x6zW58yr3Lu4bzKvbVtG5mPUurKfQ3fp8Ih/V6EwuWauVkaMvjhOKSMT543Zajp74bMr+s6bt++zfn5OSGEK8xfGKFQ+sypEJG1NyS551TZSs95naAZMv/h86bnve5aKQ3vP6X0fob/y29kHd3UnL4QAqAfKE0IDqUuHxy/Y2Yh4H1ABYPSoHU/8NYYjFFkWmG1IVjFdtugg8bQEdCsLs4pyxGzyZSDo0Mef/QEvCf4rn+5XvqjeyZdjsa40KGCAgLj8ZimXXN0sKAsS9rNCnxvqRAczgWsyZhM57z0ksJYhWtW0BV412BcR9ABrwLOhSsbJJ3cPM/pug5r7aWWiI/HpJvHmAwceOUIamdZKPAEgvwNHrNjDm43ft65nfbvwSu0Ab8zS7rO456DAEiZt5DMr3Pu0tq5RkvWWkcmXtc1cKkZNU1zhalMp9MrjE80aTl/nudst9so7J1zTKdTXnrpJcqypK7rK0JI5mI6nfLFL36Rhw8fstlsrgiYIQMbCvfhXA+ZXTqvSqkoMIZrI2VEXdfF33ddd0XbHwoGIN7n86DrtPxU202/S+8ppevGLoTAer1mMpnw8OFDJpPJFQE9ZIhDwb5arfDeUxTFFQUqPb8xhvPzc7IsY7FYkGUZzrl4LzLPIkiGwmz4vB83xnL8s84h1xsKyFSgDdfDcE/9uPRCCIBLTVFH2CcEhVLuchDCbkB8P3mWfpNn1uKzjDLL0drTdY66db0loT1KW/Adm+US3wUm0znb7ZbZbEbbtOhijKKfQFkEmbXktsC3HWWuuXU45Wg6RuPJjCU4j9VQtw02Myg0ZTnGOc/x4SHrB+/TbQyumGBsASbHk+O9whhNluVPbXJQ5HlxhVmk+oOiF0j9q90xCwfOooOF0KFtDqr/TKEIrsOF3ebrHM4HgtsJAx/w3U5AePrj3M1qiqmGzG6MxURPN4P8L9/JZvfek+f5DprraJrmKeEhv++6ju12y61bt2jblqIogJ4BCiPKsowsy+i6jvF4zGKxIM/zCP3IOdu2JcsylFKUZcnh4SGLxYL1es12u40wRcqUUrghnVf5f2gBxHm9YtkRBV7K6CL0t7s/EUAps0+F3xA6el4CQOb0R/l8SGKpybPKMyql2G63HB4eslwun2KYwzEuigLnHNvtFmMMeZ6TZRlAFOZAhNmyLKOqKs7Pzzk8PMQYE9fGZrMB+vW72WyiBfeDnu3jIJ3hur/uHEMBkVoDQ+sSbsayeyEEgDU5qB0k4M3lgzlDMLtFrXo4yDlNYDc4AYLrUMHTlSW2k82wRXcdDo82GSoEvHc02w2NzfipN3+a9z58SAgKfL+ZdGdwJqAJaGPwXc24KDmYlBTliNm4pK03hB1TaNsWoxSFNeRlRds4RqMJX/67f4d3/+B/xPnAWQNjZ8mrGSWWcjaLml4quLcQbwAAIABJREFU2WVxDTHilFWkk50pMEbjMoMhw2BRKiOEDEuLzizaKxQWVEHWBFrfM0kXPM4FusbRBkfnPc550IruhvlEysyGpn/K9FNNHYjMG4g4tvxGNF05t/c+avY/9VM/xYMHD6LWm2rHQNzgZVkymUy4c+cOk8mEtm1RSlFVVYRfsiyL83x0dMSXv/xl/uAP/oAHDx4wm83w3rNYLFBKMZlMIvMfzmuqfT4LQxYSS0jed12HMYa6rq/4LtKxSv0AKe6fQkCi2b5IlGqxgvEPsXqhoijYbDaUZRk/S60s8SF1XXdFm5b3IihkHcjno9GIsizZbreUZXlFqVBKsVwuI+Q3ZM4pfZx/YHjcDzsmQ0sqtQrk701YAS+EABAICO1BhWTyVa/zJhYAQPAedhul67Kd9t6hVEfRZrjcAQHtPV2AoDwKjzUa8Pzb/+frNEFzfHyHpmkwWXm5kbqOqtCgFYcHU44OF2jTm4KtvwrhaC43bZaPaJs1VVVh8WzX5xRmQmgbsqmm3m4pDw6uYIpD03ToIFTXLIgQAlbtECtjMF5jrAYMtijQ2qF1R3AdRoGhxQRHCB7nDKF3/eIJtMHjg6ej9xV4btYCSH0Aww001FSFhli2MGQRAql5LmMzGo0A+PrXv85isQCIQkGOFcZfFAV37txhsVgwnU4jg5WxF41U5kE0+9FodAVemM/n0Sfgvb/it0iZUzqf1+G3Q6aTQg6yJuTc3nuaprny/On6aNs2njO1Gp6XAPhxoIhnab6ipYs1IK+maXprfWfVpdZPqlSkgQRFUUQFIb2GHJ9apiEEmqahLMvocLbWsl6vKcsyHpdakvLMw3Ed7ushfp8ycln38izpPaWWXyocxYpI18snpRdCACgr5pUB41GACoGgfQ8Fhd7Z6b1HWzBWUW88WoOmQu0GtWkaAPLMsF4ZmqahDgGFpsNhaMnpUHnGF7/wJu+8/T3yPKet15RljsKDDhRFhXINt2/PmE8yMmMxrUcFjwmggGJU0gVPXlTkhaV1G/LcErxms3S0bctkvEb5x2xWnmz2Mk0rDi1HVRV43+02eUDttHqtdwxEKYwOeC8CUGHoUKHXYmzwWOXIbUArQ7AaqzReFxgdCM0aox2BAlRHZvpFZX1OYEvtPNpr+mCitoeAbthZmGpFQ2GQOtLkvSzwFP4QrVy0fonekM0lTHE06qO57t+/zzvvvIO1trfSEgd727aMx2Pm83mEf+TashGrqiKEEJmIMGXvPev1GmttdDovl8sYfSRMTHw5Q6Ge4v/pc6YMLMWn5SWMTZ41ZWSpsEm1XBGYQ0vgpumHZUDp9eX+8jy/IsQErkkZoECAJycnzOdz3n33XW7fvv2UBSTQj0A7xph4HYF1U4YrcyrwYlEUnJycRFhILA2Bi9K5S4VBCutcp8RcN0ZDZWi4L4Yafvr7dF8MFYkfl14IASDSun+oXqpqINgA3u0gHE9wPV7dBXDWotHoLGcxm7ParHn/gw+4dXTI6Wnv3Dk/P4euZ6KZtmANTb1hXlW89a1v8qUv/hzf+e4Dilzj6pqqyMmUQvuWV165x51bR4xy+kihsiDvWpbLZR9mOKpolktGozEeUL6la2rOTj6k3Zzz2ddf4/HFGXpc8fbb3+XVL2Q4lcVwQ0KH0QprzRVmIdpnP7kBrXdakwqYoEBrLAHrPVZBFgLWANpSqgzvFMo6QlaSOUWXabptP3agcE3P7JuuAxSeBuUUBOjCzWJAKZwx1PxSLT+FaYTpVlUVcfcPPviA4+NjTk9PybKMs7OzK1CQtZbtdstiseBb3/oWX/rSl/je974XNWfB87XWvPLKK9y6dSsyoKIoCCGw3W4j9BRCiBp/XdecnJxwcnLCZrPh6Ogoav5nZ2dx7sqyjBZHGn6aOrOHESbDDZ9GgaVaYcrEBD7MsoztdhuPg6uafyrYnqcP4OModW7LPcm4Pn78GKVUjKYSQSfzD8TwTMHiP//5z3N6evrMsSuK4opTWARgqlGLv0eEUJ7nfPjhh1ecxuJrEshQLDuxsOBpiGbIkK/T0lPhLoIkPT61LIbOX/lc1vFNWXUvhAC4LmTL7DQeVC8MnHMEFIoMWxoyvdOO2pq6rjk9PeUzL7+Mc47lcokxGcXRMY9Pz+g6h7YZbRcoqpw8U+ANX/u/vkpezMlsgbWa0G7J1IjgWxbzMWVhyawiL2wPQekJPQ/WKK1ZLBZoZXFdTWgbnGt559vf5LP3X+bW0YKma3jv+98FZ2i3F6yXS8o8R9k+zyAASpvIoIaYsfN9FJRSamcVgQ6ejIBVAesDhVb0Ll9NpQxeewIWmym6xmO0QmsPnUN5RaM2ZAQK50BrPIHaeVR3c5EFQqlgT83XVPsV7UmYtWCyzjlWqxVaa2azWdTYPvzwQ2azGdvtNmp5wli22y15nvOtb32Lz3zmM5yengKXjsa2ba+EfaaOX9FC07+bzYa2bfnrv/5r3nrrLf7W3/pbVFXF+++/z1tvvcV4PI7M4+joiPF4HNdz6hRO17e1Nmr0Ms/XQQepMEgx/jS0VM4l2vPQ6Q5Xcy7+JimFOmSegBhtdevWrYizn52dYYyhqqro9xAHu8BvbdtyenrK48ePuXXrVrxO0zRPwWHyv0BC6RxMJpN4f7PZjMePH/POO+9wdHQUgwPqumYymUSGLdFfqfYu57zON5DOowiq9P/hfAytntQ6lvOmAQXp55+UXggBoO0lNqrUbhADWK1R4TKZ5v/l7r2D5LzOc8/fFzqn6TA9CRhgAogMMIBEYhJFiqZkOVC0TZGypFuWV7W3JPtae13r67Al2+vdu7vWrXWtLFu+DqJWuraCKTFAJEGKmSAJAYM4gwEn557QOXd/Yf/oOYcfRqAkm+Bdlg9raqanu8Hpc77vDc/7vM9r2za2qWOtp2Au06KpqKhKk/a2GM1miwkSC0dIJNu5dGmYeChEpV6jWmvg0VVUxaJWzOIORAi6dfr6NrGWK+LChdmoEfQobOntIhH243W1qJgu3bV+KB40XWc9GcSlamSzBQI+HxY2M1MTRAMB9v38fSwuLhJqi7D06gmivjCV1TnWMmUUawtt0Ti67iIUjYHiQlVtCUdoKrLeoSiKdHwaoCut5z2WiY6NS7Vw2SauVskCt6qArrdO1TbRXWAZJqrppllv4EJFc7tAKWNhoxsGpm2jNJrYuo51jXmgTojCuZxRsBPrFJ9XwCh+v596vY7P55MGf+fOnVy6dIlQKESt1moCdBYQ3W73FZGay+WiXq/j8Xjo7u4mGAxegQ+LbMP5O03TyGaz+P1+DMOgXq/T0dHBpk2bOH/+PAMDAywsLMjnl5aW8Hq90kn5/X4Z6QnceiPd0xnBbWSPOGsI4u9xRrYiq2k2mxL2FM7HiTubpikL3P+9l6zX2fYVn12scrkszz6ZTGLbNqurq1dkXk72V6lUIhqN0t/fTzablRG5aBgMBAISMhNZna7rVxSOxXUlfl5ZWWFpaYnOzk4CgQB+v5/JyUmi0Sh+v1/WiAzDIJ/PSyqx+Dc2FuTF53aeobBbYm2sA4jlzN7Evjlft9F5XC2r/tes94UDEBFMa9PWqV7re6ZigbWOC9stfrthGGimjm0ZoKnohgu3qmBh49FdGEaDSqlIKBCkv3+Q4eFhouEIxVoFBQ3DMgELr89DamGO3i39ZHJ5gkEfXYkYPZ0xvJ4WBq8omuT9W+sXlW1ZGPUG5WaVaDRMtZCnnMvhQmXPju1MzC+SyRbJ5XLs2b6d6ZlZSvkVNL1EbipDI92ON5qkXuqhY3MfmisIqoaKgUt3wXpXtG1aqIqChYVLMVoG3zLwqjbrvc64FXCtdwy78YBioyitmx+r1S2rGA00vY6hqNjNVnprq63il8c08FgWVtPAaDSvfkDX5Fx/fDkjKmehU0S2IooWsIphGJTLZQKBAAMDAwwPDxOJRKhUKsDbN5Tf72dxcZEtW7ZQLBbx+Xy0t7fT2dkpcV8n71sYC3GDCWNTqVQoFAoEg0GSySRzc3OyO3nnzp3Mz8+vZ5saq6urpNNpIpEIvb29xGKxKxoMBQwiDPPGyF/sk9NwOKFBsZzMJrE/uq5L2EI4P2Gk/v+Cf8T6SVGq2A8RYXd2dpLNZikWi8RisSv2YtOmTSwuLkrn3NbWJqE/sQ/OWoJYTmcqDLZ4fT6fJx6Ps3XrVqrVKktLS8TjcSqVivx/uVwuwuGwrPWIa0YELBv/P87P5vyMzt9fDdffWOAV/+ZG3P9ar/eFA7iS87p+YOudwSoKyjoO3ioMa6CpYFpYhoaiqdimszCmAF7KlRLxaBuLc7Ps27eHkdHLBHxemqYFTQvVpRGLxsgXqiTa45QqZVQsujqSeN0uNHUddhHdss0mFlaLFqZpKLqOrmnUG1U0TcE0mqhYXL70Fj8avkQhl0VXFeLxOD//cz/H0PnzFIsFAiEvNbuG3wX55flWA1O0xSl36+uFJctuNWspCjYtaqqmKrisluHXVBUdEx215RRUBUVp1TkUVcAGBpblQtWbqIaOqaitzmVVwzbWZQbsFjTi8/kwqFF9jzKAn+QAnMwMYdic7CDRANZsNqnVakCLFpjP52lra5OOQERQuq5TLBYJhUJkMhmg5Vy6u7vp6uqSzkLw+avVKuFwGNM0ZabhdrspFArypqxUKjz66KMUi0USiQSFQoHBwUFGR0f58Ic/TCqVatWbaEXiKysrVCoVOjs7gZZDEjIUTmx+I+3RWTR29g04mTFORlSz2fwxGEgUzJ0wiMgS3ovlNHRiObMQp6EUzuhqBtA0TXK5nOzPSKVSUgpCPNfT00M2m2VgYIDx8XHJ2hHZF7zdGCYCClVVJQwkyANib/r7++np6WFtbY1ms0m9XmdlZYVEIiEDB5/PRyaToa2tjXA4TEdHB4VCQUJSYm+FYxGfyYnhOxl84rETvnSygQTsJV6/MdK/Wob4btb7wgE4Cz+Ksv6zpaAodisDAOx1KqNu65iKia2bWC4Ly3BjmgZoOlg2HjtIs15F192YTYNwqEEpXyDiD7JpSy+XR8doWAregA/MKuGAyvLsW3REo8RiUaJhD26Xjm21upJFVKi7VJo1g0aljNfBU2+UKtRqZZYX5lmcX2B6fpZSbo2OeIJ9+/ZRKpWYnpzAZRp0JTqwbZuOaIiGWsMyalw69xLJnuvx+LxEogk6kl00as0WjZM6mm3hVsFtNdGtOhoGLtvCi41bsXDbTTRVAZeGqiqtxjdNRVU9mE0TpWmg6jWMdcNiaFVcCng0F5pHw1Z0jFIVj62hGdc2wtjoADZGMhuNh7PA5ezkte0WRObMDMTjiYkJtm/fzuXLl2k0GrhcLokRu1wuDh06JLtJxc3lTK19Ph+maUpHA29Hi81mk7m5OU6dOiUhl0qlwsGDBymVStx1112Mjo7i9/vRdZ1oNAq0cO6pqSmazSaRSEQaqVqtdkXx0okhO43/Rgqqsz4kjMNGyEBAHwLSEhmAgEjeq3W1YqfzOeH44EqHv5ElIz5XrVajUqnQ3t4u4bV4PI5lWaytrdFoNFheXmbLli3Mzc1JI72xM1v8P0UGJ/Ze7FE8HqdcLvPiiy9SqVQolUokEgmuu+46bNums7OTSqXCysoKyWRSOqK1tTWCwaCEnESwUCqVqNVqsn7kzG6d17s4fwFXCSO+McvbCP+I94vvzszx3az3lQO4IhW2W5x9RVFQsbDXawFY68wZ1qNEl2ggszCNVkFIc+lYZovC1axV8Xg8uN1uKsUSW3s3USqVqDZbUVO1bhD0+9i7e1crCl/HCZvNOuFwmGKx2MJxGw1cuo7f7yefSbeohz4f+bUMpfQaL7/8MjWjyeGbbsY/NsYv3X8/zz7/Ag3VJlutYvu8rKbX6Oho58yZs6xlcjQNhUism3wqR1dPD359O29lFkh296C5dIKmhdVooLvdYJpoWOiYeBUDl6LgwsKtK6i6CpqK5nKvNwjo2ApoigqahmG70Fx1DM2F5nKjudxYqo7brWJYKgYalqpSucadYFc7V7HETQI/niZvpDsKJoaI6kRUK861VCrR29tLqVSi0WhISCQQCPDmm29yxx13SPy22WwSDocpFAp4vV7JM9d1nWw2S7PZxOv1kslkMAyDl19+GcMwuP7663nrrbc4cuQIIyMj2LbNyZMn2blzJysrK6iqyvT0NJlMRkauq6urtLe3s3PnTjKZDF1dXTJKFJGx0yFuZA05IZ6NRWJnLUC8VnyJqFE4TKdz+++9nNGrkw3kNIZOgw3IzExRFLZu3UoqlaKjo4NyuUwwGKRarTI3NycDA1EvEFmRiPIFgUA4+lAohGmaVKtVbNvm/PnzZLNZgsEgXV1ddHR0EI1GKRaLMtDYs2cPLpdL1iyE4a7XW/Yhm83i9XqlQGAmk7nicznP91+D42+M8n+Sw/3XrPeFA9jIZ37bATh0QtYdgC31cXQU20Rf38xms4GiedEtG9NoYNtuMC08ukalUsHv91PI59F1nb17bmF4ZJRKrUEkEsUXilAs5unv76dab0EAoWAQVVFktB/w+8lnsoT8PkzDwGo2SGUzBLxennzuh/zSL/wiYxPjHD54kJtuuolMLsvU7AxzyymqzQbzyzn6+ntYSq+SWlqgVLEJB/0ElDqNYpGRM1PMTF+m77rd5NKLqJoLn62yfXCAeqWGX7PQRR0Ao4X9K6BrCppmg6agaC1IDFXFtAFVBa0lsKcoCraloGg62CouSwVdwW3YuE0Lj2nidl/bSNFZWN3YRONcG9kVzveJxicnNGTb9hWGPp/PS/kGt9tNJBKhXq+zbds20uk0XV1dksIpnEl7ezu5XA6v10ulUpFYdCQSYXx8HJfLxTe/+U0efvhhQqEQjUaDnp4eHn30UbLZLCsrK8TjcU6fPo3f72d+fl7e4EK+oFqtYhgG8/Pz9PX1cfbsWZrNJvv27aOjo+MdKaNiD4Txd9YSxP0iDLz4nXAEArcWvxNdru/1upoxc0bATlhK/Cyz6/XP4eTci/c2m002b97MzMwM8XicUqlEvV6XtFuRCYnPLZydsCk+n49SqYRpmqTTaQYHB/H7/fzDP/wD8Xicm266CV3XSSaT+Hw+lpaW0HWdTCZDPB4nk8lQKBSoVquEQiESiYTMUE3TJBAISGZROp2WxABn/4VwSBt7BTayeZyPxc/ORj7xe2d96N2u95UD2OjdWkZ+w2tdCjbrDApLR8hIuwRVCpu2cCtaKJfL6F43uqfV0h+Lt6KFudkFBvq3MjU9S1uina7NmzHMVjTh8flwaTrFUh6vy42utDDX1dQymqJSKZYIut0srazgVlXefO01vvD7v8v5kz/i5z9wD+fH3uL5N1+hWq1y5uJ5gsEgfpeXj93zAfbv38/Q0BBRj5fzo+OEowHGpsdoj4TxeD14lRrV1WmaDQXLhmhnN+klHZ+m4Ql6UVwWqmKjqXW8Pg+aouBWPS2jrykoqgKKiq2qtDSDWpQiRQQR3nVamqphajrUFXz22zLEvmuMFV9Z2/npcMFGdoQzUhKGXZ6rrsubPRQKUa/X8fv9tLe3Mz09TXt7u2zYMk1TsksKhYIs6CmKwurqqjTaoVCIsbEx/H4/p0+f5oEHHqBUKtHW1kYmk+G1115jbW2NVCpFMBikXq9z4MAB9u/fz3e+8x18Ph8jIyMA5HI5GU2GQiGKxSLQcopra2tXOCuPxyNvbiE94OwdcBaDN2LLAoZwGlRAQi+iw/Van6uAW37a68RywhgbqbHCWJqmKTtwNU2TFM9KpUI8HiebzeLz+QAolUqEw2GZ6YlscaMAXLFYpK+vTwq/nThxgmw2y+HDh0kmkwQCAcrlMtlslrNnz8pGw7a2NiYmJggEAtJxxWIx1tbWpJaYCCrENaQoColEgmq1Sr1el814QrhQ1GSc17Xzunc2ljmhIZE5CYqssx70btf7zgH8GBSkKOtOQHxYC9DXYSEbWFfztGygVSgul1sdmx6PB6+3jYBtUcjmWp28rnorDazUSSSS+MNhkh1doOmEIxHcbi/VcoVoNIrVNEivrhGLxWh6DNyqQr1U4fHvfZfuzg72799Pb+8m3jjxGvVyhfOXhhkdn8Cqm3hUN15FIaDrHL7lRgDGz53BYzW5+9BRNAsWVle5ee9eZldWyRWKlCo1JiYmCAZDtEXj1JU6ZmWNnlicQN1HMhHFq9mE3CqaZaIqGh5NxVJ1VM0NmoalKtiahqqsQyuqimK9zcU3TRNFbUFGttsFqJjrUba3Vn1PznUjP1r8/E7ratCQbdvS8LfOtSXfIbj+gtVTrVZJJBKEQiE6Ojro7OyUXb/1ep1oNIphGLKwJwqDlUqF733vexw8eBCv10tvby+7du3ipZdeYnZ2VvYEJBIJVldXcbvd3HTTTbhcLi5evMi9994rDXAul+P6669naWmJ5eVlajVxrkHa2trk39ne3o5pmnR2dqIoinRMgDT+LpdL7p+TSSSMv6gViKKiMwMQUIoofF/L5XQCGyP/jfi+c238ndOIud1uCbsKeQan9IOQ3RAaPs6CqcgSPR4PlUpFFvV1XWd5eZlCoUA6naZSqbBlyxaSySSqqjI8PCz7Q5LJpOw4jkQibNu2jZmZGdra2rBtm4mJCer1Or29vfJ8crmcrPFEo1FKpZJkY4m6g3i8cZ+clFCxb+I+vVoNwQmpOt/3btb70gGAAzJg/Tn5aqcmztsGQlc1FMsGpaV8qasabt1Nc51C6guFSKxHcpqiYjbr2IrG5r4+Wui6iqW0GANut5u5+Rn6N2+hkMuTzWTo7OimXMzz9JNPcOftdzA/O83unTuJd7Qzc2mUbzz1FDXLwIUbpWnywAMPEPX5OHzrQSanJ0kmE5wfHWVpahFr0YM3FODIju288voblOoq+VKdgF+nocNqrUihYGAvz1GMxzHLHXjb2+nxKti2gTvoJRKJoK03pWmqhqK2cHxFUbA0QNEwsVEtu8UmUhVsW8XQ3i42GigYLh3NpePxuK4wQO/5uTp+51xO2pvz9YDsyBTG3rZtgsEg0WiUTCYjGR8COxYYMbSgBFFE3LJlC6lUikwmQ2dnJxMTE5w4cYI77riDUCjEddddRzKZ5Mknn2RoaIjR0VG2bdsmIZu77rqLW2+9lRMnTrBnzx5GRkZ4/fXXZZ/BysqKnB8gMHhFaTU9WZbF0tISyWRSPh8KhVpXttXSGHIygZwZgDMLcH4X+yTeI6iRLpdL1kmu5drYaLZxCUe18TVXa5oS0B68LdchMgDBzPH7/dL5C0cnuP8i83F21waDQTweD7FYjEQiweXLl1uwbijERz/6UarVKtlsVjK3/H4/s7Oz+P1+lpeXSSaTNJtNlpeX2b9/P8VikXK5zP79+2UWIAIPQNahMpmMdExCjdTJ+nF+1o2G3Gnwr7bE6wUc9E73z790vS8cgKroP2YYFNVZPEE+h3AI9jonV1kvrpjrOum2gsvbkluwDQO90cSwLXTVQ7leQ9E1fG4vecsiGk9guXyoioKm2CiWCS431VKFtlC4Jfnr0UG1mZq4xKk3T1DIZ3nsqSe47YN38/L5szz2zf/GpaUFyiWDG/fsI+yxuO2uozzz9FNcv2MPk5cnmJiZ5ct/948M9vcxOj7F9TcFaCg2Lzx+DF3XMBQdVYVypYZlgcfjQm0qRP0hvJoHfyhMzTLIFNMkQ378oSCavg4H0VKGU1QwVRNF0dDtloieggYK2DqtmQC2hWXYqC4V1bJQXTqay4eu+dBUA1159ynlFefqaNC6WobnfN3b53ulQJrTkIhWf9HhKVgdQgJY0ALj8bh0PolEgnK5LJ/3er3SMGqaxsTEBM8++yymaXLs2DGOHj1KoVDgW9/6FvF4nMnJSY4cOYKqqtx55508//zz9Pb2MjY2RiaT4c/+7M8YGBgglUrJDCubzcq/WdM0WWMQUE043Lq2AoEAhmGQy+Voa2uTTWpOY7/R+IvlhIGcvRTiu2AtCSdyrc/VuTbCQe8kU3C114gMBpBnKxhYLpeLWCwmB8MICQ6v1yuZP8LhCefcbDYxDIO2tjaazSYXLlyg0WjILt9sNovH42Fubo6ZmRl6enoklTOdTrNjxw42b95Mo9GgUqkwMjKC1+tlcHCQmZkZZmdnyWazdHd3y2vMSbM1TZNIJCLPZCPkI/ZrI21WfH7gqs5TrKupJryb9b5wAFeDgJwOYKOBEA7Ameoq5tuDMkQU6fF4qNk2LtvGshSwLFRFw+310N/dgy8YwkalVCphNJu4A0Ga6xdaezy6LjIWxO3WefX4szzxvSe4cf/1HDp6hP6uTXh8AY6dPItX1xncFOeTv/oh/vavv8rlNyNs6dxKutbkn3/wBCtrZbZ0tTM7t8BHPvxzHH/pBXLlOtdtH2TLli08c/yHeFwuTAV0j4bHp2MYTUqGScgTYXT8Mv2xEFvDfWAb2KUKWtSDy6uhucDWWk5Sa00BaCmqCh0hVBSlJSan2AqaS8WybOym0Zq4pipo2jrNUHNxLddGCEic5U/LBpznurFwJs51I5tEVVuiXqKrE1o48dLSEm1tbViWJWcFiCygXq/zwx/+UDYXHTlyhL6+Pvx+P6+++iqbNm0iEokwODjIiRMnGBoa4qGHHuKll17iiSeeQFVVOjo6mJ2d5fDhw7zxxhuUSiXuueceFEXh+PHjch9EV6rICKLRKGNjY3R0dBCLxaSBE+Ms34n544QInM8JKEYURDd+vZfrZ3UwTgruRqkLp26PmNEgZLADgYAkAwiGVbValUVjEWkDkt01PT0tHf7IyAjRaJQDBw4wPj7O6uoqqVSK7du309bWRj6fp6enhxtuuIG1tTVGR0cBGB0dZffu3YRCIU6dOkUikaCnp4f9+/czPT0tKasClhSZihOKEmwvJ7XXWeh2ZgHOM95YJxDPi+c2Muf+tet94QA2RjrCAQBXGJCNDuAKA6LmIc1rAAAgAElEQVS9bUDExSW8viwuNg3S1TSK24uvrY1gMIzZMPB5vNQqJbxeL4pp0BZtyc/6fD4CrhAej4fNff381u/8Dm+eOMENN9zI8R8+jwXcuOM6okE3R/bv5uLJ09z1gXsYm0sx/dYYFybHcft9JNt1PvTBD3FhZJh9+/bxzIvPs2vHNkqlMuPj43z6V3+Vp59+in033EK1XmFo6Aw7dm/j8vgUKBYebytarFQqLFfLJLROGm43Hp8bGwPb1NalM9zQGg0Gqo3Fei+Fqrakpe11g2qZYJooltmairZuRM1rLAbnLF5erb5ztXMV7xOPnRf5O52rGAYjujaFxr/X6+Wtt96iq6tLZg3iXEXheOvWrTz++ONEo1FuuOEGNE3jmWeeYdeuXYyNjfGbv/mbWJbFXXfdxczMDH/4h39IoVDA5/MRjUa55ZZbGBkZYWpqCtu22blzJ5OTk9i2zYMPPshjjz3GwYMHqdfrDA0NsWPHDiYmJgDwer3yXMXfKwwcvB39XRHoOPZIGH1hTJyFYGcz3XvZDXw15so7LWdmcLXXCmPeaDSo1+syoyoUCiiKIsd6lstlfD4flUrlCt69syC8efNmyuUyp0+fZuvWrcTjcfzro2A9Hg8HDhxgbGwMXdfZuXOnNPILCwsApFIpPvaxj1EqlTh37pws7i4vL0sHUSqVWvXB9azDtm2pSmuaJtFolHq9LmmqTv7+Rvrv1Qz6xvvCef7/pmoATkMhHqP8+JzVjQ5A/qwooLzdTCYuLVV3YxoN3HrrQOqGSTTRTtemTbgDrWaOXC1LNpOhadRJdCRoFsvrssqtrs5ytQ62ynMvvYBZrfAHv/efuHjxIntvuJ4v/9VX2BQN8dBDv8T4hbe4675f5Pf/tz/lrbVFVlN59u4eZGBLP4Nb+kgtLpFJr/L1r3+Nf/fJT/Hlr/5XNFXhppsO8L1Hv8ODDz7I0vIip08Pc/31ezk9dIGbb7mRs0Nn8LjceJsGMU0l3tOB7fWgrOOOflUDDXSPDrSYQIoKpg2KbSGkLHRNxTZNQMEENFWlaZutuonVGiVpX21q/btYThjDec5Xcwbv9P1qkJGz+1XABfF4nJ6eHvx+P4FAQCp49vT0EAgEZGFRVVUpLgbwwgsvMDAwwCc+8QkuXrxINpvlxIkTtLe3c+utt8ou4m9/+9ssLi4SDAbZvHkzfX19BINBKpUK6XSa1dVVPv3pT/NXf/VXBINBdu7cyfe+9z3uuecems0mp0+fZv/+/Zw5c4abb76ZM2fOSLVLj8dDb2+vxI1LpZLcO6FQerUoXmDfG1UlN0aI18pYXG05o/qflglsZLdcjQbp7Go2DEMyfwTtUzS4FQoFQqHQFY1VogYkGFbT09PEYjE6OzuJx+Pkcjm5t9lsll/5lV9hbm6O6elpLly4gKIo5PN5duzYwT333MP58+fJ5XJ4PB55Ll6vl3Q6TTweZ9++fTJLEaJx2WwW27ZJJBISghTEAGfA4oTw4MprfmPvgHOv4W1V0X9TGYD4oFI2F/OnOoCfZERkqqm4sC0F07QJBsIkku1E4jEM06RRb66r/9UItXXStC3cLo2VteK6V1YIt0VIp9NcPDvE1//+75iafItMdo1vfPdbpFaWGOzczcilMbo2beG3/+D3uTw3zZ69O/nIXXfjRuXIgQOMjIzQkYjwiYcfpFqt8sLLL7Nz29bWuMFQgPs+8iFOvPEKnd2bePChj/PUc8c5dOtBBrf0Uas1KJarFKsV3pieZXQpxf7NcQ7s2k2bz4tfq+MN+6hVSy1JCFsDl4K6LmTRur7W6aC2jWJb2IaJ0WhiNeo0mjWqtTL1ep1q/dpqATn7O5yc9Z/VAYhr42rnKm4UUfRrb28nFothmqaMBAVdT+DDqqqSTqeJRqP4fD7S6TTnzp1j7969TE5OkslkeP7551leXqanp4ezZ89y//3386UvfYlCocDu3bv57Gc/y4kTJ7jllltYWVlhZWWFhx9+mHK5zCuvvMK2bdu47rrrqNfr3HffffLGf+ihhzh+/LiEmWq1GuVymVqtxuXLl5mbm6Ovr4+9e/cSCARwuVxS8M65Fxv3wdkpKhgzAuaq1+vUarX3pBHMeUYbM7V3WhuN/kaH4bzfRSE5FApdUTz1+/2yOVMSQHSdUqkEIEd4Dg8Po2kaAwMD9PX1kc/nZVexgAgnJydJpVIMDQ3R3d2NaZr09vZimibz8/Osra3hdrvp7OwknU6jqirlcpkPfOAD5PN5JicnUVWV3t5eVldXsW1b4v8LCwuy/iQcmhO2cp7lxn4J5346ncHGBrN/Mw5gY/TfevzO1FBoaQVdER2qV0aJ4pFiK5QLZS5euMDtd3wANBWX24PdaKDSajALtUUwrCamaVDJ5fF4PMTjcTweD7likfPnz3PoxhtZmZ/n9Vde5vL4BHt27aZeqTI0epmqO4Q7ksAX9/I3f/rXZIbHiMVizC8uMjU9wb33fYivf+MRmrZFvdkgui4YNjMzw8zkBNGuOOFohFKlyA+efopSrc7I5QlGRkZQXT6alkWxUsasVfG5FNZSK9QqdW7s78dnWKDplC2bsNu3Doc5LiKlJaOBk1pptfBWc32GgVBbFDfRe3muV/tyrndy+huXpmkUi0UuXLjAHXfcIXsFBK0TkNPBGo0GuVwOXdfp7OyUk57Onz/PgQMHuO+++3j11VcZGxsjGAwSj8cZHh6mq6uLUqnEgQMHePjhhyUvvLu7m6mpKQYGBrh06RKWZXHmzBna2tro7e1laWlJSgiIZrOnnnqKarXK6OgoIyMjsompXC7TaDSk1LXQvRdRrYhonfviNBLOL9EJ63QC5XL5mp8rvM3IEj//S6J/8di5nM8Fg0G2bdvG+Pi4bK6yrNbgn0KhILtuRZOc0PIJhdbh2nX4p16v09nZydTUFJlMRnZ7i3/n+PHjtLW1ceDAARqNBgMDA5imSSqVIhQKMTAwIAfSB4NBCS/Oz8/j9/u5++678Xg8TExMMDg4SLVapVqtoqoqg4ODkn7r9Xol00v0I9RqNVnXcEJAiqJIppNTTt353Ul/fbfrfeEAVP1tpkLDaGGhqnJlc8RGo/BOjgF1faqSomI2DcqFKrX0GuNnT9ERCnL7B3+OXKmKxzZoNA08oTALMymS3VH0chVV1Ykk2vGHIugqeGslJkYvsHXTZr73+GMMDAxwcfQyIxfP0pmI0H3DboxijeX5Gf7ki39KvVCCrVsxTZPu/s3EYjG++nd/T6S9k3Pj05w8fQoblZ6tW7gwPEowHEQvW+QyaWyjjkeHTZt6KBRL6KqXXDqP7nHTKNfx+wKEgiHWSmnOLqWpWyrtyThmbplILIquNjEbTTw+L03TRmmaeFxQV93YlolRr6PYJkq1grtRpVorY9QrNKo1arUG5dq1jRSdDBSBcW/E+Z0//0SHr6pX3CDFYkttVYyBvOuuuygWi/L5QCDA3NwcO3fuJJVKSQcRiUTQNI1SqcTo6CibNm2iXC7T3t7OpUuXaDabtLe3c8MNNzA4OMj09DRf+MIXyOfzbNq0Ca/Xy5YtW4jFYoyPjxMMBhkdHSWVSjE8PMyWLVuYmpqSheVsNithjZ6eHorFonzO7XZTrVal9HCpVGJ6ehrDMGhvbyeTyRCLxSS04Sweut1uaTQEBi0UQRuNhtQAqtVqcsj5tT5bEYGK2ck/yRFcAe9uWM46hSiAP/3002zevJnu7m7ZNW3bNtPT01SrVYLBIOVymVwuJxlemqbJbuHZ2Vm2bt3KwsKC3CfRxDU3N0cwGOSTn/ykLNwmEgnm51vijAcOHJB9B7ZtS1hnfn4el8tFW1sb5XKrfjc8PIzP56O7u5vV1VU5sUzMrfB4PBQKBer1uuxzSKfT0oGJLNY59U0YfuFknfeACAx+Gm30Z13vDwfgSAfFfFfLbMpuxqt1Cr8TTmwr4NZdNGp1dEXF7fexumTwuc//LjWfhu538/LffYO3UpOcuXSR//xf/m8Gt21h9MIIHn+A7k1JIuvFG4+iMvT6CZ7653/m1ltvpd4weOzxx3F7WhohmUyG63ffwPjoZdo3bebRR7+PT3cT8vnJZzPUGlVcgQBbtm/nvz1+jKW1DJWGQbNpsjrcKiQVSxXclSo+TcHr9nD0yCHOn7/IDTv3kKmUWRw6w9auTrbt2M6Z00OUKxXcGhQMm9V6nUuzs+zu7iToKlOsNwi3JTCr5VYNwOXFMCw0bT0CM1pNcI16lUa1QqVSpFwskC0WyBSrrJWuraFwctOdoxMFHfJfcq4CFhAFNSGu9lu/9VvSAHzzm99kbm6Oixcv8qUvfYnBwUEmJydpNpv09PRIvFzTNI4dO8Zjjz3Grbfeyle/+lWgxefeunUr+XyevXv3cvLkSXbt2sWf//mfSwkB0dAlIKShoSFWVlYkp/zixYvSmAs9m2AwyNGjRzl//jx79uyhVCpx+vRpOjo62L59O6dPn5Y8d0E/nJqaore3V+oVRSIRGf0KaWwBqQjsvF6vy6yiWCxSKBQoFArk8/lreq5wJW9dFGOvRmP8WZZgKimKIiExAa+1t7ezuLjIJz7xCSnr/fLLL0sDK64rwbLyeDycO3eO3bt343a7KRaLMgMSLLBDhw7JCXKFQkGqywoarugGzmaz5HI5GaULG7WysoJlWdJZh0Ihzp49yy/+4i8yNjZGKpWSjKJMJiN7Q8LhsJx54AyMhIyHc/6v2EfRAeysF1zL9b5yAM4PKQZ7uN3un2oYNjqAUqGIW9Nb0VUgxI1HjhKLt6NYJmapyMz0OLNrRRQrwr133cfQ8Bl27thBulIjHI1Rb9QI+wNU0sv8P//7/8oDH7qb1y9dolQq4Qv42b59O91dPawsr/Hs6ydYWVkh/cYJpmbmOHz4FnqiUWqVKrt27eJbjz7KjQcOUi6U2JSI02iEwLLZtXMnbk3Ftiz6Brby/e9/n93br2NHXx87tw7g8nj5u29+g4jbxdrsHF7gxl27CAQCzKRSrCzNYZpN+rrbiaWzGKUSvVs3oTSqqJqCrrpQNAVTzACwLOx6HZoGZqVMvVSmWipSKBVJF4usluusFq8tVHC1cxVc7X/puSpKq61f11vnGggEOHToEPF4HNtuSTZPT0+ztLQEwAc/+EEuXLhAV1cX5XJZwkEul4tsNstzzz3Hhz/8Yc6fP09/f7+kBYob7pVXXmFmZoZz587R1dXFxYsXSSQSLC0tcfToUb773e/KJqFkMkl7ezuNRoNdu3YRjUbJ51vaUqdOnSIYDNLf309/fz8ej4evf/3reL1eFhYW0DRN4v4LCwssLy9jmqaUKa5Wq2zdulUaeCGFoCiKLECK5wSsUCqVrnAAzqal9+J8RRQritpiOSm6G5fzPnfOMxAia52dnbhcLrZt20ZnZydf/OIXKRQKfO5zn+Pee+/l6aeflgVa4XgEVCQGuayurtJsNrl8+TJ9fX2Uy2WSySTFYpFdu3YxMTGBqqokk0kymQzz8/NSwkM4VtHg5Xa7WVxcpFwus3fvXvnZYrEYuVyO22+/nYsXL9LW1sbOnTt54403ZDSvaRq9vb1XDOcRjYwCznJmBKLIu7G47/wu9vzdrveFA9AUFUX8J4sjbzfOKMrbY/p+EkQAyE22bRuvy02sLYqvLYLic2OvZHn60Sc5PTNFcMct+JMW+yNRbjp8mPMvnMCnKxRzrWipbFucO3WSj9z9ARrlArbRpC0S4vy5i/zGb/wmf/XVr/Lggw+ynM2RSqVYmJvjP//JH/PXf/tfefnlkxy5eTc3h8L80kd+ga//v98kFAoRwCTq9/KpT32KZq3Olt7NrCyleGXoR3zyoY8TDgTo7OhiaOg8f/GXf8mOwX7C4b5WPWF+nl3br2N6eppSLku9bmH6FZZzZRKaQjzUTSq1gtdfQvd5UXQXLm+QSDxBo9aQsIBL07CNOtVKgUqlSr5YJl+tkqvUKL1HWkBw5Xxgobh6Na77z3KubrebaDQqG6oKhQKPP/44Y2Nj9PX1EQqF8Pv9HDx4kKGhIVkD8Pv9NBoNTp06xe7du8lkMpimyaVLlyiXy/zGb/wGo6Oj7Nu3j3Q6zbFjx/B4PFJ3/+WXX+bhhx8mGAzy0Y9+lG9961tXyFT/9m//tiy4ut1uTp48ySc/+UnS6TSdnZ0MDQ3x5S9/mcHBQSKRCLFYTMJUU1NTFAoFiQsLlclIJEIqlZLU1ZWVFdnlWq/X5euhZVQEN11EvkIh9VoupxFyGnzR8eyUZ3gneqiAOcR1qaqqpFK2tbXhcrnYu3cvpmlKPr6qqrz44ovS8YtGNyHAtrKywuuvv87AwACLi4tSCG9wcJCuri4ymQw+n49Lly5x8uRJKetQq9VYXFxk9+7dHD16lOHhYTKZDKurq5TLZcLhMIZhsH//fqrVKouLi2SzWbZv387tt9/O0NAQU1NTzM3NUalUCIVCsodEzIc4efKkZIeJuk8gEJD0XfH5xf46Ayax36JX4meh3P6s633hAK5mBCzLAkVFX08PDcPA7Xat48DrDuMKg2Fj2i2PqxgWRrNJyOsnlohjqwpBVeG/fPW/ksrk+OHZEf7883/EW7kS3/za37JrcC+BaASvbVEpFFF0BaNWZeumLtz7d1NYXWU2leL6mw9RLVQYn5zmpoNHWcmW6evZzGLPAv/LF36XWrHM9q5u/G43/8NnPotVqXPuzVP8zuf+PZlslvZErKUpUyzg9viYmJigu7uHX/yFX6ZRrxNri6AoCm2xCP/z//Q7bOntwrYsLl0cpicZI5PPYdbLfPDoIbZt28bzz/2QsekF9g7eykytQYcGHruCzzRIJOIoVp1SOkWjbhAKBikXC9Qti3KpTCGXJV1ukMkXWSmUWClVSOeL78m5On8WRkEwI8QIyJ8U+YvoSBQ5/X4/8XgcRWk1hf3N3/wN6XSakydP8pnPfIbl5WUeeeQRrrvuOhKJhKTwCUPY29tLIpHg3LlzMqIfHx9ncnKSZ599lp//+Z9n8+bNfOITn+CjH/0os7OzHDt2DK/Xyy//8i+TSqU4deoUt956K93d3ZLyVy6XpcOIRCL8wi/8Aslkkq6uLhRFIRqN8h//43+UTJORkRE6OjrI5XI0m01uu+02tm3bxnPPPcfU1BTbtm0jn89LfX9FUQiHw9h2S3a40WjIfgZh7EulErlc7oovZ2fytVySaMDbLC/B2HHKUjvrBeI9G9lAIgsQWL7f7yebzVKtVvF6vTz99NNomkZfXx/Hjx8nHA7LvolGoyHnAvT39+P1emX2I6TCz507RywW48KFC/j9fnK5HHfccQfQ4vzv27cP27b5/ve/L2G0zZs3s3fvXhmwVCoVSevcs2cPlUqFRx55hIGBASYmJohEIhw6dIhgMMjMzMwVMxx2796N1+vlxRdfZNeuXXR1dUmHL2ir4j5wBhFOFpiz2O/c+3ez3hcO4GrMECEEBW8Pu2g2m1eo/V3xftVGVTUa64XMUr5AT7KTBjZBl8oLj36PtVKely6eR8HLrpJBKdTG1h272ZxZoqaBqaiEgyE0l8rqyiKXRobpawuzKdGGoen81d99ncN33YXP5+fAzn2E26LUqlk+/7u/Q3FpGdWl8qGP3Mvy/AqZhRQeVefQzbfQ2dPJ9TfvZ3p8lgM3H8RSVeqWhc8fJFcqYzcbtLd3UCrmMY0GnV1ddG3azMk3XmJyYoJopI2p2SkWUkv4ggHa24IszUxz7wfvZn5qijfPX6SzPUL39m2srCyRjIYp5VsdkeVyhXrTpJzNoiga5XKZfLlCPpchV7HIFMrk8gUqDZNq/d2nlBvPFa7s+hTDvuHtwdoCEnI6DOf7xeg/aLEoOjs7EcqZTzzxBIVCgaGhIfnvx2IxOdhD3IRiKIho76/X6+zcuRNFUXjkkUc4cuQIPp+PBx54gH379lGpVLjhhhvQdZ2Ojg7uu6815/ny5cuoqsrNN99MMBhkYGCAyclJXnrpJT772c9KNko+n5cdocViEdM06erqoqenhzfeeIPx8XHa2tqYmZlhaWmJQCBANBplbm6Oe+65R8JP7e3t7N+/n9XVVbkfougqBqoL51MqlWRhNJfLSRnja00DvZrhEdGrOFPnIHhncVi8V9zb4n4W0I1olrrpppsYGhrC6/WyvLzMbbfdxr59+/jLv/xLYrEYgIR6xDUkMsIXX3yRZDLJoUOHSKVSzM3N0Wg0ePnll0kkEjKaHxoaukLIrV6vs2/fPlk0D4VCNJtNZmdnsSyLcDiMqqqEQiFZiBZT5g4dOkQ+n2dlZUVKexiGwdjYGIFAgJGREYrFIr/2a7/G2NgYZ86c4cCBA3IcpugoFnsimhaFtpAgQAhoShSO3+163ziAjVmAiBSdj4WHVFX1Chqaoii40DEMC0VTMUtFutvClGt5Qu526s0mL730Ej39g8w++s9gNXli5CRf/uu/5qFPf4obD92N21ZR601wq9SKabxmjWTYTz5XpKJrRNva+dSn/h0zyyv0dPawqXsTgVCIQPQ6yvkc1aaNiU612ODIkSO8duIEd999N48fe5L23h6eOf48+3ftaeGSlkW+UMDj9xFLxEkmW0yH8alJDNNkR6IDt6aTXikTCnVQqjX45Yf+Hd3d3Xg8HkrZArOzs5w69SOyuSIf+rmPcX7oDMPzKXYNDqKYNeqKTiaTBVXBrfsoWxaLqSUM00TzekgVixRRKZoNCnWTbKXOezE3aqPMh7MbUkSI73Su4v3id8ViUc5sTSaTNBoNXnrpJXp7e5mensayLE6ePMlXvvIVPvWpT7F//34pwCbYG5VKRc6TFXjvbbfdhq7rdHV1ceONN14xyWt2dpZQKMT4+DhHjhxhdXWVLVu28OSTT3LXXXdx/Phx9uzZw2c+8xmWlpYoFAr09fVhGAbJZJK2tjZqtRqmaRKPxyVtMRKJUKvVePDBB+W55vP59XM9RTab5b777mNoaIjp6WnZWyDYLIuLi2zdupVmsymxblH0FINvqtUqxWLxPesEdjrrjfLOgMzaNhp/27YltVVkZSK4E2qfYqKaEP/79Kc/zblz5/jGN77BF77wBQYGBqTjc7lceL1eZmZmKJfL3HLLLczOzvLII4/g8XiIRqOEQiE+9KEPEY1GyeVy1Ot1Dh48yPj4OJFIRF6fU1NTdHd3SwZQs9mkv78fwzAolUqymOv3+6nValx//fUALC4uEg6HGRoa4sKFC+vBV6tjuV6vSxro5z//eTo7O7n++ut544035PS67u5uCVGl02na29vldSsCF1FvcfZ+vOszfC+7BH/W9fyTj9viAN4JNhBYmBjMsFFVD8OiaRlUakVc5SqJcJi6Dp3dfXiDAT7zwMfp2LObv/z6N3AbYKgaoaCPfC1DLZ0hM79A0zYIaC7seoWpsbfobo8zfO48u3fvptA0CMZiaB4f+WqVRt2gUCiwtjBHV7ID0zBalDPDJNqekIqPjUYDBbh06RJ33nkn+Xwe3eVBc7tomgY2KsmuzhafeHKSpmXSkWjH7/Pxg2PP8Msfux+338fyaotXbhgGPm+gxTd2qwR9Plyqht/jZi2TZ3lhgc54G/lMmqWlJSxN49h3v0s8Hudr//A1LAsSiUhr5F6zQrpYIpWvUKwZVG0F07auWTvwU0899RPP1fl7ca7OaFFRFNncVK1WpU47tG6YQCDAxz/+ca677jr+/u//XhoYJ0VQGEiRTk9OTpJMJmXUWKvVJL4u2B+iaai3t5dyucz8/LwcIvPmm29K3nhHRwcvvvgid955p6R3CoYOQGdnJ5qmMTs7K9/v8/n4wQ9+wP3334/P55P9AoZh4PP5qFar6OuT5wS7KZPJsLi4KP/GpaUlvF4v//RP/0Q8Hufpp5+WjUeaphEMBikUCnIP1guO1+xc/+iP/ugnGg1xz4roWhjSq1G6RVOWc5i9CAoAua/QcjLpdFo2ybW0ulpzHvL5PKurqzLCF9RZwewR7xeS3oJyK2pJi4uLRCIRIpGIrDeJ4fTCkAt6sVAerVQq5PN5KfsgZKj7+/tl06EQHrQsi0KhQKVSoVwuk0qlGBkZkbBhOp0mkUhQqVSIxWKydrFt2zaZ8YisTnxmRVH4kz/5k3d1ru8LB/DiD560NxoH58/O4t/GyF9+t1sD01fXlli4MEJXPEb/nu20berH1nQ++7FfZ9ZosNyosDKxgKHruF0mJ449Qd9AL+dOnWf7ln5Si9OoCtSrZQKhCLrL3eIW2yZuf6A1farewK5XGBoaIjc3y6HDR1ldy+ANBFDQiCZbHYD5fB5dUVleSnHzzTezlkm3ZpRGwvj8QbZu3cry6gomNulMhlh7oqV173Jz4cIF9t90EFXT+OY//SO79+5hoK8fl8tFeiVNo9EyXJFIhHqj+vaMUt1FNp1pRVkeN+VqBRWwTBO/24VimXz3W9/mu9/6J5pA3bQo1RtkimVMBUzr2hmKZ555RjqAd/oS5+qMFDfi/4rSGtxy+fJl2tvb2bFjB11dXaiqysc//nGJfc/NzUkY6Pjx4/T397eE/gxDGtC1tTUCgYB0Ahv589lslsuXL7O4uCihGMMwiEQiJBIJms0mKysrUkn0zjvvlM1KYvJcV1eXZN6srq7S0dEhZZkvXLjAjTfeiKZp/OM//iN79uyhv79fZgYCzxZTzcS5ulwu1tbWUBRF4sUCZhKqot/+9rf5/ve/T61Wk05oeXkZAMu6do79D//wD3+i0XBSt0VjnsfjuSpkIbq1Q6EQmUxGGt58Ps+hQ4cYHR0lHA7LYvCjjz7K5z//eensROZkmuYV2L9woCLIiMViskFMURTpcEZHR6lUKpL509vby8LCAoqi0NPTQy6XQ1VVJicnZaNYo9GQ0bppmsRiMdxuN9lsllAoRDweZ35+Xl5fAs4WlE85onb9eimXywCSSiuu10uXLnHq1CnC4TC33347vb29chCNsNv/JhzAy7OX7SYAACAASURBVM88+WOGYmOnr/y93jL+AksWqa9p2Gi2xbmhN+nQNJJtUeq6ReeufSTiHcy/NcfdH/slXOEAfb1bCHmixGMR/vP/8Sdkl1eJRdrIl8oYxTXaQiF+9KMfMbBrJ5buxoZWmqi1cGqzXKJZyPLaSy+yMDdLsVzh/l/9NbZdt53T584zNzfH1nXqXv/WVhu6z+PhR2eGiMfjVKtVllaWOXjwMMVikWgizuTMNNFYjPb2drLZLMVCgZ7eAQqlIqqmcXl8DJ/bw769e6kWSlweHWV5eRnVhoNHDuP2eECxWrxl1FZEHQmiaCqKy0uz3sClKnjcbpKxKEsLi/zxH/8xPzozROeWTSwsLtK0oVKpXTNDcfz4cdtp1K/W5et8LFQznd2v4oxTqZQcB7h7927i8TjhcJjXX3+dj3/84xw+fJjJyUnJAHr66adpNpsyMhRRXD6fl806Pl9LNry9vZ10Oo3H4+Gtt95idXWVtbU1OQzkAx/4AN3d3Tz99NOSp1+v1+nv72d4eJibbrqJy5cv4/V6mZ+f5+DBg3IIvaCILiwsyAEla2trTE5O8sADD7C0tESpVOLs2bMcPHiQZDLJuXPnSKVSdHV1EY1GZZFQyFiUy2UpGOeUl15dXWVsbIyvfOUrElMeGRkR7JprmgE4IQinXIFziRoMvK1/IxhToq7X3d3N2toa5XJZwiTVapVjx46xY8cOjh49ytmzZwF49dVXue+++2T0L7BwERiKa0jAJuL6iUajV9Aq6/U6a2trDA4O0tvby9raGsvLy/I6ue222zAMg9HRUcm48vl8LC4uSkVXQAoCVioV5ubmSCaTeDweSRsVstR+v19OCFtYWJDdxbZtU6vVZCFfdIcL6EyI+42Pj/Paa68Ri8W4/fbbcblc5HI5XC4Xf/EXf/GuzlX74he/+G7ef03W7OTYF524sKqqqMrVpQOExoPw4EITxOvxYdSr1MtlgrbC+KURevo349Z9qIpKKJHglv3XU04tMnr6JDu2beMv/q//E8XrRTNMdEXBsA3mZydpC0fo6enB6/ORr1RaVD9Nw+f1YRsmE5cu8cqzz+DVNF58+RWiiSTnz1/kzdM/QlU1utqTqMCl4WH+0+/9HvY6re+GG/fT092FZZk0G00ya2tgW/gDQeZmZ2kaBsPDwxTyeeamZ3jmmedoNJuEwiH6t27FpWp842uPcHl0hN6eHqxmk7ZwmGqlDJZJItlJpVLH6/Hj9vhoNuroqoptKaytrrK0lKItEsVWXIQjMT7y4fs48cYJMoUspVoFRdX4/d//gz++Vuc6NTUlz3XjcJONzgBaDkEUTp31ARF1K4rC/Pw8yWRS8qu3bNnC4cOHGRsbY2Zmhptvvpmvfe1rqKpKoVCQtNPl5WW8Xi9dXV14vV7Gx8el4RQ33MWLFymVSszMzPDYY4+xc+dOOehdzKTt6elhYWGBr33taxw8eBBN09ixYweRSETKTHg8HhqNBslkknQ6zeTkpJQhPn/+PK+88oqM8AVu/8ILL0jt+oWFBWkohbDY8vKy7IEQMsONRoP5+XlSqZQsiicSCe6//35+8IMfMDc3J/fwD/7g2p3rCy+88EW4ejHY+TsRaQvKLyANncgOBKYu4C5oObVkMsnx48d59tlnOXr0KC+88AKHDx+mu7tbDrwRNSPxHmGsBa1UZCKJRIJ8Pi8zhVqtxr59+xgeHubcuXMoikIkEuHGG29k+/btHDt2TDb81Wo1pqenpbREKpWSpALbtllYWJAigcJYLy4u0t/fTzKZpFAo8Nprr8mgQyiSVioVgsGgHEsprhm/3y+vW6EWK7LeRqPBzMwMg4ODJBIJdF3nzjvvfFfn+r7IAF577ql3zADEksZCVX5MUCkQCFCpVQloOmMj51kYOkvfpi7C3QnCvnbwB+i+5XpCdZulk2f4D5/7H/nmy88xs5Im4ItQU0yCboWJ88MkNnUR8fiZmZ2iVKnhCUcIR9taA8VNhbm5OX700ou89MTjfPDOOzjygbuxVYV4sp2VtQzpbIbi2ho+n48tW7ZQrlT4x2/9EwcPHuTEiddob2/npltuxu32sLKy0oIVbJW1dJruTT1s27aNxx9/nNTiEg998tN85zvf4UM/dy9jY2Pcfutt5NIZjh1/itXlFXZuu46erm6mpqap1Kr4vT66ujcRCkVaqW21wL59u2k0GoxPTrBv3/WklpZxeX3rujWbmJqf4d//h8+xvJpH0VWaTfOaRYrPP//8O0JAziWyBAEDiahRCLq5XC4uXbrE8PAwmzdvJplMyqlPe/fuBeDChQt87nOf4/jx4ywtLcmmII/Hw/DwsOwETqVSlMtl9uzZQy6XIx6P43a7eeutt3j11VeZn58nkUhwxx134Ha7aWtrI5fLsbKyQiaToaurS84mfvbZZ9m2bRsnTpwgEolw9OhROXpSZKj5fJ6Ojg62bdvGY489RiqV4td//df5zne+w733ts71tttuI5P5/7h77yg7z/u+8/O+t/c6d3oFBgNgBjPojWARi6hiibZCm7EkWllFsSMnsSXnnI0ta21Ksq1Im6wdWYkcKyvJjEUpEosoiiLYQKIXEh0YzAwwg6l3bu+9vfvHxfPwAiSV3SNoj5TnnHvmYurF+7z3+bVvSbB//37C4XCTaHhDb0iYoIiDT9gaivtrdnaW3bt3Mzc3h8lkwmazsWbNGhYXF3nhhRf4h3/4B9FWuO0zgFZo562DXvG89WsCzSKydkFea50ZCAKYELETzN7e3l6Gh4dlIBTQYOGTK/rswWBQDnCtVit+v1+iciwWCydPnpSELrvdjsvlYnBwkEAgwMrKCteuXaOzs5P29nZZrYvDXWg2FYtFlpaWyOfz2Gw2bDYb7e3t+P1+lpaWSCaTzM/P43a7JTHs7NmzdHZ2yteh1+tZXl4mk8mg0+mkaZEYaItheDAYlGZBgkG+uLjIQw89hF6v53Of+9yvfgvo2Kv7ZUkpDgi9+s6HhqJ7C7ikqk1rOKPRiNViJ5NOY1Y0iokofpcNkw7S5RqqYgaDiczqCrHlRXbu2kLZ5cdgNNFQm0SiRrVZruWTSaxGA9euzlBpNBjbsp1cuYjH0046HkOvanzza/+Jci7H1q1bGd17F1avm1whRywUZsPgWuLhEJFIiOnLk0AT3tbR1cmR40fJ5XJ86EMfJpvNYjYYuX59gZ61a6XWS3//IIVCgStXplCNhhuCVDYWFxcxG/XMz88TjyZ46Dd+nevXr2Oz2fiLL36JP/uTP+W7//273P/eB9DpdFy+Mo3b62FhYYFde3aSLRQxmkwUK1XcHh96owGbwwWNCuVKnq9+9SscO/Um5frt6xXfGgDebb4DN0vdlkol2ScV5XapVGJycpK+vj7a29vJZrMYjUbWrl1LLpfjO9/5DmvXrpWHpyifS6XSTUxhh8OBqqpSNEyU2ktLS0xOTvLaa6+xY8cONm3a1KwCzWZOnz5NrVbjySefZHh4GACv18uVK1cIBAKMjo7S0dHB0tISq6urbNq0ifb2diqVCktLS7jdbiKRCOPj46iqytGjR/H7/SQSCbZs2cLx48fxer14PB6OHTvGI488wsmTJ7FarczNzWE0GvH7/fT390vto40bNxKJRGSmKw7CXC5Hd3c3jUaDcrnMpz71KaFIetsDgNi7n8VKFa9PtINaCWCqqhKLxSTXQch79/T0sHnzZgqFAkeOHJHkPFVVJWJHoHJExSVmg60YelVVpQhguVzmwIEDrFmzRgIIxH0nAq3wbO7q6sLpdHLixAnsdrucTfT29kqHsqGhIQBmZmZuGgZv3ryZRqOBz+eTdpSlUomOjg7pLibkPMSenzlzRmpFiWppdXVVDobFnExct6WlJWKxGLt27eILX/jCr34AOH7gxbcPgRvaO2aOiu5maKiY7OsNJnSKis1kJJ+M0h0IYNRpRLPFpm1cMILf6SZVzFDTqxjtTkwWO7W6RqVWx2Zp4o2TiRg+u525a7PoDHqGN4zR09PDbDhKOhxmdmqa7oAPnU6hs7MT3/AGairojTpioRD5aIzp8xfYvXMnoeAqL734ArV6ne07t3P2wnl27NjB5cuTLC4usnnzZtoDnRjtdjZu3EgynaFQalLhi6Vmj/Pc+TPNHmk2SzoZb6JQtu6grjXYtGkTVqsVp93Jv/3sH/HCj3/C9PQ01+ZmyWazGMwmUqkU23Zsx+P109bZwbXr87jcXhZXVxhZO4zRqKehVZicvMyff+kvWY3GbttB8frrr7/jcF+sd0IDCeTPrbMAs9kss2m9Xk86nZZ0fyHOJapBMXQTAziRvQ8NDXHy5En0ej33338/iqKwsrKCzWbjpZdekqSs0dFRBgYG5JBvaWmJXC7HxYsX6ejowGg0sn//fkKhEB/84Ac5f/48O3fu5NKlSywuLnLvvfdiMBiwWq1MTExISQen0yntC8+dO0csFiOXy5FMJjl58iTbtm2j0XhrXx0OB5/97Gd5/vnnmZ6eZnp6WrqGFQoFxsfHpRSBqFZqtZrUyBFIly9+8YtEIpHbPgS+Vc//1tU66G+VhRCVWSu8t1Kp3DQ4zmazOBwO2tra5OHcKgbYSgCLx+MEAgFCoRAATqdT6idVq1WSyaRECInZDyBtOWOxmFSIHRgYYHFxkWvXrtHe3o7ZbKa7uxuDwUAwGGR1dRWPx0MkEsFgaCZo2WyTQCk8ioWAXDKZZPPmzWSzWSkUl81mmZqaore3F4vF0mT4b9xIIBBgYWFBDpldLheAZHtXKhV8Pp+E+U5OTqJpGi+88MKvfgA4+dqrmugTi5tGp9PfnPnLw6Em+8niZhCtoXw+j8lgZKCnm2I2g9vloKEqFHNpYhcnUe1WdG4vekzotCqa1rRMLJTKQKM5DFpZxuX14G9rJ9DVTbFWkbot5oYetVHn9Jun2HfX3qYphbudlfAKHW1+lufnyMRjPPXjn5DNpPnsH34Gk8nEhUsXicZi7L5jL4lEgtdfP8i+ffuabMBsHpfPD6h0dHVSbSg4vT6CoVX0ehWzycThg4dwWi3YLGYOHzpAZ0c3wWCI7Tt3cP7iJbZv2U53Zxenjh2hXq9z4MABnC47LoeTnp4e5haXWDM8jMVq5fL0DNt27cbudJCMxTEYdAwOD3LyzTd47fARDh46ctsOikOHDr3Dvr6zAFyr4FYr1llRmmbqImPP5XIyC8zn88zMzEgYZ2tGCkjNoIWFBYLBIOPj45RKJTo7O2VGurKyIm0B33zzTbZu3Uq9Xsftdkso7xtvvIFer+fZZ5/F5/Px0EMPYTKZeP3119E0jb1790r8/h133CFdorxeL+3t7XIA6fF4WF1dlRaChw4dwmazYbFYOHToEJ2dnQSDQXbs2CHRQl1dXRw/fpx6vc6lS5ckamb37t0cOXKE4eFhent7OXr0KDt37sTj8RAOhzEYDPT29nL+/HkOHjzI4cOHb9u+fu5zn3vHQ6MV5w9vtXzEe1U8b+V2VCoV6dImAorNZmNiYoLz588TiUTweDwyqIu5QrlcpqurS7KHRXYtEFFtbW3kcjk5NzEajZhMJhwOB3q9HqPRSG9vL8lkUs5qZmdnpTSHXq/n7Nmzcmbh8XikP4FISqLRqJR8TqWakjAmk4k1a9YQCASo1+vYbDbcbjfBYJCOjg4ZDM6dO4fb3WwtCxJiNpslkUhIUpxQOhX/l2w2y+DgoPQ0LpVK/NEf/dGvfgA49fqBt+HFlVuGwG9lkG/pgIvSs7V37HA4KeezOOxWLAY9WrnEiz9+mjt2bkFxuSjXFKw1I+HIMpqm4HH7iCYSNBStiZ1OpvH39dE/PExR08iWmgSM6OxVDrx6iKtXZ/F53fzrT/8uOr2KWTGg6hROnTqBy2Ylm0xwfSXE/v372Tw+jt3pZmx8E9fn59Hr9dhsNkLRKOqNzMdgtjA8so5AWwfRZAp/RwdX5xYYHh7G0ebG4/SQTaQIL60QDS7y8ks/xWy2MnN1lkqtygc/9GF+7dc+THApyPZtE1y+fAmz0cTXv/Y3hFdDDA0NsHlr0+rw8tQ01+fnue+B9xKJxdGhYHM6WAwF8Xe28+3//o8sLQdv20Fx+PBh7dbsvvXxToFBOCsJqKNAswhVTKPRiN1up1gsYjAYmJ6elpBQs9nM9PS09Gd1Op2kUimOHTuGxWLBaDTywQ9+kGKx2ITo6vWsrKxIHLjw5i2VSiQSCcbHx7ly5QqHDx8mk8lQKpWIxWJ4vV4+/OEPs7y8zM6dOwkGg+RyOaanp3n/+9/Pq6++yujoqMR8C77BunXrKBQK7N27F4PBwPe//336+/v55je/yY4dO3juuedwu908/PDDDAwMcOHCBR599FESiQQvv/wy4+PjnD59mjNnzkhZayGIdscdd0ijlL6+Pqkwqqoq3/3ud8lms78wHsCts7pbKwIR0AXGX3xdsLvFQFMoa3Z0dPD444+zfv167Ha7nBeI970whQ8Gg5TLZSKRCFarVWpEmc1m2tvb2bVrF263m3A4zJkzZzh9+rSszGq1mvQPFpIRe/fulZDP5eVlfud3fodkMkkkEiGdTkuhwHw+T0dHB16vl1AohNlsxul0Ak2byGAwSCQSYe/evczNzXHlyhV27txJo9FgcHBQsooXFhbI5XKcOdNEB46MjEi/gXw+L6VMxBzAZDKxsrJCMpnE5XJhNpv/1wkA4qZobRXcmimKDFFkFnq9Xg5NKuUyJqMZi9VMJLiCQWnQ0R7g0qEDTIwM01Dq1FQd9bqefLmKqjTI5fLoNAPpQo54OkW5XMRhdjK2cyeq2UquVGR1Jcjp06f5xje+wdWlJao0zWgeuv8+fuejH2NkqJdAoI2XX36ZBx+4j0OvHySZaA6B3zz5JgaTiZ2797AcWsWoa8oK2F0uorEYyUyaubl5xjdvotao00CP3eFi3cYx6jWN2dAiV6evsXZgDeH5BWanLjF16Sw7dt9BtVbD629jORhmZvoav/Xww/y3b/83PvOZPyC0vMJd+/bxt//prwkuL2F3NP1TO7u6cTrdxFNJXC4Pqs5AOp+je3CAxVCI148cZmFh6RceAMR+tu5ra6tAvNEFi9JkMmGxWFhdXUVVVTo6Ojh27BgbN26UbQGB7hAVg6gQlpeXJbFqdHQUl8slh6g/+MEP+M//+T9Lqr94LWKeMDExweOPP053dzfRaJREIsGlS5fQtKaN49jYGOl0WsI0+/v7JaRxZmaGiYkJOah1Op1s2LBBuk3NzMwwODjI0tISU1NTXL58md27d0uikpCd+M3f/E2+9a1v8ZnPfIbHH3+cxx57jL/5m78hEolItukjjzzChQsXSCaT9PX1kc1myefzDA8PMz8/z6FDh1hcXPyFEsFube+1SheIfRd97EKhIMmdohIQiJ5AIEAwGKS9vV3OxYTRipgfiO8V3ItKpSKze4fDwa5du9A0jRMnTrC0tITNZmPbtm24XC7efPNNQqGQbDsJsuDY2JiE+HZ2dsoKz2KxyK+JQBYIBLh27RqNRkMqyM7PzxOPx+np6WF0dJR4PM6BAwfYt28fY2NjLC0tkU6neeWVVyS/wO/388ADD3Dy5EnJPejp6ZGBo9FowroFsRGQA2nxf/+TP/mTX/0A8MbB197eK1YaNx0a8pBovPU5cUM0Gg30qE28r6KgV+qUchmcDgvf+sL/wfaJLXjb26ilC9h9PrJeKy6DFavZRjyW4PrCIiWtqSWCZmRkfILVcJwfP/UjvvPNb1KvV1hF4U+/8h84de4czz/7YwyFPCo1zr76CsFQsy+oqirZdIaZi2/Iyf758xcp1aps3bmL4aE1vPbaa2zZvh1Vp+Pq3Cy+tgBnzjXJHmuG1+P2tOF0ezCbrazmU4yPbUGpNUiFojzx7f+KzaSSK1aIxmLcdc97eOW1Q/R293Hx4mUC3e10tPnJJOIk4nH+y9f+hqd++CQvvfIsbpeXwcFBqqUqgUB7U0PGYEYzGDgzeZmL09OsRmO3dVh45MgRrXVPbw3stx7+4nMCIy0OD8GEFNfU4XDw7//9v2fLli0EAgFyuRwej0dm/haLReL4BUIEYN26dSSTSZ5++mmeeeYZIpEI5XIZn8/He97zHn784x9L/Pjhw4fZuHEjZ86cwWazEQ6HuXTpEn19fczMzHD+/HmGh4cl6ubpp5/m/vvvl0gji8UifX/Hx8fxeDy43W7MZjOpVIpNmzbRaDSIRqN85zvfwWw2UygUiEaj3HPPPbz22mv09PRISetAIMCZM2dwOp387d/+LR/72Mdke2HdunXkcjkCgQDhcBi73S7hqV/60peIRCK3dV///M//XLsV7XOr2Juo5lrbQWJQLIQAhWhcOp0mn8+zfv16GRQDgYAM3sViUSJujEYjFouFXC5H4QZE2+12o6oq9913H1arlQMHDnDx4kWefvpp3ve+90ny1Z49e8jlcuzfv5+enh45dwgEApw7dw6Px8OWLVskN0NANIUEieBglMtl+vv7SSQSXLx4Uc6mBPRY2FEqisLMzAxbt27ljjvuIBgMMjY2JmGw4v/R1dXF0tIS165dY2FhgUajwfj4uLwuuVxO+k+nUin6+voYHx9ncnKSz3/+87/6AeDNw2+HC6rKW3ZorZhe0epp9orfyjwMOj0Omw20Ehanj0w8xq/vGOMTH3wfHS4rDpsbj8tG18RGnM4uilqDRDrB5QsXCPgDRBJZMoUiVp2BvKrxjb/7b1wIJ6gYDWiVOs89/yIf+OAj3POe+/kXH/84//jyi1y88gbR86e4cuYMB17cz0Mf/g3iySRvnDqO1WTkx08/xV137qNcLbFj1y45xCsWi7g8TWXCjZvGmLu+yIYNG5idm+fAocN4fH42TYyzd/edTF+7SvdAHxa7haOvHyK4uEAkGCQSi3NxagqbxYpJb6BaLGBSm36/fqeTRr2Gz+dhcHCwSSZraPi8bXja21i7aRMrwRCFhsLU1GXOnX+TYrFIqaaRLlRu20Fx7Nixd4SBitVa9Yn+8K2DYWgibkSwz+VyfPazn2Xt2rX09/djs9lwuVwsLy/T3d3NwMAAk5OTXL16VaqGiv7p7t27ef755/mHf/gHOUS+7777+L3f+z3+4A/+gJ07d3L16lUmJyfp7e3lr/7qrySaQ5iHX7hwgQcffJBQKITb7WZqakqyUJ1OJ6+88go9PT309/czNjbG5cuXJZS1v7+f9evXk0gkZNAS8MJYLEYkEuErX/mK5LiIwfHdd9/NG2+8QT6fZ+3atSQSCR588EGuXLlCsVikvb1dHo5er5darcbevXv57Gc/K39PKpX6hVcAIhCIPa5Wq28jid0a+EUV0NHRwaZNm3jyySfp7u7m+vXrjI2NSQkJoSArGNBGo5FYLEZPTw87d+7E7/fz05/+lOvXr1Mulzl69Chf+tKX+NrXvsb58+fJZDLs3r2bT37ykwwMDHD06NGbOBXCrGVhYYFisci6detwOp10dXURj8cJhUKUSiV27NjBhQsXpCjh5s2bsVqt5HI5DAYD58+fx+FwSBmYQqGA0+nk+9//PnfffTfj4+NAk0SWSqU4cuSIhJAKQtvKyor82UwmIzkP1WqV++67D1VVefPNN9Hr9fzFX/zFr34AOH344DuiRUQUFW+wW1tAcHM/2WOxgrGCqaynTIUn/vTfEVlcYc/dd1GMLWL1OMikywxtGqfRKHFxcpJUoYzP10Z7ezurq6voVCtf//53WYmkidWhZDFBsc5H/tmjXD80id/t4VsHnuWL//UHPPn3/wUfIUrRGP/jW49z/fp1NL2OWrU5qFs/vJZwKEgwuMLV2RnqdY033niDR//ZJ7CYrSwsLHDixAnWbdh4wy5Rh8VuY/yG7ni+WCMYDmF12ClVimzfto1GucKpo0eYuTrLvnvu4bEvfIngahCPxY5FLTO+YZRYOILZqKero1nKRlIJBrt76esfIjDQz3w8ikk10jXQz+f+5H/H4bSQz+cplmqspvK/8ADQusdC40nsa+sSwd5ms93Ejvzyl7/MysqKxM+7XC7S6TQbN26k0Whw+fJl8vk8Pp+P4eFhpqen0el0HDx4kFOnTsnec6PR4OMf/zjPPfcco6OjPPfcc5w4cYLf//3fR6/X84lPfIItW7ZQrVZZXV2lWq3S2dlJV1cX4XCYo0ePSq2iaDTK3XffjcVi4Wtf+xrt7e2sX7+eUqmEx+ORg02RAIRCIWw2G5VKhW3btlGtVjl27BhXr17l7rvv5rHHHmN1dVVm86OjoxJb3tnZKYenghexbt06yWbdtWsXf/zHfyy1g27MNG7rEPjWPr/YTzGTE3vbKscg3sutkhAC2pnP5ykUCgwODkrrRaGbbzab6evrk1DLXC5HvV5neHiYer1OIpGQfs52u51UKsX999/PqVOnpLLo/Pw8VqtVWnC+/vrrDA4OSqG4lZUVCoUCrht+3ULs7ZVXXkGn09HT04Oqqpw4cYLt27dLIuL09LScBwgiYDgcJhKJAM2D/siRI3z605/m/PnzPPPMM+zZs4eenh6Jdmt1CRPXNRQK3ZhpOqQ6wPr168lkMpL412g0+OpXv/qrHwDOHjn0Nh6ABvIiC0akaA0I9ma93ngLY2w24teZUdwNDEtpMoYKh77wl2xos3JiconLSpmO7m6KuQzvn9jAm1OzLIYTJKoaZqeT3ROj5BIJfrR/P8FMjrvveS9nLl1joVrDY3UzNX2NnrV9jFq8PHfyENv+6cf43f/tn/OvP/o+7tmyk8//m8+wfmQd3/jm3/PRj3+Mp556iqHBfgIBP5FIiFBwBZfTzfJqkNnrc1jtNtr87XR3dWGy2bEYmzK2K8ElNo+Po9fryWRLlGtVnC5XMwOoVKhXqjz1o2do7+jCZLNx7OQp3GYbaHX6PGacDgd+TzMLNBtNBHwBUvEEfRs2YPW3kc8WsNisnLs2xcr8LEa9jmq5qTaYW7Iz2AAAIABJREFUL1a4EozetoPi+PHjb9tXEawFfBfeagkIIow4RAT+2WKxYLVaSSSaGkdf//rX6erq4uLFi2SzWbq6uqQK5OXLl1ldXaVUKmG327n33nuZm5vjhRdeYM+ePRQKBc6fP8/U1BTr169nZmaGvr4+Ojo6OHToEL/927/NI488wqOPPoqqqjzxxBP4/X6eeuopPvaxj3H27Fk0TZO96kgkgsvlklaAdrsdv99Pd3e3ZH2eOnWK5eVlNm/ejF6vJ5vNSvniViTMM888Q0dHB1arlRMnTki3qLa2NkkE6u7ubvJebsgLDA8Py+tptVrp6uri29/+tkS6RCIRisXiL2wG0Ir/bzX4EV8TB9WtnwckeUuYo9TrdcLhMH19fXR2dkoxtlwuJ+cHmqYRiUSoVqsEg0F8Ph+pVIrh4WGWl5elSJtAim3cuJFz587x67/+63zta1+Te71u3TopN728vCwRRYVCgWq1KuU+JiYmMJvNrK6uUqlU8Hq9km9gtVrp7e1lYWFBwlF7enro6OiQ/APhNby6uioZvW+88QaRSOQm1dTV1VWJGrJarZIEabfbCQaD9PT04Pf7mZyclCgqvV7/cweAXwo5aI23l4ZitSoDvptFmsFgQGuoaAaVTCKFWwOjpsdoNJPUCmwetJO8UOPxyf3s6ejlP82F6Qw4yZY0JheDVI0hdu3ay/DYEBump/nEjr1cmpvH53Jy/vIVDP0mjD4fHQEfPzlzEr3Vitej8S9f+iFUqrx27CgdvjY+9L4H2bd3N9NXpmjze5saQh4nNpuF2aszFItF5udm2bhpDFSVaDhMT09XE7bpdOJ0WLkUjxONhHA4bBRTBfyBNnra/dSqdQ4ePIjNZiMaS5IrVsjm8xh1KqVSHpOqUMyV6W4PsHZkHT6/n0gkRi6TZ2DTGGPjm8nXG9i8bmZnr2JXYNv4RiKhMIVsjoDbTSTxizMOEYeUSDgEfFdIeSiKIqWBhXm8UGXU6/U3eeaKQfGGDRt49dVXmZqaIhgMYjAYZAkv7h2XyyVbMT/84Q+lQJwAD9hsNux2O1euXGHLli1MTk7y/PPPy7998OBBzGYzd911F9euXZOvLZFI0NfXh9frlVDSWq2Gz+fj+vXrvPzyy7zvfe9jenoaq9WK2Wzmhz/8IcPDw6iqKtnI1WpVZrnQ9BQWmWCpVJKBslqt0tfXx+7du+W1TCaTtLe3oyiKzJJ/9KMfYTQaJUHKbrfL3307l5jRtBq/CKQPcFPmL5Zg8Is9F6qdJpNJuol1dHSQTqcpFouMj49LMTkh1BeNRuXhLXr7kUiEQ4cOYbVa2bRpE1u3bpXKm8lkkg996EPMzc3xr/7Vv+LYsWNoWhPxJ8hgTqdTevcKxJbD4WB4eFhajHZ3dxOPxykWiyQSCbxeL8FgkHA4jNVqZXFxkUwmI4l7w8PDmEwmOSwW0txCsFC0PFOplAwsQqo8nU4TCoWkAGBfX5+sPgwGw01s4Z93/VIEAFQVTVFQdToamobWaKBv0YwRmYRgEgqyhV5vkDeHQW+hXG/gsNgx6FTM9Rp5RcVcd5M1gmKMUIjWOZCcZ6yni+lsDLPOQneHl771w7znrn288txP8bhddHd1cP/D/4SMauS+Bz5MwO9maekqZqMJpaThRE80n8Vc0dMwm6iUyswtL/Pia6+yb/MWwtFI001obCPPPfcs7R0B7rnrTuLxOOvXr2NhJUhHbzd2q4XjR48x0N/D2bNnARgb3UA+n6Wnp4tsOs2Fi2eYnb3alBmuV2loddo6u1hZDTYDZ72BCbAadVhMBjo7O2koTZXPfffeSz5XIJpLU2o0UPV6SuUy0WiY9QO95IoZVL+PqNZsrbic9tu6ra0Zf6sksGjtCD0bkfm3DvXF9xsMBokEEt8nbCAB3G43L7/8suyLigrRarXS09NDoVBgZWWFtrY2vvjFL3LvvffSaDT48pe/LI1CSqWSPHTk/XTj7x45coT/+B//o8zahoeH2bhxI8899xzBYJDNmzfj9/vx+/1YLBa6u7vJZrPce++90mREaNRs376dkZERFhcXCYVC8k0uSE+izSWG32KALTJZo9GIpmmSBexwOKQ2fb1e58iRIwwMDOD3+8lkMiQSCdra2iSC5HYtEcTFvorETARrIZcsMn/BxBWBS2j5AFIrSCiCJpNJ+vv7KZVKXLhwQR52c3NztLW1SWa20+lkzZo1XLhwgcHBQfl7a7UayWRSSjOvrq4Si8WoVqvMzMxINFI0GqVcLuNyubj77ruZmpoiFosxODgoZ1InT57E5/PRaDQ4efIkLpcLi8WC1+uV8FC73S6T0YGBAZxOJ7lcjlAoRCaToV6vs2vXLoLBoOQviPtfoNYEx0GIBtbrdRwOB0ajkfb2doxGI4lEAofDcRPJUVTOP8/6pQgAmqqAolDTRKmoolNUmSUI7K/Ah4thU6uTlF41gN5AtVAklivRiMT41pNP8YHt7+HZN05xRcnx8NYJXj5znlXKfOKDD1BOZnD6vXh6u6nn4gQcRgY/+AAbN22maFVp8wf45x/6Df7PJ75FV3s/y8UMmkGlVtdBxULJAJSK6Exmjp87zcULZ6il0mzYMEKt0jxoOzrbcTvsXJ+9SjyeJJ3NopgNXHjhPIFABwNdPdQbZdatHWJlZYX+wUF58Hk7/KQKGXR6PWarhVcOvd5sDdVt1ACz0YyuXsWo1lEaNbZO7GiSh9YO079mLfFkkmi2QF9HD4rLzvWVIKdeeIV+r5OF6BIuqx2v04HTYiNXyJPIpm/rvsp2XosmjDg0BJuzFfcv3nhC1qC1ciiVSmSzWVKpFN/73vfYt28fhw8flsPdkydPUiqV+I3f+A0ymQxer5euri6JkHnwwQcJh8PodDra29v5/Oc/z7Zt2+QB2Wpooihv2fKdPn2aT3/60wwNDbFhwwaKxSLRaFS2amZnZ4nH47KVsX//fmkDWavVGB0dlQeL2FeBXBK8kNdff10ynwXEVGTXmqaxefNmtm7dit1uZ9euXSSTyZt0aIrFIj/5yU/w+/1NwuINL2GPx0MymSSdvr37KghZre1jcf2E7LE4FMX7V8hBC/N4gfvX6/VyhqNpmpTSFlBg0R4zmUxMTU1JZuzo6CiXLl2iUqkQCoXkEPX06dNSg6e9vZ1gMCgFAcUMQshQ7N27l2PHjvHEE09Ij2Xh0JXJZBgfH5fnTVdXF6qqEgqFWF5eZmRkhPHxcRYXF4lEIrjdbnK5HJqmYbFYmJiYoFAoEA6HuXr16k2mR6IiWFlZAZAaTz6f7yY+g9/vp1gscv36daDZMisWi7Kquh1ez78UAaCqmKX4W+OGOXxdq6FXVVQ9VDUNVdGhM5so1G7YpN1oIVRpoBh0ZKsZDDk9Kjq0ap1QOEpb/wa+cfwANgX+6OGHWBPoQ9NM+Dq72L1tN9VSGpvLjadzHflCjqGdu7hy8RyXZxZZO7EVTW/lT/7mr+ibGOH3/90fU02GWbduHVenr2LWNLRqjbLOyK6JCT758D/h9Refx2c3Mj6+meXgCqVqjbFt2wkFV/HZ3ZhcfgK1GooOduzcTSS8Sq1UpFTRsW79egyWJvGpu70Lu8WBqjNhXWdmcf46J18/iF7Rk89VyNRrGACbVsXYqON12BgaGiJTqbMYTdG3rZ2SwUmimiKnaQSjq/izVpRknAf3bsdiNtIorSNfbPrkFkoFjBY9NvvtbxXAzRhx0QZorQqEBZ5AerRmN+VymWq1KoXVNE3D4/Fw4MABDAYDn/jEJzhx4gQul4tIJMLQ0BCapuHz+Vi/fj3Ly8u43W6uXbvGwMAALpeLVCpFd3c3n/rUp3jyySeJxWIMDAzcVJ4L5mhbW5scukKzXeB0OhkZGaFSqUjteaEBbzabsdvtLC4uypbC0NAQAwMDLC8vA02rQQEtTaVS9Pf3Mzc3J1UgW9ubdrsdh8PB8vIy+/btIx6PEwwGpUx0KpUiFovxoQ99SOLai8WidARrNUS5Xas1WIp1qyyEwPmLgC6qA5EMtAYJgeoRwXV+fl6yngWRzufz0dvbS3t7O1arVcpFGAwGOjo6CAQCpFIp1qxZw7e//W0efvhhOWPK5XKy8gwGgyiKwokTJ3jzzTcZGBiQ7bJAICAPa5vNdhPhTrDDfT4fPp+PM2fOoNfr8Xq9GAwG+bdisRh6vZ7FxUWGh4fp7++XqB6hTZXNZiUDWZD3xsbGGBsbk2276elplpeX5YHf2gkR7aPb0QL6pRgCHzt5RhKGdKJtoL5FIdffUAAF0Onfkg3QNA3dDX1oFQVUHYVKFS2VphaN8pNvP85vfeKjJCKrmKxGBjdsIJLJUdEZcdr0GE16Cqkknb42rDYnmVyBopYjH4ySSmXoWDOMq7ePcr3BytR1fudf/C65So2MVsXu96CpCsGLk/zdl/8D85OTvP9997MQWmLDpqaQlcfrp9aok8nl0BsNLC02xcj8bW1Mz1xpahdZTLR5vQSDy1TrzXZIOpmjVqoSTUbobG+jXGwiOS5emmJucZl0sUgxmcJjtVMtldj1nrv5yEd/G73didVmZ3JyhoWVIM/85GmuzV3FY7KweXiEf/qRD+P3e6lWyygNhUI+S61Wo5TPNW0EKxX+zVe+ftuGhSdPnnwbEawV6imyQLFu5QW0fk4cBqlUin/8x3/k0UcfJRqNYrFYGBkZIZlMoiiKlNZNpVJSgld4CmSzWcLhMAMDA/T09FAqlZidneUP//APpRmL0BKanp7mc5/7HLFYjHXr1mE0GqVBeKsAm7CNFNLU09PTKIoig0IwGJQVjej3xuNx2tvbpV/vpUuXmJ+fl6JiQh/+rrvu4pFHHpFDwVAoxIULF/jxj39MPp8nk8mwYcMGHnjgAYaGhmSgFNBGYSVYqVR+buOQ1nUrDFTMA8RqlbIWh6No3YrVeoCJttHS0hJ9fX3yNedyOdxut5zzCR0lq9VKNpuViUS5XGZgYOAm562DBw9KhJVAmglZ8FAoRDgcZuPGjfT19cm5kugoCOYxIPctkUjgdrvl0N7r9cpWEDQTA0VRpAS3y9V03TMYDCSTSdnunJmZYXFxEYPBwPbt2+np6ZEBWkhEA3LIv7KyIoOmmH2J66ZpGl/5ylf+FxgCKyooCpqiygpApzTbQigKlXoDndrs/9fqdVRVD4qCokCj0dR0qVdrNFBBb8DV5mN5JYjZamPl2gLLKws89LsfJx6OERjoQzNZWLp8GY/Hg9fh5f/66lf4o8f+nEQ9j0lTiIZD7N17B6++foiOQpHOvgE8Pivvv+cO4tEEXW2dRMJhNm3axNSaYXbv2MzeO3ZhMBiYWwkxM3WR9kAHSt3J8uISTpeHaCpOe6CTWCxGXI3T3dvP4vwcxrqRYDBIsVimu7eL5eVlVFWls6ebYr1IpVbDbLUxP7/I/Pw84UiUQiJLR6cfs8XAfe97gG133kkVHeWGQmgphN3p5qX/+9uU8gXsLif1VB6nw0E4uIrdbsXmdFAsFDBaLVAqYWxYUVU9Vf3tdwW+FQXUmuGKg1HMBm79OfF9AgggymO32838/DwrKyt88pOfZHl5WeqmX7hwgb6+Pi5cuEAqlaKnp4c9e/ZIko6qqnz961/nnnvuweFwMDY2xk9/+lOefvppvve977F27VoAfu/3fo/Ozk46OzspFovMzc0xNTWFXq9nzZo1suIwm8243W4mJyex2+3yNZZKJZaWliTssFwuk06n8fv98oBvNBpcv36dvr4+QqEQ1WoVt9tNvV7nS1/6ksT3z83NyRbF9773PfL5PKVSCZ/PJ81y3G63rArE/ELAMAXs9Xatd1P+hObB3moIL/b41p9pDRiZTIZkMin77dAMCsJaMRQKSeTVe97zHl588UUcDocMCAJSKbx3hYPWK6+8wre+9S0GBgbkdbBYLFgsFt7//vejaRqLi4vs2rWL3t5estksMzMzGI1GqS1VKBTw+/10dXVJ+Wqfz0e9XpdeBsVikYWFBenv0NPTI4mDHo+Hvr4+Go0Gk5OTxONx9u3bh8fjAZrVycjIyE0yD6lUCofDgdvtlogiAWwQe/luJjz/X9cvRQCoKzdEwBQFbmgA1VER3EVFp6PWqKM1FBqKDg2lCR0CdKpCHQVFb5A/X9fqbNyymaXJSRo2I/t+7QNkQyksfV2ki0VMej2bt2whvLzMv/30v+SfPvwREiuLNGhQLGisXb+e2dlr2BQ49/J+dNt3cnp2lk//wb+mXAOLzUat2FTtXJpdwOMLUK4UePPkCTx2Iy+89CyxZAqvr52t23ewurrEYH8PhWwSq0lHvVahVtGwWy0YdAqdXU25gHQyRUegnXyhRKFcYt3IhiZJKBLF5vZx17338cwzz4CSxefx8luf+Dh9GzaQLdYxWZyYnR6mJ6/yvSf+kaWlBXK5DJ1d7bg7XfR0dlAslFhaDhLobmraN6o1FL0JvbGMVqtTzBf+f9130ZMV2vk/S1VSzH28Xi/9/f0YjUbe+973SpEtUWLv3buXYDDID37wAx5++GESiQSxWIxyuSzdm+69917JLfnJT37CQw89xN69e7n//vslcUyYg5dKJa5du4bFYmH//v1MTU2xYcMGtm/fjtPplIfBwA0HODHINBgMDA0NkUgkJLpDGIwItUpo+gY3Gg1CoRDxeJyRkRE+/vGPEwgE5GxiZGSEEydO8NJLL5FKpW4aGo+OjgKQSCQwmUw4nU45eAXkDOV2rlv3qfUwf6fn/7NWhcDA35CtlpIOc3Nz6PV6uru7iUQizM3N8Y1vfIPHHnuMO++8k+eee07uT6lUYs2aNRQKBYLBIHq9no9+9KPs3btXEuYEsUv4FFcqFckITqfT9Pb2Mj4+zrFjx4jH4+j1enp7e1EUhWQyidPppFwuk8/nb5opGI1GafAjYJs6nY41a9aQyWSoVCqcOXMGr9fLvn37gGbby+12MzIyIrH9rbMyUUn6/X7JIBfghFauxc+7fikcweZWY4+h6lAVHYqqA1UHarOlA6CoOjRFRdHp0QBNUZqoIVWVCCJN1aGgokPFpNOh1SqYjAYi2RT+7k4ahRI6u402qxutUOVqcAl3m5OHfuu3aKhmVi5Nkbh6FbPHS8NkwGAyc+7ESS6cO0G1msXuDeAx2rAreoyaThKNTl04j91m5dLZs9x//z2sRkOMbBjlJ/tf4uDpMxw4cZKFpUWG+/uYuXKRRq1MPBZlYX6Oeq1CvVqh3qhht9uIxyLkclkUvY58qYjOaMZgMqM3mXC5vTjdHtasGcLf0cbw6CjpXI41Ixuxu7yU6hrP/ehHHDtxjNmVeXRmPdlclnq5Qke7n00j69m+fRuB9gAmmxVFU9Dp9egMuqbyql4POoWN23bfNueolZWVx25t+7Rm9uJguhX62/o9rT8j2ghGo5FUKkVHRwflclmycMvlsnTuevjhhyXEcGpqCpfLJU1CTp06xdzcHLFYDI/HQ6lUkoeQ8OE9e/Ys69ev59ChQ2zevJlMJsP69etZWFjgpZde4ujRo9jtdiwWC1euXKFarZJIJJifn6dWq8l2jIApCnNzIWInWg1iYLlmzRra29vZsGED2WxWMlFrtRrPPvssx48f5/r16yhKU+Mon8/T29vLxo0bGR8fp6urS/4fBFpOQDRVVWXLli231RGsdY9a91d8bMX+/6yHmPsIkp8Qf0smk5J8lclkaGtrw2azMTo6yv79+/nrv/5rHn74YVlBCjau0WhkYmICTdNYWFhg/fr1coDv8Xjwer3Y7XaJIkskEoRCIZLJpLwnXC4XlUpFDmpjsdhNks8iCAghOaFb5Xa7ZTvJ4/FI2el4PM6mTZsIBAKyQhsdHWXTpk1kMpmmN0gL9FkED4/HI+cmrcFB3D83mOw/177+UgSA+WD0MUVpSjqr8ua40T5Qmi0hvb4JG1R0OhQUoJntq6jN56oCmopO01C0OgZVo14u49AZSEZjNAx6HDY76WwWu81ONVNEp9SIFnKYfe1sHBni6R/9gEB3N96uLlZXVqkW8oyNraNcK+FztfHGhTOsndhAgSo1qvjbvWQTSbq6OvC2+ZlbWGRuYan5+gxGlqIxlsJR2tt8xJZXGF0/zJXJy7R3dlAo5PF5PdQqZVCbSpZ2m5VQKER7RycGo4lKvUGpXGU1FMZithKNhKk3NDLFpk7J0NAa+noHMNqcPPfTF5mZnqJYLaEZoFyvkYzG6evuZai/l97ubhxWK2aLGZRmhqjoVLSGRl2ro7uBvhqZ2HZbA0Cr3IP4eGvpeqsMROvz1iWghQ6HQ5bg8Xgcm81GIpFg7dq1KIrC3Nyc/HdXV5eU8BXEq3q9zp49e3A6nXz3u9/lve99L/F4nPPnz9PW1kahUGDjxo2cP3+erVu3SnJQNpvlwIEDEtKXy+V49tlnaWtro1qtsrCwIDXmhVhZvV4nFovh8/lksFIUhVAoJAeGoo8t/AecTidtbW2cPn2as2fPkkgkmJmZIZPJSFbx8PAw9913n8xQBVdGSBWLh7i2mzZtuq0BQDxvrQZ+1h6/2xKZrIC7it67qJIEeiiVSkntIzGYf+KJJ1hZWZEQYZPJRCgUYnJyksHBQfr7+zl79iwOh4N8Pi/7/aIKK5VKmM1menp6pM+CGCy3t7fj8XiktLTQkRJscCG45/f7ZbBLJpNomobT6ZQmRMVikXA4LLWqFEVhx44dWK1Wzp49Kw1xgsEg165dY2pqShq+CDKf0A5qldsWVcADDzzwc+3rL0ULqHU1/ag0NEWhwQ1FQQVqjQaKXn+jzdNCPRdDYw00FeqoVMsl6mod3A5seiNLVxKYXCorwQjrN42SL5fxDfRQyuUJXrzEmhELKZOVR//0L0mGo2hGKwaXk/ahPuILs5hVE5tG1lBtVMgtL1IpVQlF47QZdBjrOaYuv0ks1WSk9nV3YDCqzExdxGLUoWgwObtANJvl5a+eoK+nl2RdI7oapLeniza/l7WDg6zkkgyvG0E1GFmNxgAFg94GOhW3x0+yVMDg9kO1yvt3bOf1w8coxTNkZub4/ve/TzKZJJGN0+5vo9fjY8jr59d27uLOPXfgd7uaOumNZh+2QbOPq1M0MOjfyrZ1t/92EAdQ67/hndUjW/ua79TjFNmRUMyMRqPSHWx0dFT6/vb29nL58mWp99PW1kY4HMZkMkm7yLNnz1KpVPjCF77Ad7/7XT7wgQ/Q19fHiy++yIc//GEuXrxIvV7n8OHDqKoqe7LVapU1a9YwNTXF9PQ0/f39PP/887jdbu68807OnTvH3XffTTqdxmQykc/nmZiYkHr1YtDodDqbVqY3BrYmk4nx8XEOHjwojW5ee+012RMOBALSoKSjo4Px8XH6+vqw2Wwt0ijI/rvgEohM8XauW/fl1upNfLwVKtq6WjNaeMsrWJifCP5EMpmUAn/BYBCAoaEh9Ho9Dz74IGazmaGhIRYXFyXyyul0Mjs7i9VqlY5xArff1dWF1+slHA4zODiI0+nk6tWr0jjG6/USjUblNezq6iKXyzEzMyN5ADMzM1KfSlEUHA6HZIFrmiYlLEwmEz09PXi9XslYH7gh851MJqVoYTQaJRqNYjAYGBgYkMgi8Z4QsFvxmlolcX7uvfxlQAG9+sYl7W0ZoHJz+f/WjaXe+PgOAmOaCkoDKmXMaoNKMYdLp5JNp6kVC7g9HtxeH7lCkVy+jMNuRac1SKXTmOwW6mj47FaOv/IKiklHz9pOjvyPJ/HpTFyLrjK+cxtDg2v5+//y9/z6ww9jczg4cegQazeOkcjmUXR6zl+8zGJ4GavNwfxqHCwWzl28RDabplar47BbUBsN1vR2Y9arbB5dj8NiRqfTsXX7Tiw2O8eOnyTQ0YnF6qZYrWC2WknnC7R3dlOv18lUymQzea7ONT0BopEQq6urDHZ1sG54DX2d3QTa/HhstiZKSvdWn7VcrcjMEBpoN0rvWq0G9Qbv+81Hbxta5MSJE2/fV/Xt+9e63qlVJJZAkwgbUKHRL0p70RoRSIp0Ok02myUQCOB0Ojl69CiNRoOhoSFmZ2elgffKygqf+cxn+Lu/+zs+8IEP0NbWxpEjR+jv76dardLT08OLL75IMBjEarWyurqK0Wjk7Nmz8gDXNA273S5N6kVbCWDXrl1YrVaOHz8u+QPCzSqXy9HZ2SmNwnO5HLOzs1SrVSKRCKurq/T09DA8PEx7ezudnZ2yohGIGFVVbzJCFy0Ysa+NRoOPfOQjt21f/+zP/kwTgf1WE5h3Wz/r662zAoHAEf11oQEGyOAvHMCsVqtE6AQCAXnNBwcHJaNa3Cc7duzg0qVLdHd3S+2hhYUFOTgXswOB4GklHtpsNjRNky3GQCCA2+1G0zSCwaD0YBAaPfV6vWlOdaOiECguVW2ayDidTvL5vKwixOzIbDZL/Z9b160HvrgmP68Y3C9FC2huJfzY20rJFhQQSlMbqPlRBUVFo2kag3ig0FBpIohUlQYaoFKtlECBTCbL9evz2BwOrHYb4WgUnU6hVq/jC/iJJqKUKiUUTSOyukrv0CCFRoVEMMSrL71CW0833/zO43R0dhLwt3H8xAnaAgGy2STPPPccgY4Okpk0BquVZDrN8mqQ+977Xs5duozH7SWRTFHTGlQqVfQ3lAwddnuzTWWz0dHVVEB0OJ2YzRaKhQJziyv4fH7qCmg6HVaHg+WVVXRGG8VykVKpTLVSIR1P4bDb2DA8zMT4BH29PfjcHvR6Aza7A02nYLZaQVXR6fU3rqVKc5au0ADQFFBV1m4Yu22tgqWlpcfeHsDffri/2/PWfyuKIhmmoldar9cpFovE43Gp/XLj72Kz2WQbYXJykkqlIttBmqYRjUbJZrNcuHCBgYEBcrkcKysrjI6O8sQTT3DPPffwxhtvsGnTJmnELQZzQ0NDMuvL5XI39b1DoRC1Wq05ZL9B+FldXeXy5cuoqkpbW5v8PcIHVxwa6XTnHLt4AAAgAElEQVSaZDIppX8rlQpWq5U9e/YwOjpKb2+vDCDCI0EQ5kQ2+m4tmZGRkds+A2iFJ75zsvbuvf/WvW0NDiLbFZBcTdMkZ0RwGkRwEC0VwR0xm80kk0mmp6e56667sNvttLe3UygUCIVC7Nmzh6mpKc6fPy8ZvcJnIBaLYTAYuH79uiT1iQM5m81Kop7wHRB/X/BOBMlQEMcEZFQYwIsBbk9PD+l0mng8Tjqdxmq1SrMX4YMtODGt16b1WsNb7dD777//V38GMLscfuzWm6KhcAMaCg00NKV5Rmk0P8eNFpH4qImKAUCBhqKgKTpQG9TqDdxuDy6fh3gqjc3pRGdUyRbyFCpFVKNKKpMmk01htTopKgq5aoVsOktnezfrxif4H089i8HhpKqB3eHG395BIpPh6sIiv/PPP8Xrx45z1333EUrEmZqaxmq309HVTW9XF7t27WRp/jqRWAwaUK02qNaawaC3r5ve7m50ej0Li0tMXpmWGW5HTz+qXo/RbAW9gZnZBfRGI9VynVw6Q61cZnl+np6ubkZG1nPHnt10dfdgtzsxmiwYjBbQGVBNJuqoTWSVqkdR9GiKjoaqA3Rw498oOtaOrL+tAaA142/9+G7PWw+Td/q+1iWyNavVSjgclh6wmqbJnq7AbQvP2FKpRDKZpKOjA51Ox4EDB5ifn0dVVYaHh2WZf+rUKe69915++tOfUqlUWLt2rRT7EiikO++8UwrCCaIONPvZwktWr9dLaQKLxYKiNE0/RMYHsLy8LKsIQS5bWFigra2N4eFhJiYmmkZCLQfgz6qk3ul6rVu37rbPAN6t5/9Og953Cw4igLXuKSClkU0mk/z5WCwmPydUQEVbzWAwkMvlpMyyYBPrdDq8Xi+Kokgbxp6eHlKpFNFolFqtJg99vV7P+Pi4dP1qvRcrlQpOpxOv18vy8jKxWIyZmRkSiYTs+SuKIkltAuUlJB0Ej6GVnyHgpuLwF9dBtOxaD/9bH2L9vDOAX5IAEHrbi2gtGG8uH7V3fCgKqFrzo5CKRlVRNNDr9ORzeUqVOnqjmZVIlFpNBcVAA5VkOovOYMbnDRCOxohn8gS6ujGZbSh6AyvhKL2D6zh44gThWIqJ8S28efEi88tBNNXA3Ow8V69dZ2klyMaJcQrFMoViiZ7ublKJOKl4lHQyztr+QaqVMqVSBRQdpXKZWrWC3WbGbLVQKFUwGI0k0ynS2QypTJ5ssUB3Xx/heBKr00UNjXQ6Q7VaIpGIk0onsTocjG/dSm9vPwaTBZ3OgKIzgN4IOgOKqkdRDSiqAVUxgNIMAoqq3kBc6UDVo6oG1qxdc9sOioWFhcduPZzets+3tAZElnNrIGjNfsRzYRAjsqtwOCyZxtD0aa3X6/h8PmnKLQ7neDxOKpVicHCQAwcOkE6nmZiY4MqVK0xPT6OqKqurq0xPT0sVx1KpxJkzZxgbGyOZTGIymVheXmZwcFDKCIhDoFqtSgVPkdml02kymUzTevQGiicej+NwOCTTWfSi0+k0drudLVu20Nvbi8lkkoQmwWq99XHrgSs+3kCV/EKGwP9vVitR7Ge1ggSCCd5SEhWDcXEAiyxcZP7id4rhaywWo7Ozk3Pnzklo5szMDJqmsWHDBqnaKfwHQqEQVquVu+66SwZoVVUlsEC0HUWAF8FGURT27NmD3W4nFotJIpnX672p9SYE5ISdqRg8u1wuiXgSiC3xaG2X/s8e/0uggK6uhB9DfavNg/r2jOZnlZHiuXpjgAwt2WatDhpYLRYUTUNvMuL1tZFLF4hG4xTLZSqlCpqmYLE6yOWzuNxeQtEwRosZq8GI3+vj1JtnWI3GuDY3S/ZGlF87sg6H1cm1mWvoDM3eXSabZX5hmdHRjVw8fx5V0bg6M83ohg1cvz6Pz+OlWm+QzuZQgFw2j9dlo7OzC5fLxZmzZzHeoKWbbU5cbjdX567j8HgplCukkhlUvUI6kyYWC1NvVFk/tone/n5cdicoCqqqk20zDRVFFWiqG9dXuxFg1SaaSpHtNIWhocHbdlAsLi6+awD4WQfBO1ULty5BqxfDMpfLJfvosVhMauLkcjna2tpIpVKUy2USiQSW/6e9N+uRLDvuPH938d09Fo89MnLPrIWlJmcoqqpEUi2SkB700EI/aOZDzIMG/Q34FXowQGOm0YBeBA0gYAhKGAHSACQFScVmsYosVlbWkpVLZGwe4R4evm93O/Nw3U6cuOmRmVUZUYxhugEB9/Dl+r33nGNm529mf8vltAGR4O2dO3f47LPPePvttwF0ttC1a9eo1+u6inh9PWZvvX//Pjs7OywuLmoGUsn6ES9PMo/effddms0m3W5MvTE/P8/y8rKGJgDd7vLx48c8fvyYlZUV3nzzTa5fv66Ly8zA6Wle9aTUS9u2uX797Mb1Jz/5yQ+fNnZPC/wmvVjTMJg0B1K/0G63Nb+O7PgkBVOOIbnxUqQlfZLff/99giDgT//0T7Ftmw8++ID9/X1tpB8+fMjNmzd1u9F0Os3u7q7ORFpaWmJhYUHHHqSyeWFhgfX1ddrtth538eb7/T79fp9CoUAqlWJ2dlZnFAlv0YMHD3R8Q8ZHrl0gzue5f0qpF4aALkQWkFIWUSSdoeLn1hj+B4VtW/p/ywJ7rLhAgsJWDPmo+FUrkhsEkYK0ZRNGAalshuHQo9vtEaVg8dIytVqN8tISlUoFt1ig3h9xa/0yR70+YZSmoRTpzCy/9/Z3efv7f8x/+z/+CxnHplJtoDK7bG8/5q23v833vvc9fvzjH9MfeswUM9y98wG1gxrXrrzNb5oNHm8+YnW5zMgPOTzcH+OagGPz8eYWfWwODg7wPA93Zo7Bzh6FmQ6rNtipPJ12TBOQzaT51S9+QRBaZAtzvPLv3uaNb7zF3PIilpMiNW5/GI3nkGVZqChmT42scbDNHjNOKhfsmH1VqfjxrOVZE9qUZ+0UTE/SXECpVEpDPpJLLouvWq1ydHSkW/nNzMyc6OC1ubnJ0tISc3NzuK7Lv/7rv5JOp9ne3ubP/uzP+MY3vsE777xDu90mlUrx61//mnw+z/e+9z1c1+Xu3btcuxZ3IZMmIHKuYoQkRVAogoWpUwxEs9kkn8/zwQcfEIYh5XKZjY0NNjY2dC653J/TjOOkIKFkjJxV0ZB5HpP+P6061YwTnPaeBFxNEVxflLtQKguTrHxXeoNLcF0yv7LZLPv7+/zsZz87kSjw1ltvcefOHbrdLnfv3iWdTvP5559z9epVVlZWcBxHt5zc2toik8mwuLh4wrhblkW9XicMQzqdjg4Ku67L+vq6zliTOToajeh0OliWxeXLl3X8R+4bHENpp1VaP2s3/WXkghiAJxtIT8I1TWsp/2slEirt9VrjY0ahhxoN8UIPx7VxLRtbRWRzeQLf4/KVKwyHQ4ZDj6XldRQ2jpPi0eNtXNel78XBmEIuz9zKOs36Hv/L//qf+L//9v/ijW/8D9z79HPSaZf//b/+Nz7becxieYFiqUy1ssMf//t/j2UpfvXeu7z95jf56MM7XLt+k3qrzcaldR5s7WE7KfwgoNUb8NGn98hms+TzecrLq/i+Ty5fxPMCOo2DMV1Ej2ptH683wMOmZLlkZmaI3DSjYcQgF4GKDaSrs6UUliuVtuN7FUUQRVgK/fqJ9894XAWW+SLjaiquZOWpbMWFcEzez+VyeJ6n6YSHw6HuBywNU6TYazgcsrGxwdLSEoPBgL/8y7/kb//2b+l2uxoPzuVy/NVf/RU7Ozt8//vfp1KpcOvWLb7zne9oSmKBkWZnZ0mn0zx+/BjLsgiCgG63q7uR5fN5XS+Qy+XwfZ9ms8ne3h69Xo9arcZgMNBGrlgs6nM1K3vlMek5ymsmUZj5eJ5iGvJJ3v+zMoQE0rIsSwdbpReC0FkIe6xcn0BbMhcADbMIu+jc3ByFQoF2u021WmVtbY033niDn/70p3z/+99HKcV7773H5cuXKZfLHB0dafZRiccsLi5qKM+2bc3fJPUatVpN13hIzKnf72soB+L5Wq/XT8SF+v3jqnu5HnEczPFKBn7PWi4GBLRzcOpJnOZtJBWJwxhCUjaB7xH4I3zfI+g0sZVirpQnDHzy+RxREJDPZel3+2DZ5ItFokiNB6aHY9vk8nk9GdvtNl4YV3X2ex2K+TydVpOl5SUIPf7oB3/Mv7zzc3Z3KjjKYdA9IggCOq0j3nzzW3z68ce88sqr5It55ufn+fjzh3R6g3FAWxEEEbZjkclmNVVwvV7Hsm08z6dx1CDwPfZ2H1OvV6nXariZHHYmh1soUsjPkEplSGfT2I6LhY3lxNCPxRgSGhfPWZatd01Yx0ZTHm9cXj8zqGBzc/OHpqE2PcXTxlQek0pNyNckgCa8PrOzs5rNUYrEWq2WZmpU6phe2fTkpJrYcRy9wHO5HL/3e7/HtWvXSKVS/NM//ROvvPIK9+/fJwgCnU7q+z63b9/WHO25XI4rV65oRS70FoA2QrK4W60WlmXRbrep1+s67fPo6Ih6vX4CBxclZ1b1wnGzlUlxkuRz+dyVK1fObFx/9rOf/TD52mme6bNel2sQamw47iUM6HspabNS52AaO/mcQDXZbJZcLsdwONTHkaYzh4eHzMzM8MEHHxBFEa+88goffvihHlfpBDYzM6Mrgo+OjigUCvp+yrlEUcTq6qo2UtLlzSxWlF2LVH4LE6zs7EzHNyli3E3DkFw7LxoDuBA7gKTnn/QoTGzTZAI1RRERKQsVKaIgxB8OOKxWyIc+l197hcgPyLoOuXQKwoBsNjOeLDMECsLRkGw6TT6bw7ZtVsYt/8yuRzY2kZ9hcWmZtG1Rrx2yurLM3U/usrGyxEJ5mZ/+5F/4zre+xlGjyY3rG7z//nvcun2TtJvm7qefEVkpisU86bTLyA8gVCwuz5PNZtnbi9vGCcvkTmUfK4q5UTYfPgIrotFsYkUWg2CPvh9SWljiQztDt9enH15heXGB+VKRMFBkXQcshaXUsZI3noODUmO8DRUHhc9YTMVv0gNMggSSaYXm61IQI2yaAFevXtX3Srb/4rnLAjWhBWkuIq0lRXkIX/7i4qJu5AHw3e9+l1/+8pfs7u6yt7fHt771Lc3p8+mnn5LL5VhZWeGTTz7R9MS2HVd1D4dDzefearV49dVXGY1GBEHA3t4eSinNd2NZlmaVlGyglZUVtre3tfJPpVLkcrkTHdQm4e2TXj/rHUAyJVFeO+2zTxMxeDIvxNMWhSrK3yyikwwhSZ8VegWBYDzPIwgCisUio9EIpRSLi4txL412myAIeOWVV/jkk0/IZrN85zvf4e7du7pXhFKKbrdLr9djdtyOtd/vk8/nsSxLe/62bevxymazZLNZms0ms7Oz1Ot1HfSV9o2SDGDbtn5+dHSkA8bJOZ8cN3EInvfePo9ciB3Ap4/3fvgsbOtEsHfChI6IiGKiIPrtBuFgwL2PPuTq8gLBaITrOjH+63vkMhl8P8S2gSjAJSL0BmQcGIx8UimXfDY7bkNoo6KQ5bkSh4f7qDAg9Dx2NjfjbZ1lkcIi5zjs7VW4eusm//3n/53/8VvfIgg9wtAn8ANWlteoNTps7e7SHfksLC3TajbjLHxLMRoNuX3rJtWDA6IopmYYBSGdbpdg6FGrVhmNArxRQIgiDEJGgxGD/pBWv0etdUSzNaBYKpHNZHFTKRwbHNvFsq1xBXWcGSXBFNPzF36lG5dWzsxTfPTo0Q/NcUsq99MeTTEDhkKsdffuXc3R77qu5swRJWHCSbKQpAtVNpul3+9rWKFcLrO/v49t2zoDp1Kp4Lqu3iFIcdHPf/5zstks5XLcc7nT6XDlyhUajQbvvPOO7h9QqVS0l57P57l27RoHBwfaAAqNxHA41ER1wiEjeLcUhTWbTdrtNqVSCdu2yeVyJ7J8zLUxCVoTuXz58m8tC+g0SQa1d3Z2tNKWHZsJH0pgWJS/CbOY0KFlHVdAiyHpdDoUi0VdfW3bNm+88QYffPAB+XxeQz2SZSTFaFIU5rourVZLj5/k+QvJnPDzCHWFbdva2MgO1vM8Xecg/4uYtOimgT/NMZZr/sEPfvD//x0APBlAMiGA5GcmeSBxXktcMJJOpznYr7BQniefjTHBmZkZBr2uLrSwsIiCCN/zcBwLKwqJfA/bgtD3GQz6eP6IaDx4B5U9GvUjMmmXfDrF4uIiKgi5/8kdrm1c4t1f/ILi/ALl8hyum+LHP/4xVy4vs7aywEK5zMcff0p30Oc73/0u//m//FeUncLzfWYLWSI7Lgo6ODjg5s2b7O7uxpzh+SJRGFLt7GMpCwgBG1SE4zoEwwGNWo1epOj4Q5SdY213haWFMoV8DmUM76QJZL5n3vOzEjNway5U8cxP+7zs8Mydg2zxpVnM3NwcMzMzJ7pnmdcpuwWljrvKAfR6Pd0wRSnF5uam9tJ7vZ4u8vrRj37EjRs3ePz4MbOzs/zbv/0bSsX1BX/3d3/HlStXNHXEwsICf/7nf87f/M3faJ4ZIQ2TgOL6+jqtVovNzc0TQb6kF61UXHFaqVRoNBq6neHc3By2HVNSJPsrnwapnfV4mseftEN72vyaNP+SQc/r169rVkxZp/J9mROiQOV7kxS/zBGBchYXF9na2uK9997j+vXrel7s7u7y2muv8dFHH3Hr1i09T/v9Pq+++irNZpOPP/6YhYUF7fkPBgPN3SSNY8TLBzTvv+xGZO6aSILMSUATHGazWb0TSN5L+f9pjtKXlQthAKxonLsdRlgC9ViT09yAE0rlOOgLyooIwoBOs4EVBuQtl3Q+RyrjYrtx703HsrCxCKIwzhxKxVTT6WwGz/Mo2hZhpEj5HtkoZK8aN32u7+3TOdpHRQHKili/doPC/Dye3yPCYu3aVSqVAzKVHf7n/+k/8o//+I/Ml1fwlMOP/p9/5tLlSyytXeJH/9v/ycaVdeqHDVzHIXJSDAc9wnGM4dNPPyPwI6LQot/syh0aVzZDnMPpEno+FhCNmvRrQ7xhBydXYm1/lY3L67HHNDuHm02hrBj2Ufb4PioLR1koSwKICqUi7HNSGM8jScw/iREDdDodTYgmi14WvIgZ+JQSf3ldFqnQSSilqFarXLp0iQ8//BCAhw8fsrq6qhlAFxYW2N3d5Q/+4A945ZVX+Id/+Adef/11bNvm/fff18Vcf//3f8+VK1c4PDyMY0VjIjDLipvUCJHY8yplCUI2m03q9bo2BgsLCzrwbcYKzHuYfH7WYu6uniaTHDrzPRlDyZSSBuySpmv+jnjLpnMgYyqPSh13mJPsoWKxyGAwYGNjg4WFBba2tnSPhUwmQ6FQYH19nb29Pd1/IJPJaG6e119/nfv37+uMHoGfpCG9OBdhGNLtdk9co8xLOWfpbic7UQnuS7GiadDkupNxHvN+noUhuBAGAJ7/gqxIoSZAmkopgtCj3+lS3dnBbze4vrJMNh1jpsPhkEwqjT02AI4Vwx4Zx4HRCDsd43lpRxHY4NjgWrA0P0en06E/aOOmbDYfbuOmHDY3N1lZu0ShUKSyt8sfvv0WlUqFuVKRx7sHfPvtt/l/f/oTQgWXr15mq7JLuz/gP/yHP+MXv3yXldWl8fZ/oPHLZqNteDpPW1zRseELFSifsNOlubfDo9Isi0sbeCpDOhOX0mfdsYfkjqsuVUigpIjuZMXhWcsXmainZZDI4trb29PetHj+UjWddBTES5OeueKNCdwgPC0Ctezs7GgsWfhbsuOg/OHhIblcjp2dHf7iL/6Cv/7rv9aZOpVKhW63y9e//nWq1Sqrq6vU63VdBzAajWg0GnpcnzeTQxSa9JW9f/8+rutqVkmBvMyioNPG8bzG9Xk+c9rYy05PFGC329XcScnvToK7xFM2DZHMlSAI6Pf7lEoltre3sW2bYrGoq4Jt2+by5cuEYUgYhroS+9GjRxo6knPb2dnh1q1bJ3rxyv2XVpMyrs8TB5HudFLkBmhHRs4/WTSXNAjy2lnIhTAASY8PiFtDKrAVoOIK39jfj7BVnN0SEZcxKQWW7UIY0W80aB5USAUj1l6/Tej79IOAQqkIqfGNsywsHBzL0phx6PukHJeR5WNFIRknQzblYqsIJ5/HXl+nmE3Rqh+yNaYOaB9WyeTiIhDXgnwmzXDUZ2trk2JxhrfeeotH2zvMzpfJFmNK2nv37vHtb3+bajVO+wujiH53iMyRL5zqpYAwQA0HBEd1dh7cAydHp9fDtcFZX0WpGItWUYitxqmhxiLSE+scIKCkpzjJK0xOavN18ehbrRa1Wo0oilhcXMT3fR3ok++K4reMcZWttwQQhWJXqZhb5tKlS7phy+bmJnNzc7pz1L179/j93/99nX20tbVFo9HgrbfeYmtrC8/zWFlZodfr8ejRI/7kT/6EWq1Gt9vVdAKlUolOp/PULlqnieDE7XabR48e0e/3dcbJ8vIytm2Tz+dPKENTUZgQ3FnKaYF6eS/52dOuTbx33/c1lCIcN+bvmMexLEtj7OJ9m+ci7SgHg4HmUxIWV9NRWFxcpNPpEEURS0tLLC4u6ibzkoY6MzPDN7/5Tba3t+l0OiwtLQFoqEbwfbn+0641Oa/FoDmOowvGTDJAMWLmeJqJMmcpF8IAPE2eNA7H/9sK3TVMIu0p2yYYDvn6K9fZevg5qzduMb9QxrHGnrVSuNYxpKIVhzUOlIpECjtSOApSlk3GdsmlM6ytrUEU6Xzx0WjE5uNHAKTSGfZ3q7z2xtfI5/M0mm0GftzSsbu7x+LiEnfu3KE3xhErezVs1yKVSp9ojwiyxTlt4Y49BOMViwiv1aBj2TTnd9ndyrG6WGauWMAtxvnJaTeFipmT9P08MYFfZKC+oJwG8yRFxlU8+tdff51Hjx5x9epV7c2ZW+Wk1yTYsPm78hnx8gqFAmtra3qhdTod3V6wXq9zdHSE4zh87WtfY3d3l3K5jOd5WFZcDLS0tES9Xuf999/Htm2q1aomBhN+ecGzv6hYVpwZI9ezvb3N8vKy5peRaxDvNKn8zzMO8LT/J8fpjsWsZu71eszMzJBOp+n3+yfoPJLHNJlDzV2PGH9zZyGEgdJZTXru2rbNzMwM2WxWk/o9ePCApaUlfS4SjBbKjlQqpau2hbpaFPek60+ed/J/2aXIPJHgvhgCIQk8DQKadNwvIxfCAEyaqKd5jVbiPe05hxG+59NtNmjW9nm3usftq2vkMzFGa97UiNhwWPY4AwFgfLOdcVyBKMQKQ7wx3a/lh3TbAwrZGWZn44KdXDZL7aiON/K5+/EnXL95m/d+c5e5csxMWSrNcnBwSL+/xUH1kFuvvkIQROzuVFDK4vr16zze3tLX4vvjIO8zJE7aNP6BWKkHHv7RPnv3RoT9I/LpFJlcAWtlDttyIa1I2xbKVmMIKaEszthTPG1cJwWCT5vMUujTarU4PDzknXfe4dq1a7rAyxxXfUsSkIEolGRGhuCuSsVUA0IzsLy8rCmH79y5o1ki5+bmtId6cHDAw4cPse2YRE6ydSCmEDg6OtLn82WVv9wXYQqVwGMmk9FZTbLLEX6a5L1/GjRxnvI8mLX08V1cXNQZV/Ldp1WQJ42DOYdNiMh1Xc2zJBlDUsQlPFLSY3d1dRWAbDarG/r0+32dHKBUnAAgFbyi+JMOyPOIfF4a3QwGA03xLfEPs4bhPCHaC2EAGMMPShm56CrEsuwY4omi8esWgQWOcR/s8boPw4isa5NPWRRcm163Q2p+jmzGpd9pURgXBYGNF0Q4KaPy1BaVqsg4LioMiYKQcNDD7TXxO206/RbpuVn8sE84GjAaeQRRRL/T5tXbt6jUaty/f59CoUS9FeOZv3n3fVZX19ir1vC8gA/v3GV1dZn9gyqlUobHW49Ip9IMh954MZ9MBzv1dp36DxCFBPVDqn6A74f4YcjB69/g6686bMwUyDo2OdvGdrNEVmBkU3AuENBpBkCeP0tEuYnHK7CK8PxICT6gIQEZV7OaWLKAgiDQSkD6BeTzeZ290e12cV2XbrfLysoK5XKZdDpNtVrVDJOfffYZa2trWqEI97wUANVqNZ1KKJDU84zrs+7lYDDQ0JNky7z++uu6Gb14paaRfd77/GXkhGN2yphOgobkvMwuXkEQaG5+qe0wDdikZBCZu2ZsR5RoNpvVMSLB+gHNFdRoNCiVSuzs7HDz5k09VtKMR6mY0vnw8FDPpYWFBd0I3nReknGJ5xVxgsTJyeVyGhKSHa/sYiRAfNZyIQxAEoqQx2ThEMTFq7pZvIlFRmOumzBibnaW11+9RbvdZmd7m+WVFVQgEwUsK5FCZxxbjosKcQgZDAf4gz7d+hGjZoPuYMh+pcooihh5PkdHdQr9IdliCe/gkOs3bvGTf/kpm1t72DbjlnU5PD/EtpTOFIiLW9LcuH6Le/fuf6EMkecRv9ehtl9h68HnjHBZzGcp37hOlLKx02lSeGCrE9DBWU+v0wzAJJqCJHxjQgjidc/NzfHaa6/RbrfZ3t5mZWXlhAJIBgSTvy1FQoDGeRuNBrVaTStV3/d1f1/pNXv//n1u3LjBP//zP2ulI4FDWZRSX+B5nm4M8/nnn5/I9T4L6ff7mnNoYWGBUqnEpUuXdDZNUkmeh/L/onDEE/E9gwJasHTpnyCpvuJZy2fNcTZ3N3Ics5hM8vwLhQLz8/PHDY+IHQDZcWQyMaRbrVZ1cx6JKYh3ns1mNc9Uv99ndXVV1wOchUIW5S4GanZ2VlNYNJtNOp2OzhySdXCWY3ohDEAyoi+PMthysyWAK2JCIZYK8UcerrJIuymikc/m559TtFLkUymWCnlC3yewLMDGTTu4tkMYBdgKbCyUbYOt4n7C/T6q1ybqNLBHfbqVXar9Lu3OgP3DI3brdTylIJMl3x1QrX+MnU7z/t2P8bx4y5/L5el0+3jd/vga4sWQz6dJpVwcx+Xhw4d6cj4NM/1CosAKI8P0QosAACAASURBVNRRnce//iW7W9sM61WG3/0By4uLbCzNU86mcVLj3sDypfBsIaCk9zZpXE2+n0kLShSEZVm6KvTBgwc6DU8qgMWQiPdkBl3NALFs+zudDkEQ6KCtBJmbzaaGVz777DOdyfPRRx9pKEcakOdyOc0cKZWiAsWcy7gSK9FOp8NgMNCFYq+99hqvvfYaKysr5HI5Hew27+FZy6TredYuwPxfPhuGoaZtXl5eJooinRMfBIFW/Kf9npkOawZmhamz1+vpuXMcY4vHsN1u645uMlZisKV6XGhDJKX34OBAG5ezjLGEYagLHXu93hOw5qTsoLOQC2EAkmRhZs626dGY2J9tGoJI4QCj0RAVBWQchw8/+BUHh/tYgxGDQY9Gt0157RJz5QWcVDomQlNxVkwURUQqAEvhOBZOaDEaDWnUDsjbiiAKqNYqPKhUWFxaIQhHDP0BmUKeRzs7LC6vMgp8Dg8O8CPF1atX2dx8TLstOeix9+l7Ht1OD9eNs5xUGLG4MM9gGFCv189soVowzukPiLoNRt6IRymXTHGeV9/4d5RKBXIuZHCMe6mworP1FiWwbY6rmeEhj+ZuIIlj23bc7lBw3N/85jfaYx8MBrTbbQ3VmIpAHk24wfd9PM/TxVVKxZ2chI1Tsn2EDRTQysnzPK5cucLm5iadTgeAwWCgm39ks1ldPZrL5SiVSrrS9ywXrBzL930ODw/57LPP8DyPUqlEqVSaCJV8VbsA+a1Jv5uM6Zm7AcdxuHHjhk7XlYCtmTJpwixm2qvQRcixRJlLsDebzer5I3AfwMHBAalUSndfE7I5IZSTuSPzrlgs6gIzIXNLxl1eRGRHIwFmQDOcShbZ7ywEZCoFUxmY23m9O0BhWTZKxUmgtop3AVEUYTuQyaTodtsMBj1s22Yw7NFqN7mcvq3xxUwmhyLCYuyJWoqIOKtI/EbbdnHSGaIQUrkcgVI4KTfme0GRTtksLszTH/gM+h3K82UcS9Hu9um1W6SdMVshitEoxPc88vn0eEI5hJFPOhUHqJvNJjdv3uTRo0cvjBXre0ocTraBMPDptxoc7u9Smpvl6voy5cISafFgZEGeQwxAAm7wZOZGctcnr524jvF3MpkM3W5X52NL9e7NmzeNcc08ccwkTi04scANokREqQuJ3GAwYDAY6DRTUSCA/h0JJkvbP2lZKdfQbDZRSunUxLMW8Rqr1Sq7u7tcunTpBPGc3IezNgBJIz3pvWRQNKm8zPvkeR4ffPAB6XRa329JtTTpHoAT88eyLF2ElcT7JcVTOooJtCfGXrKParWaZoTt9Xra+TQD96Kf5ufnyefzDAaDE0bpy97f5JwXg9NqtSgUCjoeUCgU6PV6Zw4TwwUxAIL5JZW/eIAi8Q0/fu5ILYBlYTmxB6Bch6vXr5FyFPu1Cq6KwFb4oUcq5ZAZt2bL5sbc41EQV9T6wXgnEvcddtJpMsU5VDDAyRZZv3Kd9Ws3qddqtJsd5ufnWVxbYSZXoHJQZWZ+ka/dusHA99mtNriyvh4XMA1i+Kff72ONA3SplIvtQP2wwfZ2Bcu2+fzzz1/4PkpyqxpnEsXGLMKKfLz2EZXHn6MIWJqfoZjL4ubH5+O6MTx1DjuAZOUmMHFck3CQ+bp4ideuXcNxHI2BS3BVCrik2xIcL9ogCHRNgEABhUJBY8bSZ1e6dRWLRS5fvqw7PQl/jGT+3L59W3PFC9YsHps8393d1cyeCwsLmrzurEWpOKZUqVT49NNPmZ+fB9A4tijPs1Yapx3vWdCPKaJoxev1PI/hcKg9evkz2yMm5xGchLfMeMtoNNLkeyYkKMrfRBm2trZOcCxJQFZSkOX3BO5zXZfFxUUODw/1fPsyclp2j5yDENklayOS1/0iciEMgD8eJFvwYQCkvSNEkZH/b8eFX/a4iXlsCCAMFZaKyBSzuKrM7bki5f0VsNO46RSz5VW8MAJ/hO06RL4F9rjikhAviNPB0qGNH3oo1yI9UyClsgS+zzff/EOCTp/OaptOr0vgxOp2cW6JQirDyAtYLy8wVy5z6+oInFihDb0Rh0dNGq0m3WHMQ98fDogii1JpjmYrDgqLAXwRT/F4Gp2cHDYuajSit/2QvW6bB4tLzK1dwZ0vsWSnSRHg2BbRGYeBZfGZCl8Wn0xseU0ezW0+cGLhAszMzLC0tKQ9+XK5rJtvmAFE+Z4sWjP4LEo9CALefPNN3Sms2+1qBVQul6lWqxweHvLaa69h2zbXr1/Xhmo0GrGzs6N7C0jDEqXiAjNJLxRq6vPYAYiiaLfb3Lt3Txcq5XI55ufnn6gQPi+ZtNtKvp/8X+aE2UfZ8zydnSNQCxxDRpOqgaWoz4T95Pdc142LLce/IWyd6XSahYUF5ufn6fV6uhWk1HZIEZ9UWgsFRLfb1f0ZJGtpMBi80L1LGjS5Bgn8CgNsKpWi2WzqncmkOokvIxfCAMiEOPk3OTsoxnvU2BCoE8VgkkPvOA6jQbyFLBTi/G5LKVJ2rIxCPyBSNsqOA5+BPyJSY/DHPb6xjuMQhYpUOssgG/edXZyfxWk0GHgjsrkc6ZlZBp7PYb1Oq9VifX2d3sin3jjCdh2Omm1y+RyjbI5RGFGr1XBTaQ4O9vE8Ia368nnizycxXIZSeP6Q7cePudU4pGGFFDIZ0riE9vl4ik/7k8+Yj6aHk5zkjuPobX6hUGB2dhbgROaGKGgTP4aTC0Y8QvEybdtmY2ODRqOh8fxisXhCGSwtLTEcDqlWq1qR5HI5bXREeVQqlRMc9l+FyLUKcZ1kr8i1nrcBeBo2nYSC5PMyB8xeByZ3k4ypeOXJnYBJFCePptMgDLGySzN/W+g51tfXgePOY9LAXcgCS6XSCXppIYOTc5CYz4vc30kQnZyv1AIUCgWd8NDr9c40E+hCGICJ0W3R9+pkVpCNrVsbxjLG4aIIUChl4boprFQaK5XWQb+13CX63R5uOl7wQ1vIpWLoxB0bHC/yiHwP5QWoMIobrNs2lpPHyrq4qRQ5N4vTG5ByXVK5GRw3S2Fvhwef3ycc+VxdWmImnWK7sk84GvLp5ia5UpGP7z+It+2dPsq28YOTxS7nka0R30qFIoIwJOxBrbLJpx++R/r1r4+bcOTJpx0c+2x3AKddj+DvZjYQnFzMcOy1yyKR5iiiXGULLlkT5g7KPKZg9QIHiUcoEEOydZ94faIANjc3mZ+fZ21tTWO04q3ats29e/cYDAY6W8lMDz2vMZX7I4+DwYD9/X1KpRLr6+t6h1MoFM7MW0z+7vOmg56mrGRdS7ZPp9NhY2NDY90SBzAVnjxOyhqTAi0x8BI7kh2icPiIYvU8T+/0BH6SZi1CB27CRWba6XkaVfkdGVeZ1zJPBdo6C7kQBgAJQKpxf1rQEAo8GXSybRvpXqhkckdSSOaQLRSwLQc/iNh5uBlnBhBzfuTHRTOjwMPzfTLZFCoMIAhQUUTKdsDzCIYDwtGI9OwsobJIWSlSmRyhinDsFFY44LBWY2Zhllwmy9rKKgQhH334AX/0h9+mlMlQzGbY8wOiKODBo4cE43OMrLhyWa7nvEUziSrA9/GbdSqf32XQ7RMRcvv6TQqZDIX02RoAUdzJKl3giSwv87kJJ5jYr7R0DIKAzc1N7eEvLy9TKBTI5/M62CdkcbK1lwCt5OoLL4xkKUmswbbj3szz8/MEQcDq6iqPHz/mww8/5Nvf/rYuNBIyMOkLKzCPXNdXMa6mSH+Chw8f6mKwK1euEIahrpo+a5l0jcm1epqREMNsHsNxHBqNRsxbZRgAMf7mbsZM7ZX/ZcclOzBRorIbmpmZ0fG4RqOhPf9r165RrVb1MWw75lgqFosnMnBO202dx1ib8Q5JjZUA9KR19WXlQhiAJCwgr5nbK/N9a1z0ZUmQgJODEESgbAc7nWZmLi4wKRaLOGMvUecLE9IfhrgootGIyA8YhQGpKMAfxC0bA88nsh1G4YjIAyyLkTcgJKQz7FK9t6fJyQqFuIz73XffZWVtjWazTavTpt8f4gUhvd6AKIJUOhXTOZ9DgG6ijAsmdLHvYEhtZ4ujvs/i4iJzM/PY5TLuGU+HSdtbUylMGtsk9GN+XxS6NHzX4zpW3GYeuDTuhjhHXzw+USiSPSJGxLaPewX3+31qtZqGmJaXl2m1Wrz77rusrq5q2odWq6XbCIqCEGXxVRsAQLcvfPDgge6AdfXq1TNPH5x0fZN+47TfNb8vmTyifOv1uu6fm81mT5D3ybiZO0i571IxLvNDOnCJkpT+ENJXwbZtDg8PqdVqrKyscPXqVe1tdzodvbsTqmYzkHwe6ZiTRCml56jMMdd1yefzmkbjReVCGADT0z+Gg57MFlFK4Vo2oXWMDSIKJYpzOFUEIxXFUE1pDse2dK6/5dg6dc6JfIbeCMtS+FGE3+mjgpBUWjEadFFeQJRK4VkWbiZLo7qDGvm4toMX+CgLeu0aB3t7tFtx/95UKoOdcml0++zdvUtnNOLe1jbdoU/kOkTEaHzsLUqRlEMUnX2AcJKMWY+IwhC/3cIPYefxI1aXLqEiC3dx/kx/b9IODo7H1TTypsdjBhXNLAjZBkuOt/yGLM52u63hHhNeGAwGmuZZFrVSca9goXiYnZ3V1cHtdpu9vT2d9pnNZjVl8XvvvYdScSMZMTJyvmJUTA/3qxL5Pali3tzcBOLg+fLy8pn+1rO84KSCT7436TmgifkEroHjOgETipHXxCGIokgbXjEmAhUCJyClWq2mvelyuaw5piR9VqAVySoTA3QeQfzTxLxfksLaarVO8D8ldeOXlQthAJSKUAri9RJn/DhY8nT8GbDsY+UP48k1VhRWFGLhY4974BIpMm4Gt1SKg3T9GOOLgpBhv8+gWaPbalPK5/D6PbrNBpYNrmuTCkK8QZ+ZwgzUjijOlKg+ekSv1wPL0X09vU6HartDcNTQqYZKKWqtRpw6aMHC3Dze0RE9PyDlWOBAvzfATTkoJVDIs9g/X/QGxw+hHF8Bfkiu06G1+4g78zM0+i0y1mtn+7PqZLm+yCSoZ9LrSeMgz6WAR/BcwfWlWXy73dZ9ADqdjvYc2+22/q6kg25vb7O9va0brQju2mw2dYN36fFbr9d14dD8/Dy7u7sAJ+AlCV5+1TsAczfleR4HBwcopTRsdpZyWlDXPJdJ42qOpRhh+Z7J4QTobCwx/DMzM1pBixfveZ5W1BJ3EeNg3hPf9/V8ksC/ZVm6t0K/39fB1eFwSCaTIZPJnOD7MZ+bFe7nIcn4hjl+uVxOZ8QJ+eCLyIUwAJM8JeuUlMTkxNJ/UYSlwFIRIQobhVLHRUgqlYJo3LYtitg/qJLLZmm2W3z4q/dBheTSGT785CNevX4dK4jIZjL4CmzLYbuyw3A4olgsUl5aZDjwGI1GPNjZ1g29y+VyvJVVIU54HGxcXlhkc28fL1Lk82nSaZvhyDcWwFezpUyKh8fwoIJnpxjU2+TtJ9s0vogk4Z7ToB4xFJNSFk+Dkcz6AFP5CZFXOp3m448/1jjvw4cPKZfLFItFvXAymQz379+n3+8zHA5ZWVnRvXoPDg40aZykIyZrDAqFAu12W2ewSBzgt2EATImiSGPc8/PzLCwsnOnxk0Hg54GDkq+bwX153dzVyaMYl2azyWg0YnV1VVd0Z7NZXQQoMR2z77NUags1hhgKyd6R49u2rat/JcZkZvjILtOk1zhPGOi0+WOmqppZcC8iF8IAmMpBKwPLnggNnWYAUBGOUkQROLaNsiBE4chkdeJeutH4N3IzM8wUC/z6l78kwEIFEds7j9ivN8jmC4x6PfL5AsNRDAt0hzEXSHBUZyOK+VjS6TSH7Q6WG/O9V5uteLKlXcJOb7yriXAzaeZnZ/AcqfZTZLPSBu68U0BPl9BSMOjR3z+AIOT+TOlMj28adrO4Lwn1mI8mJUQyBmR6jfK+CbtEUUQ+n6dUKvHee+/RbrdRSnHv3j12dnZYW1vTRT4ScJQ0wTAM2djY0OO6v79PLpfT/0sKqqmkTFZIOQdJZfxtGgCllOY8+uSTT75UM5rn/Z3k+EGCpHHCd0xJfs/MDhOJooher4frxpX4MiZSrGdW+wpEIvEek1NH5o55P+RzAgEJHYRZvS7ZRSa1yW9jfCWtVSAvgbheRC6GAQjCmKN+/GfZNpF9ciIlFYZZim1ZVkznECnsKNIVwrZtEUbhMZOgY4NSpPJZVjauYhGxsLZOJpvjs08/4ajTp9bpkTo8ottrs1S2ODxqxK0F54r0e3HRR+PBfcRrP6i3KZdn6PT79EeKteU53CiMyaaabfKlOJNgoTzHYb+nOd0HQ+8ZgbO4FuK8xY0UdJt0Qp9H6bPdAYhShJO7ARlDUfTJmIA5tiLJ78sikHQ/yZS4dOkSACsrK2SzWTY3N2m1WrRaLVKpFIPBgIWFBc31PjMzg+fFu7l79+5pGufDw0Pm5ubo9Xp4nsfS0pJWAMPhUBd7lctlHSuwbftE8NmU07zl85QwDKnVaifgi7OQp0F6p31GXkvuAuDJoib5nFnPYVbtikEWojfRB3BMpy4B0m63e8JTlvoBoZkQ5d7tdslkMvR6Pd1vQdKBwzDUlcFnmYP/ZSQMQ31u5o7ky8qFMABJCEgsddITTCqME1gxURwIVjAKg7gBuhtic4zXBeF4ew44uSyB53P5xk12H28ys7iAF0UMVYTnDyjNzmOlXQr5seJX8Q3PFfKMRjFjH0A6m6Y/9AiVhWMruv0hGcemUCixemmdIIhIZ3KENnpiSZei02KE8bXFNQ3m5D77G28RWWocOe/R36uc6eGTsI+8pn/egIEmFX4lj2XGWUwvzFyUAgksLCwwGo10t65+v89oNNIl/9lslmq1qj3CTCajGTbFE5RSfMkMmZmZoVwu0+12daqgFDIJZbBUnp6WKZOExM5DzOP6vq8hyvOW0zz+pPGbZDCS34GTuLs57p7nUa1WNUWHeMIyfyT/X5r+CKeT7PTkcyaLrGVZNBoNzfBqEsyZNCWTxtVco5PqE85K5HdFf5zFzu5CGAAT+oEnCaWSOwAzCq6NgBiASGGPaZexIQrGxwSNz9q2jbIcLCckX5phcWWVw8NDOt0+G+trDAaxZ5HL5RhlPXLZLIEV4M7OkhsHYB48eDhmHcyPqxcjCoUMoCiVSoSeT3n1EpXqAZl8jt5ooD1Ny5IS/egJL1+ux3FslLJOVSZnIVbkEBGBFYEKYfjiaWWmJPP/zR2djKOJw5rPJylLSd+zLOtERyZTUQtMtL6+TjqdZn5+noODgxPtGQXauX79OhIglsDa48ePGQ6HlEoxHNZsNrWiEZx4Y2NDF13JQhQaCZFJyl/OHzhXbzJ5TKmuPevfSK7T064luQs8DQZKwkamwZDg+sLCgjba0ntZMoPEm5fAsVktLCRw8p4YCPGipUE7oIn9BF4UJtnTrk/G9qsQ8xyEbuRF5EIYADOqLgtZPEL50xbWARWNFUZ4fPN1ADgMCaOQILDJRGlSVnzTgtBDjY8ZAXh+rHwVZLJ5bt14lSsbV9l7/IDm4QEp16bdbrPx6qs8fPiQTq9LubzIzNwcXhCST2VpdmImxpXLGwxGXnzucbt5lG3RGfSJHBc/glZ3QBg5DIYhjgNBYHr10Yl7AWBZDkFwvvEBBbHyJ85DioKzpS5IBvqSRtwUM9XPNAQiAidJcZAcSxSA/C8LVSmlu0L90R/9EVtbW7RaLSDOnnj11VfZ3NzUXcWk8Gt+fp5KpUK1WmV5eVnzBglTpBQS2batGSTFeTGhikn3Ao4J7L4qMY3lWR930nN4Eq5LfiYZxzFfN9f9pGY/gvVLzYaki0p2j4yBBOzld8RQC+237CaF7x/iQjGBFoEThWVJOgnTKRGRuXteuzvzPjxrx/y8ciEMgNkPYJKYk0VxEi/WEwMLS0VxJXEYQqSwLB/bkYIxG8tFd/2SgYpUrFhiiggoz86RsRX1epxN4gcj5hfKDEZDeoMBynJotJq4mQyLmUWclMvi6hq7lT0ODw9Zv3RF9xHd3qsQWVBvNknlsgxHoeaOOU3EgzU/c15QgbCHonSm6JmKTNJJGT7mIknCOTIPkqX3Zt63fA+OoQJ5L7kll8Ix27Y1HDIYDHQTcCm2kQKkcrkMcKIHsLSerNVqWolIkFk8zacp2mSGi1zT76JM8uInXas5vhIANo2pZN/I+/LZVCpFqVQa05jEIoRupVJJV4MvLi5qKA/QDVeEW8fM9RfiNYEJhT5CjMBp4ya6S4zJecvToKgvIxfCAJyW4WO+fgz1PBkfiL9nxe0io5jzJmb4jwngHF1MYKOUZIyMjQBx2qhSigiFFcVby06zRb1RJ5XNjIuJRnidHge1Q9LZHN2jo7giL4L9/X0qlQoRxM1KVIgXBRy1mqRyOex0inanp3/zaWLi5l99tsHZxhomtfEzjbYZ7DWfPzm2J1vnSdZGskhQlPUkIyDHaDabmp5ZskUODg7Y29sjm82yt7eni5E8z6NWqzEajSiXy/p429vbZDIZjcU+71iZ1/xVjet5QUxPw/tPe/9p52XOexPum/R5MRaSsikZPFK0JfCNOFIC88zNzekAqsyxcrmse+92u10cx6Hdbp9I/33a+JpG67zX7GmxtBeRC2EAzOrJZ0EGNpMLhrDtGOJRikhZsWfvK0IXcBwUFlFg4HjheKs2DhIHUUQQhWRyMV94tpAnatS5f/8+dsoF5VI9rJPOZdk+2CdU4xREFXuH+eIMh4eHtLsVQgsKhRKRY9HqdghVRLE0S7fde6an8CIe4iTP6/m+KDQRZ28AkhW7kwL5cLy9PQH3GfCfqdRlsct1mtlG5hZcPm9CAvl8nnq9zvb2tl681WqVfD5PpVLBtm2Ojo50cLFQKDAcDnUTdsk3lwKzUqlEp9N55sL/rYwrZ+cpnnY+5munwT2Tnsvnktl8k+CjpCR3FcKP4ziODsybvExmh61SqYRSiv39fZ3hI3NMYklCDChB4kn1KSLJONd5irkbPiu5EAbAhAP04iUEZWNbNmARKdn+W5MNgLKJwjBOBApDiAJCy8IbgW1LBexx0M2KxjsBFRCEIUEUEkYROA4LS2vYqTSXItit7MdVgakUneGQMFKkMhlUpKjV6+TzeWr1OHugUCoS9Pr0el28oImTSjNfKNJsdei2O0hWz4ss6LMUm0h3QFPEu6uzluRu5jSFkFzUpnIwC4PkGM9S+snXoyjmDZK0QPlMJpPRnqGMS71ep1Qq6SwSwZUF7slkMnGLz7HXOAnK+l2WSYr8ea77tFiBufMzaz2edkz5TUmskKK9dDqtc+SlwMuyLM0EKjs2MdyS2itBZDEcQv8gzsfzKPfzHvvkLugs5EIYADipAOLnxwv3BASUCBRqA2BFRNE4EBwEqCAEK2LAmOGT8U0b8/7bKh7Y0BpnDaiIKAxxMxkcz2OuHJNpOW7MG9Ie9Kg1m+A4WJkMzXab1fU1+v0hmTHtdKFUZHTU0CyCbtqi3x+ysLDA3t4+MmbPSiv7bYhS6szrkU0FbXryZjHNiThOIrZjwj/AiSCreHzw5D0UxS/FO0EQnGjbWCqVqFarlEolut0uqVSKdrutK0jlUbz/crmsqaflHERJCEkXnBxX8/p/m/JVnENSMU0yEObnTvPwTRgledxJvylzQMZWDLb5vsTSZC5Itpf0HxZIUYxGNpul3W7rWMCk87wochZjeyEMgCIELCzLRqEIoxBH+v5aVkwLYR3/KUud+B/LIrJia20pRRQFhOOG7yMvwLfjfsECKVqWwgpCwsgn8gNspXCCgKybxveG+LbCymcp5lcpLq1jY1EatCGV031pL61t0Om06A98OoM+xVR8K9cWlxn5HsVMjnqzQa8/pD/skcmlSDlZjTeey338ghMihBPR37NWFUnPWJS8GfSftP03/5LwjyhYc3tu5nKLcjYXvlR3Cl68tLTE/Py8bj4+OzvLwcGB7uwlpHCe5+ketZYVd99Kp9O0223dMUqCicIlcx5yEQyJKclYHZzs6XGawjRfT1J4mPNDJAm9JDl+lIorr6WJi6SFCnvoaDTSBV+AJgYUCglJsTabwEu7x9FopIvCTKN2ljDMpB3R88rvVAzAUuO62ih+Ylkx5HM8AawnFEPyDyn4MiaT4ji+oADfj5WCZSmcME4NDT0fWymah4e0Gk2icMTH9z5lbmGe27deYaYQV4NmsmnW19fpdrscNo7GpFQzuKkcB/VDNrceo5RieXWF7e1tBt6ICEWxmKfd7eO4Sistk1r2oi3u85CkIRDF/jxpbEk4wAwqi9KRhSzHF68f0OyeQRBQrVZJp+NxlKIt4Z6HOF++UqlQLpe1AahUKhQKBbLZLPv7+3r3IhBBErdOQlm/izIJuvuiXrJpMESShsXcBSZfl/iMELdJ7KbVaun1NRgMTqw54QcyM4Ek20goI6QbmG3blEolTRF9WkD6ReQizJELYQBEkjdEY8ATsEL5fNIAEEVgQEeh4MBAGAb6s1EUK2Tf84iGo7hRSODTaTbo93v0hn1y2TxXLrkEnk+r3aVYmo8rSVs2W9vbbFxeBzfGg4vFIo1Gg8PDQ1LZDMq2GPkezXaXbDYVn4eRpfIySVJhmN69iCjWZHxHvpPE880mHcmgukABnhdXbItiODg4oN/v0+12uXLlis4lz+VyGg4SA1Mul/E8j06nQ7PZRCmlIR8pDBIvVoKHL4PyP02eFwaa9PlJ98uMHcn35bnQcUhFthjkYrGoO2aJ5y6JB7Zts7KyosdS6kUKhYLmAxIxG8lLT4GvOnvrq5ILYQDMxTMpwBsjPvK/Ak7WDJiDEhc0HQdvnPGgxSRwEgSOIIgbRAwHA/B8LDdWQDIZhiOPX//61xBCykkzGHYYeRHKtmi2WhRmSnz40UfcuvUaQwPWaXbaNFpNLGycdIrV1RWGo7gc/zSv53dtUolMGlcT55fXBbpJRkA2oAAAAYlJREFU1oIk74vgtcniIPN3pPGHeHJyfDEIw+GQX/3qVwyHQ/L5vFbos7Oz2nu8c+cOt2/f1vnow+GQ0Wh0whCsrKzQarV0BkryfH+Xx9WUZ+H75vvma+aYmVBP8nOTjiGBWlHwUt0bRRG5XE7vzmTnJmMvFCGe59HtdjWsl8vlTnxGYgtSs2PuLn/XxPpdvKipTGUqU5nKs+WrIbCYylSmMpWpXDiZGoCpTGUqU3lJZWoApjKVqUzlJZWpAZjKVKYylZdUpgZgKlOZylReUpkagKlMZSpTeUllagCmMpWpTOUllakBmMpUpjKVl1SmBmAqU5nKVF5SmRqAqUxlKlN5SWVqAKYylalM5SWVqQGYylSmMpWXVKYGYCpTmcpUXlKZGoCpTGUqU3lJZWoApjKVqUzlJZWpAZjKVKYylZdUpgZgKlOZylReUpkagKlMZSpTeUllagCmMpWpTOUllakBmMpUpjKVl1SmBmAqU5nKVF5SmRqAqUxlKlN5SWVqAKYylalM5SWV/w+vJEXojaZHSQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from imageio import imread\n", + "from PIL import Image\n", + "\n", + "kitten = imread('notebook_images/kitten.jpg')\n", + "puppy = imread('notebook_images/puppy.jpg')\n", + "# kitten is wide, and puppy is already square\n", + "d = kitten.shape[1] - kitten.shape[0]\n", + "kitten_cropped = kitten[:, d//2:-d//2, :]\n", + "\n", + "img_size = 200 # Make this smaller if it runs too slow\n", + "resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n", + "resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n", + "x = np.zeros((2, 3, img_size, img_size))\n", + "x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n", + "x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n", + "\n", + "# Set up a convolutional weights holding 2 filters, each 3x3\n", + "w = np.zeros((2, 3, 3, 3))\n", + "\n", + "# The first filter converts the image to grayscale.\n", + "# Set up the red, green, and blue channels of the filter.\n", + "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n", + "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n", + "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n", + "\n", + "# Second filter detects horizontal edges in the blue channel.\n", + "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n", + "\n", + "# Vector of biases. We don't need any bias for the grayscale\n", + "# filter, but for the edge detection filter we want to add 128\n", + "# to each output so that nothing is negative.\n", + "b = np.array([0, 128])\n", + "\n", + "# Compute the result of convolving each input in x with each filter in w,\n", + "# offsetting by b, and storing the results in out.\n", + "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n", + "\n", + "def imshow_no_ax(img, normalize=True):\n", + " \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n", + " if normalize:\n", + " img_max, img_min = np.max(img), np.min(img)\n", + " img = 255.0 * (img - img_min) / (img_max - img_min)\n", + " plt.imshow(img.astype('uint8'))\n", + " plt.gca().axis('off')\n", + "\n", + "# Show the original images and the results of the conv operation\n", + "plt.subplot(2, 3, 1)\n", + "imshow_no_ax(puppy, normalize=False)\n", + "plt.title('Original image')\n", + "plt.subplot(2, 3, 2)\n", + "imshow_no_ax(out[0, 0])\n", + "plt.title('Grayscale')\n", + "plt.subplot(2, 3, 3)\n", + "imshow_no_ax(out[0, 1])\n", + "plt.title('Edges')\n", + "plt.subplot(2, 3, 4)\n", + "imshow_no_ax(kitten_cropped, normalize=False)\n", + "plt.subplot(2, 3, 5)\n", + "imshow_no_ax(out[1, 0])\n", + "plt.subplot(2, 3, 6)\n", + "imshow_no_ax(out[1, 1])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution: Naive backward pass\n", + "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n", + "\n", + "When you are done, run the following to check your backward pass with a numeric gradient check." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_backward_naive function\n", + "dw error: 2.2471264748452487e-10\n", + "db error: 3.37264006649648e-11\n", + "dx error: 1.159803161159293e-08\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(4, 3, 5, 5)\n", + "w = np.random.randn(2, 3, 3, 3)\n", + "b = np.random.randn(2,)\n", + "dout = np.random.randn(4, 2, 5, 5)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n", + "\n", + "out, cache = conv_forward_naive(x, w, b, conv_param)\n", + "dx, dw, db = conv_backward_naive(dout, cache)\n", + "\n", + "# Your errors should be around e-8 or less.\n", + "print('Testing conv_backward_naive function')\n", + "print('dw error: ', rel_error(dw, dw_num))\n", + "print('db error: ', rel_error(db, db_num))\n", + "print('dx error: ', rel_error(dx, dx_num))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Max-Pooling: Naive forward\n", + "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n", + "\n", + "Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing max_pool_forward_naive function:\n", + "difference: 4.1666665157267834e-08\n" + ] + } + ], + "source": [ + "x_shape = (2, 3, 4, 4)\n", + "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n", + "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n", + "\n", + "out, _ = max_pool_forward_naive(x, pool_param)\n", + "\n", + "correct_out = np.array([[[[-0.26315789, -0.24842105],\n", + " [-0.20421053, -0.18947368]],\n", + " [[-0.14526316, -0.13052632],\n", + " [-0.08631579, -0.07157895]],\n", + " [[-0.02736842, -0.01263158],\n", + " [ 0.03157895, 0.04631579]]],\n", + " [[[ 0.09052632, 0.10526316],\n", + " [ 0.14947368, 0.16421053]],\n", + " [[ 0.20842105, 0.22315789],\n", + " [ 0.26736842, 0.28210526]],\n", + " [[ 0.32631579, 0.34105263],\n", + " [ 0.38526316, 0.4 ]]]])\n", + "\n", + "# Compare your output with ours. Difference should be on the order of e-8.\n", + "print('Testing max_pool_forward_naive function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Max-Pooling: Naive backward\n", + "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n", + "\n", + "Check your implementation with numeric gradient checking by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing max_pool_backward_naive function:\n", + "dx error: 3.27562514223145e-12\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(3, 2, 8, 8)\n", + "dout = np.random.randn(3, 2, 4, 4)\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n", + "\n", + "out, cache = max_pool_forward_naive(x, pool_param)\n", + "dx = max_pool_backward_naive(dout, cache)\n", + "\n", + "# Your error should be on the order of e-12\n", + "print('Testing max_pool_backward_naive function:')\n", + "print('dx error: ', rel_error(dx, dx_num))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fast layers\n", + "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n", + "\n", + "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n", + "\n", + "```bash\n", + "python setup.py build_ext --inplace\n", + "```\n", + "\n", + "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n", + "\n", + "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n", + "\n", + "You can compare the performance of the naive and fast versions of these layers by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_forward_fast:\n", + "Naive: 1.647736s\n", + "Fast: 0.032071s\n", + "Speedup: 51.377958x\n", + "Difference: 4.926407851494105e-11\n", + "\n", + "Testing conv_backward_fast:\n", + "Naive: 0.889276s\n", + "Fast: 0.019460s\n", + "Speedup: 45.697725x\n", + "dx difference: 5.021244662145216e-13\n", + "dw difference: 5.155328198575201e-13\n", + "db difference: 0.0\n" + ] + } + ], + "source": [ + "# Rel errors should be around e-9 or less\n", + "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n", + "from time import time\n", + "np.random.seed(231)\n", + "x = np.random.randn(100, 3, 31, 31)\n", + "w = np.random.randn(25, 3, 3, 3)\n", + "b = np.random.randn(25,)\n", + "dout = np.random.randn(100, 25, 16, 16)\n", + "conv_param = {'stride': 2, 'pad': 1}\n", + "\n", + "t0 = time()\n", + "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n", + "t1 = time()\n", + "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n", + "t2 = time()\n", + "\n", + "print('Testing conv_forward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('Fast: %fs' % (t2 - t1))\n", + "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('Difference: ', rel_error(out_naive, out_fast))\n", + "\n", + "t0 = time()\n", + "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n", + "t1 = time()\n", + "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n", + "t2 = time()\n", + "\n", + "print('\\nTesting conv_backward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('Fast: %fs' % (t2 - t1))\n", + "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('dx difference: ', rel_error(dx_naive, dx_fast))\n", + "print('dw difference: ', rel_error(dw_naive, dw_fast))\n", + "print('db difference: ', rel_error(db_naive, db_fast))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing pool_forward_fast:\n", + "Naive: 0.538419s\n", + "fast: 0.004911s\n", + "speedup: 109.641841x\n", + "difference: 0.0\n", + "\n", + "Testing pool_backward_fast:\n", + "Naive: 0.670326s\n", + "fast: 0.016390s\n", + "speedup: 40.897667x\n", + "dx difference: 0.0\n" + ] + } + ], + "source": [ + "# Relative errors should be close to 0.0\n", + "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n", + "np.random.seed(231)\n", + "x = np.random.randn(100, 3, 32, 32)\n", + "dout = np.random.randn(100, 3, 16, 16)\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "t0 = time()\n", + "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n", + "t1 = time()\n", + "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n", + "t2 = time()\n", + "\n", + "print('Testing pool_forward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('fast: %fs' % (t2 - t1))\n", + "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('difference: ', rel_error(out_naive, out_fast))\n", + "\n", + "t0 = time()\n", + "dx_naive = max_pool_backward_naive(dout, cache_naive)\n", + "t1 = time()\n", + "dx_fast = max_pool_backward_fast(dout, cache_fast)\n", + "t2 = time()\n", + "\n", + "print('\\nTesting pool_backward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('fast: %fs' % (t2 - t1))\n", + "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('dx difference: ', rel_error(dx_naive, dx_fast))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional \"sandwich\" layers\n", + "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check they're working." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_relu_pool\n", + "dx error: 6.514336569263308e-09\n", + "dw error: 1.490843753539445e-08\n", + "db error: 2.037390356217257e-09\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 16, 16)\n", + "w = np.random.randn(3, 3, 3, 3)\n", + "b = np.random.randn(3,)\n", + "dout = np.random.randn(2, 3, 8, 8)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n", + "dx, dw, db = conv_relu_pool_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n", + "\n", + "# Relative errors should be around e-8 or less\n", + "print('Testing conv_relu_pool')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_relu:\n", + "dx error: 3.5600610115232832e-09\n", + "dw error: 2.2497700915729298e-10\n", + "db error: 1.3087619975802167e-10\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 8, 8)\n", + "w = np.random.randn(3, 3, 3, 3)\n", + "b = np.random.randn(3,)\n", + "dout = np.random.randn(2, 3, 8, 8)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "\n", + "out, cache = conv_relu_forward(x, w, b, conv_param)\n", + "dx, dw, db = conv_relu_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n", + "\n", + "# Relative errors should be around e-8 or less\n", + "print('Testing conv_relu:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Three-layer ConvNet\n", + "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n", + "\n", + "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sanity check loss\n", + "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss (no regularization): 13452.493162062709\n", + "Initial loss (with regularization): 424791.4931620627\n" + ] + } + ], + "source": [ + "model = ThreeLayerConvNet()\n", + "\n", + "N = 50\n", + "X = np.random.randn(N, 3, 32, 32)\n", + "y = np.random.randint(10, size=N)\n", + "\n", + "loss, grads = model.loss(X, y)\n", + "print('Initial loss (no regularization): ', loss)\n", + "\n", + "model.reg = 0.5\n", + "loss, grads = model.loss(X, y)\n", + "print('Initial loss (with regularization): ', loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient check\n", + "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 max relative error: 1.883892e-08\n", + "W2 max relative error: 3.007267e-06\n", + "W3 max relative error: 2.845685e-09\n", + "b1 max relative error: 4.254413e-09\n", + "b2 max relative error: 1.918815e-08\n", + "b3 max relative error: 1.262379e-09\n" + ] + } + ], + "source": [ + "num_inputs = 2\n", + "input_dim = (3, 16, 16)\n", + "reg = 0.0\n", + "num_classes = 10\n", + "np.random.seed(231)\n", + "X = np.random.randn(num_inputs, *input_dim)\n", + "y = np.random.randint(num_classes, size=num_inputs)\n", + "\n", + "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n", + " input_dim=input_dim, hidden_dim=7,\n", + " dtype=np.float64)\n", + "loss, grads = model.loss(X, y)\n", + "# Errors should be small, but correct implementations may have\n", + "# relative errors up to the order of e-2\n", + "for param_name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n", + " e = rel_error(param_grad_num, grads[param_name])\n", + " print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overfit small data\n", + "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 30) loss: 717199.768614\n", + "(Epoch 0 / 15) train acc: 0.080000; val_acc: 0.078000\n", + "(Iteration 2 / 30) loss: 463639.649887\n", + "(Epoch 1 / 15) train acc: 0.160000; val_acc: 0.110000\n", + "(Iteration 3 / 30) loss: 343272.029774\n", + "(Iteration 4 / 30) loss: 292669.387393\n", + "(Epoch 2 / 15) train acc: 0.280000; val_acc: 0.141000\n", + "(Iteration 5 / 30) loss: 312064.125680\n", + "(Iteration 6 / 30) loss: 384598.616036\n", + "(Epoch 3 / 15) train acc: 0.290000; val_acc: 0.141000\n", + "(Iteration 7 / 30) loss: 257563.263947\n", + "(Iteration 8 / 30) loss: 257558.899173\n", + "(Epoch 4 / 15) train acc: 0.400000; val_acc: 0.152000\n", + "(Iteration 9 / 30) loss: 95502.912259\n", + "(Iteration 10 / 30) loss: 180207.861385\n", + "(Epoch 5 / 15) train acc: 0.490000; val_acc: 0.170000\n", + "(Iteration 11 / 30) loss: 41301.588823\n", + "(Iteration 12 / 30) loss: 77050.201765\n", + "(Epoch 6 / 15) train acc: 0.530000; val_acc: 0.153000\n", + "(Iteration 13 / 30) loss: 83860.521969\n", + "(Iteration 14 / 30) loss: 64546.599964\n", + "(Epoch 7 / 15) train acc: 0.660000; val_acc: 0.154000\n", + "(Iteration 15 / 30) loss: 38717.756533\n", + "(Iteration 16 / 30) loss: 59231.494177\n", + "(Epoch 8 / 15) train acc: 0.630000; val_acc: 0.163000\n", + "(Iteration 17 / 30) loss: 40704.859093\n", + "(Iteration 18 / 30) loss: 45833.695036\n", + "(Epoch 9 / 15) train acc: 0.640000; val_acc: 0.167000\n", + "(Iteration 19 / 30) loss: 32820.809382\n", + "(Iteration 20 / 30) loss: 26125.181771\n", + "(Epoch 10 / 15) train acc: 0.790000; val_acc: 0.169000\n", + "(Iteration 21 / 30) loss: 12112.838558\n", + "(Iteration 22 / 30) loss: 13404.974011\n", + "(Epoch 11 / 15) train acc: 0.820000; val_acc: 0.162000\n", + "(Iteration 23 / 30) loss: 5567.485699\n", + "(Iteration 24 / 30) loss: 12903.084246\n", + "(Epoch 12 / 15) train acc: 0.730000; val_acc: 0.172000\n", + "(Iteration 25 / 30) loss: 15231.108947\n", + "(Iteration 26 / 30) loss: 6554.338610\n", + "(Epoch 13 / 15) train acc: 0.830000; val_acc: 0.178000\n", + "(Iteration 27 / 30) loss: 8495.112211\n", + "(Iteration 28 / 30) loss: 12824.357584\n", + "(Epoch 14 / 15) train acc: 0.910000; val_acc: 0.181000\n", + "(Iteration 29 / 30) loss: 6673.494962\n", + "(Iteration 30 / 30) loss: 326.647547\n", + "(Epoch 15 / 15) train acc: 0.940000; val_acc: 0.183000\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "\n", + "num_train = 100\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "model = ThreeLayerConvNet(weight_scale=1e-2)\n", + "\n", + "solver = Solver(model, small_data,\n", + " num_epochs=15, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-2,\n", + " },\n", + " verbose=True, print_every=1)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 1, 1)\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.xlabel('iteration')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(solver.train_acc_history, '-o')\n", + "plt.plot(solver.val_acc_history, '-o')\n", + "plt.legend(['train', 'val'], loc='upper left')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the net\n", + "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 490) loss: 1449929.446953\n", + "(Epoch 0 / 1) train acc: 0.112000; val_acc: 0.122000\n", + "(Iteration 21 / 490) loss: 535466.886670\n", + "(Iteration 41 / 490) loss: 392751.763424\n", + "(Iteration 61 / 490) loss: 327944.988030\n", + "(Iteration 81 / 490) loss: 322325.760300\n", + "(Iteration 101 / 490) loss: 300385.189237\n", + "(Iteration 121 / 490) loss: 209564.784938\n", + "(Iteration 141 / 490) loss: 268572.206592\n", + "(Iteration 161 / 490) loss: 223729.497319\n", + "(Iteration 181 / 490) loss: 252921.933643\n", + "(Iteration 201 / 490) loss: 186442.965186\n", + "(Iteration 221 / 490) loss: 212435.739316\n", + "(Iteration 241 / 490) loss: 178688.534029\n", + "(Iteration 261 / 490) loss: 191253.194772\n", + "(Iteration 281 / 490) loss: 139206.068505\n", + "(Iteration 301 / 490) loss: 164376.913277\n", + "(Iteration 321 / 490) loss: 134725.398874\n", + "(Iteration 341 / 490) loss: 174234.338009\n", + "(Iteration 361 / 490) loss: 211926.231433\n", + "(Iteration 381 / 490) loss: 138764.664049\n", + "(Iteration 401 / 490) loss: 159476.599020\n", + "(Iteration 421 / 490) loss: 134676.350006\n", + "(Iteration 441 / 490) loss: 133966.042822\n", + "(Iteration 461 / 490) loss: 118241.144728\n", + "(Iteration 481 / 490) loss: 111584.528408\n", + "(Epoch 1 / 1) train acc: 0.348000; val_acc: 0.315000\n" + ] + } + ], + "source": [ + "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n", + "\n", + "solver = Solver(model, data,\n", + " num_epochs=1, batch_size=100,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 0.003,\n", + " },\n", + " verbose=True, print_every=20)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Filters\n", + "You can visualize the first-layer convolutional filters from the trained network by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from cs231n.vis_utils import visualize_grid\n", + "\n", + "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n", + "plt.imshow(grid.astype('uint8'))\n", + "plt.axis('off')\n", + "plt.gcf().set_size_inches(5, 5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spatial Batch Normalization\n", + "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n", + "\n", + "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n", + "\n", + "If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n", + "\n", + "\n", + "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", + "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial batch normalization: forward\n", + "\n", + "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before spatial batch normalization:\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [9.33463814 8.90909116 9.11056338]\n", + " Stds: [3.61447857 3.19347686 3.5168142 ]\n", + "After spatial batch normalization:\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [ 6.05071548e-16 6.21724894e-16 -1.16573418e-16]\n", + " Stds: [0.99999723 0.99999687 0.99999716]\n", + "After spatial batch normalization (nontrivial gamma, beta):\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [6. 7. 8.]\n", + " Stds: [2.9999917 3.99998747 4.99998578]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after spatial batch normalization\n", + "\n", + "N, C, H, W = 2, 3, 4, 5\n", + "x = 4 * np.random.randn(N, C, H, W) + 10\n", + "\n", + "print('Before spatial batch normalization:')\n", + "print(' Shape: ', x.shape)\n", + "print(' Means: ', x.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', x.std(axis=(0, 2, 3)))\n", + "\n", + "# Means should be close to zero and stds close to one\n", + "gamma, beta = np.ones(C), np.zeros(C)\n", + "bn_param = {'mode': 'train'}\n", + "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "print('After spatial batch normalization:')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', out.std(axis=(0, 2, 3)))\n", + "\n", + "# Means should be close to beta and stds close to gamma\n", + "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n", + "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "print('After spatial batch normalization (nontrivial gamma, beta):')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', out.std(axis=(0, 2, 3)))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After spatial batch normalization (test-time):\n", + " means: [-0.08034378 0.07562855 0.05716351 0.04378368]\n", + " stds: [0.96718413 1.02996788 1.02887272 1.00585232]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the test-time forward pass by running the training-time\n", + "# forward pass many times to warm up the running averages, and then\n", + "# checking the means and variances of activations after a test-time\n", + "# forward pass.\n", + "N, C, H, W = 10, 4, 11, 12\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "gamma = np.ones(C)\n", + "beta = np.zeros(C)\n", + "for t in range(50):\n", + " x = 2.3 * np.random.randn(N, C, H, W) + 13\n", + " spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "bn_param['mode'] = 'test'\n", + "x = 2.3 * np.random.randn(N, C, H, W) + 13\n", + "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "\n", + "# Means should be close to zero and stds close to one, but will be\n", + "# noisier than training-time forward passes.\n", + "print('After spatial batch normalization (test-time):')\n", + "print(' means: ', a_norm.mean(axis=(0, 2, 3)))\n", + "print(' stds: ', a_norm.std(axis=(0, 2, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial batch normalization: backward\n", + "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx error: 3.511390713672255e-06\n", + "dgamma error: 1.795799129503502e-11\n", + "dbeta error: 3.275608725278405e-12\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, C, H, W = 2, 3, 4, 5\n", + "x = 5 * np.random.randn(N, C, H, W) + 12\n", + "gamma = np.random.randn(C)\n", + "beta = np.random.randn(C)\n", + "dout = np.random.randn(N, C, H, W)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta, dout)\n", + "\n", + "#You should expect errors of magnitudes between 1e-12~1e-06\n", + "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Group Normalization\n", + "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n", + "\n", + ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n", + "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n", + "statistics from the rest of the hidden units within the same layer.\n", + "\n", + "The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups, and a per-group per-datapoint normalization instead. \n", + "\n", + "![Comparison of normalization techniques discussed so far](notebook_images/normalization.png)\n", + "
**Visual comparison of the normalization techniques discussed so far (image edited from [3])**
\n", + "\n", + "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional Computer Vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4]-- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n", + "\n", + "You will now implement Group Normalization. Note that this normalization technique that you are to implement in the following cells was introduced and published to ECCV just in 2018 -- this truly is still an ongoing and excitingly active field of research!\n", + "\n", + "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n", + "\n", + "\n", + "[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n", + "\n", + "\n", + "[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n", + "human detection. In Computer Vision and Pattern Recognition\n", + "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group normalization: forward\n", + "\n", + "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before spatial group normalization:\n", + " Shape: (2, 6, 4, 5)\n", + " Means: [9.72505327 8.51114185 8.9147544 9.43448077]\n", + " Stds: [3.67070958 3.09892597 4.27043622 3.97521327]\n", + "After spatial group normalization:\n", + " Shape: (2, 6, 4, 5)\n", + " Means: [-2.14643118e-16 5.25505565e-16 2.65528340e-16 -3.38618023e-16]\n", + " Stds: [0.99999963 0.99999948 0.99999973 0.99999968]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after spatial batch normalization\n", + "\n", + "N, C, H, W = 2, 6, 4, 5\n", + "G = 2\n", + "x = 4 * np.random.randn(N, C, H, W) + 10\n", + "x_g = x.reshape((N*G,-1))\n", + "print('Before spatial group normalization:')\n", + "print(' Shape: ', x.shape)\n", + "print(' Means: ', x_g.mean(axis=1))\n", + "print(' Stds: ', x_g.std(axis=1))\n", + "\n", + "# Means should be close to zero and stds close to one\n", + "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n", + "bn_param = {'mode': 'train'}\n", + "\n", + "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n", + "out_g = out.reshape((N*G,-1))\n", + "print('After spatial group normalization:')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out_g.mean(axis=1))\n", + "print(' Stds: ', out_g.std(axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial group normalization: backward\n", + "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dgamma error: 9.468195772749234e-12\n", + "dbeta error: 3.354494437653335e-12\n", + "dx error: 7.413109384854475e-08\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, C, H, W = 2, 6, 4, 5\n", + "G = 2\n", + "x = 5 * np.random.randn(N, C, H, W) + 12\n", + "gamma = np.random.randn(1,C,1,1)\n", + "beta = np.random.randn(1,C,1,1)\n", + "dout = np.random.randn(N, C, H, W)\n", + "\n", + "gn_param = {}\n", + "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta, dout)\n", + "\n", + "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n", + "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n", + "#You should expect errors of magnitudes between 1e-12~1e-07\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))\n", + "print('dx error: ', rel_error(dx_num, dx))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/.ipynb_checkpoints/Dropout-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/Dropout-checkpoint.ipynb new file mode 100755 index 0000000..554f00a --- /dev/null +++ b/assignment2/.ipynb_checkpoints/Dropout-checkpoint.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Dropout\n", + "Dropout [1] is a technique for regularizing neural networks by randomly setting some output activations to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n", + "\n", + "[1] [Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012](https://arxiv.org/abs/1207.0580)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "from __future__ import print_function\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dropout forward pass\n", + "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n", + "\n", + "Once you have done so, run the cell below to test your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(500, 500) + 10\n", + "\n", + "for p in [0.25, 0.4, 0.7]:\n", + " out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n", + " out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n", + "\n", + " print('Running tests with p = ', p)\n", + " print('Mean of input: ', x.mean())\n", + " print('Mean of train-time output: ', out.mean())\n", + " print('Mean of test-time output: ', out_test.mean())\n", + " print('Fraction of train-time output set to zero: ', (out == 0).mean())\n", + " print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dropout backward pass\n", + "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(10, 10) + 10\n", + "dout = np.random.randn(*x.shape)\n", + "\n", + "dropout_param = {'mode': 'train', 'p': 0.2, 'seed': 123}\n", + "out, cache = dropout_forward(x, dropout_param)\n", + "dx = dropout_backward(dout, cache)\n", + "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n", + "\n", + "# Error should be around e-10 or less\n", + "print('dx relative error: ', rel_error(dx, dx_num))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1:\n", + "What happens if we do not divide the values being passed through inverse dropout by `p` in the dropout layer? Why does that happen?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fully-connected nets with Dropout\n", + "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specifically, if the constructor of the network receives a value that is not 1 for the `dropout` parameter, then the net should add a dropout layer immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "for dropout in [1, 0.75, 0.5]:\n", + " print('Running check with dropout = ', dropout)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " weight_scale=5e-2, dtype=np.float64,\n", + " dropout=dropout, seed=123)\n", + "\n", + " loss, grads = model.loss(X, y)\n", + " print('Initial loss: ', loss)\n", + " \n", + " # Relative errors should be around e-6 or less; Note that it's fine\n", + " # if for dropout=1 you have W2 error be on the order of e-5.\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regularization experiment\n", + "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a keep probability of 0.25. We will then visualize the training and validation accuracies of the two networks over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Train two identical nets, one with dropout and one without\n", + "np.random.seed(231)\n", + "num_train = 500\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "solvers = {}\n", + "dropout_choices = [1, 0.25]\n", + "for dropout in dropout_choices:\n", + " model = FullyConnectedNet([500], dropout=dropout)\n", + " print(dropout)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=25, batch_size=100,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 5e-4,\n", + " },\n", + " verbose=True, print_every=100)\n", + " solver.train()\n", + " solvers[dropout] = solver\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot train and validation accuracies of the two models\n", + "\n", + "train_accs = []\n", + "val_accs = []\n", + "for dropout in dropout_choices:\n", + " solver = solvers[dropout]\n", + " train_accs.append(solver.train_acc_history[-1])\n", + " val_accs.append(solver.val_acc_history[-1])\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "for dropout in dropout_choices:\n", + " plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n", + "plt.title('Train accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend(ncol=2, loc='lower right')\n", + " \n", + "plt.subplot(3, 1, 2)\n", + "for dropout in dropout_choices:\n", + " plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n", + "plt.title('Val accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend(ncol=2, loc='lower right')\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2:\n", + "Compare the validation and training accuracies with and without dropout -- what do your results suggest about dropout as a regularizer?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "Suppose we are training a deep fully-connected network for image classification, with dropout after hidden layers (parameterized by keep probability p). If we are concerned about overfitting, how should we modify p (if at all) when we decide to decrease the size of the hidden layers (that is, the number of nodes in each layer)?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/.ipynb_checkpoints/FullyConnectedNets-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/FullyConnectedNets-checkpoint.ipynb new file mode 100755 index 0000000..f55763e --- /dev/null +++ b/assignment2/.ipynb_checkpoints/FullyConnectedNets-checkpoint.ipynb @@ -0,0 +1,1559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Fully-Connected Neural Nets\n", + "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n", + "\n", + "```python\n", + "def layer_forward(x, w):\n", + " \"\"\" Receive inputs x and weights w \"\"\"\n", + " # Do some computations ...\n", + " z = # ... some intermediate value\n", + " # Do some more computations ...\n", + " out = # the output\n", + " \n", + " cache = (x, w, z, out) # Values we need to compute gradients\n", + " \n", + " return out, cache\n", + "```\n", + "\n", + "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n", + "\n", + "```python\n", + "def layer_backward(dout, cache):\n", + " \"\"\"\n", + " Receive dout (derivative of loss with respect to outputs) and cache,\n", + " and compute derivative with respect to inputs.\n", + " \"\"\"\n", + " # Unpack cache values\n", + " x, w, z, out = cache\n", + " \n", + " # Use values in cache to compute derivatives\n", + " dx = # Derivative of loss with respect to x\n", + " dw = # Derivative of loss with respect to w\n", + " \n", + " return dx, dw\n", + "```\n", + "\n", + "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n", + "\n", + "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "from __future__ import print_function\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('X_train: ', (49000, 3, 32, 32))\n", + "('y_train: ', (49000,))\n", + "('X_val: ', (1000, 3, 32, 32))\n", + "('y_val: ', (1000,))\n", + "('X_test: ', (1000, 3, 32, 32))\n", + "('y_test: ', (1000,))\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in list(data.items()):\n", + " print(('%s: ' % k, v.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Affine layer: foward\n", + "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n", + "\n", + "Once you are done you can test your implementaion by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_forward function:\n", + "difference: 9.769847728806635e-10\n" + ] + } + ], + "source": [ + "# Test the affine_forward function\n", + "\n", + "num_inputs = 2\n", + "input_shape = (4, 5, 6)\n", + "output_dim = 3\n", + "\n", + "input_size = num_inputs * np.prod(input_shape)\n", + "weight_size = output_dim * np.prod(input_shape)\n", + "\n", + "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n", + "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n", + "b = np.linspace(-0.3, 0.1, num=output_dim)\n", + "\n", + "\n", + "out, _ = affine_forward(x, w, b)\n", + "correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],\n", + " [ 3.25553199, 3.5141327, 3.77273342]])\n", + "\n", + "# Compare your output with ours. The error should be around e-9 or less.\n", + "print('Testing affine_forward function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Affine layer: backward\n", + "Now implement the `affine_backward` function and test your implementation using numeric gradient checking." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_backward function:\n", + "dx error: 5.399100368651805e-11\n", + "dw error: 9.904211865398145e-11\n", + "db error: 2.4122867568119087e-11\n" + ] + } + ], + "source": [ + "# Test the affine_backward function\n", + "\n", + "np.random.seed(231)\n", + "x = np.random.randn(10, 2, 3)\n", + "w = np.random.randn(6, 5)\n", + "b = np.random.randn(5)\n", + "dout = np.random.randn(10, 5)\n", + "\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n", + "\n", + "_, cache = affine_forward(x, w, b)\n", + "dx, dw, db = affine_backward(dout, cache)\n", + "\n", + "# The error should be around e-10 or less\n", + "print('Testing affine_backward function:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ReLU activation: forward\n", + "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing relu_forward function:\n", + "difference: 4.999999798022158e-08\n" + ] + } + ], + "source": [ + "# Test the relu_forward function\n", + "\n", + "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n", + "\n", + "out, _ = relu_forward(x)\n", + "correct_out = np.array([[ 0., 0., 0., 0., ],\n", + " [ 0., 0., 0.04545455, 0.13636364,],\n", + " [ 0.22727273, 0.31818182, 0.40909091, 0.5, ]])\n", + "\n", + "# Compare your output with ours. The error should be on the order of e-8\n", + "print('Testing relu_forward function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ReLU activation: backward\n", + "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing relu_backward function:\n", + "dx error: 1.0\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(10, 10)\n", + "dout = np.random.randn(*x.shape)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n", + "\n", + "_, cache = relu_forward(x)\n", + "dx = relu_backward(dout, cache)\n", + "\n", + "# The error should be on the order of e-12\n", + "print('Testing relu_backward function:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1: \n", + "\n", + "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n", + "1. Sigmoid\n", + "2. ReLU\n", + "3. Leaky ReLU\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# \"Sandwich\" layers\n", + "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n", + "\n", + "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_relu_forward and affine_relu_backward:\n", + "dx error: 6.750562121603446e-11\n", + "dw error: 8.162015570444288e-11\n", + "db error: 7.826724021458994e-12\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 4)\n", + "w = np.random.randn(12, 10)\n", + "b = np.random.randn(10)\n", + "dout = np.random.randn(2, 10)\n", + "\n", + "out, cache = affine_relu_forward(x, w, b)\n", + "dx, dw, db = affine_relu_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n", + "\n", + "# Relative error should be around e-10 or less\n", + "print('Testing affine_relu_forward and affine_relu_backward:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss layers: Softmax and SVM\n", + "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n", + "\n", + "You can make sure that the implementations are correct by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing svm_loss:\n", + "loss: 8.999602749096233\n", + "dx error: 1.4021566006651672e-09\n", + "\n", + "Testing softmax_loss:\n", + "loss: 2.302545844500738\n", + "dx error: 9.384673161989355e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "num_classes, num_inputs = 10, 50\n", + "x = 0.001 * np.random.randn(num_inputs, num_classes)\n", + "y = np.random.randint(num_classes, size=num_inputs)\n", + "\n", + "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n", + "loss, dx = svm_loss(x, y)\n", + "\n", + "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n", + "print('Testing svm_loss:')\n", + "print('loss: ', loss)\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "\n", + "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n", + "loss, dx = softmax_loss(x, y)\n", + "\n", + "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n", + "print('\\nTesting softmax_loss:')\n", + "print('loss: ', loss)\n", + "print('dx error: ', rel_error(dx_num, dx))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two-layer network\n", + "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n", + "\n", + "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing initialization ... \n", + "Testing test-time forward pass ... \n", + "Testing training loss (no regularization)\n", + "Running numeric gradient check with reg = 0.0\n", + "W1 relative error: 1.00e+00\n", + "W2 relative error: 1.00e+00\n", + "b1 relative error: 1.00e+00\n", + "b2 relative error: 4.33e-10\n", + "Running numeric gradient check with reg = 0.7\n", + "W1 relative error: 1.00e+00\n", + "W2 relative error: 1.00e+00\n", + "b1 relative error: 1.00e+00\n", + "b2 relative error: 1.28e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H, C = 3, 5, 50, 7\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=N)\n", + "\n", + "std = 1e-3\n", + "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n", + "\n", + "print('Testing initialization ... ')\n", + "W1_std = abs(model.params['W1'].std() - std)\n", + "b1 = model.params['b1']\n", + "W2_std = abs(model.params['W2'].std() - std)\n", + "b2 = model.params['b2']\n", + "assert W1_std < std / 10, 'First layer weights do not seem right'\n", + "assert np.all(b1 == 0), 'First layer biases do not seem right'\n", + "assert W2_std < std / 10, 'Second layer weights do not seem right'\n", + "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n", + "\n", + "print('Testing test-time forward pass ... ')\n", + "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n", + "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n", + "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n", + "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n", + "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n", + "scores = model.loss(X)\n", + "correct_scores = np.asarray(\n", + " [[11.53165108, 12.2917344, 13.05181771, 13.81190102, 14.57198434, 15.33206765, 16.09215096],\n", + " [12.05769098, 12.74614105, 13.43459113, 14.1230412, 14.81149128, 15.49994135, 16.18839143],\n", + " [12.58373087, 13.20054771, 13.81736455, 14.43418138, 15.05099822, 15.66781506, 16.2846319 ]])\n", + "scores_diff = np.abs(scores - correct_scores).sum()\n", + "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n", + "\n", + "print('Testing training loss (no regularization)')\n", + "y = np.asarray([0, 5, 1])\n", + "loss, grads = model.loss(X, y)\n", + "correct_loss = 3.4702243556\n", + "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n", + "\n", + "model.reg = 0.5\n", + "loss, grads = model.loss(X, y)\n", + "correct_loss = 26.5948426952\n", + "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n", + "\n", + "# Errors should be around e-7 or less\n", + "for reg in [0.0, 0.7]:\n", + " print('Running numeric gradient check with reg = ', reg)\n", + " model.reg = reg\n", + " loss, grads = model.loss(X, y)\n", + "\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solver\n", + "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n", + "\n", + "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 4900) loss: 2.304060\n", + "(Epoch 0 / 10) train acc: 0.091000; val_acc: 0.069000\n", + "(Iteration 101 / 4900) loss: 1.938327\n", + "(Iteration 201 / 4900) loss: 1.960257\n", + "(Iteration 301 / 4900) loss: 1.862565\n", + "(Iteration 401 / 4900) loss: 1.618868\n", + "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.404000\n", + "(Iteration 501 / 4900) loss: 1.544881\n", + "(Iteration 601 / 4900) loss: 1.732115\n", + "(Iteration 701 / 4900) loss: 1.713286\n", + "(Iteration 801 / 4900) loss: 1.684847\n", + "(Iteration 901 / 4900) loss: 1.567337\n", + "(Epoch 2 / 10) train acc: 0.461000; val_acc: 0.463000\n", + "(Iteration 1001 / 4900) loss: 1.484216\n", + "(Iteration 1101 / 4900) loss: 1.483702\n", + "(Iteration 1201 / 4900) loss: 1.752461\n", + "(Iteration 1301 / 4900) loss: 1.553389\n", + "(Iteration 1401 / 4900) loss: 1.365505\n", + "(Epoch 3 / 10) train acc: 0.486000; val_acc: 0.464000\n", + "(Iteration 1501 / 4900) loss: 1.438134\n", + "(Iteration 1601 / 4900) loss: 1.456878\n", + "(Iteration 1701 / 4900) loss: 1.379353\n", + "(Iteration 1801 / 4900) loss: 1.519290\n", + "(Iteration 1901 / 4900) loss: 1.486483\n", + "(Epoch 4 / 10) train acc: 0.514000; val_acc: 0.482000\n", + "(Iteration 2001 / 4900) loss: 1.510836\n", + "(Iteration 2101 / 4900) loss: 1.428223\n", + "(Iteration 2201 / 4900) loss: 1.395694\n", + "(Iteration 2301 / 4900) loss: 1.423331\n", + "(Iteration 2401 / 4900) loss: 1.443047\n", + "(Epoch 5 / 10) train acc: 0.498000; val_acc: 0.496000\n", + "(Iteration 2501 / 4900) loss: 1.554888\n", + "(Iteration 2601 / 4900) loss: 1.416505\n", + "(Iteration 2701 / 4900) loss: 1.232805\n", + "(Iteration 2801 / 4900) loss: 1.382266\n", + "(Iteration 2901 / 4900) loss: 1.388853\n", + "(Epoch 6 / 10) train acc: 0.505000; val_acc: 0.499000\n", + "(Iteration 3001 / 4900) loss: 1.391340\n", + "(Iteration 3101 / 4900) loss: 1.452501\n", + "(Iteration 3201 / 4900) loss: 1.395050\n", + "(Iteration 3301 / 4900) loss: 1.433530\n", + "(Iteration 3401 / 4900) loss: 1.489256\n", + "(Epoch 7 / 10) train acc: 0.513000; val_acc: 0.489000\n", + "(Iteration 3501 / 4900) loss: 1.371254\n", + "(Iteration 3601 / 4900) loss: 1.254908\n", + "(Iteration 3701 / 4900) loss: 1.400341\n", + "(Iteration 3801 / 4900) loss: 1.288566\n", + "(Iteration 3901 / 4900) loss: 1.252551\n", + "(Epoch 8 / 10) train acc: 0.515000; val_acc: 0.508000\n", + "(Iteration 4001 / 4900) loss: 1.387048\n", + "(Iteration 4101 / 4900) loss: 1.450029\n", + "(Iteration 4201 / 4900) loss: 1.342555\n", + "(Iteration 4301 / 4900) loss: 1.277807\n", + "(Iteration 4401 / 4900) loss: 1.408134\n", + "(Epoch 9 / 10) train acc: 0.536000; val_acc: 0.511000\n", + "(Iteration 4501 / 4900) loss: 1.170929\n", + "(Iteration 4601 / 4900) loss: 1.420749\n", + "(Iteration 4701 / 4900) loss: 1.225336\n", + "(Iteration 4801 / 4900) loss: 1.213172\n", + "(Epoch 10 / 10) train acc: 0.549000; val_acc: 0.513000\n" + ] + } + ], + "source": [ + "model = TwoLayerNet()\n", + "solver = None\n", + "\n", + "##############################################################################\n", + "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least #\n", + "# 50% accuracy on the validation set. #\n", + "##############################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "train_and_val = {}\n", + "\n", + "train_and_val['X_train'] = data['X_train']\n", + "train_and_val['y_train'] = data['y_train']\n", + "train_and_val['X_val'] = data['X_val']\n", + "train_and_val['y_val'] = data['y_val']\n", + "\n", + "solver = Solver(model, train_and_val,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': 0.0004,\n", + " },\n", + " lr_decay=0.95,\n", + " num_epochs=10, batch_size=100,\n", + " print_every=100)\n", + "solver.train()\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "##############################################################################\n", + "# END OF YOUR CODE #\n", + "##############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to visualize training loss and train / val accuracy\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.title('Accuracy')\n", + "plt.plot(solver.train_acc_history, '-o', label='train')\n", + "plt.plot(solver.val_acc_history, '-o', label='val')\n", + "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(loc='lower right')\n", + "plt.gcf().set_size_inches(15, 12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilayer network\n", + "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n", + "\n", + "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n", + "\n", + "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial loss and gradient check\n", + "\n", + "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n", + "\n", + "For gradient checking, you should expect to see errors around 1e-7 or less." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running check with reg = 0\n", + "Initial loss: 2.300479089768492\n", + "W1 relative error: 1.03e-07\n", + "W2 relative error: 2.21e-05\n", + "W3 relative error: 4.56e-07\n", + "b1 relative error: 4.66e-09\n", + "b2 relative error: 2.09e-09\n", + "b3 relative error: 1.69e-10\n", + "Running check with reg = 3.14\n", + "Initial loss: 7.052114776533016\n", + "W1 relative error: 6.86e-09\n", + "W2 relative error: 3.52e-08\n", + "W3 relative error: 2.62e-08\n", + "b1 relative error: 1.48e-08\n", + "b2 relative error: 1.72e-09\n", + "b3 relative error: 2.38e-10\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "for reg in [0, 3.14]:\n", + " print('Running check with reg = ', reg)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " reg=reg, weight_scale=5e-2, dtype=np.float64)\n", + "\n", + " loss, grads = model.loss(X, y)\n", + " print('Initial loss: ', loss)\n", + " \n", + " # Most of the errors should be on the order of e-7 or smaller. \n", + " # NOTE: It is fine however to see an error for W2 on the order of e-5\n", + " # for the check when reg = 0.0\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kalkidanfekadu/Desktop/CS231/assignment2/cs231n/classifiers/fc_net.py:331: RuntimeWarning: divide by zero encountered in log\n", + " loss = np.sum(-np.log(scores[range(N), y]))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 40) loss: inf\n", + "(Epoch 0 / 20) train acc: 0.020000; val_acc: 0.109000\n", + "(Epoch 1 / 20) train acc: 0.040000; val_acc: 0.112000\n", + "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.110000\n", + "(Epoch 3 / 20) train acc: 0.300000; val_acc: 0.143000\n", + "(Epoch 4 / 20) train acc: 0.300000; val_acc: 0.135000\n", + "(Epoch 5 / 20) train acc: 0.420000; val_acc: 0.159000\n", + "(Iteration 11 / 40) loss: 31.049688\n", + "(Epoch 6 / 20) train acc: 0.540000; val_acc: 0.155000\n", + "(Epoch 7 / 20) train acc: 0.560000; val_acc: 0.145000\n", + "(Epoch 8 / 20) train acc: 0.640000; val_acc: 0.145000\n", + "(Epoch 9 / 20) train acc: 0.700000; val_acc: 0.150000\n", + "(Epoch 10 / 20) train acc: 0.760000; val_acc: 0.150000\n", + "(Iteration 21 / 40) loss: 23.376963\n", + "(Epoch 11 / 20) train acc: 0.740000; val_acc: 0.160000\n", + "(Epoch 12 / 20) train acc: 0.800000; val_acc: 0.149000\n", + "(Epoch 13 / 20) train acc: 0.840000; val_acc: 0.141000\n", + "(Epoch 14 / 20) train acc: 0.880000; val_acc: 0.145000\n", + "(Epoch 15 / 20) train acc: 0.940000; val_acc: 0.146000\n", + "(Iteration 31 / 40) loss: 0.554121\n", + "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.142000\n", + "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.146000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Use a three-layer Net to overfit 50 training examples by \n", + "# tweaking just the learning rate and initialization scale.\n", + "\n", + "num_train = 50\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "weight_scale = 0.099 # Experiment with this!\n", + "learning_rate = 1e-4 # Experiment with this!\n", + "model = FullyConnectedNet([100, 100],\n", + " weight_scale=weight_scale, dtype=np.float64)\n", + "solver = Solver(model, small_data,\n", + " print_every=10, num_epochs=20, batch_size=25,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': learning_rate,\n", + " }\n", + " )\n", + "solver.train()\n", + "\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.title('Training loss history')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Training loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 40) loss: 2.302585\n", + "(Epoch 0 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 3 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 4 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 5 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 11 / 40) loss: 2.275307\n", + "(Epoch 6 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 7 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 8 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 9 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 10 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 21 / 40) loss: 2.274710\n", + "(Epoch 11 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 12 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 13 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 14 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 15 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 31 / 40) loss: 2.276649\n", + "(Epoch 16 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 17 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 18 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 19 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 20 / 20) train acc: 0.160000; val_acc: 0.079000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Use a five-layer Net to overfit 50 training examples by \n", + "# tweaking just the learning rate and initialization scale.\n", + "\n", + "num_train = 50\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "learning_rate = 10**(np.random.uniform(-6,-1)) # Experiment with this!\n", + "weight_scale = 10**(np.random.uniform(-4,-1)) # Experiment with this!\n", + "model = FullyConnectedNet([100, 100, 100, 100],\n", + " weight_scale=weight_scale, dtype=np.float64)\n", + "solver = Solver(model, small_data,\n", + " print_every=10, num_epochs=20, batch_size=25,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': learning_rate,\n", + " }\n", + " )\n", + "solver.train()\n", + "\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.title('Training loss history')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Training loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2: \n", + "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Update rules\n", + "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SGD+Momentum\n", + "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n", + "\n", + "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 8.882347033505819e-09\n", + "velocity error: 4.269287743278663e-09\n" + ] + } + ], + "source": [ + "from cs231n.optim import sgd_momentum\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-3, 'velocity': v}\n", + "next_w, _ = sgd_momentum(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789],\n", + " [ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526],\n", + " [ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263],\n", + " [ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]])\n", + "expected_velocity = np.asarray([\n", + " [ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158],\n", + " [ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105],\n", + " [ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053],\n", + " [ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]])\n", + "\n", + "# Should see relative errors around e-8 or less\n", + "print('next_w error: ', rel_error(next_w, expected_next_w))\n", + "print('velocity error: ', rel_error(expected_velocity, config['velocity']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running with sgd\n", + "(Iteration 1 / 200) loss: 2.743596\n", + "(Epoch 0 / 5) train acc: 0.073000; val_acc: 0.098000\n", + "(Iteration 11 / 200) loss: 2.310501\n", + "(Iteration 21 / 200) loss: 2.254656\n", + "(Iteration 31 / 200) loss: 2.121765\n", + "(Epoch 1 / 5) train acc: 0.198000; val_acc: 0.210000\n", + "(Iteration 41 / 200) loss: 2.045536\n", + "(Iteration 51 / 200) loss: 2.169792\n", + "(Iteration 61 / 200) loss: 2.008206\n", + "(Iteration 71 / 200) loss: 1.995862\n", + "(Epoch 2 / 5) train acc: 0.287000; val_acc: 0.256000\n", + "(Iteration 81 / 200) loss: 2.113303\n", + "(Iteration 91 / 200) loss: 1.871558\n", + "(Iteration 101 / 200) loss: 2.041598\n", + "(Iteration 111 / 200) loss: 1.894926\n", + "(Epoch 3 / 5) train acc: 0.338000; val_acc: 0.263000\n", + "(Iteration 121 / 200) loss: 2.008418\n", + "(Iteration 131 / 200) loss: 1.714670\n", + "(Iteration 141 / 200) loss: 1.909367\n", + "(Iteration 151 / 200) loss: 1.806134\n", + "(Epoch 4 / 5) train acc: 0.356000; val_acc: 0.290000\n", + "(Iteration 161 / 200) loss: 1.753934\n", + "(Iteration 171 / 200) loss: 1.883756\n", + "(Iteration 181 / 200) loss: 1.951251\n", + "(Iteration 191 / 200) loss: 1.836960\n", + "(Epoch 5 / 5) train acc: 0.369000; val_acc: 0.320000\n", + "\n", + "running with sgd_momentum\n", + "(Iteration 1 / 200) loss: 2.587765\n", + "(Epoch 0 / 5) train acc: 0.096000; val_acc: 0.089000\n", + "(Iteration 11 / 200) loss: 2.154765\n", + "(Iteration 21 / 200) loss: 2.025203\n", + "(Iteration 31 / 200) loss: 2.018126\n", + "(Epoch 1 / 5) train acc: 0.306000; val_acc: 0.285000\n", + "(Iteration 41 / 200) loss: 1.973381\n", + "(Iteration 51 / 200) loss: 1.734572\n", + "(Iteration 61 / 200) loss: 1.726248\n", + "(Iteration 71 / 200) loss: 1.850464\n", + "(Epoch 2 / 5) train acc: 0.424000; val_acc: 0.342000\n", + "(Iteration 81 / 200) loss: 1.842990\n", + "(Iteration 91 / 200) loss: 1.611516\n", + "(Iteration 101 / 200) loss: 1.615972\n", + "(Iteration 111 / 200) loss: 1.414714\n", + "(Epoch 3 / 5) train acc: 0.474000; val_acc: 0.347000\n", + "(Iteration 121 / 200) loss: 1.376354\n", + "(Iteration 131 / 200) loss: 1.596911\n", + "(Iteration 141 / 200) loss: 1.447764\n", + "(Iteration 151 / 200) loss: 1.431524\n", + "(Epoch 4 / 5) train acc: 0.465000; val_acc: 0.364000\n", + "(Iteration 161 / 200) loss: 1.377602\n", + "(Iteration 171 / 200) loss: 1.421367\n", + "(Iteration 181 / 200) loss: 1.497430\n", + "(Iteration 191 / 200) loss: 1.414507\n", + "(Epoch 5 / 5) train acc: 0.541000; val_acc: 0.358000\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n", + "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " \"Adding an axes using the same arguments as a previous axes \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "num_train = 4000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "solvers = {}\n", + "\n", + "for update_rule in ['sgd', 'sgd_momentum']:\n", + " print('running with ', update_rule)\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule=update_rule,\n", + " optim_config={\n", + " 'learning_rate': 5e-3,\n", + " },\n", + " verbose=True)\n", + " solvers[update_rule] = solver\n", + " solver.train()\n", + " print()\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Training accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Validation accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "for update_rule, solver in solvers.items():\n", + " plt.subplot(3, 1, 1)\n", + " plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n", + " \n", + " plt.subplot(3, 1, 2)\n", + " plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n", + "\n", + " plt.subplot(3, 1, 3)\n", + " plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n", + " \n", + "for i in [1, 2, 3]:\n", + " plt.subplot(3, 1, i)\n", + " plt.legend(loc='upper center', ncol=4)\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RMSProp and Adam\n", + "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n", + "\n", + "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n", + "\n", + "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n", + "\n", + "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n", + "\n", + "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 9.524687511038133e-08\n", + "cache error: 2.6477955807156126e-09\n" + ] + } + ], + "source": [ + "# Test RMSProp implementation\n", + "from cs231n.optim import rmsprop\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-2, 'cache': cache}\n", + "next_w, _ = rmsprop(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n", + " [-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774],\n", + " [ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447],\n", + " [ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]])\n", + "expected_cache = np.asarray([\n", + " [ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321],\n", + " [ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377],\n", + " [ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936],\n", + " [ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]])\n", + "\n", + "# You should see relative errors around e-7 or less\n", + "print('next_w error: ', rel_error(expected_next_w, next_w))\n", + "print('cache error: ', rel_error(expected_cache, config['cache']))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 1.1395691798535431e-07\n", + "v error: 4.208314038113071e-09\n", + "m error: 4.214963193114416e-09\n" + ] + } + ], + "source": [ + "# Test Adam implementation\n", + "from cs231n.optim import adam\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n", + "next_w, _ = adam(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n", + " [-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929],\n", + " [ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969],\n", + " [ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]])\n", + "expected_v = np.asarray([\n", + " [ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,],\n", + " [ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,],\n", + " [ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,],\n", + " [ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]])\n", + "expected_m = np.asarray([\n", + " [ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474],\n", + " [ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316],\n", + " [ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158],\n", + " [ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]])\n", + "\n", + "# You should see relative errors around e-7 or less\n", + "print('next_w error: ', rel_error(expected_next_w, next_w))\n", + "print('v error: ', rel_error(expected_v, config['v']))\n", + "print('m error: ', rel_error(expected_m, config['m']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running with adam\n", + "(Iteration 1 / 200) loss: 2.680973\n", + "(Epoch 0 / 5) train acc: 0.132000; val_acc: 0.120000\n", + "(Iteration 11 / 200) loss: 2.213776\n", + "(Iteration 21 / 200) loss: 2.082044\n", + "(Iteration 31 / 200) loss: 1.814850\n", + "(Epoch 1 / 5) train acc: 0.344000; val_acc: 0.322000\n", + "(Iteration 41 / 200) loss: 1.932607\n", + "(Iteration 51 / 200) loss: 1.732372\n", + "(Iteration 61 / 200) loss: 1.684073\n", + "(Iteration 71 / 200) loss: 1.454854\n", + "(Epoch 2 / 5) train acc: 0.446000; val_acc: 0.353000\n", + "(Iteration 81 / 200) loss: 1.562296\n", + "(Iteration 91 / 200) loss: 1.410424\n", + "(Iteration 101 / 200) loss: 1.569873\n", + "(Iteration 111 / 200) loss: 1.571657\n", + "(Epoch 3 / 5) train acc: 0.470000; val_acc: 0.330000\n", + "(Iteration 121 / 200) loss: 1.632057\n", + "(Iteration 131 / 200) loss: 1.488813\n", + "(Iteration 141 / 200) loss: 1.356309\n", + "(Iteration 151 / 200) loss: 1.371299\n", + "(Epoch 4 / 5) train acc: 0.551000; val_acc: 0.342000\n", + "(Iteration 161 / 200) loss: 1.138429\n", + "(Iteration 171 / 200) loss: 1.395116\n", + "(Iteration 181 / 200) loss: 1.189616\n", + "(Iteration 191 / 200) loss: 1.241926\n", + "(Epoch 5 / 5) train acc: 0.583000; val_acc: 0.371000\n", + "\n", + "running with rmsprop\n", + "(Iteration 1 / 200) loss: 2.525414\n", + "(Epoch 0 / 5) train acc: 0.141000; val_acc: 0.146000\n", + "(Iteration 11 / 200) loss: 2.195400\n", + "(Iteration 21 / 200) loss: 2.030609\n", + "(Iteration 31 / 200) loss: 1.811840\n", + "(Epoch 1 / 5) train acc: 0.367000; val_acc: 0.311000\n", + "(Iteration 41 / 200) loss: 1.820211\n", + "(Iteration 51 / 200) loss: 1.777108\n", + "(Iteration 61 / 200) loss: 1.707527\n", + "(Iteration 71 / 200) loss: 1.697344\n", + "(Epoch 2 / 5) train acc: 0.396000; val_acc: 0.336000\n", + "(Iteration 81 / 200) loss: 1.850499\n", + "(Iteration 91 / 200) loss: 1.560861\n", + "(Iteration 101 / 200) loss: 1.624011\n", + "(Iteration 111 / 200) loss: 1.477089\n", + "(Epoch 3 / 5) train acc: 0.461000; val_acc: 0.341000\n", + "(Iteration 121 / 200) loss: 1.601157\n", + "(Iteration 131 / 200) loss: 1.496088\n", + "(Iteration 141 / 200) loss: 1.493349\n", + "(Iteration 151 / 200) loss: 1.308786\n", + "(Epoch 4 / 5) train acc: 0.504000; val_acc: 0.348000\n", + "(Iteration 161 / 200) loss: 1.491016\n", + "(Iteration 171 / 200) loss: 1.389998\n", + "(Iteration 181 / 200) loss: 1.417710\n", + "(Iteration 191 / 200) loss: 1.520727\n", + "(Epoch 5 / 5) train acc: 0.529000; val_acc: 0.368000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n", + "for update_rule in ['adam', 'rmsprop']:\n", + " print('running with ', update_rule)\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule=update_rule,\n", + " optim_config={\n", + " 'learning_rate': learning_rates[update_rule]\n", + " },\n", + " verbose=True)\n", + " solvers[update_rule] = solver\n", + " solver.train()\n", + " print()\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Training accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Validation accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "for update_rule, solver in list(solvers.items()):\n", + " plt.subplot(3, 1, 1)\n", + " plt.plot(solver.loss_history, 'o', label=update_rule)\n", + " \n", + " plt.subplot(3, 1, 2)\n", + " plt.plot(solver.train_acc_history, '-o', label=update_rule)\n", + "\n", + " plt.subplot(3, 1, 3)\n", + " plt.plot(solver.val_acc_history, '-o', label=update_rule)\n", + " \n", + "for i in [1, 2, 3]:\n", + " plt.subplot(3, 1, i)\n", + " plt.legend(loc='upper center', ncol=4)\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "\n", + "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n", + "\n", + "```\n", + "cache += dw**2\n", + "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n", + "```\n", + "\n", + "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n", + "\n", + "\n", + "## Answer: \n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train a good model!\n", + "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n", + "\n", + "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n", + "\n", + "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'self' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'self' is not defined" + ] + } + ], + "source": [ + "best_model = None\n", + "################################################################################\n", + "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might #\n", + "# find batch/layer normalization and dropout useful. Store your best model in #\n", + "# the best_model variable. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "train_and_val = {}\n", + "\n", + "train_and_val['X_train'] = data['X_train']\n", + "train_and_val['y_train'] = data['y_train']\n", + "train_and_val['X_val'] = data['X_val']\n", + "train_and_val['y_val'] = data['y_val']\n", + "\n", + "normalize = ['batchnorm', 'layernorm']\n", + "dropouts = [1, 0.5, 0.75]\n", + "best_val = -1\n", + "best_model = None\n", + "\n", + "for norm in normalize:\n", + " for dropout in dropouts:\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100],\n", + " dropout=dropout, normalization=norm, reg=0,\n", + " weight_scale=1e-2, dtype=np.float32, seed=123)\n", + "\n", + " solver = Solver(model, train_and_val ,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule = 'adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3\n", + " },\n", + " verbose=False)\n", + "\n", + " solver.train()\n", + " if(solver.val_acc_history[-1] > best_val):\n", + " best_val = solver.val_acc_history[-1]\n", + " best_model = model\n", + "\n", + "\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE #\n", + "################################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.089, 0.44, 0.492, 0.508, 0.554, 0.586]\n", + "[0.082, 0.43, 0.502, 0.511, 0.519, 0.505]\n" + ] + } + ], + "source": [ + "print(solver.train_acc_history)\n", + "print(solver.val_acc_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test your model!\n", + "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n", + "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n", + "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n", + "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/.ipynb_checkpoints/PyTorch-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/PyTorch-checkpoint.ipynb new file mode 100755 index 0000000..4998ba5 --- /dev/null +++ b/assignment2/.ipynb_checkpoints/PyTorch-checkpoint.ipynb @@ -0,0 +1,1466 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# What's this PyTorch business?\n", + "\n", + "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n", + "\n", + "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, PyTorch (or TensorFlow, if you choose to use that notebook)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### What is PyTorch?\n", + "\n", + "PyTorch is a system for executing dynamic computational graphs over Tensor objects that behave similarly as numpy ndarray. It comes with a powerful automatic differentiation engine that removes the need for manual back-propagation. \n", + "\n", + "### Why?\n", + "\n", + "* Our code will now run on GPUs! Much faster training. When using a framework like PyTorch or TensorFlow you can harness the power of the GPU for your own custom neural network architectures without having to write CUDA code directly (which is beyond the scope of this class).\n", + "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n", + "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n", + "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry.\n", + "\n", + "### PyTorch versions\n", + "This notebook assumes that you are using **PyTorch version 1.0**. In some of the previous versions (e.g. before 0.4), Tensors had to be wrapped in Variable objects to be used in autograd; however Variables have now been deprecated. In addition 1.0 also separates a Tensor's datatype from its device, and uses numpy-style factories for constructing Tensors rather than directly invoking Tensor constructors." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## How will I learn PyTorch?\n", + "\n", + "Justin Johnson has made an excellent [tutorial](https://github.com/jcjohnson/pytorch-examples) for PyTorch. \n", + "\n", + "You can also find the detailed [API doc](http://pytorch.org/docs/stable/index.html) here. If you have other questions that are not addressed by the API docs, the [PyTorch forum](https://discuss.pytorch.org/) is a much better place to ask than StackOverflow.\n", + "\n", + "\n", + "# Table of Contents\n", + "\n", + "This assignment has 5 parts. You will learn PyTorch on **three different levels of abstraction**, which will help you understand it better and prepare you for the final project. \n", + "\n", + "1. Part I, Preparation: we will use CIFAR-10 dataset.\n", + "2. Part II, Barebones PyTorch: **Abstraction level 1**, we will work directly with the lowest-level PyTorch Tensors. \n", + "3. Part III, PyTorch Module API: **Abstraction level 2**, we will use `nn.Module` to define arbitrary neural network architecture. \n", + "4. Part IV, PyTorch Sequential API: **Abstraction level 3**, we will use `nn.Sequential` to define a linear feed-forward network very conveniently. \n", + "5. Part V, CIFAR-10 open-ended challenge: please implement your own network to get as high accuracy as possible on CIFAR-10. You can experiment with any layer, optimizer, hyperparameters or other advanced features. \n", + "\n", + "Here is a table of comparison:\n", + "\n", + "| API | Flexibility | Convenience |\n", + "|---------------|-------------|-------------|\n", + "| Barebone | High | Low |\n", + "| `nn.Module` | High | Medium |\n", + "| `nn.Sequential` | Low | High |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part I. Preparation\n", + "\n", + "First, we load the CIFAR-10 dataset. This might take a couple minutes the first time you do it, but the files should stay cached after that.\n", + "\n", + "In previous parts of the assignment we had to write our own code to download the CIFAR-10 dataset, preprocess it, and iterate through it in minibatches; PyTorch provides convenient tools to automate this process for us." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import sampler\n", + "\n", + "import torchvision.datasets as dset\n", + "import torchvision.transforms as T\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "NUM_TRAIN = 49000\n", + "\n", + "# The torchvision.transforms package provides tools for preprocessing data\n", + "# and for performing data augmentation; here we set up a transform to\n", + "# preprocess the data by subtracting the mean RGB value and dividing by the\n", + "# standard deviation of each RGB value; we've hardcoded the mean and std.\n", + "transform = T.Compose([\n", + " T.ToTensor(),\n", + " T.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n", + " ])\n", + "\n", + "# We set up a Dataset object for each split (train / val / test); Datasets load\n", + "# training examples one at a time, so we wrap each Dataset in a DataLoader which\n", + "# iterates through the Dataset and forms minibatches. We divide the CIFAR-10\n", + "# training set into train and val sets by passing a Sampler object to the\n", + "# DataLoader telling how it should sample from the underlying Dataset.\n", + "cifar10_train = dset.CIFAR10('./cs231n/datasets', train=True, download=True,\n", + " transform=transform)\n", + "loader_train = DataLoader(cifar10_train, batch_size=64, \n", + " sampler=sampler.SubsetRandomSampler(range(NUM_TRAIN)))\n", + "\n", + "cifar10_val = dset.CIFAR10('./cs231n/datasets', train=True, download=True,\n", + " transform=transform)\n", + "loader_val = DataLoader(cifar10_val, batch_size=64, \n", + " sampler=sampler.SubsetRandomSampler(range(NUM_TRAIN, 50000)))\n", + "\n", + "cifar10_test = dset.CIFAR10('./cs231n/datasets', train=False, download=True, \n", + " transform=transform)\n", + "loader_test = DataLoader(cifar10_test, batch_size=64)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "You have an option to **use GPU by setting the flag to True below**. It is not necessary to use GPU for this assignment. Note that if your computer does not have CUDA enabled, `torch.cuda.is_available()` will return False and this notebook will fallback to CPU mode.\n", + "\n", + "The global variables `dtype` and `device` will control the data types throughout this assignment. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "using device: cpu\n" + ] + } + ], + "source": [ + "USE_GPU = True\n", + "\n", + "dtype = torch.float32 # we will be using float throughout this tutorial\n", + "\n", + "if USE_GPU and torch.cuda.is_available():\n", + " device = torch.device('cuda')\n", + "else:\n", + " device = torch.device('cpu')\n", + "\n", + "# Constant to control how frequently we print train loss\n", + "print_every = 100\n", + "\n", + "print('using device:', device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part II. Barebones PyTorch\n", + "\n", + "PyTorch ships with high-level APIs to help us define model architectures conveniently, which we will cover in Part II of this tutorial. In this section, we will start with the barebone PyTorch elements to understand the autograd engine better. After this exercise, you will come to appreciate the high-level model API more.\n", + "\n", + "We will start with a simple fully-connected ReLU network with two hidden layers and no biases for CIFAR classification. \n", + "This implementation computes the forward pass using operations on PyTorch Tensors, and uses PyTorch autograd to compute gradients. It is important that you understand every line, because you will write a harder version after the example.\n", + "\n", + "When we create a PyTorch Tensor with `requires_grad=True`, then operations involving that Tensor will not just compute values; they will also build up a computational graph in the background, allowing us to easily backpropagate through the graph to compute gradients of some Tensors with respect to a downstream loss. Concretely if x is a Tensor with `x.requires_grad == True` then after backpropagation `x.grad` will be another Tensor holding the gradient of x with respect to the scalar loss at the end." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### PyTorch Tensors: Flatten Function\n", + "A PyTorch Tensor is conceptionally similar to a numpy array: it is an n-dimensional grid of numbers, and like numpy PyTorch provides many functions to efficiently operate on Tensors. As a simple example, we provide a `flatten` function below which reshapes image data for use in a fully-connected neural network.\n", + "\n", + "Recall that image data is typically stored in a Tensor of shape N x C x H x W, where:\n", + "\n", + "* N is the number of datapoints\n", + "* C is the number of channels\n", + "* H is the height of the intermediate feature map in pixels\n", + "* W is the height of the intermediate feature map in pixels\n", + "\n", + "This is the right way to represent the data when we are doing something like a 2D convolution, that needs spatial understanding of where the intermediate features are relative to each other. When we use fully connected affine layers to process the image, however, we want each datapoint to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data. So, we use a \"flatten\" operation to collapse the `C x H x W` values per representation into a single long vector. The flatten function below first reads in the N, C, H, and W values from a given batch of data, and then returns a \"view\" of that data. \"View\" is analogous to numpy's \"reshape\" method: it reshapes x's dimensions to be N x ??, where ?? is allowed to be anything (in this case, it will be C x H x W, but we don't need to specify that explicitly). " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before flattening: tensor([[[[ 0, 1],\n", + " [ 2, 3],\n", + " [ 4, 5]]],\n", + "\n", + "\n", + " [[[ 6, 7],\n", + " [ 8, 9],\n", + " [10, 11]]]])\n", + "After flattening: tensor([[ 0, 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10, 11]])\n" + ] + } + ], + "source": [ + "def flatten(x):\n", + " N = x.shape[0] # read in N, C, H, W\n", + " return x.view(N, -1) # \"flatten\" the C * H * W values into a single vector per image\n", + "\n", + "def test_flatten():\n", + " x = torch.arange(12).view(2, 1, 3, 2)\n", + " print('Before flattening: ', x)\n", + " print('After flattening: ', flatten(x))\n", + "\n", + "test_flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### Barebones PyTorch: Two-Layer Network\n", + "\n", + "Here we define a function `two_layer_fc` which performs the forward pass of a two-layer fully-connected ReLU network on a batch of image data. After defining the forward pass we check that it doesn't crash and that it produces outputs of the right shape by running zeros through the network.\n", + "\n", + "You don't have to write any code here, but it's important that you read and understand the implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "import torch.nn.functional as F # useful stateless functions\n", + "\n", + "def two_layer_fc(x, params):\n", + " \"\"\"\n", + " A fully-connected neural networks; the architecture is:\n", + " NN is fully connected -> ReLU -> fully connected layer.\n", + " Note that this function only defines the forward pass; \n", + " PyTorch will take care of the backward pass for us.\n", + " \n", + " The input to the network will be a minibatch of data, of shape\n", + " (N, d1, ..., dM) where d1 * ... * dM = D. The hidden layer will have H units,\n", + " and the output layer will produce scores for C classes.\n", + " \n", + " Inputs:\n", + " - x: A PyTorch Tensor of shape (N, d1, ..., dM) giving a minibatch of\n", + " input data.\n", + " - params: A list [w1, w2] of PyTorch Tensors giving weights for the network;\n", + " w1 has shape (D, H) and w2 has shape (H, C).\n", + " \n", + " Returns:\n", + " - scores: A PyTorch Tensor of shape (N, C) giving classification scores for\n", + " the input data x.\n", + " \"\"\"\n", + " # first we flatten the image\n", + " x = flatten(x) # shape: [batch_size, C x H x W]\n", + " \n", + " w1, w2 = params\n", + " \n", + " # Forward pass: compute predicted y using operations on Tensors. Since w1 and\n", + " # w2 have requires_grad=True, operations involving these Tensors will cause\n", + " # PyTorch to build a computational graph, allowing automatic computation of\n", + " # gradients. Since we are no longer implementing the backward pass by hand we\n", + " # don't need to keep references to intermediate values.\n", + " # you can also use `.clamp(min=0)`, equivalent to F.relu()\n", + " x = F.relu(x.mm(w1))\n", + " x = x.mm(w2)\n", + " return x\n", + " \n", + "\n", + "def two_layer_fc_test():\n", + " hidden_layer_size = 42\n", + " x = torch.zeros((64, 50), dtype=dtype) # minibatch size 64, feature dimension 50\n", + " w1 = torch.zeros((50, hidden_layer_size), dtype=dtype)\n", + " w2 = torch.zeros((hidden_layer_size, 10), dtype=dtype)\n", + " scores = two_layer_fc(x, [w1, w2])\n", + " print(scores.size()) # you should see [64, 10]\n", + "\n", + "two_layer_fc_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Three-Layer ConvNet\n", + "\n", + "Here you will complete the implementation of the function `three_layer_convnet`, which will perform the forward pass of a three-layer convolutional network. Like above, we can immediately test our implementation by passing zeros through the network. The network should have the following architecture:\n", + "\n", + "1. A convolutional layer (with bias) with `channel_1` filters, each with shape `KW1 x KH1`, and zero-padding of two\n", + "2. ReLU nonlinearity\n", + "3. A convolutional layer (with bias) with `channel_2` filters, each with shape `KW2 x KH2`, and zero-padding of one\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer with bias, producing scores for C classes.\n", + "\n", + "Note that we have **no softmax activation** here after our fully-connected layer: this is because PyTorch's cross entropy loss performs a softmax activation for you, and by bundling that step in makes computation more efficient.\n", + "\n", + "**HINT**: For convolutions: http://pytorch.org/docs/stable/nn.html#torch.nn.functional.conv2d; pay attention to the shapes of convolutional filters!" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def three_layer_convnet(x, params):\n", + " \"\"\"\n", + " Performs the forward pass of a three-layer convolutional network with the\n", + " architecture defined above.\n", + "\n", + " Inputs:\n", + " - x: A PyTorch Tensor of shape (N, 3, H, W) giving a minibatch of images\n", + " - params: A list of PyTorch Tensors giving the weights and biases for the\n", + " network; should contain the following:\n", + " - conv_w1: PyTorch Tensor of shape (channel_1, 3, KH1, KW1) giving weights\n", + " for the first convolutional layer\n", + " - conv_b1: PyTorch Tensor of shape (channel_1,) giving biases for the first\n", + " convolutional layer\n", + " - conv_w2: PyTorch Tensor of shape (channel_2, channel_1, KH2, KW2) giving\n", + " weights for the second convolutional layer\n", + " - conv_b2: PyTorch Tensor of shape (channel_2,) giving biases for the second\n", + " convolutional layer\n", + " - fc_w: PyTorch Tensor giving weights for the fully-connected layer. Can you\n", + " figure out what the shape should be?\n", + " - fc_b: PyTorch Tensor giving biases for the fully-connected layer. Can you\n", + " figure out what the shape should be?\n", + " \n", + " Returns:\n", + " - scores: PyTorch Tensor of shape (N, C) giving classification scores for x\n", + " \"\"\"\n", + " conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b = params\n", + " scores = None\n", + " ################################################################################\n", + " # TODO: Implement the forward pass for the three-layer ConvNet. #\n", + " ################################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " out_channel_1, in_channel_1, KH1, KW1 = conv_w1.shape\n", + " out_channel_2, in_channel_2, KH2, KW2 = conv_w2.shape\n", + " batch, channel, H, W = x.shape\n", + " flat_x, out = fc_w.shape\n", + " \n", + " \n", + " conv1 = nn.Conv2d(in_channel_1, out_channel_1, KH1, padding=2)\n", + " conv1.weight = conv_w1\n", + " conv1.bias = conv_b1\n", + " \n", + " conv2 = nn.Conv2d(in_channel_2, out_channel_2, KH2, padding=1)\n", + " conv2.weight = conv_w2\n", + " conv2.bias = conv_b2\n", + " \n", + " \n", + " x = F.relu(conv1(x))\n", + " x = F.relu(conv2(x))\n", + " x = flatten(x) \n", + " scores = x.mm(fc_w) + fc_b\n", + "\n", + " \n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ################################################################################\n", + " # END OF YOUR CODE #\n", + " ################################################################################\n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After defining the forward pass of the ConvNet above, run the following cell to test your implementation.\n", + "\n", + "When you run this function, scores should have shape (64, 10)." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "def three_layer_convnet_test():\n", + " x = torch.zeros((64, 3, 32, 32), dtype=dtype) # minibatch size 64, image size [3, 32, 32]\n", + "\n", + " conv_w1 = nn.Parameter(torch.zeros((6, 3, 5, 5), dtype=dtype)) # [out_channel, in_channel, kernel_H, kernel_W]\n", + " conv_b1 = nn.Parameter(torch.zeros((6,))) # out_channel\n", + " conv_w2 = nn.Parameter(torch.zeros((9, 6, 3, 3), dtype=dtype)) # [out_channel, in_channel, kernel_H, kernel_W]\n", + " conv_b2 = nn.Parameter(torch.zeros((9,))) # out_channel\n", + "\n", + " # you must calculate the shape of the tensor after two conv layers, before the fully-connected layer\n", + " fc_w = torch.zeros((9 * 32 * 32, 10))\n", + " fc_b = torch.zeros(10)\n", + "\n", + " scores = three_layer_convnet(x, [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b])\n", + " \n", + " print(scores.size()) # you should see [64, 10]\n", + "three_layer_convnet_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Initialization\n", + "Let's write a couple utility methods to initialize the weight matrices for our models.\n", + "\n", + "- `random_weight(shape)` initializes a weight tensor with the Kaiming normalization method.\n", + "- `zero_weight(shape)` initializes a weight tensor with all zeros. Useful for instantiating bias parameters.\n", + "\n", + "The `random_weight` function uses the Kaiming normal initialization method, described in:\n", + "\n", + "He et al, *Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification*, ICCV 2015, https://arxiv.org/abs/1502.01852" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-0.9709, 0.8014, -0.7096, -1.3630, 0.2763],\n", + " [ 0.0339, -0.0283, -0.5006, 1.0902, -0.4276],\n", + " [ 0.2078, -1.4354, 0.7377, -1.7247, -1.3787]], requires_grad=True)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def random_weight(shape):\n", + " \"\"\"\n", + " Create random Tensors for weights; setting requires_grad=True means that we\n", + " want to compute gradients for these Tensors during the backward pass.\n", + " We use Kaiming normalization: sqrt(2 / fan_in)\n", + " \"\"\"\n", + " if len(shape) == 2: # FC weight\n", + " fan_in = shape[0]\n", + " else:\n", + " fan_in = np.prod(shape[1:]) # conv weight [out_channel, in_channel, kH, kW]\n", + " # randn is standard normal distribution generator. \n", + " w = torch.randn(shape, device=device, dtype=dtype) * np.sqrt(2. / fan_in)\n", + " w.requires_grad = True\n", + " return w\n", + "\n", + "def zero_weight(shape):\n", + " return torch.zeros(shape, device=device, dtype=dtype, requires_grad=True)\n", + "\n", + "# create a weight of shape [3 x 5]\n", + "# you should see the type `torch.cuda.FloatTensor` if you use GPU. \n", + "# Otherwise it should be `torch.FloatTensor`\n", + "random_weight((3, 5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Check Accuracy\n", + "When training the model we will use the following function to check the accuracy of our model on the training or validation sets.\n", + "\n", + "When checking accuracy we don't need to compute any gradients; as a result we don't need PyTorch to build a computational graph for us when we compute scores. To prevent a graph from being built we scope our computation under a `torch.no_grad()` context manager." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def check_accuracy_part2(loader, model_fn, params):\n", + " \"\"\"\n", + " Check the accuracy of a classification model.\n", + " \n", + " Inputs:\n", + " - loader: A DataLoader for the data split we want to check\n", + " - model_fn: A function that performs the forward pass of the model,\n", + " with the signature scores = model_fn(x, params)\n", + " - params: List of PyTorch Tensors giving parameters of the model\n", + " \n", + " Returns: Nothing, but prints the accuracy of the model\n", + " \"\"\"\n", + " split = 'val' if loader.dataset.train else 'test'\n", + " print('Checking accuracy on the %s set' % split)\n", + " num_correct, num_samples = 0, 0\n", + " with torch.no_grad():\n", + " for x, y in loader:\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.int64)\n", + " scores = model_fn(x, params)\n", + " _, preds = scores.max(1)\n", + " num_correct += (preds == y).sum()\n", + " num_samples += preds.size(0)\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f%%)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Training Loop\n", + "We can now set up a basic training loop to train our network. We will train the model using stochastic gradient descent without momentum. We will use `torch.functional.cross_entropy` to compute the loss; you can [read about it here](http://pytorch.org/docs/stable/nn.html#cross-entropy).\n", + "\n", + "The training loop takes as input the neural network function, a list of initialized parameters (`[w1, w2]` in our example), and learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def train_part2(model_fn, params, learning_rate):\n", + " \"\"\"\n", + " Train a model on CIFAR-10.\n", + " \n", + " Inputs:\n", + " - model_fn: A Python function that performs the forward pass of the model.\n", + " It should have the signature scores = model_fn(x, params) where x is a\n", + " PyTorch Tensor of image data, params is a list of PyTorch Tensors giving\n", + " model weights, and scores is a PyTorch Tensor of shape (N, C) giving\n", + " scores for the elements in x.\n", + " - params: List of PyTorch Tensors giving weights for the model\n", + " - learning_rate: Python scalar giving the learning rate to use for SGD\n", + " \n", + " Returns: Nothing\n", + " \"\"\"\n", + " for t, (x, y) in enumerate(loader_train):\n", + " # Move the data to the proper device (GPU or CPU)\n", + " x = x.to(device=device, dtype=dtype)\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " # Forward pass: compute scores and loss\n", + " scores = model_fn(x, params)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Backward pass: PyTorch figures out which Tensors in the computational\n", + " # graph has requires_grad=True and uses backpropagation to compute the\n", + " # gradient of the loss with respect to these Tensors, and stores the\n", + " # gradients in the .grad attribute of each Tensor.\n", + " loss.backward()\n", + "\n", + " # Update parameters. We don't want to backpropagate through the\n", + " # parameter updates, so we scope the updates under a torch.no_grad()\n", + " # context manager to prevent a computational graph from being built.\n", + " with torch.no_grad():\n", + " for w in params:\n", + " \n", + " w -= learning_rate * w.grad\n", + "\n", + " # Manually zero the gradients after running the backward pass\n", + " w.grad.zero_()\n", + "\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss.item()))\n", + " check_accuracy_part2(loader_val, model_fn, params)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Train a Two-Layer Network\n", + "Now we are ready to run the training loop. We need to explicitly allocate tensors for the fully connected weights, `w1` and `w2`. \n", + "\n", + "Each minibatch of CIFAR has 64 examples, so the tensor shape is `[64, 3, 32, 32]`. \n", + "\n", + "After flattening, `x` shape should be `[64, 3 * 32 * 32]`. This will be the size of the first dimension of `w1`. \n", + "The second dimension of `w1` is the hidden layer size, which will also be the first dimension of `w2`. \n", + "\n", + "Finally, the output of the network is a 10-dimensional vector that represents the probability distribution over 10 classes. \n", + "\n", + "You don't need to tune any hyperparameters but you should see accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.8139\n", + "Checking accuracy on the val set\n", + "Got 140 / 1000 correct (14.00%)\n", + "\n", + "Iteration 100, loss = 2.3674\n", + "Checking accuracy on the val set\n", + "Got 352 / 1000 correct (35.20%)\n", + "\n", + "Iteration 200, loss = 1.9672\n", + "Checking accuracy on the val set\n", + "Got 331 / 1000 correct (33.10%)\n", + "\n", + "Iteration 300, loss = 1.9417\n", + "Checking accuracy on the val set\n", + "Got 412 / 1000 correct (41.20%)\n", + "\n", + "Iteration 400, loss = 1.7889\n", + "Checking accuracy on the val set\n", + "Got 392 / 1000 correct (39.20%)\n", + "\n", + "Iteration 500, loss = 1.8344\n", + "Checking accuracy on the val set\n", + "Got 386 / 1000 correct (38.60%)\n", + "\n", + "Iteration 600, loss = 1.6571\n", + "Checking accuracy on the val set\n", + "Got 374 / 1000 correct (37.40%)\n", + "\n", + "Iteration 700, loss = 1.2890\n", + "Checking accuracy on the val set\n", + "Got 458 / 1000 correct (45.80%)\n", + "\n" + ] + } + ], + "source": [ + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "\n", + "w1 = random_weight((3 * 32 * 32, hidden_layer_size))\n", + "w2 = random_weight((hidden_layer_size, 10))\n", + "\n", + "train_part2(two_layer_fc, [w1, w2], learning_rate)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Training a ConvNet\n", + "\n", + "In the below you should use the functions defined above to train a three-layer convolutional network on CIFAR. The network should have the following architecture:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You should initialize your weight matrices using the `random_weight` function defined above, and you should initialize your bias vectors using the `zero_weight` function above.\n", + "\n", + "You don't need to tune any hyperparameters, but if everything works correctly you should achieve an accuracy above 42% after one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.7931\n", + "Checking accuracy on the val set\n", + "Got 136 / 1000 correct (13.60%)\n", + "\n", + "Iteration 100, loss = 2.0767\n", + "Checking accuracy on the val set\n", + "Got 351 / 1000 correct (35.10%)\n", + "\n", + "Iteration 200, loss = 1.9719\n", + "Checking accuracy on the val set\n", + "Got 371 / 1000 correct (37.10%)\n", + "\n", + "Iteration 300, loss = 1.5589\n", + "Checking accuracy on the val set\n", + "Got 422 / 1000 correct (42.20%)\n", + "\n", + "Iteration 400, loss = 1.5141\n", + "Checking accuracy on the val set\n", + "Got 452 / 1000 correct (45.20%)\n", + "\n", + "Iteration 500, loss = 1.5812\n", + "Checking accuracy on the val set\n", + "Got 445 / 1000 correct (44.50%)\n", + "\n", + "Iteration 600, loss = 1.4372\n", + "Checking accuracy on the val set\n", + "Got 461 / 1000 correct (46.10%)\n", + "\n", + "Iteration 700, loss = 1.5749\n", + "Checking accuracy on the val set\n", + "Got 460 / 1000 correct (46.00%)\n", + "\n" + ] + } + ], + "source": [ + "learning_rate = 3e-3\n", + "\n", + "channel_1 = 32\n", + "channel_2 = 16\n", + "\n", + "conv_w1 = None\n", + "conv_b1 = None\n", + "conv_w2 = None\n", + "conv_b2 = None\n", + "fc_w = None\n", + "fc_b = None\n", + "\n", + "################################################################################\n", + "# TODO: Initialize the parameters of a three-layer ConvNet. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "conv_w1 = nn.Parameter(random_weight((32,3,5,5)))\n", + "conv_b1 = nn.Parameter(zero_weight(32))\n", + "conv_w2 = nn.Parameter(random_weight((16, 32,3, 3)))\n", + "conv_b2 = nn.Parameter(zero_weight(16))\n", + "fc_w = nn.Parameter(random_weight((32*32*16, 10)))\n", + "fc_b = nn.Parameter(zero_weight(10))\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE #\n", + "################################################################################\n", + "\n", + "params = [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b]\n", + "train_part2(three_layer_convnet, params, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part III. PyTorch Module API\n", + "\n", + "Barebone PyTorch requires that we track all the parameter tensors by hand. This is fine for small networks with a few tensors, but it would be extremely inconvenient and error-prone to track tens or hundreds of tensors in larger networks.\n", + "\n", + "PyTorch provides the `nn.Module` API for you to define arbitrary network architectures, while tracking every learnable parameters for you. In Part II, we implemented SGD ourselves. PyTorch also provides the `torch.optim` package that implements all the common optimizers, such as RMSProp, Adagrad, and Adam. It even supports approximate second-order methods like L-BFGS! You can refer to the [doc](http://pytorch.org/docs/master/optim.html) for the exact specifications of each optimizer.\n", + "\n", + "To use the Module API, follow the steps below:\n", + "\n", + "1. Subclass `nn.Module`. Give your network class an intuitive name like `TwoLayerFC`. \n", + "\n", + "2. In the constructor `__init__()`, define all the layers you need as class attributes. Layer objects like `nn.Linear` and `nn.Conv2d` are themselves `nn.Module` subclasses and contain learnable parameters, so that you don't have to instantiate the raw tensors yourself. `nn.Module` will track these internal parameters for you. Refer to the [doc](http://pytorch.org/docs/master/nn.html) to learn more about the dozens of builtin layers. **Warning**: don't forget to call the `super().__init__()` first!\n", + "\n", + "3. In the `forward()` method, define the *connectivity* of your network. You should use the attributes defined in `__init__` as function calls that take tensor as input and output the \"transformed\" tensor. Do *not* create any new layers with learnable parameters in `forward()`! All of them must be declared upfront in `__init__`. \n", + "\n", + "After you define your Module subclass, you can instantiate it as an object and call it just like the NN forward function in part II.\n", + "\n", + "### Module API: Two-Layer Network\n", + "Here is a concrete example of a 2-layer fully connected network:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "class TwoLayerFC(nn.Module):\n", + " def __init__(self, input_size, hidden_size, num_classes):\n", + " super().__init__()\n", + " # assign layer objects to class attributes\n", + " self.fc1 = nn.Linear(input_size, hidden_size)\n", + " # nn.init package contains convenient initialization methods\n", + " # http://pytorch.org/docs/master/nn.html#torch-nn-init \n", + " nn.init.kaiming_normal_(self.fc1.weight)\n", + " self.fc2 = nn.Linear(hidden_size, num_classes)\n", + " nn.init.kaiming_normal_(self.fc2.weight)\n", + " \n", + " def forward(self, x):\n", + " # forward always defines connectivity\n", + " x = flatten(x)\n", + " scores = self.fc2(F.relu(self.fc1(x)))\n", + " return scores\n", + "\n", + "def test_TwoLayerFC():\n", + " input_size = 50\n", + " x = torch.zeros((64, input_size), dtype=dtype) # minibatch size 64, feature dimension 50\n", + " model = TwoLayerFC(input_size, 42, 10)\n", + " scores = model(x)\n", + " print(scores.size()) # you should see [64, 10]\n", + "test_TwoLayerFC()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Three-Layer ConvNet\n", + "It's your turn to implement a 3-layer ConvNet followed by a fully connected layer. The network architecture should be the same as in Part II:\n", + "\n", + "1. Convolutional layer with `channel_1` 5x5 filters with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer with `channel_2` 3x3 filters with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer to `num_classes` classes\n", + "\n", + "You should initialize the weight matrices of the model using the Kaiming normal initialization method.\n", + "\n", + "**HINT**: http://pytorch.org/docs/stable/nn.html#conv2d\n", + "\n", + "After you implement the three-layer ConvNet, the `test_ThreeLayerConvNet` function will run your implementation; it should print `(64, 10)` for the shape of the output scores." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "class ThreeLayerConvNet(nn.Module):\n", + " def __init__(self, in_channel, channel_1, channel_2, num_classes):\n", + " super().__init__()\n", + " ########################################################################\n", + " # TODO: Set up the layers you need for a three-layer ConvNet with the #\n", + " # architecture defined above. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + " self.conv1 = nn.Conv2d(in_channel, channel_1, 5, padding=2)\n", + " nn.init.kaiming_normal_(self.conv1.weight)\n", + " self.conv2 = nn.Conv2d(channel_1, channel_2, 3, padding=1)\n", + " nn.init.kaiming_normal_(self.conv2.weight)\n", + " self.fc1 = nn.Linear(channel_2 * 32 * 32, num_classes)\n", + " nn.init.kaiming_normal_(self.fc1.weight)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE # \n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " scores = None\n", + " ########################################################################\n", + " # TODO: Implement the forward function for a 3-layer ConvNet. you #\n", + " # should use the layers you defined in __init__ and specify the #\n", + " # connectivity of those layers in forward() #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = F.relu(self.conv1(x))\n", + " x = F.relu(self.conv2(x))\n", + " x = flatten(x)\n", + " scores = self.fc1(x)\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + " return scores\n", + "\n", + "\n", + "def test_ThreeLayerConvNet():\n", + " x = torch.zeros((64, 3, 32, 32), dtype=dtype) # minibatch size 64, image size [3, 32, 32]\n", + " model = ThreeLayerConvNet(in_channel=3, channel_1=12, channel_2=8, num_classes=10)\n", + " scores = model(x)\n", + " print(scores.size()) # you should see [64, 10]\n", + "test_ThreeLayerConvNet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Check Accuracy\n", + "Given the validation or test set, we can check the classification accuracy of a neural network. \n", + "\n", + "This version is slightly different from the one in part II. You don't manually pass in the parameters anymore." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "def check_accuracy_part34(loader, model):\n", + " if loader.dataset.train:\n", + " print('Checking accuracy on validation set')\n", + " else:\n", + " print('Checking accuracy on test set') \n", + " num_correct = 0\n", + " num_samples = 0\n", + " model.eval() # set model to evaluation mode\n", + " with torch.no_grad():\n", + " for x, y in loader:\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + " scores = model(x)\n", + " _, preds = scores.max(1)\n", + " num_correct += (preds == y).sum()\n", + " num_samples += preds.size(0)\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Training Loop\n", + "We also use a slightly different training loop. Rather than updating the values of the weights ourselves, we use an Optimizer object from the `torch.optim` package, which abstract the notion of an optimization algorithm and provides implementations of most of the algorithms commonly used to optimize neural networks." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def train_part34(model, optimizer, epochs=1):\n", + " \"\"\"\n", + " Train a model on CIFAR-10 using the PyTorch Module API.\n", + " \n", + " Inputs:\n", + " - model: A PyTorch Module giving the model to train.\n", + " - optimizer: An Optimizer object we will use to train the model\n", + " - epochs: (Optional) A Python integer giving the number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints model accuracies during training.\n", + " \"\"\"\n", + " model = model.to(device=device) # move the model parameters to CPU/GPU\n", + " for e in range(epochs):\n", + " for t, (x, y) in enumerate(loader_train):\n", + " model.train() # put model to training mode\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " scores = model(x)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Zero out all of the gradients for the variables which the optimizer\n", + " # will update.\n", + " optimizer.zero_grad()\n", + "\n", + " # This is the backwards pass: compute the gradient of the loss with\n", + " # respect to each parameter of the model.\n", + " loss.backward()\n", + "\n", + " # Actually update the parameters of the model using the gradients\n", + " # computed by the backwards pass.\n", + " optimizer.step()\n", + "\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss.item()))\n", + " check_accuracy_part34(loader_val, model)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Train a Two-Layer Network\n", + "Now we are ready to run the training loop. In contrast to part II, we don't explicitly allocate parameter tensors anymore.\n", + "\n", + "Simply pass the input size, hidden layer size, and number of classes (i.e. output size) to the constructor of `TwoLayerFC`. \n", + "\n", + "You also need to define an optimizer that tracks all the learnable parameters inside `TwoLayerFC`.\n", + "\n", + "You don't need to tune any hyperparameters, but you should see model accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 4.0420\n", + "Checking accuracy on validation set\n", + "Got 145 / 1000 correct (14.50)\n", + "\n", + "Iteration 100, loss = 1.8651\n", + "Checking accuracy on validation set\n", + "Got 365 / 1000 correct (36.50)\n", + "\n", + "Iteration 200, loss = 1.6438\n", + "Checking accuracy on validation set\n", + "Got 387 / 1000 correct (38.70)\n", + "\n", + "Iteration 300, loss = 1.6083\n", + "Checking accuracy on validation set\n", + "Got 385 / 1000 correct (38.50)\n", + "\n", + "Iteration 400, loss = 1.7412\n", + "Checking accuracy on validation set\n", + "Got 437 / 1000 correct (43.70)\n", + "\n", + "Iteration 500, loss = 1.9077\n", + "Checking accuracy on validation set\n", + "Got 424 / 1000 correct (42.40)\n", + "\n", + "Iteration 600, loss = 1.8101\n", + "Checking accuracy on validation set\n", + "Got 456 / 1000 correct (45.60)\n", + "\n", + "Iteration 700, loss = 1.8079\n", + "Checking accuracy on validation set\n", + "Got 452 / 1000 correct (45.20)\n", + "\n" + ] + } + ], + "source": [ + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "model = TwoLayerFC(3 * 32 * 32, hidden_layer_size, 10)\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Train a Three-Layer ConvNet\n", + "You should now use the Module API to train a three-layer ConvNet on CIFAR. This should look very similar to training the two-layer network! You don't need to tune any hyperparameters, but you should achieve above above 45% after training for one epoch.\n", + "\n", + "You should train the model using stochastic gradient descent without momentum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 3e-3\n", + "channel_1 = 32\n", + "channel_2 = 16\n", + "\n", + "model = None\n", + "optimizer = None\n", + "################################################################################\n", + "# TODO: Instantiate your ThreeLayerConvNet model and a corresponding optimizer #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "model = ThreeLayerConvNet(3, channel_1, channel_2, 10)\n", + "\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part IV. PyTorch Sequential API\n", + "\n", + "Part III introduced the PyTorch Module API, which allows you to define arbitrary learnable layers and their connectivity. \n", + "\n", + "For simple models like a stack of feed forward layers, you still need to go through 3 steps: subclass `nn.Module`, assign layers to class attributes in `__init__`, and call each layer one by one in `forward()`. Is there a more convenient way? \n", + "\n", + "Fortunately, PyTorch provides a container Module called `nn.Sequential`, which merges the above steps into one. It is not as flexible as `nn.Module`, because you cannot specify more complex topology than a feed-forward stack, but it's good enough for many use cases.\n", + "\n", + "### Sequential API: Two-Layer Network\n", + "Let's see how to rewrite our two-layer fully connected network example with `nn.Sequential`, and train it using the training loop defined above.\n", + "\n", + "Again, you don't need to tune any hyperparameters here, but you shoud achieve above 40% accuracy after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We need to wrap `flatten` function in a module in order to stack it\n", + "# in nn.Sequential\n", + "class Flatten(nn.Module):\n", + " def forward(self, x):\n", + " return flatten(x)\n", + "\n", + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "\n", + "model = nn.Sequential(\n", + " Flatten(),\n", + " nn.Linear(3 * 32 * 32, hidden_layer_size),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_layer_size, 10),\n", + ")\n", + "\n", + "# you can use Nesterov momentum in optim.SGD\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate,\n", + " momentum=0.9, nesterov=True)\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sequential API: Three-Layer ConvNet\n", + "Here you should use `nn.Sequential` to define and train a three-layer ConvNet with the same architecture we used in Part III:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You should initialize your weight matrices using the `random_weight` function defined above, and you should initialize your bias vectors using the `zero_weight` function above.\n", + "\n", + "You should optimize your model using stochastic gradient descent with Nesterov momentum 0.9.\n", + "\n", + "Again, you don't need to tune any hyperparameters but you should see accuracy above 55% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "channel_1 = 32\n", + "channel_2 = 16\n", + "learning_rate = 1e-2\n", + "\n", + "model = None\n", + "optimizer = None\n", + "\n", + "################################################################################\n", + "# TODO: Rewrite the 2-layer ConvNet with bias from Part III with the #\n", + "# Sequential API. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "pass\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part V. CIFAR-10 open-ended challenge\n", + "\n", + "In this section, you can experiment with whatever ConvNet architecture you'd like on CIFAR-10. \n", + "\n", + "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves **at least 70%** accuracy on the CIFAR-10 **validation** set within 10 epochs. You can use the check_accuracy and train functions from above. You can use either `nn.Module` or `nn.Sequential` API. \n", + "\n", + "Describe what you did at the end of this notebook.\n", + "\n", + "Here are the official API documentation for each component. One note: what we call in the class \"spatial batch norm\" is called \"BatchNorm2D\" in PyTorch.\n", + "\n", + "* Layers in torch.nn package: http://pytorch.org/docs/stable/nn.html\n", + "* Activations: http://pytorch.org/docs/stable/nn.html#non-linear-activations\n", + "* Loss functions: http://pytorch.org/docs/stable/nn.html#loss-functions\n", + "* Optimizers: http://pytorch.org/docs/stable/optim.html\n", + "\n", + "\n", + "### Things you might try:\n", + "- **Filter size**: Above we used 5x5; would smaller filters be more efficient?\n", + "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n", + "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n", + "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n", + "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n", + " - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", + " - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", + " - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n", + "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n", + "- **Regularization**: Add l2 weight regularization, or perhaps use Dropout.\n", + "\n", + "### Tips for training\n", + "For each network architecture that you try, you should tune the learning rate and other hyperparameters. When doing this there are a couple important things to keep in mind:\n", + "\n", + "- If the parameters are working well, you should see improvement within a few hundred iterations\n", + "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n", + "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n", + "- You should use the validation set for hyperparameter search, and save your test set for evaluating your architecture on the best parameters as selected by the validation set.\n", + "\n", + "### Going above and beyond\n", + "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these, but don't miss the fun if you have time!\n", + "\n", + "- Alternative optimizers: you can try Adam, Adagrad, RMSprop, etc.\n", + "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n", + "- Model ensembles\n", + "- Data augmentation\n", + "- New Architectures\n", + " - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n", + " - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n", + " - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n", + "\n", + "### Have fun and happy training! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "################################################################################\n", + "# TODO: # \n", + "# Experiment with any architectures, optimizers, and hyperparameters. #\n", + "# Achieve AT LEAST 70% accuracy on the *validation set* within 10 epochs. #\n", + "# #\n", + "# Note that you can use the check_accuracy function to evaluate on either #\n", + "# the test set or the validation set, by passing either loader_test or #\n", + "# loader_val as the second argument to check_accuracy. You should not touch #\n", + "# the test set until you have finished your architecture and hyperparameter #\n", + "# tuning, and only run the test set once at the end to report a final value. #\n", + "################################################################################\n", + "model = None\n", + "optimizer = None\n", + "\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "pass\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "# You should get at least 70% accuracy\n", + "train_part34(model, optimizer, epochs=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Describe what you did \n", + "\n", + "In the cell below you should write an explanation of what you did, any additional features that you implemented, and/or any graphs that you made in the process of training and evaluating your network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "TODO: Describe what you did" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test set -- run this only once\n", + "\n", + "Now that we've gotten a result we're happy with, we test our final model on the test set (which you should store in best_model). Think about how this compares to your validation set accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_model = model\n", + "check_accuracy_part34(loader_test, best_model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment2/.ipynb_checkpoints/TensorFlow-checkpoint.ipynb b/assignment2/.ipynb_checkpoints/TensorFlow-checkpoint.ipynb new file mode 100755 index 0000000..4cd00dc --- /dev/null +++ b/assignment2/.ipynb_checkpoints/TensorFlow-checkpoint.ipynb @@ -0,0 +1,1888 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# What's this TensorFlow business?\n", + "\n", + "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n", + "\n", + "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you choose to work with that notebook)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "#### What is it?\n", + "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n", + "\n", + "#### Why?\n", + "\n", + "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n", + "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n", + "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n", + "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## How will I learn TensorFlow?\n", + "\n", + "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n", + "\n", + "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here.\n", + "\n", + "**NOTE: This notebook is meant to teach you the latest version of Tensorflow 2.0. Most examples on the web today are still in 1.x, so be careful not to confuse the two when looking up documentation**.\n", + "\n", + "## Install Tensorflow 2.0\n", + "Tensorflow 2.0 is still not in a fully 100% stable release, but it's still usable and more intuitive than TF 1.x. Please make sure you have it installed before moving on in this notebook! Here are some steps to get started:\n", + "\n", + "1. Have the latest version of Anaconda installed on your machine.\n", + "2. Create a new conda environment starting from Python 3.7. In this setup example, we'll call it `tf_20_env`.\n", + "3. Run the command: `source activate tf_20_env`\n", + "4. Then pip install TF 2.0 as described here: https://www.tensorflow.org/install/pip \n", + "\n", + "A guide on creating Anaconda enviornments: https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/\n", + "\n", + "This will give you an new enviornemnt to play in TF 2.0. Generally, if you plan to also use TensorFlow in your other projects, you might also want to keep a seperate Conda environment or virtualenv in Python 3.7 that has Tensorflow 1.9, so you can switch back and forth at will. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Table of Contents\n", + "\n", + "This notebook has 5 parts. We will walk through TensorFlow at **three different levels of abstraction**, which should help you better understand it and prepare you for working on your project.\n", + "\n", + "1. Part I, Preparation: load the CIFAR-10 dataset.\n", + "2. Part II, Barebone TensorFlow: **Abstraction Level 1**, we will work directly with low-level TensorFlow graphs. \n", + "3. Part III, Keras Model API: **Abstraction Level 2**, we will use `tf.keras.Model` to define arbitrary neural network architecture. \n", + "4. Part IV, Keras Sequential + Functional API: **Abstraction Level 3**, we will use `tf.keras.Sequential` to define a linear feed-forward network very conveniently, and then explore the functional libraries for building unique and uncommon models that require more flexibility.\n", + "5. Part V, CIFAR-10 open-ended challenge: please implement your own network to get as high accuracy as possible on CIFAR-10. You can experiment with any layer, optimizer, hyperparameters or other advanced features. \n", + "\n", + "We will discuss Keras in more detail later in the notebook.\n", + "\n", + "Here is a table of comparison:\n", + "\n", + "| API | Flexibility | Convenience |\n", + "|---------------|-------------|-------------|\n", + "| Barebone | High | Low |\n", + "| `tf.keras.Model` | High | Medium |\n", + "| `tf.keras.Sequential` | Low | High |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part I: Preparation\n", + "\n", + "First, we load the CIFAR-10 dataset. This might take a few minutes to download the first time you run it, but after that the files should be cached on disk and loading should be faster.\n", + "\n", + "In previous parts of the assignment we used CS231N-specific code to download and read the CIFAR-10 dataset; however the `tf.keras.datasets` package in TensorFlow provides prebuilt utility functions for loading many common datasets.\n", + "\n", + "For the purposes of this assignment we will still write our own code to preprocess the data and iterate through it in minibatches. The `tf.data` package in TensorFlow provides tools for automating this process, but working with this package adds extra complication and is beyond the scope of this notebook. However using `tf.data` can be much more efficient than the simple approach used in this notebook, so you should consider using it for your project." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math\n", + "import timeit\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(tf.version)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "important (49000, 32, 32, 3)\n", + "Train data shape: (49000, 32, 32, 3)\n", + "Train labels shape: (49000,) int32\n", + "Validation data shape: (1000, 32, 32, 3)\n", + "Validation labels shape: (1000,)\n", + "Test data shape: (10000, 32, 32, 3)\n", + "Test labels shape: (10000,)\n" + ] + } + ], + "source": [ + "def load_cifar10(num_training=49000, num_validation=1000, num_test=10000):\n", + " \"\"\"\n", + " Fetch the CIFAR-10 dataset from the web and perform preprocessing to prepare\n", + " it for the two-layer neural net classifier. These are the same steps as\n", + " we used for the SVM, but condensed to a single function.\n", + " \"\"\"\n", + " # Load the raw CIFAR-10 dataset and use appropriate data types and shapes\n", + " cifar10 = tf.keras.datasets.cifar10.load_data()\n", + " (X_train, y_train), (X_test, y_test) = cifar10\n", + " X_train = np.asarray(X_train, dtype=np.float32)\n", + " y_train = np.asarray(y_train, dtype=np.int32).flatten()\n", + " X_test = np.asarray(X_test, dtype=np.float32)\n", + " y_test = np.asarray(y_test, dtype=np.int32).flatten()\n", + "\n", + " # Subsample the data\n", + " mask = range(num_training, num_training + num_validation)\n", + " X_val = X_train[mask]\n", + " y_val = y_train[mask]\n", + " mask = range(num_training)\n", + " X_train = X_train[mask]\n", + " y_train = y_train[mask]\n", + " mask = range(num_test)\n", + " X_test = X_test[mask]\n", + " y_test = y_test[mask]\n", + " \n", + " print(\"important\", X_train.shape)\n", + " # Normalize the data: subtract the mean pixel and divide by std\n", + " mean_pixel = X_train.mean(axis=(0, 1, 2), keepdims=True)\n", + " std_pixel = X_train.std(axis=(0, 1, 2), keepdims=True)\n", + " X_train = (X_train - mean_pixel) / std_pixel\n", + " X_val = (X_val - mean_pixel) / std_pixel\n", + " X_test = (X_test - mean_pixel) / std_pixel\n", + "\n", + " return X_train, y_train, X_val, y_val, X_test, y_test\n", + "\n", + "# If there are errors with SSL downloading involving self-signed certificates,\n", + "# it may be that your Python version was recently installed on the current machine.\n", + "# See: https://github.com/tensorflow/tensorflow/issues/10779\n", + "# To fix, run the command: /Applications/Python\\ 3.7/Install\\ Certificates.command\n", + "# ...replacing paths as necessary.\n", + "\n", + "# Invoke the above function to get our data.\n", + "NHW = (0, 1, 2)\n", + "X_train, y_train, X_val, y_val, X_test, y_test = load_cifar10()\n", + "print('Train data shape: ', X_train.shape)\n", + "print('Train labels shape: ', y_train.shape, y_train.dtype)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Validation labels shape: ', y_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "class Dataset(object):\n", + " def __init__(self, X, y, batch_size, shuffle=False):\n", + " \"\"\"\n", + " Construct a Dataset object to iterate over data X and labels y\n", + " \n", + " Inputs:\n", + " - X: Numpy array of data, of any shape\n", + " - y: Numpy array of labels, of any shape but with y.shape[0] == X.shape[0]\n", + " - batch_size: Integer giving number of elements per minibatch\n", + " - shuffle: (optional) Boolean, whether to shuffle the data on each epoch\n", + " \"\"\"\n", + " assert X.shape[0] == y.shape[0], 'Got different numbers of data and labels'\n", + " self.X, self.y = X, y\n", + " self.batch_size, self.shuffle = batch_size, shuffle\n", + "\n", + " def __iter__(self):\n", + " N, B = self.X.shape[0], self.batch_size\n", + " idxs = np.arange(N)\n", + " if self.shuffle:\n", + " np.random.shuffle(idxs)\n", + " return iter((self.X[i:i+B], self.y[i:i+B]) for i in range(0, N, B))\n", + "\n", + "\n", + "train_dset = Dataset(X_train, y_train, batch_size=64, shuffle=True)\n", + "val_dset = Dataset(X_val, y_val, batch_size=64, shuffle=False)\n", + "test_dset = Dataset(X_test, y_test, batch_size=64)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 (64, 32, 32, 3) (64,)\n", + "1 (64, 32, 32, 3) (64,)\n", + "2 (64, 32, 32, 3) (64,)\n", + "3 (64, 32, 32, 3) (64,)\n", + "4 (64, 32, 32, 3) (64,)\n", + "5 (64, 32, 32, 3) (64,)\n", + "6 (64, 32, 32, 3) (64,)\n" + ] + } + ], + "source": [ + "# We can iterate through a dataset like this:\n", + "for t, (x, y) in enumerate(train_dset):\n", + " print(t, x.shape, y.shape)\n", + " if t > 5: break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can optionally **use GPU by setting the flag to True below**. It's not neccessary to use a GPU for this assignment; if you are working on Google Cloud then we recommend that you do not use a GPU, as it will be significantly more expensive." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: /cpu:0\n" + ] + } + ], + "source": [ + "# Set up some global variables\n", + "USE_GPU = False\n", + "\n", + "if USE_GPU:\n", + " device = '/device:GPU:0'\n", + "else:\n", + " device = '/cpu:0'\n", + "\n", + "# Constant to control how often we print when training models\n", + "print_every = 100\n", + "\n", + "print('Using device: ', device)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Part II: Barebones TensorFlow\n", + "TensorFlow ships with various high-level APIs which make it very convenient to define and train neural networks; we will cover some of these constructs in Part III and Part IV of this notebook. In this section we will start by building a model with basic TensorFlow constructs to help you better understand what's going on under the hood of the higher-level APIs.\n", + "\n", + "**\"Barebones Tensorflow\" is important to understanding the building blocks of TensorFlow, but much of it involves concepts from TensorFlow 1.x.** We will be working with legacy modules such as `tf.Variable`.\n", + "\n", + "Therefore, please read and understand the differences between legacy (1.x) TF and the new (2.0) TF.\n", + "\n", + "### Historical background on TensorFlow 1.x\n", + "\n", + "TensorFlow 1.x is primarily a framework for working with **static computational graphs**. Nodes in the computational graph are Tensors which will hold n-dimensional arrays when the graph is run; edges in the graph represent functions that will operate on Tensors when the graph is run to actually perform useful computation.\n", + "\n", + "Before Tensorflow 2.0, we had to configure the graph into two phases. There are plenty of tutorials online that explain this two-step process. The process generally looks like the following for TF 1.x:\n", + "1. **Build a computational graph that describes the computation that you want to perform**. This stage doesn't actually perform any computation; it just builds up a symbolic representation of your computation. This stage will typically define one or more `placeholder` objects that represent inputs to the computational graph.\n", + "2. **Run the computational graph many times.** Each time the graph is run (e.g. for one gradient descent step) you will specify which parts of the graph you want to compute, and pass a `feed_dict` dictionary that will give concrete values to any `placeholder`s in the graph.\n", + "\n", + "### The new paradigm in Tensorflow 2.0\n", + "Now, with Tensorflow 2.0, we can simply adopt a functional form that is more Pythonic and similar in spirit to PyTorch and direct Numpy operation. Instead of the 2-step paradigm with computation graphs, making it (among other things) easier to debug TF code. You can read more details at https://www.tensorflow.org/guide/eager.\n", + "\n", + "The main difference between the TF 1.x and 2.0 approach is that the 2.0 approach doesn't make use of `tf.Session`, `tf.run`, `placeholder`, `feed_dict`. To get more details of what's different between the two version and how to convert between the two, check out the official migration guide: https://www.tensorflow.org/alpha/guide/migration_guide\n", + "\n", + "Later, in the rest of this notebook we'll focus on this new, simpler approach." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### TensorFlow warmup: Flatten Function\n", + "\n", + "We can see this in action by defining a simple `flatten` function that will reshape image data for use in a fully-connected network.\n", + "\n", + "In TensorFlow, data for convolutional feature maps is typically stored in a Tensor of shape N x H x W x C where:\n", + "\n", + "- N is the number of datapoints (minibatch size)\n", + "- H is the height of the feature map\n", + "- W is the width of the feature map\n", + "- C is the number of channels in the feature map\n", + "\n", + "This is the right way to represent the data when we are doing something like a 2D convolution, that needs spatial understanding of where the intermediate features are relative to each other. When we use fully connected affine layers to process the image, however, we want each datapoint to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data. So, we use a \"flatten\" operation to collapse the `H x W x C` values per representation into a single long vector. \n", + "\n", + "Notice the `tf.reshape` call has the target shape as `(N, -1)`, meaning it will reshape/keep the first dimension to be N, and then infer as necessary what the second dimension is in the output, so we can collapse the remaining dimensions from the input properly.\n", + "\n", + "**NOTE**: TensorFlow and PyTorch differ on the default Tensor layout; TensorFlow uses N x H x W x C but PyTorch uses N x C x H x W." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def flatten(x):\n", + " \"\"\" \n", + " Input:\n", + " - TensorFlow Tensor of shape (N, D1, ..., DM)\n", + " \n", + " Output:\n", + " - TensorFlow Tensor of shape (N, D1 * ... * DM)\n", + " \"\"\"\n", + " N = tf.shape(x)[0]\n", + " return tf.reshape(x, (N, -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_np:\n", + " [[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n", + "\n", + " [[12 13 14 15]\n", + " [16 17 18 19]\n", + " [20 21 22 23]]] \n", + "\n", + "x_np:\n", + " (2, 3, 4) \n", + "\n", + "x_flat_np:\n", + " tf.Tensor(\n", + "[[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + " [12 13 14 15 16 17 18 19 20 21 22 23]], shape=(2, 12), dtype=int64) \n", + "\n" + ] + } + ], + "source": [ + "def test_flatten():\n", + " # Construct concrete values of the input data x using numpy\n", + " x_np = np.arange(24).reshape((2, 3, 4))\n", + " print('x_np:\\n', x_np, '\\n')\n", + " print('x_np:\\n', x_np.shape, '\\n')\n", + " # Compute a concrete output value.\n", + " x_flat_np = flatten(x_np)\n", + " print('x_flat_np:\\n', x_flat_np, '\\n')\n", + "\n", + "test_flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Define a Two-Layer Network\n", + "We will now implement our first neural network with TensorFlow: a fully-connected ReLU network with two hidden layers and no biases on the CIFAR10 dataset. For now we will use only low-level TensorFlow operators to define the network; later we will see how to use the higher-level abstractions provided by `tf.keras` to simplify the process.\n", + "\n", + "We will define the forward pass of the network in the function `two_layer_fc`; this will accept TensorFlow Tensors for the inputs and weights of the network, and return a TensorFlow Tensor for the scores. \n", + "\n", + "After defining the network architecture in the `two_layer_fc` function, we will test the implementation by checking the shape of the output.\n", + "\n", + "**It's important that you read and understand this implementation.**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def two_layer_fc(x, params):\n", + " \"\"\"\n", + " A fully-connected neural network; the architecture is:\n", + " fully-connected layer -> ReLU -> fully connected layer.\n", + " Note that we only need to define the forward pass here; TensorFlow will take\n", + " care of computing the gradients for us.\n", + " \n", + " The input to the network will be a minibatch of data, of shape\n", + " (N, d1, ..., dM) where d1 * ... * dM = D. The hidden layer will have H units,\n", + " and the output layer will produce scores for C classes.\n", + "\n", + " Inputs:\n", + " - x: A TensorFlow Tensor of shape (N, d1, ..., dM) giving a minibatch of\n", + " input data.\n", + " - params: A list [w1, w2] of TensorFlow Tensors giving weights for the\n", + " network, where w1 has shape (D, H) and w2 has shape (H, C).\n", + " \n", + " Returns:\n", + " - scores: A TensorFlow Tensor of shape (N, C) giving classification scores\n", + " for the input data x.\n", + " \"\"\"\n", + " w1, w2 = params # Unpack the parameters\n", + " x = flatten(x) # Flatten the input; now x has shape (N, D)\n", + " h = tf.nn.relu(tf.matmul(x, w1)) # Hidden layer: h has shape (N, H)\n", + " scores = tf.matmul(h, w2) # Compute scores of shape (N, C)\n", + " return scores" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def two_layer_fc_test():\n", + " hidden_layer_size = 42\n", + "\n", + " # Scoping our TF operations under a tf.device context manager \n", + " # lets us tell TensorFlow where we want these Tensors to be\n", + " # multiplied and/or operated on, e.g. on a CPU or a GPU.\n", + " with tf.device(device): \n", + " x = tf.zeros((64, 32, 32, 3))\n", + " w1 = tf.zeros((32 * 32 * 3, hidden_layer_size))\n", + " w2 = tf.zeros((hidden_layer_size, 10))\n", + "\n", + " # Call our two_layer_fc function for the forward pass of the network.\n", + " scores = two_layer_fc(x, [w1, w2])\n", + "\n", + " print(scores.shape)\n", + "\n", + "two_layer_fc_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Three-Layer ConvNet\n", + "Here you will complete the implementation of the function `three_layer_convnet` which will perform the forward pass of a three-layer convolutional network. The network should have the following architecture:\n", + "\n", + "1. A convolutional layer (with bias) with `channel_1` filters, each with shape `KW1 x KH1`, and zero-padding of two\n", + "2. ReLU nonlinearity\n", + "3. A convolutional layer (with bias) with `channel_2` filters, each with shape `KW2 x KH2`, and zero-padding of one\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer with bias, producing scores for `C` classes.\n", + "\n", + "**HINT**: For convolutions: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/nn/conv2d; be careful with padding!\n", + "\n", + "**HINT**: For biases: https://www.tensorflow.org/performance/xla/broadcasting" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def three_layer_convnet(x, params):\n", + " \"\"\"\n", + " A three-layer convolutional network with the architecture described above.\n", + " \n", + " Inputs:\n", + " - x: A TensorFlow Tensor of shape (N, H, W, 3) giving a minibatch of images\n", + " - params: A list of TensorFlow Tensors giving the weights and biases for the\n", + " network; should contain the following:\n", + " - conv_w1: TensorFlow Tensor of shape (KH1, KW1, 3, channel_1) giving\n", + " weights for the first convolutional layer.\n", + " - conv_b1: TensorFlow Tensor of shape (channel_1,) giving biases for the\n", + " first convolutional layer.\n", + " - conv_w2: TensorFlow Tensor of shape (KH2, KW2, channel_1, channel_2)\n", + " giving weights for the second convolutional layer\n", + " - conv_b2: TensorFlow Tensor of shape (channel_2,) giving biases for the\n", + " second convolutional layer.\n", + " - fc_w: TensorFlow Tensor giving weights for the fully-connected layer.\n", + " Can you figure out what the shape should be?\n", + " - fc_b: TensorFlow Tensor giving biases for the fully-connected layer.\n", + " Can you figure out what the shape should be?\n", + " \"\"\"\n", + " conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b = params\n", + " scores = None\n", + " ############################################################################\n", + " # TODO: Implement the forward pass for the three-layer ConvNet. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " # Flatten the input; now x has shape (N, D)\n", + " \n", + " padded_x = tf.pad(x, [[0,0], [2, 2], [2, 2], [0,0]], \"CONSTANT\")\n", + " \n", + " h1 = tf.nn.conv2d(padded_x, conv_w1, strides=[1,1,1,1], padding=\"VALID\") + conv_b1\n", + " h1 = tf.nn.relu(h1)\n", + " \n", + " padded_h1 = tf.pad(h1, [[0,0], [1, 1], [1, 1], [0,0]], \"CONSTANT\") \n", + " \n", + " h2 = tf.nn.conv2d(padded_h1, conv_w2, strides=[1,1,1,1], padding='VALID') + conv_b2\n", + " \n", + " h2 = tf.nn.relu(h2)\n", + " \n", + " r_h2_flatten = flatten(h2) \n", + " scores = tf.matmul(r_h2_flatten, fc_w) + fc_b\n", + " \n", + "\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After defing the forward pass of the three-layer ConvNet above, run the following cell to test your implementation. Like the two-layer network, we run the graph on a batch of zeros just to make sure the function doesn't crash, and produces outputs of the correct shape.\n", + "\n", + "When you run this function, `scores_np` should have shape `(64, 10)`." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores_np has shape: (64, 10)\n" + ] + } + ], + "source": [ + "def three_layer_convnet_test():\n", + " \n", + " with tf.device(device):\n", + " x = tf.zeros((64, 32, 32, 3))\n", + " conv_w1 = tf.zeros((5, 5, 3, 6))\n", + " conv_b1 = tf.zeros((6,))\n", + " conv_w2 = tf.zeros((3, 3, 6, 9))\n", + " conv_b2 = tf.zeros((9,))\n", + " fc_w = tf.zeros((32 * 32 * 9, 10))\n", + " fc_b = tf.zeros((10,))\n", + " params = [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b]\n", + " scores = three_layer_convnet(x, params)\n", + "\n", + " # Inputs to convolutional layers are 4-dimensional arrays with shape\n", + " # [batch_size, height, width, channels]\n", + " print('scores_np has shape: ', scores.shape)\n", + "\n", + "three_layer_convnet_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Training Step\n", + "\n", + "We now define the `training_step` function performs a single training step. This will take three basic steps:\n", + "\n", + "1. Compute the loss\n", + "2. Compute the gradient of the loss with respect to all network weights\n", + "3. Make a weight update step using (stochastic) gradient descent.\n", + "\n", + "\n", + "We need to use a few new TensorFlow functions to do all of this:\n", + "- For computing the cross-entropy loss we'll use `tf.nn.sparse_softmax_cross_entropy_with_logits`: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits\n", + "\n", + "- For averaging the loss across a minibatch of data we'll use `tf.reduce_mean`:\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/reduce_mean\n", + "\n", + "- For computing gradients of the loss with respect to the weights we'll use `tf.GradientTape` (useful for Eager execution): https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/GradientTape\n", + "\n", + "- We'll mutate the weight values stored in a TensorFlow Tensor using `tf.assign_sub` (\"sub\" is for subtraction): https://www.tensorflow.org/api_docs/python/tf/assign_sub \n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def training_step(model_fn, x, y, params, learning_rate):\n", + " with tf.GradientTape() as tape:\n", + " scores = model_fn(x, params) # Forward pass of the model\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=scores)\n", + " total_loss = tf.reduce_mean(loss)\n", + " grad_params = tape.gradient(total_loss, params)\n", + "\n", + " # Make a vanilla gradient descent step on all of the model parameters\n", + " # Manually update the weights using assign_sub()\n", + " for w, grad_w in zip(params, grad_params):\n", + " w.assign_sub(learning_rate * grad_w)\n", + " \n", + " return total_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def train_part2(model_fn, init_fn, learning_rate):\n", + " \"\"\"\n", + " Train a model on CIFAR-10.\n", + " \n", + " Inputs:\n", + " - model_fn: A Python function that performs the forward pass of the model\n", + " using TensorFlow; it should have the following signature:\n", + " scores = model_fn(x, params) where x is a TensorFlow Tensor giving a\n", + " minibatch of image data, params is a list of TensorFlow Tensors holding\n", + " the model weights, and scores is a TensorFlow Tensor of shape (N, C)\n", + " giving scores for all elements of x.\n", + " - init_fn: A Python function that initializes the parameters of the model.\n", + " It should have the signature params = init_fn() where params is a list\n", + " of TensorFlow Tensors holding the (randomly initialized) weights of the\n", + " model.\n", + " - learning_rate: Python float giving the learning rate to use for SGD.\n", + " \"\"\"\n", + " \n", + " \n", + " params = init_fn() # Initialize the model parameters \n", + " \n", + " for t, (x_np, y_np) in enumerate(train_dset):\n", + " # Run the graph on a batch of training data.\n", + " loss = training_step(model_fn, x_np, y_np, params, learning_rate)\n", + " \n", + " # Periodically print the loss and check accuracy on the val set.\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss))\n", + " check_accuracy(val_dset, x_np, model_fn, params)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def check_accuracy(dset, x, model_fn, params):\n", + " \"\"\"\n", + " Check accuracy on a classification model, e.g. for validation.\n", + " \n", + " Inputs:\n", + " - dset: A Dataset object against which to check accuracy\n", + " - x: A TensorFlow placeholder Tensor where input images should be fed\n", + " - model_fn: the Model we will be calling to make predictions on x\n", + " - params: parameters for the model_fn to work with\n", + " \n", + " Returns: Nothing, but prints the accuracy of the model\n", + " \"\"\"\n", + " num_correct, num_samples = 0, 0\n", + " for x_batch, y_batch in dset:\n", + " scores_np = model_fn(x_batch, params).numpy()\n", + " y_pred = scores_np.argmax(axis=1)\n", + " num_samples += x_batch.shape[0]\n", + " num_correct += (y_pred == y_batch).sum()\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f%%)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Initialization\n", + "We'll use the following utility method to initialize the weight matrices for our models using Kaiming's normalization method.\n", + "\n", + "[1] He et al, *Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification\n", + "*, ICCV 2015, https://arxiv.org/abs/1502.01852" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def create_matrix_with_kaiming_normal(shape):\n", + " if len(shape) == 2:\n", + " fan_in, fan_out = shape[0], shape[1]\n", + " elif len(shape) == 4:\n", + " fan_in, fan_out = np.prod(shape[:3]), shape[3]\n", + " return tf.keras.backend.random_normal(shape) * np.sqrt(2.0 / fan_in)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Train a Two-Layer Network\n", + "We are finally ready to use all of the pieces defined above to train a two-layer fully-connected network on CIFAR-10.\n", + "\n", + "We just need to define a function to initialize the weights of the model, and call `train_part2`.\n", + "\n", + "Defining the weights of the network introduces another important piece of TensorFlow API: `tf.Variable`. A TensorFlow Variable is a Tensor whose value is stored in the graph and persists across runs of the computational graph; however unlike constants defined with `tf.zeros` or `tf.random_normal`, the values of a Variable can be mutated as the graph runs; these mutations will persist across graph runs. Learnable parameters of the network are usually stored in Variables.\n", + "\n", + "You don't need to tune any hyperparameters, but you should achieve validation accuracies above 40% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.2493\n", + "Got 121 / 1000 correct (12.10%)\n", + "Iteration 100, loss = 1.8525\n", + "Got 385 / 1000 correct (38.50%)\n", + "Iteration 200, loss = 1.4725\n", + "Got 373 / 1000 correct (37.30%)\n", + "Iteration 300, loss = 1.8654\n", + "Got 366 / 1000 correct (36.60%)\n", + "Iteration 400, loss = 1.8284\n", + "Got 416 / 1000 correct (41.60%)\n", + "Iteration 500, loss = 1.7518\n", + "Got 441 / 1000 correct (44.10%)\n", + "Iteration 600, loss = 1.9832\n", + "Got 434 / 1000 correct (43.40%)\n", + "Iteration 700, loss = 1.9373\n", + "Got 445 / 1000 correct (44.50%)\n" + ] + } + ], + "source": [ + "def two_layer_fc_init():\n", + " \"\"\"\n", + " Initialize the weights of a two-layer network, for use with the\n", + " two_layer_network function defined above. \n", + " You can use the `create_matrix_with_kaiming_normal` helper!\n", + " \n", + " Inputs: None\n", + " \n", + " Returns: A list of:\n", + " - w1: TensorFlow tf.Variable giving the weights for the first layer\n", + " - w2: TensorFlow tf.Variable giving the weights for the second layer\n", + " \"\"\"\n", + " hidden_layer_size = 4000\n", + " w1 = tf.Variable(create_matrix_with_kaiming_normal((3 * 32 * 32, 4000)))\n", + " w2 = tf.Variable(create_matrix_with_kaiming_normal((4000, 10)))\n", + " return [w1, w2]\n", + "\n", + "learning_rate = 1e-2\n", + "train_part2(two_layer_fc, two_layer_fc_init, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Train a three-layer ConvNet\n", + "We will now use TensorFlow to train a three-layer ConvNet on CIFAR-10.\n", + "\n", + "You need to implement the `three_layer_convnet_init` function. Recall that the architecture of the network is:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You don't need to do any hyperparameter tuning, but you should see validation accuracies above 43% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 4.0431\n", + "Got 118 / 1000 correct (11.80%)\n", + "Iteration 100, loss = 1.8898\n", + "Got 323 / 1000 correct (32.30%)\n", + "Iteration 200, loss = 1.5676\n", + "Got 391 / 1000 correct (39.10%)\n", + "Iteration 300, loss = 1.8205\n", + "Got 387 / 1000 correct (38.70%)\n", + "Iteration 400, loss = 1.5285\n", + "Got 438 / 1000 correct (43.80%)\n", + "Iteration 500, loss = 1.7125\n", + "Got 445 / 1000 correct (44.50%)\n", + "Iteration 600, loss = 1.6236\n", + "Got 466 / 1000 correct (46.60%)\n", + "Iteration 700, loss = 1.6076\n", + "Got 482 / 1000 correct (48.20%)\n" + ] + } + ], + "source": [ + "def three_layer_convnet_init():\n", + " \"\"\"\n", + " Initialize the weights of a Three-Layer ConvNet, for use with the\n", + " three_layer_convnet function defined above.\n", + " You can use the `create_matrix_with_kaiming_normal` helper!\n", + " \n", + " Inputs: None\n", + " \n", + " Returns a list containing:\n", + " - conv_w1: TensorFlow tf.Variable giving weights for the first conv layer\n", + " - conv_b1: TensorFlow tf.Variable giving biases for the first conv layer\n", + " - conv_w2: TensorFlow tf.Variable giving weights for the second conv layer\n", + " - conv_b2: TensorFlow tf.Variable giving biases for the second conv layer\n", + " - fc_w: TensorFlow tf.Variable giving weights for the fully-connected layer\n", + " - fc_b: TensorFlow tf.Variable giving biases for the fully-connected layer\n", + " \"\"\"\n", + " params = None\n", + " ############################################################################\n", + " # TODO: Initialize the parameters of the three-layer network. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " conv_w1 = tf.Variable(create_matrix_with_kaiming_normal((5,5,3,32)))\n", + " conv_b1 = tf.Variable(create_matrix_with_kaiming_normal((1,32)))\n", + " conv_w2 = tf.Variable(create_matrix_with_kaiming_normal((3,3,32,16)))\n", + " conv_b2 = tf.Variable(create_matrix_with_kaiming_normal((1,16)))\n", + " fc_w = tf.Variable(create_matrix_with_kaiming_normal((32*32*16, 10)))\n", + " fc_b = tf.Variable(create_matrix_with_kaiming_normal((1, 10)))\n", + " params = conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return params\n", + "\n", + "learning_rate = 3e-3\n", + "train_part2(three_layer_convnet, three_layer_convnet_init, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Part III: Keras Model Subclassing API\n", + "\n", + "Implementing a neural network using the low-level TensorFlow API is a good way to understand how TensorFlow works, but it's a little inconvenient - we had to manually keep track of all Tensors holding learnable parameters. This was fine for a small network, but could quickly become unweildy for a large complex model.\n", + "\n", + "Fortunately TensorFlow 2.0 provides higher-level APIs such as `tf.keras` which make it easy to build models out of modular, object-oriented layers. Further, TensorFlow 2.0 uses eager execution that evaluates operations immediately, without explicitly constructing any computational graphs. This makes it easy to write and debug models, and reduces the boilerplate code.\n", + "\n", + "In this part of the notebook we will define neural network models using the `tf.keras.Model` API. To implement your own model, you need to do the following:\n", + "\n", + "1. Define a new class which subclasses `tf.keras.Model`. Give your class an intuitive name that describes it, like `TwoLayerFC` or `ThreeLayerConvNet`.\n", + "2. In the initializer `__init__()` for your new class, define all the layers you need as class attributes. The `tf.keras.layers` package provides many common neural-network layers, like `tf.keras.layers.Dense` for fully-connected layers and `tf.keras.layers.Conv2D` for convolutional layers. Under the hood, these layers will construct `Variable` Tensors for any learnable parameters. **Warning**: Don't forget to call `super(YourModelName, self).__init__()` as the first line in your initializer!\n", + "3. Implement the `call()` method for your class; this implements the forward pass of your model, and defines the *connectivity* of your network. Layers defined in `__init__()` implement `__call__()` so they can be used as function objects that transform input Tensors into output Tensors. Don't define any new layers in `call()`; any layers you want to use in the forward pass should be defined in `__init__()`.\n", + "\n", + "After you define your `tf.keras.Model` subclass, you can instantiate it and use it like the model functions from Part II.\n", + "\n", + "### Keras Model Subclassing API: Two-Layer Network\n", + "\n", + "Here is a concrete example of using the `tf.keras.Model` API to define a two-layer network. There are a few new bits of API to be aware of here:\n", + "\n", + "We use an `Initializer` object to set up the initial values of the learnable parameters of the layers; in particular `tf.initializers.VarianceScaling` gives behavior similar to the Kaiming initialization method we used in Part II. You can read more about it here: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/initializers/VarianceScaling\n", + "\n", + "We construct `tf.keras.layers.Dense` objects to represent the two fully-connected layers of the model. In addition to multiplying their input by a weight matrix and adding a bias vector, these layer can also apply a nonlinearity for you. For the first layer we specify a ReLU activation function by passing `activation='relu'` to the constructor; the second layer uses softmax activation function. Finally, we use `tf.keras.layers.Flatten` to flatten the output from the previous fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "class TwoLayerFC(tf.keras.Model):\n", + " def __init__(self, hidden_size, num_classes):\n", + " super(TwoLayerFC, self).__init__() \n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.fc1 = tf.keras.layers.Dense(hidden_size, activation='relu',\n", + " kernel_initializer=initializer)\n", + " self.fc2 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " def call(self, x, training=False):\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "\n", + "def test_TwoLayerFC():\n", + " \"\"\" A small unit test to exercise the TwoLayerFC model above. \"\"\"\n", + " input_size, hidden_size, num_classes = 50, 42, 10\n", + " x = tf.zeros((64, input_size))\n", + " model = TwoLayerFC(hidden_size, num_classes)\n", + " with tf.device(device):\n", + " scores = model(x)\n", + " print(scores.shape)\n", + " \n", + "test_TwoLayerFC()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Three-Layer ConvNet\n", + "Now it's your turn to implement a three-layer ConvNet using the `tf.keras.Model` API. Your model should have the same architecture used in Part II:\n", + "\n", + "1. Convolutional layer with 5 x 5 kernels, with zero-padding of 2\n", + "2. ReLU nonlinearity\n", + "3. Convolutional layer with 3 x 3 kernels, with zero-padding of 1\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer to give class scores\n", + "6. Softmax nonlinearity\n", + "\n", + "You should initialize the weights of your network using the same initialization method as was used in the two-layer network above.\n", + "\n", + "**Hint**: Refer to the documentation for `tf.keras.layers.Conv2D` and `tf.keras.layers.Dense`:\n", + "\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Conv2D\n", + "\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dense" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "class ThreeLayerConvNet(tf.keras.Model):\n", + " def __init__(self, channel_1, channel_2, num_classes):\n", + " super(ThreeLayerConvNet, self).__init__()\n", + " ########################################################################\n", + " # TODO: Implement the __init__ method for a three-layer ConvNet. You #\n", + " # should instantiate layer objects to be used in the forward pass. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.pad1 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\")\n", + " self.pad2 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\")\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " self.conv1 = tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer)\n", + " self.conv2 = tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer) \n", + " self.fc1 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + " \n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + " \n", + " def call(self, x, training=False):\n", + " scores = None\n", + " ########################################################################\n", + " # TODO: Implement the forward pass for a three-layer ConvNet. You #\n", + " # should use the layer objects defined in the __init__ method. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = self.pad1(x)\n", + " x = self.conv1(x)\n", + " x = self.pad2(x)\n", + " x = self.conv2(x)\n", + " x = self.flatten(x)\n", + " scores = self.fc1(x)\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ######################################################################## \n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you complete the implementation of the `ThreeLayerConvNet` above you can run the following to ensure that your implementation does not crash and produces outputs of the expected shape." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def test_ThreeLayerConvNet(): \n", + " channel_1, channel_2, num_classes = 12, 8, 10\n", + " model = ThreeLayerConvNet(channel_1, channel_2, num_classes)\n", + " with tf.device(device):\n", + " x = tf.zeros((64, 3, 32, 32))\n", + " scores = model(x)\n", + " print(scores.shape)\n", + "\n", + "test_ThreeLayerConvNet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Eager Training\n", + "\n", + "While keras models have a builtin training loop (using the `model.fit`), sometimes you need more customization. Here's an example, of a training loop implemented with eager execution.\n", + "\n", + "In particular, notice `tf.GradientTape`. Automatic differentiation is used in the backend for implementing backpropagation in frameworks like TensorFlow. During eager execution, `tf.GradientTape` is used to trace operations for computing gradients later. A particular `tf.GradientTape` can only compute one gradient; subsequent calls to tape will throw a runtime error. \n", + "\n", + "TensorFlow 2.0 ships with easy-to-use built-in metrics under `tf.keras.metrics` module. Each metric is an object, and we can use `update_state()` to add observations and `reset_state()` to clear all observations. We can get the current result of a metric by calling `result()` on the metric object." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def train_part34(model_init_fn, optimizer_init_fn, num_epochs=1, is_training=False):\n", + " \"\"\"\n", + " Simple training loop for use with models defined using tf.keras. It trains\n", + " a model for one epoch on the CIFAR-10 training set and periodically checks\n", + " accuracy on the CIFAR-10 validation set.\n", + " \n", + " Inputs:\n", + " - model_init_fn: A function that takes no parameters; when called it\n", + " constructs the model we want to train: model = model_init_fn()\n", + " - optimizer_init_fn: A function which takes no parameters; when called it\n", + " constructs the Optimizer object we will use to optimize the model:\n", + " optimizer = optimizer_init_fn()\n", + " - num_epochs: The number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints progress during trainingn\n", + " \"\"\" \n", + " with tf.device(device):\n", + "\n", + " # Compute the loss like we did in Part II\n", + " loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()\n", + " \n", + " model = model_init_fn()\n", + " optimizer = optimizer_init_fn()\n", + " \n", + " train_loss = tf.keras.metrics.Mean(name='train_loss')\n", + " train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n", + " \n", + " val_loss = tf.keras.metrics.Mean(name='val_loss')\n", + " val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='val_accuracy')\n", + " \n", + " t = 0\n", + " for epoch in range(num_epochs):\n", + " \n", + " # Reset the metrics - https://www.tensorflow.org/alpha/guide/migration_guide#new-style_metrics\n", + " train_loss.reset_states()\n", + " train_accuracy.reset_states()\n", + " \n", + " for x_np, y_np in train_dset:\n", + " with tf.GradientTape() as tape:\n", + " \n", + " # Use the model function to build the forward pass.\n", + " scores = model(x_np, training=is_training)\n", + " loss = loss_fn(y_np, scores)\n", + " \n", + " gradients = tape.gradient(loss, model.trainable_variables)\n", + " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", + " \n", + " # Update the metrics\n", + " train_loss.update_state(loss)\n", + " train_accuracy.update_state(y_np, scores)\n", + " \n", + " if t % print_every == 0:\n", + " val_loss.reset_states()\n", + " val_accuracy.reset_states()\n", + " for test_x, test_y in val_dset:\n", + " # During validation at end of epoch, training set to False\n", + " prediction = model(test_x, training=False)\n", + " t_loss = loss_fn(test_y, prediction)\n", + "\n", + " val_loss.update_state(t_loss)\n", + " val_accuracy.update_state(test_y, prediction)\n", + " \n", + " template = 'Iteration {}, Epoch {}, Loss: {}, Accuracy: {}, Val Loss: {}, Val Accuracy: {}'\n", + " print (template.format(t, epoch+1,\n", + " train_loss.result(),\n", + " train_accuracy.result()*100,\n", + " val_loss.result(),\n", + " val_accuracy.result()*100))\n", + " t += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Train a Two-Layer Network\n", + "We can now use the tools defined above to train a two-layer network on CIFAR-10. We define the `model_init_fn` and `optimizer_init_fn` that construct the model and optimizer respectively when called. Here we want to train the model using stochastic gradient descent with no momentum, so we construct a `tf.keras.optimizers.SGD` function; you can [read about it here](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/optimizers/SGD).\n", + "\n", + "You don't need to tune any hyperparameters here, but you should achieve validation accuracies above 40% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0917 13:39:06.645046 140735629599616 deprecation.py:323] From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1220: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.820213794708252, Accuracy: 21.875, Val Loss: 2.874540328979492, Val Accuracy: 14.30000114440918\n", + "Iteration 100, Epoch 1, Loss: 2.211560010910034, Accuracy: 28.712871551513672, Val Loss: 1.9114350080490112, Val Accuracy: 38.10000228881836\n", + "Iteration 200, Epoch 1, Loss: 2.061293601989746, Accuracy: 32.23725128173828, Val Loss: 1.8562566041946411, Val Accuracy: 40.400001525878906\n", + "Iteration 300, Epoch 1, Loss: 1.9950498342514038, Accuracy: 33.93376159667969, Val Loss: 1.88795804977417, Val Accuracy: 38.20000076293945\n", + "Iteration 400, Epoch 1, Loss: 1.927003264427185, Accuracy: 35.95698165893555, Val Loss: 1.7473491430282593, Val Accuracy: 41.10000228881836\n", + "Iteration 500, Epoch 1, Loss: 1.8845645189285278, Accuracy: 37.11327362060547, Val Loss: 1.6804167032241821, Val Accuracy: 42.599998474121094\n", + "Iteration 600, Epoch 1, Loss: 1.857049584388733, Accuracy: 37.95497131347656, Val Loss: 1.691178798675537, Val Accuracy: 42.0\n", + "Iteration 700, Epoch 1, Loss: 1.8302688598632812, Accuracy: 38.64568328857422, Val Loss: 1.648596167564392, Val Accuracy: 45.20000076293945\n" + ] + } + ], + "source": [ + "hidden_size, num_classes = 4000, 10\n", + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " return TwoLayerFC(hidden_size, num_classes)\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate)\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Train a Three-Layer ConvNet\n", + "Here you should use the tools we've defined above to train a three-layer ConvNet on CIFAR-10. Your ConvNet should use 32 filters in the first convolutional layer and 16 filters in the second layer.\n", + "\n", + "To train the model you should use gradient descent with Nesterov momentum 0.9. \n", + "\n", + "**HINT**: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/optimizers/SGD\n", + "\n", + "You don't need to perform any hyperparameter tuning, but you should achieve validation accuracies above 50% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.977733612060547, Accuracy: 14.0625, Val Loss: 11.000207901000977, Val Accuracy: 11.200000762939453\n", + "Iteration 100, Epoch 1, Loss: 2.1598904132843018, Accuracy: 26.670793533325195, Val Loss: 1.8142485618591309, Val Accuracy: 36.79999923706055\n", + "Iteration 200, Epoch 1, Loss: 1.931534767150879, Accuracy: 33.333335876464844, Val Loss: 1.54546320438385, Val Accuracy: 48.20000076293945\n", + "Iteration 300, Epoch 1, Loss: 1.8104909658432007, Accuracy: 36.975704193115234, Val Loss: 1.452940583229065, Val Accuracy: 48.79999923706055\n", + "Iteration 400, Epoch 1, Loss: 1.7208037376403809, Accuracy: 39.7015266418457, Val Loss: 1.4160033464431763, Val Accuracy: 48.89999771118164\n", + "Iteration 500, Epoch 1, Loss: 1.6586426496505737, Accuracy: 41.68537902832031, Val Loss: 1.3770642280578613, Val Accuracy: 52.60000228881836\n", + "Iteration 600, Epoch 1, Loss: 1.6175931692123413, Accuracy: 43.055843353271484, Val Loss: 1.3665345907211304, Val Accuracy: 54.20000076293945\n", + "Iteration 700, Epoch 1, Loss: 1.5854710340499878, Accuracy: 44.1534423828125, Val Loss: 1.353290319442749, Val Accuracy: 51.79999923706055\n" + ] + } + ], + "source": [ + "learning_rate = 3e-3\n", + "channel_1, channel_2, num_classes = 32, 16, 10\n", + "\n", + "def model_init_fn():\n", + " model = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " model = ThreeLayerConvNet(channel_1, channel_2, num_classes)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return model\n", + "\n", + "def optimizer_init_fn():\n", + " optimizer = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " optimizer = tf.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True, name='SGD')\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return optimizer\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part IV: Keras Sequential API\n", + "In Part III we introduced the `tf.keras.Model` API, which allows you to define models with any number of learnable layers and with arbitrary connectivity between layers.\n", + "\n", + "However for many models you don't need such flexibility - a lot of models can be expressed as a sequential stack of layers, with the output of each layer fed to the next layer as input. If your model fits this pattern, then there is an even easier way to define your model: using `tf.keras.Sequential`. You don't need to write any custom classes; you simply call the `tf.keras.Sequential` constructor with a list containing a sequence of layer objects.\n", + "\n", + "One complication with `tf.keras.Sequential` is that you must define the shape of the input to the model by passing a value to the `input_shape` of the first layer in your model.\n", + "\n", + "### Keras Sequential API: Two-Layer Network\n", + "In this subsection, we will rewrite the two-layer fully-connected network using `tf.keras.Sequential`, and train it using the training loop defined above.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.3860318660736084, Accuracy: 9.375, Val Loss: 3.1342945098876953, Val Accuracy: 12.100000381469727\n", + "Iteration 100, Epoch 1, Loss: 2.229124069213867, Accuracy: 28.821165084838867, Val Loss: 1.879478931427002, Val Accuracy: 39.20000076293945\n", + "Iteration 200, Epoch 1, Loss: 2.0733325481414795, Accuracy: 32.49378204345703, Val Loss: 1.8507494926452637, Val Accuracy: 41.400001525878906\n", + "Iteration 300, Epoch 1, Loss: 1.9997358322143555, Accuracy: 34.39057159423828, Val Loss: 1.7963671684265137, Val Accuracy: 38.70000076293945\n", + "Iteration 400, Epoch 1, Loss: 1.9314937591552734, Accuracy: 36.20246505737305, Val Loss: 1.7118778228759766, Val Accuracy: 42.89999771118164\n", + "Iteration 500, Epoch 1, Loss: 1.8869222402572632, Accuracy: 37.26921081542969, Val Loss: 1.64309561252594, Val Accuracy: 43.70000076293945\n", + "Iteration 600, Epoch 1, Loss: 1.857375144958496, Accuracy: 38.11355972290039, Val Loss: 1.6449146270751953, Val Accuracy: 45.0\n", + "Iteration 700, Epoch 1, Loss: 1.8303626775741577, Accuracy: 38.754905700683594, Val Loss: 1.607826828956604, Val Accuracy: 44.70000076293945\n" + ] + } + ], + "source": [ + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " input_shape = (32, 32, 3)\n", + " hidden_layer_size, num_classes = 4000, 10\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " layers = [\n", + " tf.keras.layers.Flatten(input_shape=input_shape),\n", + " tf.keras.layers.Dense(hidden_layer_size, activation='relu',\n", + " kernel_initializer=initializer),\n", + " tf.keras.layers.Dense(num_classes, activation='softmax', \n", + " kernel_initializer=initializer),\n", + " ]\n", + " model = tf.keras.Sequential(layers)\n", + " return model\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate) \n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Abstracting Away the Training Loop\n", + "In the previous examples, we used a customised training loop to train models (e.g. `train_part34`). Writing your own training loop is only required if you need more flexibility and control during training your model. Alternately, you can also use built-in APIs like `tf.keras.Model.fit()` and `tf.keras.Model.evaluate` to train and evaluate a model. Also remember to configure your model for training by calling `tf.keras.Model.compile.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation and test accuracies above 42% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 49000 samples, validate on 1000 samples\n", + "49000/49000 [==============================] - 56s 1ms/sample - loss: 1.8096 - sparse_categorical_accuracy: 0.3908 - val_loss: 1.6444 - val_sparse_categorical_accuracy: 0.4480\n", + "10000/10000 [==============================] - 6s 627us/sample - loss: 1.6521 - sparse_categorical_accuracy: 0.4300\n" + ] + }, + { + "data": { + "text/plain": [ + "[1.6520895320892335, 0.43]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = model_init_fn()\n", + "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=[tf.keras.metrics.sparse_categorical_accuracy])\n", + "model.fit(X_train, y_train, batch_size=64, epochs=1, validation_data=(X_val, y_val))\n", + "model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Sequential API: Three-Layer ConvNet\n", + "Here you should use `tf.keras.Sequential` to reimplement the same three-layer ConvNet architecture used in Part II and Part III. As a reminder, your model should have the following architecture:\n", + "\n", + "1. Convolutional layer with 32 5x5 kernels, using zero padding of 2\n", + "2. ReLU nonlinearity\n", + "3. Convolutional layer with 16 3x3 kernels, using zero padding of 1\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer giving class scores\n", + "6. Softmax nonlinearity\n", + "\n", + "You should initialize the weights of the model using a `tf.initializers.VarianceScaling` as above.\n", + "\n", + "You should train the model using Nesterov momentum 0.9.\n", + "\n", + "You don't need to perform any hyperparameter search, but you should achieve accuracy above 45% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.066777229309082, Accuracy: 10.9375, Val Loss: 7.942768096923828, Val Accuracy: 7.90000057220459\n", + "Iteration 100, Epoch 1, Loss: 2.0578784942626953, Accuracy: 27.04207992553711, Val Loss: 1.7399837970733643, Val Accuracy: 39.89999771118164\n", + "Iteration 200, Epoch 1, Loss: 1.8581743240356445, Accuracy: 33.76865768432617, Val Loss: 1.5444930791854858, Val Accuracy: 45.5\n", + "Iteration 300, Epoch 1, Loss: 1.7626904249191284, Accuracy: 37.089908599853516, Val Loss: 1.5184237957000732, Val Accuracy: 46.39999771118164\n", + "Iteration 400, Epoch 1, Loss: 1.6890290975570679, Accuracy: 39.66645812988281, Val Loss: 1.480480670928955, Val Accuracy: 48.10000228881836\n", + "Iteration 500, Epoch 1, Loss: 1.641016960144043, Accuracy: 41.43587875366211, Val Loss: 1.4048315286636353, Val Accuracy: 50.5\n", + "Iteration 600, Epoch 1, Loss: 1.6024343967437744, Accuracy: 42.87385559082031, Val Loss: 1.3888776302337646, Val Accuracy: 52.10000228881836\n", + "Iteration 700, Epoch 1, Loss: 1.5693012475967407, Accuracy: 44.24259948730469, Val Loss: 1.3774913549423218, Val Accuracy: 54.20000076293945\n" + ] + } + ], + "source": [ + "def model_init_fn():\n", + " model = None\n", + " ############################################################################\n", + " # TODO: Construct a three-layer ConvNet using tf.keras.Sequential. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " channel_1, channel_2 = 32, 16\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " layers = [\n", + " \n", + " tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\"),\n", + " \n", + " tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\"),\n", + " \n", + " tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer),\n", + " \n", + " tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer), \n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(num_classes, activation='softmax', \n", + " kernel_initializer=initializer),\n", + " ]\n", + " model = tf.keras.Sequential(layers)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return model\n", + "\n", + "learning_rate = 5e-4\n", + "def optimizer_init_fn():\n", + " optimizer = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " optimizer = tf.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True, name='SGD')\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return optimizer\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also train this model with the built-in training loop APIs provided by TensorFlow." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 49000 samples, validate on 1000 samples\n", + "49000/49000 [==============================] - 93s 2ms/sample - loss: 1.5981 - sparse_categorical_accuracy: 0.4382 - val_loss: 1.3696 - val_sparse_categorical_accuracy: 0.5330\n", + "10000/10000 [==============================] - 11s 1ms/sample - loss: 1.3767 - sparse_categorical_accuracy: 0.5067\n" + ] + }, + { + "data": { + "text/plain": [ + "[1.3766763814926148, 0.5067]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = model_init_fn()\n", + "model.compile(optimizer='sgd',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=[tf.keras.metrics.sparse_categorical_accuracy])\n", + "model.fit(X_train, y_train, batch_size=64, epochs=1, validation_data=(X_val, y_val))\n", + "model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part IV: Functional API\n", + "### Demonstration with a Two-Layer Network \n", + "\n", + "In the previous section, we saw how we can use `tf.keras.Sequential` to stack layers to quickly build simple models. But this comes at the cost of losing flexibility.\n", + "\n", + "Often we will have to write complex models that have non-sequential data flows: a layer can have **multiple inputs and/or outputs**, such as stacking the output of 2 previous layers together to feed as input to a third! (Some examples are residual connections and dense blocks.)\n", + "\n", + "In such cases, we can use Keras functional API to write models with complex topologies such as:\n", + "\n", + " 1. Multi-input models\n", + " 2. Multi-output models\n", + " 3. Models with shared layers (the same layer called several times)\n", + " 4. Models with non-sequential data flows (e.g. residual connections)\n", + "\n", + "Writing a model with Functional API requires us to create a `tf.keras.Model` instance and explicitly write input tensors and output tensors for this model. " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def two_layer_fc_functional(input_shape, hidden_size, num_classes): \n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " inputs = tf.keras.Input(shape=input_shape)\n", + " flattened_inputs = tf.keras.layers.Flatten()(inputs)\n", + " fc1_output = tf.keras.layers.Dense(hidden_size, activation='relu',\n", + " kernel_initializer=initializer)(flattened_inputs)\n", + " scores = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)(fc1_output)\n", + "\n", + " # Instantiate the model given inputs and outputs.\n", + " model = tf.keras.Model(inputs=inputs, outputs=scores)\n", + " return model\n", + "\n", + "def test_two_layer_fc_functional():\n", + " \"\"\" A small unit test to exercise the TwoLayerFC model above. \"\"\"\n", + " input_size, hidden_size, num_classes = 50, 42, 10\n", + " input_shape = (50,)\n", + " \n", + " x = tf.zeros((64, input_size))\n", + " model = two_layer_fc_functional(input_shape, hidden_size, num_classes)\n", + " \n", + " with tf.device(device):\n", + " scores = model(x)\n", + " print(scores.shape)\n", + " \n", + "test_two_layer_fc_functional()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Functional API: Train a Two-Layer Network\n", + "You can now train this two-layer network constructed using the functional API.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.1926515102386475, Accuracy: 6.25, Val Loss: 2.9305005073547363, Val Accuracy: 11.90000057220459\n", + "Iteration 100, Epoch 1, Loss: 2.252453565597534, Accuracy: 28.790224075317383, Val Loss: 1.8985050916671753, Val Accuracy: 37.900001525878906\n", + "Iteration 200, Epoch 1, Loss: 2.0828073024749756, Accuracy: 32.37717819213867, Val Loss: 1.8634843826293945, Val Accuracy: 39.599998474121094\n", + "Iteration 300, Epoch 1, Loss: 2.003688335418701, Accuracy: 34.29194259643555, Val Loss: 1.8453090190887451, Val Accuracy: 38.10000228881836\n", + "Iteration 400, Epoch 1, Loss: 1.935245394706726, Accuracy: 36.03881072998047, Val Loss: 1.7412177324295044, Val Accuracy: 41.20000076293945\n", + "Iteration 500, Epoch 1, Loss: 1.8904175758361816, Accuracy: 37.12574768066406, Val Loss: 1.647827386856079, Val Accuracy: 43.900001525878906\n", + "Iteration 600, Epoch 1, Loss: 1.8617217540740967, Accuracy: 38.00956726074219, Val Loss: 1.6854203939437866, Val Accuracy: 42.0\n", + "Iteration 700, Epoch 1, Loss: 1.835993766784668, Accuracy: 38.59218978881836, Val Loss: 1.6190838813781738, Val Accuracy: 46.0\n" + ] + } + ], + "source": [ + "input_shape = (32, 32, 3)\n", + "hidden_size, num_classes = 4000, 10\n", + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " return two_layer_fc_functional(input_shape, hidden_size, num_classes)\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate)\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part V: CIFAR-10 open-ended challenge\n", + "\n", + "In this section you can experiment with whatever ConvNet architecture you'd like on CIFAR-10.\n", + "\n", + "You should experiment with architectures, hyperparameters, loss functions, regularization, or anything else you can think of to train a model that achieves **at least 70%** accuracy on the **validation** set within 10 epochs. You can use the built-in train function, the `train_part34` function from above, or implement your own training loop.\n", + "\n", + "Describe what you did at the end of the notebook.\n", + "\n", + "### Some things you can try:\n", + "- **Filter size**: Above we used 5x5 and 3x3; is this optimal?\n", + "- **Number of filters**: Above we used 16 and 32 filters. Would more or fewer do better?\n", + "- **Pooling**: We didn't use any pooling above. Would this improve the model?\n", + "- **Normalization**: Would your model be improved with batch normalization, layer normalization, group normalization, or some other normalization strategy?\n", + "- **Network architecture**: The ConvNet above has only three layers of trainable parameters. Would a deeper model do better?\n", + "- **Global average pooling**: Instead of flattening after the final convolutional layer, would global average pooling do better? This strategy is used for example in Google's Inception network and in Residual Networks.\n", + "- **Regularization**: Would some kind of regularization improve performance? Maybe weight decay or dropout?\n", + "\n", + "### NOTE: Batch Normalization / Dropout\n", + "If you are using Batch Normalization and Dropout, remember to pass `is_training=True` if you use the `train_part34()` function. BatchNorm and Dropout layers have different behaviors at training and inference time. `training` is a specific keyword argument reserved for this purpose in any `tf.keras.Model`'s `call()` function. Read more about this here : https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/BatchNormalization#methods\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dropout#methods\n", + "\n", + "### Tips for training\n", + "For each network architecture that you try, you should tune the learning rate and other hyperparameters. When doing this there are a couple important things to keep in mind: \n", + "\n", + "- If the parameters are working well, you should see improvement within a few hundred iterations\n", + "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n", + "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n", + "- You should use the validation set for hyperparameter search, and save your test set for evaluating your architecture on the best parameters as selected by the validation set.\n", + "\n", + "### Going above and beyond\n", + "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these, but don't miss the fun if you have time!\n", + "\n", + "- Alternative optimizers: you can try Adam, Adagrad, RMSprop, etc.\n", + "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n", + "- Model ensembles\n", + "- Data augmentation\n", + "- New Architectures\n", + " - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n", + " - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n", + " - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n", + " \n", + "### Have fun and happy training! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.9480481147766113, Accuracy: 14.0625, Val Loss: 6.719646453857422, Val Accuracy: 12.0\n" + ] + } + ], + "source": [ + "class CustomConvNet(tf.keras.Model):\n", + " def __init__(self):\n", + " super(CustomConvNet, self).__init__()\n", + " ############################################################################\n", + " # TODO: Construct a model that performs well on CIFAR-10 #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.pad1 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\")\n", + " self.pad2 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\")\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " self.conv1 = tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer)\n", + " \n", + " self.conv2 = tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer) \n", + " self.fc1 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " \n", + " def call(self, input_tensor, training=False):\n", + " ############################################################################\n", + " # TODO: Construct a model that performs well on CIFAR-10 #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = self.pad1(input_tensor) \n", + " x = self.conv1(x) \n", + " x = self.pad2(x) \n", + " x = self.conv2(x) \n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " \n", + " return x\n", + "\n", + "# device = '/device:GPU:0' # Change this to a CPU/GPU as you wish!\n", + "device = '/cpu:0' # Change this to a CPU/GPU as you wish!\n", + "print_every = 700\n", + "num_epochs = 10\n", + "\n", + "model = CustomConvNet()\n", + "\n", + "def model_init_fn():\n", + " return CustomConvNet()\n", + "\n", + "def optimizer_init_fn():\n", + " learning_rate = 1e-3\n", + " return tf.keras.optimizers.Adam(learning_rate) \n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn, num_epochs=num_epochs, is_training=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Describe what you did \n", + "\n", + "In the cell below you should write an explanation of what you did, any additional features that you implemented, and/or any graphs that you made in the process of training and evaluating your network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "TODO: Tell us what you did" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/BatchNormalization.ipynb b/assignment2/BatchNormalization.ipynb new file mode 100755 index 0000000..c3fb9c8 --- /dev/null +++ b/assignment2/BatchNormalization.ipynb @@ -0,0 +1,1148 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Batch Normalization\n", + "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n", + "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n", + "\n", + "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n", + "\n", + "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n", + "\n", + "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n", + "\n", + "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", + "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", + "\n", + "def print_mean_std(x,axis=0):\n", + " print(' means: ', x.mean(axis=axis))\n", + " print(' stds: ', x.std(axis=axis))\n", + " print() " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: forward\n", + "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n", + "\n", + "Referencing the paper linked to above in [1] may be helpful!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before batch normalization:\n", + " means: [ -2.3814598 -13.18038246 1.91780462]\n", + " stds: [27.18502186 34.21455511 37.68611762]\n", + "\n", + "After batch normalization (gamma=1, beta=0)\n", + " means: [5.10702591e-17 6.21724894e-17 3.98986399e-17]\n", + " stds: [0.99999963 0.99999971 0.99999973]\n", + "\n", + "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n", + " means: [11. 12. 13.]\n", + " stds: [0.99999963 1.99999942 2.9999992 ]\n", + "\n" + ] + } + ], + "source": [ + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after batch normalization \n", + "\n", + "# Simulate the forward pass for a two-layer network\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 = 200, 50, 60, 3\n", + "X = np.random.randn(N, D1)\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "\n", + "print('Before batch normalization:')\n", + "print_mean_std(a,axis=0)\n", + "\n", + "gamma = np.ones((D3,))\n", + "beta = np.zeros((D3,))\n", + "# Means should be close to zero and stds close to one\n", + "print('After batch normalization (gamma=1, beta=0)')\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=0)\n", + "\n", + "gamma = np.asarray([1.0, 2.0, 3.0])\n", + "beta = np.asarray([11.0, 12.0, 13.0])\n", + "# Now means should be close to beta and stds close to gamma\n", + "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After batch normalization (test-time):\n", + " means: [-0.03927353 -0.04349151 -0.10452686]\n", + " stds: [1.01531399 1.01238345 0.97819961]\n", + "\n" + ] + } + ], + "source": [ + "# Check the test-time forward pass by running the training-time\n", + "# forward pass many times to warm up the running averages, and then\n", + "# checking the means and variances of activations after a test-time\n", + "# forward pass.\n", + "\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 = 200, 50, 60, 3\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "gamma = np.ones(D3)\n", + "beta = np.zeros(D3)\n", + "\n", + "for t in range(50):\n", + " X = np.random.randn(N, D1)\n", + " a = np.maximum(0, X.dot(W1)).dot(W2)\n", + " batchnorm_forward(a, gamma, beta, bn_param)\n", + "\n", + "bn_param['mode'] = 'test'\n", + "X = np.random.randn(N, D1)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n", + "\n", + "# Means should be close to zero and stds close to one, but will be\n", + "# noisier than training-time forward passes.\n", + "print('After batch normalization (test-time):')\n", + "print_mean_std(a_norm,axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: backward\n", + "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n", + "\n", + "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n", + "\n", + "Once you have finished, run the following to numerically check your backward pass." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx error: 2.4825083150038806e-05\n", + "dgamma error: 5.9824277839050605e-12\n", + "dbeta error: 2.8795057655839487e-12\n" + ] + } + ], + "source": [ + "# Gradient check batchnorm backward pass\n", + "np.random.seed(231)\n", + "N, D = 4, 5\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n", + "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", + "\n", + "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", + "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n", + "#You should expect to see relative errors between 1e-13 and 1e-8\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch normalization: alternative backward\n", + "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n", + "\n", + "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too! \n", + "\n", + "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n", + "\n", + "we first calculate the mean $\\mu$ and variance $v$.\n", + "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$ and normalized data $Y$.\n", + "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n", + "\n", + "\\begin{align}\n", + "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k & v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n", + "& \\sigma=\\sqrt{v+\\epsilon} & y_i=\\frac{x_i-\\mu}{\\sigma}\n", + "\\end{align}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n", + "\n", + "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n", + "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n", + "$\\frac{\\partial \\sigma}{\\partial v}$, \n", + "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n", + "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n", + "\n", + "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n", + "\n", + "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n", + "\n", + "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx difference: 0.0\n", + "dgamma difference: 0.0\n", + "dbeta difference: 0.0\n", + "speedup: 1.33x\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D = 100, 500\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", + "\n", + "t1 = time.time()\n", + "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n", + "t2 = time.time()\n", + "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n", + "t3 = time.time()\n", + "\n", + "print('dx difference: ', rel_error(dx1, dx2))\n", + "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n", + "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n", + "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fully Connected Nets with Batch Normalization\n", + "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n", + "\n", + "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n", + "\n", + "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running check with reg = 0\n", + "Initial loss: 2.1631431253070756\n", + "W1 relative error: 6.03e-01\n", + "W2 relative error: 3.33e-01\n", + "W3 relative error: 3.74e-10\n", + "b1 relative error: 2.22e-03\n", + "b2 relative error: 2.22e-03\n", + "b3 relative error: 1.18e-10\n", + "beta1 relative error: 3.34e-01\n", + "beta2 relative error: 1.84e-09\n", + "gamma1 relative error: 3.34e-01\n", + "gamma2 relative error: 2.90e-09\n", + "\n", + "Running check with reg = 3.14\n", + "Initial loss: 6.992501965152032\n", + "W1 relative error: 2.30e-03\n", + "W2 relative error: 1.00e+00\n", + "W3 relative error: 1.14e-08\n", + "b1 relative error: 4.44e-03\n", + "b2 relative error: 2.85e-08\n", + "b3 relative error: 2.01e-10\n", + "beta1 relative error: 3.33e-01\n", + "beta2 relative error: 5.72e-09\n", + "gamma1 relative error: 3.33e-01\n", + "gamma2 relative error: 4.08e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "# You should expect losses between 1e-4~1e-10 for W, \n", + "# losses between 1e-08~1e-10 for b,\n", + "# and losses between 1e-08~1e-09 for beta and gammas.\n", + "for reg in [0, 3.14]:\n", + " print('Running check with reg = ', reg)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " reg=reg, weight_scale=5e-2, dtype=np.float64,\n", + " normalization='batchnorm')\n", + "\n", + " loss, grads = model.loss(X, y) \n", + " print('Initial loss: ', loss)\n", + "\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", + " if reg == 0: print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batchnorm for deep networks\n", + "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solver with batch norm:\n", + "(Iteration 1 / 200) loss: 2.340994\n", + "(Epoch 0 / 10) train acc: 0.106000; val_acc: 0.116000\n", + "(Epoch 1 / 10) train acc: 0.346000; val_acc: 0.279000\n", + "(Iteration 21 / 200) loss: 2.003612\n", + "(Epoch 2 / 10) train acc: 0.425000; val_acc: 0.299000\n", + "(Iteration 41 / 200) loss: 2.051566\n", + "(Epoch 3 / 10) train acc: 0.493000; val_acc: 0.292000\n", + "(Iteration 61 / 200) loss: 1.713268\n", + "(Epoch 4 / 10) train acc: 0.549000; val_acc: 0.309000\n", + "(Iteration 81 / 200) loss: 1.280356\n", + "(Epoch 5 / 10) train acc: 0.576000; val_acc: 0.295000\n", + "(Iteration 101 / 200) loss: 1.329176\n", + "(Epoch 6 / 10) train acc: 0.662000; val_acc: 0.318000\n", + "(Iteration 121 / 200) loss: 1.072926\n", + "(Epoch 7 / 10) train acc: 0.650000; val_acc: 0.295000\n", + "(Iteration 141 / 200) loss: 1.326556\n", + "(Epoch 8 / 10) train acc: 0.688000; val_acc: 0.342000\n", + "(Iteration 161 / 200) loss: 0.905949\n", + "(Epoch 9 / 10) train acc: 0.782000; val_acc: 0.339000\n", + "(Iteration 181 / 200) loss: 1.044589\n", + "(Epoch 10 / 10) train acc: 0.814000; val_acc: 0.313000\n", + "\n", + "Solver without batch norm:\n", + "(Iteration 1 / 200) loss: 2.302332\n", + "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n", + "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n", + "(Iteration 21 / 200) loss: 2.041970\n", + "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n", + "(Iteration 41 / 200) loss: 1.900473\n", + "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n", + "(Iteration 61 / 200) loss: 1.713156\n", + "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n", + "(Iteration 81 / 200) loss: 1.662213\n", + "(Epoch 5 / 10) train acc: 0.431000; val_acc: 0.298000\n", + "(Iteration 101 / 200) loss: 1.703401\n", + "(Epoch 6 / 10) train acc: 0.525000; val_acc: 0.348000\n", + "(Iteration 121 / 200) loss: 1.569302\n", + "(Epoch 7 / 10) train acc: 0.540000; val_acc: 0.322000\n", + "(Iteration 141 / 200) loss: 1.416812\n", + "(Epoch 8 / 10) train acc: 0.625000; val_acc: 0.336000\n", + "(Iteration 161 / 200) loss: 1.087573\n", + "(Epoch 9 / 10) train acc: 0.649000; val_acc: 0.337000\n", + "(Iteration 181 / 200) loss: 0.934002\n", + "(Epoch 10 / 10) train acc: 0.699000; val_acc: 0.337000\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Try training a very deep net with batchnorm\n", + "hidden_dims = [100, 100, 100, 100, 100]\n", + "\n", + "num_train = 1000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "weight_scale = 2e-2\n", + "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", + "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + "\n", + "print('Solver with batch norm:')\n", + "bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=True,print_every=20)\n", + "bn_solver.train()\n", + "\n", + "print('\\nSolver without batch norm:')\n", + "solver = Solver(model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=True, print_every=20)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n", + " \"\"\"utility function for plotting training history\"\"\"\n", + " plt.title(title)\n", + " plt.xlabel(label)\n", + " bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n", + " bl_plot = plot_fn(baseline)\n", + " num_bn = len(bn_plots)\n", + " for i in range(num_bn):\n", + " label='with_norm'\n", + " if labels is not None:\n", + " label += str(labels[i])\n", + " plt.plot(bn_plots[i], bn_marker, label=label)\n", + " label='baseline'\n", + " if labels is not None:\n", + " label += str(labels[0])\n", + " plt.plot(bl_plot, bl_marker, label=label)\n", + " plt.legend(loc='lower center', ncol=num_bn+1) \n", + "\n", + " \n", + "plt.subplot(3, 1, 1)\n", + "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n", + " lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n", + "plt.subplot(3, 1, 2)\n", + "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n", + " lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n", + "plt.subplot(3, 1, 3)\n", + "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n", + " lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batch normalization and initialization\n", + "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n", + "\n", + "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running weight scale 1 / 20\n", + "Running weight scale 2 / 20\n", + "Running weight scale 3 / 20\n", + "Running weight scale 4 / 20\n", + "Running weight scale 5 / 20\n", + "Running weight scale 6 / 20\n", + "Running weight scale 7 / 20\n", + "Running weight scale 8 / 20\n", + "Running weight scale 9 / 20\n", + "Running weight scale 10 / 20\n", + "Running weight scale 11 / 20\n", + "Running weight scale 12 / 20\n", + "Running weight scale 13 / 20\n", + "Running weight scale 14 / 20\n", + "Running weight scale 15 / 20\n", + "Running weight scale 16 / 20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kalkidanfekadu/Desktop/CS231/assignment2/cs231n/classifiers/fc_net.py:341: RuntimeWarning: divide by zero encountered in log\n", + " scores = np.exp(scores)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running weight scale 17 / 20\n", + "Running weight scale 18 / 20\n", + "Running weight scale 19 / 20\n", + "Running weight scale 20 / 20\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Try training a very deep net with batchnorm\n", + "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n", + "num_train = 1000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "bn_solvers_ws = {}\n", + "solvers_ws = {}\n", + "weight_scales = np.logspace(-4, 0, num=20)\n", + "for i, weight_scale in enumerate(weight_scales):\n", + " print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n", + " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", + " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + "\n", + " bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=False, print_every=200)\n", + " bn_solver.train()\n", + " bn_solvers_ws[weight_scale] = bn_solver\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=10, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=False, print_every=200)\n", + " solver.train()\n", + " solvers_ws[weight_scale] = solver" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "scrolled": true, + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results of weight scale experiment\n", + "best_train_accs, bn_best_train_accs = [], []\n", + "best_val_accs, bn_best_val_accs = [], []\n", + "final_train_loss, bn_final_train_loss = [], []\n", + "\n", + "for ws in weight_scales:\n", + " best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n", + " bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n", + " \n", + " best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n", + " bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n", + " \n", + " final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n", + " bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n", + " \n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Best val accuracy vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Best val accuracy')\n", + "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n", + "plt.legend(ncol=2, loc='lower right')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Best train accuracy vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Best training accuracy')\n", + "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n", + "plt.legend()\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Final training loss vs weight initialization scale')\n", + "plt.xlabel('Weight initialization scale')\n", + "plt.ylabel('Final training loss')\n", + "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n", + "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n", + "plt.legend()\n", + "plt.gca().set_ylim(1.0, 3.5)\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1:\n", + "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Batch normalization and batch size\n", + "We will now run a small experiment to study the interaction of batch normalization and batch size.\n", + "\n", + "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No normalization: batch size = 5\n", + "Normalization: batch size = 5\n", + "Normalization: batch size = 10\n", + "Normalization: batch size = 50\n" + ] + } + ], + "source": [ + "def run_batchsize_experiments(normalization_mode):\n", + " np.random.seed(231)\n", + " # Try training a very deep net with batchnorm\n", + " hidden_dims = [100, 100, 100, 100, 100]\n", + " num_train = 1000\n", + " small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + " }\n", + " n_epochs=10\n", + " weight_scale = 2e-2\n", + " batch_sizes = [5,10,50]\n", + " lr = 10**(-3.5)\n", + " solver_bsize = batch_sizes[0]\n", + "\n", + " print('No normalization: batch size = ',solver_bsize)\n", + " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", + " solver = Solver(model, small_data,\n", + " num_epochs=n_epochs, batch_size=solver_bsize,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': lr,\n", + " },\n", + " verbose=False)\n", + " solver.train()\n", + " \n", + " bn_solvers = []\n", + " for i in range(len(batch_sizes)):\n", + " b_size=batch_sizes[i]\n", + " print('Normalization: batch size = ',b_size)\n", + " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n", + " bn_solver = Solver(bn_model, small_data,\n", + " num_epochs=n_epochs, batch_size=b_size,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': lr,\n", + " },\n", + " verbose=False)\n", + " bn_solver.train()\n", + " bn_solvers.append(bn_solver)\n", + " \n", + " return bn_solvers, solver, batch_sizes\n", + "\n", + "batch_sizes = [5,10,50]\n", + "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 1, 1)\n", + "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", + " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "plt.subplot(2, 1, 2)\n", + "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", + " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "\n", + "plt.gcf().set_size_inches(15, 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2:\n", + "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization\n", + "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n", + "\n", + "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n", + "\n", + "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n", + "\n", + "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n", + "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1. \n", + "3. Subtracting the mean image of the dataset from each image in the dataset.\n", + "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization: Implementation\n", + "\n", + "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n", + "\n", + "Here's what you need to do:\n", + "\n", + "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n", + "\n", + "Run the cell below to check your results.\n", + "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n", + "\n", + "Run the second cell below to check your results.\n", + "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n", + "\n", + "Run the third cell below to run the batch size experiment on layer normalization." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before layer normalization:\n", + " means: [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n", + " stds: [10.07429373 28.39478981 35.28360729 4.01831507]\n", + "\n", + "After layer normalization (gamma=1, beta=0)\n", + " means: [-4.81096644e-16 -7.40148683e-17 7.40148683e-17 -5.55111512e-16]\n", + " stds: [0.99999901 0.99999965 0.99999972 0.99999751]\n", + "\n", + "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n", + " means: [5. 5. 5. 5.]\n", + " stds: [2.99999702 2.99999894 2.99999915 2.99999253]\n", + "\n" + ] + } + ], + "source": [ + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after layer normalization \n", + "\n", + "# Simulate the forward pass for a two-layer network\n", + "np.random.seed(231)\n", + "N, D1, D2, D3 =4, 50, 60, 3\n", + "X = np.random.randn(N, D1)\n", + "W1 = np.random.randn(D1, D2)\n", + "W2 = np.random.randn(D2, D3)\n", + "a = np.maximum(0, X.dot(W1)).dot(W2)\n", + "\n", + "print('Before layer normalization:')\n", + "print_mean_std(a,axis=1)\n", + "\n", + "gamma = np.ones(D3)\n", + "beta = np.zeros(D3)\n", + "# Means should be close to zero and stds close to one\n", + "print('After layer normalization (gamma=1, beta=0)')\n", + "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=1)\n", + "\n", + "gamma = np.asarray([3.0,3.0,3.0])\n", + "beta = np.asarray([5.0,5.0,5.0])\n", + "# Now means should be close to beta and stds close to gamma\n", + "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n", + "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", + "print_mean_std(a_norm,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx error: 1.0\n", + "dgamma error: 3.648665989818157e-12\n", + "dbeta error: 3.685413092846043e-12\n" + ] + } + ], + "source": [ + "# Gradient check batchnorm backward pass\n", + "np.random.seed(231)\n", + "N, D = 4, 5\n", + "x = 5 * np.random.randn(N, D) + 12\n", + "gamma = np.random.randn(D)\n", + "beta = np.random.randn(D)\n", + "dout = np.random.randn(N, D)\n", + "\n", + "ln_param = {}\n", + "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n", + "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n", + "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", + "\n", + "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n", + "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n", + "\n", + "#You should expect to see relative errors between 1e-12 and 1e-8\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer Normalization and batch size\n", + "\n", + "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No normalization: batch size = 5\n", + "Normalization: batch size = 5\n", + "Normalization: batch size = 10\n", + "Normalization: batch size = 50\n" + ] + }, + { + "data": { + "image/png": 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HLGZyqAwOPwmeVnC4oehyuPTbULIF4tKi3UvBxALdSINh9Shtbwb+pLUOjXRSa/0L4BcApaWlo11DCCGEEEIIMVW8HXDkGbPJd/lzEOgBVzIsu8rMhyu6DGLio91LMcREAt1JYFHE84VA7Shtbwa+OIF7CSGEEEIIISZbd6MZRnnwcbPhdzgACQvgvI+YEFewCRwx0e6lGMNEAt0eoFgptQSowYS2jw1tpJRaBqQCr07gXkIIIYQQQojJ0HrMDKU8WAbVuwENqUtgwxfMHnF5pWCzRbuXYpzOONBprYNKqduApzHbFtyvtX5XKXU3sFdr/ZjV9KPA77XWMpRSCCGEEEKI6aY1NLw7EOIa3jbHs8+F930DVmyFrLNke4FZSs20nFVaWqr37t0b7W4IIYQQQggxexx4CJ6/22zunbzQLFySWmDmwx0sg7ZjgDKbey/fakJcakGUOy1Go5Tap7UuHU/bCW0sLoQQQgghhIiyAw/B4182K1MCdFTDI58z39ucUHgxbPx7WHYNJGRFr58zyKP7a/jx04epbfeQmxLLnVuWccPqvGh364xIoBNCCCGEEGImCoegpxm6G6Cn0Sxg0vfV02iOdzdC8xHQ4eGvj02H2/eDO3n6+z6DPbq/hm88/DaegFmAv6bdwzceNsNQZ2Ook0AnhBBCCCHEdAmHzZ5ufWFsaDiLPNbTzIi7gjnjTaUtIQsyiqHp0Mj38rTOuzCntcYfCuP1h/EGQ3j8IbzBEN5AuP/7ux9/tz/M9fEEQvz46cMS6IQQQgghhJh3tAZPmxXGGqCnaZTA1mTOjbQ1s8NtAlp8lpnbtmit2T4gPtM8JiyAhExz3pUw+LU/PccMsxwqeeGUvN3TpbXGFxwIVB6/CVjeYAhv/7Ew3sDA+f72gRCewEjthx4buP6ZLhFS2+6Z3Dc+TSTQCSGEEEIIMZTWZqPtscJZf3hrNPu3DWVzDgSxpDzIWWU9t6prCQtMQEvIAlfiGa8yuWfplzhn37eIVf7+Yx4dwztLv8TaUV4TDuuICtZAePJFhCsTpMx57zjCVV/7oeHNFxxhOOg42BS4nXbcTjuxTjsup41Y63lcjIO0eBsu65w74tzAV+QxW//xLzywj8Yu37D75abEnlE/o00CnRBCCCGEmB+0Bn/3kHDWOEJlzXoMDf/Qj7JblTSrcrbg7IHKWl9I6wts7pRp2Qrg798r4vzANr7meIhc1UKtTudHwZt4anc+K6pfHhK8TPjyh84sZNltqj9ARYYtt9NGottBZqLLOmaLCGKDw1Vf+4EwZh92TbfTRozdhpqCn983r1kxaA4dQKzTzp1blk36vaaDBDohhBBCCDHzDF2G/7LvwMqbRm7r7x0jnA05Fugd4QLKCmhWEEsvHlJFixj2GJs6Izbdbu728fLRZl480kxNu5caNvKYf+OQVmFSYp24k1wRoWlwxWp4kBpe8YqsjDnt0X/vE9U3T26urHIp+9AJIYQQQoiZZegy/AB2J5RYy+4Prab5u0a+Tlx6ROVsaDiLOBaXDjb79Ly3M+QNhNh7vI1d5U3sKm/mvbpOAFLinPgCITyB4RW3vJRYXv76pdPdVTEJZB86IYQQQggxO3U1wJNfGxzmAEIBOPg/ZhhjXxDrn5MWUUHrC2zxGSYEzlJaaw7WdfHSURPgXj/Wii8YxmlXnL84lTu3LGNTcQZn5ybz+Fu1c2oIoTg9EuiEEEIIIUT0aA2N78HhJ81XzVgjtRR8/cS0dW26NXZ62VXezK7yJl462kJzt5nDV7IggY+vX8ym4gzWF6YRFzP4I/xcG0IoTo8EOiGEEEIIMb2Cfqh6xQpxT0B7lTmeuwYu+Rbs+aUZTjnUDFmGf7J4/CF2H2thV3kzL5U3c7jBDB3NSIjhoqIMNhVnsrEog+xk9ymvdcPqPAlw85QEOiGEEEIIMfU8bVD+nAlwR58DX6fZe63wfbDpDii5ChKzTdvUxcPn0DljzcIos1g4rHm3tpNdR5vYdaSZfSfa8IfCxDhsrCtI44Nr8thYnMGK7CRstqlfHVPMDRLohBBCCCHE1GipMFW4I0/BiVfMhtrxmXDW9bDsGhPmYuKGv65vNcvxrnI5g9W2e3ipvJkXy5t4paKF1h6zV9zy7ERuvaiAjUUZrFuShts5sxdlETOXBDohhBBCCDE5wiE4uWdgPlzzYXM862zY+PcmxOWuGd+y/ytvmpUBrtsXZHdlS/9cuIqmHgCyEl28b1kmm4szuagog8xEV5R7KuYKCXRCCCGEEOLM+bqh4s+mCnfkKehtAZsDFl8EpZ+GZVdBakG0ezllQmHNgZPtvFTezK6jzbxxoo1gWON22li/JJ2PrstnU3EmJQsSpmSTbCEk0AkhhBBCiNPTUQNHnoTDT8Gxv0LID+5kKL4Sll0NRZeb53NUdWtvfwXulYoWOjwBlIKzc5P47OZCNhVlcH5BKi6HDKMUU08CnRBCCCGEGJvWUPeWNR/uSfM9QOoSWPtZE+LyN8zqfd/G0ukN8GpFi9lOoLyZ4y29AOQku9ly9gI2WcMo0+JjotxTMR9JoBNCCCGEEMMFvHB8l1mV8sjT0FkDKFi0Di7fbubDZZTAHBxGGAyFebO63WwncLSZN6vbCYU18TF2NhSmc+uFBWwszmRpZrwMoxRRJ4FOCCGEEEIYPc0mvB1+Air+AoEecMbD0kvgkn8yQyoTMqPdy0mnteZ4Sy8vlTfxYnkzr1W00OULYlOwcmEKf/e+pWwsymB1fioxjnEs6CLENJJAJ4QQQggxX2kNzUdMgDv8JFS/DmhIzIXzPmKqcAWbwHnqja1nm/ZeP69Ywyh3lTdzss3sebcwNZat5+WyuTiDC5dmkBw3N4eRirlDAp0QQgghxHwSCkLVqwPz4VorzfHslXDxP5r5cDnnzbmhlP5gmDeq2vpXozxwsh2tIdHl4IKl6Xz+4qVsKspgcXqcDKOcJwKNjdR89Q4W/vQnODJnb+VZAp0QQgghxFzn7YCjz5kQV/4seNvBHgNLNsMFX4SSq8zm3XOI1pqKpm5rNcpmXqtsodcfwm5TrFqUwu2XFbOpOIPzFqbgsMswyvlGa03jT36KZ98+mv7t38i5665od+mMSaATQgghhJiL2o6bbQUOPwEnXoZwEOLSzTDKZVebeXGuxGj3clK1dPt4uaKFXUeaeOloM3UdXgCWZMTzoTUL2VicwQVL00lyyzDKuS7U3U2wro5AXR2BunoCdbUE6+rN83rzSCAAQMcjj5J5222ztkongU4IIYQQYi4Ih6H2jYH5cI3vmeMZy0wVbtk1sHAt2ObO3mi+YIh9x9t4sbyZl4428U5NJwDJsU4uKkrny8WZbCzKYFFaXJR7KiZT2O8n2NBAoLaOYP0Ioa2+nnBX1+AX2Ww4FizAmZ1N7DlnY4uLw1deDqEQWmua7r2PnO9+JzpvaIKU1jrafRiktLRU7927N9rdEEIIIYSY+fy9UPnCwNYCPY2g7JB/ganCLbsa0pdGu5dn5NH9Nfz46cPUtnvITYnlzi3LuH5VLocbunipvJkXy5t5/VgL3kAYh02xZnEqm4sz2Ficybl5ydhtMg9uNtLhMMGmZiuomUpasL6OQK1VWauvI9TUPOx19tRUHDnZOHNycWZn48zJxpGTg9P6cmRmohymlhVobKTiiivRPl//65XLRdFzz86YKp1Sap/WunQ8baVCJ4QQQggxm3TVw5GnTBWu8gUIesGVBEWXmSpc0eUQlxbtXk7Io/tr+MbDb+MJhACoafdwx0Nv8e1H36bLZ44VZSVw89p8NpdksH5JOvEu+Vg702mtCXd29g95DNZFhLa+4ZGNjf1DIfuouDgrpOXgWlZiQlp2zkBoy87GFhs77n4033sfOhwe3LdweNZW6Sb0J18pdRVwD2AHdmit/2WENjcB2wENvKW1/thE7imEEEIIMa9oDQ3vmgB3+AkzrBIgOR/WfNJU4RZfBI6Y6PZzEmitqW71cNfj7/aHuT4hrQmG4Uc3rmRTcQY5yeP/AC+mR9jrJVhvhjyaitrw0Bbu7R38IocDZ1YWjtwcYlevJiknx6q0WdW17GxsycmTuvKo5803h4VGAgE8+/dP2j2m0xkHOqWUHfg5cAVwEtijlHpMa/1eRJti4BvARVrrNqVU1kQ7LIQQQggx5wX9cOIlK8Q9CR3V5nheKVz6LVOJyzpr1m8tEAyFOVTfxZ7jrew93sae4600dvlGbe8NhLipdNE09lD00aEQwaamYZW1QH0dQWs4ZKi1ddjr7BkZOLOzcRUuIf6iC01lLdcENUdOLo6MdJR9eud1Fj76yLTeb6pNpEK3Djiqta4EUEr9HrgeeC+izWeBn2ut2wC01o0TuJ8QQgghxNzV22q2FDj8BBx9Hvxd4IiFwvfB5juhZAskZke7lxPS6w/yZlU7e463sfdEK2+caKPHbypxeSmxXLg0ndKCNH72fPmIwS43RapyYznTfdW01oTa201QG2E4ZKC+jmBDI4QGV01t8fE4c3Nw5OTgPueciCGQJrQ5FizA5nJN9tsUQ0wk0OUB1RHPTwLrh7QpAVBKvYwZlrlda/3U0AsppT4HfA4gPz9/Al0SQgghhJihDjwEz98NHSfNnm+XfQdy15jNvQ8/aTb71mFIWADnfMBU4ZZcDDGzd4XGpi4f+060mgB3vJV3ajsJhTVKwfLsJD50/kJKC9IoXZw6KKwluByD5tABxDrt3LllWTTexqzRfO99Zl+1IXPBwr29w4PakOGQ2usddC3ldOKw5q3Fr107ENQiFhuxJ86tbS9mq4kEupFq/EOXzHQAxcD7gIXALqXUOVrr9kEv0voXwC/ArHI5gT4JIYQQQsw8Bx6Cx78MAY953lEND3+O/o9OC86BjV81IS53Ndhm30bXWmuONff0D53ce6KNY809ALgcNlYtSuFvL15KaUEqaxanjrkX3A2r8wCGrXLZd1wM0FoTam7G8847tP/pT6A17Q89hP/ECUJtbQRrawl1dAx+kVI4MjJw5ObgKikh4eKLB68KmZ2NPT0dNQv/HM5HEwl0J4HIQcwLgdoR2rymtQ4Ax5RShzEBb88E7iuEEEIIMTt4O+DkHth5x0CY66fBnQKffxFSF0elexMRCIV5r7aTPcdb++fAtfT4AUiNc1JakMZH1y2itCCNc3KTiXGcXji4YXWeBDgGlvEP1NSYr9rage9rakx1zTdkeGoohPfdd4lbtYrYVecNm7fmzMpExcz+RXSEMZFAtwcoVkotAWqAm4GhK1g+CnwU+LVSKgMzBLNyAvcUQgghhJiZtIb2KqjeDVWvmceGdxk+gCmCt2PWhLluX5D9VW39wyf3V7X3D4nMT4vj4mWZrC1IY21BKoUZCdhkH7hx6V9sZGhQq6khUFNLoLYWPWRFRntaGs68PFzLl5Nw6aXYkhJp/vm9g1Zu1F4vOd//3ozZV01MnTMOdFrroFLqNuBpzPy4+7XW7yql7gb2aq0fs85dqZR6DwgBd2qtWyaj40IIIYQQURUKQP0BqNoN1a9B9evQVWfOxSTAwlK4+B8hfz38z23QWTP8GskLp7fPp6Gh0xsxfLKV92o7CWuwKTgrN4mPrF3E2oI0SgtSWZDkjnZ3ZywdChFsaOgPbP5BlbZaAnV1w5bQt2dk4MzLxXXWChKvuBxnXp75ys3FmZuLLW7wvMq67XcNv+8s3ldNnJ4J7UOntX4CeGLIse9EfK+Br1pfQgghhBCzl6cNqvcMhLeafRCw9tRKXmT2gsvfAIvWw4KzwRaxFPvl2wfPoQNwxpqFUWYArTUVTd3s6Qtwx9uoajXvLdZpZ3V+CrddWszaglRW56eSIJt499PBIMGGBiuo1Q4fFllfD8HgoNfYMzOIyc0j9pxzSNqyxQpsueYxJ+e0NsmGubevmjg98rdRCCGEEGIoraHt2ED1rWo3NB0055Qdss+F1beY6tuiDZB8irleK28yj0NXuew7Ps38wTBv13Sw97hZgXLfiVbaek0gyEiIoXRxGn9zwWLWFqRxVm4STvv8XRxDBwIEGhoInBxlDltDw7Dl/B1ZWTjz8ohdtYqk3NyBClueVWGb5KX859q+auL0SKATQgghhAj6oe4tK7xZFbgea/tP0fBFAAAgAElEQVRcVxIsXAvnfNBU3/LOB1fC6d9j5U1RC3AdngBvVLX1B7i3qtvxBcMAFGbEc8VZCygtSGNtQRoF6XGoWb5h+enQfr9Z0n9IWOuruAUbGiAcHniBUjgWLDCB7fzzSbIqazFWaHPk5GCTBUfENJJAJ4QQQoj5p7fVLFpSvdtU32rfgKC1D1fKYlh6iQlv+Rsgc/ng4ZOzQG27p3/o5J7jrRxu6EJrcNgUZ+clc8uGxZQWpHH+4lQyE+f2xs9hv59grVlcxB+52IgV4IINDaYi28dmswJbLvHr1g6ev5aXhzM7W1aIFDOKBDohhBBCzG1aQ0tFRPVtNzQfMedsDsg5D0o/PRDgErOj29/TFA5rjjR29a8+ufd4GzXtZq5efIydNYtTuebcHEoLUlm1KIW4mNnz8S/Q2EjNV+9g4U9/MupqjWGfb2CBkcihkFa1LdjUNDiw2e04rQpb/IYNg8PawjycCxagnKPvkSfETDN7/kYLIYQQQoxH0Ae1+weGTlbvht5mc86dbILbyo+Y8Ja7BmLixr7eDOMNhHi7psPs/3aslX0n2uj0mkU3shJdrC1IY9umJawtSGN5diKOWTz/rfne+/Ds20f9v/yQlBuuH7ycf00N/toaQk3Ng19kt5vNsfPyiL/ookHz12Ly8nAsWIByyEdgMXfIn2YhhBBCzG49zYP3fqvdDyGzwTVphVB8pbV4yXrIWAa22RVw2nv97DsxsP/bgZMd+ENmTldRVgLXrsyhdLGZ/7YoLXZOzH/TWtP5xBO0/+EPoDVdO3fStXOnOel0msCWm0vC5s0D89esKpsjK0sCm5hX5E+7EEIIIWYPrc1wyb7wVvUatFaYczYn5K6CdZ8b2D4gISu6/T1NWmtOtnnYe6K1P8AdaegGwGlXnJuXzKcuKuif/5YWP7fmculgkK5nnqF5xw587x0cOGG3k3DJ+8j+1rdwZGai7LNrTqMQU0kCnRBCCCFmroAHat4YWMCkerfZDw4gNs2EtjW3mK0DcleZvd1mqEf31/Djpw9T2+4hNyWWO7cs4/3n5XKovnNgA+/jbdR3msVZEl0Ozi9I5fpVeZQuTuW8RSm4nXMzyIS9XtoffpjW//w1gepqnAsXgsMxsH9bKETPrpdQdruEOSGGUDpykugMUFpaqvfu3RvtbgghhBAiGrobB1ff6t6CsLVhcnrxwNDJRRsgoxhmyfDCR/fX8I2H38YTGNivzKbAaVP4QuazWE6ym7UFaawtSKW0II2SBYnYbbPj/Z2pUEcHbb/7Ha0P/JZQayvu81aSvm0bPS+/TPt/Pzx4s2ynk5QbbyTnuzNjM3YhppJSap/WunQ8baVCJ4QQQojoCIeh6dDAxt3Vu81m3gB2F+Suhgv+zoS3ReshPj26/T0NobCmqrWXQ3WdHKzv4hd/rcAbDA9qE9bgsNv40YfPpbQgjbyUmVtdnGyBujpaf/0b2v74R3RvL/GbN5G+bRtxa9eilKL55/cODnMAgQCe/fuj02EhZjAJdEIIIYSYHv4ea/ikFeBOvg7eDnMuLsPMeyv9tHnMOQ8cs2N/tPZeP4fquzhU18mh+i4O1ndxpL6rvxpnUya8jaTXH+L6VXnT2Nvo8h09SsuOX9FRVgZak3TNNaRv+wzuZcsGtSt89JEo9VCI2UcCnRBCCCHOzIGH4Pm7oeMkJC+Ey74DK28aON9ZF1F9ew3q34awNScqczmcdb2pvuVvMKtRzvDhk4FQmMqmHg7Vd3KwrotD9Z0cquvqn/MGkBrnZEVOEh9dl8/ynERWZCdRvCCBy/71r/17w0XKnSdVud59+2jZ8Su6//IXVGwsqR/9KGmf/CQxC+dPmBViqkigE0IIIcTpO/AQPP5ls2gJQEc1PHYbVPwZwiET4NqrzDmHG/LOhwu/bMLbwrUQlxa9vp+C1pqmLp+pulmh7WB9F0cbuwhY892cdkVRViIXLk1nWXYiy3OSWJGdSGaia8RtA+7csmzYHLpYp507tywb1nau0OEw3S+8QMsvd+DZvx97SgoZt91G6sc/hiM1NdrdE2LOkEAnhBBCiNP33PaBMNcn6IO3HoSEBWbO2/ovmMfsleCYmcvrewMhyhu6OWgFt0P1Zthka4+/v012kpvlOYlcXJLJipxElmcnUZgZj/M0Nuy+YbWpRA1d5bLv+Fyi/X46ynbScv+v8B+twJmby4J/+idSPvRBbHGzaxN3IWYDCXRCCCGEOLWOk4NXn+ysGaWhgjsOz7jhk337ux22qm4HrTlvx5p7+ue3uZ02lmUnccWKBSy3gtvy7ERSJ2mvtxtW583JANcn1N1D+x//SOtvfkOwvh5XSQm5P/4RSVddhXI6o909IeYsCXRCCCGEGCwUhMZ3B+a+Ve2GzpPmnDPODJ90JYGvc/hrkxdGPcx1eQMcaegaNM/tcH0XXb5gf5v8tDiWZydy7cpcVlhDJvPT4ub8NgFTIdjSQusDD9D2uwcJd3YSt3YtOXffRfymTSMOPxVCTC4JdEIIIcR85+2Ek3sGqm81+8Dfbc4l5lp7v91mDZ88F+zO4XPowGzqfdn07REWCmuOt/SYqpu1PcCh+k6qWwf6lOh2sDw7kRtW5/VX3ZZlJ5Lgko9AE+WvqqLl/vvpeORRtN9P4uWXk77tM8Sed160uzYv7azcyT1v3EN9Tz3Z8dncvuZ2ri28NtrdEtNA/msmhBBCzCdam8VK+sJb9eumGqfDoGyQdTacd7O1+uR6SF40csWtbzXLsVa5nERtPf5h89yONHThDZi93WwKCjMTOG9hCjevzWe5VXXLTXZLlWiSed59l5YdO+h6+hmU3U7yDdeT9qlP4ypcEu2uzVs7K3ey/ZXteENmxdW6njq2v7IdQELdPKC0HmVjlCgpLS3Ve/fujXY3hBBCiLkhFDDbBfQHuN3QVWfOxSTAwtKB8JZXCu6kqHbXHwxT0dTN4fquQQGuodPX3yY9PmbQHLcVOUkUZSXgdtqj2PO5TWtN76uv0rJjBz2vvIotIYHUmz9C6i1/g3NBVrS7N+9d+acrqeupG3Y8Ky6Lpz74FE67zGGcbZRS+7TWpeNpKxU6IYQQYi7xtJvhk33hrWYfBHrNueRFsPgiM3Qyf72pxtmj81FAa01jl4+D1mbcfZtyH23sJmitUhJjt1GUlcBFRRmsyE7qD3GZibNjw/G5QIdCdD3zDC07foX33XexZ2aQecdXSb35ZuyJidHungA6/Z0jhjmAxt5G1vx2DQnOBFJcKaS6U0d+dKWS4h54TI5Jxm6TfyCZLSTQCSGEELOV1tB2zAyb7AtwjQcBDcoO2efA6lusOXAbIHlyV1h8dH/NuJbh9/hDHGnoGlZ1a+sN9LfJTXazLDuRS5Zn9VfdlmSc3tYAYvKEvV46HnmElvv/k0B1NTGLF5N9910kX389NpcE6mgLhALsqtlFWWUZf63+66jtkmOSueWsW2j3tdPma6Pd206Lt4WK9grafG14gsM3uwdQKJJcSSbguVIGhb2+Y0NDYWJMIjYlf1+jQQKdEEIIMVsE/VB/wApv1vy37gZzzpVkNuw++wOmApd3PrgSpqwrj+6vGbRRdk27h288fICWHh+LUuMGbcp9rKWHvhkesU47y7ITueqc7P4hk8uzk0iOkyFhM0Goo4O2Bx+k9YHfEmppwb1yJVl3/gOJl12GskvFJprCOsybjW9SVlnG08efptPfSZo7jRtLbiTFlcL979zfP4cOwG1384313xhzDp036KXd124Cn7dt5EdfG3XddbzX8h5t3jYC4cCI17IrO8mu5GHVvtECYKo7lThHnMxxnQQS6IQQQoiZqrfVhLbq3QPDJ4PWB7aUxbDk4oHqW9YKmKYhUuGw5gdPHuwPc308gTDfKzvY/3xxutka4P3n5fZvyJ2fFodNtgaYcQL19bT++je0P/QQ4d5e4jdtIn3bNuLWrZUP3FFW2V5JWWUZTxx7gpruGtx2N5fmX8rWwq1syN2A02b+MSQ/Kf+0V7l0O9xkO7LJjs8eV1+01niCnv5qX5uvbVgA7Pv+eOdx2hrNsZAOjXg9p805LACONhS075jb4T69H+Ao5tKqoLIoihBCCDETaA2tlQPVt6rd0HzYnLM5IHsl5G8w1bdF6yEpZ8q71OkNUNnUQ2VTt3lsNo/HW3r6V5ccySN/dyElCxKJl60BZjxfRQUtO35FR1kZhMMkXX016ds+g3v58mh3bV5r6m3iyWNPUlZZxsHWg9iUjQ05G9hauJVL8y8l3hkf7S6OW1iH6Q509wfAYY9WAIysCHb6OtGMnFFiHbFmGOg45gL2VQeHLgozdFVQMBXN7RdunzGhThZFEUIIIWa6oA9q3xwIb9W7obfZnHMnm9C28qaB4ZMxcVPSjUAoTHVrb39gO9bcQ0VTD5VNPTR3D6wsabcpFqXGUpiZwMaiDP607yTtnuFDr/JSYlmdnzolfRWTp/eN/bTs2EH3n/+McrtJvekm0j71KWIWTu48SzF+PYEe/lz1Z8oqy3it7jXCOsxZ6WfxtbVf4+olV5MRmxHtLp4Rm7KRFJNEUkwS+eSP6zXBcJBOf+eo4S/ysaqzinZfO92B7lGvN3RRmL0NeweFOQBvyMs9b9wzYwLd6ZBAJ4QQQkyHnmZr+KQV4Gr3Q8gKTGmFUHyFtfrkBshYBrbJW1xAa01Lj3+g2tbc0x/gqlp6+1eVBEiLj6EwI55Ll2dSmJlAYUY8hZnx5KfFE+MY6NM5ecmD5tCBmR9355Zlk9ZvMbl0OEz3C3+lZccOPG+8gT05mYwvfpHUj38MR1patLs3LwXCAV6tfZWyyjL+UvUXvCEveQl5fOacz7C1cCuFKYXR7mJUOGwO0txppLnH/+cyEAoMWvxltCDY7GkedTGY+p76yXoL02pCgU4pdRVwD2AHdmit/2XI+VuBHwM11qF/01rvmMg9hRBCiBlPa2guj6i+vQYtR805mxNyV8G6zw4MoUyYnH28vIEQx1t6hge3pm46vcH+djF2GwUZcZRkJXLV2dkUZiawJCOepZnxpMTFjOtefatZjmeVSxFd2u+nY+cTtN7/K3zlR3Hk5rDgm98k5cYPYYubmsqvGJ3Wmnea36Gssoynjj9Fq7eVpJgkrlt6HVuXbmVV5iqZt3gGnHYnmXGZZMZlnrLtaPv2jXcu4UxzxoFOKWUHfg5cAZwE9iilHtNavzek6R+01rdNoI9CCCHEzBbwQu0b1ubd1vBJT6s5F5tmQtvqT5jH3NXgjD3jW2mtqevwDprTZoJbNzXtHiKnxmcnuSnMjOe6VbkUZiRQmBlPYUYCeamx2CdhYZIbVudJgJvBwj09tP3xj7T++jcE6+txlZSQ+6MfknT11SinrCo63ao6q9hZuZOyyjKquqqIscVw8aKL2Vq4lU15m2Tz72l0+5rbR5xDd/ua26PYqzM3kQrdOuCo1roSQCn1e+B6YGigE0IIIeaW7kYrvFl7v9W+CX1LeacXwbJrBlafzCiGM/jX9m5fMGIxkoGFSY419wwa5hgfY2dJZjxr8lO58fyF/cMkl2TEy6Ik81SwpYXW3/6Wtt89SLijg7jSUnLu2k785s1S+Zlmrd5Wnj7+NGWVZRxoOoBCsTZ7LdvO3cbliy8nMUY2Z4+Gvnlyc2WVy4n8lz4PqI54fhJYP0K7DymlNgNHgK9orauHNlBKfQ74HEB+/vgmSwohhBCT7sBD8Pzd0HESkhfCZd+Bc240q032hbfq3WY1SgC7y1TcLvg7E94WrYf49HHfLhgKc7LNM6zSVtnUQ2PXwIIkNgULU+MozIxnQ2G6qbRlxrM0M4GsRJd8SBcA+Kurabn/fjoefgTt95Nw2aVkbNtG7KpV0e7avOIJenih+gXKKst4peYVgjpISWoJXzn/K1yz5JpZO6xvrrm28NpZG+CGOuNtC5RSHwa2aK23Wc9vAdZprb8U0SYd6NZa+5RSXwBu0lpfOtZ1ZdsCIYQQUXHgIXj8yxCImCyvbCa09U2gj8uw5r2tMwEudxU4XKe8dGuPf3i1rbmHEy09BEID/x9OiXNai5AMDI9cmhlPfnocLods6ixG5n3vPVp27KDzqafBbif5+utI//SncRXOzwU1oiEUDvF6/euUVZbx3Inn6A32khWXZULDkmtZliaLBYnTM13bFpwEFkU8XwjURjbQWrdEPP0l8MMJ3E8IIYSYOs9tHxzmAHTYhLrr7zVBLq1w1OGTvmCIEy29VDZ1U2ENjewLbu29A8v7O+2KxenxFGbEc/mKBValzYS31PjxLUgihNaa3tdeo+WXO+h55RVs8fGkfepW0v7mkzgXTM4iO2JsWmsOtR6irLKMJ489SZOniQRnAlsKtrC1cCvnLzgfu03+IUZMvYkEuj1AsVJqCWYVy5uBj0U2UErlaK37lpC5Djg4gfsJIYQQk6+3FXb/O3TWjHw+0AurPw6YD3ANHd5hS/9XNvVwsq2XiNX/yUp0UZgZzzXn5lCYYYZHFmbGk5cSi8M+eVsSiPlFh0J0PfssLb/cgffdd7FnZJD51a+SevNHsCclRbt780Jtdy1PHHuCsooyKjoqcNgcbMrbxNbCrWxeuBm3wx3tLop55owDndY6qJS6DXgas23B/Vrrd5VSdwN7tdaPAV9WSl0HBIFW4NZJ6LMQQggxcd1N8NrP4fUd4O8iaHPjCHuHNWtxZHLXg/vNpttNPfT4B++7tiQjnpULk7lhdV5/pa0gI45Et6xYJyZP2Oej45FHafnP+wmcqMK5OJ/su+4i+YbrsblOPexXTEyHr4NnTjxDWUUZbzS+AcCarDV8e8O3uXLxlaS4U6LcQzGfnfEcuqkic+iEEEJMqa56ePlnsPd+dNBLa8G1vJp3K3996QXu4hfEKX9/014dw9cD23gj+Yr+1SOXZg7McVuQ6MY2Ccv/CzGaUGcnbb97kNYHHiDU0oL73HNJ37aNxMsvQ9llON9U8oV87Dq5i7LKMl48+SKBcIAlyUvYWriVa5Zcw8LEhdHuopjDpmsOnRBCCDHjaa2p7fBSfewISft+TnHNI9h0kCfYxE9976fyUC4c8gMX4rOF+ZrjIXJVC7U6nR8Fb+Lx8EaO/eOY63nNa4HGRmq+egcLf/oTHJmn3tBXjE+goYHWX/+G9j/8gXBvL/EbN5K+bRtx69fJqqZTKKzD7GvYx87KnTxz/Bm6Al2ku9O5efnNXFt4LWelnSU/fzHjSKATQggxJwRDYU609nK0sZujjd1UNHZztKkbb2MFnwo/wofsLwLwmHoff874OEm5JXw8K4Ei6+vD//4Kj7Vv5DH/xkHXzUs5803A54Pme+/Ds28fTffeR853vxPt7sx6vooKWn51Px2PPw6hEElXX036ts/gXrEi2l2b08rbytlZuZOdx3ZS31NPrCOWy/MvZ2vhVtblrMNhk4/MYuaSP51CCCFmFW8gREXT4NB2tLGbY82DtwBYn9jCnc7/4SLbX9AOO03FH8N18Vf4UO5SPjTCdb+2ZTnfePjtQZt2xzrt3LlFlhuPFPZ68VVU4DtSjufNN2n/4x9Ba9p//3v8x4/jSE1BuWOxxcZii3WjYmOxxcYNfO+OxRYXi3K7zfG4WGxuNyouzjy63fOyAtK7fz8tO35F9/PPo9xuUj/8YdI+/SliFsqwvqnS0NPAk8eepKyyjMNth7ErOxfmXsjfr/l7Lll0CXHOuGh3UYhxkUAnhBBiRur0Bky1rWEgtB1t7Ka6rZe+6d82BYvTzQqSly5fQFFWAuc6aig8dB/Og4+CdsOGL8CFXyI7KWfM+92wOg+AHz99mNp2D7kpsdy5ZVn/8flGh0L4q6rwHSnHV16O78gRfEeO4K+qgnDYNLLZBr4HfIcOEUxJIezxoD0e8+j3j3KHUShlBT83tthYVKwV/CK/d7tNKBwpIA4JkeZ1fQHTtFO26K0yGjlE1Z6eTvdf/0rLjl/h2bcPW3IyGX/3t6R+4hM40tKi1se5rNvfzXNVz1FWWcbrda+j0ZybcS5fX/d1riq4ivTY9Gh3UYjTJouiCCGEiBqtNU3dvv5qW3njQHBr7PL1t4tx2CjMiO8fHtn3VZAej9tpLQxR+ya8+GM4VAYxCbDus7Dhi5Ag87rGorUm2Ng0KLT5ysvxVVSgvdaqn0rhzF+Eu6QEV3EJrpIS7BnpVH9mG9o38HtSLhdFzz07aC6dDoUIe7xorwl44V7PwPcRwS/s8RL29KI9Xut5xPdeD7rXaucd3K6/j6dBud1WVTB2ICAODX6x7hEriyYUxg5UFiOrjH3tx1ispG77XbT/4Q/ErV9HqKUFX/lRHDk5pH/qVlI+9CFs8fGn/X7E2AKhAC/XvkxZZRkvVL+AL+RjUeIithZu5drCa1mctDjaXRRiGFkURQghxIwSDmtq2j39Ye1oYzfljV0cbeym0xvsb5fgcrA0K4HNJZkmtGWa4LYoLQ77aKtJVu8xQa78aXAlw8X/COu/AHFS4Rgq1N1tKm59oc0KcKGOjv429swM3MUlpH7kI7hKTHhzFS3FFjt4LmHd9rvQEdU5AB0OD5tLp+x27AnxkDA1QUWHw2ivtz8Uak+vCX29Vijs+95rhcfI74eEx1B7O8H6OquNtz9wcpr/+K1iYgYFxL5wiN2OZ+9e0Jre13YTs2QJuT/8F5KuuQbllG0uJpPWmrea3qKssoynjz9Nu6+dFFcKHyj6AFuXbmVlxsp5ObRXzE0S6IQQQkwafzDMiZaegeDWN9etqRtvYODDf0ZCDEszE3j/ebkUZyVQlJVIUVYCC5Jc4/+QdfxlePFHUPkCxKbBpd82VTl38tS8uVlE+/34jh0fqLhZAS5QW9vfxhYXh6u4mMQrrxwIbiXFOFJTx3UPz5tvQiAw+GAggGf//sl8K6ekbDYz/y5uauY7aa3RPt/gauKgKmNfxTDi+xFDpBdf+ZGBIap2O3Eb1pN8/fVT0u/56ljHMbO4SeVOTnafxGV3cemiS7m28FouzLsQp02Cs5h7ZMilEEKI09brD1LZ1NNfZev7OtHSSzA88P+VvJTYQUMki63HlLiYM7ux1lD5F/jrj6HqFYjPggu/BKWfBlfCJL272UOHwwRqa63Q1ld5O4Lv2HEIWpVPhwPXkiUDoa24GFdJCc68XKlQTKNAYyMVV1x5yiGq4vQ1e5p5+vjTlFWU8U7LOygU63PWs7VwK5flX0ZCzPz7b4OY/WTIpRBCiEnR3uuPGCI5ENxq2j39bew2RUF6HEVZCVx1TrY1VDKRpVnxxMVM0v9mtIbyZ+CvP4KavZCYC1f9EM7/JDjnx7YCwdbWQaHNe+QI/vKjhHt7+9s48/JwFReTcMml/RU3V0EBKuYMA7SYNM333jeuIapifHoDvfy5+s+UVZbxWu1rhHSIFWkr+IfSf+DqJVeTFZcV7S4KMW0k0AkhxBz26P6aU67aqLWmodNnhbUujjZ1U95ghkk2dw+sUOh22ijMSKC0IJWbMxf1V90Wp8cT45iiVQPDYTi808yRq3sLUvJh609h1cfB4Zqae0ZZ2OPBd/ToQNWt/AjeI+WEmpv729hTUnCVlJD8wQ9aFbdiXMXF2BOkEjFTzZQhqrPJzsqd3PPGPdT31JMdn81tq24jPTadssoynq96Hk/QQ058Dp8651Ncu+RailKLot1lIaJChlwKIcQc9ej+mmH7qrkcNm7ZsJiMRNegDbi7fAMLkyS5HRFDJBP7v89LicU22sIkky0cgncfgV3/Co3vQdpS2HQHrLwJ7HNjDowOBvGfONG/OIm3b55bVXX/IhzK7cZVVNQ/TLIvuDkyM2W4pJjTdlbuZPsr2/GGhq9imhiTyJaCLWwt3MrqrNXYVPS2oRBiqsiQSyGEmOcCoTD//MTBQWEOwBcMs+OlYwBkJbooykrgA2vyBs1zy0w4jYVJJlsoCG8/ZIJcy1HIXA4f3AFnfwDss/N/WVprgg0NgxYn8R4px19RMbBHm81GzOLFuJevIPm663AVF+MuKcG5aNGYS+ALMdsEw0Hafe20edto87bR6mul1dNKm8967m2lzdvGm41vEtTBYa9PdaXy3IefI8Yuw4iF6DM7/+8ohBBikMZOL29UtbG/qp03qto4cLIDXzA8YlsFvPndK0mOnUGVrqAf3vod7PoJtJ+ABefCh38DK64zm1fPEqHOzsEVN2tT7nBnZ38bx4IFuIqLib/gAlwlJrjFFBZic7uj2HMhzkxfQGvxtAwLZYMerXMdvg40I48OS3GlkOpOJdWVOmKYA2j3tUuYE2IICXRCCDHL+IIh3qvt5I2qdvZbIa5vkZIYu42z85L4xIbFPPzGSdp6A8Nen5sSO3PCXMAL+x+Al/4PdJ6E3DVw9Q+h5CqIUpUw0NhIzVfvYOFPfzLq6oNhvx9/RcWgoZK+I+UE6+v729gSEnCVlJB0zdW4SkpwF1vz3FJSpuutCHHaAqHAoGA2UiiLPNbp7xzxOgpFiiuFNHcaqe5UilKK+r9PdaWSFptGmst67k4lxZWCwzbwsfTKP11JXU/dsOtmx2dP2XsXYraSQCeEEDNcbbunv/K2v6qNd2o78VvVt7yUWFblp/DpjUtYnZ/C2blJuBxmiN65ecnD5tDFOu3cuWVZVN7HIP4e2Puf8MrPoLsBFm2A6+6BpZdFLcj1ab73Pjz79tF0731kf/tbBKqrrWGSA1sD+E+cgJD5uSqnk5ilS4lbu7a/4uYqKcGRnS3z3ETU+UP+/gDWN8RxpCpam6+NVk8rXYGuEa9jU7ZBAW1Z2jITzNwDoSzNndb/PDkmGbvtzIcL377m9mFz6Nx2N7evuf2MrynEXCWLogghxAziDYR4p6YjIsC1U99pPtC4HDZWLkxmdX4qa/JTWJ2fyoKksYfpjWeVy2nl7YQ9O+DVf4PeFliyGTZ/DQo2Rj3IAXgPHeLYjR82e7gpBS4XeAc+UDoXLepfnPe2l2EAACAASURBVMRtLVQSs3gxyjlDKp5iRhu6auPta27n2sJrT+savpBv5OpZRCjrC21t3ja6A90jXsehHKS4zRDHvkpZXxgbqZKW5Eqa9sVHJuPnJcRsdTqLokigE0KIKNFac7LN0x/c9le18V5dJ4GQ+e/yorRY1uSnsnpRCmsWp7I8O2nqtgeYap422P0f8Np94G2HostNkMtfH+2eEaitpevZZ+l89lk8e/cNnFCKmJJi0m+5xYS4pUuxxcdHr6NiVhtp1Ua33c0313+T9TnrBw1rHBrK2rxttHhbaPO20RvsHfH6Dptj0BDGoRWzyHNp7jSSYpKkgizEDCaBTgghZiCPP8SBk+0Dc9+q22nq8gFmKOTKhcmsWWwC3Or8VDIT58A+az0t8NrP4fVfgq8Tll0Lm++AvPOj2i3fsWN0PfMsXc8+i/edd/4/e/cdX2V5/3/8dZ+RdbL3JmEECCGsyAoiGMAqimJRcaK2P62tlbrqahFt3bbWfl1dCK5SGYIMlSUiSwiEkYQdQsgme+es+/fHDYccSFjZ8Hk+HnkkOec+57pOInjeXNf1+QDg0rOn01ZKAMXVld5rVrd4lk6IC9Foa+S6hddR2lB6wY8x6oz4ufkR4BZwOqS5nhHSmmx39DJ6SUAT4jIibQuEEKKTqarKsdI60o6Xs/NYBWnHy9lXUI3Nrv0jWmygiat7BzLkZIDrF+qFQd9NV9+aU12knY9LnQOWeoi/GcY+BaEDO2U6qqrSeODAyRC3isZDhwFwS0wk6Mkn8JowgbJ5n2A+ftwp0Kl2Oyc++JCwF2d1yrxF92NX7WRXZrO3ZC97S/aSXpLOgfIDWO3NV20EeGn0S2etpJmMJgloQogLIoFOCCHaQG2jld3HK0g7XsHOY9rqW1mt1mPM5KJncLQvj1zTi6E9fBkc5Ye/6TItu12ZB5vehZ3zwGaGgbdpDcGDOr4Qi2q307BnD1WrV1O9eg2WnBxQFDyGDSPk+efxmjgBY1iY4/r6XbvAckZVUIuF+rS0Dp656E6K64odwW1vyV4ySjIc59ZMRhMJAQnMiJ/B4kOLKW8sP+vxYaYwbu1za0dPWwhxGZFAJ4QQF0lVVbJKah3Bbeexcg4WVXNy8Y1eQSZS+gVrxUt6+NIn2Au97jL/l/bybNj4DqR9DqgwaDqMeQICenXoNFSrlbodO6letYrqNWuwFhWBwYBp5EgCfvkLvK69FkNgYLOP7bnkqw6dq+h+asw1ZJZmOlbf9pbspbiuGNCKjMT5xzG552QSAhMYGDiQGO8YR6XHPn59pGqjEKJdSKATQojzqGqwsPt4hWPrZFpOBZX12kqOl5uBwVG+XDcglCHRvgyJ8sPH4wqqeFh6BH78C+yeDzo9DL0Xkn8Hfj06bAp2s5m6rVupWrWKmrXrsJWXo7i6Yrp6DN5PPI7nuHHofXw6bD7i8mCxWzhYfpD0E+mOFbisyixHU+xor2iSQpJIDEokITCBfv79cNW3fO71VHVGqdoohGhrUhRFCCGasNtVDp+oIS3n9Nm3Q8U1qKpWxT4u2Ish0b5a9cloX3oFeaK73FffmlO8Dza8DRmLQe8Cwx6A5MfAO7xDhrfX1VGzcSPVq9dQ8/332Gtq0JlMeI4bh9fEiXiOvRqdh0eHzEV0f6qqcrz6uNPWyX2l+zDbtW3T/m7+DAwc6Fh5GxAwAF83aRAvhGg/UhRFCCEuUEWdmbTjFY62AbtyKqhu1IoX+HoYGRLly42J4QyN9iMxygdvtyto9a05BXtgw1uw72swmmD0b2HUo+AZ3O5D26qrqVm/nupVq6n58UfUhgb0Pj54TZqE16SJmEaNQud6GVQGFe2utL6UjNIMbdvkib2kl6ZT2VgJgLvBnf7+/bmz350kBGkBLtwULgVKhBBdlgQ6IcQVw2ZXOVhU7ej7tjOnnKwTtQDoFOgb6s2UweGOxt2xgVJlziF3hxbkDn4Drt4w9mkY+Wvw8G/XYa1lZVSvXUv16tXUbtkKFguGoCB8b52K16RJeCQloRjkf2WiZfXWevaV7nOqOplXkweATtHR27c3E6InOFbfevn2wqCT/6aEEN2H/I0lhLhsldY0sut4hSPA7T5eQa1ZK0nvb3JhaLQvPx8ayZBoXwZF+mJylb8Sz3JsC2x4E46sA3c/GP8CDH8I3Ntvu5mlsJDqNWupXrWKutRUsNsxRkTgf889eE2ciPvgQSi6y6jFg2gzNruNwxWHHdsm00vSOVxxGJuq/bkPN4WTEJjA9L7TGRg0kP7+/fEwytZcIUT31qp3L4qi/Ax4F9AD/1ZV9fUWrpsGLACuUlVVDsgJIS7ZkrQ83vruAPkV9YT7uvP0dX25ZUgEVpud/YXV2tm3k9sns0vrANDrFOLDvPn5sEjH2bdofw9ZfWuJqsLRH+CHt+DYRvAIhAkvwVW/AFevdhnSnJND9erVVK1aRcPuPQC49OpFwMMP4T1xIq79+8vvSzhRVZWC2gKnc2+ZpZnUW+sB8HbxZmDgQMZFjdPOvQUOINC9+QqnQgjRnV1yURRFUfTAQWAikAtsB+5UVTXzjOu8gBWAC/Do+QKdFEURQrRkSVoezy3eS73ldONng04h2t+DgsoGx+1BXq4MjfY9uXXSj4ERPri76Dtr2t2HqsLhNfDDm5C7DTxDIXkmDLsfXNp2FUNVVRoPHaL6ZI+4xv37AXCLj9fOxE2cgGuvjm15ILq2ysZKMkoynFoGlDWUAeCic6FfQD+nwiXRXtHyjwBCiG6ro4qiDAcOq6qadXLQ+cDNQOYZ1/0JeBN4qhVjCSEEb3y73ynMAVjtKsfL67hnZA/H2bcIX3d5I3cx7HbtbNyGtyA/DXyiYPJfYPA9YHRrs2FUVaUhPUPrEbd6NebsbFAU3IcMIfiZZ/CaOBGXyIg2G090X422Rg6UHXA693as6hgACgqxPrGMiRjDwMCBDAwaSJxvHEb9FV6wSAhxxWpNoIsAjjf5PhcY0fQCRVGGAFGqqi5XFKXFQKcoykPAQwDR0dGtmJIQ4nJ0vKyOeZuzKahsaPZ+q03lxZsGdPCsLgN2G2Qu1frIFaWDXwxM+T9InA4GlzYZQrXZqE9Lo2rVKqpXr8FaUAB6PaYRw/GfcR+eKSkYg9u/QqbouuyqnezKbKfwdqD8AFa7Vm02yD2IgYEDuaX3LQwMHEh8QDxeLu2z9VcIIbqj1gS65v7527F/U1EUHfAOcP/5nkhV1X8C/wRty2Ur5iSEuEyoqsr27HLmbDzKqsxCdIqCu1F/1godQLiveyfMsBuzWSF9oRbkSg5CQB+Y+g9ImAb61heGUS0Wan/apm2nXLsWW0kJiosLpuRkvH77WzzHj8Pg59cGL0R0R8V1xU7n3jJKMqix1ABgMppICEhgRvwMx/bJEFNIJ89YCCG6ttb8nzsXiGryfSSQ3+R7LyABWH9y61Mo8LWiKFOkMIoQoiWNVhvLdxfw8eajpOdV4eth5FfX9OLeUT34KavsrDN07kY9T1/XtxNn3MXt+RLWvgyVueATAb2uhaM/QvlRCB4A0z6G+JtB17ozhvaGBmo3baJ61Wqqv/8ee1UViocHnteMxXviRExjr0HvaWqjFyW6ihVZK3h357sU1hYSagpl5tCZTO452XF/jbmGzNJMp3NvxXXFABgUA3H+cUzuOdlx7i3GOwZ9K/9bFEKIK01riqIY0IqipAB5aEVR7lJVNaOF69cDT0lRFCFEc0pqGvl8aw6fbj1GSU0jfYI9eXBMLLcMjnAqaNJSlUvRjD1fwrLHwFLvfLtPNFz/OsRdD60o/2+rqaXmh/VUr15DzYYNqHV16Ly98Ro/Hq/rJmEaPRqdW9udwRNdy4qsFczePJsG2+mt0C56F26IuQEVlfSSdLIqs1BPbt6J9op2BLeBQQPp598PV700ghdCiOZ0SFEUVVWtiqI8CnyH1rZgjqqqGYqivAykqqr69aU+txDiypGZX8XHm46ydFc+Zpud8X2DeHBMLGN6BzZb2OSWIRGXR4Cz28Far4WtUx/WerA0gKUOrCc/O33fzH1nPUeT+2oKQbU3M7gK/SY3c/v5WcvLqfl+PdWrVlG7eTOq2Yw+IACfm27Ca+JETCOGoxilOMWV4G87/+YU5gDMNjNLjizB382fgYED+Vnsz7SWAQED8HVrv96FQghxJWvVYQlVVVcCK8+4bVYL145rzVhCiMuHza6ydl8RczYdZWtWGe5GPXdcFcX9yTH0CvI894OdthBGQsosSLy9jSZmdQ5IzYaqC7nv1HM0F7hOfraZL22Oih6MHlr1SYM7GN21r40eWo84z2DtNoM77Pqs+eeozL2oIS3FxdSsXUvVqlXUbdsONhuG8DD87pyuNfoeMgRFL9vkLneqqnK08iib8jexOX8zhbWFzV6noLD+9vVSaVYIITpI60+/CyHEBapusLAgNZe5m7PJKasjwted52/oxx1J0fh4XMCqzplbCCuPw9LfQM5PEJbYQqhqGsCaua/p7XbLpb0wnfFksHIHg5tz4HLzBa+w5u8zNvkwnApm7ue4zwMupjT70R+0n9GZfCLP+1Bzbp5W1GTVKup37QJVxSUmhoBf/AKviRNxSxggb9ivAJWNlWwt2Mrm/M1OIS7GOwYPgwd11rqzHhNqCpX/NoQQogNJoBNCtLuc0jrmbs7my9Tj1DRaSerhx7PX92NSfAgGfQtnuFQVqguhKEMrqV+UARlfnR26bGZI/ffZj9e7ng5BBjfncOQReI5Q1TSYtRCqzgxmbVAZsl2kzDr7DJ3RXbu9GY1ZWVqPuFWracjUWoq69utH4KO/wXvSJFx695Y36pc5q93K3pK9WoDL20x6aTp21Y6X0YuR4SN5KPEhRoePJsIzotkzdG56N2YOndmJr0AIIa48XfRdiBCiu1NVla1ZZczZdJQ1+4rQKwo3DQrngeQYEiPPOEtjroMT+0+GtyYBrr7s9DXeEedYQVPgd3udw5hUyju9FbWFLaqqqtK4b5+jR5z5yBEA3AcNIvjpp7RG39Ib9LKXV5PHprxNbMnfwk8FP1FtqUan6EgITODhxIcZHT6ahMAEDDrntwynqlmeq8qlEEKI9nfJVS7bi1S5FKJ7a7DYWLY7nzmbstlXUIW/yYW7R0Rzz8gehHi6QGXOGcEtE8qOnC7eYfSA4P4QMgBCErTPwfHg4Q/vJLSwhTAKHk/v2BfajViKi8l74kki3/kr+oAA6nft1rZTrl6NJTcXdDo8rroKr4kT8ZqQgjE0tLOnLNpRnaWO7YXbHWfhjlUdA7StksnhyYwOH82IsBH4uPp08kyFEOLK1SFVLoUQoqni6gY+25rD51uPUVprZkiwnv+MtzLWJx9jyUpYkKGFN3P16Qf5xWqBLeHnJwPcAO22lkrpX+QWQqEpef996nfs4NiDD2KvqMR64gQYjZhGjSTwVw/jee21GPz9O3uaop3YVTv7y/Y7zsGlFadhtVtxN7iTFJLE9L7TGR0xmljvWNlSK4QQ3ZCs0AkhWiX9eBnL1/9I/oFU+pDDGO8i+ivHcattUknRzUdrYn0qtIUkaKtwruepaNmc9qxy2Y2pqoqttBTzsWOYs7NPfhyj8fBhzEePOq4zjR2Lz0034jluHHovr06csWhPJfUlbM7fzKa8TWwt2EpZg7Z9ua9fX0ZHjGZ0+GiGBg/FRe/SyTMVQgjRHFmhE0K0j9pSKErHXphO/sFUGnP30tuSzbOKBQygKnoUUx8IGQEhD57eMukdAW31L/+Jt1/RAc5WXe0Ia47gdjLE2WtqTl9oNOISGYm9sVFb8bTbwWjEGBGBz003dd4LEO2i0dZIWnEam/M2syl/EwfLDwLg7+bPqPBRJIcnMyp8FIHugZ08UyGEEG1NVuiEEGezNkLJwSZn3U5+1JzuO3VC9SFbH4Nb5EB6JYzEIyoRAvtqlR9Fq9gbGjAfy3EKa6e+tpWWnr5QUTCGh+PSowcuMTEnP7SvjeHhWMvKODJxEmpj4+mHuLrSe81qDEFBnfDKRFs51RNuc74W4FILU2mwNWDQGRgaPNQR4vr690WntLCFWQghRJclK3RCiAujqlBd4FxZsihDC3N2q3aN3oVG/zj2uQxllT2A3dZIPCIT+fnYoUyMD0GvkzM3l0K1WLDk5dF4xiqbOfsY1oICp2v1QYG49ojBc/w4XE8Ftx49MEZHo3N1bXGMkg8+RLXbnce12znxwYeEvSjnDrubc/WEu7XPrSRHJJMUkoSH0aOTZyqEEKIjSaAT4kphroMT+85YdUuH+vLT1/hEaVsk436GGjKAtMYIPtyrsOZgKQad1nbgueRYEiKk+t2FUO12rIWFZ51rM2dnY87LA6vVca3O2xuXmBg8rkpyXnHr0QO95yWcNQStIbjljFYPFgv1aWmteVmig1jtVtJL0rVqlGf0hBsRNsKpJ5wQQogrl2y5FOJyY7dDxbGze7qVZQEn/7wbTRASf0ZrgP7g7keDxcbSXXnM2ZjNgaJqAkwu3DOyB3ePjCbYS7ZTnklVVWxlZc2fazt2zHm7o5vbWWHNJSYGl9gY9L6+UmFQnLMn3KmWAs31hBNCCHF5kS2XQlwpGiq1VgBNt0sWZ4L5VHEMBfx7aoEt8Y7TIc435qzWAEVVDXy64QBfbMuhrNZM/zBv3pqWyE2DwnEzSpNurRjJsWbPtdmrm7RiMBhwiYrCpUcPTKNHO51rMwQHo7TUkkFckZr2hNuSv4XsqmxA6wk3KWaS9IQTQghxXhLohOhsF1KG32bVmm83DW5FGc5Ntt18tdW2wXc3aQ3QD1xM5x4+t4I5G4+yfE8BNlVlQv8QHkyOZWRP/ytuxcje0IA558xiJNrns4qRhIXhEhODz003Oq24GSMiUAzyV6toXks94dz0biSFJnFH3zukJ5wQQoiLIu86hOhMe750bpRdeRy+fgyK94Ep8PSWyeL9YDu5dU9ngMA4iB4JIb843d/NO/yCWwNYbXZWZRYxZ+NRUo+V4+lq4L5RMcwY3YMeAecOgN2darViycs7q+R/Y3Y21oJCrVDMSfrAQFxieuA5fpxje6RrTMx5i5EI0dSpnnCb8zezJX+LU0+4e+PvlZ5wQgghWkUCnRCdae3Lp8PcKdZ62PhX7WvPEC2sjXjo9Fm3wDgwXFqYqKyzMH97Dp9sOUZeRT3R/h7MujGe25Ii8XIztvLFdBxLcTF5TzxJ5Dt/bbb8vmq3Yy0qOr3KdrTJilturnMxEi8vrRjJsCRta2SP0+X/L7UYibiymW1mdhbvZHOeFuIOlB8ApCecEEKI9iGBTojOoqrOWyadKPD0YW2Vrg0cOVHD3E3ZLNyRS73FxqieAbx4Uzwp/btn24GSDz6kfscOit95B99p05ovRtLQ4Lj+VDES17g4vCZNcjrXpvfzk61tolXO1xNu5tCZ0hNOCCFEu5FAJ0RnKDkEK55o+X6fyFaHOVVV+fFQCR9vOsr3B07gotdx8+BwHkiOJT7cu1XP3VnM2dlULF1Kxf/+B6pK5eKvqFz8lXanwYBLZCQuMTGYRo7EJfb0uTZDSIgUIxFt6lRPuC35W9iUv+msnnCjw0dzVehV0hNOCCFEu5NAJ0RHstTDj3+FTX8Do7tWwCRjsfO2S6O7VhjlEtWbbXyVlsfHm45yqLiGQE9XHp8Qx10jogny6l7nvlS7nYaMDKrXrKV67RrMh484X6DXYxozhtDnn8MYHo5i7D7bRjvKiqwVvLvzXQprCwk1hTJz6Ewm95zc2dPqslr6eTn1hMvfTHqJ9IQTQgjRNUgfOiE6yuE1sOIpKD+qtRCY9GfwDL6wKpcXoKCynk+3HOOLbTlU1FkYEO7Ng8mx3DgoDFdD92k7oJrN1G7fTs3atVSvXYe1qAj0ejySkvAYMYLSjz5CNZsd1yuurvRes7rZs3RXuhVZK5i9eTYNttPbT930bswePVtCXTOa+3kZdUbifOPIqc453RMuIIHREaNJDk+WnnBCCCHaxcX0oZNAJ0R7qyqA756DjK8goDdM/iv0vKbNnj4tp5w5m7L5Zm8BdlVlUnwoD46J5aqY7nM2zFZTS+3GH6les5aaH37AXl2N4uaG59Vj8ExJwfOaazD4+VEw+yUqFi0Ci+X0g41GfKdNI+zFS1/VvJzUW+sprC2koKaA32/4PZXmyrOucdO7MTZybCfMrmvbkLvBKcydolN0TO09lVHhoxgZNlJ6wgkhhGh30lhciK7AboNt/4J1fwabGca/AMkzL7lCZVMWm51v0wuZs+koaTkVeLkauH90DDNGxxDl3z3O7FhLSqhet47qtWup27wF1WJB7+uL18SJeE1IwTRqFDp3d6fH1O/a5RzmACwW6tPSOnDmnUdVVSoaKyioLaCgpoD82nzH1wW12sepkvjn0mBr4HDF4Q6YcffSXJgD7ec+e/Tsjp2MEEIIcYEk0AnRHvJ2wPLHoWA39LoWbngbAnq1+mkr6sz8d9txPtmSTUFlAzEBHrw0ZQA/HxaJp2vX/+NsPnbs5Hm4tVoIU1WMERH43XUXXhNScB8y5JxNuXsu+aoDZ9vxrHYrxXXF5NfkOwJa0/BWWFtIvdW5zYWb3o0wzzDCTeH08+9HuGc4YaYwwkxhPLPhGYrri88aJ8wUxtJblnbUy+o2Ji2cREFtwVm3h5pCO2E2QgghxIXp+u8AhehOGiph7Z9g+7+1HnLTPoYBUy+44XdLDhdXM2dTNot35tJgsZPcO4A/35LA+L7B6Lpw2wFVVWlIz6B67Rpq1q6l8ZC2KuQa35/AR3+D14QJuMbFdZutoa1VZ6mjoLbAKbDl12hBLb82n+K6Yuyq3ekx/m7+hJpC6eXTizERYwgzaeEt1DOUcFM4vq6+Lf78nkh6otkzdDOHzmzX19ldzRw6U35eQgghuh0JdEK0BVWF9EXw3fNQewJGPKxtsXS79PYAdrvKhkMnmLMpmw0HT+Bi0HHrkAjuT46hX2jXbTugWizUbd+urcStW4e1sNBR1CTk+dvxSrkWY8TlVwlQVVVKG0q1cNZCYKtsdD7PZlAMhJhCCDOFcVXIVYR5hjkCW5hnGKGmUNwN7i2MeH6nCp9IlcsLIz8vIYQQ3ZEURRGitUqPaD3lstZD+BC48R3t8wVakpbHW98dIL+innBfd2am9KbRpvLxpqNknagl2MuV+0b14M7h0QR4ds22A/baWmp+3Ej12pNFTaqqmi1q0p1ZbBYK6wod59VObYF0hLeaAsx2s9NjPAweTlsgT22NPBXcgtyD0Ou6TwVSIYQQQnQMqXIpREewNGj95H78q1boJGUWJD0IF/EGfUlaHs8t3ku9xXbWfYmRPvxiTCzXJ4ThYuh6TbGtJSVUf/89NWvWUrtlC6rZjN7XF89rr22xqElXVm2udiowkl+bf7rYSE0BJ+pPoOL892Wge6C2/dEUSrjnyc+mcMfX3i7eV8x2UiGEEEK0nQ6rcqkoys+AdwE98G9VVV8/4/5fAb8BbEAN8JCqqpmtGVOILuHIOljxJJRlQcI0uO4V8Lq4wgl1Zit/XpHZbJgL9HRh6W+Su1wYaLGoyZ3T8Zow4bxFTdrCpTTKtqt2SupLnKtD1jgXHam2VDs9xqgzOgLaqPBRp1faTq6yhZhCcNV3zRVTIYQQQlw5Lvmdl6IoeuB9YCKQC2xXFOXrMwLbF6qqfnTy+inAX4GftWK+QnSu6iLtnFz6QvDvBfcugV7jz/sws9XO/sIqdudWsud4BXtyKzlUXI29hQXy0hpzlwhzqqrSkJGpFTVZs5bGQ4cAcO3fn8Df/AavCSm49u3bYXM9s/FzQW0BszfPxmK3MCR4iNN5NcdKW00+hXWFWO1Wp+fycvFynFcbFjLMEdZO3RbgHoBO6Xoro0IIIYQQTbXmn9KHA4dVVc0CUBRlPnAz4Ah0qqpWNbneBHSt/Z1CXCi7DVLnwNqXwdoA456D5N+B0e2sS212lSMnath9Mrjtya1gX0E1ZptWvdDf5EJipA/XJYTy+dZjlNaaz3qOcN/O26qoWizUpaY6VuKshYWg050savIcntem4BLZ8UVNLDYLb6e+fVavsAZbA3/c9Een2xQUgjyCCDeFMzBwIJM8J2lBrcl5Nk8Xz46cvhBCCCFEu2hNoIsAjjf5PhcYceZFiqL8BngCcAGube6JFEV5CHgIIDo6uhVTEqId5KdpPeXy06DnOLjhLxDYG9BWsI6X1bM7t4I9uRXszq0kI6+SWrO2jdLT1UBChDcPJMeQGOlLYqQPkX7ujhWtnoGms87QuRv1PH1d3w59ifbaWmo2btJW4tafLmpiGpOM18yZeI7ruKImqqpyov4EB8sPOn0crTiKVbW2+LhXxrziCGshHiEY9cYOma8QQgghRGdqTaBrbo/VWStwqqq+D7yvKMpdwB+AGc1c80/gn6AVRWnFnIRoOw2VsO4V2P4v8AiEn/+H4ujJ7M6rYs/OA+zOrWRvbgXldRYAXAw64sO8mTYsksRIXwZF+dAz0POcfeJuGaKtdDWtcvn0dX0dt7cna2kpNd9/T/WatdRu3uwoauKVkqIVNRk9ut2LmtRb6zlScYRD5YecwltFY4XjmlBTKHF+cYyNGMuiQ4uc7jslzBTGlF5T2nWuQgghhBBdUWsCXS4Q1eT7SCD/HNfPBz5sxXhCdAxVhYyvsH/zLEptMXvDb+M/xrv5aZmNwqp1AOh1Cn2CPZkUH0pilA+DIn2JC/G6pGqUtwyJ6JAAB2DOyTld1GTnTq2oSXg4fndOxzMlBY+hQ9ulqIldtZNfk+8U2g6VH+JY1TFH5Uh3gzt9fPuQEp1CnF8ccX5x9PHrg4+rj+N5+vj1kcbPQgghhBBNtOad23agj6IosUAeMB24q+kFiqL0UVX10MlvJwOHEKILqjNbSc+r4ujBvQzc/Sfi67aTYY/hBcvL7MnqRWwgjOjpr628RfowINwHd5eu3z+seyLX4wAAIABJREFUM4qaVJurOVxxmINlTcJbxSFqLbWAdr4tyiuKOL84ro+93hHeIr0iz1uERBo/CyGEEEI4a1UfOkVRbgD+hta2YI6qqq8oivIykKqq6teKorwLTAAsQDnwqKqqGed6TulDJ9pbcxUnjxWX8f90y3jUsBSrYmBZ4C8oH3AfiVEBJET44OPefc5jqRYLdTt2nC5qUlDgKGriNSGlzYqaWO1WcqpztNBWdtCxbTK/9vRCvZeLlyOwnfro7dsbD6NHq8cXQgghhLhcSWNxIU66kIqTdwRk8f+q3sO/IYeGuCm43fgmeId18swvjr2ujpqNG6lZu5bq9T9gr6xEcXXFNGYMXikpeI4f16qiJmUNZWedcztScYRGWyMAekVPrE8sffz6OIW3EI+QLtF+QQghhBCiO+mwxuJCdCUXW3FysL+Z8J/+jLL3S/CLhWmLcOs9oZNfxYWzlpU5FzVpbETv44PX+PGni5p4XNxKmNlm5mjl0bMqTJbUlziuCXALIM4vjul9pxPnrwW3nj49cdG7tPVLFEIIIYQQ5yGBTnRbxVUN2rbJk+HtgitO2u2w42P49CWw1MHY38PVT4Cx83q/XSjz8eMnt1KuoX5nGtjtGMPD8b3jdrxSJuAx7MKKmqiqSnFd8VnBLbsy29EawEXnQi/fXiSHJztW3vr49SHQPbC9X6YQQgghhLhAEuhEt1BZZ2FPnrZt8tT2ycIqrdLhRVWcLNij9ZTLS4WYq2HyXyEoroNfzYVTVZWGzExtK+WatTQePAiAa79+BD7yiFbUpF+/c25rrLPUcaTiiKM4yanwVtlY6bgmzBRGnF8c46PGO7ZLRntHY9DJXxFCCCGEEF2ZvFsTXU6d2UpGfpXTubfs0jrH/bGBpouvONlYDd+/Cj99BB4BcOu/YOBt0EXOd1mKi8l74kki3/krel/f00VN1q3Fmn+yqMmwYYQ89yyeKSm4REae9Rx21U5eTZ5TW4CD5QfJqcpxtAbwMHjQx68Pk3pMclp183bx7uiXLIQQQggh2oAEOtGpzFY7BwqrHefe9uRWcrCoGvvJWj1hPm4kRvpwW1IUgyJ9GRh5kRUnVRX2fQ3fPAvVBZD0AKTMAvdLLxDSHk783/9Rv2MHx+69D2t5uXNRk0d/e1ZRkypzlSOwNf1cZ9WCr4JCtHc0cX5xTO452bHqFuEZcd7WAEIIIYQQovuQQCfa3JK0PN767gD5FfWE+7rz9HV9uWVIBDa7StaJGqdzb/vyqxwVJ/08jCRG+jIpPoTESF8So3wI9nK79ImUHYWVT8Ph1RA6EO74FCIvqFjQeal2O/a6Ouy1tef8sDl93/z1luoqdFatcEtjdja1Vw+i3x2/xDR6NHY3F3Kqctha/hMHj54+61ZQW+CYi7eLN339+zK1z1T6+Gqrbr18e0lrACGEEEKIK4C0LRBtaklaHs8t3ku9xea4zaBT6OHvQWFVw1kVJwdF+mrhLdKHSD/3tilxbzXD5r/DhrdAZ4DxL6Be9f9QzZaLClpOwazO+Vq1vv7C5qIo6Ewm5w8PD8fXubYSynZsJapYRa+CRQ/fD9az855h1FvrOVJxBLPdrP0cFQMxPjFn9XUL9giW1gBCCCGEEJcRaVsgOs1b3+2n3mLDr6GKZ7d/xutX3UO5mzc55XXcNTz67IqTLVBVFbWx8byrYGcFsxM52I+nY6+vx64Lx66YsC+ag73uPW375QVQPDzQmTwcwUvvYcIYFIwuxnR2OHN8nA5p+ia3K+7nDqm/nTOeF1dpYQ7AaINrdttYlJxGfJ/RjOw/0nHWLdYnVloDCCGEEEIIJxLoRJspqzWTV6FVnvxF+jISSrN4LG0Ba6KvwsPayMx+Zdi312JfX0vxqRBWd2YwOxng6urAar2gcRUXF3Qe7uiURnRqNTo3F/QxgzAG92gxaLX44e6Ooj9PgZU2UFBTwKf7PmXs6iKUM3KmToVbN1r53a8/avd5CCGEEEKI7k0CnWgTqzOLeG7xHtysjfxm12JSctMAGFm0j5FF+wAoSjt5scHgWNXSm0zoPEzoTZ4Yg0POuwJ2Vihzc0PZ+wWsmQ3mWkh+DK5+Cly65vmxfaX7mJsxl++yv0NB4bV8BaPNOdEZbTCgQP5oCiGEEEKI85N3jaJVqhosvLwsk4U7crleOcEvf/oE9xMF2FDQo2JVdGyNSCT86Se57qqe2jZEF5e2OfNVuBcWPAG526DHGLjxrxDUt/XP28ZUVWVz/mY+zviYnwp+wsPgwd397+be+HvZMWYH922eTYOtwXG9m96N2aNnd96EhRBCCCFEtyGBTlyyjYdK+P3C3ZRW1vJ/tdvovfYrDEFBmA0G9Ce3SxpUO6OLM+k7tAcGf/+2GbixBta/Bls/BHdfuOUjGDS9y/SUO8Vis/BN9jfMzZjLofJDBLsH8/iwx5kWN83R921yz8kAvLvzXQprCwk1hTJz6EzH7UIIIYQQQpyLVLkUF63ObOW1lfv5dOsxxugreC7tf+iyDuHz81tBUahc+jVYLKcfYDTiO20aYS/Oat3Aqgr7l8M3z0BVHgydARNmg0cbBcU2Um2uZuHBhXy27zOK64rp7dub+wfczw2xN2DUX0QPPSGEEEIIcUWSKpei3aRml/Hkgt3kltbwhjWdxBX/Re/lRdj77+GVkkLWLVOdwxyAxUJ9WlrzT3ihyo/BN7+Hg99C8ACY9jFEj2jdc7axwtpCPsv8jIWHFlJrqWVE2AheGv0SyeHJ0lZACCGEEEK0Cwl04oI0WGy8s/og//wxi0GGWt47/BWG9F14Tkgh7KWXMAQEANBzyVdtO7DVDFvegx/eBEUHk/4MI34FXWil60DZAeZmzOXbo9+iojIpZhL3D7if+ID4zp6aEEIIIYS4zEmgE+e1N7eSJ77cxaGiav5oOELyik9QFIWQV1/FZ+ot7bf6lL0JVjwBJ/ZDvxvh+jfAJ7J9xrpIqqqypWALc9PnsqVgCx4GD+7sfyf39L+HcM/wzp6eEEIIIYS4QkigEy2y2Oy8//1h3lt3mFh9A18VrMRt2ybchw8n/LVXMUZEtM/AtaWwehbs+gx8ouHO+dD3+vYZ6yJZ7Ba+Pfot8zLmcaD8AEHuQcwcOpPb4m7Dx9Wns6cnhBBCCCGuMBLoRLMOFlXzxJe7SM+r4nG3fH727ceodbUEPfMM/jPuQ9Hp2n5Qu10LcatnQWM1jHkcxj4NLqa2H+si1ZhrWHRoEZ9mfkpRXRG9fHrx8uiXmdxzMi56l86enhBCCCGEuEJJoBNObHaV/2zM4u1VBwlWLCyoWIfn+u8wxvcn4o03cO3Tp30GLsqA5U/A8a0QPVrrKRfcv33GugiFtYV8se8LFhxcQI2lhqtCr2LWqFmMiRiDTmmHUCuEEEIIIcRFkEAnHLJLanlqwW5Sj5XzC1Mpt6+di724iIBHfkXQI4+guLTDSpS5Fn54A7a8D67ecPMHMPiuTu8pd6DsAJ9kfsLKrJXYsXNdj+uYMWAGAwIHdOq8hBBCCCGEaEoCncBuV/n8p2O8unI/7qqFz80/4b9kEYYePQj/4nPcBw9un4H3r9RaEVQehyH3wsSXO7WnnKqq/FT4E3PT57IpfxPuBnfu6HcH98bfS4RnO50XFEIIIYQQohUk0F3h8ivq+f3CPWw8XMJt3rX88sd52I9m4XfXnQQ/9RQ6D4+2H7TiuNYc/MAKCOoPD3wLPUa1/TgXyGK3sCp7FfMy5rGvbB8BbgE8NuQxbu97uxQ6EUIIIYQQXZoEuiuUqqos2pnHS19noNqs/Ee3l/AvPkfn70/Ev/6F59Vj2n5QmwW2fgDrX9e+n/gyjPx1p/WUq7XUsujgIj7b9xkFtQXE+sTy0uiXmNxzMq56106ZkxBCCCGEEBdDAt0VqLi6gecXp7NmXxHX+5j53fYvsGem433DDYTO+iN6X9+2HzRnKyx/HIozoe8NWk853+i2H+cCFNcV8/m+z1lwYAHVlmqSQpJ4YcQLXB15tRQ6EUIIIYQQ3YoEuivMyr0FvPDVXmobrbzncYTe//sYXF0J/8vb+Eye3DaD7PkS1r4MlbngHQ5+MXBsE3hHwvQvoF8bjXORDpUfYl7GPFYcXYFdtTMhegL3D7ifgUEDO2U+QgghhBBCtJYEuitERZ2ZWUsz+Hp3Plf72nnh4CLU1J/wGDOGsFdewRgS3DYD7fkSlj0Glnrt+6o87aPPdTBtDrh6ts04F0hVVbYXbufjjI/ZmLcRd4M7t8Xdxr3x9xLlFdWhcxFCCCGEEKKttSrQKYryM+BdQA/8W1XV18+4/wngl4AVOAE8qKrqsdaMKS7euv1FPLNoL+W1Zt70K2DQon+hWiyEvjgL3+nTUdqyRcCa2afDXFPFmR0a5qx2K6uPrebj9I/ZV7YPfzd/Hh38KHf0vQNft3bYUiqEEEIIIUQnuORApyiKHngfmAjkAtsVRflaVdXMJpelAUmqqtYpivII8CZwR2smLC5cdYOFPy/fx/9SjzPUV8e88m9QFq/BddAgwt94HZeYmLYZyNIAh1dD+iJtNa45lbltM9Z51FnqWHxoMZ9mfkp+bT4x3jG8OOpFbup1kxQ6EUIIIYQQl53WrNANBw6rqpoFoCjKfOBmwBHoVFX9vsn1W4F7WjGeuAibj5Tw9II9FFTW82JIJcmLP8JWXk7Q72YS8MtfohhaudvWZoGsH7QQt385NFaBRyC4eIK55uzrfSJbN955nKg7wRf7v+B/B/5HtbmaocFDeXb4s1wTdY0UOhFCCCGEEJet1ryrjwCON/k+Fxhxjut/AXzT3B2KojwEPAQQHd05lQ8vF/VmG298u5+5m7Pp62NgiXkTxn98hb53L6L/8RFu8fGX/uR2O+Rs1kJc5lKoKwVXH+g/BRJuhdhrIGOx8xk6AKM7pMxq/YtrxpGKI8zLmMfyrOVY7VYm9NAKnSQGJbbLeEIIIYQQQnQlrQl0zR28Upu9UFHuAZKAa5q7X1XVfwL/BEhKSmr2OcT57cwp56kvd5NVUsuTEY38bNnfsR4/jv8DDxD0u5noXC9hy6GqQt5OLcRlLIbqAjB6QN/rIeHn0HsCGJo8b+Lt2udTVS59IrUwd+r2NqCqKqlFqczNmMuG3A246d24tc+tzIifQZS3FDoRQgghhBBXjtYEulyg6bvnSCD/zIsURZkAvABco6pqYyvGEy1otNp4d80hPvrhCJGeRha77Mb9w89RQkOJnjcX0/DhF/eEqqoVMdm7UAtyFcdA7wK9J2orcX2vBxdTy49PvL1NA9wpVruVNTlrmJs+l4zSDPzd/Pn14F8zve90/Nz82nw8IYQQQgghurrWBLrtQB9FUWKBPGA6cFfTCxRFGQL8A/iZqqrFrRhLtCAjv5Inv9zN/sJqHo5SuW3Vh1gO7Mfn1lsJef459J4XUVmy9IgW4NIXwYn9oOih5zVwzTNa7zj3zqkOWWep46vDX/Fp5qfk1eTRw7sHfxz5R6b0moKbwa1T5iSEEEIIIURXcMmBTlVVq6IojwLfobUtmKOqaoaiKC8Dqaqqfg28BXgCC06Wxs9RVXVKG8z7ime12flw/RHeXXsIf3cD//M9gs9H/8bu5UXk++/hlZJyYU9UmQvpi7UQV7BLuy16NEz+C/S/GTyD2u9FnEdJfQlf7NMKnVSZqxgcNJinr3qa8VHjpdCJEEIIIYQQtLIPnaqqK4GVZ9w2q8nXE1rz/KJ5h4trePLLXezOreTuHkYe+PETLDt3YEpJIezllzAEBJz7CWqKtaIm6YsgZ4t2W/gQmPQKDJgKPhHt/yLOIasyi08yPuHrI19jtVtJiU5hxoAZDA4e3KnzEkIIIYQQoqtpZe160ZHsdpU5m47y1ncH8DDqmBdWROi/P8AGhL36Kj5Tb2m5SXh9OexbDukL4egGUO0QHA/X/gEG3AoBvTr0tZxJVVV2FO1gXsY81ueux1XvytTeU7lvwH308O7RqXMTQgghhBCiq5JA100cL6vjyQW72Xa0jJui3Zi5eyGW/67H7aqrCHvtNVwim1lVa6yBg99qxU0OrwG7BfxiYcwTWoXKkFa0MGgjNruNNTlrmJcxj70le/F19eWRQY8wvd90/N38O3t6QgghhBBCdGkS6Lo4VVX577bj/HlFJnpF4R+xtcTOexVrTQ3BzzyD/4z7UHRNzpNZGuDwam075YFvwVoPXuEw4mEtxIUPgZZW8TpQnaWOpUeW8knGJ+TW5BLlFcUfRvyBKb2n4G5w7+zpCSGEEEII0S1IoOvCCisb+P2iPWw4eIKUKA+ey/oGyzvLMMT3J+KNubj26aNdaLNA1g9aiNu/HBqrwCMQhtythbiokaDrGkVESupLmL9/PvMPzKeysZLEoESeTHqS8VHj0ev0nT09IYQQQgghuhUJdF2Qqqos2ZXHi0szsNhU/tbXxoBP/oSlqIiAXz1M0K9/jWIwQPZGLcRlLoW6UnD1gf5TtF5xsdeAvnN+vSuyVvDuzncprC0k1BTKzKEziQ+I55PMT/j68NdY7BbGRY3jgYQHGBI8pFPmKIQQQgghxOVAAl0XU1LTyB++SufbjEJGRnjycskGbG98gdIjmpjPP8c9yAbrZkPGYqguAKOH1ug74efQewIYXDt1/iuyVjB782wabA0AFNQW8PzG57Grdlx0LkzpPYX74u8j1ie2U+cphBBCCCHE5UACXRfybXohL3y1l+oGK68MMDBy/luYjxzB9+brCLnGG92GGVCeDXoX6D1RW4nrez24mDp76g7v7nzXEeZOsat2PI2eLJ+6nAD387RUEEIIIYQQQlwwCXRdQGWdhdnLMvgqLY/EMBNvumWgvv5v7F6uRE3xwNP9Y9iuh57XwNinod+N4O7b2dN2KG8oZ2fxTlILUymoLWj2mlpLrYQ5IYQQQggh2pgEuk72w8ETPLNwDyU1jfy5Xx3JX75FQ1YR3tF1hA7LRd9nFAx8BPrfDJ5BnT1dAE7UnWBH0Q5Si1LZUbSDwxWHAXDVu+Kic8FsN5/1mFBTaEdPUwghhBBCiMueBLpOUtNo5dWV+1j1015m+KRxW9mPVLxVjlkP4Tf44TPtNzBgKvg001+ug+XX5DsFuGNVxwBwN7gzJHgIN8TewLCQYSQEJrD62GqnM3QAbno3Zg6d2VnTF0IIIYQQ4rIlga4TpO7LYvVX/+b6hg28aN9H0XfelBe6YRoQRdgrr2DsN6LT5qaqKjnVOaQWpjpC3KltlF4uXgwLHsa0PtNICk2in38/DDrn/4Qm95wMcFaVy1O3CyGEEEIIIdqOoqpqZ8/BSVJSkpqamtrZ02h7jTWY963k6PpPiC3fjItio6Q4itKtelS7Qsgzz+A7fTpKBzf9tqt2jlQccVqBK6kvAcDfzZ9hIcMYFjKMpJAk+vj1Qad0jX52QgghhBBCXK4URdmhqmrShVwrK3TtydIAh9dA+kLs+7/BxdaAt+pPqvctRB60U/vDT7gNGkDEG2/gEhPTIVOy2W0cKD/gWIHbWbyTisYKAII9ghkeOlwLcKFJxHrHdnjAFEIIIYQQQlw4CXRtzWaBoz/A3kWwfzk0VlFn8GWxeQwb3cZxf88eBH3wNrXl5QT9biYBv/yl1iS8nVjsFjJKMthRtIMdRTtIK06jxlIDQKRnJNdEXkNSaBLDQoYR6RkpAU4IIYQQQohuRAJdW7DbIWczpC+CzKVQVwquPlTE/Iy38hKYXxLLtIQQnjuwkrqXPkDXuxcxH32I+4ABbT6VRlsje07scWyf3HNiD/XWegBifWK5PvZ6xzZKqTx5+bJYLOTm5tLQ0HD+i4UQQog25ObmRmRkJEajsbOnIsQVQQLd+ez5Eta+DJW54BMJKbMg8XZQVcjbqYW4jMVQXQBGD+h7PbYBt/Kvgp78ZW02Pu5G/jNCT+RHs6jLOY7//fcT9Pjv0Lm6tsn06ix17Cre5Qhwe0v2YrFbUFCI84tjau+pjgAnfeCuHLm5uXh5eRETEyOrrkIIITqMqqqUlpaSm5tLbGxsZ09HiCuCBLpz2fMlLHsMLNoKF5XH4etHIeMrKM6E8mzQu0DviZBwK/S9nqxKlScX7CYt5yg3xgfy+6JN1L7wHwgNJXruXEwjhrdqSlXmKtKK0hxFTDJLM7GpNvSKnv7+/bm7/90MCxnGkOAh+Lj6tP5nILqlhoYGCXNCCCE6nKIoBAQEcOLEic6eihBXDAl057L25dNh7hRrIxxYCb2uhbFPQ78bwd0Xu11l3pZs3vh2P64GPR+N8SPuP29Qu28fPlOnEvLC8+g9PS96CmUNZY7zbzuKdnCg7AAqKgadgcTARB5MeJBhIcMYHDwYk9HUNq9bXBYkzAkhhOgM8v8fITqWBLpzqcxt4Q4F7v3K8V1ueR1PL9jDlqxSxscF8GLjHhqefx+rpyeR7/0fXhMmXPCQRbVFjvCWWpRKVmUWoDXnHhQ0iEcGPUJSaBIDAwfiZnBrzasTQgghhBBCdHMS6M7FJ1LbZtnc7Wj7xL9MPc6flu9DVVX+OjaIof99j/rt2/G89lrC/vQyhoCWz62pqkpeTZ5TD7jj1dp4JqOJwcGDuanXTSSFJDEgYABGvRwuFkIIIS5H2dnZ3HjjjaSnp7f5c69fv563336b5cuX8/XXX5OZmcmzzz57Sc8VExODl5cXer0eg8HAZdk7WIhuRrpEn0vKLDC6O99mdIeUWRRXNfCLeak8s2gvCeFeLOtdwYA//prGzEzCXnmFyPffOyvMqarK0cqjLDi4gGd/fJaJCydy/eLr+cOmP7AuZx29fXvzVNJTzL9xPhunb+SjCR/xy4G/ZHDwYAlzol0tScsj+fV1xD67guTX17EkLa/dx7zhhhuoqKigoqKCDz74wHH7+vXrufHGG9t9/Is1e/ZsIiIiGDx4MIMHD2blypWdM5E9X8I7CTDbV/u858t2H7K7/a4WLFjAgAED0Ol0Z73ZfO211+jduzd9+/blu+++65T5rchawaSFk0icl8ikhZNYkbWi3cfsbr/Dc/156wq/QwBLcTHZ99yLtZudFZsyZcolh7lTvv/+e3bt2iVhToguQlboziXxdu3zGVUuv1bH8Md3NtBotfGna8IZt+zf1Kxbh8dVVxH22mu4REYAYFftHCo/5LQCV9ZQBkCAW4CjgfewkGH09u2NTpF8LTrekrQ8nlu8l3qLDYC8inqeW7wXgFuGRLTbuKfeoGVnZ/PBBx/w61//ut3GaonVasVwEX0gH3/8cZ566ql2nNF5NFeoadlj2ten/r5qB93td5WQkMDixYt5+OGHnW7PzMxk/vz5ZGRkkJ+fz4QJEzh48CB6vb49ptysFVkrmL15Ng02raVIQW0BszfPBmByz8ntNm53+x1C83/eusLv8JSSDz6kfscOTnzwIWEvzmqT57RarcyYMYO0tDTi4uL45JNPePvtt1m2bBn19fWMHj2af/zjHyiKwt///nc++ugjDAYD8fHxzJ8/n9raWn7729+yd+9erFYrs2fP5uabb3YaY+7cuaSmpvLee+9x//334+3tTWpqKoWFhbz55ptMmzYNgLfeeosvv/ySxsZGpk6dyksvvdQmr1EI0fYk0J3HElsybzX+nfyGekJd3QjZ4Mqu3DSGRPvyZmgF6p8epbamhuBnnsH73rs4WHGI1Iw1pBalsrNoJ1XmKgBCTaGMDh+thbiQJHp495BDw6JDvLQsg8z8qhbvT8upwGyzO91Wb7Hx+4V7+O+2nGYfEx/uzYs3nbuP4ptvvombmxuPPfYYjz/+OLt372bdunWsXbuWjz/+mI0bN5Kamsqzzz7LkSNHGDx4MBMnTmTy5MnU1NQwbdo00tPTGTZsGJ999lmLf15iYmKYMWMGy5Ytw2KxsGDBAvr160dZWRkPPvggWVlZeHh48M9//pPExERmz55Nfn4+2dnZBAYGMmnSJJYsWYLNZiM9PZ0nn3wSs9nMp59+iqurKytXrsTf3/88P+U28s2zULi35ftzt4Ot0fk2Sz0sfRR2zGv+MaED4frXzzns5fa76t+/f7PjL126lOnTp+Pq6kpsbCy9e/dm27ZtjBo16pw/n4vxxrY32F+2v8X795zYg9ludrqtwdbArE2zWHhwYbOP6effj2eGP3POcS+332FLOuJ3WPjqqzTua/l3CKCazdTv2QOqSsX8+TTu24dyjp5rrv37Efr88+cd+8CBA/znP/8hOTmZBx98kA8++IBHH32UWbO0wHjvvfeyfPlybrrpJl5//XWOHj2Kq6srFRUVALzyyitce+21zJkzh4qKCoYPH86E85zjLygoYOPGjezfv58pU6Ywbdo0Vq1axaFDh9i2bRuqqjJlyhQ2bNjA2LFjURSFSZMmoSgKDz/8MA899NB5X5cQon3JktA5LEnL4/lV86gIeBFTv2epCpxNetV6pvb15r3jK7A+/xSWAC92vnYnL0RuZcz/rmb6ium8nfo2WRVZpESn8MqYV/j259+y6uereO3q15gWN40YHyknL7qOM8Pc+W6/UGPHjuXHH38EIDU1lZqaGiwWCxs3buTqq692XPf666/Tq1cvdu3axVtvvQVAWloaf/vb38jMzCQrK4tNmzadc6zAwEB27tzJI488wttvvw3Aiy++yJAhQ9izZw+vvvoq9913n+P6HTt2sHTpUr744gsA0tPT+eKLL9i2bRsvvPACHh4epKWlMWrUKD755BPH49577z0SExN58MEHKS8vb9XP55KcGebOd/sFuhx/V83Jy8sjKirK8X1kZCR5ee2/vbipM8Pc+W6/UJfj77C5P29d4XcIYM7Pd/6+jeYQFRVFcnIyAPfccw8bN27k+++/Z8SIEQwcOJB169aRkZEBQGJiInfffTefffaZY+Vz1apVvP766wwePJhx48bR0NBATk7z/zB3yi233IJOpyM+Pp6ioiLH86xatYohQ4YwdOhQ9u/fz6FDhwDL4TuXAAAgAElEQVTYtGkTO3fu5JtvvuH9999nw4YNbfLahRCXTlbozuGVHz5HF7wQvzozv/vSxju3lBNuWcDUDxZQUWNmWbKR+cm52Eo+p5dPL27qdRPDQoYxNHgoIaaQzp6+EADnXUlLfn0deRX1Z90e4evO/x6+9H/1HjZsGDt27KC6uhpXV1eGDh1KamoqP/74I3//+9957bXXWnzs8OHDiYzUig8NHjyY7OxsxowZ0+L1t956q2PMxYsXA7Bx40YWLVoE/5+9O4+Pqr73P/76zJKZ7AkJCSEJIBCQHRVxQRC1ChYXVATXe+vttfd20+633l9t6XJvl2urtbu319a2briAVrTaqoAbKsgi+w6ZEMi+J7N+f3+ck2SyQQhJJgmf5+Mxj5w5c+ac7+RMYN7z+X6/B7j88sspLy+nuroasMaQxMe3jo+97LLLSE5OJjk5mdTUVK699loApk2bxtatWwH47Gc/y/3334+IcP/99/PVr36VRx99tKe/ns6dpJLGg1O7mKgpH+7q+TisoXauumKM6bCut79cO1kl7apnr6K4vrjD+pzEHP6w8A89Pu5QO4dd/b31xzk8WSUtWFLC/iuvgua2GEOkpobcn/0U1/Dhp3Xs9q9FRPjc5z7Hhg0byM/PZ/ny5TQ1Wd11V69ezbp163jxxRf5/ve/z/bt2zHG8NxzzzFx4sQ2+2kOap3xeDwty82/X2MM9913X4duywAjR44EICsrixtuuIEPPviAefPm9ewFK6V6hVboTqAh8a+II8hNb0c4uxD+8+kw33kyQMgV4P8+Nxb5t9t54IqHWLtsLasWr+JbF36Lq8+6WsOcGlS+vmAi8e6240/i3U6+vmBiF8/oHrfbzZgxY/jDH/7AxRdfzNy5c3nzzTfZv39/l13imkV/wHA6nYRCoW5tH73tiT74JSa2vWZj9PEcDkfLfYfD0bK/7OxsnE4nDoeDu+++mw8++OCEbeoTJ5io6XQMtXPVlby8PAoLWwOxz+dr+XDaX+499168zraXnPE6vdx77r2ntd+hdg67+nsbCOew7Ne/wUTa9mAwkQilv/7Nae/7yJEjvPfeewA8+eSTLcE6MzOTuro6nn3W6pYbiUQoLCzksssu4yc/+QlVVVXU1dWxYMECfvGLX7Scj02bNvWoHQsWLODRRx+lrq4OsCqjJSUl1NfXU1tbC0B9fT2vvfYaU6dOPa3XrJQ6facV6ERkoYjsFpF9ItJhyiQRmSciH4lISESWnM6xYsHhriKtznD5VoMDOKsE1k6Fb9zl5GdfXM1/zP4PPjH6Ewzz9tP4GqX6wOJzcvnhjdPITYtHsCpzP7xxWq9MiDJv3jweeOAB5s2bx9y5c/ntb3/LzJkz23wLnZyc3PIBoTfNmzePxx9/HLBm8svMzCQlJaXH+ysubq2qrFy5MjYfYqYvhWsftipyiPXz2od7ZUKUoXSuunLdddfx1FNP4ff7OXjwIHv37mX27Nm9fpwTWTR2EcsvXk5OYg6CkJOYw/KLl/fKhChD6Rx29fc2EM5h4+bNEAy2XRkM0tjD8BRt0qRJPPbYY0yfPp2Kigo++9nPcvfddzNt2jQWL17M+eefD0A4HOaOO+5g2rRpnHPOOXz5y18mLS2N+++/n2AwyPTp05k6dSr3339/j9px1VVXcdttt3HRRRcxbdo0lixZQm1tLcePH+eSSy5hxowZzJ49m0WLFrFw4cLTft1KqdPT4y6XIuIEfgVcCfiAD0XkRWPMjqjNjgCfAmI4LVzPpcZlcdOrxWB/8RhygD9O8CZpBU4NLYvPye2TGS3nzp3Lf/3Xf3HRRReRmJiI1+ttM54HICMjgzlz5jB16lSuvvpqFi3qnZn+li9fzl133cX06dNJSEjgsce6mDSkm77xjW+wefNmRIQxY8bwu9/9rlfaecqmL+2TGS2H0rlauXIlX/ziFyktLWXRokXMnDmTV199lSlTprB06VImT56My+XiV7/6VUxmR1w0dlGfzGg5lM5hV39vA+Ecjl21sk/2O2bMGHbs2NFh/Q9+8AN+8IMfdFj/9ttvd1gXHx/f6b9N8+fPZ/78+QB86lOf4lOf+hRgzXgZrbkiB3Dvvfdy770dK8dbtmw50ctQSsWAdNZNoltPFLkIWG6MWWDfvw/AGNOho76I/BF4yRjT+RReUWbNmmUGynVN/rbhSXI+9T3ionqf+F1w7I/fZuGsW2PXMKVOYufOnSftZqWUUkr1Ff1/SKnTIyIbjTGzurPt6XS5zAWiR+f77HWnTEQ+IyIbRGRD6QC6QOeMl/bgou03f26czFi9N0YtUkoppZRSSqlWpzPLZWfTSvWo3GeMeQR4BKwK3Wm0qVc1bt6MIxRus84RCvdKP3ml1Km54YYbOHjwYJt1P/7xj1mwYEGMWqS6oudq8NNzqJRSg8fpBDofkB91Pw842sW2g1Jf9ZNXqj8YY4bU9Q5XrtS/x8FCz9Xgp+dQnY6eDudRSvXM6XS5/BAoEJGzRCQOuAV4sXeapZQ6HV6vl/Lycv1PVSmlVL8yxlBeXo7X6z35xkqpXtHjCp0xJiQiXwBeBZzAo8aY7SLyPWCDMeZFETkfWAmkA9eKyHeNMSe+yrFS6rTl5eXh8/kYSGNSlVJKnRm8Xm/LxeqVUn2vx7Nc9pWBNMulUkoppZRSSvW3/prlUimllFJKKaVUDGmgU0oppZRSSqlBSgOdUkoppZRSSg1SA24MnYiUAodj3Y5OZAJlsW6EGtL0Pab6kr6/VF/S95fqS/r+Un1poL6/RhtjhndnwwEX6AYqEdnQ3YGJSvWEvsdUX9L3l+pL+v5SfUnfX6ovDYX3l3a5VEoppZRSSqlBSgOdUkoppZRSSg1SGui675FYN0ANefoeU31J31+qL+n7S/UlfX+pvjTo3186hk4ppZRSSimlBimt0CmllFJKKaXUIKWBTimllFJKKaUGKQ103SAiC0Vkt4jsE5Fvxro9augQkXwReVNEdorIdhG5N9ZtUkOPiDhFZJOIvBTrtqihR0TSRORZEdll/1t2UazbpIYOEfmy/f/jNhF5UkS8sW6TGrxE5FERKRGRbVHrhonI30Vkr/0zPZZt7AkNdCchIk7gV8DVwGTgVhGZHNtWqSEkBHzVGDMJuBD4vL6/VB+4F9gZ60aoIevnwN+MMWcDM9D3muolIpIL3APMMsZMBZzALbFtlRrk/ggsbLfum8DrxpgC4HX7/qCige7kZgP7jDEHjDEB4Cng+hi3SQ0RxphiY8xH9nIt1geh3Ni2Sg0lIpIHLAJ+H+u2qKFHRFKAecD/ARhjAsaYqti2Sg0xLiBeRFxAAnA0xu1Rg5gxZh1Q0W719cBj9vJjwOJ+bVQv0EB3crlAYdR9H/qBW/UBERkDnAO8H9uWqCHmIeAbQCTWDVFD0ligFPiD3a339yKSGOtGqaHBGFMEPAAcAYqBamPMa7FtlRqCso0xxWB90Q5kxbg9p0wD3clJJ+v0Wg+qV4lIEvAc8CVjTE2s26OGBhG5BigxxmyMdVvUkOUCzgV+Y4w5B6hnEHZXUgOTPZbpeuAsYCSQKCJ3xLZVSg08GuhOzgfkR93PQ8v9qheJiBsrzD1ujHk+1u1RQ8oc4DoROYTVXfxyEflLbJukhhgf4DPGNPcseBYr4CnVGz4BHDTGlBpjgsDzwMUxbpMaeo6LSA6A/bMkxu05ZRroTu5DoEBEzhKROKzBuC/GuE1qiBARwRp7stMY87NYt0cNLcaY+4wxecaYMVj/dr1hjNFvt1WvMcYcAwpFZKK96gpgRwybpIaWI8CFIpJg/395BTrpjup9LwL/bC//M/BCDNvSI65YN2CgM8aEROQLwKtYsys9aozZHuNmqaFjDnAn8LGIbLbX/acx5uUYtkkppU7FF4HH7S89DwB3xbg9aogwxrwvIs8CH2HNCr0JeCS2rVKDmYg8CcwHMkXEB3wH+BGwQkQ+jfUlws2xa2HPiDE6HEwppZRSSimlBiPtcqmUUkoppZRSg5QGOqWUUkoppZQapDTQKaWUUkoppdQgpYFOKaWUUkoppQYpDXRKKaWUUkopNUhpoFNKKTVkiUhYRDZH3b7Zi/seIyLbemt/SimlVE/odeiUUkoNZY3GmJmxboRSSinVV7RCp5RS6owjIodE5Mci8oF9G2+vHy0ir4vIVvvnKHt9toisFJEt9u1ie1dOEflfEdkuIq+JSHzMXpRSSqkzkgY6pZRSQ1l8uy6Xy6IeqzHGzAZ+CTxkr/sl8CdjzHTgceBhe/3DwFpjzAzgXGC7vb4A+JUxZgpQBdzUx69HKaWUakOMMbFug1JKKdUnRKTOGJPUyfpDwOXGmAMi4gaOGWMyRKQMyDHGBO31xcaYTBEpBfKMMf6ofYwB/m6MKbDv/wfgNsb8oO9fmVJKKWXRCp1SSqkzleliuattOuOPWg6jY9OVUkr1Mw10SimlzlTLon6+Zy+/C9xiL98OvG0vvw58FkBEnCKS0l+NVEoppU5Ev0lUSik1lMWLyOao+38zxjRfusAjIu9jfbl5q73uHuBREfk6UArcZa+/F3hERD6NVYn7LFDc561XSimlTkLH0CmllDrj2GPoZhljymLdFqWUUup0aJdLpZRSSimllBqktEKnlFJKKaWUUoOUVuiUUkoppZRSapDSQKeUUkoppZRSg5QGOqWU6iMiMkZEjIi47PuviMg/d2fbHhzrP0Xk96fT3jOdiHhEZIeIjIh1WwYzEVkjIv9qL98uIq/1wTF65f0uItNF5N3eaJNSSsWKBjqllOqCiLwqIt/rZP31InLsVMOXMeZqY8xjvdCu+SLia7fv/zbG/Ovp7vsM9xlgnTHmGICI/FFEfhDjNrURFfxXt1v/FxFZHqNmdckY87gx5qrT2Udfvt+NMVuBKhG59nT3pZRSsaKBTimluvZH4E4RkXbr7wQeN8aE+r9JZ5aeVix76N+AP/fj8U7oJK/9QhGZ08fHOFM8jnXulVJqUNJAp5RSXVsFDAPmNq8QkXTgGuBP9v1FIrJJRGpEpPBEVZJ2XdGcIvKAiJSJyAFgUbtt7xKRnSJSKyIHROTf7PWJwCvASBGps28jRWS5iPwl6vnXich2Eamyjzsp6rFDIvI1EdkqItUi8rSIeLto8zgReUNEyu22Pi4iaVGP54vI8yJSam/zy6jH7o56DTtE5Fx7vRGR8VHbtVTCmqsxIvIfInIM+IOIpIvIS/YxKu3lvKjnDxORP4jIUfvxVfb6bdGVFxFx269hZievcxQwDni/q/PXbvuf2+e7RkQ2ishce/0IEWkQkYyobc+z2+627/+L/XuptKvAo6O2NSLyeRHZC+w9QRN+AnRZPbR/9/tEpEJEXhSRkSc6hr3ucyKy1z5f37fP/Xv2a1whInH2tic8H+3a8SkRedte/kbUe7ZORIIi8kf7sVi+39cAV4iI5wS/b6WUGrA00CmlVBeMMY3ACuCfolYvBXYZY7bY9+vtx9OwQtlnRWRxN3Z/N1YwPAeYBSxp93iJ/XgKcBfwoIica4ypB64Gjhpjkuzb0egnisgE4EngS8Bw4GXgr80fyKNex0LgLGA68Kku2inAD4GRwCQgH1huH8cJvAQcBsYAucBT9mM329v9k/0argPKu/F7ARiBFaRHY3WDdAB/sO+PAhqBX0Zt/2cgAZgCZAEP2uv/BNwRtd0ngWJjzOZOjjkNOHAKVdcPgZl2O58AnhERr91dcw3W77fZHcBTxpig/d74T+BGrHPzFta5irYYuACYfILj/wqYICKfaP+AiFyOdc6WAjlY5+epbhxjIXAecCHwDeAR4Hascz4VuNXe7mTno1PGmJ80v2ex3kulWH9fEMP3uzGmCAgCE0/2GpRSaiDSQKeUUif2GHCziMTb9//JXgeAMWaNMeZjY0zEHo/zJHBpN/a7FHjIGFNojKnA+gDewhiz2hiz31jWAq8RVSk8iWXAamPM340xQeABIB64OGqbh40xR+1j/xUrnHRgjNln78dvjCkFfhb1+mZjBb2vG2PqjTFNxpi37cf+FfiJMeZD+zXsM8Yc7mb7I8B37GM2GmPKjTHPGWMajDG1wH81t0FEcrA+8P+7MabSGBO0f18AfwE+KSIp9v076bpLZRpQ2832YYz5i92ukDHmp4CH1kDwGHaQtEPvrVHH/Tfgh8aYnXZ4/G9gZnSVzn68wv5CoStNWL+Hzqp0twOPGmM+Msb4gfuAi0RkzEmO8WNjTI0xZjuwDXjNGHPAGFONVSU7x37tXZ6P7rD/llYBPzfGvGzvM9bv91qs94BSSg06GuiUUuoE7IBSClwvImOB87EqMgCIyAUi8qbd/awa+Hcgsxu7HgkURt1vE3ZE5GoRWW93mavCqi51Z7/N+27ZnzEmYh8rN2qbY1HLDUBSZzsSkSwReUpEikSkBiskNbcjHzjcRVUrH9jfzfa2V2qMaYpqQ4KI/E5EDtttWAek2WEpH6gwxlS234ldyXkHuEmsbqJXY42X6kwlkNzdBorIV+0ugtX2+Uml9ffyAjDZfr9cCVQbYz6wHxsN/NzuGlgFVGBVQaPPTfT74kT+F8iWjhN6tD//dVjV0ZMd43jUcmMn95PgpOejO/4P2G2M+XHzigHwfk8Gqrp5PKWUGlA00Cml1Mn9CasydydW1SL6g+4TwItAvjEmFfgt1gf0kynGCiPNRjUv2GN5nsOqNGQbY9KwupE179ecZN9HsYJD8/7EPlZRN9rV3g/t4003xqRgVZ6a21EIjJLOJ9YoxBqT1pkGrC6SzdpfJqD96/sqVvXrArsN8+z1Yh9nmESN62unuVp2M/Ce3b2uM1uBsV28ljbEGi/3H1hV1nT7/FTb7cEOoyuwKmXtq4KFwL8ZY9KibvHGmOip8092frGPEwS+C3yftu+59uc/Ecig7fnv1jG6cKLzcUIi8k37uZ+OWhfT97s9vjAO2N2d7ZVSaqDRQKeUUif3J+ATWOPe2l92IBmrQtQkIrOB27q5zxXAPSKSJ9ZEK9+MeiwOqwtfKRASkauB6KnfjwMZIpJ6gn0vEpErxJqI46uAH+jJ9baSgTqsqd1zga9HPfYBVjD9kYgkiohXWmde/D3wNbEmBBERGR/VrXAzcJtYE8Ms5OTd9ZKxKkRVIjIM+E7zA8aYYqzugL8Wa7IOt4jMi3ruKuBc4F7siWw6Y4zxYU0QMrvdQ077dTXf4uz2hLDOj0tEvo019ivan7DGaV2HVdVs9lvgPhGZAiAiqfZ4w576M9Z7ZWHUuieAu0Rkph2W/ht43xhz6DSOE63L83Ei9vv4HmBxu66esX6/zwfesLunKqXUoKOBTimlTsL+IPwukIhVjYv2OeB7IlILfJvWSR5O5n+BV4EtwEfA81HHq8X64LsCqyvgbdHHNcbswhqrd8Duujcyar8YY3ZjVaV+AZQB1wLXGmMC3WxbtO9iBaJqYHW7dobtfY8HjgA+rPFMGGOewRpb9QTW+KTmGUPBClfXYnVxu91+7EQewhoTVQasB/7W7vE7sSa12IU1ucaXotrYiFX9OSu67V34nb2vaN/ECi/NtzewztsrwB6srn5NtOvCaIx5B2ss4EfRQcoYsxL4MfCU3V1xG1ZX0B6xz8F3aP3dYox5Hbgf63UXY1VKb+npMTpxsvPRlWVYk5bslNYZK387AN7vt2MFbaWUGpTEmNPpdaGUUkoNbHYFbYIx5o6TbOcBNgFX2JW/0z3uG8ATxpjfn+6+VN8QkWnAI8aYi2LdFqWU6ikNdEoppYYsu0vgJuBOY8y6fjzu+cDfscZWdnv2TKWUUupUaZdLpZRSQ5KI3I3VFfKVfg5zjwH/AL6kYU4ppVRf0wqdUkoppZRSSg1SWqFTSimllFJKqUHqpNfb6W+ZmZlmzJgxsW6GUkoppZRSSsXExo0by4wxw7uz7YALdGPGjGHDhg2xboZSSimllFJKxYSIHO7uttrlUimllFJKKaUGKQ10SimllFJKKTVIaaBTSimllFJKqUFKA51SSimllFJKDVIa6JRSSimllFJqkNJAp5RSSimllFKDlAY6pZRSSqn+sHUFPDgVlqdZP7euiHWLlFJDwIC7Dp1SSiml1JCzdQX89R4INlr3qwut+wDTl8auXUqdqbaugNe/B9U+SM2DK749aP8WtUKnlFJKKdXXXv9ua5hrFmyEf3w3Nu1R6kzW/AVLdSFgWr9gGaRVcw10SimllOqRVZuKmPOjNzjrm6uZ86M3WLWpKNZNGlhCAdjzGqz8rFUF6EyNDx69Gt74ARxYA4GGfm2iGlqCJSUcuuNOQqWlsW7KwPb69zr/guX178WmPadJu1wqpZRS6pSt2lTEfc9/TGMwDEBRVSP3Pf8xAIvPyY1l02IrHISD62D787DzJWiqAk8quBMg2ElYi0uGUCO89VNY9z/gcEPuuTB6Doy5BPIvAE9S/78ONSiV/fo3NG7cSOmvf0POd74d6+YMPI1V1t9mdWHnj3f1xcsAp4FOKaWUUicVDEcormqisLKBwooGfrB6Z0uYa9YYDPOtVdsoq/MzPNnD8CQPmfbP1Hg3DofEqPV9LBKGQ29bHxR3vAiNFVZQO/uTMOVGGHcZ7Hih7Rg6AHc8XPMza9xOUzUceR8Ovw2H3oF3fg5v/wwcLsiZCWPmwJi5VsDzpsTutfazYEkJRV/5KnkP/gzX8OGxbs5JGWMgHMYEAtYtGMQEAkRaloNt1ptgwFoXDLRb3/o8gkHr+V3tw16O1Nfj37sXjKHqmWcYduedeMaeFetfSexFwnBwLWx6HHa9BKEm6+8qEuq4bWpe/7evF4gxJtZtaGPWrFlmw4YNsW6GUkopdUYxxlBWF+BIRQM+O7QVVjRypKKBwsoGiqubCEd6/pnB5RAykuIYnuwhM6n1Zt2PY3jLsoe0BDciAzz8RcJw5D3YvtIKa/Wl4E6EiQutEDf+E+D2tn3OqUzC4K+FwvetcHf4HSj6CCJBEAfkzLCqd6MvgVEXQnxa37/eGCle/l2qnn6atFtuaVNxMpGIFWjahZquliPt158gGLVZDrYPVV0cM2odvfnZWgSJi0Pc7rY/2yy7EbebwKHDhI4daz2+00nq9deTvmwp3unTB/7fVG8r3w+bn4AtT0JNEXjTYNrNMPM2KN/X+Rcs1z48YCZGEZGNxphZ3dpWA51SSil1ZqjzhyisaLBCWkUDvsrGlvu+ysYOFbfMJA/5w+IZNSyB/PQE8ofFk28vL3vkPY5WNXU4Rm6al9X3zKWszk9pbcD+6aeszh+1HGi5Hwx3/BzSWfhrXY5h+ItEwPcBbHveCnF1x8AVDxMWwJQboOAqiEvom2MH6qHwAyvcHXoHijZAOAAIjJhmVe/GzIFRF0HCsL5pQz9r2r2bgzfeBGHrfelITsaEw5hgEILB3j2YyxUVktw43F2Fp86W3YjbDlb2OseJntfpvq3n0O65uFzden8HS0rYc8WVOIKBlnXG4UA8HmhsxDNpEunLlpJyzbU4kxJ793c3kPhrrS9ZNj9hfeEiDhh3hRXiJn6y7ZcsA3yWSw10Siml1BkoEIpwtKqRwsrm0GYt++zQVtnQ9kNwksdFXroV0qzQ1rqcl55AfJyzy2O1H0MHEO928sMbp3V7DJ0xhurGIGV1fkqag16tv10IDFBa66e8vuvwl5nkITM5zgp+djfPlvDX3PWzp+HPGPBtsCtxq6xv+p0eKLgSpt4IBQtiM8Yt2Ai+D1sreIUfQNgPCGRPsSt4c6xbYkb/t+80RBoaqPjTnyj95a8gZHeLcziIGzeOpDlzTlCl6iRUdTeUOQb3PIHvfuEbJL3xMu5I699j0OGk/tKrOPvS2VQ+9TT+XbuQhARSFy0ibdky4qdOiWGLe1EkYnVV3vQ47HzRGquaUQDn3A7Tl0HKyFi3sEc00CmllFJDUCRiKK3zW90hKxs4Ut7YMqbNV9lIcXUj0b0i3U4hN82uqkVX2dKt0Ha61a1Vm4r4n1d3c7SqkZFp8Xx9wcQ+mxClOfyV1vopjQp6ZXX+1hBY56fMrgqGOuke6nYKGYmdh7/orp+ZiXGkVW9Htq+E7aug+gg446xv+qfeCBMWDrxxbMEmKNpoV/DetgJeyO5ONnySFfDG2AEvKSu2be2CCYWoWrmSsod/Yc3S6HBYH9Zt4vEw/h9/HxRj6fqLMYZjNU1sXngtYyo7zjJ7JD2PBe/9HWMMTVu3Uvn0CmpefhnT1IR36lTSli0l9ZOfxJE4CKt2lYdg85Ow5QmoOgKeFOvvc+YdkDcLBnkXUw10Siml1CBV0xTkSHnzOLboapsV2vyhSJvts1M8dlBrrbA130akeHEO1YlITiASaa38lbbr5hnd/bNt+DNMlsNc41zPIsd6RjtKCOFkk/sctqRcxuHh80lKy4wKga1dP1PjuxeM+zMAEwrA0Y+scHf4HWvClWC99VjmhNYK3phLIHlE37Shm4wx1K1ZQ8lPf0pg337iZ87EmZlB3dp1bbtWut2kLVlyRs/eWNUQYIuvmq2FVWzxVbPFV0Vprf+Ez3lw2QwWTslpqbiHa2qofvGvVD39FP69+3AkJpJy3bWkL1uG9+yz++Nl9Fyg3pp4aPPjcOgtQGDsfJh5O0y6xhoHN0RooFNKKaUGKH8oTFFl82Qjjfiaq212F8nqxrbdIpO9ro5j2Oz7eenxeN1dd4tUJ2EMkeM78G95FueOVcRVHyAiTo6mz2Zr6uW8F3cRhU2elhBYXhc4YeWvucrXZsyfve5jXxUP/mMvTcHWQH6qXVRPSzgIRze3zqJ5ZD0Eaq3Hho2zK3h2yEvtv8tONG7dSsn/PEDDhx8SN2YMw7/yZZKvvJKDN9yIf9euDtt7zj6bsatW9lv7YqkhEGJbUQ1bfVZ42+qr4nB566Uvxg1PZEZeGtPzUvnVm/spresY7JwOIRwxJHlcXDM9h5tn5XHuqHREBGMMjZs2UfX0082YYJwAACAASURBVNS88jdMIED8jBmkLVtGytULccQPkHBkjDUebvPjVsU8UAfDxlrj4qbfAmn5sW5hn9BAp5RSSsVIJGI4XtvUOkOkHdh89v3jtU1tJsGLczpaxrE1d4dsHdOWQGqCO3YvZqgq3W2Nidv2PJTttiZOGDPXmthk0nVdjjmLRAxVduWvrPYk1b+6QLdmBU1PcPPnT1/A+Kyk/g3n4RAc29I6Bu/we+Cvths1pnUWzTFzIG1Urx8+cOQIJQ8+SO0rf8OZkcHwL3yetCVLrIlAzkDBcITdx2rZ4qtiS2EVW33V7Dle29KFemSqlxn5aUzPS2NGXipT81JJ8bb+rroa0/rfi6eSkx7PMxt8vPxxMY3BMGMzE7npvDxuPDeXnFQrtIWrqqh+4QUqn3qawMGDOFJSSL3uOtKXLcVTUNCvv4sWVYWw5SkryFUehLgkmLLY6lI56sJB36XyZDTQKaWUUj3QnS5xzWO52neHbK62+SobCYSjxv0IjEjxtnaLbBfaspI9Q/f6bANJ+X4rwG1fCSXbAbGqUVMWw+Tre31cWfvwd9vv3z/h9g6B0RmJFGQlMSE7mQkjkpmQncTYzCTiXP0wYUckDMc+bp1F8/A71kXRAVJHtR2Dlz6mxx+mQ5WVlP36N1Q+9RTicpFx110M+5d/GdozL7YTiRgOlNWz1WcFt82FVeworiFgd6dOT3BbwS3fCm/T89IYnuw56X5P9u9XnT/Eyx8X8+xGHx8crMAhcEnBcG4+L48rJ2fjdTsxxtDw4YdUPb2C2tdewwSDxJ93HunLlpK8YAEOz8nbcVqCjbDzJdj8FziwFjDWly0zb4fJ10HcmfM+0UCnlFJKnaLOvuGOczq4dkYOwxLj2swaWdvU9oK0aQnuDtP6N49py02Px+PSbpExUXHQCnDbV8Kxrda6/AutiRMmXQcpOf3WlDk/eoOiqsYO67OSPXz72snsOV7HnmO17Cmp5XB5Q0t1z+kQzspMZEJ2EgVZyUzITmbiiCRGZyTidvZh0ItErOB76B2rm+bhd6Gh3HosJa813I25xOr+dpKAF2lspOJPf6b8f/+XSEMDaUuWkPmFz+POGpgTtPQWYwxHq5taxrxt9VXxsa+aWr/1b0hCnJOpuanMyEu1A1waeenxfX4pjsPl9Ty30cdzHxVRVNVIitfFdTNHsuS8fGbkpSIihCoqqF65isoVTxM8fARnaiqpixeTtmxZ716wvHkm2c1/sb508ddYXyLMvA1m3mp9gXAG0kCnlFJqyDLG4A9FaAyEaQzat0CYpqjlxqB9PxCmMRhpd99+vN3z95bUddlFzuNytJl0pHla/+YAF931ScVY1RFrnM325+HoJmtd7iwrxE2+3rreVAycymUemoJhDpTWs7eklj3Ha62wd7yWIxUNLd113U5hbGaSVcnLSqIg26rojc5I7JuJcCIRKN3VOovm4Xesi6kDJOfY4W6O1U0zs6Al4JlwmOpVqyh9+BeEjh8n6YoryPrKl/GMG9f7bRwAKusDdrfJ6paxb2X22Da3U5iUk8J0u+o2Iy+N8VlJMZ24KBIxvHegnGc2FPLKtmP4QxEKspJYcl4eN5ybS1ayFxOJ0PD++1Q+vYLaf/wDQiESZs8mbdlSkq+80rp+Xk/UFMPWp6xrxpXtsa7pOPl663IDoy+xZjk9g2mgU0opBfTzrHpY40A6C0utgSrSZaDqNJC1PBZp81hPeN0O4t1O4t1OvHHOluX4OCdet5O/7zje6fMEOPDDT/bfxavVqasusq4Rt32ldW02gJHnWGPiJi+G9NGxbZ/tdP8eGwNh9pfWsduu5O21g56vsrXy53E5GDc8iQnZzWHPqurlpcf3btdeY6wP4c3h7tA71oXWARKzMKMvpr56JCXPf4T/4BG8M6aT/fWvkzCrW59PB4V6f4htRdVWt0lfFVt9VRRWWOdCBMYNT2JGXhoz8q0ANykneUBX62uagqzeWswzGwr56EgVTocwf8JwlpyXxxWTsolzOQiVlVH1/EqqVqwg6PPhTE8n9cYbSF+6lLjR3fg7CzbB7petELf/dTARGHWRVY2bvHjgXQ4khjTQKaWU6rQi4HU7+MaCicybMLxNuDpxhauz+5E2gax5ubMZAE/G7RS8UeEq3u3s/H7ciQNZx+1bt/G4HCf9MNtVl7jctHje+eblp/y6VB+rPQY7XrC6aBWut9aNmAZTbrTGxQ0bG9v29aN6f4i9JVa4s7pt1rH3eC3F1U0t28S7nRS0dNu0w152MiNTvb3zZYUxUHEADr1F49t/o+SFLTQcBXdSiKzzDcmXXoicNdeq5GVNHnTVl0Aowq5jNVGXDKhiX0ldy6QluWnxzMhPtWedTGNqbgrJg7hyv7+0jmc3+nj+Ix/Ha/ykJ7i5fmYuS87LY8rIFDCG+nfepWrF09S+8SaEwyRefBFpS5eRfMXlbSe3Mcaqlm9+HD5+1hqbmZILM261glzG0KzWni4NdEopNQT4Q2Hq/WHq/SFqm0LUB0LU+UPUNYWo99vL/ublcNSytc3u47XdmmWvKyK0Bqh2AckKVI6OgaqbActrhzOv29m344BOwal0iVMxUlcKO1+AbSutqhAGsqZYlbgpN0Dm+Fi3cECpbgyyr6S1y+be43XsPl7b5rplSR4X47OSmJidTEF2kj1GL5msZM8pB71AYSGlDz5Ezcsv4xw2jMxPLSN9ZgpStN6q4FUfsTaMT7eCXXM3zeyp4Bg4lStr0pI6Nkd1m9x5tKZlsqOMxLjWbpN29S0zqY8nC4mRcMTw1t5Snt3o47UdxwmEIpw9IpmbZ+WzeOZIMpI8BI+XUP38c1SueIZQcTHOzEzSbryRtEWXE1e+zqrGlewAlxfOvsbqUnnWpQPqnA9EGuiUUioGmsd21XYauNqFr+ZtAp0HtHp/uM1MiSfidTtI8rhJ8jhJ9LhIsm+v7yrp8jm/uPWcqNDl6DSAeVyOM66bYX93UVXdUF8OO1+0ulMeesvqopU5wa7E3QBZA/xCyANQVUOAPXa423u8dZxeRX2gZZsUr4sJ2ckUZCcz0Q56BdnJZCbFdfh3IVRZSflvf0vFE08iTifD7voUGZ/+NM6kpLYHrjwcNYvm21B5yFrvTYVRF7dOtDJiOjhdsHUFvP49qPZZYx+v+DZMX9qrvwtjDEVVjWz1VbPFrrxtK6qhzp60JNGetGSmfcmA6Xmp/TJpSU+sPrCan3/0c47VH2NE4gjuPfdeFo1d1Gv7r24I8uKWIp7d6GOLrxqXQ7j87CxunpXP/InDcWGoW/MmVX/8NXUbdoIxJOb4ST8/h6Sb/gWZvgTi03qtPUOdBjql1JDV2x+4jTE0BMJtA1eTvRxoXg6fOKC1bB/udkUsMc5JktfVJoB1XHa2XR+1fcvPOCeuLipc2oVQDVqNldbU5dtXwoE1YMLWxa+nNoe4yYPyGlR9/YH7dJXV+dtU8vbaQS/6YvfDEuNaLq0wMd3NlPV/I/6ZxzEN9aTddCOZX/gi7uxuzlxZ7WsNd4fegYr91npPinXtu9LdEGk9Nu54uPbh0wp15XV+K7zZlwzYUlhFuR1k45wOJuUkt7lkwNjhsZ20pLtWH1jN8neX0xRu7WbrdXpZfvHyPnmP7T5Wy3Mf+Xj+oyLK6vxcnFjEvRkfcl7NP3A1VRBkBFVVU6nacIxQWQWurCzSltxE2pIluEeO7PX2DEUa6JRSQ1JnXeI8LgefnT+OWaOHUecPdh6+OgQ0qwLWvK47/ww6hDZhK9HjItnrIjEuatmukCVHb9N+e4+LBLezX647pl0I1aDSVA27XrZmp9z/pvVBPm20HeJutMbHDcIQ16y/P3D3FmMMpbV+dtvhbu/xWvYUVzPygzdZ+vHLDG+s5v3sSaw8/waSzi5oubTChGxr5s3U+FMYR1ZT3DqL5qY/QyTUcZu4ZLjyu9YYyYxx1lisLrru1flDfGxfKqA5xDVPICMCBVlJLRfqnpGfxsQRA3fSEn/YT3ljuXVrKqessazN8prCNQSjw68t3hXPZ6Z/hrykPHKTcslNziXdk947Fcb6csJbV9Dw/mMkV+0kYFy8FjmPDWlXM/aCa7nu3FGkxjmoW7uWyqefpv6tt0GEpHnzSFu2lKR58xDnwPx9DwQa6JRSQ1JXFaeuuJ3SReXLCl9tuil6myteUctRz/O6B2f3Q+1CqAY0fy3s/psV4vb9A8IBSM23JjWZcqM1U+Ug/LtrLxgO8olnP0FFU0WHx9wON/Py5pHmSSPdm97mZ5onjXRPOmneNJLcSTH/N8gYQ/3bb1PyPw/g37MH5+QplN7+GbYPH9ca9o7XtfkSaUSKl4Jsa4zeBHucXkF2Mkke14kPtjwN6MZnVKcH0scQHjaW8rhcDkSy2VyXyVsVybxX7iVirB4MeenxbS7UPTU39eRt6GOBcIDyRjucNZV3WG752VhObbC2030kxyWTGZ/JweqD3T5ugiuB3ORccpNyyUvKIy/ZDnv2LcGd0PWTw0Hrb3Xz49bfbiQIOTPhnDuoHHstq3Y38swGHzuKa4hzOrhycjZLzstjbkEmkeJiqp55hqrnnyNcWoYrJ4e0m5eQdtOS7ld1zyAa6JRSQ04gFGHCt17p8vFn/v0iK4y1dE10DthvWpU6owXqYc/frO6Ue/8OoSZIHtk6sUnerCER4iqaKnjL9xZrfWt59+i71Afru9y2IL2AqqYqKv2VhDqrSgEucZHmTWsb/Oyw1+lPTxrxrt4b69W4fTslDzxAw3vrcefnk/WVL5O8cGGH/Uci1pi06Ovn7Tley76SOvyh1nHBuWnx1myb2cktt/FZScTH2f9uPzgVqgs7NiQ1j/Bdr3L0wDaOH9xB4/G9uKoOkuEvZBTH8UprlSokcfiT83EPLyAua3xrVW/YWOvi6H0w02ZzSOusinYqIS3Dm0FmfCYZ8RkdljPirfvDvMOIc1rXgLvq2asori/usK+cxBxWXb+KoroiiuqK8NX6rJ91vpb7jaG2X5QO8w5rqei1hL1QmLyD7zJix2pc9SWQOBymL7Nmqcye0uG4249W8+xGHy9sPkpFfYCsZA83nJvLzeflMS7dS+0bb1L19NPUv/suOJ0kXTaf9GXLSJwzBxlkM6D2FQ10Sqkh5WBZPfc8uYmPi6o7fVzHhCkVO8GSEoq+8lXyHvwZruHDu9ioEfa+Zl1iYM+rEGqEpGzrulNTb4S82YNuGvv2jDHsrdrLOt861hSuYWvpVgyGrPgs5uXP4/XDr1Ppr+zwvJzEHF5b8lrLPuqD9VT6K1sCXpW/isqmzn823yKm8wmUPE7PKQXANG8aHmfb2RoDviJKf/5zav76V5xpaWR+7nOk37IMOcWLSYcjhsKKhjZj8/Ycr+VAaX3LBFAikJ+ewITsZC6o/Qe3l/6UBGmdqKXBxPGg9ws80XgB9QGrCpjkcTEtN5Xp+anMzE3hnPRGsoNFSMUBa0xexUEo3w+VB60vD5o5PTDsLCvcRQe9Yc3dOFvfj9EhrbMqWnRYqw2cOKQ1h7HOlpt/Noe0U9HTLr3GGCr9lRTVtg15RXVF+GqOcKz+GCFa319OYIQnndy0ceSljGqp6jUHvwxvRpuQHwhFeGNXCc9u9PHm7hLCEcPM/DRunpXHNdNHEl9y1K7arSRcUYE7N5e0pUtJu/GGrv89OUNooFNKDRnPf+Tj/lXbcDkd3HRuLk9+UKhjwlTf6YdZ9Yaa4u98h6oVz5B2yy3kfOfbrQ8Em6wLB297Hna/AsF6SMiEyddbIW7URYN+2vJAOMCHxz5kTeEa1vnWcbT+KABTMqZwad6lXJp/KZOGTUJE+mwMXcREqA3Udgx90aGwXTisCdR0ub8EVwLp3nRGhJO4ck01M9YdBRGKrjmP2puvIDUjp004TPWk4nb0/HproXCEQ+UN7D1ea4c9+xILJXVc53ibb7hWMFLKOWoy+EloKa/IXG6bPapl4pKxmYndG5MciUDtUSvcVRwgUL6HivK9lFUfpryumHKJUO50UuZ0Uu5yU+5NoMzpplwi1JqOY9MAkt3JVhDrpIoWvTwsfliHoNwXemXSnUgY9r9hdanctZpQOEDJiMn4Ci6nKHsivmB1S+ArqiuirLGszdPjXfFtum9Ghz0vw/n79iqe2eBj9/FaPC4HC6aMYMl5eVw0KoXGN16n8ukVNLz/PrhcJF9xBenLlpJw4YVnZNVOA51SatCr84e4f9U2Vm4qYvaYYTx0y0xGpsXrmDDVd7augL/eY1WTmrni4ZqfwbSlVvgYAl0Be8qEwwR9Pvz79uHfuw//vn00bVpPoMj+QCeQ/c8LSLliLq7C12D3y+CvgfhhMPk6qzvl6Eus6egHsbLGsjZdKRtDjXidXi4ceSHz8+YzN28uWQmdjwcaKLNchiIhqv3VnQbA6tpSRrzyEVNW78bTFOL9cxNZMdeBL77r8cvJcckdqn2djQdsfjwlLgXnScL8Wd9cjTNlE57hryLuKkwwDX/pAsI153DwR53/zoLhYJeVs/bLXVbSHB4yxEVGBDICTWQ01ZIZDJARDpMRjpCJg4zkXIaljcOTOb61qjdsbIfK3qBSthc2/QW2Pg21xdbf7fSlVpfKnBldPq0x1MjRuqMtlT1fnY+i2qKWbp3tuxqne9LJTcol0ZlFRXUS+4riqG9IJdMzghunT2XprLMYWVNC1YoVVK9cSbi6GvfoUaQvXUrqDTfgGjasr38TA4YGOqXUoLbVV8U9T27iSEUD91xRwBcvLxgU00arQa6rMTvRxAHiBIfLDnhO62fLssv6QHfSbZzWvtps09fPtR8Tp7Vty3Lzegc4nBgcBEur8RcW4z90lMDho/gP+/AfLsL4W7u/uTKSobGKUIMDEKwJLATEED88QvJ5E0i67lbi5t6MuE69C9lAYYxhT+Welircx2UfYzBkJ2S3VOFmj5iN1+WNdVNPi4lEqH7xRUp//jCh4mISL51H1le+infiBMCqRp60AthU1eax6GpkNEFI9aR2Gvyag+H3Xn2DQMJbiCMU1UYn7sbZfO6S8zvt/thV5bG5kjbMO6zLKlpmfGbnlbRIBGqK7O6bB1oqfNbtIIRbL9KOywvpdjfOjKiglzHOGic60MJeU7VVQd/8BPg+sP49KLgSZt4OExaA6/SqisYYqv3VLeEuurLXfIseM2qMYEKpJDqyKBg2iouyz2LK9kqyXtuMY+suxO0m+corSVu2jITZ58d8kqC+poFOKTUoRSKG/3v7ID95dRfDkzw8dMs5zD7rzPk2TsVQKAA/OMF4jcv+nzWFeiRsXRMtEm63HLKXI1HLzesj7bY5zeeaSLu2dD6JxskYA6FGB/5qN/5qV+vPGhcm1PrB0xUfxpMaxJMaavkZlxIiEhL2v5SNCbd+qBKHIX1ShPrIFPw7dwHgzs8n6bL5JM+fT8KsWac89ioW/GE/HxR/wFrfWtb61nKs/hgA0zKntYS4iekTh8wHyrq336HkgQfw79qFd8oUsr7+dRIvvOC099sYaqTaX01lU2WX4wKjw2CFv6LLSWHa6yqkdRiXFp/Rd90dI2GoOWqFvTZB7wRhL2OcPXZvXOu4vf4Me5EIHFxrdanc+VdrXOHws60QN30ZJGf3TzuAcCRMaWNpS2VvT/lh3i/cx4GqIwQow+FuDei5ZYaFm4W5H0dIaIpQOyKFioWzcC+6itzcieQm5ZIcl9xvbe8PGuiUUoNOaa2frz2zhbV7SlkwJZsf3zSdtISB/8FPDQGle+D5u6F4c+ePp+bDl7f1b5tOVSTSGu7ahU4TCREqLSGw7wD+A4fs22H8hwuJ1DW07MKZnoJn9Ejrlp+NJ38EnvzhOBM8nYbR4m/9B1UHEiESFWochrSxDeS8dITgsWPUrVlL3ZtvUr9+Pcbvx5GYSOIll5B02XyS5s0bUN2nShtKWedbx1rfWtYXr6cx1Ei8K56Lci5ifr7VlTIzPjPWzexVTTt2UPLAT6l/913ceXkM//KXSLn66piNVzLG0BBqoLKpkqufv7rTbQThwzs+7JcxaaclErYre+2qes0TtIRbq91tw167SVp6K+yV74ctT8LmJ6HGB95UmLoEzrkdRp47oLqTG2PYVFjFUx8e4JWdO2gwpQxLrWX8yADDPJWM3LCX6e8UM94XJuCE9WcLfz/HQfHYFPJS8lsuxxA9fm9k0sgOk80MlC7QXdFAp5QaVNbtKeUrK7ZQ2xTk/msmc/sFo4bMN99qADMGNvwfvPotcMfDjGWw8Y9tx9C54+HahwfNxCihigp7fNte/Hv34t+3j8DefYSrW2eIdaal4SkowFMwnrjx4/GMH4+noABXevopHevAnEn4yzuu92TA2Hd2tlkXaWyk/r311L35JnVr1hAqLQUR4mfOJOmyy0iafymegoJ+/bs3xrCrYhdrfGtYV7iObeVWaM9JzGmpwp0/4vyBHxx6IFhUROnDD1P94l9xpqSQ+bnPknbrrTgGUPX0RNPwN88KOmg1h732Qa/iQCdhL77r2TiTc9qGvfaTOl36DUCsLpVH3rW6VY+73BoXN3ERuAd+N+GmYJhXtx/j2Y0+3t5XhjFw4dhh3HxePhc4jlLzzJOYv63B0dBEdW4qmy4azhuTwxwIHycQaf09CkJWQlZLyKsP1LOuaF2bi7H3xiRFvUkDnVJqUAiEIvz077v53doDFGQl8cvbzmXiiKHVZUINULXH4cUvWFPpj7sCFv8akkcMmlkuw9XV9uQke1smKPHv3Uu4ovXC1Y6UFCusNd8mFOAZPx5nRkbvBKfOJpHpRgA2xtC0fQd1a9ZQ9+abNG3fbj01N5ek+fNJuuwyEmaf3yfhoinUxPvF77d0pSxpKEEQpg2fxvy8+czLm8eE9AlD9gulcHU1Zb97hMq//AVEGPZPd5Jx9904U1Ji3bQO+mpW0AEvErb+/Wl/2YWK/VB5qOuwFw7CgTesn+1ljLe6VM64BVJG9ttL6W1FVY2s/MjHsxt9HCpvIDHOyaLpOSyZkknBtveoWrGCpo8/Rrxekq9eCNdfxfExKRTVH22dsMUeu9fcjbq9gfSFgQY6pdSAd7jcurbcFl81t10wivsXTW69oKxSfWnXanjxi9YFrq/8Psy+e0B1N4oWrqtrrbRFBbhQaWnLNo6EBOIKWittnvFW9c2VldX3waQXAnDweAl1a9dQ9+Ya6t97D9PUhCMhgcQ5c6zq3aXzcGVk9LiJJQ0lrPWtZV3hOtYXr6cp3ESCK4E5uXOYlzePublzyYjv+f4Hg4jfT+XjT1D2u98RqakhdfFiht/zRdw5ObFu2gkN9C5x/a4l7DVP0HKgNfiV7en8OYlZ8LU9A/bfuJ4wxrDhcCXPbChk9dZi6gNhRmcksOTcPBYn1uJ6+QVqXnqJSEMDnrPPJn3ZUlKuvRZnUlLLPqY/Nh1DxwwkCFv/eWt/vpwuaaBTSg1oL2wu4v+t3IZD4Mc3TefqaQP7Q4UaIvx18Op98NGfYMQ0uPH3kHV2rFsFQKShAf/+/Va1zQ5w/n37CBW3djkTrxfPuHEt3SVbukrm5AyZilKkqYn69evt6t0aQsePW10zp0+3xt1ddhmeCSeuoEVMhJ3lO1nrW8uawjXsrLC6f+Ym5bZ0pZyVPatHF28ebEwkQs3q1ZQ++BDBo0dJnDuXrK99Fe/EibFumupty9Ogk4ACAsur+rs1/aYhEOKVj4/xzMZC1h+oQAQuGZ/JzZOHceGBDdQ99wz+HTuRhARSFy0ibdky4qdOGRRdejXQKaUGpHp/iG+/sJ3nPvIxa3Q6D90yk7z0hFg3S50JCj+ElZ+xui9d8iWY/58Qg6n0I01NBA4caA1tdoALFhW1bCNxccSNG9faVdIOcO7c3DPq4rrGGPw7d1Jrh7umjz8GwDUyh+T580maP5+ECy7A4fHQGGpk/dH1ViXOt47SxlIEYcbwGVyafynz8+YzLm3ckAm+3VH/7rscf+AB/Dt24pk8ieyvfY3Eiy+OdbNUX+nqsiuDYVKnXlJY0cCzG60umUVVjSR7XFwzPYelybVkr3mZmldewTQ24p0yhcIrJvGf3leQxia+tCrMg4ud+FPjB1SX3l4PdCKyEPg54AR+b4z5UbvH/x34PBAG6oDPGGN22I/dB3zafuweY8yrJzqWBjqlhqZtRdV88clNHC6v5wuXF3DP5eNxOc+cD6cqRsIheOsBWPsTa+zIDb+DMXP6/LCRQIDAwYNRE5RYP4OFPmtGSgC3G8+YMVa1raCgZYKSuPx8xDW4L77dF0KlpdStXUvtmjXUv/MuprGRsDeOQxNSeDO/hvfHhgmlJzFn5Bwuzb+UublzSfee2kQvQ0HTrl3WzJVvv4175Ehr5spFi86oLwPOSD0c0zoURSKG9QfLeXaDj5e3FdMUjDBueCLLJg9jwdFNhF94Hv+ePUTiPRxLDjOiJMQ7sxPJXf7dARPmoJcDnYg4gT3AlYAP+BC4tTmw2dukGGNq7OXrgM8ZYxaKyGTgSWA2MBL4BzDBGBPu6nga6JQaWowxPPrOIX70yk4yEj08dMtMLhw7tMerqAGifD88/xko2mBdX+mT/2NN1d2LTDBI4PBhq9q2p7WrZODwYQjb/9U5ncSNHm2Pbxvf0l0ybvRoxO3u1fYMZRETYXvZdtb61vLOwTdxbt7FrH2G2fsdpFdbv2vP1KkkX34ZyfPn45k06YyqyAWPHqX05w9T/eKLOFJSyPz3fyf9tltxeIbeLJ2qC4NkUqf+VNsU5OWPi3l2o48PD1XiEJhXkMkdyTUk/fUZUj54CwGCDheHf/UE1142LdZNbtHbge4iYLkxZoF9/z4AY8wPu9j+VuCfjDFXt99WRF619/VeV8fTQKfU0FFeZ11b7s3dpVw5OZuf3DSd9MShP25FxZgx1ji5v90HThdc8yBMvanbTw+WlFD0la+S9+DPcA23LjZuwmECR460U4DVgAAAIABJREFUm6BkH/5DhyBozyonQtyoUR0mKIk7a8yAmg5+MGkINvBe8XusLbS6UpY3leMQBzOHz2zpSjkmZQyBvXutSyK8uYbGrVvBGFwjRpA0/1KS5s8n8cILcXgH/hTtPRGuqaH8kUeo+NOfAUi/8w4yP/MZnKm9++WFUoPdwbJ6ntvo47mPfBRXN/H5zc+x4PAHuE2YgDh5feyFjP3Bd1l8Tm6smwr0fqBbAiw0xvyrff9O4AJjzBfabfd54CtAHHC5MWaviPwSWG+M+Yu9zf8Brxhjnm333M8AnwEYNWrUeYcPH+5O25VSA9g7+8r40tObqW4M8q1Fk7jzwtFn1LflKkbqy+DFe2D3ajhrHiz+LaR2/M/ZRCKYYBATCFi3qOXSX/yS2tdewzttGnGjR+Pfu5fAgQOYQOt04e68vDbj2zzjxxM3duyQDQ39qbiu2JrQxLeGD4s/JBAJkOxOZk6u1ZXykpGXkOZN6/L5obIy6tauo27NGurfeYdIQwPi9ZJ40UXWxCqXzsedndWPr6hvRAIBKp94gvLf/JZwTQ2p113L8HvuwZ07MD6MKjVQhSOGq771HD9d9V08kVDLer/DzX1Lvssr37shhq1rdSqBrjud9Dv7BNYhBRpjfgX8SkRuA74F/PMpPPcR4BGwKnTdaJNSaoAKhiM8+Pc9/GbtfsYNT+JP/zKbSTkD7xpHZ5LOKk79wRgD4XCH0BRpWQ62DVPBgLUu2DFkmWDQel6b9cG221QXY4p3YIJhTOIMzEbBPPFp67nBttsTCp20/U1btxI8dgzv2RNJvPji1u6SY8fiSEzsh9/gmSEcCbOtfBtrC61rw+2ptKZfH5U8imVnL2N+3nzOyT4Ht6N73VNdmZmk3XQjaTfdSCQQoOGDD+3qnXUD8E6ZYl/QfD7eKZMH1ZdN1syVL1P60EMEi4pInDPHmrly0qRYN02pQcHpEBZ+9DLSrqglJsLlH7wEDIxAdyq6E+h8QH7U/Tzg6Am2fwr4TQ+fq5QaxAorGvjik5vYXFjFrbPzuf+aySTE6eQOsWQCAY7/8Ec0btxI8Xe+Q/qyZZ0Eq07CUWfLUYGry1D2/9m77/CoyuyB4987k5n0SgqEJPQSegfpzYKKAiqoa1l1xVVUVgEF9AeIihR1RUUUdXUtKyACgtjpgkiH0AMB0iCF9Drt/f1xIXRImTBJOJ/nmWcyd+597xmB3Zx53/ecC37GmZWUNQ3NbEYzmfTnMz+b3NCKTqEVpaN5eGKo1xZ8AjGYzWimS5x/qTHM+uvsZcso+Guzvv/NZMJ3wADqTJ7kvM8gAMi35vNn8p+sSVjD+qT1ZBRlYNSMtA9tz5iOY+gT2YcG/g0qfB+D2YxPzx749OyBevklimNjyVu9hrw1a0ifM4f099/HLTQUnz598OnXD+8bumHw9HTCJ6wc+Zs2kTpzFkX79uHevDmRn3yCT8/KL/IjRE3TJjsB8wUlPczKTpvseBdFVDGlWXLphl4UZQCQhF4U5X6l1N5zzmmilIo9/fNgYLJSqpOmaS2B/3G2KMpKoIkURRGi5lm+K5mJi2NAgzeGteb2NuGuDum6omy203u8zu1jFovl6LGzxTlKy83tvETHcLmk6AqJ0ZnjhsudbzLr515ybJO+58ykP585jpvbxTMpyTvgu8fhVCzc8DT0/z8wlW/ZozU1lSM33oQqLi45prm70/j3367pzGZNlZSXxJqENaxLXMeWk1uwOqz4mn3pWbcnfSP60qNuD/zdr92+L1tGBnnr1ukNzf/4A0d+Ppq7O97dup2eveuDqXbtaxbPlRQdPETqW2+Sv249bnXqEPqv0fgNHiyVK4Uop6U7kpiwOIZC69n/f/Q0GXljWOtquYfuql+dK6VsmqY9DfyC3rbgP0qpvZqmTQW2KqWWAU9rmjYQsAKZ6MstOX3eQmAfYANGXSmZE0JUPwUWG1OW7WXh1kQ6RAUw+972RAZJb7nKoux2rAkJJdUUzyRwlqNHUecU5zBFRuLeuDGayUzxoUN6Uufmhk//foQ8+eTlEzOTqXr8kuiww4Z3YPU08A6Fh76Hhn0rNGT6B3NRZ1oKnKYcDtI+mCuzdJexIm4Fs7fP5mT+SWp712Z0h9ElZb/tDjsx6TGsSVjD2sS1HM46DEB9v/r8Lfpv9I7oTfvQ9rgZXDOL7xYURMCQIQQMGYKyWCjYupXc1Wv0pZlr1wLg3iJa73nXrx8eLVte838b1pMnSXv3PbKXLMHg60vouHEEPvA3qVwpRAWdSdpm/XKQ5KxCwgM8GXdzsyqTzJWVNBYXQpTb3mS9t9zR9HxG9W3M6IFNMElvOadQDgfW5ORzGlCfrq54JO68GSRTeDjmJo3xKOlj1gT3Rg0xeHrW3BmnzOOw5AmI/xNaDoXb3gavoAoPGzdkKMUHDlx03L15cxouXVLh8WuaFXErmLJxCkX2opJjHkYPhjYZSr41n/WJ68kszsRNc6NDWAf6RPShT2Qf6vnVc2HUV6eUwnLkCHlr1pC7eg2FO3aAw4ExJBifPn3w7dcP7xtuwOBVeV9c2XNzOTXvYzK++AIcDgIfeIDgJ0ZiDLh8MRghRM3i9Mbi15IkdEJUfUopPt94jDd+PECAl4l3RrSje+NgV4dVLSmlsJ08eVEfs+IjR1AFBSXnuYWFXdzHrFFjjD6XL85xYsorZH333dmy+gAmEwF33109Z5yUgl3z4cdxoGlw65t6j6VqVNCiJrlp0U2cyD9xyff83f3pVbcXfSL60L1ud/zM1bcwki0zk/z168lbs4a89X/gyM1FM5vx6tYVn7598e3bF1O4c5aYK4uFzPnzSf9gLvasLPwGDyZk9GjMEdVz1kAIUX6S0AkhKk1GvoVx3+5i5YFUBjQPZdY9bQmS3nJXpZTClpqm72s7k7QdiqX4yBEceXkl5xlDgvWkrfGZcvhNcG/cCKNf2X8hrlEzTgUZ8MNzsG8pRHWHoR9CYNWe6amplFLsz9jPiB9GXPacHQ/ucNlSysqkrFYKtm0jb/UactesxnpcL6Dg3qwZPv305M6jTZsyL81UDge5P/9M6r/fwZqQgNcN3QgdOxbPli0r42MIIaoBSeiEEJVi45F0nluwk8x8KxNubc7fu9e/9uW+dy+ElVMhOxH8I2DAJH2WpgqxnTql7207fG6BksM4srNLzjEGBl7cx6xxY9wCA10YeRV1ZDUsfVLvMddvIvQYDQajq6O6rlgdVranbGdV/CpWJ6y+7MwcQB3vOvx696/XMDrXUEphOXpUr5q5ejUFO3aA3Y6xVi29ambfPnh373HRLPqFbUTy/9pM6qxZFO3Zg3vTpoSOG4d3zx7VqpWCEML5JKETQjiVze7gnd9jmbPmMA2CvXnvvva0DL921ehK7F4Iy58Fa+HZYyZPGPyuS5I6e1bW2aTtnATOnplZco7Bz+/sUslzEji3WrWuebzVjrUIVr4Cmz6A4KYw7GMIb+fqqK4bBdYCNiZvZFX8KtYmriXHkoO70Z0bwm+gf2R/LA4Lb25586I9dFO6TykpjHI9sWdlkbf+j9NLM9fjyMlBM5nw6tKlpOedOaKuvhR6wQJ8B92Cyi8gb+1a3GrXJmT0aPzvGIxmlC8rhBCS0AkhnCgho4DR83ewPT6L4Z0imHJHS9f1lnu7BeQkXXzcPxKe21Npt7Xn5p5O2M4vUGJPSy85x+Dtrc+yXVCgxC00RL5pL4+TMXo7grT90GUkDHwFzFI9tbJlFGWwNmEtq+JX8eeJPym2F+Nn9qNvZF/6R/bnhvAb8DKd/XO4UpXL65myWinYsaNk9s5y7BgA5gYNsMTHl7QS0by9Cf7nEwQ9+CAGj/K12xBC1EyS0AkhnGLF7hOMX7wbFLw2tBV3tqvEjfkOO+Se1JdS5iTqzyWPBP25MPMyF2swJaviIeTnU3zkyAW93A5jO3ny7J08PXFv1OiiAiVudepI4uYMDgf8+T6sehU8A+HOOdDkRldHVaMl5CSwKmEVq+JXsTNtJw7lINw7nP5R/ekf1d+lrQVqiuKjR8lbs5ZTn32GPTVVP2gw4D9kCOHTXndtcEKIKkkSOiFEhRRa7Ez9YS/fbE6gXWQA797bnqhaFZwdKcq5OEE795GbDA7b+de4++v75M489iyCouyLxzaY4LY3ofU9YL581cczHIWFFMfF6f3bzunlZk1OLjlHc3fH3Kjh+QVKmjTBFB5ePfq0VUfZibDkn3BsPTS/XV9K6y1LU51NKcW+jH2siteTuDP94ZoFNitJ4poFNpMvKJysxrYREUJUCqc2FhdCXF/2n8jhmW92cCQtjyf7NuL5G5tevbec3Qa5J66csBVfkIhpRvCrqydqUd3OSdwiTz/XBY8L9ulFdYPlz2LNKSZpYyAR3TNx8zGCVygsHw2/ToJ290Pnf0BwYxwWC5a4uIsKlFgTEvQS+IBmMmFu0ADPdu0IGH5PyV43U2Sk7GW5lmIWwYrn9b9Ld7wP7R+QdgROZHVY2ZayrSSJSylIwaAZ6BDagRc6v0C/yH5E+Ea4OswaTRrXCyEqiyR0QghA/9b+y03HeW3Ffvw9TXz5aFd6NgnWE5/CrKvPrqnzf1HBM1BPzALrQb3uFydsvrXLXqnwdOGT9CmTKUxTpMXWps6UV1DN7sSyaTnFq76k+NP/UfzWfIoL/LBk2fUlfABubpjr1cOjRQv877hDT9yaNsEcFYXmJv9T6DKFWXpfuZiFENEZhs2DoIaujqpGKLAW8EfSH6xKWMW6xHXkWnLxMHrQPbw7z7R/ht4RvQn0kKqq10rhzp3n94QEsFr1xuVCCFEBsuRSiOudzUJ2ynHm/bCO5OOx9Awt5NYoO54FZ2bcksCSe/41BpM+g1YymxZxfsLmVxfcfSolXGtKKocHDtR/MTIYMEVFYk1MAtvp5ZoGA+YQH9w9czB75eJRxw9zzyG43zIKLVCa81YpR9frSyxzT0Df8dDzeTBKcl0R6YXpelGThFVsSt6ExWEhwD2APhF96B+lFzXxdPN0dZhCCCGuQpZcCiF0SumFRM6bVTt/hk3lnsQfxTgAM5AFWGrpyVmtxtCw78Wza96h4IJ9ZEUHDhA/8omz33I7HCiLlVqPPnq2l1vDhhjc3fWle4d+hi0fw8E5EDsPWtwJXR6HyK6ynM+VbMWw+nXY8C4ENYDHfoOIjq6OqtqKz4nXl1ImrGJn6k4Uiro+dRnebLgUNRFCiOuAzNAJ4WoVaZRtK9bL+F+qIuSZh7Xg/GuM7uBfF4dfBPsK/FiVbKbYO5y7+3ejQaPm+uxaFSsPbzt1irR3ZpP17bcXvVeqogLpsbDlU9j5P30vX1gr6PwYtB5eaTOJ4jJS98Pix/W2BB3/Dje9Ln8GZaSUYu+pvSVNvs8UNWke1Jz+kXpRk6aBTaWoiRBCVGNS5VKI6uJKjbJb3wP56ZfYs3b6dU4S5KVcPKZ36CUKjJzz2juYpOwi/jV/B1uOZXJXhwim3tkSb/eq9w2+w2Ih88svSf9gLo7iYtwbNaI4Lu78fSgmEwF33126ogKWfIj5FjZ/Aikx4O4Hbe/Ti6iENK28DyL0vYyb58Fvk8DdF+58H5oNcnVU1YbVbmVLypaSJC61IBWjZqRjWEf6R/Wnb2Rf6vrIkmIhhKgpJKETorr4dys9QbuQwU1/2IrOP+7meXGCdu5rv7pgunJz2p/3nOCFRbtxKHhtSCuGtK96vwQqpchbuZKUmbOwxsfj07cvoS++QNJzz1N84MBF57s3b07DpUvKcgNI2Kwvx9y7FBxWaNAbOj8OzW6VfVzOlnMCvn8KjqyCJjfryZxPqKujqvLyrfl6UZP4VaxPXE+uVS9q0qNuD/pH9ad33d4EeAS4OkwhhBCVQBI6IaqLKQHAZf4N3vD0BQlbJHgFlXvvV5HVzqs/7OPrv+JpG+HPu/e1p16tq/dsu9aKDh4kZdobFPz1F+bGjQgbPwGfnj0q74Z5abD9v7D1M72huW84dHoEOjwMvmGVd9/rxb5lp2ehi+Dm16HTo7J/8QrSC9NZk7CGVfGr2HRiE1aHlUD3QPpE9qF/ZH+6hXeToiZCCHEdkIROiOrirWi95P+F/CPhuT1Ou83Bk7k88812DqXk8UTvhoy5qRlmt6rVHNt26hRps98la9EijL6+BD/7DIEjRly7lgJ2G8T+Als+0WeSDG4QfYdeRCXqBklCyqooB34eDzu/hvD2MOxjCG7i6qiqpGPZx1iVoPeH2522u6SoyYCoAfSP6k+7kHYYy9riQwghRLUmVS6FqA6UAs+AixM6k6deGMUpt1B8/Vc8r/6wD18PE1882oXeTa9QPMQFlMVCxldfk/7BBziKigh84G+EjBqF0d//6hc7k9ENmt+mP9IPw9b/wM6vYO9iCG2pF1FpM0IKeJRG/CZYPFJfTtx7HPR5EYwmV0dVZTiUg73pe0uSuLjsOACig6J5qt1T9I/qT5OAJlLURAghRKnIDJ0QrrL5Y/hxrF6U49gf5atyeQVZBRbGfxfDz3tP0rtpCG/d05YQX3cnBO4cSinyVq0iZeZMrMfj8e7Tm7AXX8S9YRVqKm0pgD2L9D+rk7vB7AvtzhRRaebq6KoeuxXWzoD1b+mzzMPmQVQ3V0dVJVjtVrac3MKqhFWsjl9NaqFe1KRTWCf6RfWjf2R/6vjUcXWYQgghqghZcilEVZd+GD7sCfW6wwPfOX053+ajGfxr/g7S8op54ebmPNazAQZD1fm2v+jgIVKmv0HBn5swN2pE2PgX8enVy9VhXZ5SkLj1dBGVJWC3QP1e+nLMZrfK7BPorSEWPw7JO6DdA3DLG+Dh5+qoXCrPkne2qEnSevKseXi6edKzbk/6Rfajd0Rv/N2v8Uy0EEKIakESOiGqMrsN/nMTZMTBk3+Cn/O+lbc7FO+vOszslYeIDPLivfva0yai6lTBs2VkkPbuu2Qt/FbfJ/fMMwSOGI5mqkYJUV4a7PhSL6KSHQ++daDjI9DxYfCt7eroLrIibgWzt8/mZP5JanvXZnSH0dzW8Dbn3UApfXnqLy/pFVYHz9YbuF+n0grSWJ2wmlUJq/jrxF/YHDaCPILoG9mX/pH96VqnKx5uV65EK4QQQkhCJ0RVtmYGrJkG93wOLYc6bdgT2YWMnr+TzUczGNq+Lq8OaYVPFektpywWMr7+n75PrqCAwPvvJ2TUUxgDqk6yWWYOO8T+qi/HPLLydBGVwfpyzHo9qkQRlRVxK5iycQpF9rPtLzyMHkzpPsU5SV1eKnz/tF5MplF/uPMDp35BUV3EZcexOl5P4nan7QYg0jeypMl325C2UtRECCFEmUhRFCGqqqRt+h6j1sOdmsz9uvckL3y3G4vNwdvD2zKsQ4TTxq4IpRR5q9eQOmMGluPH8e7dS98n16iRq0OrOINRb4zdbBCcOqLPUu34Sl+SGRKtF1Fpe6/eRPsacSgHuZZcsoqzyCzKZMbmGeclcwBF9iKmb56Oj8kHDzcP/WE8/9nTzROTwXTlohwHf9KTueJcGDRT7+FnqFqVUyuLQzmISY9hVbxe1ORYzjEAWtZqyTPtn6FfZD8aBzSWoiZCCCGuCZmhE+JasRTAR73BWgBPbtQrXFZQkdXOtB/388Wfx2lV14/37utAg+Cq0Vuu6NAhUqdPJ3/jn5gbNtT3yfXu7eqwKpelAPZ8p++1O7ELzD56Utf5HxAaXaahlFIU2ArILMosSdDOey7OJKvo/Ofs4mzsyu6Uj2LQDOcleCVJn8GMR9ZxPLMS8XD3x6NBHzx8auvnXJAUXvT6zBinX7sb3XEzVK3vFS+3RNVit7D55GZWxa9idcJq0gvTcdPc6FS7E/2j+tMvsh+1vaveklshhBDVkyy5FKIq+vEF2PwRPPQ9NOxb4eFiU3J55psdHDiZyz96NuCFW5pXid5ytsxMfZ/cgoUYfH0JGTWKwPvurV775CpKKX02dssnsGcx2Ispqt+DrLbDyYzoSKY192wydk6SdmGCZnVYLzm8UTPi7+5PoHsgAR4B5z+7BxDooT9P2jiJ9ML0i64P8Qzhvf7vUWgrpMheRJGt6Lyfz31dbCumyH76dV4KRSkx+vs+IRR5+FJkLy45vzzJpMlg0hM8o+fZGcMLX18mSTwvQbzC9e5Gdwza1f9tXGqJqslgIjoomiPZR8i35pcUNekf1Z9edXtJURMhhBCVQpZcClHVHFmlJ3Ndn6xwMqeU4pvNCUz9YS/eZjc+e6Qz/ZqFOiXMCsVlsZDxv/+RPuf0Prn77iP46VG4BQa6OjSns9gt5ydi5yZj586iuWWS1bwNWYWnKFQJsPst2H3+WBoa/u7+JYlYXZ+6tApupb8+nagFuAecl6j5mn1LlaCM7TT2knvoxnQaQ8vglqX/wHab3opg2+fgFw5D/wP1e150mtVhLUkIi2xFFNoLz74+kxRe6vW5SeU5r3MKcs5LMs9cUx7nJn8exkskh24erElYc9ESVavDyp70PQxtMpT+UXpRE3dj1Wn/IYQQQkhCJ0RlK8yEpaMguBkMnFyhobILrExYspsfY07Ss3Ewb49oS6ivayvmKaXIW7OG1BkzsRw7hnfPnoSNfxH3xo0r5X7Ortpoc9jILs6+6nLGcxO2fGv+ZcfzNfmWzJaFeIXQJLDJ6ZkzfwKzkwk4tpHAxB0EKAhsNBC/zk9gbNC7UoqonPnvUqH/XhlxepPwxC363s9bZ112ubDJYMJkNuFrrrx9g0opik/PCl6YFJYmYSy59nTCWWArIKMo44rJokIxpfuUSvtMQgghREVIQidEZVsxFvJT4b7/gcmz3MNsO57Bs9/sJCWniPGDmjOyV0OX95Yrjo0lZfoM8jdswNygAZEffYh3796VVgziwiVxJ/JPMGXjFEBPXs4UBSnNfrMz7+dYci57P083z/OWM9bzr3fRssZzn/3N/piu1pOuF3qSdKaIyv4VENJc32fXZoTTe7fd1vC28iW8Sunx/TweNCPc9Sm0vtupsZWHpmklM2rOdtOimziRf+Ki47I3TgghRFUme+iEqEwxi+C7x6Dfy9BnXLmGsDsUc9cc5t+/xxIe4MG797anfZRrlzHaMjNJf+89MhcsxODtTcjTowi8775K3SenlGLgtwNJLUy96D03zQ0/dz+yirNwKMclrzcbzAR6BJ5NwC61/+z085klkJXeL8xaqO+x2/Kx3pDb7KMndZ3/AWEtKvfeV5J/CpY/Cwd+0BuoD/0Q/KtG5dTKVOltHoQQQohSkqIoQlQFOcnwQTcIbgqP/AzGsk+In8wu4l8LdrApLoM72obz2tBW+Hm4rriIslrJ/OYb0t6fgyM/n8ARIwh+5mmn75NzKAeJuYnsy9jH/lP79UfGfrKKsy57zfCmwy+boAW4B+Dp5lm1y8gnbYPNn+hVMu3Fei+7zo9B88HgZr52ccT+Dt8/pS8VHjAJuo26btoRwDVoxC6EEEKUgiR0QriaUvDVMIjfBP/8A2qVve/a7/tSGLdoF0VWB1PvbMndHSNclpAopchft46U6TOwHD2Kd/fuhE0Yj3uTJhUe2+6wcyznGPtO7WN/hp68Hcg4QJ41DwA3gxtNApoQXSua34//fsklknW86/Dr3b9WOJYqoSADdnwJWz6FrOPgEwYd/64//MIr777WQvhtEmyep/fRu+tjqN268u4nhBBCiMuSKpdCuNqWT/TKlre9XeZkrshqZ/pPB/h84zFa1PHjvfvb0yjEp5ICvbriw4f1fXJ//IG5fn0i5n6AT9++5UourXYrR7KPsP/U/pIE7lDmoZJiFB5GD5oGNeW2hrcRHRRNdK1oGgc0xmzUZ6i61O5yySVxozuMds6HrQq8gqDHaLjhGTj8u/53ae1MWPcmNL9NX47p7CIqyTv1wifpB6HbUzBgMphcW2xHCCGEEKUjM3RCOFt6LHzYSy/r/rdvr/qL99IdScz65SDJWYWE+LrjZtBIzi7ikR71GT+oOe5uxmsU+PlsmZmkvz+HzPnzMXh5ETzqKYLuvx/NXLrlf0W2Ig5lHipZLrk/Yz+xmbElvdW8Td40D2pOdFA0LWq1IDoomvr+9a/aaPq6XBKXcfR0EZUv9aWQwU31xK7tveBRgT5oDjtsmA2rp4F3MAyZC436OS9uIYQQQpSLLLkUwlXsVvj0Jsg8Ck9tAt8rV8dbuiOJCYtjKLSe35D5H70a8PJtrimKoe+Tm0/anDk4cnMJGDGckGefveI+uXxrPgcyDpQkb/tO7eNo9tGSRtP+7v4lM24tgloQXSuaSN/IUvVSE+ewFsLeJfqsXdI2MHlDm+HQ5XEIK0NfOYDM47DknxC/EVrcCbe/o88OCiGEEMLlZMmlEK6y7k1I3g73/PeqyRzArF8OXJTMAfwUc9IlCV3emX1ycXF4d7+B0PHj8Wja9LxzsouzS/a6nUngjuccR6F/ORTsGUx0UDT9o/qXJG91vOtU7YIk1YXJE9rdrz+Stuv77HZ9A9s+g6gb9Fm76DuuXERFKdi9AH4cp/885EN9pk/+fIQQQohqSRI6IZwlcRusm6WXnW855IqnZhdYWboziaSsoku+n5x16QbHlaX4yBFSZswgf916zPXqEfHBB/j068upolNsSVx/NoHL2E9SXlLJdeHe4UTXiub2hrcTXSua6KBoQrxCrmns1626HfTHTa/Czq/15O67x8A7FDo+rBdR8Y+A3Qth5VTITtSLqvjWgaStegI49CMIrOfqTyKEEEKICpAll0I4g6UAPuoF1iJ4cgN4Blx0ilKKTXEZLNgSz497TmKxOTAZNaz2i/8N1g3wZMP4/pUetj0ri7T355D5zTfg6UH2/TexrVcY+3L0vW9phWkl59bzq1eybDI6SH8EeFz8OYWLOBx6IZ4tH8OhX0Az6FUqU/frbRDO1WIo3P0pGFyzP1MIIYQQVyZLLoW41n6bBKcOw0PLLkrmUnOK+HZbIt8IeJ+WAAAgAElEQVRuTeDYqQJ8Pdy4t3MkwztFcjg176I9dJ4mI+NublZpoTqUg4TMYyR+9Sk+X6zALb+YdR09+LJHEbleyzHsN9DQvyHd6nQrSd6aBzXHx+y6SpuiFAwGaDJQf2Qe14uobHwXLtVoPWmrJHNCCCFEDSEJnRAVdXilPivS7Slo2AcAm93BmoNpzN+SwOqDqdgdiq4Nghg9sAmDWtXBw6T/Mt2qrl6h8EyVy/AAT8bd3Iwh7es6JbRL9Xgzbo5hxC/5RJyCPfUNrHu8MbVad2R0UDTNazWnaWBTPN08nXJ/4SKB9eDGV/QKlpeSnXht4xFCCCFEpZGEToiKKMiA70dBSHMYMInjp/JZuDWBb7cmkppbTLCPOyN7N2R4p0gaBHtfcogh7es6JYGz2q0czjpcUmVyf8Z+DmUcKunZ1iDTzD/WutFkfz6WOkEwfSR33n4f91ypgIao3vwjIDvh0seFEEIIUSNIQidERawYg8pPY23H95j3+S42HjmFQYN+zUIZ0TmSfs1DMRmdX5r/wh5v+07tIzYrFpvDBpzt8XZ307tpZa5P48XbcSz+EYOHgeBx4wh88AEMpewnJ6qxAZNg+bN6u4MzTJ76cSGEEELUCKVK6DRNuwWYDRiBT5RS0y94/3ngH4ANSAMeVUodP/2eHYg5fWq8UuoOJ8UuhEslrf+CunsX8z738tbPFiKDChh3czPu6hBBbX+PUo9ztUbZpenx1iKoBQ+2ePC8Hm+a3UHmggWkv/tv7Lm5BNx9NyGjn8WtVi2n/7cQVVSb4frzmSqX/hF6MnfmuBBCCCGqvatWudQ0zQgcAm4EEoEtwH1KqX3nnNMP+EspVaBp2pNAX6XUiNPv5SmlSl1NQapciqost8jK8l0n+H3Tdv6d8SRHVF2+aD6X4V0a0K1hLQyGsvXyWhG3gikbp5QsiwQwG8z0j9IrXB7IOMCxnGMl74V4hpytMnm6SXdt79oX9XjL+2MDKdPfwHL4CF5duxI2YTwezZuX/4MLIYQQQohrxtlVLrsAh5VScacHnw/cCZQkdEqp1eecvwl4oPThClG1KaXYdjyT+VsSWLH7BEVWK9/5/BsvN0WjR77inYjyJ0qzt88+L5kDsDgs/Hzs53L1eCuOO0rqzJnkrVmDKSqKiPffw2fAAGnqLYQQQghRQ5UmoasLnLurPhHoeoXzHwN+Oue1h6ZpW9GXY05XSi298AJN00YCIwGioqJKEZIQle9UXjGLtycxf0s8R9Ly8TYbGdI+nFFeK4nYtBNufwf/CiRzACfzT17yuIbGL3f/Uupx7NnZpH/wARlf/w+Duzuh48YS+OCDsk9OCCGEEKKGK01Cd6mv9i+5TlPTtAeATkCfcw5HKaWSNU1rCKzSNC1GKXXkvMGUmgfMA33JZakiF6IS2B2KPw6ns2BLPL/tS8FqV3SsF8jMuxpxW5s6eOfEwUfTocnN0PHvFb6fv7s/WcVZFx2v7V27VNcrm43MhQtJf/c97NnZZ/fJBQdXODYhhBBCCFH1lSahSwQiz3kdASRfeJKmaQOBl4A+SqniM8eVUsmnn+M0TVsDtAeOXHi9EK6UmFnAt1sTWbQtkaSsQgK9TDx8Q31GdI6kSZivfpLdCosfB5MX3PEeVHAZY1x2HPmWfDQ01DnfkXgYPRjdYfRVr8/bsIHU6dMpjj2MV5cu+j656OgKxSSEEEIIIaqX0iR0W4AmmqY1AJKAe4H7zz1B07T2wEfALUqp1HOOBwIFSqliTdOCgR7ATGcFL0RFWGwOft+fwvwtCayPTQOgZ+NgJt4azcAWobi7Gc+/YN0sOLEThn8JvmEVunehrZAxa8bg6+7LE22e4PO9n1+2yuWFio8eJXXmLPJWr8YUEUHdd2fje+ONsk9OCCGEEOI6dNWETill0zTtaeAX9LYF/1FK7dU0bSqwVSm1DJgF+ADfnv6l8kx7gmjgI03THIABfQ/dvkveSIhr5HBqLgu2JPDd9iQy8i3U8ffgmf5NuKdjBJFBXpe+KHErrHsT2t4HLSreeWPaX9M4knWED2/8kO7h3bk/+v6rXmPPySH9g7lkfP01BrOZkDHPE/TQQxjc3SscjxBCCCGEqJ5K1YdOKfUj8OMFxyad8/PAy1y3EWhdkQCFcIYCi40fdp9g4ZYEth7PxM2gcWOLMEZ0jqRXkxCMV2o3YMmHxSPBLxwGzahwLN8f/p6lh5fyRJsn6B7e/arnK5uNrEWLSJv9LvasLPzvGkbo6NG4hVy54qUQQgghhKj5SpXQCVEdKaXYnZjN/C0JLN+VTF6xjYYh3ky8tTnDOkQQ7FPKma3fJkFGHDy8HDz8KxTT4czDvLbpNbrU7sKTbZ+86vn5f/5JyrQ3KI6NxatTJ8ImTsCjRYsKxSCEEEIIIWoOSehEjZNVYGHpjiTmb0ngwMlcPEwGbmsdzr1dIulUL7Bse81if4ctn8ANT0ODXhWKq8BawJi1Y/A2eTOj9wyMBuNlz7UcO0bKzFnkrVqFqW5d6s6eje9Nsk9OCCGEEEKcTxI6USM4HIpNcaeYvyWBn/eexGJz0CbCn9eHtmJw23D8PExlH7QgA74fBSHR0P//KhSfUorXNr3GsZxjzLtxHsGe57cVsKamkvT8GOq8OpWshd+S8dVXGEwmQp5/nqCHZZ+cEEIIIYS4NEnoRLWWklPEom2JLNiSQHxGAX4ebtzXOZLhnSNpGV6B5ZFKwYrnoeAU/G0hmDwqFOeSw0tYHrecp9o9Rdc6XS96P33OBxRu20bckKFgseA/bCgho0djCg2t0H2FEEIIIUTNJgmdqHZsdgerD6axYEs8qw6k4lDQrWEQz9/YlFta1cbDdPmljKUWswj2LoEBk6BO2woNdTDjINP+mka3Ot0Y2XokoM/YWRMTKdy5i/w//yR7yRI9ibRYiJg3D99ePSv+GYQQQgghRI0nCZ2oNo6l57NgawLfbUskNbeYEF93nujTiOGdImkQ7O28G2UnwooxENkVevyrQkPlW/MZu3Yswfgw1XMEmZ/8h8KdOynctQv7qVP6ScZzElA3N/JWrZKETgghhBBClIokdKJKK7La+XnPSeZviWdTXAYGDfo3D2VE5yj6NgvBZDQ494YOByx9Chw2GPohXKFwyeUopbDGx1OwYyerfprLkwfiqZ+mkeV4BgBz/fr49OqFZ7u2mKKiSHzyKVRxsX6x1Ur24sWEPPWktCUQQgghhBBXJQmdqJL2JeewYEs8S3YkkVNkIyrIi3E3N+OuDhHU9q/YfrYr2jwPjq6FwbMhqGGpLnHk51MYE0Phzl1nZ98yMwGoZ4biZlEED7sVr3bt8GjTBrfAwJJrT0x5BeVwnDeecjhI+2AudSZPQgghhBBCiCuRhE5UGTlFVpbtTGbh1gR2J2ZjdjMwqFVtRnSKpFvDWhiu1PzbGdIOwu+Toekt0OHhS56ilMJy9BiFu04nbzt3Uhwbq8/sAeZGjfDp14/sJmGMz/iM8NZdmHPjXAzapWcSC3fuBKv1/INWK4U7djj1owkhhBBCiJpJEjrhUkopth7PZP7mBFbEJFNkddC8ti9TBrdgSPu6BHiZr00gdissHglmbxj8Lpzu92bPy6No924KTidvRbt2Y8/OBsDg64tnmzb4DhyIZ7t2eLZpjdHfnzxLHo//MIIinyCm9Z5+2WQOoOHSJdfk4wkhhBBCiJpJEjrhEul5xSzensj8LQnEpeXjbTYytH0E93aOpE2E/7VvoL12Jip5J5Yeb1H46x+nZ992UXz4sF59UtNwb9wI35tu1JO3tm0xN2yIZjg/WVNKMXnjZJLykvjsls8I9Ai8zA2FEEIIIYSoOEnoxDVjdyjWxaaxYHMCv+9PweZQdKwXyMy7G3Fb6zp4u1/bv472nBwKd+2mcN0KCn9bQGFWFI4FbwFg8PfHs20bfAfdgmfbtni2aYPR1/eqY84/OJ9fj//Kcx2fo31o+8r+CEIIIYQQ4jonCZ1wuqU7kpj1y0GSswoJD/Dk0Z71yS608e3WBE5kFxHkbeaRHvUZ0TmSxqFXT5KcQdntFB85UlK0pHDnLixHjuhvauAe6I7f7YPx7NAZz3btMNevd9Hs29XsPbWXWVtm0TuiN39v+XfnfwghhBBCCCEuoCmlXB3DeTp16qS2bt3q6jBEOS3dkcSExTEUWu0Xvde7aQj3do5kYHQYZjcntxu4gD0rS0/czhQv2R2DIy8PAGNAgL5ssl1bPAv/xCN9GcbHl0P98vd+y7HkMHz5cOzKzre3f0uAR4CzPooQQgghhLjOaJq2TSnVqTTnygydcKpZvxy8ZDIX5ufOF492qZR7Krud4tjYs20Ddu7EcuyY/qbRiHuzpvgNvh2vdu3wbNcOU1SUvkcv9jf4+kXo/XSFkjmlFJM2TCIlP4XPB30uyZwQQgghhLhmJKETTpWcVXjJ46k5xU67hy0jo2TZZOHOnRTFxOAoKADAGBSEZ7t2+A8bps/AtWqFwcvr4kEKMuD7URDaAvr/X4Xi+Xr/16yMX8nYTmNpG9K2QmMJIYQQQghRFpLQCafy8zSRXWi96Hh4gGe5xlM2G8WHDpW0DSjctQvr8Xj9TTc3PJo1w3/o0JIllKaIiKtXyFQKfviXntQ98B2Yyt+oPCYthre2vUW/yH481OKhco8jhBBCCCFEeUhCJ5wmMbOAQosNgwaOc7ZmepqMjLu5WanGsKWnn9O0exeFe/agCvVZP2NIMF7t2hE4fDiebdvi0bIlBs9yJIq7F8K+72HgFKjduuzXn5ZdnM3YtWMJ8wrj1R6vXvtWC0IIIYQQ4ronCZ1wCqUUExbH4GY08OKgpvznj2MlVS7H3dyMIe3rXnyN1UrRgYPnVJ7ciTUxUX/TZMIjOpqAe+7Gs21bvNq1wy08vOJJU1YC/DgOom6A7s+WexilFC9veJnUwlS+HPQl/u7+FYtLCCGEEEKIcpCETjjFt9sSWR+bzqt3tuTBG+rzUFMfkp4fQ8SYt3ELCQHAmpp6XtuAoj17UMX63jq3sDA827Uj8P778WzXDo+WLTC4uzs3SIcDlj4Jyg5D5oLBWO6hvtj3BWsS1jC+y3haBbdyYpBCCCGEEEKUniR0osJScop49Yd9dGkQxN+61gMg/f33Kdy2jcTR/8JUO4yCnTuxJZ8AQDOZ8GjRgsB778WzfTs827bFVKdO5Qf614dwbD3c8R4ENSj3MDtTd/LOtne4sd6N3N/8ficGKIQQQgghRNlIQicqRCnFS0tisNodzLyrDQaDRt6mTWQt/BaAwu3bsYSG4tWxA14PP4xn27a4t2iBwWy+toGmHoDfp0DTQdD+wXIPk1WUxbh146jtXZtXur8i++aEEEIIIYRLSUInKmTZrmR+35/Ky7dFUz/YG2W1kvTcc2dPMLnhO2AAdSZPcl2QNgssfhzcfeGOd6GcSZhDOZj4x0ROFZ7iq1u/wtfs6+RAhRBCCCGEKBuDqwMQ1VdabjGTl+2lfVQAj/TQlzCmvv1vHJlZZ0+y2shevBhbWpqLogTWzoCTu2HwbPAJLfcwn+35jPVJ63mh8wu0qNXCiQEKIYQQQghRPpLQiXKbsmwvBcV2Zt3dBqNBo3DvXjI+//yiGTDlcJD2wVzXBJmwGf54G9o9ANG3l3uY7SnbeW/He9xS/xZGNBvhxACFEEIIIYQoP0noRLn8FHOCFTEnGD2wCY1DfXFYLJyYMBEMBr1x97msVgp37Lj2QRbnweKR4B8Bt7xR7mEyijIYt3YcEb4RTL5hsuybE0IIIYQQVYbsoRNllplv4f++30Orun6M7N0QgPQ5H1B86BARH87Ft29f1wZ4xq8vQ+Yx+PsK8PAr1xAO5WDi+olkFWcxZ+AcfMw+zo1RCCGEEEKICpAZOlFmU3/YR1aBlVl3t8VkNFC4ezenPv4Y/2HDqk4yd+hX2PYZdH8G6vco9zCfxHzChuQNjO86nuZBzZ0YoBBCCCGEEBUnCZ0ok5X7U1iyI4lR/RoTXccPR3ExyeMn4BYWRtiE8a4OT5d/Cr4fBaEtof/L5R5my8ktzNk5h1sb3MrdTe52YoBCCCGEEEI4hyy5FKWWXWhl4pIYmoX5MqpfYwDSZr+LJS6OyE8/wehbBcr4KwU//AuKsuDBJeDmXq5h0gvTeWHdC0T5Rsm+OSGEEEIIUWVJQidKbdqK/aTnWfj4oU6Y3QwUbN9OxmefETBiBD49yr+s0al2L4D9y2DgK1C7VbmGsDvsjF8/njxLHh/d+BFeJi8nBymEEEIIIYRzyJJLUSrrY9NYsDWBkb0b0iYiAEdhIckTJmAKDyd03DhXh6fLSoAfx0FUd33vXDnN2z2Pv078xcSuE2ka2NSJAQohhBBCCOFcMkMnriqv2Mb472JoGOLN6AFNAL2BuPV4PFH//S9GH28XRwg4HLD0SVAOGDoXDMZyDbPpxCbm7prLHY3uYEjjIU4OUgghhBBCCOeShE5c1YyfDpCcXciif96Ah8lI/l+byfzySwIfeADvrl1cHZ7ur7lwbD3c8T4E1i/XEGkFaby47kUa+Dfgpa4vyb45IYQQQghR5UlCJ65oU9wpvtx0nEd7NKBjvSAc+fmcmDgRU70oQp9/ztXh6VL3w++vQLNbof0D5RrC5rDx4voXKbQV8ulNn8q+OSGEEEIIUS1IQicuq9Bi58XvdlOvlhfjbm4GQMqsWViTk6n39VcYvKpA0mOzwOLHwd0XBr8L5ZxVm7trLltObuG1Hq/ROLCxk4MUQgghhBCickhCJy7rrV8PcvxUAd883g1Ps5G8DRvImr+AoEcewatDB1eHp1s7HU7GwL3fgE9IuYbYmLSRj3d/zJDGQ7iz8Z1ODlAIIYQQQojKI1UuxSVtO57JpxuO8kC3KG5oVAt7bi4nXv4/zA0bEjL6WVeHp4v/C/74t77Msvmt5RoiJT+F8evH0yigERO7TnRygEIIIYQQQlQumaETFymy2nlh0S7C/T0ZPygagJTp07GlpFB//jcYPDxcHCFQnAdLRoJ/JNwyvVxD2Bw2Xlj3AkX2It7q+xaebp5ODlIIIYQQQojKJQmduMi7K2M5kpbPF492wcfdjdw1a8j+bjG1Ro7Es00bV4en+/UlyDwOj/yo758rh/d3vM/21O280esNGvo3dHKAQgghhBBCVD5ZcinOE5OYzUfr4hjeKYLeTUOwZ2dz8v8m4d60KcFPj3J1eLqDP8O2z6HHaKjXvVxDrEtcx6d7PuWuJndxe8PbnRufEEIIIYQQ10ipEjpN027RNO2gpmmHNU0bf4n3n9c0bZ+mabs1TVupaVq9c957WNO02NOPh50ZvHAui83BuEW7qOVt5qXbWgBw8vXXsWVmUueNaRjMZhdHCOSnw7JnIKwV9CvfnreT+SeZ+MdEmgU2Y3yXi/46CyGEEEIIUW1cNaHTNM0IzAEGAS2A+zRNa3HBaTuATkqpNsAiYObpa4OAyUBXoAswWdO0QOeFL5zpgzWHOXAyl2lDW+PvaSL399/JWbac4CeewLNlS1eHB0rB8tFQlAXD5oGbe5mHsDqsjF07Fqvdypt93sTDrQrsBxRCCCGEEKKcSjND1wU4rJSKU0pZgPnAebXdlVKrlVIFp19uAiJO/3wz8JtSKkMplQn8BtzinNCFM+0/kcP7qw4zpF04A1uEYcvM5MTkKbi3iCb4n0+4Ojzdrm/gwA/Q//8grHwJ5rvb32VX2i5e6f4K9f3rOzc+IYQQQgghrrHSJHR1gYRzXieePnY5jwE/leVaTdNGapq2VdO0rWlpaaUISTiTze7ghUW7CfAyMXmwniidnDoVe04O4W9MRzOZXBwhegGUH1+Aej3ghvLt5VuTsIbP937OiGYjuKWBfK8ghBBCCCGqv9IkdNoljqlLnqhpDwCdgFlluVYpNU8p1Ukp1SkkpHzNoUX5zVsfR0xSNlPvbEWgt5mcn34i96efCRk1Co9mTV0dHjgcsPQp/echc8FgLPMQyXnJvPTHS0QHRTOu8zgnByiEEEIIIYRrlCahSwQiz3kdASRfeJKmaQOBl4A7lFLFZblWuM7h1Fze+T2WQa1qc2vrOtjS0zn5ylQ82rSh1j8ec3V4uk1z4PgfMGgGBNa7+vkXsNr1fXMO5eCtPm/hbiz73jshhBBCCCGqotIkdFuAJpqmNdA0zQzcCyw79wRN09oDH6Enc6nnvPULcJOmaYGni6HcdPqYqALsDsULi3bjZTYy9c5WKKU4MWUKjoICwt+YhuZWBdoUpuyDlVOh+e3Q7v5yDfH2treJSY9hao+pRPpFXv0CIYQQQgghqomr/saulLJpmvY0eiJmBP6jlNqradpUYKtSahn6Eksf4FtN0wDilVJ3KKUyNE17FT0pBJiqlMqolE8iyuzzjcfYHp/FOyPaEeLrTvayZeT9vpLQceNwb9TI1eGBrRgWjwQPfxg8G7RLreC9spXHV/LV/q+4v/n93FjvxkoIUgghhBBCCNfRlLrkdjiX6dSpk9q6daurw6jxjqXnc8vsdfRoFMwnD3fClppK3OA7cG/cmHpffoFmLPs+Naf7fQr88W+4bz40G1TmyxNyExixfAT1/Orx30H/xWysAn30hBBCCCGEuApN07YppTqV5txSNRYXNYvDoXjxu92YjAZeH9oagBOTJqEsFsKnvV41krnjf8KG2dDhoXIlcxa7hbFrx4IGs/rMkmROCCGEEELUSJLQXYe+3hzPX0cz+L/bWlDb34PsxYvJX7uO0Oefx1y/vqvDg+JcWPIE+EfCzdPKNcSbW99k36l9vNrjVSJ8I65+gRBCCCGEENVQFah6Ia6lxMwCpv+4n15NgrmnUwTW5GRSpr2BV5cuBD7wN1eHp/tlImQnwCM/gbtv2S8/9gvfHPiGB1s8yICoAZUQoBBCCCGEEFWDzNBdR5RSTFgcA8Abw04vtXz5ZZRS1Jn2OpqhCvx1OPgTbP8CeoyGqG5lvjw+J57JGyfTJrgNz3V4rhICFEIIIYQQouqoAr/Bi2vl262JrI9NZ/yg5kQEepG1YAH5G/8k7IVxmCOqwLLE/HRY9gyEtYa+E8t8ebG9mDFrx2DUjMzqMwuT0VQJQQohhBBCCFF1yJLL68TJ7CJeXbGPrg2C+FvXelgSEkiZOQvv7t0JGDHC1eGBUrB8NBRlw0PLwK3sRUxmbp7JgYwDvN//fcJ9wishSCGEEEIIIaoWmaG7DiileGlJDFa7gxl3tUFDcWLiS2gGA3Vefw2tHP3dnG7n/+DADzBgEoS1KPPlP8b9yMJDC3mk5SP0iexTCQEKIYQQQghR9UhCdx34fmcyKw+kMvamZtQP9ibzq68p2LKFsAnjMdWp4+rwIPM4/PQi1OsJ3UaV+fKj2Ud55c9XaBfSjmc6PFMJAQohhBBCCFE1SUJXw6XlFjNl+V46RAXwSI8GFB89Surbb+Pdpzf+w4a5Ojxw2GHpk/rPQ+dCGQuzFNmKGLt2LGajWd83Z5B9c0IIIYQQ4vohe+hquMnL9lBgsTPz7rYYlENfamk2U2fqq1VjqeWfc+D4BhgyFwKiynz59M3TOZR5iA8GfEBt79qVEKAQQgghhBBVl8zQ1WA/xpzgx5iT/GtgExqH+pDx+X8p3LGD2i+/hCks1NXhwck9sOpVaH47tL2vzJcvP7Kc72K/4x+t/0GviF6VEKAQQgghhBBVmyR0NVRGvoVJ3++hdV1/RvZqSPHhw6TNno3PwAH4DR7s6vDAVgxLngCPABg8G8o4WxiXFcerm16lY1hHRrUr+747IYQQQgghagJZcllDTV2+l+xCK18+1hWjcpAwYSIGLy/qTJlSNZZarp4GKXvg/oXgHVymSwusBYxZOwZPN09m9p6Jm0H+GgshhBBCiOuT/CZcA/2+L4WlO5P518AmRNfxI/3DjyiKiaHuv9/GLbhsyVOlOL4RNsyGjn+HpjeX+fJpf03jSNYRPrzxQ0K9qsDSUSGEEEIIIVxEllzWMNmFVl5aGkPz2r481bcxRQcPkjZnDr6DbsFv0CBXhwfFubDknxBYH256vcyXLz28lO+PfM/INiPpHt7d+fEJIYQQQghRjcgMXQ3z+op9pOdZ+OShzpiUnaPjJ2D086P2pEmuDk338wTIToBHfgZ3nzJdGpsZy+ubXqdL7S482fbJSgpQCCGEEEKI6kMSuhpk3aE0Fm5N5Mm+jWgd4U/ae+9TvH8/Ee+/h1tgoKvDgwMrYMeX0GsMRHUt06Vn9s15m7yZ0XsGRoOxkoIUQgghhBCi+pCErobIK7YxYXEMjUK8GT2gCYV795L+0Uf43TEY34EDXR0e5KXBsmehdhvoM75MlyqleHXTqxzPOc68G+cR7FkF9gEKIYQQQghRBcgeuhpixk8HSM4uZObdbTErOyfGT8AtMJDaL73k6tBAKVg+Wt8/N2weuJnLdPni2MX8EPcD/2z7T7rWKdvMnhBCCCGEEDWZzNDVAH8eOcWXm47zWM8GdKwXSOrb/6Y4NpbIjz7E6O/v6vBgx1dwcAXcPA1Co8t06cGMg7yx+Q261enGyNYjKylAIYQQQgghqieZoavmCiw2XvxuN/VqeTH2pmYU7trFqU8+wf+uYfj06ePq8CDzGPw8Hur3gq5lK2SSb81n7Nqx+Jn9mN5ruuybE0IIIYQQ4gIyQ1fNvfXrIeIzCvjm8W64O6wcnTARt7AwwsaXbZ9apXDYYcmToBlgyFwwlP77A6UUr2x8hfjceD656RNqedaqxECFEEIIIYSoniShq8a2Hc/kPxuO8mC3etzQqBYpM2ZiiYsj8tNPMPr6ui6w3Qth5VS9PQFAp0chILJMQ3x76Ft+OvYTz7Z/ls61O1dCkEIIIYQQQlR/suSymiqy2nlh0S7C/T15cVBzCrZtI+Pzzwm4dwQ+PXq4LrDdC2H5s2eTOYBd3+jHS2n/qf3M2DyDHnV78FjrxyohSCGEEEIIIWoGSeiqqSWbNfsAACAASURBVNkrYzmSls8bw1rjZbeQPGEipvBwwsaNc21gK6eCtfD8Y9ZC/Xgp5FpyGbN2DAEeAbzR8w0MmvwVFUIIIYQQ4nJkyWU1tDsxi3nr4hjeKYLeTUM4+drrWOPjifrvfzF4e7suMKXOn5k7V3ZiKS5XTN44meS8ZD675TMCPapAM3QhhBBCCCGqMJn+qGYsNgcvLNpNsI+Zl25rQf6mv8j86isCH3wQ765dXBdYTjJ8c9/l3/ePuOoQ3xz4ht+O/8azHZ6lfWh7JwYnhBBCCCFEzSQJXTUzZ/VhDpzMZdrQ1vjYLZx46SVM9aIIff451wSkFGz7HOZ0hbg10OoeMHmef47JEwZMuuIwe9P3MmvrLHpH9ObvLf9eWdEKIYQQQghRo8iSy2pk/4kc5qw+zJB24QyIDuPE5ClYk5Op9/VXGDw9rz6As2XEwbJn4dh6vc/cHe9CUMNzqlwm6jNzAyZBm+GXHSbHksOYtWMI9gzm9R6vy745IYQQQgghSkkSumrCZncwbtEuArxMTB7ckrw/NpC1YAFBjz6KV4cO1zYYhx02zYVVr4HRBINnQ4eHQdP099sMv2ICdy6lFJM2TCIlP4XPB31OgEdAJQYuhBBCCCFEzSIJXTUxb30ce5JymPu3DvjZi4h7+WXMDRsSMvrZaxtI6n74fhQkbYOmt8Btb4N/3XIP99X+r1gZv5KxncbSNqStEwMVQgghhBCi5pOErho4nJrLO7/Fcmvr2gxqXYfkiS9hS02l/vxvMLi7X5sgbBb449+wbhZ4+MFdn0Kru87OypXD7rTdvL31bfpF9uOhFg85MVghhBBCCCGuD5LQVXF2h2Lcot14uRt55Y5W5K5eTfbixdR64gk827S5NkEkbYPvn4HUvdD6HrhlOngHV2jI7OJsxq4dS5h3GK/2eBWtAomhEEIIIYQQ1ytJ6Kq4zzYcZUd8Fu+MaEeQvZAjkybh3rQpwaOeqvybWwpgzTT4cw741Ib75kOzQRUe9v/bu/O4qut8j+OvL4uCuKGYC6hgCm54XchcRisn90xsmqZrljZNdivLrHSsxpG6Oi5jm6mT3XJpanLMslwqLbPMalIUQk1xyxQBN9xAUJbv/eMgiSIgnOORw/v5ePA453zP73y/n8Pvd+D3Od/lZ63lL+v/wpHMI/yz/z+pVbWWE4IVEREREal8lNBdw/YdzWDG6kRubXUdg9s3Inncn8k9foImc+fiVaWKixtfD8sec6xk2WkE9H4B/JyTeC3ctpCvkr5ifOfxtA1q65Q6RUREREQqIyV016i8PMu4DxLw9fZiUnQkp7/4glPLlxM0ahR+rVu7ruGsU/DFRIidB4FhMHw5hPV0WvXxh+N5ZfMr9G7am6EthzqtXhERERGRykgJ3TXq3R9+YcPPaUz/XTuCcs+wd2IMVVu3Iuihka5rdOcqWDEGTqdA11Fwy3NQpZrTqj+edZynv36ahgENeb7b85o3JyIiIiJSTkrorkEH0s4w5dMd9GgRxO+jQjg45klyT5+myfz5GF9f5zeYcQw+Gw9bFkO9VnDX2xAS5dQm8mwez65/lrSsNN4Z8A41qtRwav0iIiIiIpWRErprjLWWZ5duwQBT7ojk9Kefcvqzz6g3Zgx+EeHObgy2fQifjIOsk3DTeOjxFPg4f37e/K3zWX9wPc/d+Byt67pwyKiIiIiISCWihO4aszj2AN/sOsr/RrelQU4Ge59/Ab927aj7wB+d29CpFFj5JCR+Ao06wuBZUL+Nc9vIt/nQZl6Le41+of34Q8QfXNKGiIiIiEhl5FWajYwx/YwxicaY3caY8UU839MYs9kYk2OMufOi53KNMfH5P8ucFbgnSj2ZxaQV27kxrA5Db2hMSszz5GVm0mjK3zA+Tsq9rYVNC2H2jbDnS+gzCR743GXJXFpWGmO/HktIjRAmdp2oeXMiIiIiIk5UYpZgjPEGZgO9gSRgozFmmbX2pws22w+MAJ4uoopMa217J8Tq0ay1PLd0C9l5eUz7XTvSVywnfc0arhs3jqrXX++cRtJ+huWPw8/roOlv4PaZUNdJdRchz+bx7DfPcuLsCWbfOpvqVaq7rC0RERERkcqoNN0+nYHd1tq9AMaYRcBgoCChs9buy38uzwUxVgofxyezZsdhJtzWmuDcdPZOmox/x47UGX5f+SvPy4Uf5sKX/wvGG257GTqOAK9SddBesZV7V/Lq5ldJyUgBYEjzIbSs09IlbYmIiIiIVGalOaMPBg5c8Dgpv6y0/IwxscaY/xhjoovawBgzMn+b2CNHjlxB1Z7h8OksYpZvo2OT2gzv2pSUCROw2dk0+ttkjLd3OSvfDm/1gVXPQGgPePQHiPqjS5O5mO9iCpI5gE9//pSVe1e6pD0RERERkcqsNGf1RU16slfQRhNrbRQwFHjFGHPJGD9r7RvW2ihrbVS9evWuoGrPMPHjbZw5l8v0O/+L9KUfkrHuG6576imqhIaWvdKcc/D1dHi9B6TthTvehKH/hlpXkouX3pnsM/x45Eem/DCFrNysQs9l5Wbx6uZXXdKuiIiIiEhlVpohl0lA4wsehwDJpW3AWpucf7vXGPMV0AHYcwUxerRPtqTw6dZUxvWLoGn2SfZOmUq1zp0JvGdo2Ss9uBmWPQaHtkLb30H/6RAQ5JR4rbUkZySTmJbIzuM7C372n9qPLSbPT81IdUr7IiIiIiLyq9IkdBuBFsaYMOAgcDeO3rYSGWMCgTPW2rPGmCCgOzC9rMF6mrSMc/z1461EBtfiwd+Ekfzgn8BaGv5tMqYsQyKzM2Ht3+D7WVC9Ptz9HrQcUOb4zmSfYfeJ3ew8vrNQApeenQ6AwdC4RmMi6kQwsNlAIgIjmPyfyRzOPHxJXQ0CGpQ5DhERERERKVqJCZ21NscYMwpYBXgD86y124wxLwCx1tplxpgbgKVAIDDIGPO8tbYN0AqYm79Yihcw9aLVMSu155dv42RmNu/86UbS31/Mme//Q4OYGKqEhFx5Zfu+hWWjHMMrOw6H3i+Af+1SvdRaS0pGSkHSlng88ZJetwDfAMIDwx2JW50IwgPDaVG7BdV8qxWqKzMnk5jvYgoNu/Tz9mN0x9FX/p5ERERERKRYpbq4mbX2E+CTi8r+esH9jTiGYl78uu+AyHLG6JE+/+kQH8cn88StLWiWfZK9f59BQLdu1P7DXVdWUdYp+CIGYt+CwFC4bxk0u+mym2fmZLL7+O6CpC0xLZFdx3dxOvt0wTZNajQpSN7CA8OJCIwguHpwqa4hN7DZQABe3fwqqRmpNAhowOiOowvKRURERETEeZx0tWq5Eiczs3lu6RZaNqjBwz2bkfLH+zFeXjScPOnKLry9czWsGAOnDkKXR6HXc1AlAHD0uqVmpJJ4PLHQcMlfTv1ySa/bgGYDCA8ML/i5uNftSg1sNlAJnIiIiIjIVaCEzg0mr/yJYxnneGv4DWQs+hdnYmNpOHkyvg0blq6CjGOOyxAk/BvqtSRzxAr2VK9N4r7PCnredh7fyelzv/a6Na7R2JG8hQ0gvI4jcQuuHoyXcc3lC0RERERExPWU0F1lX+88wuLYJB65+XrCs9P4+aWXqX7TTdS6Y0iJr7V5eaTGL2Dnuikkco7ENt3Z6ePF/nWPkmcd13Sv5lON8MBw+of2/3WuW2ALAnwDXP3WRERERETkKlNCdxWdzsrmmQ8SuL5eAI/d3IyUEcMxVavS4IUXLhlqmZWTVXiFyaNb2Xl0G6fIhUA/wI8QL0tE7eb0DxtARGAE4XXU6yYiIiIiUpkoobuKpn22g5RTWSz5n26cefefZMbH03D6NI5Vz2Nn0joS0xILhkz+cuqXgl43fy9fwrMy6XfuHOFhtxLR8UFa1G2lXjcRERERkUpOCd1V8t2eo7zzwx7uuNGb1N3/puorr7GnXSB/Sp/KqSW/znULrh5MRGAE/UL7Ee5bm4jYfxK85zu8mnaHO16Dute78V2IiIiIiMi1RAmdC1hrOXTmUMHiJNuObufLvT9SI+Iwa47nccsruZzxMXzyu8b0bdKmYLhki9otqF6lOuTlwoY3YOVTYLxh4EvQ6X4oy8XGRURERETEYymhK8HKvSuLvaba2dyzjrluaTsLXZT75NmTBdsEeF3H2cwghrTpx+D/HKJW6goavfwic/v3v7TBwztg2WOQtAGa94ZBr0CtMlxoXEREREREPJ4SumKs3LuSmO9iyMrNAiAlI4UJ305g7f61eBkvEo8n8supX8i1uQD4+/jTIrAFvZv2JiIwgog6EWScDuLeNxMYdmNTnmvow8//uouaA/pT6+JkLjcb1r8C66ZDlepwx/9B5O/hSq5LJyIiIiIilYoSumK8uvnVgmTuvOy8bFb9sorg6sGEB4Y7krc6EUQERhBSI6TQCpNZ2bkM+Oc3NKrlz7hbryd52FC8a9ak/oQJhRtKjoOPR8GhrdDmDug/HarXuxpvUUREREREKjAldMVIzUgtstxg+Ox3n5X4+le+2MXeIxn884HOZM17k7M7dhAyexY+gYGODbIz4aup8N1rEFAP7v4XtBxYfKUiIiIiIiL5lNAVo0FAA1IyUoosL8mPB07wxro9/CGqMVFnD7Nv7lxqDb6dGr/9rWODfd865sql7YEO90KfSeBf29lvQUREREREPJiWTSzG6I6j8fP2K1Tm5+3H6I6ji33duZw8xi1JoF6NqjzT+3pSnhmPT9261H/2WTh72rF65YIBkJcD930Mg2cpmRMRERERkSumHrpinF/NsrhVLosya+1uEg+d5q3hUZx7cy5nd+2m8Rtz8T68AZY/AacOQpdHoNdfoIouDi4iIiIiImWjhK4EA5sNLDGBu9BPyaeYs3Y3QzoE0+1sCvveeota0bdR/ei78OUiCIqAB1ZD484ujFpERERERCoDJXROlJ2bx9glP1K7mi8Tbm1G8rC78alTg/o1lsLW49BzHPR8GnyqujtUERERERHxAEronOiNdXvZlnyKf9zTkezZMzj38880ufko3kGtYfBSaBDp7hBFRERERMSDKKFzkl2HTvPqF7sY2LYBN22dzy//Wkzt8LMEDHsOuo4Cb/2qRURERETEuZRlOEFunmXskgRaVDnKi+mzSHpzJ761/Kg/631ool45ERERERFxDSV0TrBg/W7aJy/iL37vc/TTALLTq9Bk4Zt4KZkTEREREREXUkJXTkk74+mw5k884LuLdLpyPPEXAu+7l4Abb3R3aCIiIiIi4uF0YfGyys0m7+u/U/9ft9LMpHD8phdJXZtNlaZNuW7MGHdHJyIiIiIilYB66MoiOR4+HoXXoS18ktuF3H5T6bzqI7JTU2n6zjt4+fu7O0IREREREakElNCVJGExrHkBTiZBzWCo3xZ2f06uf12eyHuaE6F9mJOXTtLixdR54I9U69jB3RGLkJ2dTVJSEllZWe4ORUREKhk/Pz9CQkLw9fV1dygilYISuuIkLIblj0N2puPxqSQ4lYRt0p2Hc57k2/QcVvUJI3XYXVS5/nrqPf64e+MVyZeUlESNGjUIDQ3FGOPucEREpJKw1nLs2DGSkpIICwtzdzgilYLm0BVnzQu/JnMXOHN4L6v3nuWZAa3wmv0yOUeP0mjqFLyqVnVDkCKXysrKom7dukrmRETkqjLGULduXY0QEbmKlNAV52RSkcX+mal0aVaH2zP2cPKjj6j74J/wj9QlCuTaomRORETcQf9/RK4uDbksTq0QOHngkuIU6jL11iakDr+bquHhBD3yiBuCExERERGRyk49dMX57V/Bt/CKlWdsFXa2fRLf2S+Te/wEjaZNxatKFTcFKOIcH8UdpPvULwkbv5LuU7/ko7iD7g5JLidhMbzcFmJqO24TFrs7IrlCK/eupM+SPrRb2I4+S/qwcu9Kd4ckZZB9+DD7ht1LzpEjTqlv3759tG3b1il1Xeyrr77itttuA2DZsmVMnTq1zHWFhoYSGRlJ+/btiYqKclaIIlIOSuiK0+4uNkY+Tyr1yLOGJBvEi1UfpUON5pxauZKgh/8Hv1at3B2lSLl8FHeQZz7cwsETmVjg4IlMnvlwi8uTugEDBnDixAlOnDjBnDlzCsovPPG4lsTExBAcHEz79u1p3749n3zyydUP4vxCTScPANZxu/xxlyd1FW1fvf/++7Rp0wYvLy9iY2MLPTdlyhSaN29OREQEq1atuuqxrdy7kpjvYkjJSMFiSclIIea7GJcndRVtHxb3eXP3Pjzv6Jx/kLlpE0fm/MNtMZTF7bffzvjx48tVx9q1a4mPj7/k8yUi7qEhl8X4KO4gz2xsSmb2qwVl9c6cIfqvMVRv3ZqgkSPdGJ1I6Ty/fBs/JZ+67PNx+09wLjevUFlmdi7jliTw3ob9Rb6mdaOaTBzUplxxnT9B27dvH3PmzOERNwxdzsnJwcen9H8Gx4wZw9NPP+26gD4dD6lbLv980kbIPVu4LDsTPh4FmxYW/ZoGkdC/7N/GQ8XbV23btuXDDz/koYceKlT+008/sWjRIrZt20ZycjK33norO3fuxNvb22lxTtswjR1pOy77fMKRBM7lnStUlpWbxV+//StLdi4p8jUt67Tkz53/XK64Kto+hKI/b1djH6b+7W+c3X75fQhgz50jMyEBrOXEokWc3b4dU8wS/VVbtaTBs8+W2HZOTg7Dhw8nLi6O8PBw3n77bWbMmMHy5cvJzMykW7duzJ07F2MMM2fO5PXXX8fHx4fWrVuzaNEiMjIyeOyxx9iyZQs5OTnExMQwePDgQm0sWLCA2NhYZs2axYgRI6hZsyaxsbGkpqYyffp07rzzTgD+/ve/s3jxYs6ePcuQIUN4/vnnS/HbExF3UA9dMf6+KpHM7NxfC6xl5Kb3senpNJw6pdg/3iIVxcXJXEnlpTV9+nRmzpwJOE7MevXqBcCaNWsYNmwYoaGhHD16lPHjx7Nnzx7at2/P2LFjAUhPT+fOO++kZcuW3HPPPVhrL9tOaGgoEydOpGPHjkRGRrJjh+NELC0tjejoaNq1a0eXLl1ISEgAHN/8jxw5kj59+nDfffexYMECoqOjGTRoEGFhYcyaNYuXXnqJDh060KVLF9LS0sr1e3Cqi5O5kspLydP2VatWrYiIiLik/Y8//pi7776bqlWrEhYWRvPmzdmwYUO5fndX6uJkrqTy0vK0fXg518I+BDiXnFz48UHnjGhITExk5MiRJCQkULNmTebMmcOoUaPYuHEjW7duJTMzkxUrVgAwdepU4uLiSEhI4PXXXwdg8uTJ9OrVi40bN7J27VrGjh1LRkZGsW2mpKSwfv16VqxYUdBzt3r1anbt2sWGDRuIj49n06ZNrFu3DnAseNKnTx86derEG2+84ZT3LSLlox66YiSfKHzJgpsOxvOb5C3Mbz2A6eHhbopK5MqU1JPWfeqXHDxx6eU5gmv78++Hupa53Z49e/Liiy/y+OOPExsby9mzZ8nOzmb9+vX06NGD9evXA46Tkq1btxIfHw84hoDFxcWxbds2GjVqRPfu3fn222/5zW9+c9m2goKC2Lx5M3PmzGHGjBm8+eabTJw4kQ4dOvDRRx/x5Zdfct999xW0sWnTJtavX4+/vz8LFixg69atxMXFkZWVRfPmzZk2bRpxcXGMGTOGt99+myeeeAKAWbNm8fbbbxMVFcWLL75IYGBgmX8/RSqpJ+3ltkUu1EStxnB/2YfseeK+KsrBgwfp0qVLweOQkBAOOulE/LySetL6LOlDSkbKJeUNAxoyv9/8MrfrifuwqM/b1diHJfWkZR8+zJ7efeB84msteadOEfzSi/jUq1euths3bkz37t0BGDZsGDNnziQsLIzp06dz5swZ0tLSaNOmDYMGDaJdu3bcc889REdHEx0dDTgSsWXLljFjxgzAcQmb/fuLHmlxXnR0NF5eXrRu3ZpDhw4V1LN69Wo6dOgAOJL+Xbt20bNnT7799lsaNWrE4cOH6d27Ny1btqRnz57let8iUj7qoStGo9qOBVECs07x0tczeTT+Q3YENub7qH5ujkzEecb2jcDft/BwJX9fb8b2vbSH40p06tSJTZs2cfr0aapWrUrXrl2JjY3lm2++oUePHsW+tnPnzoSEhODl5UX79u3Zt29fsdvfcccdBW2e33b9+vXce++9APTq1Ytjx45x8uRJwDGHxN//1wWPbrnlFmrUqEG9evWoVasWgwYNAiAyMrKgvocffpg9e/YQHx9Pw4YNeeqpp670V1J+RSzUhK+/o7wcPG1fXU5RPU9Xe3n10R1H4+ftV6jMz9uP0R1Hl6teT9uHl/u8XQv78Oicf2DzCo9gsHl5TplLd/F7McbwyCOPsGTJErZs2cKDDz5YcH23lStX8uijj7Jp0yY6depETk4O1lo++OAD4uPjiY+PZ//+/bQqYa5/1QuuoXv+92ut5ZlnnimoZ/fu3TzwwAMANGrUCIDrrruOIUOGuKWHVEQKU0JXjPMnukN3fE7L4/vxz8lidud7eKp/a3eHJuI00R2CmXJHJMG1/TE4euam3BFJdIfgctXr6+tLaGgo8+fPp1u3bvTo0YO1a9eyZ8+eKzrB8Pb2Jicnp1TbX7htcSd+AQEBl23Py8ur4LGXl1dBffXr18fb2xsvLy8efPBB95zEtLsLBs109MhhHLeDZjrKy8HT9tXlhISEcODArz2cSUlJBSenV8vAZgOJ6RZDw4CGGAwNAxoS0y2Ggc0GlqteT9uHl/u8XQv7MDM+HrKzCxdmZ5MZF1fuuvfv38/3338PwHvvvVfQUxoUFER6ejpLljjmWebl5XHgwAFuueUWpk+fzokTJ0hPT6dv37689tprBfsjrowx9e3bl3nz5pGeng44ercPHz5MRkYGp0+fBiAjI4PVq1e7bGVOESk9DbksRnSHYLyPHyP0gw0YAGMYHd2RQeU80RW51kR3CC53AleUnj17MmPGDObNm0dkZCRPPvkknTp1KvQtdI0aNQpOEJzd9rvvvsuECRP46quvCAoKombNmmWuLyUlhYYNGwKwdOlS953EtLur3AlcUTxpX13O7bffztChQ3nyySdJTk5m165ddO7c2entlGRgs4HlTuCK4kn78HKft2thHzb7aKnL6m7VqhULFy7koYceokWLFjz88MMcP36cyMhIQkNDueGGGwDIzc1l2LBhnDx5EmstY8aMoXbt2kyYMIEnnniCdu3aYa0lNDS0YM7dlejTpw/bt2+na1fHsPvq1avzzjvvkJ6ezpAhQwDHAi5Dhw6lXz+NWhJxNyV0JYhat5QT3l6Ql4uPjzdR65bCLZHuDkukQujRoweTJ0+ma9euBAQE4Ofnd8nwr7p169K9e3fatm1L//79GTjQOSe6MTEx3H///bRr145q1aqxcOFlVoEspXHjxhEfH48xhtDQUObOneuUOK8VnrSvli5dymOPPcaRI0cYOHAg7du3Z9WqVbRp04a77rqL1q1b4+Pjw+zZs526OqK7edI+vNznzZP3YWhoKD/99NMl5ZMmTWLSpEmXlJ+fF3khf3//Iv823Xzzzdx8880AjBgxghEjRgCOFS8vdL5HDmD06NGMHn3pUOAff/yxuLchIm5gilvNyh2ioqLstXJdk/MTn+3ZX1eQM1Wr0vyLz8s98VnElbZv317iMCsRERFX0f8hkfIxxmyy1kaVZlvNoSuGKyc+i4iIiIiIlJeGXBbDlROfReTKDBkyhJ9//rlQ2bRp0+jbt6+bIpLL0b6q+LQPRUQqjlINuTTG9ANeBbyBN621Uy96vifwCtAOuNtau+SC54YDf8l/OMlaW+zA+mtpyKVIRbV9+3Zatmx51ZfzFhERsdayY8cODbkUKQenDrk0xngDs4H+QGvgv40xF6/bvx8YAfzrotfWASYCNwKdgYnGGCdfiVdELubn58exY8eKXEpcRETEVay1HDt2DD8/v5I3FhGnKM2Qy87AbmvtXgBjzCJgMFCwFJO1dl/+c3kXvbYv8Lm1Ni3/+c+BfsB75Y5cRC4rJCSEpKQkjhw54u5QRESkkvHz8yMkJMTdYYhUGqVJ6IKBAxc8TsLR41YaRb32kotdGWNGAiMBmjRpUsqqReRyfH19CQsLc3cYIiIiIuJipVnlsqhJOKUdx1Wq11pr37DWRllro+rpcgAiIiIiIiKlUpqELglofMHjECC5lPWX57UiIiIiIiJSjNIkdBuBFsaYMGNMFeBuYFkp618F9DHGBOYvhtInv0xERERERETKqbSXLRiA47IE3sA8a+1kY8wLQKy1dpkx5gZgKRAIZAGp1to2+a/9I/BsflWTrbXzS2jrCPBLWd+QCwUBR90dhHg0HWPiSjq+xJV0fIkr6fgSV7pWj6+m1tpSzUUrVUInYIyJLe21IETKQseYuJKOL3ElHV/iSjq+xJU84fgqzZBLERERERERuQYpoRMREREREamglNCV3hvuDkA8no4xcSUdX+JKOr7ElXR8iStV+ONLc+hEREREREQqKPXQiYiIiIiIVFBK6ERERERERCooJXSlYIzpZ4xJNMbsNsaMd3c84jmMMY2NMWuNMduNMduMMaPdHZN4HmOMtzEmzhizwt2xiOcxxtQ2xiwxxuzI/1vW1d0xiecwxozJ//+41RjznjHGz90xScVljJlnjDlsjNl6QVkdY8znxphd+beB7oyxLJTQlcAY4w3MBvoDrYH/Nsa0dm9U4kFygKesta2ALsCjOr7EBUYD290dhHisV4HPrLUtgf9Cx5o4iTEmGHgciLLWtgW8gbvdG5VUcAuAfheVjQfWWGtbAGvyH1coSuhK1hnYba3da609BywCBrs5JvEQ1toUa+3m/PuncZwIBbs3KvEkxpgQYCDwprtjEc9jjKkJ9ATeArDWnrPWnnBvVOJhfAB/Y4wPUA1IdnM8UoFZa9cBaRcVDwYW5t9fCERf1aCcQAldyYKBAxc8TkIn3OICxphQoAPwv5zHjQAAA9VJREFUg3sjEQ/zCjAOyHN3IOKRmgFHgPn5w3rfNMYEuDso8QzW2oPADGA/kAKctNaudm9U4oHqW2tTwPFFO3Cdm+O5YkroSmaKKNO1HsSpjDHVgQ+AJ6y1p9wdj3gGY8xtwGFr7SZ3xyIeywfoCPzDWtsByKACDleSa1P+XKbBQBjQCAgwxgxzb1Qi1x4ldCVLAhpf8DgEdfeLExljfHEkc+9aaz90dzziUboDtxtj9uEYLt7LGPOOe0MSD5MEJFlrz48sWIIjwRNxhluBn621R6y12cCHQDc3xySe55AxpiFA/u1hN8dzxZTQlWwj0MIYE2aMqYJjMu4yN8ckHsIYY3DMPdlurX3J3fGIZ7HWPmOtDbHWhuL42/WltVbfbovTWGtTgQPGmIj8ot8CP7kxJPEs+4Euxphq+f8vf4sW3RHnWwYMz78/HPjYjbGUiY+7A7jWWWtzjDGjgFU4VleaZ63d5uawxHN0B+4Fthhj4vPLnrXWfuLGmERErsRjwLv5X3ruBe53czziIay1PxhjlgCbcawKHQe84d6opCIzxrwH3AwEGWOSgInAVGCxMeYBHF8i/N59EZaNsVbTwURERERERCoiDbkUERERERGpoJTQiYiIiIiIVFBK6ERERERERCooJXQiIiIiIiIVlBI6ERERERGRCkoJnYiIeCxjTK4xJv6Cn/FOrDvUGLPVWfWJiIiUha5DJyIinizTWtve3UGIiIi4inroRESk0jHG7DPGTDPGbMj/aZ5f3tQYs8YYk5B/2yS/vL4xZqkx5sf8n275VXkbY/7PGLPNGLPaGOPvtjclIiKVkhI6ERHxZP4XDbn8wwXPnbLWdgZmAa/kl80C3rbWtgPeBWbml88EvrbW/hfQEdiWX94CmG2tbQOcAH7n4vcjIiJSiLHWujsGERERlzDGpFtrqxdRvg/oZa3da4zxBVKttXWNMUeBhtba7PzyFGttkDHmCBBirT17QR2hwOfW2hb5j/8M+FprJ7n+nYmIiDioh05ERCore5n7l9umKGcvuJ+L5qaLiMhVpoROREQqqz9ccPt9/v3vgLvz798DrM+/vwZ4GMAY422MqXm1ghQRESmOvkkUERFP5m+Mib/g8WfW2vOXLqhqjPkBx5eb/51f9jgwzxgzFjgC3J9fPhp4wxjzAI6euIeBFJdHLyIiUgLNoRMRkUonfw5dlLX2qLtjERERKQ8NuRQREREREamg1EMnIiIiIiJSQamHTkREREREpIJSQiciIiIiIlJBKaETERERERGpoJTQiYiIiIiIVFBK6ERERERERCqo/wdPd6TRE77+7gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", + " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "plt.subplot(2, 1, 2)\n", + "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", + " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", + "\n", + "plt.gcf().set_size_inches(15, 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 4:\n", + "When is layer normalization likely to not work well, and why?\n", + "\n", + "1. Using it in a very deep network\n", + "2. Having a very small dimension of features\n", + "3. Having a high regularization term\n", + "\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/ConvolutionalNetworks.ipynb b/assignment2/ConvolutionalNetworks.ipynb new file mode 100755 index 0000000..746356a --- /dev/null +++ b/assignment2/ConvolutionalNetworks.ipynb @@ -0,0 +1,1275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Convolutional Networks\n", + "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n", + "\n", + "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# As usual, a bit of setup\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.cnn import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n", + "from cs231n.layers import *\n", + "from cs231n.fast_layers import *\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution: Naive forward pass\n", + "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n", + "\n", + "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n", + "\n", + "You can test your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_forward_naive\n", + "difference: 2.2121476417505994e-08\n" + ] + } + ], + "source": [ + "x_shape = (2, 3, 4, 4)\n", + "w_shape = (3, 3, 4, 4)\n", + "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n", + "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n", + "b = np.linspace(-0.1, 0.2, num=3)\n", + "\n", + "conv_param = {'stride': 2, 'pad': 1}\n", + "out, _ = conv_forward_naive(x, w, b, conv_param)\n", + "correct_out = np.array([[[[-0.08759809, -0.10987781],\n", + " [-0.18387192, -0.2109216 ]],\n", + " [[ 0.21027089, 0.21661097],\n", + " [ 0.22847626, 0.23004637]],\n", + " [[ 0.50813986, 0.54309974],\n", + " [ 0.64082444, 0.67101435]]],\n", + " [[[-0.98053589, -1.03143541],\n", + " [-1.19128892, -1.24695841]],\n", + " [[ 0.69108355, 0.66880383],\n", + " [ 0.59480972, 0.56776003]],\n", + " [[ 2.36270298, 2.36904306],\n", + " [ 2.38090835, 2.38247847]]]])\n", + "\n", + "# Compare your output to ours; difference should be around e-8\n", + "print('Testing conv_forward_naive')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aside: Image processing via convolutions\n", + "\n", + "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from imageio import imread\n", + "from PIL import Image\n", + "\n", + "kitten = imread('notebook_images/kitten.jpg')\n", + "puppy = imread('notebook_images/puppy.jpg')\n", + "# kitten is wide, and puppy is already square\n", + "d = kitten.shape[1] - kitten.shape[0]\n", + "kitten_cropped = kitten[:, d//2:-d//2, :]\n", + "\n", + "img_size = 200 # Make this smaller if it runs too slow\n", + "resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n", + "resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n", + "x = np.zeros((2, 3, img_size, img_size))\n", + "x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n", + "x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n", + "\n", + "# Set up a convolutional weights holding 2 filters, each 3x3\n", + "w = np.zeros((2, 3, 3, 3))\n", + "\n", + "# The first filter converts the image to grayscale.\n", + "# Set up the red, green, and blue channels of the filter.\n", + "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n", + "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n", + "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n", + "\n", + "# Second filter detects horizontal edges in the blue channel.\n", + "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n", + "\n", + "# Vector of biases. We don't need any bias for the grayscale\n", + "# filter, but for the edge detection filter we want to add 128\n", + "# to each output so that nothing is negative.\n", + "b = np.array([0, 128])\n", + "\n", + "# Compute the result of convolving each input in x with each filter in w,\n", + "# offsetting by b, and storing the results in out.\n", + "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n", + "\n", + "def imshow_no_ax(img, normalize=True):\n", + " \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n", + " if normalize:\n", + " img_max, img_min = np.max(img), np.min(img)\n", + " img = 255.0 * (img - img_min) / (img_max - img_min)\n", + " plt.imshow(img.astype('uint8'))\n", + " plt.gca().axis('off')\n", + "\n", + "# Show the original images and the results of the conv operation\n", + "plt.subplot(2, 3, 1)\n", + "imshow_no_ax(puppy, normalize=False)\n", + "plt.title('Original image')\n", + "plt.subplot(2, 3, 2)\n", + "imshow_no_ax(out[0, 0])\n", + "plt.title('Grayscale')\n", + "plt.subplot(2, 3, 3)\n", + "imshow_no_ax(out[0, 1])\n", + "plt.title('Edges')\n", + "plt.subplot(2, 3, 4)\n", + "imshow_no_ax(kitten_cropped, normalize=False)\n", + "plt.subplot(2, 3, 5)\n", + "imshow_no_ax(out[1, 0])\n", + "plt.subplot(2, 3, 6)\n", + "imshow_no_ax(out[1, 1])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution: Naive backward pass\n", + "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n", + "\n", + "When you are done, run the following to check your backward pass with a numeric gradient check." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_backward_naive function\n", + "dw error: 2.2471264748452487e-10\n", + "db error: 3.37264006649648e-11\n", + "dx error: 1.159803161159293e-08\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(4, 3, 5, 5)\n", + "w = np.random.randn(2, 3, 3, 3)\n", + "b = np.random.randn(2,)\n", + "dout = np.random.randn(4, 2, 5, 5)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n", + "\n", + "out, cache = conv_forward_naive(x, w, b, conv_param)\n", + "dx, dw, db = conv_backward_naive(dout, cache)\n", + "\n", + "# Your errors should be around e-8 or less.\n", + "print('Testing conv_backward_naive function')\n", + "print('dw error: ', rel_error(dw, dw_num))\n", + "print('db error: ', rel_error(db, db_num))\n", + "print('dx error: ', rel_error(dx, dx_num))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Max-Pooling: Naive forward\n", + "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n", + "\n", + "Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing max_pool_forward_naive function:\n", + "difference: 4.1666665157267834e-08\n" + ] + } + ], + "source": [ + "x_shape = (2, 3, 4, 4)\n", + "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n", + "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n", + "\n", + "out, _ = max_pool_forward_naive(x, pool_param)\n", + "\n", + "correct_out = np.array([[[[-0.26315789, -0.24842105],\n", + " [-0.20421053, -0.18947368]],\n", + " [[-0.14526316, -0.13052632],\n", + " [-0.08631579, -0.07157895]],\n", + " [[-0.02736842, -0.01263158],\n", + " [ 0.03157895, 0.04631579]]],\n", + " [[[ 0.09052632, 0.10526316],\n", + " [ 0.14947368, 0.16421053]],\n", + " [[ 0.20842105, 0.22315789],\n", + " [ 0.26736842, 0.28210526]],\n", + " [[ 0.32631579, 0.34105263],\n", + " [ 0.38526316, 0.4 ]]]])\n", + "\n", + "# Compare your output with ours. Difference should be on the order of e-8.\n", + "print('Testing max_pool_forward_naive function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Max-Pooling: Naive backward\n", + "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n", + "\n", + "Check your implementation with numeric gradient checking by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing max_pool_backward_naive function:\n", + "dx error: 3.27562514223145e-12\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(3, 2, 8, 8)\n", + "dout = np.random.randn(3, 2, 4, 4)\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n", + "\n", + "out, cache = max_pool_forward_naive(x, pool_param)\n", + "dx = max_pool_backward_naive(dout, cache)\n", + "\n", + "# Your error should be on the order of e-12\n", + "print('Testing max_pool_backward_naive function:')\n", + "print('dx error: ', rel_error(dx, dx_num))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fast layers\n", + "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n", + "\n", + "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n", + "\n", + "```bash\n", + "python setup.py build_ext --inplace\n", + "```\n", + "\n", + "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n", + "\n", + "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n", + "\n", + "You can compare the performance of the naive and fast versions of these layers by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_forward_fast:\n", + "Naive: 1.647736s\n", + "Fast: 0.032071s\n", + "Speedup: 51.377958x\n", + "Difference: 4.926407851494105e-11\n", + "\n", + "Testing conv_backward_fast:\n", + "Naive: 0.889276s\n", + "Fast: 0.019460s\n", + "Speedup: 45.697725x\n", + "dx difference: 5.021244662145216e-13\n", + "dw difference: 5.155328198575201e-13\n", + "db difference: 0.0\n" + ] + } + ], + "source": [ + "# Rel errors should be around e-9 or less\n", + "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n", + "from time import time\n", + "np.random.seed(231)\n", + "x = np.random.randn(100, 3, 31, 31)\n", + "w = np.random.randn(25, 3, 3, 3)\n", + "b = np.random.randn(25,)\n", + "dout = np.random.randn(100, 25, 16, 16)\n", + "conv_param = {'stride': 2, 'pad': 1}\n", + "\n", + "t0 = time()\n", + "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n", + "t1 = time()\n", + "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n", + "t2 = time()\n", + "\n", + "print('Testing conv_forward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('Fast: %fs' % (t2 - t1))\n", + "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('Difference: ', rel_error(out_naive, out_fast))\n", + "\n", + "t0 = time()\n", + "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n", + "t1 = time()\n", + "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n", + "t2 = time()\n", + "\n", + "print('\\nTesting conv_backward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('Fast: %fs' % (t2 - t1))\n", + "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('dx difference: ', rel_error(dx_naive, dx_fast))\n", + "print('dw difference: ', rel_error(dw_naive, dw_fast))\n", + "print('db difference: ', rel_error(db_naive, db_fast))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing pool_forward_fast:\n", + "Naive: 0.538419s\n", + "fast: 0.004911s\n", + "speedup: 109.641841x\n", + "difference: 0.0\n", + "\n", + "Testing pool_backward_fast:\n", + "Naive: 0.670326s\n", + "fast: 0.016390s\n", + "speedup: 40.897667x\n", + "dx difference: 0.0\n" + ] + } + ], + "source": [ + "# Relative errors should be close to 0.0\n", + "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n", + "np.random.seed(231)\n", + "x = np.random.randn(100, 3, 32, 32)\n", + "dout = np.random.randn(100, 3, 16, 16)\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "t0 = time()\n", + "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n", + "t1 = time()\n", + "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n", + "t2 = time()\n", + "\n", + "print('Testing pool_forward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('fast: %fs' % (t2 - t1))\n", + "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('difference: ', rel_error(out_naive, out_fast))\n", + "\n", + "t0 = time()\n", + "dx_naive = max_pool_backward_naive(dout, cache_naive)\n", + "t1 = time()\n", + "dx_fast = max_pool_backward_fast(dout, cache_fast)\n", + "t2 = time()\n", + "\n", + "print('\\nTesting pool_backward_fast:')\n", + "print('Naive: %fs' % (t1 - t0))\n", + "print('fast: %fs' % (t2 - t1))\n", + "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n", + "print('dx difference: ', rel_error(dx_naive, dx_fast))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional \"sandwich\" layers\n", + "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check they're working." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_relu_pool\n", + "dx error: 6.514336569263308e-09\n", + "dw error: 1.490843753539445e-08\n", + "db error: 2.037390356217257e-09\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 16, 16)\n", + "w = np.random.randn(3, 3, 3, 3)\n", + "b = np.random.randn(3,)\n", + "dout = np.random.randn(2, 3, 8, 8)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n", + "\n", + "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n", + "dx, dw, db = conv_relu_pool_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n", + "\n", + "# Relative errors should be around e-8 or less\n", + "print('Testing conv_relu_pool')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing conv_relu:\n", + "dx error: 3.5600610115232832e-09\n", + "dw error: 2.2497700915729298e-10\n", + "db error: 1.3087619975802167e-10\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 8, 8)\n", + "w = np.random.randn(3, 3, 3, 3)\n", + "b = np.random.randn(3,)\n", + "dout = np.random.randn(2, 3, 8, 8)\n", + "conv_param = {'stride': 1, 'pad': 1}\n", + "\n", + "out, cache = conv_relu_forward(x, w, b, conv_param)\n", + "dx, dw, db = conv_relu_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n", + "\n", + "# Relative errors should be around e-8 or less\n", + "print('Testing conv_relu:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Three-layer ConvNet\n", + "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n", + "\n", + "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sanity check loss\n", + "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss (no regularization): 2.302585856800178\n", + "Initial loss (with regularization): 2.5090564357090983\n" + ] + } + ], + "source": [ + "model = ThreeLayerConvNet()\n", + "\n", + "N = 50\n", + "X = np.random.randn(N, 3, 32, 32)\n", + "y = np.random.randint(10, size=N)\n", + "\n", + "loss, grads = model.loss(X, y)\n", + "print('Initial loss (no regularization): ', loss)\n", + "\n", + "model.reg = 0.5\n", + "loss, grads = model.loss(X, y)\n", + "print('Initial loss (with regularization): ', loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient check\n", + "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 max relative error: 1.380104e-04\n", + "W2 max relative error: 1.822723e-02\n", + "W3 max relative error: 3.064049e-04\n", + "b1 max relative error: 3.477652e-05\n", + "b2 max relative error: 2.516375e-03\n", + "b3 max relative error: 7.945660e-10\n" + ] + } + ], + "source": [ + "num_inputs = 2\n", + "input_dim = (3, 16, 16)\n", + "reg = 0.0\n", + "num_classes = 10\n", + "np.random.seed(231)\n", + "X = np.random.randn(num_inputs, *input_dim)\n", + "y = np.random.randint(num_classes, size=num_inputs)\n", + "\n", + "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n", + " input_dim=input_dim, hidden_dim=7,\n", + " dtype=np.float64)\n", + "loss, grads = model.loss(X, y)\n", + "# Errors should be small, but correct implementations may have\n", + "# relative errors up to the order of e-2\n", + "for param_name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n", + " e = rel_error(param_grad_num, grads[param_name])\n", + " print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overfit small data\n", + "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 30) loss: 2.414060\n", + "(Epoch 0 / 15) train acc: 0.160000; val_acc: 0.093000\n", + "(Iteration 2 / 30) loss: 2.290796\n", + "(Epoch 1 / 15) train acc: 0.210000; val_acc: 0.115000\n", + "(Iteration 3 / 30) loss: 2.089441\n", + "(Iteration 4 / 30) loss: 2.091442\n", + "(Epoch 2 / 15) train acc: 0.270000; val_acc: 0.127000\n", + "(Iteration 5 / 30) loss: 1.914290\n", + "(Iteration 6 / 30) loss: 2.002273\n", + "(Epoch 3 / 15) train acc: 0.350000; val_acc: 0.145000\n", + "(Iteration 7 / 30) loss: 1.828825\n", + "(Iteration 8 / 30) loss: 1.834136\n", + "(Epoch 4 / 15) train acc: 0.480000; val_acc: 0.161000\n", + "(Iteration 9 / 30) loss: 1.520318\n", + "(Iteration 10 / 30) loss: 1.910616\n", + "(Epoch 5 / 15) train acc: 0.510000; val_acc: 0.169000\n", + "(Iteration 11 / 30) loss: 1.676075\n", + "(Iteration 12 / 30) loss: 1.547881\n", + "(Epoch 6 / 15) train acc: 0.550000; val_acc: 0.176000\n", + "(Iteration 13 / 30) loss: 1.649479\n", + "(Iteration 14 / 30) loss: 1.268677\n", + "(Epoch 7 / 15) train acc: 0.680000; val_acc: 0.187000\n", + "(Iteration 15 / 30) loss: 1.201789\n", + "(Iteration 16 / 30) loss: 1.304679\n", + "(Epoch 8 / 15) train acc: 0.700000; val_acc: 0.210000\n", + "(Iteration 17 / 30) loss: 1.379660\n", + "(Iteration 18 / 30) loss: 1.153268\n", + "(Epoch 9 / 15) train acc: 0.760000; val_acc: 0.207000\n", + "(Iteration 19 / 30) loss: 1.108503\n", + "(Iteration 20 / 30) loss: 1.031183\n", + "(Epoch 10 / 15) train acc: 0.790000; val_acc: 0.197000\n", + "(Iteration 21 / 30) loss: 1.011405\n", + "(Iteration 22 / 30) loss: 0.906427\n", + "(Epoch 11 / 15) train acc: 0.850000; val_acc: 0.203000\n", + "(Iteration 23 / 30) loss: 0.798424\n", + "(Iteration 24 / 30) loss: 0.909893\n", + "(Epoch 12 / 15) train acc: 0.860000; val_acc: 0.194000\n", + "(Iteration 25 / 30) loss: 0.736356\n", + "(Iteration 26 / 30) loss: 0.691900\n", + "(Epoch 13 / 15) train acc: 0.900000; val_acc: 0.188000\n", + "(Iteration 27 / 30) loss: 0.675176\n", + "(Iteration 28 / 30) loss: 0.618615\n", + "(Epoch 14 / 15) train acc: 0.950000; val_acc: 0.194000\n", + "(Iteration 29 / 30) loss: 0.540485\n", + "(Iteration 30 / 30) loss: 0.625012\n", + "(Epoch 15 / 15) train acc: 0.940000; val_acc: 0.203000\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "\n", + "num_train = 100\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "model = ThreeLayerConvNet(weight_scale=1e-2)\n", + "\n", + "solver = Solver(model, small_data,\n", + " num_epochs=15, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-4,\n", + " },\n", + " verbose=True, print_every=1)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 1, 1)\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.xlabel('iteration')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(solver.train_acc_history, '-o')\n", + "plt.plot(solver.val_acc_history, '-o')\n", + "plt.legend(['train', 'val'], loc='upper left')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the net\n", + "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 980) loss: 2.304740\n", + "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n", + "(Iteration 21 / 980) loss: 2.098229\n", + "(Iteration 41 / 980) loss: 1.949788\n", + "(Iteration 61 / 980) loss: 1.888398\n", + "(Iteration 81 / 980) loss: 1.877093\n", + "(Iteration 101 / 980) loss: 1.851877\n", + "(Iteration 121 / 980) loss: 1.859353\n", + "(Iteration 141 / 980) loss: 1.800181\n", + "(Iteration 161 / 980) loss: 2.143292\n", + "(Iteration 181 / 980) loss: 1.830573\n", + "(Iteration 201 / 980) loss: 2.037280\n", + "(Iteration 221 / 980) loss: 2.020304\n", + "(Iteration 241 / 980) loss: 1.823728\n", + "(Iteration 261 / 980) loss: 1.692679\n", + "(Iteration 281 / 980) loss: 1.882594\n", + "(Iteration 301 / 980) loss: 1.798261\n", + "(Iteration 321 / 980) loss: 1.851960\n", + "(Iteration 341 / 980) loss: 1.716323\n", + "(Iteration 361 / 980) loss: 1.897655\n", + "(Iteration 381 / 980) loss: 1.319744\n", + "(Iteration 401 / 980) loss: 1.738790\n", + "(Iteration 421 / 980) loss: 1.488866\n", + "(Iteration 441 / 980) loss: 1.718409\n", + "(Iteration 461 / 980) loss: 1.744440\n", + "(Iteration 481 / 980) loss: 1.605460\n", + "(Iteration 501 / 980) loss: 1.494847\n", + "(Iteration 521 / 980) loss: 1.835179\n", + "(Iteration 541 / 980) loss: 1.483923\n", + "(Iteration 561 / 980) loss: 1.676871\n", + "(Iteration 581 / 980) loss: 1.438325\n", + "(Iteration 601 / 980) loss: 1.443469\n", + "(Iteration 621 / 980) loss: 1.529369\n", + "(Iteration 641 / 980) loss: 1.763475\n", + "(Iteration 661 / 980) loss: 1.790329\n", + "(Iteration 681 / 980) loss: 1.693343\n", + "(Iteration 701 / 980) loss: 1.637078\n", + "(Iteration 721 / 980) loss: 1.644564\n", + "(Iteration 741 / 980) loss: 1.708919\n", + "(Iteration 761 / 980) loss: 1.494252\n", + "(Iteration 781 / 980) loss: 1.901751\n", + "(Iteration 801 / 980) loss: 1.898991\n", + "(Iteration 821 / 980) loss: 1.489988\n", + "(Iteration 841 / 980) loss: 1.377615\n", + "(Iteration 861 / 980) loss: 1.763751\n", + "(Iteration 881 / 980) loss: 1.540284\n", + "(Iteration 901 / 980) loss: 1.525582\n", + "(Iteration 921 / 980) loss: 1.674166\n", + "(Iteration 941 / 980) loss: 1.714316\n", + "(Iteration 961 / 980) loss: 1.534668\n", + "(Epoch 1 / 1) train acc: 0.504000; val_acc: 0.499000\n" + ] + } + ], + "source": [ + "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n", + "\n", + "solver = Solver(model, data,\n", + " num_epochs=1, batch_size=50,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3,\n", + " },\n", + " verbose=True, print_every=20)\n", + "solver.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Filters\n", + "You can visualize the first-layer convolutional filters from the trained network by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from cs231n.vis_utils import visualize_grid\n", + "\n", + "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n", + "plt.imshow(grid.astype('uint8'))\n", + "plt.axis('off')\n", + "plt.gcf().set_size_inches(5, 5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spatial Batch Normalization\n", + "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n", + "\n", + "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n", + "\n", + "If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n", + "\n", + "\n", + "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", + "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial batch normalization: forward\n", + "\n", + "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before spatial batch normalization:\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [9.33463814 8.90909116 9.11056338]\n", + " Stds: [3.61447857 3.19347686 3.5168142 ]\n", + "After spatial batch normalization:\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [ 6.05071548e-16 6.21724894e-16 -1.16573418e-16]\n", + " Stds: [0.99999723 0.99999687 0.99999716]\n", + "After spatial batch normalization (nontrivial gamma, beta):\n", + " Shape: (2, 3, 4, 5)\n", + " Means: [6. 7. 8.]\n", + " Stds: [2.9999917 3.99998747 4.99998578]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after spatial batch normalization\n", + "\n", + "N, C, H, W = 2, 3, 4, 5\n", + "x = 4 * np.random.randn(N, C, H, W) + 10\n", + "\n", + "print('Before spatial batch normalization:')\n", + "print(' Shape: ', x.shape)\n", + "print(' Means: ', x.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', x.std(axis=(0, 2, 3)))\n", + "\n", + "# Means should be close to zero and stds close to one\n", + "gamma, beta = np.ones(C), np.zeros(C)\n", + "bn_param = {'mode': 'train'}\n", + "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "print('After spatial batch normalization:')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', out.std(axis=(0, 2, 3)))\n", + "\n", + "# Means should be close to beta and stds close to gamma\n", + "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n", + "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "print('After spatial batch normalization (nontrivial gamma, beta):')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out.mean(axis=(0, 2, 3)))\n", + "print(' Stds: ', out.std(axis=(0, 2, 3)))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After spatial batch normalization (test-time):\n", + " means: [-0.08034378 0.07562855 0.05716351 0.04378368]\n", + " stds: [0.96718413 1.02996788 1.02887272 1.00585232]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the test-time forward pass by running the training-time\n", + "# forward pass many times to warm up the running averages, and then\n", + "# checking the means and variances of activations after a test-time\n", + "# forward pass.\n", + "N, C, H, W = 10, 4, 11, 12\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "gamma = np.ones(C)\n", + "beta = np.zeros(C)\n", + "for t in range(50):\n", + " x = 2.3 * np.random.randn(N, C, H, W) + 13\n", + " spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "bn_param['mode'] = 'test'\n", + "x = 2.3 * np.random.randn(N, C, H, W) + 13\n", + "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "\n", + "# Means should be close to zero and stds close to one, but will be\n", + "# noisier than training-time forward passes.\n", + "print('After spatial batch normalization (test-time):')\n", + "print(' means: ', a_norm.mean(axis=(0, 2, 3)))\n", + "print(' stds: ', a_norm.std(axis=(0, 2, 3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial batch normalization: backward\n", + "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx error: 3.511390713672255e-06\n", + "dgamma error: 1.795799129503502e-11\n", + "dbeta error: 3.275608725278405e-12\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, C, H, W = 2, 3, 4, 5\n", + "x = 5 * np.random.randn(N, C, H, W) + 12\n", + "gamma = np.random.randn(C)\n", + "beta = np.random.randn(C)\n", + "dout = np.random.randn(N, C, H, W)\n", + "\n", + "bn_param = {'mode': 'train'}\n", + "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta, dout)\n", + "\n", + "#You should expect errors of magnitudes between 1e-12~1e-06\n", + "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n", + "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Group Normalization\n", + "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n", + "\n", + ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n", + "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n", + "statistics from the rest of the hidden units within the same layer.\n", + "\n", + "The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups, and a per-group per-datapoint normalization instead. \n", + "\n", + "![Comparison of normalization techniques discussed so far](notebook_images/normalization.png)\n", + "
**Visual comparison of the normalization techniques discussed so far (image edited from [3])**
\n", + "\n", + "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional Computer Vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4]-- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n", + "\n", + "You will now implement Group Normalization. Note that this normalization technique that you are to implement in the following cells was introduced and published to ECCV just in 2018 -- this truly is still an ongoing and excitingly active field of research!\n", + "\n", + "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n", + "\n", + "\n", + "[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n", + "\n", + "\n", + "[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n", + "human detection. In Computer Vision and Pattern Recognition\n", + "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group normalization: forward\n", + "\n", + "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before spatial group normalization:\n", + " Shape: (2, 6, 4, 5)\n", + " Means: [9.72505327 8.51114185 8.9147544 9.43448077]\n", + " Stds: [3.67070958 3.09892597 4.27043622 3.97521327]\n", + "After spatial group normalization:\n", + " Shape: (2, 6, 4, 5)\n", + " Means: [-2.14643118e-16 5.25505565e-16 2.65528340e-16 -3.38618023e-16]\n", + " Stds: [0.99999963 0.99999948 0.99999973 0.99999968]\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "# Check the training-time forward pass by checking means and variances\n", + "# of features both before and after spatial batch normalization\n", + "\n", + "N, C, H, W = 2, 6, 4, 5\n", + "G = 2\n", + "x = 4 * np.random.randn(N, C, H, W) + 10\n", + "x_g = x.reshape((N*G,-1))\n", + "print('Before spatial group normalization:')\n", + "print(' Shape: ', x.shape)\n", + "print(' Means: ', x_g.mean(axis=1))\n", + "print(' Stds: ', x_g.std(axis=1))\n", + "\n", + "# Means should be close to zero and stds close to one\n", + "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n", + "bn_param = {'mode': 'train'}\n", + "\n", + "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n", + "out_g = out.reshape((N*G,-1))\n", + "print('After spatial group normalization:')\n", + "print(' Shape: ', out.shape)\n", + "print(' Means: ', out_g.mean(axis=1))\n", + "print(' Stds: ', out_g.std(axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial group normalization: backward\n", + "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dgamma error: 9.468195772749234e-12\n", + "dbeta error: 3.354494437653335e-12\n", + "dx error: 7.413109384854475e-08\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, C, H, W = 2, 6, 4, 5\n", + "G = 2\n", + "x = 5 * np.random.randn(N, C, H, W) + 12\n", + "gamma = np.random.randn(1,C,1,1)\n", + "beta = np.random.randn(1,C,1,1)\n", + "dout = np.random.randn(N, C, H, W)\n", + "\n", + "gn_param = {}\n", + "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n", + "\n", + "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", + "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n", + "db_num = eval_numerical_gradient_array(fb, beta, dout)\n", + "\n", + "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n", + "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n", + "#You should expect errors of magnitudes between 1e-12~1e-07\n", + "print('dgamma error: ', rel_error(da_num, dgamma))\n", + "print('dbeta error: ', rel_error(db_num, dbeta))\n", + "print('dx error: ', rel_error(dx_num, dx))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/Dropout.ipynb b/assignment2/Dropout.ipynb new file mode 100755 index 0000000..cb2421a --- /dev/null +++ b/assignment2/Dropout.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Dropout\n", + "Dropout [1] is a technique for regularizing neural networks by randomly setting some output activations to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n", + "\n", + "[1] [Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012](https://arxiv.org/abs/1207.0580)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "from __future__ import print_function\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (49000, 3, 32, 32)\n", + "y_train: (49000,)\n", + "X_val: (1000, 3, 32, 32)\n", + "y_val: (1000,)\n", + "X_test: (1000, 3, 32, 32)\n", + "y_test: (1000,)\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in data.items():\n", + " print('%s: ' % k, v.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dropout forward pass\n", + "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n", + "\n", + "Once you have done so, run the cell below to test your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running tests with p = 0.25\n", + "Mean of input: 10.000207878477502\n", + "Mean of train-time output: 10.014059116977283\n", + "Mean of test-time output: 10.000207878477502\n", + "Fraction of train-time output set to zero: 0.749784\n", + "Fraction of test-time output set to zero: 0.0\n", + "\n", + "Running tests with p = 0.4\n", + "Mean of input: 10.000207878477502\n", + "Mean of train-time output: 9.977917658761159\n", + "Mean of test-time output: 10.000207878477502\n", + "Fraction of train-time output set to zero: 0.600796\n", + "Fraction of test-time output set to zero: 0.0\n", + "\n", + "Running tests with p = 0.7\n", + "Mean of input: 10.000207878477502\n", + "Mean of train-time output: 9.987811912159426\n", + "Mean of test-time output: 10.000207878477502\n", + "Fraction of train-time output set to zero: 0.30074\n", + "Fraction of test-time output set to zero: 0.0\n", + "\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(500, 500) + 10\n", + "\n", + "for p in [0.25, 0.4, 0.7]:\n", + " out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n", + " out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n", + "\n", + " print('Running tests with p = ', p)\n", + " print('Mean of input: ', x.mean())\n", + " print('Mean of train-time output: ', out.mean())\n", + " print('Mean of test-time output: ', out_test.mean())\n", + " print('Fraction of train-time output set to zero: ', (out == 0).mean())\n", + " print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dropout backward pass\n", + "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dx relative error: 5.44560814873387e-11\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(10, 10) + 10\n", + "dout = np.random.randn(*x.shape)\n", + "\n", + "dropout_param = {'mode': 'train', 'p': 0.2, 'seed': 123}\n", + "out, cache = dropout_forward(x, dropout_param)\n", + "dx = dropout_backward(dout, cache)\n", + "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n", + "\n", + "# Error should be around e-10 or less\n", + "print('dx relative error: ', rel_error(dx, dx_num))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1:\n", + "What happens if we do not divide the values being passed through inverse dropout by `p` in the dropout layer? Why does that happen?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fully-connected nets with Dropout\n", + "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specifically, if the constructor of the network receives a value that is not 1 for the `dropout` parameter, then the net should add a dropout layer immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running check with dropout = 1\n", + "Initial loss: 2.300479089768492\n", + "W1 relative error: 1.03e-07\n", + "W2 relative error: 2.21e-05\n", + "W3 relative error: 4.56e-07\n", + "b1 relative error: 4.66e-09\n", + "b2 relative error: 2.09e-09\n", + "b3 relative error: 1.69e-10\n", + "\n", + "Running check with dropout = 0.75\n", + "Initial loss: 2.302371489704412\n", + "W1 relative error: 1.85e-07\n", + "W2 relative error: 2.15e-06\n", + "W3 relative error: 4.56e-08\n", + "b1 relative error: 1.16e-08\n", + "b2 relative error: 1.82e-09\n", + "b3 relative error: 1.48e-10\n", + "\n", + "Running check with dropout = 0.5\n", + "Initial loss: 2.30427592207859\n", + "W1 relative error: 3.11e-07\n", + "W2 relative error: 2.48e-08\n", + "W3 relative error: 6.43e-08\n", + "b1 relative error: 5.37e-09\n", + "b2 relative error: 1.91e-09\n", + "b3 relative error: 1.85e-10\n", + "\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "for dropout in [1, 0.75, 0.5]:\n", + " print('Running check with dropout = ', dropout)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " weight_scale=5e-2, dtype=np.float64,\n", + " dropout=dropout, seed=123)\n", + "\n", + " loss, grads = model.loss(X, y)\n", + " print('Initial loss: ', loss)\n", + " \n", + " # Relative errors should be around e-6 or less; Note that it's fine\n", + " # if for dropout=1 you have W2 error be on the order of e-5.\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regularization experiment\n", + "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a keep probability of 0.25. We will then visualize the training and validation accuracies of the two networks over time." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "(Iteration 1 / 125) loss: 7.856644\n", + "(Epoch 0 / 25) train acc: 0.260000; val_acc: 0.184000\n", + "(Epoch 1 / 25) train acc: 0.416000; val_acc: 0.258000\n", + "(Epoch 2 / 25) train acc: 0.482000; val_acc: 0.276000\n", + "(Epoch 3 / 25) train acc: 0.532000; val_acc: 0.277000\n", + "(Epoch 4 / 25) train acc: 0.600000; val_acc: 0.271000\n", + "(Epoch 5 / 25) train acc: 0.708000; val_acc: 0.299000\n", + "(Epoch 6 / 25) train acc: 0.722000; val_acc: 0.282000\n", + "(Epoch 7 / 25) train acc: 0.832000; val_acc: 0.255000\n", + "(Epoch 8 / 25) train acc: 0.878000; val_acc: 0.269000\n", + "(Epoch 9 / 25) train acc: 0.902000; val_acc: 0.275000\n", + "(Epoch 10 / 25) train acc: 0.890000; val_acc: 0.261000\n", + "(Epoch 11 / 25) train acc: 0.930000; val_acc: 0.283000\n", + "(Epoch 12 / 25) train acc: 0.958000; val_acc: 0.300000\n", + "(Epoch 13 / 25) train acc: 0.964000; val_acc: 0.305000\n", + "(Epoch 14 / 25) train acc: 0.962000; val_acc: 0.317000\n", + "(Epoch 15 / 25) train acc: 0.962000; val_acc: 0.304000\n", + "(Epoch 16 / 25) train acc: 0.980000; val_acc: 0.307000\n", + "(Epoch 17 / 25) train acc: 0.970000; val_acc: 0.321000\n", + "(Epoch 18 / 25) train acc: 0.992000; val_acc: 0.317000\n", + "(Epoch 19 / 25) train acc: 0.984000; val_acc: 0.303000\n", + "(Epoch 20 / 25) train acc: 0.986000; val_acc: 0.309000\n", + "(Iteration 101 / 125) loss: 0.074303\n", + "(Epoch 21 / 25) train acc: 0.996000; val_acc: 0.304000\n", + "(Epoch 22 / 25) train acc: 0.964000; val_acc: 0.311000\n", + "(Epoch 23 / 25) train acc: 0.982000; val_acc: 0.314000\n", + "(Epoch 24 / 25) train acc: 0.982000; val_acc: 0.305000\n", + "(Epoch 25 / 25) train acc: 0.972000; val_acc: 0.305000\n", + "\n", + "0.25\n", + "(Iteration 1 / 125) loss: 17.318480\n", + "(Epoch 0 / 25) train acc: 0.230000; val_acc: 0.177000\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kalkidanfekadu/Desktop/CS231/assignment2/cs231n/classifiers/fc_net.py:350: RuntimeWarning: divide by zero encountered in log\n", + " loss = np.sum(-np.log(scores[range(N), y]))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Epoch 1 / 25) train acc: 0.378000; val_acc: 0.243000\n", + "(Epoch 2 / 25) train acc: 0.402000; val_acc: 0.254000\n", + "(Epoch 3 / 25) train acc: 0.502000; val_acc: 0.276000\n", + "(Epoch 4 / 25) train acc: 0.528000; val_acc: 0.298000\n", + "(Epoch 5 / 25) train acc: 0.562000; val_acc: 0.297000\n", + "(Epoch 6 / 25) train acc: 0.620000; val_acc: 0.290000\n", + "(Epoch 7 / 25) train acc: 0.626000; val_acc: 0.298000\n", + "(Epoch 8 / 25) train acc: 0.680000; val_acc: 0.311000\n", + "(Epoch 9 / 25) train acc: 0.716000; val_acc: 0.299000\n", + "(Epoch 10 / 25) train acc: 0.730000; val_acc: 0.301000\n", + "(Epoch 11 / 25) train acc: 0.750000; val_acc: 0.311000\n", + "(Epoch 12 / 25) train acc: 0.774000; val_acc: 0.284000\n", + "(Epoch 13 / 25) train acc: 0.826000; val_acc: 0.317000\n", + "(Epoch 14 / 25) train acc: 0.810000; val_acc: 0.350000\n", + "(Epoch 15 / 25) train acc: 0.864000; val_acc: 0.352000\n", + "(Epoch 16 / 25) train acc: 0.856000; val_acc: 0.316000\n", + "(Epoch 17 / 25) train acc: 0.810000; val_acc: 0.278000\n", + "(Epoch 18 / 25) train acc: 0.858000; val_acc: 0.349000\n", + "(Epoch 19 / 25) train acc: 0.872000; val_acc: 0.327000\n", + "(Epoch 20 / 25) train acc: 0.854000; val_acc: 0.297000\n", + "(Iteration 101 / 125) loss: 3.263317\n", + "(Epoch 21 / 25) train acc: 0.888000; val_acc: 0.305000\n", + "(Epoch 22 / 25) train acc: 0.902000; val_acc: 0.305000\n", + "(Epoch 23 / 25) train acc: 0.894000; val_acc: 0.298000\n", + "(Epoch 24 / 25) train acc: 0.916000; val_acc: 0.314000\n", + "(Epoch 25 / 25) train acc: 0.904000; val_acc: 0.318000\n", + "\n" + ] + } + ], + "source": [ + "# Train two identical nets, one with dropout and one without\n", + "np.random.seed(231)\n", + "num_train = 500\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "solvers = {}\n", + "dropout_choices = [1, 0.25]\n", + "for dropout in dropout_choices:\n", + " model = FullyConnectedNet([500], dropout=dropout)\n", + " print(dropout)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=25, batch_size=100,\n", + " update_rule='adam',\n", + " optim_config={\n", + " 'learning_rate': 5e-4,\n", + " },\n", + " verbose=True, print_every=100)\n", + " solver.train()\n", + " solvers[dropout] = solver\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot train and validation accuracies of the two models\n", + "\n", + "train_accs = []\n", + "val_accs = []\n", + "for dropout in dropout_choices:\n", + " solver = solvers[dropout]\n", + " train_accs.append(solver.train_acc_history[-1])\n", + " val_accs.append(solver.val_acc_history[-1])\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "for dropout in dropout_choices:\n", + " plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n", + "plt.title('Train accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend(ncol=2, loc='lower right')\n", + " \n", + "plt.subplot(3, 1, 2)\n", + "for dropout in dropout_choices:\n", + " plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n", + "plt.title('Val accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend(ncol=2, loc='lower right')\n", + "\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2:\n", + "Compare the validation and training accuracies with and without dropout -- what do your results suggest about dropout as a regularizer?\n", + "\n", + "## Answer:\n", + "\n", + "The accuracy of the validation set with drop out is higher than without. Hence, we can say drop out is effective as a regularizer in reducing overfitting.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "Suppose we are training a deep fully-connected network for image classification, with dropout after hidden layers (parameterized by keep probability p). If we are concerned about overfitting, how should we modify p (if at all) when we decide to decrease the size of the hidden layers (that is, the number of nodes in each layer)?\n", + "\n", + "## Answer:\n", + "Decresing the side of the hidden layers is similar to reduing the model size, in turn reducing the number of parameters. Comparing to larger models, smaller models are less concerned with the probability of overfitting. Hence we can decrease the likelihood a node will be dropped(increase the value of p).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/FullyConnectedNets.ipynb b/assignment2/FullyConnectedNets.ipynb new file mode 100755 index 0000000..4458d33 --- /dev/null +++ b/assignment2/FullyConnectedNets.ipynb @@ -0,0 +1,1596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Fully-Connected Neural Nets\n", + "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n", + "\n", + "```python\n", + "def layer_forward(x, w):\n", + " \"\"\" Receive inputs x and weights w \"\"\"\n", + " # Do some computations ...\n", + " z = # ... some intermediate value\n", + " # Do some more computations ...\n", + " out = # the output\n", + " \n", + " cache = (x, w, z, out) # Values we need to compute gradients\n", + " \n", + " return out, cache\n", + "```\n", + "\n", + "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n", + "\n", + "```python\n", + "def layer_backward(dout, cache):\n", + " \"\"\"\n", + " Receive dout (derivative of loss with respect to outputs) and cache,\n", + " and compute derivative with respect to inputs.\n", + " \"\"\"\n", + " # Unpack cache values\n", + " x, w, z, out = cache\n", + " \n", + " # Use values in cache to compute derivatives\n", + " dx = # Derivative of loss with respect to x\n", + " dw = # Derivative of loss with respect to w\n", + " \n", + " return dx, dw\n", + "```\n", + "\n", + "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n", + "\n", + "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run the following from the cs231n directory and try again:\n", + "python setup.py build_ext --inplace\n", + "You may also need to restart your iPython kernel\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "from __future__ import print_function\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cs231n.classifiers.fc_net import *\n", + "from cs231n.data_utils import get_CIFAR10_data\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.solver import Solver\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('X_train: ', (49000, 3, 32, 32))\n", + "('y_train: ', (49000,))\n", + "('X_val: ', (1000, 3, 32, 32))\n", + "('y_val: ', (1000,))\n", + "('X_test: ', (1000, 3, 32, 32))\n", + "('y_test: ', (1000,))\n" + ] + } + ], + "source": [ + "# Load the (preprocessed) CIFAR10 data.\n", + "\n", + "data = get_CIFAR10_data()\n", + "for k, v in list(data.items()):\n", + " print(('%s: ' % k, v.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Affine layer: foward\n", + "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n", + "\n", + "Once you are done you can test your implementaion by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_forward function:\n", + "difference: 9.769847728806635e-10\n" + ] + } + ], + "source": [ + "# Test the affine_forward function\n", + "\n", + "num_inputs = 2\n", + "input_shape = (4, 5, 6)\n", + "output_dim = 3\n", + "\n", + "input_size = num_inputs * np.prod(input_shape)\n", + "weight_size = output_dim * np.prod(input_shape)\n", + "\n", + "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n", + "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n", + "b = np.linspace(-0.3, 0.1, num=output_dim)\n", + "\n", + "\n", + "out, _ = affine_forward(x, w, b)\n", + "correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],\n", + " [ 3.25553199, 3.5141327, 3.77273342]])\n", + "\n", + "# Compare your output with ours. The error should be around e-9 or less.\n", + "print('Testing affine_forward function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Affine layer: backward\n", + "Now implement the `affine_backward` function and test your implementation using numeric gradient checking." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_backward function:\n", + "dx error: 5.399100368651805e-11\n", + "dw error: 9.904211865398145e-11\n", + "db error: 2.4122867568119087e-11\n" + ] + } + ], + "source": [ + "# Test the affine_backward function\n", + "\n", + "np.random.seed(231)\n", + "x = np.random.randn(10, 2, 3)\n", + "w = np.random.randn(6, 5)\n", + "b = np.random.randn(5)\n", + "dout = np.random.randn(10, 5)\n", + "\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n", + "\n", + "_, cache = affine_forward(x, w, b)\n", + "dx, dw, db = affine_backward(dout, cache)\n", + "\n", + "# The error should be around e-10 or less\n", + "print('Testing affine_backward function:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ReLU activation: forward\n", + "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing relu_forward function:\n", + "difference: 4.999999798022158e-08\n" + ] + } + ], + "source": [ + "# Test the relu_forward function\n", + "\n", + "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n", + "\n", + "out, _ = relu_forward(x)\n", + "correct_out = np.array([[ 0., 0., 0., 0., ],\n", + " [ 0., 0., 0.04545455, 0.13636364,],\n", + " [ 0.22727273, 0.31818182, 0.40909091, 0.5, ]])\n", + "\n", + "# Compare your output with ours. The error should be on the order of e-8\n", + "print('Testing relu_forward function:')\n", + "print('difference: ', rel_error(out, correct_out))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ReLU activation: backward\n", + "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing relu_backward function:\n", + "dx error: 1.0\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "x = np.random.randn(10, 10)\n", + "dout = np.random.randn(*x.shape)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n", + "\n", + "_, cache = relu_forward(x)\n", + "dx = relu_backward(dout, cache)\n", + "\n", + "# The error should be on the order of e-12\n", + "print('Testing relu_backward function:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 1: \n", + "\n", + "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n", + "1. Sigmoid\n", + "2. ReLU\n", + "3. Leaky ReLU\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# \"Sandwich\" layers\n", + "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n", + "\n", + "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing affine_relu_forward and affine_relu_backward:\n", + "dx error: 6.750562121603446e-11\n", + "dw error: 8.162015570444288e-11\n", + "db error: 7.826724021458994e-12\n" + ] + } + ], + "source": [ + "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n", + "np.random.seed(231)\n", + "x = np.random.randn(2, 3, 4)\n", + "w = np.random.randn(12, 10)\n", + "b = np.random.randn(10)\n", + "dout = np.random.randn(2, 10)\n", + "\n", + "out, cache = affine_relu_forward(x, w, b)\n", + "dx, dw, db = affine_relu_backward(dout, cache)\n", + "\n", + "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n", + "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n", + "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n", + "\n", + "# Relative error should be around e-10 or less\n", + "print('Testing affine_relu_forward and affine_relu_backward:')\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "print('dw error: ', rel_error(dw_num, dw))\n", + "print('db error: ', rel_error(db_num, db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss layers: Softmax and SVM\n", + "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n", + "\n", + "You can make sure that the implementations are correct by running the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing svm_loss:\n", + "loss: 8.999602749096233\n", + "dx error: 1.4021566006651672e-09\n", + "\n", + "Testing softmax_loss:\n", + "loss: 2.302545844500738\n", + "dx error: 9.384673161989355e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "num_classes, num_inputs = 10, 50\n", + "x = 0.001 * np.random.randn(num_inputs, num_classes)\n", + "y = np.random.randint(num_classes, size=num_inputs)\n", + "\n", + "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n", + "loss, dx = svm_loss(x, y)\n", + "\n", + "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n", + "print('Testing svm_loss:')\n", + "print('loss: ', loss)\n", + "print('dx error: ', rel_error(dx_num, dx))\n", + "\n", + "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n", + "loss, dx = softmax_loss(x, y)\n", + "\n", + "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n", + "print('\\nTesting softmax_loss:')\n", + "print('loss: ', loss)\n", + "print('dx error: ', rel_error(dx_num, dx))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two-layer network\n", + "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n", + "\n", + "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing initialization ... \n", + "Testing test-time forward pass ... \n", + "Testing training loss (no regularization)\n", + "Running numeric gradient check with reg = 0.0\n", + "W1 relative error: 1.00e+00\n", + "W2 relative error: 1.00e+00\n", + "b1 relative error: 1.00e+00\n", + "b2 relative error: 4.33e-10\n", + "Running numeric gradient check with reg = 0.7\n", + "W1 relative error: 1.00e+00\n", + "W2 relative error: 1.00e+00\n", + "b1 relative error: 1.00e+00\n", + "b2 relative error: 1.28e-09\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H, C = 3, 5, 50, 7\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=N)\n", + "\n", + "std = 1e-3\n", + "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n", + "\n", + "print('Testing initialization ... ')\n", + "W1_std = abs(model.params['W1'].std() - std)\n", + "b1 = model.params['b1']\n", + "W2_std = abs(model.params['W2'].std() - std)\n", + "b2 = model.params['b2']\n", + "assert W1_std < std / 10, 'First layer weights do not seem right'\n", + "assert np.all(b1 == 0), 'First layer biases do not seem right'\n", + "assert W2_std < std / 10, 'Second layer weights do not seem right'\n", + "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n", + "\n", + "print('Testing test-time forward pass ... ')\n", + "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n", + "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n", + "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n", + "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n", + "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n", + "scores = model.loss(X)\n", + "correct_scores = np.asarray(\n", + " [[11.53165108, 12.2917344, 13.05181771, 13.81190102, 14.57198434, 15.33206765, 16.09215096],\n", + " [12.05769098, 12.74614105, 13.43459113, 14.1230412, 14.81149128, 15.49994135, 16.18839143],\n", + " [12.58373087, 13.20054771, 13.81736455, 14.43418138, 15.05099822, 15.66781506, 16.2846319 ]])\n", + "scores_diff = np.abs(scores - correct_scores).sum()\n", + "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n", + "\n", + "print('Testing training loss (no regularization)')\n", + "y = np.asarray([0, 5, 1])\n", + "loss, grads = model.loss(X, y)\n", + "correct_loss = 3.4702243556\n", + "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n", + "\n", + "model.reg = 0.5\n", + "loss, grads = model.loss(X, y)\n", + "correct_loss = 26.5948426952\n", + "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n", + "\n", + "# Errors should be around e-7 or less\n", + "for reg in [0.0, 0.7]:\n", + " print('Running numeric gradient check with reg = ', reg)\n", + " model.reg = reg\n", + " loss, grads = model.loss(X, y)\n", + "\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solver\n", + "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n", + "\n", + "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 4900) loss: 2.304060\n", + "(Epoch 0 / 10) train acc: 0.091000; val_acc: 0.069000\n", + "(Iteration 101 / 4900) loss: 1.938327\n", + "(Iteration 201 / 4900) loss: 1.960257\n", + "(Iteration 301 / 4900) loss: 1.862565\n", + "(Iteration 401 / 4900) loss: 1.618868\n", + "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.404000\n", + "(Iteration 501 / 4900) loss: 1.544881\n", + "(Iteration 601 / 4900) loss: 1.732115\n", + "(Iteration 701 / 4900) loss: 1.713286\n", + "(Iteration 801 / 4900) loss: 1.684847\n", + "(Iteration 901 / 4900) loss: 1.567337\n", + "(Epoch 2 / 10) train acc: 0.461000; val_acc: 0.463000\n", + "(Iteration 1001 / 4900) loss: 1.484216\n", + "(Iteration 1101 / 4900) loss: 1.483702\n", + "(Iteration 1201 / 4900) loss: 1.752461\n", + "(Iteration 1301 / 4900) loss: 1.553389\n", + "(Iteration 1401 / 4900) loss: 1.365505\n", + "(Epoch 3 / 10) train acc: 0.486000; val_acc: 0.464000\n", + "(Iteration 1501 / 4900) loss: 1.438134\n", + "(Iteration 1601 / 4900) loss: 1.456878\n", + "(Iteration 1701 / 4900) loss: 1.379353\n", + "(Iteration 1801 / 4900) loss: 1.519290\n", + "(Iteration 1901 / 4900) loss: 1.486483\n", + "(Epoch 4 / 10) train acc: 0.514000; val_acc: 0.482000\n", + "(Iteration 2001 / 4900) loss: 1.510836\n", + "(Iteration 2101 / 4900) loss: 1.428223\n", + "(Iteration 2201 / 4900) loss: 1.395694\n", + "(Iteration 2301 / 4900) loss: 1.423331\n", + "(Iteration 2401 / 4900) loss: 1.443047\n", + "(Epoch 5 / 10) train acc: 0.498000; val_acc: 0.496000\n", + "(Iteration 2501 / 4900) loss: 1.554888\n", + "(Iteration 2601 / 4900) loss: 1.416505\n", + "(Iteration 2701 / 4900) loss: 1.232805\n", + "(Iteration 2801 / 4900) loss: 1.382266\n", + "(Iteration 2901 / 4900) loss: 1.388853\n", + "(Epoch 6 / 10) train acc: 0.505000; val_acc: 0.499000\n", + "(Iteration 3001 / 4900) loss: 1.391340\n", + "(Iteration 3101 / 4900) loss: 1.452501\n", + "(Iteration 3201 / 4900) loss: 1.395050\n", + "(Iteration 3301 / 4900) loss: 1.433530\n", + "(Iteration 3401 / 4900) loss: 1.489256\n", + "(Epoch 7 / 10) train acc: 0.513000; val_acc: 0.489000\n", + "(Iteration 3501 / 4900) loss: 1.371254\n", + "(Iteration 3601 / 4900) loss: 1.254908\n", + "(Iteration 3701 / 4900) loss: 1.400341\n", + "(Iteration 3801 / 4900) loss: 1.288566\n", + "(Iteration 3901 / 4900) loss: 1.252551\n", + "(Epoch 8 / 10) train acc: 0.515000; val_acc: 0.508000\n", + "(Iteration 4001 / 4900) loss: 1.387048\n", + "(Iteration 4101 / 4900) loss: 1.450029\n", + "(Iteration 4201 / 4900) loss: 1.342555\n", + "(Iteration 4301 / 4900) loss: 1.277807\n", + "(Iteration 4401 / 4900) loss: 1.408134\n", + "(Epoch 9 / 10) train acc: 0.536000; val_acc: 0.511000\n", + "(Iteration 4501 / 4900) loss: 1.170929\n", + "(Iteration 4601 / 4900) loss: 1.420749\n", + "(Iteration 4701 / 4900) loss: 1.225336\n", + "(Iteration 4801 / 4900) loss: 1.213172\n", + "(Epoch 10 / 10) train acc: 0.549000; val_acc: 0.513000\n" + ] + } + ], + "source": [ + "model = TwoLayerNet()\n", + "solver = None\n", + "\n", + "##############################################################################\n", + "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least #\n", + "# 50% accuracy on the validation set. #\n", + "##############################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "train_and_val = {}\n", + "\n", + "train_and_val['X_train'] = data['X_train']\n", + "train_and_val['y_train'] = data['y_train']\n", + "train_and_val['X_val'] = data['X_val']\n", + "train_and_val['y_val'] = data['y_val']\n", + "\n", + "solver = Solver(model, train_and_val,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': 0.0004,\n", + " },\n", + " lr_decay=0.95,\n", + " num_epochs=10, batch_size=100,\n", + " print_every=100)\n", + "solver.train()\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "##############################################################################\n", + "# END OF YOUR CODE #\n", + "##############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to visualize training loss and train / val accuracy\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.title('Accuracy')\n", + "plt.plot(solver.train_acc_history, '-o', label='train')\n", + "plt.plot(solver.val_acc_history, '-o', label='val')\n", + "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(loc='lower right')\n", + "plt.gcf().set_size_inches(15, 12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilayer network\n", + "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n", + "\n", + "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n", + "\n", + "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial loss and gradient check\n", + "\n", + "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n", + "\n", + "For gradient checking, you should expect to see errors around 1e-7 or less." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running check with reg = 0\n", + "Initial loss: 2.300479089768492\n", + "W1 relative error: 1.03e-07\n", + "W2 relative error: 2.21e-05\n", + "W3 relative error: 4.56e-07\n", + "b1 relative error: 4.66e-09\n", + "b2 relative error: 2.09e-09\n", + "b3 relative error: 1.69e-10\n", + "Running check with reg = 3.14\n", + "Initial loss: 7.052114776533016\n", + "W1 relative error: 6.86e-09\n", + "W2 relative error: 3.52e-08\n", + "W3 relative error: 2.62e-08\n", + "b1 relative error: 1.48e-08\n", + "b2 relative error: 1.72e-09\n", + "b3 relative error: 2.38e-10\n" + ] + } + ], + "source": [ + "np.random.seed(231)\n", + "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", + "X = np.random.randn(N, D)\n", + "y = np.random.randint(C, size=(N,))\n", + "\n", + "for reg in [0, 3.14]:\n", + " print('Running check with reg = ', reg)\n", + " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", + " reg=reg, weight_scale=5e-2, dtype=np.float64)\n", + "\n", + " loss, grads = model.loss(X, y)\n", + " print('Initial loss: ', loss)\n", + " \n", + " # Most of the errors should be on the order of e-7 or smaller. \n", + " # NOTE: It is fine however to see an error for W2 on the order of e-5\n", + " # for the check when reg = 0.0\n", + " for name in sorted(grads):\n", + " f = lambda _: model.loss(X, y)[0]\n", + " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", + " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kalkidanfekadu/Desktop/CS231/assignment2/cs231n/classifiers/fc_net.py:331: RuntimeWarning: divide by zero encountered in log\n", + " loss = np.sum(-np.log(scores[range(N), y]))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 40) loss: inf\n", + "(Epoch 0 / 20) train acc: 0.020000; val_acc: 0.109000\n", + "(Epoch 1 / 20) train acc: 0.040000; val_acc: 0.112000\n", + "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.110000\n", + "(Epoch 3 / 20) train acc: 0.300000; val_acc: 0.143000\n", + "(Epoch 4 / 20) train acc: 0.300000; val_acc: 0.135000\n", + "(Epoch 5 / 20) train acc: 0.420000; val_acc: 0.159000\n", + "(Iteration 11 / 40) loss: 31.049688\n", + "(Epoch 6 / 20) train acc: 0.540000; val_acc: 0.155000\n", + "(Epoch 7 / 20) train acc: 0.560000; val_acc: 0.145000\n", + "(Epoch 8 / 20) train acc: 0.640000; val_acc: 0.145000\n", + "(Epoch 9 / 20) train acc: 0.700000; val_acc: 0.150000\n", + "(Epoch 10 / 20) train acc: 0.760000; val_acc: 0.150000\n", + "(Iteration 21 / 40) loss: 23.376963\n", + "(Epoch 11 / 20) train acc: 0.740000; val_acc: 0.160000\n", + "(Epoch 12 / 20) train acc: 0.800000; val_acc: 0.149000\n", + "(Epoch 13 / 20) train acc: 0.840000; val_acc: 0.141000\n", + "(Epoch 14 / 20) train acc: 0.880000; val_acc: 0.145000\n", + "(Epoch 15 / 20) train acc: 0.940000; val_acc: 0.146000\n", + "(Iteration 31 / 40) loss: 0.554121\n", + "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.142000\n", + "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.146000\n", + "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.146000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Use a three-layer Net to overfit 50 training examples by \n", + "# tweaking just the learning rate and initialization scale.\n", + "\n", + "num_train = 50\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "weight_scale = 0.099 # Experiment with this!\n", + "learning_rate = 1e-4 # Experiment with this!\n", + "model = FullyConnectedNet([100, 100],\n", + " weight_scale=weight_scale, dtype=np.float64)\n", + "solver = Solver(model, small_data,\n", + " print_every=10, num_epochs=20, batch_size=25,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': learning_rate,\n", + " }\n", + " )\n", + "solver.train()\n", + "\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.title('Training loss history')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Training loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Iteration 1 / 40) loss: 2.302585\n", + "(Epoch 0 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 3 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 4 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 5 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 11 / 40) loss: 2.275307\n", + "(Epoch 6 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 7 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 8 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 9 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 10 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 21 / 40) loss: 2.274710\n", + "(Epoch 11 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 12 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 13 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 14 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 15 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Iteration 31 / 40) loss: 2.276649\n", + "(Epoch 16 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 17 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 18 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 19 / 20) train acc: 0.160000; val_acc: 0.079000\n", + "(Epoch 20 / 20) train acc: 0.160000; val_acc: 0.079000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: Use a five-layer Net to overfit 50 training examples by \n", + "# tweaking just the learning rate and initialization scale.\n", + "\n", + "num_train = 50\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "learning_rate = 10**(np.random.uniform(-6,-1)) # Experiment with this!\n", + "weight_scale = 10**(np.random.uniform(-4,-1)) # Experiment with this!\n", + "model = FullyConnectedNet([100, 100, 100, 100],\n", + " weight_scale=weight_scale, dtype=np.float64)\n", + "solver = Solver(model, small_data,\n", + " print_every=10, num_epochs=20, batch_size=25,\n", + " update_rule='sgd',\n", + " optim_config={\n", + " 'learning_rate': learning_rate,\n", + " }\n", + " )\n", + "solver.train()\n", + "\n", + "plt.plot(solver.loss_history, 'o')\n", + "plt.title('Training loss history')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Training loss')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 2: \n", + "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n", + "\n", + "## Answer:\n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Update rules\n", + "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SGD+Momentum\n", + "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n", + "\n", + "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 8.882347033505819e-09\n", + "velocity error: 4.269287743278663e-09\n" + ] + } + ], + "source": [ + "from cs231n.optim import sgd_momentum\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-3, 'velocity': v}\n", + "next_w, _ = sgd_momentum(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789],\n", + " [ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526],\n", + " [ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263],\n", + " [ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]])\n", + "expected_velocity = np.asarray([\n", + " [ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158],\n", + " [ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105],\n", + " [ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053],\n", + " [ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]])\n", + "\n", + "# Should see relative errors around e-8 or less\n", + "print('next_w error: ', rel_error(next_w, expected_next_w))\n", + "print('velocity error: ', rel_error(expected_velocity, config['velocity']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running with sgd\n", + "(Iteration 1 / 200) loss: 2.743596\n", + "(Epoch 0 / 5) train acc: 0.073000; val_acc: 0.098000\n", + "(Iteration 11 / 200) loss: 2.310501\n", + "(Iteration 21 / 200) loss: 2.254656\n", + "(Iteration 31 / 200) loss: 2.121765\n", + "(Epoch 1 / 5) train acc: 0.198000; val_acc: 0.210000\n", + "(Iteration 41 / 200) loss: 2.045536\n", + "(Iteration 51 / 200) loss: 2.169792\n", + "(Iteration 61 / 200) loss: 2.008206\n", + "(Iteration 71 / 200) loss: 1.995862\n", + "(Epoch 2 / 5) train acc: 0.287000; val_acc: 0.256000\n", + "(Iteration 81 / 200) loss: 2.113303\n", + "(Iteration 91 / 200) loss: 1.871558\n", + "(Iteration 101 / 200) loss: 2.041598\n", + "(Iteration 111 / 200) loss: 1.894926\n", + "(Epoch 3 / 5) train acc: 0.338000; val_acc: 0.263000\n", + "(Iteration 121 / 200) loss: 2.008418\n", + "(Iteration 131 / 200) loss: 1.714670\n", + "(Iteration 141 / 200) loss: 1.909367\n", + "(Iteration 151 / 200) loss: 1.806134\n", + "(Epoch 4 / 5) train acc: 0.356000; val_acc: 0.290000\n", + "(Iteration 161 / 200) loss: 1.753934\n", + "(Iteration 171 / 200) loss: 1.883756\n", + "(Iteration 181 / 200) loss: 1.951251\n", + "(Iteration 191 / 200) loss: 1.836960\n", + "(Epoch 5 / 5) train acc: 0.369000; val_acc: 0.320000\n", + "\n", + "running with sgd_momentum\n", + "(Iteration 1 / 200) loss: 2.587765\n", + "(Epoch 0 / 5) train acc: 0.096000; val_acc: 0.089000\n", + "(Iteration 11 / 200) loss: 2.154765\n", + "(Iteration 21 / 200) loss: 2.025203\n", + "(Iteration 31 / 200) loss: 2.018126\n", + "(Epoch 1 / 5) train acc: 0.306000; val_acc: 0.285000\n", + "(Iteration 41 / 200) loss: 1.973381\n", + "(Iteration 51 / 200) loss: 1.734572\n", + "(Iteration 61 / 200) loss: 1.726248\n", + "(Iteration 71 / 200) loss: 1.850464\n", + "(Epoch 2 / 5) train acc: 0.424000; val_acc: 0.342000\n", + "(Iteration 81 / 200) loss: 1.842990\n", + "(Iteration 91 / 200) loss: 1.611516\n", + "(Iteration 101 / 200) loss: 1.615972\n", + "(Iteration 111 / 200) loss: 1.414714\n", + "(Epoch 3 / 5) train acc: 0.474000; val_acc: 0.347000\n", + "(Iteration 121 / 200) loss: 1.376354\n", + "(Iteration 131 / 200) loss: 1.596911\n", + "(Iteration 141 / 200) loss: 1.447764\n", + "(Iteration 151 / 200) loss: 1.431524\n", + "(Epoch 4 / 5) train acc: 0.465000; val_acc: 0.364000\n", + "(Iteration 161 / 200) loss: 1.377602\n", + "(Iteration 171 / 200) loss: 1.421367\n", + "(Iteration 181 / 200) loss: 1.497430\n", + "(Iteration 191 / 200) loss: 1.414507\n", + "(Epoch 5 / 5) train acc: 0.541000; val_acc: 0.358000\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n", + "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " \"Adding an axes using the same arguments as a previous axes \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "num_train = 4000\n", + "small_data = {\n", + " 'X_train': data['X_train'][:num_train],\n", + " 'y_train': data['y_train'][:num_train],\n", + " 'X_val': data['X_val'],\n", + " 'y_val': data['y_val'],\n", + "}\n", + "\n", + "solvers = {}\n", + "\n", + "for update_rule in ['sgd', 'sgd_momentum']:\n", + " print('running with ', update_rule)\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule=update_rule,\n", + " optim_config={\n", + " 'learning_rate': 5e-3,\n", + " },\n", + " verbose=True)\n", + " solvers[update_rule] = solver\n", + " solver.train()\n", + " print()\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Training accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Validation accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "for update_rule, solver in solvers.items():\n", + " plt.subplot(3, 1, 1)\n", + " plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n", + " \n", + " plt.subplot(3, 1, 2)\n", + " plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n", + "\n", + " plt.subplot(3, 1, 3)\n", + " plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n", + " \n", + "for i in [1, 2, 3]:\n", + " plt.subplot(3, 1, i)\n", + " plt.legend(loc='upper center', ncol=4)\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RMSProp and Adam\n", + "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n", + "\n", + "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n", + "\n", + "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n", + "\n", + "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n", + "\n", + "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 9.524687511038133e-08\n", + "cache error: 2.6477955807156126e-09\n" + ] + } + ], + "source": [ + "# Test RMSProp implementation\n", + "from cs231n.optim import rmsprop\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-2, 'cache': cache}\n", + "next_w, _ = rmsprop(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n", + " [-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774],\n", + " [ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447],\n", + " [ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]])\n", + "expected_cache = np.asarray([\n", + " [ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321],\n", + " [ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377],\n", + " [ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936],\n", + " [ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]])\n", + "\n", + "# You should see relative errors around e-7 or less\n", + "print('next_w error: ', rel_error(expected_next_w, next_w))\n", + "print('cache error: ', rel_error(expected_cache, config['cache']))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "next_w error: 1.1395691798535431e-07\n", + "v error: 4.208314038113071e-09\n", + "m error: 4.214963193114416e-09\n" + ] + } + ], + "source": [ + "# Test Adam implementation\n", + "from cs231n.optim import adam\n", + "\n", + "N, D = 4, 5\n", + "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n", + "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n", + "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n", + "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n", + "\n", + "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n", + "next_w, _ = adam(w, dw, config=config)\n", + "\n", + "expected_next_w = np.asarray([\n", + " [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n", + " [-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929],\n", + " [ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969],\n", + " [ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]])\n", + "expected_v = np.asarray([\n", + " [ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,],\n", + " [ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,],\n", + " [ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,],\n", + " [ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]])\n", + "expected_m = np.asarray([\n", + " [ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474],\n", + " [ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316],\n", + " [ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158],\n", + " [ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]])\n", + "\n", + "# You should see relative errors around e-7 or less\n", + "print('next_w error: ', rel_error(expected_next_w, next_w))\n", + "print('v error: ', rel_error(expected_v, config['v']))\n", + "print('m error: ', rel_error(expected_m, config['m']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running with adam\n", + "(Iteration 1 / 200) loss: 2.680973\n", + "(Epoch 0 / 5) train acc: 0.132000; val_acc: 0.120000\n", + "(Iteration 11 / 200) loss: 2.213776\n", + "(Iteration 21 / 200) loss: 2.082044\n", + "(Iteration 31 / 200) loss: 1.814850\n", + "(Epoch 1 / 5) train acc: 0.344000; val_acc: 0.322000\n", + "(Iteration 41 / 200) loss: 1.932607\n", + "(Iteration 51 / 200) loss: 1.732372\n", + "(Iteration 61 / 200) loss: 1.684073\n", + "(Iteration 71 / 200) loss: 1.454854\n", + "(Epoch 2 / 5) train acc: 0.446000; val_acc: 0.353000\n", + "(Iteration 81 / 200) loss: 1.562296\n", + "(Iteration 91 / 200) loss: 1.410424\n", + "(Iteration 101 / 200) loss: 1.569873\n", + "(Iteration 111 / 200) loss: 1.571657\n", + "(Epoch 3 / 5) train acc: 0.470000; val_acc: 0.330000\n", + "(Iteration 121 / 200) loss: 1.632057\n", + "(Iteration 131 / 200) loss: 1.488813\n", + "(Iteration 141 / 200) loss: 1.356309\n", + "(Iteration 151 / 200) loss: 1.371299\n", + "(Epoch 4 / 5) train acc: 0.551000; val_acc: 0.342000\n", + "(Iteration 161 / 200) loss: 1.138429\n", + "(Iteration 171 / 200) loss: 1.395116\n", + "(Iteration 181 / 200) loss: 1.189616\n", + "(Iteration 191 / 200) loss: 1.241926\n", + "(Epoch 5 / 5) train acc: 0.583000; val_acc: 0.371000\n", + "\n", + "running with rmsprop\n", + "(Iteration 1 / 200) loss: 2.525414\n", + "(Epoch 0 / 5) train acc: 0.141000; val_acc: 0.146000\n", + "(Iteration 11 / 200) loss: 2.195400\n", + "(Iteration 21 / 200) loss: 2.030609\n", + "(Iteration 31 / 200) loss: 1.811840\n", + "(Epoch 1 / 5) train acc: 0.367000; val_acc: 0.311000\n", + "(Iteration 41 / 200) loss: 1.820211\n", + "(Iteration 51 / 200) loss: 1.777108\n", + "(Iteration 61 / 200) loss: 1.707527\n", + "(Iteration 71 / 200) loss: 1.697344\n", + "(Epoch 2 / 5) train acc: 0.396000; val_acc: 0.336000\n", + "(Iteration 81 / 200) loss: 1.850499\n", + "(Iteration 91 / 200) loss: 1.560861\n", + "(Iteration 101 / 200) loss: 1.624011\n", + "(Iteration 111 / 200) loss: 1.477089\n", + "(Epoch 3 / 5) train acc: 0.461000; val_acc: 0.341000\n", + "(Iteration 121 / 200) loss: 1.601157\n", + "(Iteration 131 / 200) loss: 1.496088\n", + "(Iteration 141 / 200) loss: 1.493349\n", + "(Iteration 151 / 200) loss: 1.308786\n", + "(Epoch 4 / 5) train acc: 0.504000; val_acc: 0.348000\n", + "(Iteration 161 / 200) loss: 1.491016\n", + "(Iteration 171 / 200) loss: 1.389998\n", + "(Iteration 181 / 200) loss: 1.417710\n", + "(Iteration 191 / 200) loss: 1.520727\n", + "(Epoch 5 / 5) train acc: 0.529000; val_acc: 0.368000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n", + "for update_rule in ['adam', 'rmsprop']:\n", + " print('running with ', update_rule)\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n", + "\n", + " solver = Solver(model, small_data,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule=update_rule,\n", + " optim_config={\n", + " 'learning_rate': learning_rates[update_rule]\n", + " },\n", + " verbose=True)\n", + " solvers[update_rule] = solver\n", + " solver.train()\n", + " print()\n", + "\n", + "plt.subplot(3, 1, 1)\n", + "plt.title('Training loss')\n", + "plt.xlabel('Iteration')\n", + "\n", + "plt.subplot(3, 1, 2)\n", + "plt.title('Training accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.subplot(3, 1, 3)\n", + "plt.title('Validation accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "for update_rule, solver in list(solvers.items()):\n", + " plt.subplot(3, 1, 1)\n", + " plt.plot(solver.loss_history, 'o', label=update_rule)\n", + " \n", + " plt.subplot(3, 1, 2)\n", + " plt.plot(solver.train_acc_history, '-o', label=update_rule)\n", + "\n", + " plt.subplot(3, 1, 3)\n", + " plt.plot(solver.val_acc_history, '-o', label=update_rule)\n", + " \n", + "for i in [1, 2, 3]:\n", + " plt.subplot(3, 1, i)\n", + " plt.legend(loc='upper center', ncol=4)\n", + "plt.gcf().set_size_inches(15, 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Inline Question 3:\n", + "\n", + "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n", + "\n", + "```\n", + "cache += dw**2\n", + "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n", + "```\n", + "\n", + "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n", + "\n", + "\n", + "## Answer: \n", + "[FILL THIS IN]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train a good model!\n", + "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n", + "\n", + "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n", + "\n", + "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training with batchnorm normalization and dropout p= 1\n", + "training accuracies : [0.089, 0.46, 0.496, 0.562, 0.562, 0.58]\n", + "validation accuracies: [0.105, 0.459, 0.482, 0.503, 0.501, 0.516]\n", + "====================================================\n", + "training with batchnorm normalization and dropout p= 0.5\n", + "training accuracies : [0.098, 0.1, 0.152, 0.171, 0.171, 0.173]\n", + "validation accuracies: [0.104, 0.099, 0.158, 0.156, 0.167, 0.167]\n", + "====================================================\n", + "training with batchnorm normalization and dropout p= 0.75\n", + "training accuracies : [0.099, 0.19, 0.24, 0.236, 0.248, 0.234]\n", + "validation accuracies: [0.089, 0.178, 0.225, 0.196, 0.236, 0.215]\n", + "====================================================\n", + "training with layernorm normalization and dropout p= 1\n", + "training accuracies : [0.102, 0.323, 0.431, 0.466, 0.488, 0.572]\n", + "validation accuracies: [0.087, 0.309, 0.431, 0.483, 0.5, 0.514]\n", + "====================================================\n", + "training with layernorm normalization and dropout p= 0.5\n", + "training accuracies : [0.085, 0.137, 0.109, 0.141, 0.152, 0.127]\n", + "validation accuracies: [0.079, 0.128, 0.12, 0.134, 0.161, 0.134]\n", + "====================================================\n", + "training with layernorm normalization and dropout p= 0.75\n", + "training accuracies : [0.125, 0.158, 0.195, 0.215, 0.204, 0.226]\n", + "validation accuracies: [0.087, 0.128, 0.177, 0.182, 0.188, 0.186]\n", + "====================================================\n" + ] + } + ], + "source": [ + "best_model = None\n", + "################################################################################\n", + "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might #\n", + "# find batch/layer normalization and dropout useful. Store your best model in #\n", + "# the best_model variable. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "train_and_val = {}\n", + "\n", + "train_and_val['X_train'] = data['X_train']\n", + "train_and_val['y_train'] = data['y_train']\n", + "train_and_val['X_val'] = data['X_val']\n", + "train_and_val['y_val'] = data['y_val']\n", + "\n", + "normalize = ['batchnorm', 'layernorm']\n", + "dropouts = [1, 0.5, 0.75]\n", + "best_val = -1\n", + "best_model = None\n", + "\n", + "for norm in normalize:\n", + " for dropout in dropouts:\n", + " model = FullyConnectedNet([100, 100, 100, 100, 100],\n", + " dropout=dropout, normalization=norm, reg=0,\n", + " weight_scale=1e-2, dtype=np.float32, seed=123)\n", + "\n", + " solver = Solver(model, train_and_val ,\n", + " num_epochs=5, batch_size=100,\n", + " update_rule = 'adam',\n", + " optim_config={\n", + " 'learning_rate': 1e-3\n", + " },\n", + " verbose=False)\n", + "\n", + " solver.train()\n", + " if(solver.val_acc_history[-1] > best_val):\n", + " best_val = solver.val_acc_history[-1]\n", + " best_model = model\n", + " print(\"training with \", norm, \"normalization and dropout p=\", dropout)\n", + " print(\"training accuracies :\", solver.train_acc_history)\n", + " print(\"validation accuracies: \", solver.val_acc_history)\n", + " print(\"====================================================\")\n", + " \n", + "\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE #\n", + "################################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.516\n" + ] + } + ], + "source": [ + "print(best_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test your model!\n", + "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation set accuracy: 0.516\n", + "Test set accuracy: 0.53\n" + ] + } + ], + "source": [ + "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n", + "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n", + "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n", + "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/PyTorch.ipynb b/assignment2/PyTorch.ipynb new file mode 100755 index 0000000..5ce3723 --- /dev/null +++ b/assignment2/PyTorch.ipynb @@ -0,0 +1,1563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# What's this PyTorch business?\n", + "\n", + "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n", + "\n", + "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, PyTorch (or TensorFlow, if you choose to use that notebook)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### What is PyTorch?\n", + "\n", + "PyTorch is a system for executing dynamic computational graphs over Tensor objects that behave similarly as numpy ndarray. It comes with a powerful automatic differentiation engine that removes the need for manual back-propagation. \n", + "\n", + "### Why?\n", + "\n", + "* Our code will now run on GPUs! Much faster training. When using a framework like PyTorch or TensorFlow you can harness the power of the GPU for your own custom neural network architectures without having to write CUDA code directly (which is beyond the scope of this class).\n", + "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n", + "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n", + "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry.\n", + "\n", + "### PyTorch versions\n", + "This notebook assumes that you are using **PyTorch version 1.0**. In some of the previous versions (e.g. before 0.4), Tensors had to be wrapped in Variable objects to be used in autograd; however Variables have now been deprecated. In addition 1.0 also separates a Tensor's datatype from its device, and uses numpy-style factories for constructing Tensors rather than directly invoking Tensor constructors." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## How will I learn PyTorch?\n", + "\n", + "Justin Johnson has made an excellent [tutorial](https://github.com/jcjohnson/pytorch-examples) for PyTorch. \n", + "\n", + "You can also find the detailed [API doc](http://pytorch.org/docs/stable/index.html) here. If you have other questions that are not addressed by the API docs, the [PyTorch forum](https://discuss.pytorch.org/) is a much better place to ask than StackOverflow.\n", + "\n", + "\n", + "# Table of Contents\n", + "\n", + "This assignment has 5 parts. You will learn PyTorch on **three different levels of abstraction**, which will help you understand it better and prepare you for the final project. \n", + "\n", + "1. Part I, Preparation: we will use CIFAR-10 dataset.\n", + "2. Part II, Barebones PyTorch: **Abstraction level 1**, we will work directly with the lowest-level PyTorch Tensors. \n", + "3. Part III, PyTorch Module API: **Abstraction level 2**, we will use `nn.Module` to define arbitrary neural network architecture. \n", + "4. Part IV, PyTorch Sequential API: **Abstraction level 3**, we will use `nn.Sequential` to define a linear feed-forward network very conveniently. \n", + "5. Part V, CIFAR-10 open-ended challenge: please implement your own network to get as high accuracy as possible on CIFAR-10. You can experiment with any layer, optimizer, hyperparameters or other advanced features. \n", + "\n", + "Here is a table of comparison:\n", + "\n", + "| API | Flexibility | Convenience |\n", + "|---------------|-------------|-------------|\n", + "| Barebone | High | Low |\n", + "| `nn.Module` | High | Medium |\n", + "| `nn.Sequential` | Low | High |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part I. Preparation\n", + "\n", + "First, we load the CIFAR-10 dataset. This might take a couple minutes the first time you do it, but the files should stay cached after that.\n", + "\n", + "In previous parts of the assignment we had to write our own code to download the CIFAR-10 dataset, preprocess it, and iterate through it in minibatches; PyTorch provides convenient tools to automate this process for us." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import sampler\n", + "\n", + "import torchvision.datasets as dset\n", + "import torchvision.transforms as T\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "NUM_TRAIN = 49000\n", + "\n", + "# The torchvision.transforms package provides tools for preprocessing data\n", + "# and for performing data augmentation; here we set up a transform to\n", + "# preprocess the data by subtracting the mean RGB value and dividing by the\n", + "# standard deviation of each RGB value; we've hardcoded the mean and std.\n", + "transform = T.Compose([\n", + " T.ToTensor(),\n", + " T.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n", + " ])\n", + "\n", + "# We set up a Dataset object for each split (train / val / test); Datasets load\n", + "# training examples one at a time, so we wrap each Dataset in a DataLoader which\n", + "# iterates through the Dataset and forms minibatches. We divide the CIFAR-10\n", + "# training set into train and val sets by passing a Sampler object to the\n", + "# DataLoader telling how it should sample from the underlying Dataset.\n", + "cifar10_train = dset.CIFAR10('./cs231n/datasets', train=True, download=True,\n", + " transform=transform)\n", + "loader_train = DataLoader(cifar10_train, batch_size=64, \n", + " sampler=sampler.SubsetRandomSampler(range(NUM_TRAIN)))\n", + "\n", + "cifar10_val = dset.CIFAR10('./cs231n/datasets', train=True, download=True,\n", + " transform=transform)\n", + "loader_val = DataLoader(cifar10_val, batch_size=64, \n", + " sampler=sampler.SubsetRandomSampler(range(NUM_TRAIN, 50000)))\n", + "\n", + "cifar10_test = dset.CIFAR10('./cs231n/datasets', train=False, download=True, \n", + " transform=transform)\n", + "loader_test = DataLoader(cifar10_test, batch_size=64)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "You have an option to **use GPU by setting the flag to True below**. It is not necessary to use GPU for this assignment. Note that if your computer does not have CUDA enabled, `torch.cuda.is_available()` will return False and this notebook will fallback to CPU mode.\n", + "\n", + "The global variables `dtype` and `device` will control the data types throughout this assignment. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "using device: cpu\n" + ] + } + ], + "source": [ + "USE_GPU = True\n", + "\n", + "dtype = torch.float32 # we will be using float throughout this tutorial\n", + "\n", + "if USE_GPU and torch.cuda.is_available():\n", + " device = torch.device('cuda')\n", + "else:\n", + " device = torch.device('cpu')\n", + "\n", + "# Constant to control how frequently we print train loss\n", + "print_every = 100\n", + "\n", + "print('using device:', device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part II. Barebones PyTorch\n", + "\n", + "PyTorch ships with high-level APIs to help us define model architectures conveniently, which we will cover in Part II of this tutorial. In this section, we will start with the barebone PyTorch elements to understand the autograd engine better. After this exercise, you will come to appreciate the high-level model API more.\n", + "\n", + "We will start with a simple fully-connected ReLU network with two hidden layers and no biases for CIFAR classification. \n", + "This implementation computes the forward pass using operations on PyTorch Tensors, and uses PyTorch autograd to compute gradients. It is important that you understand every line, because you will write a harder version after the example.\n", + "\n", + "When we create a PyTorch Tensor with `requires_grad=True`, then operations involving that Tensor will not just compute values; they will also build up a computational graph in the background, allowing us to easily backpropagate through the graph to compute gradients of some Tensors with respect to a downstream loss. Concretely if x is a Tensor with `x.requires_grad == True` then after backpropagation `x.grad` will be another Tensor holding the gradient of x with respect to the scalar loss at the end." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### PyTorch Tensors: Flatten Function\n", + "A PyTorch Tensor is conceptionally similar to a numpy array: it is an n-dimensional grid of numbers, and like numpy PyTorch provides many functions to efficiently operate on Tensors. As a simple example, we provide a `flatten` function below which reshapes image data for use in a fully-connected neural network.\n", + "\n", + "Recall that image data is typically stored in a Tensor of shape N x C x H x W, where:\n", + "\n", + "* N is the number of datapoints\n", + "* C is the number of channels\n", + "* H is the height of the intermediate feature map in pixels\n", + "* W is the height of the intermediate feature map in pixels\n", + "\n", + "This is the right way to represent the data when we are doing something like a 2D convolution, that needs spatial understanding of where the intermediate features are relative to each other. When we use fully connected affine layers to process the image, however, we want each datapoint to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data. So, we use a \"flatten\" operation to collapse the `C x H x W` values per representation into a single long vector. The flatten function below first reads in the N, C, H, and W values from a given batch of data, and then returns a \"view\" of that data. \"View\" is analogous to numpy's \"reshape\" method: it reshapes x's dimensions to be N x ??, where ?? is allowed to be anything (in this case, it will be C x H x W, but we don't need to specify that explicitly). " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before flattening: tensor([[[[ 0, 1],\n", + " [ 2, 3],\n", + " [ 4, 5]]],\n", + "\n", + "\n", + " [[[ 6, 7],\n", + " [ 8, 9],\n", + " [10, 11]]]])\n", + "After flattening: tensor([[ 0, 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10, 11]])\n" + ] + } + ], + "source": [ + "def flatten(x):\n", + " N = x.shape[0] # read in N, C, H, W\n", + " return x.view(N, -1) # \"flatten\" the C * H * W values into a single vector per image\n", + "\n", + "def test_flatten():\n", + " x = torch.arange(12).view(2, 1, 3, 2)\n", + " print('Before flattening: ', x)\n", + " print('After flattening: ', flatten(x))\n", + "\n", + "test_flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### Barebones PyTorch: Two-Layer Network\n", + "\n", + "Here we define a function `two_layer_fc` which performs the forward pass of a two-layer fully-connected ReLU network on a batch of image data. After defining the forward pass we check that it doesn't crash and that it produces outputs of the right shape by running zeros through the network.\n", + "\n", + "You don't have to write any code here, but it's important that you read and understand the implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "import torch.nn.functional as F # useful stateless functions\n", + "\n", + "def two_layer_fc(x, params):\n", + " \"\"\"\n", + " A fully-connected neural networks; the architecture is:\n", + " NN is fully connected -> ReLU -> fully connected layer.\n", + " Note that this function only defines the forward pass; \n", + " PyTorch will take care of the backward pass for us.\n", + " \n", + " The input to the network will be a minibatch of data, of shape\n", + " (N, d1, ..., dM) where d1 * ... * dM = D. The hidden layer will have H units,\n", + " and the output layer will produce scores for C classes.\n", + " \n", + " Inputs:\n", + " - x: A PyTorch Tensor of shape (N, d1, ..., dM) giving a minibatch of\n", + " input data.\n", + " - params: A list [w1, w2] of PyTorch Tensors giving weights for the network;\n", + " w1 has shape (D, H) and w2 has shape (H, C).\n", + " \n", + " Returns:\n", + " - scores: A PyTorch Tensor of shape (N, C) giving classification scores for\n", + " the input data x.\n", + " \"\"\"\n", + " # first we flatten the image\n", + " x = flatten(x) # shape: [batch_size, C x H x W]\n", + " \n", + " w1, w2 = params\n", + " \n", + " # Forward pass: compute predicted y using operations on Tensors. Since w1 and\n", + " # w2 have requires_grad=True, operations involving these Tensors will cause\n", + " # PyTorch to build a computational graph, allowing automatic computation of\n", + " # gradients. Since we are no longer implementing the backward pass by hand we\n", + " # don't need to keep references to intermediate values.\n", + " # you can also use `.clamp(min=0)`, equivalent to F.relu()\n", + " x = F.relu(x.mm(w1))\n", + " x = x.mm(w2)\n", + " return x\n", + " \n", + "\n", + "def two_layer_fc_test():\n", + " hidden_layer_size = 42\n", + " x = torch.zeros((64, 50), dtype=dtype) # minibatch size 64, feature dimension 50\n", + " w1 = torch.zeros((50, hidden_layer_size), dtype=dtype)\n", + " w2 = torch.zeros((hidden_layer_size, 10), dtype=dtype)\n", + " scores = two_layer_fc(x, [w1, w2])\n", + " print(scores.size()) # you should see [64, 10]\n", + "\n", + "two_layer_fc_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Three-Layer ConvNet\n", + "\n", + "Here you will complete the implementation of the function `three_layer_convnet`, which will perform the forward pass of a three-layer convolutional network. Like above, we can immediately test our implementation by passing zeros through the network. The network should have the following architecture:\n", + "\n", + "1. A convolutional layer (with bias) with `channel_1` filters, each with shape `KW1 x KH1`, and zero-padding of two\n", + "2. ReLU nonlinearity\n", + "3. A convolutional layer (with bias) with `channel_2` filters, each with shape `KW2 x KH2`, and zero-padding of one\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer with bias, producing scores for C classes.\n", + "\n", + "Note that we have **no softmax activation** here after our fully-connected layer: this is because PyTorch's cross entropy loss performs a softmax activation for you, and by bundling that step in makes computation more efficient.\n", + "\n", + "**HINT**: For convolutions: http://pytorch.org/docs/stable/nn.html#torch.nn.functional.conv2d; pay attention to the shapes of convolutional filters!" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def three_layer_convnet(x, params):\n", + " \"\"\"\n", + " Performs the forward pass of a three-layer convolutional network with the\n", + " architecture defined above.\n", + "\n", + " Inputs:\n", + " - x: A PyTorch Tensor of shape (N, 3, H, W) giving a minibatch of images\n", + " - params: A list of PyTorch Tensors giving the weights and biases for the\n", + " network; should contain the following:\n", + " - conv_w1: PyTorch Tensor of shape (channel_1, 3, KH1, KW1) giving weights\n", + " for the first convolutional layer\n", + " - conv_b1: PyTorch Tensor of shape (channel_1,) giving biases for the first\n", + " convolutional layer\n", + " - conv_w2: PyTorch Tensor of shape (channel_2, channel_1, KH2, KW2) giving\n", + " weights for the second convolutional layer\n", + " - conv_b2: PyTorch Tensor of shape (channel_2,) giving biases for the second\n", + " convolutional layer\n", + " - fc_w: PyTorch Tensor giving weights for the fully-connected layer. Can you\n", + " figure out what the shape should be?\n", + " - fc_b: PyTorch Tensor giving biases for the fully-connected layer. Can you\n", + " figure out what the shape should be?\n", + " \n", + " Returns:\n", + " - scores: PyTorch Tensor of shape (N, C) giving classification scores for x\n", + " \"\"\"\n", + " conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b = params\n", + " scores = None\n", + " ################################################################################\n", + " # TODO: Implement the forward pass for the three-layer ConvNet. #\n", + " ################################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " out_channel_1, in_channel_1, KH1, KW1 = conv_w1.shape\n", + " out_channel_2, in_channel_2, KH2, KW2 = conv_w2.shape\n", + " batch, channel, H, W = x.shape\n", + " flat_x, out = fc_w.shape\n", + " \n", + " \n", + " conv1 = nn.Conv2d(in_channel_1, out_channel_1, KH1, padding=2)\n", + " conv1.weight = conv_w1\n", + " conv1.bias = conv_b1\n", + " \n", + " conv2 = nn.Conv2d(in_channel_2, out_channel_2, KH2, padding=1)\n", + " conv2.weight = conv_w2\n", + " conv2.bias = conv_b2\n", + " \n", + " \n", + " x = F.relu(conv1(x))\n", + " x = F.relu(conv2(x))\n", + " x = flatten(x) \n", + " scores = x.mm(fc_w) + fc_b\n", + "\n", + " \n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ################################################################################\n", + " # END OF YOUR CODE #\n", + " ################################################################################\n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After defining the forward pass of the ConvNet above, run the following cell to test your implementation.\n", + "\n", + "When you run this function, scores should have shape (64, 10)." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "def three_layer_convnet_test():\n", + " x = torch.zeros((64, 3, 32, 32), dtype=dtype) # minibatch size 64, image size [3, 32, 32]\n", + "\n", + " conv_w1 = nn.Parameter(torch.zeros((6, 3, 5, 5), dtype=dtype)) # [out_channel, in_channel, kernel_H, kernel_W]\n", + " conv_b1 = nn.Parameter(torch.zeros((6,))) # out_channel\n", + " conv_w2 = nn.Parameter(torch.zeros((9, 6, 3, 3), dtype=dtype)) # [out_channel, in_channel, kernel_H, kernel_W]\n", + " conv_b2 = nn.Parameter(torch.zeros((9,))) # out_channel\n", + "\n", + " # you must calculate the shape of the tensor after two conv layers, before the fully-connected layer\n", + " fc_w = torch.zeros((9 * 32 * 32, 10))\n", + " fc_b = torch.zeros(10)\n", + "\n", + " scores = three_layer_convnet(x, [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b])\n", + " \n", + " print(scores.size()) # you should see [64, 10]\n", + "three_layer_convnet_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Initialization\n", + "Let's write a couple utility methods to initialize the weight matrices for our models.\n", + "\n", + "- `random_weight(shape)` initializes a weight tensor with the Kaiming normalization method.\n", + "- `zero_weight(shape)` initializes a weight tensor with all zeros. Useful for instantiating bias parameters.\n", + "\n", + "The `random_weight` function uses the Kaiming normal initialization method, described in:\n", + "\n", + "He et al, *Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification*, ICCV 2015, https://arxiv.org/abs/1502.01852" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[-0.9709, 0.8014, -0.7096, -1.3630, 0.2763],\n", + " [ 0.0339, -0.0283, -0.5006, 1.0902, -0.4276],\n", + " [ 0.2078, -1.4354, 0.7377, -1.7247, -1.3787]], requires_grad=True)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def random_weight(shape):\n", + " \"\"\"\n", + " Create random Tensors for weights; setting requires_grad=True means that we\n", + " want to compute gradients for these Tensors during the backward pass.\n", + " We use Kaiming normalization: sqrt(2 / fan_in)\n", + " \"\"\"\n", + " if len(shape) == 2: # FC weight\n", + " fan_in = shape[0]\n", + " else:\n", + " fan_in = np.prod(shape[1:]) # conv weight [out_channel, in_channel, kH, kW]\n", + " # randn is standard normal distribution generator. \n", + " w = torch.randn(shape, device=device, dtype=dtype) * np.sqrt(2. / fan_in)\n", + " w.requires_grad = True\n", + " return w\n", + "\n", + "def zero_weight(shape):\n", + " return torch.zeros(shape, device=device, dtype=dtype, requires_grad=True)\n", + "\n", + "# create a weight of shape [3 x 5]\n", + "# you should see the type `torch.cuda.FloatTensor` if you use GPU. \n", + "# Otherwise it should be `torch.FloatTensor`\n", + "random_weight((3, 5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones PyTorch: Check Accuracy\n", + "When training the model we will use the following function to check the accuracy of our model on the training or validation sets.\n", + "\n", + "When checking accuracy we don't need to compute any gradients; as a result we don't need PyTorch to build a computational graph for us when we compute scores. To prevent a graph from being built we scope our computation under a `torch.no_grad()` context manager." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def check_accuracy_part2(loader, model_fn, params):\n", + " \"\"\"\n", + " Check the accuracy of a classification model.\n", + " \n", + " Inputs:\n", + " - loader: A DataLoader for the data split we want to check\n", + " - model_fn: A function that performs the forward pass of the model,\n", + " with the signature scores = model_fn(x, params)\n", + " - params: List of PyTorch Tensors giving parameters of the model\n", + " \n", + " Returns: Nothing, but prints the accuracy of the model\n", + " \"\"\"\n", + " split = 'val' if loader.dataset.train else 'test'\n", + " print('Checking accuracy on the %s set' % split)\n", + " num_correct, num_samples = 0, 0\n", + " with torch.no_grad():\n", + " for x, y in loader:\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.int64)\n", + " scores = model_fn(x, params)\n", + " _, preds = scores.max(1)\n", + " num_correct += (preds == y).sum()\n", + " num_samples += preds.size(0)\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f%%)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Training Loop\n", + "We can now set up a basic training loop to train our network. We will train the model using stochastic gradient descent without momentum. We will use `torch.functional.cross_entropy` to compute the loss; you can [read about it here](http://pytorch.org/docs/stable/nn.html#cross-entropy).\n", + "\n", + "The training loop takes as input the neural network function, a list of initialized parameters (`[w1, w2]` in our example), and learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [], + "source": [ + "def train_part2(model_fn, params, learning_rate):\n", + " \"\"\"\n", + " Train a model on CIFAR-10.\n", + " \n", + " Inputs:\n", + " - model_fn: A Python function that performs the forward pass of the model.\n", + " It should have the signature scores = model_fn(x, params) where x is a\n", + " PyTorch Tensor of image data, params is a list of PyTorch Tensors giving\n", + " model weights, and scores is a PyTorch Tensor of shape (N, C) giving\n", + " scores for the elements in x.\n", + " - params: List of PyTorch Tensors giving weights for the model\n", + " - learning_rate: Python scalar giving the learning rate to use for SGD\n", + " \n", + " Returns: Nothing\n", + " \"\"\"\n", + " for t, (x, y) in enumerate(loader_train):\n", + " # Move the data to the proper device (GPU or CPU)\n", + " x = x.to(device=device, dtype=dtype)\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " # Forward pass: compute scores and loss\n", + " scores = model_fn(x, params)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Backward pass: PyTorch figures out which Tensors in the computational\n", + " # graph has requires_grad=True and uses backpropagation to compute the\n", + " # gradient of the loss with respect to these Tensors, and stores the\n", + " # gradients in the .grad attribute of each Tensor.\n", + " loss.backward()\n", + "\n", + " # Update parameters. We don't want to backpropagate through the\n", + " # parameter updates, so we scope the updates under a torch.no_grad()\n", + " # context manager to prevent a computational graph from being built.\n", + " with torch.no_grad():\n", + " for w in params:\n", + " \n", + " w -= learning_rate * w.grad\n", + "\n", + " # Manually zero the gradients after running the backward pass\n", + " w.grad.zero_()\n", + "\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss.item()))\n", + " check_accuracy_part2(loader_val, model_fn, params)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Train a Two-Layer Network\n", + "Now we are ready to run the training loop. We need to explicitly allocate tensors for the fully connected weights, `w1` and `w2`. \n", + "\n", + "Each minibatch of CIFAR has 64 examples, so the tensor shape is `[64, 3, 32, 32]`. \n", + "\n", + "After flattening, `x` shape should be `[64, 3 * 32 * 32]`. This will be the size of the first dimension of `w1`. \n", + "The second dimension of `w1` is the hidden layer size, which will also be the first dimension of `w2`. \n", + "\n", + "Finally, the output of the network is a 10-dimensional vector that represents the probability distribution over 10 classes. \n", + "\n", + "You don't need to tune any hyperparameters but you should see accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.8139\n", + "Checking accuracy on the val set\n", + "Got 140 / 1000 correct (14.00%)\n", + "\n", + "Iteration 100, loss = 2.3674\n", + "Checking accuracy on the val set\n", + "Got 352 / 1000 correct (35.20%)\n", + "\n", + "Iteration 200, loss = 1.9672\n", + "Checking accuracy on the val set\n", + "Got 331 / 1000 correct (33.10%)\n", + "\n", + "Iteration 300, loss = 1.9417\n", + "Checking accuracy on the val set\n", + "Got 412 / 1000 correct (41.20%)\n", + "\n", + "Iteration 400, loss = 1.7889\n", + "Checking accuracy on the val set\n", + "Got 392 / 1000 correct (39.20%)\n", + "\n", + "Iteration 500, loss = 1.8344\n", + "Checking accuracy on the val set\n", + "Got 386 / 1000 correct (38.60%)\n", + "\n", + "Iteration 600, loss = 1.6571\n", + "Checking accuracy on the val set\n", + "Got 374 / 1000 correct (37.40%)\n", + "\n", + "Iteration 700, loss = 1.2890\n", + "Checking accuracy on the val set\n", + "Got 458 / 1000 correct (45.80%)\n", + "\n" + ] + } + ], + "source": [ + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "\n", + "w1 = random_weight((3 * 32 * 32, hidden_layer_size))\n", + "w2 = random_weight((hidden_layer_size, 10))\n", + "\n", + "train_part2(two_layer_fc, [w1, w2], learning_rate)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BareBones PyTorch: Training a ConvNet\n", + "\n", + "In the below you should use the functions defined above to train a three-layer convolutional network on CIFAR. The network should have the following architecture:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You should initialize your weight matrices using the `random_weight` function defined above, and you should initialize your bias vectors using the `zero_weight` function above.\n", + "\n", + "You don't need to tune any hyperparameters, but if everything works correctly you should achieve an accuracy above 42% after one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.7931\n", + "Checking accuracy on the val set\n", + "Got 136 / 1000 correct (13.60%)\n", + "\n", + "Iteration 100, loss = 2.0767\n", + "Checking accuracy on the val set\n", + "Got 351 / 1000 correct (35.10%)\n", + "\n", + "Iteration 200, loss = 1.9719\n", + "Checking accuracy on the val set\n", + "Got 371 / 1000 correct (37.10%)\n", + "\n", + "Iteration 300, loss = 1.5589\n", + "Checking accuracy on the val set\n", + "Got 422 / 1000 correct (42.20%)\n", + "\n", + "Iteration 400, loss = 1.5141\n", + "Checking accuracy on the val set\n", + "Got 452 / 1000 correct (45.20%)\n", + "\n", + "Iteration 500, loss = 1.5812\n", + "Checking accuracy on the val set\n", + "Got 445 / 1000 correct (44.50%)\n", + "\n", + "Iteration 600, loss = 1.4372\n", + "Checking accuracy on the val set\n", + "Got 461 / 1000 correct (46.10%)\n", + "\n", + "Iteration 700, loss = 1.5749\n", + "Checking accuracy on the val set\n", + "Got 460 / 1000 correct (46.00%)\n", + "\n" + ] + } + ], + "source": [ + "learning_rate = 3e-3\n", + "\n", + "channel_1 = 32\n", + "channel_2 = 16\n", + "\n", + "conv_w1 = None\n", + "conv_b1 = None\n", + "conv_w2 = None\n", + "conv_b2 = None\n", + "fc_w = None\n", + "fc_b = None\n", + "\n", + "################################################################################\n", + "# TODO: Initialize the parameters of a three-layer ConvNet. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "conv_w1 = nn.Parameter(random_weight((32,3,5,5)))\n", + "conv_b1 = nn.Parameter(zero_weight(32))\n", + "conv_w2 = nn.Parameter(random_weight((16, 32,3, 3)))\n", + "conv_b2 = nn.Parameter(zero_weight(16))\n", + "fc_w = nn.Parameter(random_weight((32*32*16, 10)))\n", + "fc_b = nn.Parameter(zero_weight(10))\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE #\n", + "################################################################################\n", + "\n", + "params = [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b]\n", + "train_part2(three_layer_convnet, params, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part III. PyTorch Module API\n", + "\n", + "Barebone PyTorch requires that we track all the parameter tensors by hand. This is fine for small networks with a few tensors, but it would be extremely inconvenient and error-prone to track tens or hundreds of tensors in larger networks.\n", + "\n", + "PyTorch provides the `nn.Module` API for you to define arbitrary network architectures, while tracking every learnable parameters for you. In Part II, we implemented SGD ourselves. PyTorch also provides the `torch.optim` package that implements all the common optimizers, such as RMSProp, Adagrad, and Adam. It even supports approximate second-order methods like L-BFGS! You can refer to the [doc](http://pytorch.org/docs/master/optim.html) for the exact specifications of each optimizer.\n", + "\n", + "To use the Module API, follow the steps below:\n", + "\n", + "1. Subclass `nn.Module`. Give your network class an intuitive name like `TwoLayerFC`. \n", + "\n", + "2. In the constructor `__init__()`, define all the layers you need as class attributes. Layer objects like `nn.Linear` and `nn.Conv2d` are themselves `nn.Module` subclasses and contain learnable parameters, so that you don't have to instantiate the raw tensors yourself. `nn.Module` will track these internal parameters for you. Refer to the [doc](http://pytorch.org/docs/master/nn.html) to learn more about the dozens of builtin layers. **Warning**: don't forget to call the `super().__init__()` first!\n", + "\n", + "3. In the `forward()` method, define the *connectivity* of your network. You should use the attributes defined in `__init__` as function calls that take tensor as input and output the \"transformed\" tensor. Do *not* create any new layers with learnable parameters in `forward()`! All of them must be declared upfront in `__init__`. \n", + "\n", + "After you define your Module subclass, you can instantiate it as an object and call it just like the NN forward function in part II.\n", + "\n", + "### Module API: Two-Layer Network\n", + "Here is a concrete example of a 2-layer fully connected network:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "class TwoLayerFC(nn.Module):\n", + " def __init__(self, input_size, hidden_size, num_classes):\n", + " super().__init__()\n", + " # assign layer objects to class attributes\n", + " self.fc1 = nn.Linear(input_size, hidden_size)\n", + " # nn.init package contains convenient initialization methods\n", + " # http://pytorch.org/docs/master/nn.html#torch-nn-init \n", + " nn.init.kaiming_normal_(self.fc1.weight)\n", + " self.fc2 = nn.Linear(hidden_size, num_classes)\n", + " nn.init.kaiming_normal_(self.fc2.weight)\n", + " \n", + " def forward(self, x):\n", + " # forward always defines connectivity\n", + " x = flatten(x)\n", + " scores = self.fc2(F.relu(self.fc1(x)))\n", + " return scores\n", + "\n", + "def test_TwoLayerFC():\n", + " input_size = 50\n", + " x = torch.zeros((64, input_size), dtype=dtype) # minibatch size 64, feature dimension 50\n", + " model = TwoLayerFC(input_size, 42, 10)\n", + " scores = model(x)\n", + " print(scores.size()) # you should see [64, 10]\n", + "test_TwoLayerFC()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Three-Layer ConvNet\n", + "It's your turn to implement a 3-layer ConvNet followed by a fully connected layer. The network architecture should be the same as in Part II:\n", + "\n", + "1. Convolutional layer with `channel_1` 5x5 filters with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer with `channel_2` 3x3 filters with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer to `num_classes` classes\n", + "\n", + "You should initialize the weight matrices of the model using the Kaiming normal initialization method.\n", + "\n", + "**HINT**: http://pytorch.org/docs/stable/nn.html#conv2d\n", + "\n", + "After you implement the three-layer ConvNet, the `test_ThreeLayerConvNet` function will run your implementation; it should print `(64, 10)` for the shape of the output scores." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([64, 10])\n" + ] + } + ], + "source": [ + "class ThreeLayerConvNet(nn.Module):\n", + " def __init__(self, in_channel, channel_1, channel_2, num_classes):\n", + " super().__init__()\n", + " ########################################################################\n", + " # TODO: Set up the layers you need for a three-layer ConvNet with the #\n", + " # architecture defined above. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + " self.conv1 = nn.Conv2d(in_channel, channel_1, 5, padding=2)\n", + " nn.init.kaiming_normal_(self.conv1.weight)\n", + " self.conv2 = nn.Conv2d(channel_1, channel_2, 3, padding=1)\n", + " nn.init.kaiming_normal_(self.conv2.weight)\n", + " self.fc1 = nn.Linear(channel_2 * 32 * 32, num_classes)\n", + " nn.init.kaiming_normal_(self.fc1.weight)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE # \n", + " ########################################################################\n", + "\n", + " def forward(self, x):\n", + " scores = None\n", + " ########################################################################\n", + " # TODO: Implement the forward function for a 3-layer ConvNet. you #\n", + " # should use the layers you defined in __init__ and specify the #\n", + " # connectivity of those layers in forward() #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = F.relu(self.conv1(x))\n", + " x = F.relu(self.conv2(x))\n", + " x = flatten(x)\n", + " scores = self.fc1(x)\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + " return scores\n", + "\n", + "\n", + "def test_ThreeLayerConvNet():\n", + " x = torch.zeros((64, 3, 32, 32), dtype=dtype) # minibatch size 64, image size [3, 32, 32]\n", + " model = ThreeLayerConvNet(in_channel=3, channel_1=12, channel_2=8, num_classes=10)\n", + " scores = model(x)\n", + " print(scores.size()) # you should see [64, 10]\n", + "test_ThreeLayerConvNet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Check Accuracy\n", + "Given the validation or test set, we can check the classification accuracy of a neural network. \n", + "\n", + "This version is slightly different from the one in part II. You don't manually pass in the parameters anymore." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "def check_accuracy_part34(loader, model):\n", + " if loader.dataset.train:\n", + " print('Checking accuracy on validation set')\n", + " else:\n", + " print('Checking accuracy on test set') \n", + " num_correct = 0\n", + " num_samples = 0\n", + " model.eval() # set model to evaluation mode\n", + " with torch.no_grad():\n", + " for x, y in loader:\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + " scores = model(x)\n", + " _, preds = scores.max(1)\n", + " num_correct += (preds == y).sum()\n", + " num_samples += preds.size(0)\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Training Loop\n", + "We also use a slightly different training loop. Rather than updating the values of the weights ourselves, we use an Optimizer object from the `torch.optim` package, which abstract the notion of an optimization algorithm and provides implementations of most of the algorithms commonly used to optimize neural networks." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def train_part34(model, optimizer, epochs=1):\n", + " \"\"\"\n", + " Train a model on CIFAR-10 using the PyTorch Module API.\n", + " \n", + " Inputs:\n", + " - model: A PyTorch Module giving the model to train.\n", + " - optimizer: An Optimizer object we will use to train the model\n", + " - epochs: (Optional) A Python integer giving the number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints model accuracies during training.\n", + " \"\"\"\n", + " model = model.to(device=device) # move the model parameters to CPU/GPU\n", + " for e in range(epochs):\n", + " for t, (x, y) in enumerate(loader_train):\n", + " model.train() # put model to training mode\n", + " x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n", + " y = y.to(device=device, dtype=torch.long)\n", + "\n", + " scores = model(x)\n", + " loss = F.cross_entropy(scores, y)\n", + "\n", + " # Zero out all of the gradients for the variables which the optimizer\n", + " # will update.\n", + " optimizer.zero_grad()\n", + "\n", + " # This is the backwards pass: compute the gradient of the loss with\n", + " # respect to each parameter of the model.\n", + " loss.backward()\n", + "\n", + " # Actually update the parameters of the model using the gradients\n", + " # computed by the backwards pass.\n", + " optimizer.step()\n", + "\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss.item()))\n", + " check_accuracy_part34(loader_val, model)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Train a Two-Layer Network\n", + "Now we are ready to run the training loop. In contrast to part II, we don't explicitly allocate parameter tensors anymore.\n", + "\n", + "Simply pass the input size, hidden layer size, and number of classes (i.e. output size) to the constructor of `TwoLayerFC`. \n", + "\n", + "You also need to define an optimizer that tracks all the learnable parameters inside `TwoLayerFC`.\n", + "\n", + "You don't need to tune any hyperparameters, but you should see model accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 4.0420\n", + "Checking accuracy on validation set\n", + "Got 145 / 1000 correct (14.50)\n", + "\n", + "Iteration 100, loss = 1.8651\n", + "Checking accuracy on validation set\n", + "Got 365 / 1000 correct (36.50)\n", + "\n", + "Iteration 200, loss = 1.6438\n", + "Checking accuracy on validation set\n", + "Got 387 / 1000 correct (38.70)\n", + "\n", + "Iteration 300, loss = 1.6083\n", + "Checking accuracy on validation set\n", + "Got 385 / 1000 correct (38.50)\n", + "\n", + "Iteration 400, loss = 1.7412\n", + "Checking accuracy on validation set\n", + "Got 437 / 1000 correct (43.70)\n", + "\n", + "Iteration 500, loss = 1.9077\n", + "Checking accuracy on validation set\n", + "Got 424 / 1000 correct (42.40)\n", + "\n", + "Iteration 600, loss = 1.8101\n", + "Checking accuracy on validation set\n", + "Got 456 / 1000 correct (45.60)\n", + "\n", + "Iteration 700, loss = 1.8079\n", + "Checking accuracy on validation set\n", + "Got 452 / 1000 correct (45.20)\n", + "\n" + ] + } + ], + "source": [ + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "model = TwoLayerFC(3 * 32 * 32, hidden_layer_size, 10)\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module API: Train a Three-Layer ConvNet\n", + "You should now use the Module API to train a three-layer ConvNet on CIFAR. This should look very similar to training the two-layer network! You don't need to tune any hyperparameters, but you should achieve above above 45% after training for one epoch.\n", + "\n", + "You should train the model using stochastic gradient descent without momentum." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.0471\n", + "Checking accuracy on validation set\n", + "Got 127 / 1000 correct (12.70)\n", + "\n", + "Iteration 100, loss = 1.9514\n", + "Checking accuracy on validation set\n", + "Got 342 / 1000 correct (34.20)\n", + "\n", + "Iteration 200, loss = 1.5484\n", + "Checking accuracy on validation set\n", + "Got 380 / 1000 correct (38.00)\n", + "\n", + "Iteration 300, loss = 1.4663\n", + "Checking accuracy on validation set\n", + "Got 404 / 1000 correct (40.40)\n", + "\n", + "Iteration 400, loss = 1.5519\n", + "Checking accuracy on validation set\n", + "Got 423 / 1000 correct (42.30)\n", + "\n", + "Iteration 500, loss = 1.6152\n", + "Checking accuracy on validation set\n", + "Got 440 / 1000 correct (44.00)\n", + "\n", + "Iteration 600, loss = 1.7878\n", + "Checking accuracy on validation set\n", + "Got 474 / 1000 correct (47.40)\n", + "\n", + "Iteration 700, loss = 1.4453\n", + "Checking accuracy on validation set\n", + "Got 461 / 1000 correct (46.10)\n", + "\n" + ] + } + ], + "source": [ + "learning_rate = 3e-3\n", + "channel_1 = 32\n", + "channel_2 = 16\n", + "\n", + "model = None\n", + "optimizer = None\n", + "################################################################################\n", + "# TODO: Instantiate your ThreeLayerConvNet model and a corresponding optimizer #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "model = ThreeLayerConvNet(3, channel_1, channel_2, 10)\n", + "\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part IV. PyTorch Sequential API\n", + "\n", + "Part III introduced the PyTorch Module API, which allows you to define arbitrary learnable layers and their connectivity. \n", + "\n", + "For simple models like a stack of feed forward layers, you still need to go through 3 steps: subclass `nn.Module`, assign layers to class attributes in `__init__`, and call each layer one by one in `forward()`. Is there a more convenient way? \n", + "\n", + "Fortunately, PyTorch provides a container Module called `nn.Sequential`, which merges the above steps into one. It is not as flexible as `nn.Module`, because you cannot specify more complex topology than a feed-forward stack, but it's good enough for many use cases.\n", + "\n", + "### Sequential API: Two-Layer Network\n", + "Let's see how to rewrite our two-layer fully connected network example with `nn.Sequential`, and train it using the training loop defined above.\n", + "\n", + "Again, you don't need to tune any hyperparameters here, but you shoud achieve above 40% accuracy after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 2.3036\n", + "Checking accuracy on validation set\n", + "Got 148 / 1000 correct (14.80)\n", + "\n", + "Iteration 100, loss = 1.7822\n", + "Checking accuracy on validation set\n", + "Got 366 / 1000 correct (36.60)\n", + "\n", + "Iteration 200, loss = 1.8760\n", + "Checking accuracy on validation set\n", + "Got 407 / 1000 correct (40.70)\n", + "\n", + "Iteration 300, loss = 1.8839\n", + "Checking accuracy on validation set\n", + "Got 402 / 1000 correct (40.20)\n", + "\n", + "Iteration 400, loss = 1.5104\n", + "Checking accuracy on validation set\n", + "Got 446 / 1000 correct (44.60)\n", + "\n", + "Iteration 500, loss = 1.8335\n", + "Checking accuracy on validation set\n", + "Got 418 / 1000 correct (41.80)\n", + "\n", + "Iteration 600, loss = 1.2792\n", + "Checking accuracy on validation set\n", + "Got 453 / 1000 correct (45.30)\n", + "\n", + "Iteration 700, loss = 1.5895\n", + "Checking accuracy on validation set\n", + "Got 453 / 1000 correct (45.30)\n", + "\n" + ] + } + ], + "source": [ + "# We need to wrap `flatten` function in a module in order to stack it\n", + "# in nn.Sequential\n", + "class Flatten(nn.Module):\n", + " def forward(self, x):\n", + " return flatten(x)\n", + "\n", + "hidden_layer_size = 4000\n", + "learning_rate = 1e-2\n", + "\n", + "model = nn.Sequential(\n", + " Flatten(),\n", + " nn.Linear(3 * 32 * 32, hidden_layer_size),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_layer_size, 10),\n", + ")\n", + "\n", + "# you can use Nesterov momentum in optim.SGD\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate,\n", + " momentum=0.9, nesterov=True)\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sequential API: Three-Layer ConvNet\n", + "Here you should use `nn.Sequential` to define and train a three-layer ConvNet with the same architecture we used in Part III:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding of 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding of 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You should initialize your weight matrices using the `random_weight` function defined above, and you should initialize your bias vectors using the `zero_weight` function above.\n", + "\n", + "You should optimize your model using stochastic gradient descent with Nesterov momentum 0.9.\n", + "\n", + "Again, you don't need to tune any hyperparameters but you should see accuracy above 55% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 20)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m20\u001b[0m\n\u001b[0;31m nn.Linear(channel_2 * 32 * 32, num_classes),\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "channel_1 = 32\n", + "channel_2 = 16\n", + "learning_rate = 1e-2\n", + "\n", + "model = None\n", + "optimizer = None\n", + "\n", + "################################################################################\n", + "# TODO: Rewrite the 2-layer ConvNet with bias from Part III with the #\n", + "# Sequential API. #\n", + "################################################################################\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "model = nn.Sequential(\n", + " nn.Conv2d(in_channel, channel_1, 5, padding=2),\n", + " nn.ReLU(),\n", + " nn.Conv2d(channel_1, channel_2, 3, padding=1),\n", + " nn.ReLU(),\n", + " Flatten()\n", + " nn.Linear(channel_2 * 32 * 32, num_classes),\n", + ")\n", + "\n", + "optimizer = optim.SGD(model.parameters(), lr=learning_rate,\n", + " momentum=0.9, nesterov=True)\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "train_part34(model, optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part V. CIFAR-10 open-ended challenge\n", + "\n", + "In this section, you can experiment with whatever ConvNet architecture you'd like on CIFAR-10. \n", + "\n", + "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves **at least 70%** accuracy on the CIFAR-10 **validation** set within 10 epochs. You can use the check_accuracy and train functions from above. You can use either `nn.Module` or `nn.Sequential` API. \n", + "\n", + "Describe what you did at the end of this notebook.\n", + "\n", + "Here are the official API documentation for each component. One note: what we call in the class \"spatial batch norm\" is called \"BatchNorm2D\" in PyTorch.\n", + "\n", + "* Layers in torch.nn package: http://pytorch.org/docs/stable/nn.html\n", + "* Activations: http://pytorch.org/docs/stable/nn.html#non-linear-activations\n", + "* Loss functions: http://pytorch.org/docs/stable/nn.html#loss-functions\n", + "* Optimizers: http://pytorch.org/docs/stable/optim.html\n", + "\n", + "\n", + "### Things you might try:\n", + "- **Filter size**: Above we used 5x5; would smaller filters be more efficient?\n", + "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n", + "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n", + "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n", + "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n", + " - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", + " - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", + " - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n", + "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n", + "- **Regularization**: Add l2 weight regularization, or perhaps use Dropout.\n", + "\n", + "### Tips for training\n", + "For each network architecture that you try, you should tune the learning rate and other hyperparameters. When doing this there are a couple important things to keep in mind:\n", + "\n", + "- If the parameters are working well, you should see improvement within a few hundred iterations\n", + "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n", + "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n", + "- You should use the validation set for hyperparameter search, and save your test set for evaluating your architecture on the best parameters as selected by the validation set.\n", + "\n", + "### Going above and beyond\n", + "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these, but don't miss the fun if you have time!\n", + "\n", + "- Alternative optimizers: you can try Adam, Adagrad, RMSprop, etc.\n", + "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n", + "- Model ensembles\n", + "- Data augmentation\n", + "- New Architectures\n", + " - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n", + " - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n", + " - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n", + "\n", + "### Have fun and happy training! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "################################################################################\n", + "# TODO: # \n", + "# Experiment with any architectures, optimizers, and hyperparameters. #\n", + "# Achieve AT LEAST 70% accuracy on the *validation set* within 10 epochs. #\n", + "# #\n", + "# Note that you can use the check_accuracy function to evaluate on either #\n", + "# the test set or the validation set, by passing either loader_test or #\n", + "# loader_val as the second argument to check_accuracy. You should not touch #\n", + "# the test set until you have finished your architecture and hyperparameter #\n", + "# tuning, and only run the test set once at the end to report a final value. #\n", + "################################################################################\n", + "model = None\n", + "optimizer = None\n", + "\n", + "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "pass\n", + "\n", + "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "################################################################################\n", + "# END OF YOUR CODE \n", + "################################################################################\n", + "\n", + "# You should get at least 70% accuracy\n", + "train_part34(model, optimizer, epochs=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Describe what you did \n", + "\n", + "In the cell below you should write an explanation of what you did, any additional features that you implemented, and/or any graphs that you made in the process of training and evaluating your network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "TODO: Describe what you did" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test set -- run this only once\n", + "\n", + "Now that we've gotten a result we're happy with, we test our final model on the test set (which you should store in best_model). Think about how this compares to your validation set accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_model = model\n", + "check_accuracy_part34(loader_test, best_model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment2/TensorFlow.ipynb b/assignment2/TensorFlow.ipynb new file mode 100755 index 0000000..aea7dec --- /dev/null +++ b/assignment2/TensorFlow.ipynb @@ -0,0 +1,1903 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# What's this TensorFlow business?\n", + "\n", + "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n", + "\n", + "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you choose to work with that notebook)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "#### What is it?\n", + "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n", + "\n", + "#### Why?\n", + "\n", + "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n", + "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n", + "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n", + "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## How will I learn TensorFlow?\n", + "\n", + "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n", + "\n", + "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here.\n", + "\n", + "**NOTE: This notebook is meant to teach you the latest version of Tensorflow 2.0. Most examples on the web today are still in 1.x, so be careful not to confuse the two when looking up documentation**.\n", + "\n", + "## Install Tensorflow 2.0\n", + "Tensorflow 2.0 is still not in a fully 100% stable release, but it's still usable and more intuitive than TF 1.x. Please make sure you have it installed before moving on in this notebook! Here are some steps to get started:\n", + "\n", + "1. Have the latest version of Anaconda installed on your machine.\n", + "2. Create a new conda environment starting from Python 3.7. In this setup example, we'll call it `tf_20_env`.\n", + "3. Run the command: `source activate tf_20_env`\n", + "4. Then pip install TF 2.0 as described here: https://www.tensorflow.org/install/pip \n", + "\n", + "A guide on creating Anaconda enviornments: https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/\n", + "\n", + "This will give you an new enviornemnt to play in TF 2.0. Generally, if you plan to also use TensorFlow in your other projects, you might also want to keep a seperate Conda environment or virtualenv in Python 3.7 that has Tensorflow 1.9, so you can switch back and forth at will. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Table of Contents\n", + "\n", + "This notebook has 5 parts. We will walk through TensorFlow at **three different levels of abstraction**, which should help you better understand it and prepare you for working on your project.\n", + "\n", + "1. Part I, Preparation: load the CIFAR-10 dataset.\n", + "2. Part II, Barebone TensorFlow: **Abstraction Level 1**, we will work directly with low-level TensorFlow graphs. \n", + "3. Part III, Keras Model API: **Abstraction Level 2**, we will use `tf.keras.Model` to define arbitrary neural network architecture. \n", + "4. Part IV, Keras Sequential + Functional API: **Abstraction Level 3**, we will use `tf.keras.Sequential` to define a linear feed-forward network very conveniently, and then explore the functional libraries for building unique and uncommon models that require more flexibility.\n", + "5. Part V, CIFAR-10 open-ended challenge: please implement your own network to get as high accuracy as possible on CIFAR-10. You can experiment with any layer, optimizer, hyperparameters or other advanced features. \n", + "\n", + "We will discuss Keras in more detail later in the notebook.\n", + "\n", + "Here is a table of comparison:\n", + "\n", + "| API | Flexibility | Convenience |\n", + "|---------------|-------------|-------------|\n", + "| Barebone | High | Low |\n", + "| `tf.keras.Model` | High | Medium |\n", + "| `tf.keras.Sequential` | Low | High |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part I: Preparation\n", + "\n", + "First, we load the CIFAR-10 dataset. This might take a few minutes to download the first time you run it, but after that the files should be cached on disk and loading should be faster.\n", + "\n", + "In previous parts of the assignment we used CS231N-specific code to download and read the CIFAR-10 dataset; however the `tf.keras.datasets` package in TensorFlow provides prebuilt utility functions for loading many common datasets.\n", + "\n", + "For the purposes of this assignment we will still write our own code to preprocess the data and iterate through it in minibatches. The `tf.data` package in TensorFlow provides tools for automating this process, but working with this package adds extra complication and is beyond the scope of this notebook. However using `tf.data` can be much more efficient than the simple approach used in this notebook, so you should consider using it for your project." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math\n", + "import timeit\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(tf.version)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "important (49000, 32, 32, 3)\n", + "Train data shape: (49000, 32, 32, 3)\n", + "Train labels shape: (49000,) int32\n", + "Validation data shape: (1000, 32, 32, 3)\n", + "Validation labels shape: (1000,)\n", + "Test data shape: (10000, 32, 32, 3)\n", + "Test labels shape: (10000,)\n" + ] + } + ], + "source": [ + "def load_cifar10(num_training=49000, num_validation=1000, num_test=10000):\n", + " \"\"\"\n", + " Fetch the CIFAR-10 dataset from the web and perform preprocessing to prepare\n", + " it for the two-layer neural net classifier. These are the same steps as\n", + " we used for the SVM, but condensed to a single function.\n", + " \"\"\"\n", + " # Load the raw CIFAR-10 dataset and use appropriate data types and shapes\n", + " cifar10 = tf.keras.datasets.cifar10.load_data()\n", + " (X_train, y_train), (X_test, y_test) = cifar10\n", + " X_train = np.asarray(X_train, dtype=np.float32)\n", + " y_train = np.asarray(y_train, dtype=np.int32).flatten()\n", + " X_test = np.asarray(X_test, dtype=np.float32)\n", + " y_test = np.asarray(y_test, dtype=np.int32).flatten()\n", + "\n", + " # Subsample the data\n", + " mask = range(num_training, num_training + num_validation)\n", + " X_val = X_train[mask]\n", + " y_val = y_train[mask]\n", + " mask = range(num_training)\n", + " X_train = X_train[mask]\n", + " y_train = y_train[mask]\n", + " mask = range(num_test)\n", + " X_test = X_test[mask]\n", + " y_test = y_test[mask]\n", + " \n", + " print(\"important\", X_train.shape)\n", + " # Normalize the data: subtract the mean pixel and divide by std\n", + " mean_pixel = X_train.mean(axis=(0, 1, 2), keepdims=True)\n", + " std_pixel = X_train.std(axis=(0, 1, 2), keepdims=True)\n", + " X_train = (X_train - mean_pixel) / std_pixel\n", + " X_val = (X_val - mean_pixel) / std_pixel\n", + " X_test = (X_test - mean_pixel) / std_pixel\n", + "\n", + " return X_train, y_train, X_val, y_val, X_test, y_test\n", + "\n", + "# If there are errors with SSL downloading involving self-signed certificates,\n", + "# it may be that your Python version was recently installed on the current machine.\n", + "# See: https://github.com/tensorflow/tensorflow/issues/10779\n", + "# To fix, run the command: /Applications/Python\\ 3.7/Install\\ Certificates.command\n", + "# ...replacing paths as necessary.\n", + "\n", + "# Invoke the above function to get our data.\n", + "NHW = (0, 1, 2)\n", + "X_train, y_train, X_val, y_val, X_test, y_test = load_cifar10()\n", + "print('Train data shape: ', X_train.shape)\n", + "print('Train labels shape: ', y_train.shape, y_train.dtype)\n", + "print('Validation data shape: ', X_val.shape)\n", + "print('Validation labels shape: ', y_val.shape)\n", + "print('Test data shape: ', X_test.shape)\n", + "print('Test labels shape: ', y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "class Dataset(object):\n", + " def __init__(self, X, y, batch_size, shuffle=False):\n", + " \"\"\"\n", + " Construct a Dataset object to iterate over data X and labels y\n", + " \n", + " Inputs:\n", + " - X: Numpy array of data, of any shape\n", + " - y: Numpy array of labels, of any shape but with y.shape[0] == X.shape[0]\n", + " - batch_size: Integer giving number of elements per minibatch\n", + " - shuffle: (optional) Boolean, whether to shuffle the data on each epoch\n", + " \"\"\"\n", + " assert X.shape[0] == y.shape[0], 'Got different numbers of data and labels'\n", + " self.X, self.y = X, y\n", + " self.batch_size, self.shuffle = batch_size, shuffle\n", + "\n", + " def __iter__(self):\n", + " N, B = self.X.shape[0], self.batch_size\n", + " idxs = np.arange(N)\n", + " if self.shuffle:\n", + " np.random.shuffle(idxs)\n", + " return iter((self.X[i:i+B], self.y[i:i+B]) for i in range(0, N, B))\n", + "\n", + "\n", + "train_dset = Dataset(X_train, y_train, batch_size=64, shuffle=True)\n", + "val_dset = Dataset(X_val, y_val, batch_size=64, shuffle=False)\n", + "test_dset = Dataset(X_test, y_test, batch_size=64)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 (64, 32, 32, 3) (64,)\n", + "1 (64, 32, 32, 3) (64,)\n", + "2 (64, 32, 32, 3) (64,)\n", + "3 (64, 32, 32, 3) (64,)\n", + "4 (64, 32, 32, 3) (64,)\n", + "5 (64, 32, 32, 3) (64,)\n", + "6 (64, 32, 32, 3) (64,)\n" + ] + } + ], + "source": [ + "# We can iterate through a dataset like this:\n", + "for t, (x, y) in enumerate(train_dset):\n", + " print(t, x.shape, y.shape)\n", + " if t > 5: break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can optionally **use GPU by setting the flag to True below**. It's not neccessary to use a GPU for this assignment; if you are working on Google Cloud then we recommend that you do not use a GPU, as it will be significantly more expensive." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: /cpu:0\n" + ] + } + ], + "source": [ + "# Set up some global variables\n", + "USE_GPU = False\n", + "\n", + "if USE_GPU:\n", + " device = '/device:GPU:0'\n", + "else:\n", + " device = '/cpu:0'\n", + "\n", + "# Constant to control how often we print when training models\n", + "print_every = 100\n", + "\n", + "print('Using device: ', device)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Part II: Barebones TensorFlow\n", + "TensorFlow ships with various high-level APIs which make it very convenient to define and train neural networks; we will cover some of these constructs in Part III and Part IV of this notebook. In this section we will start by building a model with basic TensorFlow constructs to help you better understand what's going on under the hood of the higher-level APIs.\n", + "\n", + "**\"Barebones Tensorflow\" is important to understanding the building blocks of TensorFlow, but much of it involves concepts from TensorFlow 1.x.** We will be working with legacy modules such as `tf.Variable`.\n", + "\n", + "Therefore, please read and understand the differences between legacy (1.x) TF and the new (2.0) TF.\n", + "\n", + "### Historical background on TensorFlow 1.x\n", + "\n", + "TensorFlow 1.x is primarily a framework for working with **static computational graphs**. Nodes in the computational graph are Tensors which will hold n-dimensional arrays when the graph is run; edges in the graph represent functions that will operate on Tensors when the graph is run to actually perform useful computation.\n", + "\n", + "Before Tensorflow 2.0, we had to configure the graph into two phases. There are plenty of tutorials online that explain this two-step process. The process generally looks like the following for TF 1.x:\n", + "1. **Build a computational graph that describes the computation that you want to perform**. This stage doesn't actually perform any computation; it just builds up a symbolic representation of your computation. This stage will typically define one or more `placeholder` objects that represent inputs to the computational graph.\n", + "2. **Run the computational graph many times.** Each time the graph is run (e.g. for one gradient descent step) you will specify which parts of the graph you want to compute, and pass a `feed_dict` dictionary that will give concrete values to any `placeholder`s in the graph.\n", + "\n", + "### The new paradigm in Tensorflow 2.0\n", + "Now, with Tensorflow 2.0, we can simply adopt a functional form that is more Pythonic and similar in spirit to PyTorch and direct Numpy operation. Instead of the 2-step paradigm with computation graphs, making it (among other things) easier to debug TF code. You can read more details at https://www.tensorflow.org/guide/eager.\n", + "\n", + "The main difference between the TF 1.x and 2.0 approach is that the 2.0 approach doesn't make use of `tf.Session`, `tf.run`, `placeholder`, `feed_dict`. To get more details of what's different between the two version and how to convert between the two, check out the official migration guide: https://www.tensorflow.org/alpha/guide/migration_guide\n", + "\n", + "Later, in the rest of this notebook we'll focus on this new, simpler approach." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### TensorFlow warmup: Flatten Function\n", + "\n", + "We can see this in action by defining a simple `flatten` function that will reshape image data for use in a fully-connected network.\n", + "\n", + "In TensorFlow, data for convolutional feature maps is typically stored in a Tensor of shape N x H x W x C where:\n", + "\n", + "- N is the number of datapoints (minibatch size)\n", + "- H is the height of the feature map\n", + "- W is the width of the feature map\n", + "- C is the number of channels in the feature map\n", + "\n", + "This is the right way to represent the data when we are doing something like a 2D convolution, that needs spatial understanding of where the intermediate features are relative to each other. When we use fully connected affine layers to process the image, however, we want each datapoint to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data. So, we use a \"flatten\" operation to collapse the `H x W x C` values per representation into a single long vector. \n", + "\n", + "Notice the `tf.reshape` call has the target shape as `(N, -1)`, meaning it will reshape/keep the first dimension to be N, and then infer as necessary what the second dimension is in the output, so we can collapse the remaining dimensions from the input properly.\n", + "\n", + "**NOTE**: TensorFlow and PyTorch differ on the default Tensor layout; TensorFlow uses N x H x W x C but PyTorch uses N x C x H x W." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def flatten(x):\n", + " \"\"\" \n", + " Input:\n", + " - TensorFlow Tensor of shape (N, D1, ..., DM)\n", + " \n", + " Output:\n", + " - TensorFlow Tensor of shape (N, D1 * ... * DM)\n", + " \"\"\"\n", + " N = tf.shape(x)[0]\n", + " return tf.reshape(x, (N, -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_np:\n", + " [[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n", + "\n", + " [[12 13 14 15]\n", + " [16 17 18 19]\n", + " [20 21 22 23]]] \n", + "\n", + "x_np:\n", + " (2, 3, 4) \n", + "\n", + "x_flat_np:\n", + " tf.Tensor(\n", + "[[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + " [12 13 14 15 16 17 18 19 20 21 22 23]], shape=(2, 12), dtype=int64) \n", + "\n" + ] + } + ], + "source": [ + "def test_flatten():\n", + " # Construct concrete values of the input data x using numpy\n", + " x_np = np.arange(24).reshape((2, 3, 4))\n", + " print('x_np:\\n', x_np, '\\n')\n", + " print('x_np:\\n', x_np.shape, '\\n')\n", + " # Compute a concrete output value.\n", + " x_flat_np = flatten(x_np)\n", + " print('x_flat_np:\\n', x_flat_np, '\\n')\n", + "\n", + "test_flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Define a Two-Layer Network\n", + "We will now implement our first neural network with TensorFlow: a fully-connected ReLU network with two hidden layers and no biases on the CIFAR10 dataset. For now we will use only low-level TensorFlow operators to define the network; later we will see how to use the higher-level abstractions provided by `tf.keras` to simplify the process.\n", + "\n", + "We will define the forward pass of the network in the function `two_layer_fc`; this will accept TensorFlow Tensors for the inputs and weights of the network, and return a TensorFlow Tensor for the scores. \n", + "\n", + "After defining the network architecture in the `two_layer_fc` function, we will test the implementation by checking the shape of the output.\n", + "\n", + "**It's important that you read and understand this implementation.**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def two_layer_fc(x, params):\n", + " \"\"\"\n", + " A fully-connected neural network; the architecture is:\n", + " fully-connected layer -> ReLU -> fully connected layer.\n", + " Note that we only need to define the forward pass here; TensorFlow will take\n", + " care of computing the gradients for us.\n", + " \n", + " The input to the network will be a minibatch of data, of shape\n", + " (N, d1, ..., dM) where d1 * ... * dM = D. The hidden layer will have H units,\n", + " and the output layer will produce scores for C classes.\n", + "\n", + " Inputs:\n", + " - x: A TensorFlow Tensor of shape (N, d1, ..., dM) giving a minibatch of\n", + " input data.\n", + " - params: A list [w1, w2] of TensorFlow Tensors giving weights for the\n", + " network, where w1 has shape (D, H) and w2 has shape (H, C).\n", + " \n", + " Returns:\n", + " - scores: A TensorFlow Tensor of shape (N, C) giving classification scores\n", + " for the input data x.\n", + " \"\"\"\n", + " w1, w2 = params # Unpack the parameters\n", + " x = flatten(x) # Flatten the input; now x has shape (N, D)\n", + " h = tf.nn.relu(tf.matmul(x, w1)) # Hidden layer: h has shape (N, H)\n", + " scores = tf.matmul(h, w2) # Compute scores of shape (N, C)\n", + " return scores" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def two_layer_fc_test():\n", + " hidden_layer_size = 42\n", + "\n", + " # Scoping our TF operations under a tf.device context manager \n", + " # lets us tell TensorFlow where we want these Tensors to be\n", + " # multiplied and/or operated on, e.g. on a CPU or a GPU.\n", + " with tf.device(device): \n", + " x = tf.zeros((64, 32, 32, 3))\n", + " w1 = tf.zeros((32 * 32 * 3, hidden_layer_size))\n", + " w2 = tf.zeros((hidden_layer_size, 10))\n", + "\n", + " # Call our two_layer_fc function for the forward pass of the network.\n", + " scores = two_layer_fc(x, [w1, w2])\n", + "\n", + " print(scores.shape)\n", + "\n", + "two_layer_fc_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Three-Layer ConvNet\n", + "Here you will complete the implementation of the function `three_layer_convnet` which will perform the forward pass of a three-layer convolutional network. The network should have the following architecture:\n", + "\n", + "1. A convolutional layer (with bias) with `channel_1` filters, each with shape `KW1 x KH1`, and zero-padding of two\n", + "2. ReLU nonlinearity\n", + "3. A convolutional layer (with bias) with `channel_2` filters, each with shape `KW2 x KH2`, and zero-padding of one\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer with bias, producing scores for `C` classes.\n", + "\n", + "**HINT**: For convolutions: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/nn/conv2d; be careful with padding!\n", + "\n", + "**HINT**: For biases: https://www.tensorflow.org/performance/xla/broadcasting" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def three_layer_convnet(x, params):\n", + " \"\"\"\n", + " A three-layer convolutional network with the architecture described above.\n", + " \n", + " Inputs:\n", + " - x: A TensorFlow Tensor of shape (N, H, W, 3) giving a minibatch of images\n", + " - params: A list of TensorFlow Tensors giving the weights and biases for the\n", + " network; should contain the following:\n", + " - conv_w1: TensorFlow Tensor of shape (KH1, KW1, 3, channel_1) giving\n", + " weights for the first convolutional layer.\n", + " - conv_b1: TensorFlow Tensor of shape (channel_1,) giving biases for the\n", + " first convolutional layer.\n", + " - conv_w2: TensorFlow Tensor of shape (KH2, KW2, channel_1, channel_2)\n", + " giving weights for the second convolutional layer\n", + " - conv_b2: TensorFlow Tensor of shape (channel_2,) giving biases for the\n", + " second convolutional layer.\n", + " - fc_w: TensorFlow Tensor giving weights for the fully-connected layer.\n", + " Can you figure out what the shape should be?\n", + " - fc_b: TensorFlow Tensor giving biases for the fully-connected layer.\n", + " Can you figure out what the shape should be?\n", + " \"\"\"\n", + " conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b = params\n", + " scores = None\n", + " ############################################################################\n", + " # TODO: Implement the forward pass for the three-layer ConvNet. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " # Flatten the input; now x has shape (N, D)\n", + " \n", + " padded_x = tf.pad(x, [[0,0], [2, 2], [2, 2], [0,0]], \"CONSTANT\")\n", + " \n", + " h1 = tf.nn.conv2d(padded_x, conv_w1, strides=[1,1,1,1], padding=\"VALID\") + conv_b1\n", + " h1 = tf.nn.relu(h1)\n", + " \n", + " padded_h1 = tf.pad(h1, [[0,0], [1, 1], [1, 1], [0,0]], \"CONSTANT\") \n", + " \n", + " h2 = tf.nn.conv2d(padded_h1, conv_w2, strides=[1,1,1,1], padding='VALID') + conv_b2\n", + " \n", + " h2 = tf.nn.relu(h2)\n", + " \n", + " r_h2_flatten = flatten(h2) \n", + " scores = tf.matmul(r_h2_flatten, fc_w) + fc_b\n", + " \n", + "\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After defing the forward pass of the three-layer ConvNet above, run the following cell to test your implementation. Like the two-layer network, we run the graph on a batch of zeros just to make sure the function doesn't crash, and produces outputs of the correct shape.\n", + "\n", + "When you run this function, `scores_np` should have shape `(64, 10)`." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores_np has shape: (64, 10)\n" + ] + } + ], + "source": [ + "def three_layer_convnet_test():\n", + " \n", + " with tf.device(device):\n", + " x = tf.zeros((64, 32, 32, 3))\n", + " conv_w1 = tf.zeros((5, 5, 3, 6))\n", + " conv_b1 = tf.zeros((6,))\n", + " conv_w2 = tf.zeros((3, 3, 6, 9))\n", + " conv_b2 = tf.zeros((9,))\n", + " fc_w = tf.zeros((32 * 32 * 9, 10))\n", + " fc_b = tf.zeros((10,))\n", + " params = [conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b]\n", + " scores = three_layer_convnet(x, params)\n", + "\n", + " # Inputs to convolutional layers are 4-dimensional arrays with shape\n", + " # [batch_size, height, width, channels]\n", + " print('scores_np has shape: ', scores.shape)\n", + "\n", + "three_layer_convnet_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Training Step\n", + "\n", + "We now define the `training_step` function performs a single training step. This will take three basic steps:\n", + "\n", + "1. Compute the loss\n", + "2. Compute the gradient of the loss with respect to all network weights\n", + "3. Make a weight update step using (stochastic) gradient descent.\n", + "\n", + "\n", + "We need to use a few new TensorFlow functions to do all of this:\n", + "- For computing the cross-entropy loss we'll use `tf.nn.sparse_softmax_cross_entropy_with_logits`: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits\n", + "\n", + "- For averaging the loss across a minibatch of data we'll use `tf.reduce_mean`:\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/reduce_mean\n", + "\n", + "- For computing gradients of the loss with respect to the weights we'll use `tf.GradientTape` (useful for Eager execution): https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/GradientTape\n", + "\n", + "- We'll mutate the weight values stored in a TensorFlow Tensor using `tf.assign_sub` (\"sub\" is for subtraction): https://www.tensorflow.org/api_docs/python/tf/assign_sub \n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def training_step(model_fn, x, y, params, learning_rate):\n", + " with tf.GradientTape() as tape:\n", + " scores = model_fn(x, params) # Forward pass of the model\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=scores)\n", + " total_loss = tf.reduce_mean(loss)\n", + " grad_params = tape.gradient(total_loss, params)\n", + "\n", + " # Make a vanilla gradient descent step on all of the model parameters\n", + " # Manually update the weights using assign_sub()\n", + " for w, grad_w in zip(params, grad_params):\n", + " w.assign_sub(learning_rate * grad_w)\n", + " \n", + " return total_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def train_part2(model_fn, init_fn, learning_rate):\n", + " \"\"\"\n", + " Train a model on CIFAR-10.\n", + " \n", + " Inputs:\n", + " - model_fn: A Python function that performs the forward pass of the model\n", + " using TensorFlow; it should have the following signature:\n", + " scores = model_fn(x, params) where x is a TensorFlow Tensor giving a\n", + " minibatch of image data, params is a list of TensorFlow Tensors holding\n", + " the model weights, and scores is a TensorFlow Tensor of shape (N, C)\n", + " giving scores for all elements of x.\n", + " - init_fn: A Python function that initializes the parameters of the model.\n", + " It should have the signature params = init_fn() where params is a list\n", + " of TensorFlow Tensors holding the (randomly initialized) weights of the\n", + " model.\n", + " - learning_rate: Python float giving the learning rate to use for SGD.\n", + " \"\"\"\n", + " \n", + " \n", + " params = init_fn() # Initialize the model parameters \n", + " \n", + " for t, (x_np, y_np) in enumerate(train_dset):\n", + " # Run the graph on a batch of training data.\n", + " loss = training_step(model_fn, x_np, y_np, params, learning_rate)\n", + " \n", + " # Periodically print the loss and check accuracy on the val set.\n", + " if t % print_every == 0:\n", + " print('Iteration %d, loss = %.4f' % (t, loss))\n", + " check_accuracy(val_dset, x_np, model_fn, params)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def check_accuracy(dset, x, model_fn, params):\n", + " \"\"\"\n", + " Check accuracy on a classification model, e.g. for validation.\n", + " \n", + " Inputs:\n", + " - dset: A Dataset object against which to check accuracy\n", + " - x: A TensorFlow placeholder Tensor where input images should be fed\n", + " - model_fn: the Model we will be calling to make predictions on x\n", + " - params: parameters for the model_fn to work with\n", + " \n", + " Returns: Nothing, but prints the accuracy of the model\n", + " \"\"\"\n", + " num_correct, num_samples = 0, 0\n", + " for x_batch, y_batch in dset:\n", + " scores_np = model_fn(x_batch, params).numpy()\n", + " y_pred = scores_np.argmax(axis=1)\n", + " num_samples += x_batch.shape[0]\n", + " num_correct += (y_pred == y_batch).sum()\n", + " acc = float(num_correct) / num_samples\n", + " print('Got %d / %d correct (%.2f%%)' % (num_correct, num_samples, 100 * acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Initialization\n", + "We'll use the following utility method to initialize the weight matrices for our models using Kaiming's normalization method.\n", + "\n", + "[1] He et al, *Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification\n", + "*, ICCV 2015, https://arxiv.org/abs/1502.01852" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def create_matrix_with_kaiming_normal(shape):\n", + " if len(shape) == 2:\n", + " fan_in, fan_out = shape[0], shape[1]\n", + " elif len(shape) == 4:\n", + " fan_in, fan_out = np.prod(shape[:3]), shape[3]\n", + " return tf.keras.backend.random_normal(shape) * np.sqrt(2.0 / fan_in)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Train a Two-Layer Network\n", + "We are finally ready to use all of the pieces defined above to train a two-layer fully-connected network on CIFAR-10.\n", + "\n", + "We just need to define a function to initialize the weights of the model, and call `train_part2`.\n", + "\n", + "Defining the weights of the network introduces another important piece of TensorFlow API: `tf.Variable`. A TensorFlow Variable is a Tensor whose value is stored in the graph and persists across runs of the computational graph; however unlike constants defined with `tf.zeros` or `tf.random_normal`, the values of a Variable can be mutated as the graph runs; these mutations will persist across graph runs. Learnable parameters of the network are usually stored in Variables.\n", + "\n", + "You don't need to tune any hyperparameters, but you should achieve validation accuracies above 40% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 3.2493\n", + "Got 121 / 1000 correct (12.10%)\n", + "Iteration 100, loss = 1.8525\n", + "Got 385 / 1000 correct (38.50%)\n", + "Iteration 200, loss = 1.4725\n", + "Got 373 / 1000 correct (37.30%)\n", + "Iteration 300, loss = 1.8654\n", + "Got 366 / 1000 correct (36.60%)\n", + "Iteration 400, loss = 1.8284\n", + "Got 416 / 1000 correct (41.60%)\n", + "Iteration 500, loss = 1.7518\n", + "Got 441 / 1000 correct (44.10%)\n", + "Iteration 600, loss = 1.9832\n", + "Got 434 / 1000 correct (43.40%)\n", + "Iteration 700, loss = 1.9373\n", + "Got 445 / 1000 correct (44.50%)\n" + ] + } + ], + "source": [ + "def two_layer_fc_init():\n", + " \"\"\"\n", + " Initialize the weights of a two-layer network, for use with the\n", + " two_layer_network function defined above. \n", + " You can use the `create_matrix_with_kaiming_normal` helper!\n", + " \n", + " Inputs: None\n", + " \n", + " Returns: A list of:\n", + " - w1: TensorFlow tf.Variable giving the weights for the first layer\n", + " - w2: TensorFlow tf.Variable giving the weights for the second layer\n", + " \"\"\"\n", + " hidden_layer_size = 4000\n", + " w1 = tf.Variable(create_matrix_with_kaiming_normal((3 * 32 * 32, 4000)))\n", + " w2 = tf.Variable(create_matrix_with_kaiming_normal((4000, 10)))\n", + " return [w1, w2]\n", + "\n", + "learning_rate = 1e-2\n", + "train_part2(two_layer_fc, two_layer_fc_init, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barebones TensorFlow: Train a three-layer ConvNet\n", + "We will now use TensorFlow to train a three-layer ConvNet on CIFAR-10.\n", + "\n", + "You need to implement the `three_layer_convnet_init` function. Recall that the architecture of the network is:\n", + "\n", + "1. Convolutional layer (with bias) with 32 5x5 filters, with zero-padding 2\n", + "2. ReLU\n", + "3. Convolutional layer (with bias) with 16 3x3 filters, with zero-padding 1\n", + "4. ReLU\n", + "5. Fully-connected layer (with bias) to compute scores for 10 classes\n", + "\n", + "You don't need to do any hyperparameter tuning, but you should see validation accuracies above 43% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, loss = 4.0431\n", + "Got 118 / 1000 correct (11.80%)\n", + "Iteration 100, loss = 1.8898\n", + "Got 323 / 1000 correct (32.30%)\n", + "Iteration 200, loss = 1.5676\n", + "Got 391 / 1000 correct (39.10%)\n", + "Iteration 300, loss = 1.8205\n", + "Got 387 / 1000 correct (38.70%)\n", + "Iteration 400, loss = 1.5285\n", + "Got 438 / 1000 correct (43.80%)\n", + "Iteration 500, loss = 1.7125\n", + "Got 445 / 1000 correct (44.50%)\n", + "Iteration 600, loss = 1.6236\n", + "Got 466 / 1000 correct (46.60%)\n", + "Iteration 700, loss = 1.6076\n", + "Got 482 / 1000 correct (48.20%)\n" + ] + } + ], + "source": [ + "def three_layer_convnet_init():\n", + " \"\"\"\n", + " Initialize the weights of a Three-Layer ConvNet, for use with the\n", + " three_layer_convnet function defined above.\n", + " You can use the `create_matrix_with_kaiming_normal` helper!\n", + " \n", + " Inputs: None\n", + " \n", + " Returns a list containing:\n", + " - conv_w1: TensorFlow tf.Variable giving weights for the first conv layer\n", + " - conv_b1: TensorFlow tf.Variable giving biases for the first conv layer\n", + " - conv_w2: TensorFlow tf.Variable giving weights for the second conv layer\n", + " - conv_b2: TensorFlow tf.Variable giving biases for the second conv layer\n", + " - fc_w: TensorFlow tf.Variable giving weights for the fully-connected layer\n", + " - fc_b: TensorFlow tf.Variable giving biases for the fully-connected layer\n", + " \"\"\"\n", + " params = None\n", + " ############################################################################\n", + " # TODO: Initialize the parameters of the three-layer network. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " conv_w1 = tf.Variable(create_matrix_with_kaiming_normal((5,5,3,32)))\n", + " conv_b1 = tf.Variable(create_matrix_with_kaiming_normal((1,32)))\n", + " conv_w2 = tf.Variable(create_matrix_with_kaiming_normal((3,3,32,16)))\n", + " conv_b2 = tf.Variable(create_matrix_with_kaiming_normal((1,16)))\n", + " fc_w = tf.Variable(create_matrix_with_kaiming_normal((32*32*16, 10)))\n", + " fc_b = tf.Variable(create_matrix_with_kaiming_normal((1, 10)))\n", + " params = conv_w1, conv_b1, conv_w2, conv_b2, fc_w, fc_b\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return params\n", + "\n", + "learning_rate = 3e-3\n", + "train_part2(three_layer_convnet, three_layer_convnet_init, learning_rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Part III: Keras Model Subclassing API\n", + "\n", + "Implementing a neural network using the low-level TensorFlow API is a good way to understand how TensorFlow works, but it's a little inconvenient - we had to manually keep track of all Tensors holding learnable parameters. This was fine for a small network, but could quickly become unweildy for a large complex model.\n", + "\n", + "Fortunately TensorFlow 2.0 provides higher-level APIs such as `tf.keras` which make it easy to build models out of modular, object-oriented layers. Further, TensorFlow 2.0 uses eager execution that evaluates operations immediately, without explicitly constructing any computational graphs. This makes it easy to write and debug models, and reduces the boilerplate code.\n", + "\n", + "In this part of the notebook we will define neural network models using the `tf.keras.Model` API. To implement your own model, you need to do the following:\n", + "\n", + "1. Define a new class which subclasses `tf.keras.Model`. Give your class an intuitive name that describes it, like `TwoLayerFC` or `ThreeLayerConvNet`.\n", + "2. In the initializer `__init__()` for your new class, define all the layers you need as class attributes. The `tf.keras.layers` package provides many common neural-network layers, like `tf.keras.layers.Dense` for fully-connected layers and `tf.keras.layers.Conv2D` for convolutional layers. Under the hood, these layers will construct `Variable` Tensors for any learnable parameters. **Warning**: Don't forget to call `super(YourModelName, self).__init__()` as the first line in your initializer!\n", + "3. Implement the `call()` method for your class; this implements the forward pass of your model, and defines the *connectivity* of your network. Layers defined in `__init__()` implement `__call__()` so they can be used as function objects that transform input Tensors into output Tensors. Don't define any new layers in `call()`; any layers you want to use in the forward pass should be defined in `__init__()`.\n", + "\n", + "After you define your `tf.keras.Model` subclass, you can instantiate it and use it like the model functions from Part II.\n", + "\n", + "### Keras Model Subclassing API: Two-Layer Network\n", + "\n", + "Here is a concrete example of using the `tf.keras.Model` API to define a two-layer network. There are a few new bits of API to be aware of here:\n", + "\n", + "We use an `Initializer` object to set up the initial values of the learnable parameters of the layers; in particular `tf.initializers.VarianceScaling` gives behavior similar to the Kaiming initialization method we used in Part II. You can read more about it here: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/initializers/VarianceScaling\n", + "\n", + "We construct `tf.keras.layers.Dense` objects to represent the two fully-connected layers of the model. In addition to multiplying their input by a weight matrix and adding a bias vector, these layer can also apply a nonlinearity for you. For the first layer we specify a ReLU activation function by passing `activation='relu'` to the constructor; the second layer uses softmax activation function. Finally, we use `tf.keras.layers.Flatten` to flatten the output from the previous fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "tags": [ + "pdf-ignore-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "class TwoLayerFC(tf.keras.Model):\n", + " def __init__(self, hidden_size, num_classes):\n", + " super(TwoLayerFC, self).__init__() \n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.fc1 = tf.keras.layers.Dense(hidden_size, activation='relu',\n", + " kernel_initializer=initializer)\n", + " self.fc2 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " def call(self, x, training=False):\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "\n", + "def test_TwoLayerFC():\n", + " \"\"\" A small unit test to exercise the TwoLayerFC model above. \"\"\"\n", + " input_size, hidden_size, num_classes = 50, 42, 10\n", + " x = tf.zeros((64, input_size))\n", + " model = TwoLayerFC(hidden_size, num_classes)\n", + " with tf.device(device):\n", + " scores = model(x)\n", + " print(scores.shape)\n", + " \n", + "test_TwoLayerFC()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Three-Layer ConvNet\n", + "Now it's your turn to implement a three-layer ConvNet using the `tf.keras.Model` API. Your model should have the same architecture used in Part II:\n", + "\n", + "1. Convolutional layer with 5 x 5 kernels, with zero-padding of 2\n", + "2. ReLU nonlinearity\n", + "3. Convolutional layer with 3 x 3 kernels, with zero-padding of 1\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer to give class scores\n", + "6. Softmax nonlinearity\n", + "\n", + "You should initialize the weights of your network using the same initialization method as was used in the two-layer network above.\n", + "\n", + "**Hint**: Refer to the documentation for `tf.keras.layers.Conv2D` and `tf.keras.layers.Dense`:\n", + "\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Conv2D\n", + "\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dense" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "class ThreeLayerConvNet(tf.keras.Model):\n", + " def __init__(self, channel_1, channel_2, num_classes):\n", + " super(ThreeLayerConvNet, self).__init__()\n", + " ########################################################################\n", + " # TODO: Implement the __init__ method for a three-layer ConvNet. You #\n", + " # should instantiate layer objects to be used in the forward pass. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.pad1 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\")\n", + " self.pad2 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\")\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " self.conv1 = tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer)\n", + " self.conv2 = tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer) \n", + " self.fc1 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + " \n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ########################################################################\n", + " \n", + " def call(self, x, training=False):\n", + " scores = None\n", + " ########################################################################\n", + " # TODO: Implement the forward pass for a three-layer ConvNet. You #\n", + " # should use the layer objects defined in the __init__ method. #\n", + " ########################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = self.pad1(x)\n", + " x = self.conv1(x)\n", + " x = self.pad2(x)\n", + " x = self.conv2(x)\n", + " x = self.flatten(x)\n", + " scores = self.fc1(x)\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ########################################################################\n", + " # END OF YOUR CODE #\n", + " ######################################################################## \n", + " return scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you complete the implementation of the `ThreeLayerConvNet` above you can run the following to ensure that your implementation does not crash and produces outputs of the expected shape." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def test_ThreeLayerConvNet(): \n", + " channel_1, channel_2, num_classes = 12, 8, 10\n", + " model = ThreeLayerConvNet(channel_1, channel_2, num_classes)\n", + " with tf.device(device):\n", + " x = tf.zeros((64, 3, 32, 32))\n", + " scores = model(x)\n", + " print(scores.shape)\n", + "\n", + "test_ThreeLayerConvNet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Eager Training\n", + "\n", + "While keras models have a builtin training loop (using the `model.fit`), sometimes you need more customization. Here's an example, of a training loop implemented with eager execution.\n", + "\n", + "In particular, notice `tf.GradientTape`. Automatic differentiation is used in the backend for implementing backpropagation in frameworks like TensorFlow. During eager execution, `tf.GradientTape` is used to trace operations for computing gradients later. A particular `tf.GradientTape` can only compute one gradient; subsequent calls to tape will throw a runtime error. \n", + "\n", + "TensorFlow 2.0 ships with easy-to-use built-in metrics under `tf.keras.metrics` module. Each metric is an object, and we can use `update_state()` to add observations and `reset_state()` to clear all observations. We can get the current result of a metric by calling `result()` on the metric object." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "def train_part34(model_init_fn, optimizer_init_fn, num_epochs=1, is_training=False):\n", + " \"\"\"\n", + " Simple training loop for use with models defined using tf.keras. It trains\n", + " a model for one epoch on the CIFAR-10 training set and periodically checks\n", + " accuracy on the CIFAR-10 validation set.\n", + " \n", + " Inputs:\n", + " - model_init_fn: A function that takes no parameters; when called it\n", + " constructs the model we want to train: model = model_init_fn()\n", + " - optimizer_init_fn: A function which takes no parameters; when called it\n", + " constructs the Optimizer object we will use to optimize the model:\n", + " optimizer = optimizer_init_fn()\n", + " - num_epochs: The number of epochs to train for\n", + " \n", + " Returns: Nothing, but prints progress during trainingn\n", + " \"\"\" \n", + " with tf.device(device):\n", + "\n", + " # Compute the loss like we did in Part II\n", + " loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()\n", + " \n", + " model = model_init_fn()\n", + " optimizer = optimizer_init_fn()\n", + " \n", + " train_loss = tf.keras.metrics.Mean(name='train_loss')\n", + " train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n", + " \n", + " val_loss = tf.keras.metrics.Mean(name='val_loss')\n", + " val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='val_accuracy')\n", + " \n", + " t = 0\n", + " for epoch in range(num_epochs):\n", + " \n", + " # Reset the metrics - https://www.tensorflow.org/alpha/guide/migration_guide#new-style_metrics\n", + " train_loss.reset_states()\n", + " train_accuracy.reset_states()\n", + " \n", + " for x_np, y_np in train_dset:\n", + " with tf.GradientTape() as tape:\n", + " \n", + " # Use the model function to build the forward pass.\n", + " scores = model(x_np, training=is_training)\n", + " loss = loss_fn(y_np, scores)\n", + " \n", + " gradients = tape.gradient(loss, model.trainable_variables)\n", + " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", + " \n", + " # Update the metrics\n", + " train_loss.update_state(loss)\n", + " train_accuracy.update_state(y_np, scores)\n", + " \n", + " if t % print_every == 0:\n", + " val_loss.reset_states()\n", + " val_accuracy.reset_states()\n", + " for test_x, test_y in val_dset:\n", + " # During validation at end of epoch, training set to False\n", + " prediction = model(test_x, training=False)\n", + " t_loss = loss_fn(test_y, prediction)\n", + "\n", + " val_loss.update_state(t_loss)\n", + " val_accuracy.update_state(test_y, prediction)\n", + " \n", + " template = 'Iteration {}, Epoch {}, Loss: {}, Accuracy: {}, Val Loss: {}, Val Accuracy: {}'\n", + " print (template.format(t, epoch+1,\n", + " train_loss.result(),\n", + " train_accuracy.result()*100,\n", + " val_loss.result(),\n", + " val_accuracy.result()*100))\n", + " t += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Train a Two-Layer Network\n", + "We can now use the tools defined above to train a two-layer network on CIFAR-10. We define the `model_init_fn` and `optimizer_init_fn` that construct the model and optimizer respectively when called. Here we want to train the model using stochastic gradient descent with no momentum, so we construct a `tf.keras.optimizers.SGD` function; you can [read about it here](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/optimizers/SGD).\n", + "\n", + "You don't need to tune any hyperparameters here, but you should achieve validation accuracies above 40% after one epoch of training." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0917 13:39:06.645046 140735629599616 deprecation.py:323] From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1220: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.820213794708252, Accuracy: 21.875, Val Loss: 2.874540328979492, Val Accuracy: 14.30000114440918\n", + "Iteration 100, Epoch 1, Loss: 2.211560010910034, Accuracy: 28.712871551513672, Val Loss: 1.9114350080490112, Val Accuracy: 38.10000228881836\n", + "Iteration 200, Epoch 1, Loss: 2.061293601989746, Accuracy: 32.23725128173828, Val Loss: 1.8562566041946411, Val Accuracy: 40.400001525878906\n", + "Iteration 300, Epoch 1, Loss: 1.9950498342514038, Accuracy: 33.93376159667969, Val Loss: 1.88795804977417, Val Accuracy: 38.20000076293945\n", + "Iteration 400, Epoch 1, Loss: 1.927003264427185, Accuracy: 35.95698165893555, Val Loss: 1.7473491430282593, Val Accuracy: 41.10000228881836\n", + "Iteration 500, Epoch 1, Loss: 1.8845645189285278, Accuracy: 37.11327362060547, Val Loss: 1.6804167032241821, Val Accuracy: 42.599998474121094\n", + "Iteration 600, Epoch 1, Loss: 1.857049584388733, Accuracy: 37.95497131347656, Val Loss: 1.691178798675537, Val Accuracy: 42.0\n", + "Iteration 700, Epoch 1, Loss: 1.8302688598632812, Accuracy: 38.64568328857422, Val Loss: 1.648596167564392, Val Accuracy: 45.20000076293945\n" + ] + } + ], + "source": [ + "hidden_size, num_classes = 4000, 10\n", + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " return TwoLayerFC(hidden_size, num_classes)\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate)\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Model Subclassing API: Train a Three-Layer ConvNet\n", + "Here you should use the tools we've defined above to train a three-layer ConvNet on CIFAR-10. Your ConvNet should use 32 filters in the first convolutional layer and 16 filters in the second layer.\n", + "\n", + "To train the model you should use gradient descent with Nesterov momentum 0.9. \n", + "\n", + "**HINT**: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/optimizers/SGD\n", + "\n", + "You don't need to perform any hyperparameter tuning, but you should achieve validation accuracies above 50% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.977733612060547, Accuracy: 14.0625, Val Loss: 11.000207901000977, Val Accuracy: 11.200000762939453\n", + "Iteration 100, Epoch 1, Loss: 2.1598904132843018, Accuracy: 26.670793533325195, Val Loss: 1.8142485618591309, Val Accuracy: 36.79999923706055\n", + "Iteration 200, Epoch 1, Loss: 1.931534767150879, Accuracy: 33.333335876464844, Val Loss: 1.54546320438385, Val Accuracy: 48.20000076293945\n", + "Iteration 300, Epoch 1, Loss: 1.8104909658432007, Accuracy: 36.975704193115234, Val Loss: 1.452940583229065, Val Accuracy: 48.79999923706055\n", + "Iteration 400, Epoch 1, Loss: 1.7208037376403809, Accuracy: 39.7015266418457, Val Loss: 1.4160033464431763, Val Accuracy: 48.89999771118164\n", + "Iteration 500, Epoch 1, Loss: 1.6586426496505737, Accuracy: 41.68537902832031, Val Loss: 1.3770642280578613, Val Accuracy: 52.60000228881836\n", + "Iteration 600, Epoch 1, Loss: 1.6175931692123413, Accuracy: 43.055843353271484, Val Loss: 1.3665345907211304, Val Accuracy: 54.20000076293945\n", + "Iteration 700, Epoch 1, Loss: 1.5854710340499878, Accuracy: 44.1534423828125, Val Loss: 1.353290319442749, Val Accuracy: 51.79999923706055\n" + ] + } + ], + "source": [ + "learning_rate = 3e-3\n", + "channel_1, channel_2, num_classes = 32, 16, 10\n", + "\n", + "def model_init_fn():\n", + " model = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " model = ThreeLayerConvNet(channel_1, channel_2, num_classes)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return model\n", + "\n", + "def optimizer_init_fn():\n", + " optimizer = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " optimizer = tf.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True, name='SGD')\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return optimizer\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part IV: Keras Sequential API\n", + "In Part III we introduced the `tf.keras.Model` API, which allows you to define models with any number of learnable layers and with arbitrary connectivity between layers.\n", + "\n", + "However for many models you don't need such flexibility - a lot of models can be expressed as a sequential stack of layers, with the output of each layer fed to the next layer as input. If your model fits this pattern, then there is an even easier way to define your model: using `tf.keras.Sequential`. You don't need to write any custom classes; you simply call the `tf.keras.Sequential` constructor with a list containing a sequence of layer objects.\n", + "\n", + "One complication with `tf.keras.Sequential` is that you must define the shape of the input to the model by passing a value to the `input_shape` of the first layer in your model.\n", + "\n", + "### Keras Sequential API: Two-Layer Network\n", + "In this subsection, we will rewrite the two-layer fully-connected network using `tf.keras.Sequential`, and train it using the training loop defined above.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.3860318660736084, Accuracy: 9.375, Val Loss: 3.1342945098876953, Val Accuracy: 12.100000381469727\n", + "Iteration 100, Epoch 1, Loss: 2.229124069213867, Accuracy: 28.821165084838867, Val Loss: 1.879478931427002, Val Accuracy: 39.20000076293945\n", + "Iteration 200, Epoch 1, Loss: 2.0733325481414795, Accuracy: 32.49378204345703, Val Loss: 1.8507494926452637, Val Accuracy: 41.400001525878906\n", + "Iteration 300, Epoch 1, Loss: 1.9997358322143555, Accuracy: 34.39057159423828, Val Loss: 1.7963671684265137, Val Accuracy: 38.70000076293945\n", + "Iteration 400, Epoch 1, Loss: 1.9314937591552734, Accuracy: 36.20246505737305, Val Loss: 1.7118778228759766, Val Accuracy: 42.89999771118164\n", + "Iteration 500, Epoch 1, Loss: 1.8869222402572632, Accuracy: 37.26921081542969, Val Loss: 1.64309561252594, Val Accuracy: 43.70000076293945\n", + "Iteration 600, Epoch 1, Loss: 1.857375144958496, Accuracy: 38.11355972290039, Val Loss: 1.6449146270751953, Val Accuracy: 45.0\n", + "Iteration 700, Epoch 1, Loss: 1.8303626775741577, Accuracy: 38.754905700683594, Val Loss: 1.607826828956604, Val Accuracy: 44.70000076293945\n" + ] + } + ], + "source": [ + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " input_shape = (32, 32, 3)\n", + " hidden_layer_size, num_classes = 4000, 10\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " layers = [\n", + " tf.keras.layers.Flatten(input_shape=input_shape),\n", + " tf.keras.layers.Dense(hidden_layer_size, activation='relu',\n", + " kernel_initializer=initializer),\n", + " tf.keras.layers.Dense(num_classes, activation='softmax', \n", + " kernel_initializer=initializer),\n", + " ]\n", + " model = tf.keras.Sequential(layers)\n", + " return model\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate) \n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Abstracting Away the Training Loop\n", + "In the previous examples, we used a customised training loop to train models (e.g. `train_part34`). Writing your own training loop is only required if you need more flexibility and control during training your model. Alternately, you can also use built-in APIs like `tf.keras.Model.fit()` and `tf.keras.Model.evaluate` to train and evaluate a model. Also remember to configure your model for training by calling `tf.keras.Model.compile.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation and test accuracies above 42% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 49000 samples, validate on 1000 samples\n", + "49000/49000 [==============================] - 56s 1ms/sample - loss: 1.8096 - sparse_categorical_accuracy: 0.3908 - val_loss: 1.6444 - val_sparse_categorical_accuracy: 0.4480\n", + "10000/10000 [==============================] - 6s 627us/sample - loss: 1.6521 - sparse_categorical_accuracy: 0.4300\n" + ] + }, + { + "data": { + "text/plain": [ + "[1.6520895320892335, 0.43]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = model_init_fn()\n", + "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=[tf.keras.metrics.sparse_categorical_accuracy])\n", + "model.fit(X_train, y_train, batch_size=64, epochs=1, validation_data=(X_val, y_val))\n", + "model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Sequential API: Three-Layer ConvNet\n", + "Here you should use `tf.keras.Sequential` to reimplement the same three-layer ConvNet architecture used in Part II and Part III. As a reminder, your model should have the following architecture:\n", + "\n", + "1. Convolutional layer with 32 5x5 kernels, using zero padding of 2\n", + "2. ReLU nonlinearity\n", + "3. Convolutional layer with 16 3x3 kernels, using zero padding of 1\n", + "4. ReLU nonlinearity\n", + "5. Fully-connected layer giving class scores\n", + "6. Softmax nonlinearity\n", + "\n", + "You should initialize the weights of the model using a `tf.initializers.VarianceScaling` as above.\n", + "\n", + "You should train the model using Nesterov momentum 0.9.\n", + "\n", + "You don't need to perform any hyperparameter search, but you should achieve accuracy above 45% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.066777229309082, Accuracy: 10.9375, Val Loss: 7.942768096923828, Val Accuracy: 7.90000057220459\n", + "Iteration 100, Epoch 1, Loss: 2.0578784942626953, Accuracy: 27.04207992553711, Val Loss: 1.7399837970733643, Val Accuracy: 39.89999771118164\n", + "Iteration 200, Epoch 1, Loss: 1.8581743240356445, Accuracy: 33.76865768432617, Val Loss: 1.5444930791854858, Val Accuracy: 45.5\n", + "Iteration 300, Epoch 1, Loss: 1.7626904249191284, Accuracy: 37.089908599853516, Val Loss: 1.5184237957000732, Val Accuracy: 46.39999771118164\n", + "Iteration 400, Epoch 1, Loss: 1.6890290975570679, Accuracy: 39.66645812988281, Val Loss: 1.480480670928955, Val Accuracy: 48.10000228881836\n", + "Iteration 500, Epoch 1, Loss: 1.641016960144043, Accuracy: 41.43587875366211, Val Loss: 1.4048315286636353, Val Accuracy: 50.5\n", + "Iteration 600, Epoch 1, Loss: 1.6024343967437744, Accuracy: 42.87385559082031, Val Loss: 1.3888776302337646, Val Accuracy: 52.10000228881836\n", + "Iteration 700, Epoch 1, Loss: 1.5693012475967407, Accuracy: 44.24259948730469, Val Loss: 1.3774913549423218, Val Accuracy: 54.20000076293945\n" + ] + } + ], + "source": [ + "def model_init_fn():\n", + " model = None\n", + " ############################################################################\n", + " # TODO: Construct a three-layer ConvNet using tf.keras.Sequential. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " channel_1, channel_2 = 32, 16\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " layers = [\n", + " \n", + " tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\"),\n", + " \n", + " tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\"),\n", + " \n", + " tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer),\n", + " \n", + " tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer), \n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(num_classes, activation='softmax', \n", + " kernel_initializer=initializer),\n", + " ]\n", + " model = tf.keras.Sequential(layers)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return model\n", + "\n", + "learning_rate = 5e-4\n", + "def optimizer_init_fn():\n", + " optimizer = None\n", + " ############################################################################\n", + " # TODO: Complete the implementation of model_fn. #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " optimizer = tf.optimizers.SGD(learning_rate=0.01, momentum=0.9, nesterov=True, name='SGD')\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " return optimizer\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also train this model with the built-in training loop APIs provided by TensorFlow." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 49000 samples, validate on 1000 samples\n", + "49000/49000 [==============================] - 93s 2ms/sample - loss: 1.5981 - sparse_categorical_accuracy: 0.4382 - val_loss: 1.3696 - val_sparse_categorical_accuracy: 0.5330\n", + "10000/10000 [==============================] - 11s 1ms/sample - loss: 1.3767 - sparse_categorical_accuracy: 0.5067\n" + ] + }, + { + "data": { + "text/plain": [ + "[1.3766763814926148, 0.5067]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = model_init_fn()\n", + "model.compile(optimizer='sgd',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=[tf.keras.metrics.sparse_categorical_accuracy])\n", + "model.fit(X_train, y_train, batch_size=64, epochs=1, validation_data=(X_val, y_val))\n", + "model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part IV: Functional API\n", + "### Demonstration with a Two-Layer Network \n", + "\n", + "In the previous section, we saw how we can use `tf.keras.Sequential` to stack layers to quickly build simple models. But this comes at the cost of losing flexibility.\n", + "\n", + "Often we will have to write complex models that have non-sequential data flows: a layer can have **multiple inputs and/or outputs**, such as stacking the output of 2 previous layers together to feed as input to a third! (Some examples are residual connections and dense blocks.)\n", + "\n", + "In such cases, we can use Keras functional API to write models with complex topologies such as:\n", + "\n", + " 1. Multi-input models\n", + " 2. Multi-output models\n", + " 3. Models with shared layers (the same layer called several times)\n", + " 4. Models with non-sequential data flows (e.g. residual connections)\n", + "\n", + "Writing a model with Functional API requires us to create a `tf.keras.Model` instance and explicitly write input tensors and output tensors for this model. " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 10)\n" + ] + } + ], + "source": [ + "def two_layer_fc_functional(input_shape, hidden_size, num_classes): \n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " inputs = tf.keras.Input(shape=input_shape)\n", + " flattened_inputs = tf.keras.layers.Flatten()(inputs)\n", + " fc1_output = tf.keras.layers.Dense(hidden_size, activation='relu',\n", + " kernel_initializer=initializer)(flattened_inputs)\n", + " scores = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)(fc1_output)\n", + "\n", + " # Instantiate the model given inputs and outputs.\n", + " model = tf.keras.Model(inputs=inputs, outputs=scores)\n", + " return model\n", + "\n", + "def test_two_layer_fc_functional():\n", + " \"\"\" A small unit test to exercise the TwoLayerFC model above. \"\"\"\n", + " input_size, hidden_size, num_classes = 50, 42, 10\n", + " input_shape = (50,)\n", + " \n", + " x = tf.zeros((64, input_size))\n", + " model = two_layer_fc_functional(input_shape, hidden_size, num_classes)\n", + " \n", + " with tf.device(device):\n", + " scores = model(x)\n", + " print(scores.shape)\n", + " \n", + "test_two_layer_fc_functional()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keras Functional API: Train a Two-Layer Network\n", + "You can now train this two-layer network constructed using the functional API.\n", + "\n", + "You don't need to perform any hyperparameter tuning here, but you should see validation accuracies above 40% after training for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 3.1926515102386475, Accuracy: 6.25, Val Loss: 2.9305005073547363, Val Accuracy: 11.90000057220459\n", + "Iteration 100, Epoch 1, Loss: 2.252453565597534, Accuracy: 28.790224075317383, Val Loss: 1.8985050916671753, Val Accuracy: 37.900001525878906\n", + "Iteration 200, Epoch 1, Loss: 2.0828073024749756, Accuracy: 32.37717819213867, Val Loss: 1.8634843826293945, Val Accuracy: 39.599998474121094\n", + "Iteration 300, Epoch 1, Loss: 2.003688335418701, Accuracy: 34.29194259643555, Val Loss: 1.8453090190887451, Val Accuracy: 38.10000228881836\n", + "Iteration 400, Epoch 1, Loss: 1.935245394706726, Accuracy: 36.03881072998047, Val Loss: 1.7412177324295044, Val Accuracy: 41.20000076293945\n", + "Iteration 500, Epoch 1, Loss: 1.8904175758361816, Accuracy: 37.12574768066406, Val Loss: 1.647827386856079, Val Accuracy: 43.900001525878906\n", + "Iteration 600, Epoch 1, Loss: 1.8617217540740967, Accuracy: 38.00956726074219, Val Loss: 1.6854203939437866, Val Accuracy: 42.0\n", + "Iteration 700, Epoch 1, Loss: 1.835993766784668, Accuracy: 38.59218978881836, Val Loss: 1.6190838813781738, Val Accuracy: 46.0\n" + ] + } + ], + "source": [ + "input_shape = (32, 32, 3)\n", + "hidden_size, num_classes = 4000, 10\n", + "learning_rate = 1e-2\n", + "\n", + "def model_init_fn():\n", + " return two_layer_fc_functional(input_shape, hidden_size, num_classes)\n", + "\n", + "def optimizer_init_fn():\n", + " return tf.keras.optimizers.SGD(learning_rate=learning_rate)\n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Part V: CIFAR-10 open-ended challenge\n", + "\n", + "In this section you can experiment with whatever ConvNet architecture you'd like on CIFAR-10.\n", + "\n", + "You should experiment with architectures, hyperparameters, loss functions, regularization, or anything else you can think of to train a model that achieves **at least 70%** accuracy on the **validation** set within 10 epochs. You can use the built-in train function, the `train_part34` function from above, or implement your own training loop.\n", + "\n", + "Describe what you did at the end of the notebook.\n", + "\n", + "### Some things you can try:\n", + "- **Filter size**: Above we used 5x5 and 3x3; is this optimal?\n", + "- **Number of filters**: Above we used 16 and 32 filters. Would more or fewer do better?\n", + "- **Pooling**: We didn't use any pooling above. Would this improve the model?\n", + "- **Normalization**: Would your model be improved with batch normalization, layer normalization, group normalization, or some other normalization strategy?\n", + "- **Network architecture**: The ConvNet above has only three layers of trainable parameters. Would a deeper model do better?\n", + "- **Global average pooling**: Instead of flattening after the final convolutional layer, would global average pooling do better? This strategy is used for example in Google's Inception network and in Residual Networks.\n", + "- **Regularization**: Would some kind of regularization improve performance? Maybe weight decay or dropout?\n", + "\n", + "### NOTE: Batch Normalization / Dropout\n", + "If you are using Batch Normalization and Dropout, remember to pass `is_training=True` if you use the `train_part34()` function. BatchNorm and Dropout layers have different behaviors at training and inference time. `training` is a specific keyword argument reserved for this purpose in any `tf.keras.Model`'s `call()` function. Read more about this here : https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/BatchNormalization#methods\n", + "https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dropout#methods\n", + "\n", + "### Tips for training\n", + "For each network architecture that you try, you should tune the learning rate and other hyperparameters. When doing this there are a couple important things to keep in mind: \n", + "\n", + "- If the parameters are working well, you should see improvement within a few hundred iterations\n", + "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n", + "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n", + "- You should use the validation set for hyperparameter search, and save your test set for evaluating your architecture on the best parameters as selected by the validation set.\n", + "\n", + "### Going above and beyond\n", + "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these, but don't miss the fun if you have time!\n", + "\n", + "- Alternative optimizers: you can try Adam, Adagrad, RMSprop, etc.\n", + "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n", + "- Model ensembles\n", + "- Data augmentation\n", + "- New Architectures\n", + " - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n", + " - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n", + " - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n", + " \n", + "### Have fun and happy training! " + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0, Epoch 1, Loss: 2.553211212158203, Accuracy: 6.25, Val Loss: 4.809482097625732, Val Accuracy: 7.800000190734863\n", + "Iteration 700, Epoch 1, Loss: 2.19158935546875, Accuracy: 16.96906280517578, Val Loss: 2.0810210704803467, Val Accuracy: 18.799999237060547\n", + "Iteration 1400, Epoch 2, Loss: 1.92042076587677, Accuracy: 25.661909103393555, Val Loss: 1.7447700500488281, Val Accuracy: 33.89999771118164\n", + "Iteration 2100, Epoch 3, Loss: 1.6454426050186157, Accuracy: 34.61665344238281, Val Loss: 1.6614642143249512, Val Accuracy: 34.70000076293945\n", + "Iteration 2800, Epoch 4, Loss: 1.5549455881118774, Accuracy: 37.86965560913086, Val Loss: 1.5995800495147705, Val Accuracy: 38.0\n", + "Iteration 3500, Epoch 5, Loss: 1.4994430541992188, Accuracy: 39.73112106323242, Val Loss: 1.5962034463882446, Val Accuracy: 39.099998474121094\n", + "Iteration 4200, Epoch 6, Loss: 1.4527039527893066, Accuracy: 41.370452880859375, Val Loss: 1.570074439048767, Val Accuracy: 41.5\n", + "Iteration 4900, Epoch 7, Loss: 1.4144495725631714, Accuracy: 42.853485107421875, Val Loss: 1.6536961793899536, Val Accuracy: 39.79999923706055\n", + "Iteration 5600, Epoch 8, Loss: 1.391770362854004, Accuracy: 44.12918472290039, Val Loss: 1.5999575853347778, Val Accuracy: 41.0\n", + "Iteration 6300, Epoch 9, Loss: 1.3563306331634521, Accuracy: 45.4028205871582, Val Loss: 1.674006700515747, Val Accuracy: 39.70000076293945\n", + "Iteration 7000, Epoch 10, Loss: 1.3255177736282349, Accuracy: 46.962615966796875, Val Loss: 1.7243903875350952, Val Accuracy: 41.20000076293945\n" + ] + } + ], + "source": [ + "class CustomConvNet(tf.keras.Model):\n", + " def __init__(self):\n", + " super(CustomConvNet, self).__init__()\n", + " ############################################################################\n", + " # TODO: Construct a model that performs well on CIFAR-10 #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " initializer = tf.initializers.VarianceScaling(scale=2.0)\n", + " self.pad1 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(2, 2), data_format=\"channels_last\")\n", + " self.pad2 = tf.compat.v2.keras.layers.ZeroPadding2D(padding=(1, 1), data_format=\"channels_last\")\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " \n", + " self.conv1 = tf.keras.layers.Conv2D(filters=channel_1, kernel_size=(5,5), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer)\n", + " \n", + " self.conv2 = tf.keras.layers.Conv2D(filters=channel_2, kernel_size=(3,3), strides=(1, 1), \n", + " padding='valid', activation='relu',\n", + " kernel_initializer=initializer) \n", + " \n", + " self.fc1 = tf.keras.layers.Dense(num_classes, activation='relu',\n", + " kernel_initializer=initializer)\n", + " \n", + " self.fc2 = tf.keras.layers.Dense(num_classes, activation='softmax',\n", + " kernel_initializer=initializer)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " \n", + " def call(self, input_tensor, training=False):\n", + " ############################################################################\n", + " # TODO: Construct a model that performs well on CIFAR-10 #\n", + " ############################################################################\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " x = self.pad1(input_tensor) \n", + " x = self.conv1(x) \n", + " x = self.pad2(x) \n", + " x = self.conv2(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.fc2(x)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ############################################################################\n", + " # END OF YOUR CODE #\n", + " ############################################################################\n", + " \n", + " return x\n", + "\n", + "# device = '/device:GPU:0' # Change this to a CPU/GPU as you wish!\n", + "device = '/cpu:0' # Change this to a CPU/GPU as you wish!\n", + "print_every = 700\n", + "num_epochs = 10\n", + "\n", + "model = CustomConvNet()\n", + "\n", + "def model_init_fn():\n", + " return CustomConvNet()\n", + "\n", + "def optimizer_init_fn():\n", + " learning_rate = 1e-3\n", + " return tf.keras.optimizers.Adam(learning_rate) \n", + "\n", + "train_part34(model_init_fn, optimizer_init_fn, num_epochs=num_epochs, is_training=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## Describe what you did \n", + "\n", + "In the cell below you should write an explanation of what you did, any additional features that you implemented, and/or any graphs that you made in the process of training and evaluating your network." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "TODO: Tell us what you did" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assignment2/collectSubmission.sh b/assignment2/collectSubmission.sh new file mode 100755 index 0000000..6ca95b9 --- /dev/null +++ b/assignment2/collectSubmission.sh @@ -0,0 +1,48 @@ +#!/bin/bash +#NOTE: DO NOT EDIT THIS FILE-- MAY RESULT IN INCOMPLETE SUBMISSIONS + +NOTEBOOKS="FullyConnectedNets.ipynb +BatchNormalization.ipynb +Dropout.ipynb +ConvolutionalNetworks.ipynb +PyTorch.ipynb +TensorFlow.ipynb" + +CODE="cs231n/layers.py +cs231n/classifiers/fc_net.py +cs231n/optim.py +cs231n/classifiers/cnn.py" + +REMOTE_DIR="cs231n-2019-assignment2" +ZIP_FILENAME="a2.zip" + +FILES="${NOTEBOOKS} ${CODE}" +for FILE in ${FILES} +do + if [ ! -f ${FILE} ]; then + echo "Required file ${FILE} not found, Exiting." + exit 0 + fi +done +if [ -d ${REMOTE_DIR} ]; then + rm -r ${REMOTE_DIR} +fi +mkdir -p ${REMOTE_DIR} +cp ${FILES} ${REMOTE_DIR} + +echo "### Zipping file ###" +zip -r ${REMOTE_DIR}/${ZIP_FILENAME} . -x "*.git*" "*cs231n/datasets*" "*.ipynb_checkpoints*" "*README.md" "collectSubmission.sh" "*requirements.txt" "*__pycache__*" ".env/*" > assignment_zip.log +echo "" + +echo "### Submitting to myth ###" +echo "Type in your Stanford student ID (alphanumeric, *not* the 8-digit ID):" +read -p "Student ID: " SUID +echo "" + +echo "### Copying to ${SUID}@myth.stanford.edu:${REMOTE_DIR} ###" +echo "Note: if myth is under heavy use, this may hang: If this happens, rerun the script." +scp -r ${REMOTE_DIR} ${SUID}@myth.stanford.edu:~/ +echo "" + +echo "### Running remote submission script from ${SUID}@myth.stanford.edu:${REMOTE_DIR} ###" +ssh ${SUID}@myth.stanford.edu "cd ${REMOTE_DIR} && /afs/ir/class/cs231n/grading/submit_a2 && exit" diff --git a/assignment2/cs231n/.gitignore b/assignment2/cs231n/.gitignore new file mode 100755 index 0000000..fbb42c2 --- /dev/null +++ b/assignment2/cs231n/.gitignore @@ -0,0 +1,3 @@ +build/* +im2col_cython.c +im2col_cython.so diff --git a/assignment2/cs231n/__init__.py b/assignment2/cs231n/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/assignment2/cs231n/classifiers/__init__.py b/assignment2/cs231n/classifiers/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/assignment2/cs231n/classifiers/cnn.py b/assignment2/cs231n/classifiers/cnn.py new file mode 100755 index 0000000..13105c9 --- /dev/null +++ b/assignment2/cs231n/classifiers/cnn.py @@ -0,0 +1,154 @@ +from builtins import object +import numpy as np + +from cs231n.layers import * +from cs231n.fast_layers import * +from cs231n.layer_utils import * + + +class ThreeLayerConvNet(object): + """ + A three-layer convolutional network with the following architecture: + + conv - relu - 2x2 max pool - affine - relu - affine - softmax + + The network operates on minibatches of data that have shape (N, C, H, W) + consisting of N images, each with height H and width W and with C input + channels. + """ + + def __init__(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7, + hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0, + dtype=np.float32): + """ + Initialize a new network. + + Inputs: + - input_dim: Tuple (C, H, W) giving size of input data + - num_filters: Number of filters to use in the convolutional layer + - filter_size: Width/height of filters to use in the convolutional layer + - hidden_dim: Number of units to use in the fully-connected hidden layer + - num_classes: Number of scores to produce from the final affine layer. + - weight_scale: Scalar giving standard deviation for random initialization + of weights. + - reg: Scalar giving L2 regularization strength + - dtype: numpy datatype to use for computation. + """ + self.params = {} + self.reg = reg + self.dtype = dtype + + ############################################################################ + # TODO: Initialize weights and biases for the three-layer convolutional # + # network. Weights should be initialized from a Gaussian centered at 0.0 # + # with standard deviation equal to weight_scale; biases should be # + # initialized to zero. All weights and biases should be stored in the # + # dictionary self.params. Store weights and biases for the convolutional # + # layer using the keys 'W1' and 'b1'; use keys 'W2' and 'b2' for the # + # weights and biases of the hidden affine layer, and keys 'W3' and 'b3' # + # for the weights and biases of the output affine layer. # + # # + # IMPORTANT: For this assignment, you can assume that the padding # + # and stride of the first convolutional layer are chosen so that # + # **the width and height of the input are preserved**. Take a look at # + # the start of the loss() function to see how that happens. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + C, H, W = input_dim + flatten_dim = num_filters * int(H/2) * int(W/2) + self.params['W1'] = weight_scale * np.random.randn(num_filters, C, filter_size, filter_size) + self.params['b1'] = np.zeros(num_filters) + self.params['W2'] = weight_scale * np.random.randn(flatten_dim, hidden_dim) + self.params['b2'] = np.zeros(hidden_dim) + self.params['W3'] = weight_scale * np.random.randn(hidden_dim, num_classes) + self.params['b3'] = np.zeros(num_classes) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + for k, v in self.params.items(): + self.params[k] = v.astype(dtype) + + + def loss(self, X, y=None): + """ + Evaluate loss and gradient for the three-layer convolutional network. + + Input / output: Same API as TwoLayerNet in fc_net.py. + """ + W1, b1 = self.params['W1'], self.params['b1'] + W2, b2 = self.params['W2'], self.params['b2'] + W3, b3 = self.params['W3'], self.params['b3'] + + # pass conv_param to the forward pass for the convolutional layer + # Padding and stride chosen to preserve the input spatial size + filter_size = W1.shape[2] + conv_param = {'stride': 1, 'pad': (filter_size - 1) // 2} + + # pass pool_param to the forward pass for the max-pooling layer + pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2} + + scores = None + ############################################################################ + # TODO: Implement the forward pass for the three-layer convolutional net, # + # computing the class scores for X and storing them in the scores # + # variable. # + # # + # Remember you can use the functions defined in cs231n/fast_layers.py and # + # cs231n/layer_utils.py in your implementation (already imported). # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + out, cache_relu_pool = conv_relu_pool_forward(X, W1, b1, conv_param, pool_param) + out, cache_aff_relu = affine_relu_forward(out, W2, b2) + scores, cache_aff = affine_forward(out, W3, b3) + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + if y is None: + return scores + + loss, grads = 0, {} + ############################################################################ + # TODO: Implement the backward pass for the three-layer convolutional net, # + # storing the loss and gradients in the loss and grads variables. Compute # + # data loss using softmax, and make sure that grads[k] holds the gradients # + # for self.params[k]. Don't forget to add L2 regularization! # + # # + # NOTE: To ensure that your implementation matches ours and you pass the # + # automated tests, make sure that your L2 regularization includes a factor # + # of 0.5 to simplify the expression for the gradient. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N = X.shape[0] + + loss, dx = softmax_loss(scores, y) + loss += 0.5 *self.reg * (np.sum(W1**2) + np.sum(W2**2) + np.sum(W3**2)) + + + + dx3, dW3, db3 = affine_backward(dx, cache_aff) + dx2, dW2, db2 = affine_relu_backward(dx3, cache_aff_relu) + dx1, dW1, db1 = conv_relu_pool_backward(dx2, cache_relu_pool) + + dW3 += self.reg * W3 + dW2 += self.reg * W2 + dW1 += self.reg * W1 + + + grads['W1'] = dW1 + grads['b1'] = db1 + grads['W2'] = dW2 + grads['b2'] = db2 + grads['W3'] = dW3 + grads['b3'] = db3 + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + return loss, grads diff --git a/assignment2/cs231n/classifiers/fc_net.py b/assignment2/cs231n/classifiers/fc_net.py new file mode 100755 index 0000000..4ccc933 --- /dev/null +++ b/assignment2/cs231n/classifiers/fc_net.py @@ -0,0 +1,395 @@ +from builtins import range +from builtins import object +import numpy as np + +from cs231n.layers import * +from cs231n.layer_utils import * + + +class TwoLayerNet(object): + """ + A two-layer fully-connected neural network with ReLU nonlinearity and + softmax loss that uses a modular layer design. We assume an input dimension + of D, a hidden dimension of H, and perform classification over C classes. + + The architecure should be affine - relu - affine - softmax. + + Note that this class does not implement gradient descent; instead, it + will interact with a separate Solver object that is responsible for running + optimization. + + The learnable parameters of the model are stored in the dictionary + self.params that maps parameter names to numpy arrays. + """ + + def __init__(self, input_dim=3*32*32, hidden_dim=100, num_classes=10, + weight_scale=1e-3, reg=0.0): + """ + Initialize a new network. + + Inputs: + - input_dim: An integer giving the size of the input + - hidden_dim: An integer giving the size of the hidden layer + - num_classes: An integer giving the number of classes to classify + - weight_scale: Scalar giving the standard deviation for random + initialization of the weights. + - reg: Scalar giving L2 regularization strength. + """ + self.params = {} + self.reg = reg + + ############################################################################ + # TODO: Initialize the weights and biases of the two-layer net. Weights # + # should be initialized from a Gaussian centered at 0.0 with # + # standard deviation equal to weight_scale, and biases should be # + # initialized to zero. All weights and biases should be stored in the # + # dictionary self.params, with first layer weights # + # and biases using the keys 'W1' and 'b1' and second layer # + # weights and biases using the keys 'W2' and 'b2'. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + + self.params['W1'] = weight_scale * np.random.randn(input_dim, hidden_dim) + self.params['b1'] = np.zeros(hidden_dim) + self.params['W2'] = weight_scale * np.random.randn(hidden_dim, num_classes) + self.params['b2'] = np.zeros(num_classes) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + + def loss(self, X, y=None): + """ + Compute loss and gradient for a minibatch of data. + + Inputs: + - X: Array of input data of shape (N, d_1, ..., d_k) + - y: Array of labels, of shape (N,). y[i] gives the label for X[i]. + + Returns: + If y is None, then run a test-time forward pass of the model and return: + - scores: Array of shape (N, C) giving classification scores, where + scores[i, c] is the classification score for X[i] and class c. + + If y is not None, then run a training-time forward and backward pass and + return a tuple of: + - loss: Scalar value giving the loss + - grads: Dictionary with the same keys as self.params, mapping parameter + names to gradients of the loss with respect to those parameters. + """ + scores = None + ############################################################################ + # TODO: Implement the forward pass for the two-layer net, computing the # + # class scores for X and storing them in the scores variable. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + W1 = self.params['W1'] + b1 = self.params['b1'] + W2 = self.params['W2'] + b2 = self.params['b2'] + out1, cache1 = affine_relu_forward(X, W1, b1) + out2, cache2 = affine_forward(out1, W2, b2) + scores = out2 + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + # If y is None then we are in test mode so just return scores + if y is None: + return scores + + loss, grads = 0, {} + ############################################################################ + # TODO: Implement the backward pass for the two-layer net. Store the loss # + # in the loss variable and gradients in the grads dictionary. Compute data # + # loss using softmax, and make sure that grads[k] holds the gradients for # + # self.params[k]. Don't forget to add L2 regularization! # + # # + # NOTE: To ensure that your implementation matches ours and you pass the # + # automated tests, make sure that your L2 regularization includes a factor # + # of 0.5 to simplify the expression for the gradient. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N = X.shape[0] + + scores -= np.max(scores, axis=1).reshape(scores.shape[0], 1) + scores = np.exp(scores) + scores /= np.sum(scores, axis=1).reshape(scores.shape[0],1) + + loss = np.sum(-np.log(scores[np.arange(N), y])) + loss /= N + loss += self.reg * (np.sum(W1**2) + np.sum(W2**2)) + + dsoftmax = scores + dsoftmax[range(N), y] -= 1 + dsoftmax /= N + dW2 = out1.T.dot(dsoftmax) + db2 = dsoftmax.sum(axis=0) + + dout = dsoftmax.dot(W2.T) + dX, dW1, db1 = affine_relu_backward(dout, cache1) + + dW1 += W1 + dW2 += W2 + + grads = {'W1': dW1, 'W2': dW2, 'b1': b1, 'b2': db2} + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + return loss, grads + + +class FullyConnectedNet(object): + """ + A fully-connected neural network with an arbitrary number of hidden layers, + ReLU nonlinearities, and a softmax loss function. This will also implement + dropout and batch/layer normalization as options. For a network with L layers, + the architecture will be + + {affine - [batch/layer norm] - relu - [dropout]} x (L - 1) - affine - softmax + + where batch/layer normalization and dropout are optional, and the {...} block is + repeated L - 1 times. + + Similar to the TwoLayerNet above, learnable parameters are stored in the + self.params dictionary and will be learned using the Solver class. + """ + + def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10, + dropout=1, normalization=None, reg=0.0, + weight_scale=1e-2, dtype=np.float32, seed=None): + """ + Initialize a new FullyConnectedNet. + + Inputs: + - hidden_dims: A list of integers giving the size of each hidden layer. + - input_dim: An integer giving the size of the input. + - num_classes: An integer giving the number of classes to classify. + - dropout: Scalar between 0 and 1 giving dropout strength. If dropout=1 then + the network should not use dropout at all. + - normalization: What type of normalization the network should use. Valid values + are "batchnorm", "layernorm", or None for no normalization (the default). + - reg: Scalar giving L2 regularization strength. + - weight_scale: Scalar giving the standard deviation for random + initialization of the weights. + - dtype: A numpy datatype object; all computations will be performed using + this datatype. float32 is faster but less accurate, so you should use + float64 for numeric gradient checking. + - seed: If not None, then pass this random seed to the dropout layers. This + will make the dropout layers deteriminstic so we can gradient check the + model. + """ + self.normalization = normalization + self.use_dropout = dropout != 1 + self.reg = reg + self.num_layers = 1 + len(hidden_dims) + self.dtype = dtype + self.params = {} + + ############################################################################ + # TODO: Initialize the parameters of the network, storing all values in # + # the self.params dictionary. Store weights and biases for the first layer # + # in W1 and b1; for the second layer use W2 and b2, etc. Weights should be # + # initialized from a normal distribution centered at 0 with standard # + # deviation equal to weight_scale. Biases should be initialized to zero. # + # # + # When using batch normalization, store scale and shift parameters for the # + # first layer in gamma1 and beta1; for the second layer use gamma2 and # + # beta2, etc. Scale parameters should be initialized to ones and shift # + # parameters should be initialized to zeros. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + dims = [input_dim] + hidden_dims + [num_classes] + for i in range(self.num_layers): + self.params['W'+ str(i+1)] = weight_scale * np.random.randn(dims[i], dims[i+1]) + self.params['b'+ str(i+1)] = np.zeros(dims[i+1]) + if(self.normalization=='batchnorm' and i < self.num_layers-1): + self.params['gamma' + str(i+1)] = np.ones(dims[i+1]) + self.params['beta' + str(i+1)] = np.zeros(dims[i+1]) + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + # When using dropout we need to pass a dropout_param dictionary to each + # dropout layer so that the layer knows the dropout probability and the mode + # (train / test). You can pass the same dropout_param to each dropout layer. + self.dropout_param = {} + if self.use_dropout: + self.dropout_param = {'mode': 'train', 'p': dropout} + if seed is not None: + self.dropout_param['seed'] = seed + + # With batch normalization we need to keep track of running means and + # variances, so we need to pass a special bn_param object to each batch + # normalization layer. You should pass self.bn_params[0] to the forward pass + # of the first batch normalization layer, self.bn_params[1] to the forward + # pass of the second batch normalization layer, etc. + self.bn_params = [] + if self.normalization=='batchnorm': + self.bn_params = [{'mode': 'train'} for i in range(self.num_layers - 1)] + if self.normalization=='layernorm': + self.bn_params = [{} for i in range(self.num_layers - 1)] + + # Cast all parameters to the correct datatype + for k, v in self.params.items(): + self.params[k] = v.astype(dtype) + + + def loss(self, X, y=None): + """ + Compute loss and gradient for the fully-connected net. + + Input / output: Same as TwoLayerNet above. + """ + X = X.astype(self.dtype) + mode = 'test' if y is None else 'train' + + # Set train/test mode for batchnorm params and dropout param since they + # behave differently during training and testing. + if self.use_dropout: + self.dropout_param['mode'] = mode + if self.normalization=='batchnorm': + for bn_param in self.bn_params: + bn_param['mode'] = mode + scores = None + ############################################################################ + # TODO: Implement the forward pass for the fully-connected net, computing # + # the class scores for X and storing them in the scores variable. # + # # + # When using dropout, you'll need to pass self.dropout_param to each # + # dropout forward pass. # + # # + # When using batch normalization, you'll need to pass self.bn_params[0] to # + # the forward pass for the first batch normalization layer, pass # + # self.bn_params[1] to the forward pass for the second batch normalization # + # layer, etc. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + caches = [] + + for i in range(self.num_layers): + if(i==0): + out = X + W = 'W' + str(i+1) + b = 'b' + str(i+1) + cache = [] + + if(i!=self.num_layers-1): + #affine + out, cache_aff = affine_forward(out, self.params[W], self.params[b]) + cache += [cache_aff,] + #batch norm + if(self.normalization=='batchnorm'): + gamma = 'gamma' + str(i+1) + beta = 'beta' + str(i+1) + out, cache_norm = batchnorm_forward(out,self.params[gamma], + self.params[beta], self.bn_params[i]) + cache += [cache_norm] + + #Relu + out, cache_relu = relu_forward(out) + cache += [cache_relu] + + #dropout + if(self.use_dropout): + out, cache_drop = dropout_forward(out, self.dropout_param) + cache += [cache_drop] + + + caches.append(cache) + + else: + out, cache = affine_forward(out, self.params[W], self.params[b]) + caches.append(cache) + + scores = out + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + # If test mode return early + if mode == 'test': + return scores + + loss, grads = 0.0, {} + ############################################################################ + # TODO: Implement the backward pass for the fully-connected net. Store the # + # loss in the loss variable and gradients in the grads dictionary. Compute # + # data loss using softmax, and make sure that grads[k] holds the gradients # + # for self.params[k]. Don't forget to add L2 regularization! # + # # + # When using batch/layer normalization, you don't need to regularize the scale # + # and shift parameters. # + # # + # NOTE: To ensure that your implementation matches ours and you pass the # + # automated tests, make sure that your L2 regularization includes a factor # + # of 0.5 to simplify the expression for the gradient. # + ############################################################################ + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + N = X.shape[0] + + scores -= np.max(scores, axis=1).reshape(scores.shape[0],1) + scores = np.exp(scores) + scores /= np.sum(scores, axis=1).reshape(scores.shape[0],1) + + loss = np.sum(-np.log(scores[range(N), y])) + loss /= N + + loss += 0.5 * self.reg * sum([np.sum(self.params['W' + str(i+1)] **2) for i in range(self.num_layers)]) + + + dsoftmax = scores + dsoftmax[range(N), y] -= 1 + dsoftmax /= N + dout = dsoftmax +# dx, dout = softmax_loss(scores, y) + + for i in range(self.num_layers, 0, -1): + W = 'W' + str(i) + b = 'b' + str(i) + if(i == self.num_layers): + dx, grads[W], grads[b] = affine_backward(dout, caches[i-1]) + + else: + if(self.normalization=='batchnorm' or self.use_dropout): + + if(self.use_dropout): + dx = dropout_backward(dx, caches[i-1].pop()) + + dx = relu_backward(dx, caches[i-1].pop()) + + if(self.normalization=='batchnorm'): + gamma = 'gamma' + str(i) + beta = 'beta' + str(i) + dx, grads[gamma], grads[beta] = batchnorm_backward(dx, caches[i-1].pop()) + + dx, grads[W], grads[b] = affine_backward(dx, caches[i-1].pop()) + + else: + dx, grads[W], grads[b] = affine_relu_backward(dx, caches[i-1]) + + grads[W] += (self.reg * self.params[W]) + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ############################################################################ + # END OF YOUR CODE # + ############################################################################ + + return loss, grads diff --git a/assignment2/cs231n/data_utils.py b/assignment2/cs231n/data_utils.py new file mode 100755 index 0000000..a0fd6a0 --- /dev/null +++ b/assignment2/cs231n/data_utils.py @@ -0,0 +1,262 @@ +from __future__ import print_function + +from builtins import range +from six.moves import cPickle as pickle +import numpy as np +import os +from imageio import imread +import platform + +def load_pickle(f): + version = platform.python_version_tuple() + if version[0] == '2': + return pickle.load(f) + elif version[0] == '3': + return pickle.load(f, encoding='latin1') + raise ValueError("invalid python version: {}".format(version)) + +def load_CIFAR_batch(filename): + """ load single batch of cifar """ + with open(filename, 'rb') as f: + datadict = load_pickle(f) + X = datadict['data'] + Y = datadict['labels'] + X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float") + Y = np.array(Y) + return X, Y + +def load_CIFAR10(ROOT): + """ load all of cifar """ + xs = [] + ys = [] + for b in range(1,6): + f = os.path.join(ROOT, 'data_batch_%d' % (b, )) + X, Y = load_CIFAR_batch(f) + xs.append(X) + ys.append(Y) + Xtr = np.concatenate(xs) + Ytr = np.concatenate(ys) + del X, Y + Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) + return Xtr, Ytr, Xte, Yte + + +def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, + subtract_mean=True): + """ + Load the CIFAR-10 dataset from disk and perform preprocessing to prepare + it for classifiers. These are the same steps as we used for the SVM, but + condensed to a single function. + """ + # Load the raw CIFAR-10 data + cifar10_dir = 'cs231n/datasets/cifar-10-batches-py' + X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) + + # Subsample the data + mask = list(range(num_training, num_training + num_validation)) + X_val = X_train[mask] + y_val = y_train[mask] + mask = list(range(num_training)) + X_train = X_train[mask] + y_train = y_train[mask] + mask = list(range(num_test)) + X_test = X_test[mask] + y_test = y_test[mask] + + # Normalize the data: subtract the mean image + if subtract_mean: + mean_image = np.mean(X_train, axis=0) + X_train -= mean_image + X_val -= mean_image + X_test -= mean_image + + # Transpose so that channels come first + X_train = X_train.transpose(0, 3, 1, 2).copy() + X_val = X_val.transpose(0, 3, 1, 2).copy() + X_test = X_test.transpose(0, 3, 1, 2).copy() + + # Package data into a dictionary + return { + 'X_train': X_train, 'y_train': y_train, + 'X_val': X_val, 'y_val': y_val, + 'X_test': X_test, 'y_test': y_test, + } + + +def load_tiny_imagenet(path, dtype=np.float32, subtract_mean=True): + """ + Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and + TinyImageNet-200 have the same directory structure, so this can be used + to load any of them. + + Inputs: + - path: String giving path to the directory to load. + - dtype: numpy datatype used to load the data. + - subtract_mean: Whether to subtract the mean training image. + + Returns: A dictionary with the following entries: + - class_names: A list where class_names[i] is a list of strings giving the + WordNet names for class i in the loaded dataset. + - X_train: (N_tr, 3, 64, 64) array of training images + - y_train: (N_tr,) array of training labels + - X_val: (N_val, 3, 64, 64) array of validation images + - y_val: (N_val,) array of validation labels + - X_test: (N_test, 3, 64, 64) array of testing images. + - y_test: (N_test,) array of test labels; if test labels are not available + (such as in student code) then y_test will be None. + - mean_image: (3, 64, 64) array giving mean training image + """ + # First load wnids + with open(os.path.join(path, 'wnids.txt'), 'r') as f: + wnids = [x.strip() for x in f] + + # Map wnids to integer labels + wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)} + + # Use words.txt to get names for each class + with open(os.path.join(path, 'words.txt'), 'r') as f: + wnid_to_words = dict(line.split('\t') for line in f) + for wnid, words in wnid_to_words.items(): + wnid_to_words[wnid] = [w.strip() for w in words.split(',')] + class_names = [wnid_to_words[wnid] for wnid in wnids] + + # Next load training data. + X_train = [] + y_train = [] + for i, wnid in enumerate(wnids): + if (i + 1) % 20 == 0: + print('loading training data for synset %d / %d' + % (i + 1, len(wnids))) + # To figure out the filenames we need to open the boxes file + boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid) + with open(boxes_file, 'r') as f: + filenames = [x.split('\t')[0] for x in f] + num_images = len(filenames) + + X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype) + y_train_block = wnid_to_label[wnid] * \ + np.ones(num_images, dtype=np.int64) + for j, img_file in enumerate(filenames): + img_file = os.path.join(path, 'train', wnid, 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + ## grayscale file + img.shape = (64, 64, 1) + X_train_block[j] = img.transpose(2, 0, 1) + X_train.append(X_train_block) + y_train.append(y_train_block) + + # We need to concatenate all training data + X_train = np.concatenate(X_train, axis=0) + y_train = np.concatenate(y_train, axis=0) + + # Next load validation data + with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f: + img_files = [] + val_wnids = [] + for line in f: + img_file, wnid = line.split('\t')[:2] + img_files.append(img_file) + val_wnids.append(wnid) + num_val = len(img_files) + y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids]) + X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype) + for i, img_file in enumerate(img_files): + img_file = os.path.join(path, 'val', 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + img.shape = (64, 64, 1) + X_val[i] = img.transpose(2, 0, 1) + + # Next load test images + # Students won't have test labels, so we need to iterate over files in the + # images directory. + img_files = os.listdir(os.path.join(path, 'test', 'images')) + X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype) + for i, img_file in enumerate(img_files): + img_file = os.path.join(path, 'test', 'images', img_file) + img = imread(img_file) + if img.ndim == 2: + img.shape = (64, 64, 1) + X_test[i] = img.transpose(2, 0, 1) + + y_test = None + y_test_file = os.path.join(path, 'test', 'test_annotations.txt') + if os.path.isfile(y_test_file): + with open(y_test_file, 'r') as f: + img_file_to_wnid = {} + for line in f: + line = line.split('\t') + img_file_to_wnid[line[0]] = line[1] + y_test = [wnid_to_label[img_file_to_wnid[img_file]] + for img_file in img_files] + y_test = np.array(y_test) + + mean_image = X_train.mean(axis=0) + if subtract_mean: + X_train -= mean_image[None] + X_val -= mean_image[None] + X_test -= mean_image[None] + + return { + 'class_names': class_names, + 'X_train': X_train, + 'y_train': y_train, + 'X_val': X_val, + 'y_val': y_val, + 'X_test': X_test, + 'y_test': y_test, + 'class_names': class_names, + 'mean_image': mean_image, + } + + +def load_models(models_dir): + """ + Load saved models from disk. This will attempt to unpickle all files in a + directory; any files that give errors on unpickling (such as README.txt) + will be skipped. + + Inputs: + - models_dir: String giving the path to a directory containing model files. + Each model file is a pickled dictionary with a 'model' field. + + Returns: + A dictionary mapping model file names to models. + """ + models = {} + for model_file in os.listdir(models_dir): + with open(os.path.join(models_dir, model_file), 'rb') as f: + try: + models[model_file] = load_pickle(f)['model'] + except pickle.UnpicklingError: + continue + return models + + +def load_imagenet_val(num=None): + """Load a handful of validation images from ImageNet. + + Inputs: + - num: Number of images to load (max of 25) + + Returns: + - X: numpy array with shape [num, 224, 224, 3] + - y: numpy array of integer image labels, shape [num] + - class_names: dict mapping integer label to class name + """ + imagenet_fn = 'cs231n/datasets/imagenet_val_25.npz' + if not os.path.isfile(imagenet_fn): + print('file %s not found' % imagenet_fn) + print('Run the following:') + print('cd cs231n/datasets') + print('bash get_imagenet_val.sh') + assert False, 'Need to download imagenet_val_25.npz' + f = np.load(imagenet_fn) + X = f['X'] + y = f['y'] + class_names = f['label_map'].item() + if num is not None: + X = X[:num] + y = y[:num] + return X, y, class_names diff --git a/assignment2/cs231n/datasets/.gitignore b/assignment2/cs231n/datasets/.gitignore new file mode 100755 index 0000000..0232c3a --- /dev/null +++ b/assignment2/cs231n/datasets/.gitignore @@ -0,0 +1,4 @@ +cifar-10-batches-py/* +tiny-imagenet-100-A* +tiny-imagenet-100-B* +tiny-100-A-pretrained/* diff --git a/assignment2/cs231n/datasets/get_datasets.sh b/assignment2/cs231n/datasets/get_datasets.sh new file mode 100755 index 0000000..0dd9362 --- /dev/null +++ b/assignment2/cs231n/datasets/get_datasets.sh @@ -0,0 +1,4 @@ +# Get CIFAR10 +wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz +tar -xzvf cifar-10-python.tar.gz +rm cifar-10-python.tar.gz diff --git a/assignment2/cs231n/fast_layers.py b/assignment2/cs231n/fast_layers.py new file mode 100755 index 0000000..cc839a0 --- /dev/null +++ b/assignment2/cs231n/fast_layers.py @@ -0,0 +1,293 @@ +from __future__ import print_function +import numpy as np +import torch +import torch.nn as nn +try: + from cs231n.im2col_cython import col2im_cython, im2col_cython + from cs231n.im2col_cython import col2im_6d_cython +except ImportError: + print('run the following from the cs231n directory and try again:') + print('python setup.py build_ext --inplace') + print('You may also need to restart your iPython kernel') + +from cs231n.im2col import * + + +def conv_forward_im2col(x, w, b, conv_param): + """ + A fast implementation of the forward pass for a convolutional layer + based on im2col and col2im. + """ + N, C, H, W = x.shape + num_filters, _, filter_height, filter_width = w.shape + stride, pad = conv_param['stride'], conv_param['pad'] + + # Check dimensions + assert (W + 2 * pad - filter_width) % stride == 0, 'width does not work' + assert (H + 2 * pad - filter_height) % stride == 0, 'height does not work' + + # Create output + out_height = (H + 2 * pad - filter_height) // stride + 1 + out_width = (W + 2 * pad - filter_width) // stride + 1 + out = np.zeros((N, num_filters, out_height, out_width), dtype=x.dtype) + + # x_cols = im2col_indices(x, w.shape[2], w.shape[3], pad, stride) + x_cols = im2col_cython(x, w.shape[2], w.shape[3], pad, stride) + res = w.reshape((w.shape[0], -1)).dot(x_cols) + b.reshape(-1, 1) + + out = res.reshape(w.shape[0], out.shape[2], out.shape[3], x.shape[0]) + out = out.transpose(3, 0, 1, 2) + + cache = (x, w, b, conv_param, x_cols) + return out, cache + + +def conv_forward_pytorch(x, w, b, conv_param): + N, C, H, W = x.shape + F, _, HH, WW = w.shape + stride, pad = conv_param['stride'], conv_param['pad'] + layer = nn.Conv2d(C, F, (HH, WW), stride=stride, padding=pad) + layer.weight = nn.Parameter(torch.tensor(w)) + layer.bias = nn.Parameter(torch.tensor(b)) + tx = torch.tensor(x, requires_grad=True) + out = layer(tx) + cache = (x, w, b, conv_param, tx, out, layer) + return out, cache + +def conv_backward_pytorch(dout, cache): + x, _, _, _, tx, out, layer = cache + out.backward(torch.tensor(dout)) + dx = tx.grad.detach().numpy() + dw = layer.weight.grad.detach().numpy() + db = layer.bias.grad.detach().numpy() + return dx, dw, db + +def conv_forward_strides(x, w, b, conv_param): + N, C, H, W = x.shape + F, _, HH, WW = w.shape + stride, pad = conv_param['stride'], conv_param['pad'] + + # Check dimensions + #assert (W + 2 * pad - WW) % stride == 0, 'width does not work' + #assert (H + 2 * pad - HH) % stride == 0, 'height does not work' + + # Pad the input + p = pad + x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant') + + # Figure out output dimensions + H += 2 * pad + W += 2 * pad + out_h = (H - HH) // stride + 1 + out_w = (W - WW) // stride + 1 + + # Perform an im2col operation by picking clever strides + shape = (C, HH, WW, N, out_h, out_w) + strides = (H * W, W, 1, C * H * W, stride * W, stride) + strides = x.itemsize * np.array(strides) + x_stride = np.lib.stride_tricks.as_strided(x_padded, + shape=shape, strides=strides) + x_cols = np.ascontiguousarray(x_stride) + x_cols.shape = (C * HH * WW, N * out_h * out_w) + + # Now all our convolutions are a big matrix multiply + res = w.reshape(F, -1).dot(x_cols) + b.reshape(-1, 1) + + # Reshape the output + res.shape = (F, N, out_h, out_w) + out = res.transpose(1, 0, 2, 3) + + # Be nice and return a contiguous array + # The old version of conv_forward_fast doesn't do this, so for a fair + # comparison we won't either + out = np.ascontiguousarray(out) + + cache = (x, w, b, conv_param, x_cols) + return out, cache + + +def conv_backward_strides(dout, cache): + x, w, b, conv_param, x_cols = cache + stride, pad = conv_param['stride'], conv_param['pad'] + + N, C, H, W = x.shape + F, _, HH, WW = w.shape + _, _, out_h, out_w = dout.shape + + db = np.sum(dout, axis=(0, 2, 3)) + + dout_reshaped = dout.transpose(1, 0, 2, 3).reshape(F, -1) + dw = dout_reshaped.dot(x_cols.T).reshape(w.shape) + + dx_cols = w.reshape(F, -1).T.dot(dout_reshaped) + dx_cols.shape = (C, HH, WW, N, out_h, out_w) + dx = col2im_6d_cython(dx_cols, N, C, H, W, HH, WW, pad, stride) + + return dx, dw, db + + +def conv_backward_im2col(dout, cache): + """ + A fast implementation of the backward pass for a convolutional layer + based on im2col and col2im. + """ + x, w, b, conv_param, x_cols = cache + stride, pad = conv_param['stride'], conv_param['pad'] + + db = np.sum(dout, axis=(0, 2, 3)) + + num_filters, _, filter_height, filter_width = w.shape + dout_reshaped = dout.transpose(1, 2, 3, 0).reshape(num_filters, -1) + dw = dout_reshaped.dot(x_cols.T).reshape(w.shape) + + dx_cols = w.reshape(num_filters, -1).T.dot(dout_reshaped) + # dx = col2im_indices(dx_cols, x.shape, filter_height, filter_width, pad, stride) + dx = col2im_cython(dx_cols, x.shape[0], x.shape[1], x.shape[2], x.shape[3], + filter_height, filter_width, pad, stride) + + return dx, dw, db + + +conv_forward_fast = conv_forward_strides +conv_backward_fast = conv_backward_strides + + +def max_pool_forward_fast(x, pool_param): + """ + A fast implementation of the forward pass for a max pooling layer. + + This chooses between the reshape method and the im2col method. If the pooling + regions are square and tile the input image, then we can use the reshape + method which is very fast. Otherwise we fall back on the im2col method, which + is not much faster than the naive method. + """ + N, C, H, W = x.shape + pool_height, pool_width = pool_param['pool_height'], pool_param['pool_width'] + stride = pool_param['stride'] + + same_size = pool_height == pool_width == stride + tiles = H % pool_height == 0 and W % pool_width == 0 + if same_size and tiles: + out, reshape_cache = max_pool_forward_reshape(x, pool_param) + cache = ('reshape', reshape_cache) + else: + out, im2col_cache = max_pool_forward_im2col(x, pool_param) + cache = ('im2col', im2col_cache) + return out, cache + + +def max_pool_backward_fast(dout, cache): + """ + A fast implementation of the backward pass for a max pooling layer. + + This switches between the reshape method an the im2col method depending on + which method was used to generate the cache. + """ + method, real_cache = cache + if method == 'reshape': + return max_pool_backward_reshape(dout, real_cache) + elif method == 'im2col': + return max_pool_backward_im2col(dout, real_cache) + else: + raise ValueError('Unrecognized method "%s"' % method) + + +def max_pool_forward_reshape(x, pool_param): + """ + A fast implementation of the forward pass for the max pooling layer that uses + some clever reshaping. + + This can only be used for square pooling regions that tile the input. + """ + N, C, H, W = x.shape + pool_height, pool_width = pool_param['pool_height'], pool_param['pool_width'] + stride = pool_param['stride'] + assert pool_height == pool_width == stride, 'Invalid pool params' + assert H % pool_height == 0 + assert W % pool_height == 0 + x_reshaped = x.reshape(N, C, H // pool_height, pool_height, + W // pool_width, pool_width) + out = x_reshaped.max(axis=3).max(axis=4) + + cache = (x, x_reshaped, out) + return out, cache + + +def max_pool_backward_reshape(dout, cache): + """ + A fast implementation of the backward pass for the max pooling layer that + uses some clever broadcasting and reshaping. + + This can only be used if the forward pass was computed using + max_pool_forward_reshape. + + NOTE: If there are multiple argmaxes, this method will assign gradient to + ALL argmax elements of the input rather than picking one. In this case the + gradient will actually be incorrect. However this is unlikely to occur in + practice, so it shouldn't matter much. One possible solution is to split the + upstream gradient equally among all argmax elements; this should result in a + valid subgradient. You can make this happen by uncommenting the line below; + however this results in a significant performance penalty (about 40% slower) + and is unlikely to matter in practice so we don't do it. + """ + x, x_reshaped, out = cache + + dx_reshaped = np.zeros_like(x_reshaped) + out_newaxis = out[:, :, :, np.newaxis, :, np.newaxis] + mask = (x_reshaped == out_newaxis) + dout_newaxis = dout[:, :, :, np.newaxis, :, np.newaxis] + dout_broadcast, _ = np.broadcast_arrays(dout_newaxis, dx_reshaped) + dx_reshaped[mask] = dout_broadcast[mask] + dx_reshaped /= np.sum(mask, axis=(3, 5), keepdims=True) + dx = dx_reshaped.reshape(x.shape) + + return dx + + +def max_pool_forward_im2col(x, pool_param): + """ + An implementation of the forward pass for max pooling based on im2col. + + This isn't much faster than the naive version, so it should be avoided if + possible. + """ + N, C, H, W = x.shape + pool_height, pool_width = pool_param['pool_height'], pool_param['pool_width'] + stride = pool_param['stride'] + + assert (H - pool_height) % stride == 0, 'Invalid height' + assert (W - pool_width) % stride == 0, 'Invalid width' + + out_height = (H - pool_height) // stride + 1 + out_width = (W - pool_width) // stride + 1 + + x_split = x.reshape(N * C, 1, H, W) + x_cols = im2col(x_split, pool_height, pool_width, padding=0, stride=stride) + x_cols_argmax = np.argmax(x_cols, axis=0) + x_cols_max = x_cols[x_cols_argmax, np.arange(x_cols.shape[1])] + out = x_cols_max.reshape(out_height, out_width, N, C).transpose(2, 3, 0, 1) + + cache = (x, x_cols, x_cols_argmax, pool_param) + return out, cache + + +def max_pool_backward_im2col(dout, cache): + """ + An implementation of the backward pass for max pooling based on im2col. + + This isn't much faster than the naive version, so it should be avoided if + possible. + """ + x, x_cols, x_cols_argmax, pool_param = cache + N, C, H, W = x.shape + pool_height, pool_width = pool_param['pool_height'], pool_param['pool_width'] + stride = pool_param['stride'] + + dout_reshaped = dout.transpose(2, 3, 0, 1).flatten() + dx_cols = np.zeros_like(x_cols) + dx_cols[x_cols_argmax, np.arange(dx_cols.shape[1])] = dout_reshaped + dx = col2im_indices(dx_cols, (N * C, 1, H, W), pool_height, pool_width, + padding=0, stride=stride) + dx = dx.reshape(x.shape) + + return dx diff --git a/assignment2/cs231n/gradient_check.py b/assignment2/cs231n/gradient_check.py new file mode 100755 index 0000000..e1189fc --- /dev/null +++ b/assignment2/cs231n/gradient_check.py @@ -0,0 +1,129 @@ +from __future__ import print_function +from builtins import range +from past.builtins import xrange + +import numpy as np +from random import randrange + +def eval_numerical_gradient(f, x, verbose=True, h=0.00001): + """ + a naive implementation of numerical gradient of f at x + - f should be a function that takes a single argument + - x is the point (numpy array) to evaluate the gradient at + """ + + fx = f(x) # evaluate function value at original point + grad = np.zeros_like(x) + # iterate over all indexes in x + it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) + while not it.finished: + + # evaluate function at x+h + ix = it.multi_index + oldval = x[ix] + x[ix] = oldval + h # increment by h + fxph = f(x) # evalute f(x + h) + x[ix] = oldval - h + fxmh = f(x) # evaluate f(x - h) + x[ix] = oldval # restore + + # compute the partial derivative with centered formula + grad[ix] = (fxph - fxmh) / (2 * h) # the slope + if verbose: + print(ix, grad[ix]) + it.iternext() # step to next dimension + + return grad + + +def eval_numerical_gradient_array(f, x, df, h=1e-5): + """ + Evaluate a numeric gradient for a function that accepts a numpy + array and returns a numpy array. + """ + grad = np.zeros_like(x) + it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite']) + while not it.finished: + ix = it.multi_index + + oldval = x[ix] + x[ix] = oldval + h + pos = f(x).copy() + x[ix] = oldval - h + neg = f(x).copy() + x[ix] = oldval + + grad[ix] = np.sum((pos - neg) * df) / (2 * h) + it.iternext() + return grad + + +def eval_numerical_gradient_blobs(f, inputs, output, h=1e-5): + """ + Compute numeric gradients for a function that operates on input + and output blobs. + + We assume that f accepts several input blobs as arguments, followed by a + blob where outputs will be written. For example, f might be called like: + + f(x, w, out) + + where x and w are input Blobs, and the result of f will be written to out. + + Inputs: + - f: function + - inputs: tuple of input blobs + - output: output blob + - h: step size + """ + numeric_diffs = [] + for input_blob in inputs: + diff = np.zeros_like(input_blob.diffs) + it = np.nditer(input_blob.vals, flags=['multi_index'], + op_flags=['readwrite']) + while not it.finished: + idx = it.multi_index + orig = input_blob.vals[idx] + + input_blob.vals[idx] = orig + h + f(*(inputs + (output,))) + pos = np.copy(output.vals) + input_blob.vals[idx] = orig - h + f(*(inputs + (output,))) + neg = np.copy(output.vals) + input_blob.vals[idx] = orig + + diff[idx] = np.sum((pos - neg) * output.diffs) / (2.0 * h) + + it.iternext() + numeric_diffs.append(diff) + return numeric_diffs + + +def eval_numerical_gradient_net(net, inputs, output, h=1e-5): + return eval_numerical_gradient_blobs(lambda *args: net.forward(), + inputs, output, h=h) + + +def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5): + """ + sample a few random elements and only return numerical + in this dimensions. + """ + + for i in range(num_checks): + ix = tuple([randrange(m) for m in x.shape]) + + oldval = x[ix] + x[ix] = oldval + h # increment by h + fxph = f(x) # evaluate f(x + h) + x[ix] = oldval - h # increment by h + fxmh = f(x) # evaluate f(x - h) + x[ix] = oldval # reset + + grad_numerical = (fxph - fxmh) / (2 * h) + grad_analytic = analytic_grad[ix] + rel_error = (abs(grad_numerical - grad_analytic) / + (abs(grad_numerical) + abs(grad_analytic))) + print('numerical: %f analytic: %f, relative error: %e' + %(grad_numerical, grad_analytic, rel_error)) diff --git a/assignment2/cs231n/im2col.py b/assignment2/cs231n/im2col.py new file mode 100755 index 0000000..df098e8 --- /dev/null +++ b/assignment2/cs231n/im2col.py @@ -0,0 +1,54 @@ +from builtins import range +import numpy as np + + +def get_im2col_indices(x_shape, field_height, field_width, padding=1, stride=1): + # First figure out what the size of the output should be + N, C, H, W = x_shape + assert (H + 2 * padding - field_height) % stride == 0 + assert (W + 2 * padding - field_height) % stride == 0 + out_height = (H + 2 * padding - field_height) / stride + 1 + out_width = (W + 2 * padding - field_width) / stride + 1 + + i0 = np.repeat(np.arange(field_height), field_width) + i0 = np.tile(i0, C) + i1 = stride * np.repeat(np.arange(out_height), out_width) + j0 = np.tile(np.arange(field_width), field_height * C) + j1 = stride * np.tile(np.arange(out_width), out_height) + i = i0.reshape(-1, 1) + i1.reshape(1, -1) + j = j0.reshape(-1, 1) + j1.reshape(1, -1) + + k = np.repeat(np.arange(C), field_height * field_width).reshape(-1, 1) + + return (k, i, j) + + +def im2col_indices(x, field_height, field_width, padding=1, stride=1): + """ An implementation of im2col based on some fancy indexing """ + # Zero-pad the input + p = padding + x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant') + + k, i, j = get_im2col_indices(x.shape, field_height, field_width, padding, + stride) + + cols = x_padded[:, k, i, j] + C = x.shape[1] + cols = cols.transpose(1, 2, 0).reshape(field_height * field_width * C, -1) + return cols + + +def col2im_indices(cols, x_shape, field_height=3, field_width=3, padding=1, + stride=1): + """ An implementation of col2im based on fancy indexing and np.add.at """ + N, C, H, W = x_shape + H_padded, W_padded = H + 2 * padding, W + 2 * padding + x_padded = np.zeros((N, C, H_padded, W_padded), dtype=cols.dtype) + k, i, j = get_im2col_indices(x_shape, field_height, field_width, padding, + stride) + cols_reshaped = cols.reshape(C * field_height * field_width, -1, N) + cols_reshaped = cols_reshaped.transpose(2, 0, 1) + np.add.at(x_padded, (slice(None), k, i, j), cols_reshaped) + if padding == 0: + return x_padded + return x_padded[:, :, padding:-padding, padding:-padding] diff --git a/assignment2/cs231n/im2col_cython.cpython-37m-darwin.so b/assignment2/cs231n/im2col_cython.cpython-37m-darwin.so new file mode 100755 index 0000000..ed629bf Binary files /dev/null and b/assignment2/cs231n/im2col_cython.cpython-37m-darwin.so differ diff --git a/assignment2/cs231n/im2col_cython.pyx b/assignment2/cs231n/im2col_cython.pyx new file mode 100755 index 0000000..d6e33c6 --- /dev/null +++ b/assignment2/cs231n/im2col_cython.pyx @@ -0,0 +1,121 @@ +import numpy as np +cimport numpy as np +cimport cython + +# DTYPE = np.float64 +# ctypedef np.float64_t DTYPE_t + +ctypedef fused DTYPE_t: + np.float32_t + np.float64_t + +def im2col_cython(np.ndarray[DTYPE_t, ndim=4] x, int field_height, + int field_width, int padding, int stride): + cdef int N = x.shape[0] + cdef int C = x.shape[1] + cdef int H = x.shape[2] + cdef int W = x.shape[3] + + cdef int HH = (H + 2 * padding - field_height) / stride + 1 + cdef int WW = (W + 2 * padding - field_width) / stride + 1 + + cdef int p = padding + cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.pad(x, + ((0, 0), (0, 0), (p, p), (p, p)), mode='constant') + + cdef np.ndarray[DTYPE_t, ndim=2] cols = np.zeros( + (C * field_height * field_width, N * HH * WW), + dtype=x.dtype) + + # Moving the inner loop to a C function with no bounds checking works, but does + # not seem to help performance in any measurable way. + + im2col_cython_inner(cols, x_padded, N, C, H, W, HH, WW, + field_height, field_width, padding, stride) + return cols + + +@cython.boundscheck(False) +cdef int im2col_cython_inner(np.ndarray[DTYPE_t, ndim=2] cols, + np.ndarray[DTYPE_t, ndim=4] x_padded, + int N, int C, int H, int W, int HH, int WW, + int field_height, int field_width, int padding, int stride) except? -1: + cdef int c, ii, jj, row, yy, xx, i, col + + for c in range(C): + for yy in range(HH): + for xx in range(WW): + for ii in range(field_height): + for jj in range(field_width): + row = c * field_width * field_height + ii * field_height + jj + for i in range(N): + col = yy * WW * N + xx * N + i + cols[row, col] = x_padded[i, c, stride * yy + ii, stride * xx + jj] + + + +def col2im_cython(np.ndarray[DTYPE_t, ndim=2] cols, int N, int C, int H, int W, + int field_height, int field_width, int padding, int stride): + cdef np.ndarray x = np.empty((N, C, H, W), dtype=cols.dtype) + cdef int HH = (H + 2 * padding - field_height) / stride + 1 + cdef int WW = (W + 2 * padding - field_width) / stride + 1 + cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.zeros((N, C, H + 2 * padding, W + 2 * padding), + dtype=cols.dtype) + + # Moving the inner loop to a C-function with no bounds checking improves + # performance quite a bit for col2im. + col2im_cython_inner(cols, x_padded, N, C, H, W, HH, WW, + field_height, field_width, padding, stride) + if padding > 0: + return x_padded[:, :, padding:-padding, padding:-padding] + return x_padded + + +@cython.boundscheck(False) +cdef int col2im_cython_inner(np.ndarray[DTYPE_t, ndim=2] cols, + np.ndarray[DTYPE_t, ndim=4] x_padded, + int N, int C, int H, int W, int HH, int WW, + int field_height, int field_width, int padding, int stride) except? -1: + cdef int c, ii, jj, row, yy, xx, i, col + + for c in range(C): + for ii in range(field_height): + for jj in range(field_width): + row = c * field_width * field_height + ii * field_height + jj + for yy in range(HH): + for xx in range(WW): + for i in range(N): + col = yy * WW * N + xx * N + i + x_padded[i, c, stride * yy + ii, stride * xx + jj] += cols[row, col] + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef col2im_6d_cython_inner(np.ndarray[DTYPE_t, ndim=6] cols, + np.ndarray[DTYPE_t, ndim=4] x_padded, + int N, int C, int H, int W, int HH, int WW, + int out_h, int out_w, int pad, int stride): + + cdef int c, hh, ww, n, h, w + for n in range(N): + for c in range(C): + for hh in range(HH): + for ww in range(WW): + for h in range(out_h): + for w in range(out_w): + x_padded[n, c, stride * h + hh, stride * w + ww] += cols[c, hh, ww, n, h, w] + + +def col2im_6d_cython(np.ndarray[DTYPE_t, ndim=6] cols, int N, int C, int H, int W, + int HH, int WW, int pad, int stride): + cdef np.ndarray x = np.empty((N, C, H, W), dtype=cols.dtype) + cdef int out_h = (H + 2 * pad - HH) / stride + 1 + cdef int out_w = (W + 2 * pad - WW) / stride + 1 + cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.zeros((N, C, H + 2 * pad, W + 2 * pad), + dtype=cols.dtype) + + col2im_6d_cython_inner(cols, x_padded, N, C, H, W, HH, WW, out_h, out_w, pad, stride) + + if pad > 0: + return x_padded[:, :, pad:-pad, pad:-pad] + return x_padded diff --git a/assignment2/cs231n/layer_utils.py b/assignment2/cs231n/layer_utils.py new file mode 100755 index 0000000..8a7f7ad --- /dev/null +++ b/assignment2/cs231n/layer_utils.py @@ -0,0 +1,107 @@ +from cs231n.layers import * +from cs231n.fast_layers import * + + +def affine_relu_forward(x, w, b): + """ + Convenience layer that perorms an affine transform followed by a ReLU + + Inputs: + - x: Input to the affine layer + - w, b: Weights for the affine layer + + Returns a tuple of: + - out: Output from the ReLU + - cache: Object to give to the backward pass + """ + a, fc_cache = affine_forward(x, w, b) + out, relu_cache = relu_forward(a) + cache = (fc_cache, relu_cache) + return out, cache + + +def affine_relu_backward(dout, cache): + """ + Backward pass for the affine-relu convenience layer + """ + fc_cache, relu_cache = cache + + + da = relu_backward(dout, relu_cache) + dx, dw, db = affine_backward(da, fc_cache) + return dx, dw, db + + +def conv_relu_forward(x, w, b, conv_param): + """ + A convenience layer that performs a convolution followed by a ReLU. + + Inputs: + - x: Input to the convolutional layer + - w, b, conv_param: Weights and parameters for the convolutional layer + + Returns a tuple of: + - out: Output from the ReLU + - cache: Object to give to the backward pass + """ + a, conv_cache = conv_forward_fast(x, w, b, conv_param) + out, relu_cache = relu_forward(a) + cache = (conv_cache, relu_cache) + return out, cache + + +def conv_relu_backward(dout, cache): + """ + Backward pass for the conv-relu convenience layer. + """ + conv_cache, relu_cache = cache + da = relu_backward(dout, relu_cache) + dx, dw, db = conv_backward_fast(da, conv_cache) + return dx, dw, db + + +def conv_bn_relu_forward(x, w, b, gamma, beta, conv_param, bn_param): + a, conv_cache = conv_forward_fast(x, w, b, conv_param) + an, bn_cache = spatial_batchnorm_forward(a, gamma, beta, bn_param) + out, relu_cache = relu_forward(an) + cache = (conv_cache, bn_cache, relu_cache) + return out, cache + + +def conv_bn_relu_backward(dout, cache): + conv_cache, bn_cache, relu_cache = cache + dan = relu_backward(dout, relu_cache) + da, dgamma, dbeta = spatial_batchnorm_backward(dan, bn_cache) + dx, dw, db = conv_backward_fast(da, conv_cache) + return dx, dw, db, dgamma, dbeta + + +def conv_relu_pool_forward(x, w, b, conv_param, pool_param): + """ + Convenience layer that performs a convolution, a ReLU, and a pool. + + Inputs: + - x: Input to the convolutional layer + - w, b, conv_param: Weights and parameters for the convolutional layer + - pool_param: Parameters for the pooling layer + + Returns a tuple of: + - out: Output from the pooling layer + - cache: Object to give to the backward pass + """ + a, conv_cache = conv_forward_fast(x, w, b, conv_param) + s, relu_cache = relu_forward(a) + out, pool_cache = max_pool_forward_fast(s, pool_param) + cache = (conv_cache, relu_cache, pool_cache) + return out, cache + + +def conv_relu_pool_backward(dout, cache): + """ + Backward pass for the conv-relu-pool convenience layer + """ + conv_cache, relu_cache, pool_cache = cache + ds = max_pool_backward_fast(dout, pool_cache) + da = relu_backward(ds, relu_cache) + dx, dw, db = conv_backward_fast(da, conv_cache) + return dx, dw, db diff --git a/assignment2/cs231n/layers.py b/assignment2/cs231n/layers.py new file mode 100755 index 0000000..3e3ed2b --- /dev/null +++ b/assignment2/cs231n/layers.py @@ -0,0 +1,1031 @@ +from builtins import range +import numpy as np + + +def affine_forward(x, w, b): + """ + Computes the forward pass for an affine (fully-connected) layer. + + The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N + examples, where each example x[i] has shape (d_1, ..., d_k). We will + reshape each input into a vector of dimension D = d_1 * ... * d_k, and + then transform it to an output vector of dimension M. + + Inputs: + - x: A numpy array containing input data, of shape (N, d_1, ..., d_k) + - w: A numpy array of weights, of shape (D, M) + - b: A numpy array of biases, of shape (M,) + + Returns a tuple of: + - out: output, of shape (N, M) + - cache: (x, w, b) + """ + out = None + ########################################################################### + # TODO: Implement the affine forward pass. Store the result in out. You # + # will need to reshape the input into rows. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + x_reshaped = x.reshape(x.shape[0],np.prod(x.shape[1:])) + out = x_reshaped.dot(w) + b + cache = (x, w, b) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + cache = (x, w, b) + return out, cache + + +def affine_backward(dout, cache): + """ + Computes the backward pass for an affine layer. + + Inputs: + - dout: Upstream derivative, of shape (N, M) + - cache: Tuple of: + - x: Input data, of shape (N, d_1, ... d_k) + - w: Weights, of shape (D, M) + - b: Biases, of shape (M,) + + Returns a tuple of: + - dx: Gradient with respect to x, of shape (N, d1, ..., d_k) + - dw: Gradient with respect to w, of shape (D, M) + - db: Gradient with respect to b, of shape (M,) + """ + x, w, b = cache + dx, dw, db = None, None, None + ########################################################################### + # TODO: Implement the affine backward pass. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + x_reshaped = x.reshape(x.shape[0],np.prod(x.shape[1:])) + + dw = x_reshaped.T.dot(dout) + + db = dout.sum(axis=0) + + dx = dout.dot(w.T) + + dx = dx.reshape(x.shape) + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return dx, dw, db + + +def relu_forward(x): + """ + Computes the forward pass for a layer of rectified linear units (ReLUs). + + Input: + - x: Inputs, of any shape + + Returns a tuple of: + - out: Output, of the same shape as x + - cache: x + """ + out = None + ########################################################################### + # TODO: Implement the ReLU forward pass. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + out = x + out[out<0] = 0 + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + cache = x + return out, cache + + +def relu_backward(dout, cache): + """ + Computes the backward pass for a layer of rectified linear units (ReLUs). + + Input: + - dout: Upstream derivatives, of any shape + - cache: Input x, of same shape as dout + + Returns: + - dx: Gradient with respect to x + """ + dx, x = None, cache + ########################################################################### + # TODO: Implement the ReLU backward pass. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + dx = x + dx[dx>0] = 1 + dx[dx<0] = 0 + dx *= dout + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return dx + + +def batchnorm_forward(x, gamma, beta, bn_param): + """ + Forward pass for batch normalization. + + During training the sample mean and (uncorrected) sample variance are + computed from minibatch statistics and used to normalize the incoming data. + During training we also keep an exponentially decaying running mean of the + mean and variance of each feature, and these averages are used to normalize + data at test-time. + + At each timestep we update the running averages for mean and variance using + an exponential decay based on the momentum parameter: + + running_mean = momentum * running_mean + (1 - momentum) * sample_mean + running_var = momentum * running_var + (1 - momentum) * sample_var + + Note that the batch normalization paper suggests a different test-time + behavior: they compute sample mean and variance for each feature using a + large number of training images rather than using a running average. For + this implementation we have chosen to use running averages instead since + they do not require an additional estimation step; the torch7 + implementation of batch normalization also uses running averages. + + Input: + - x: Data of shape (N, D) + - gamma: Scale parameter of shape (D,) + - beta: Shift paremeter of shape (D,) + - bn_param: Dictionary with the following keys: + - mode: 'train' or 'test'; required + - eps: Constant for numeric stability + - momentum: Constant for running mean / variance. + - running_mean: Array of shape (D,) giving running mean of features + - running_var Array of shape (D,) giving running variance of features + + Returns a tuple of: + - out: of shape (N, D) + - cache: A tuple of values needed in the backward pass + """ + mode = bn_param['mode'] + eps = bn_param.get('eps', 1e-5) + momentum = bn_param.get('momentum', 0.9) + + N, D = x.shape + running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype)) + running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype)) + + out, cache = None, None + if mode == 'train': + ####################################################################### + # TODO: Implement the training-time forward pass for batch norm. # + # Use minibatch statistics to compute the mean and variance, use # + # these statistics to normalize the incoming data, and scale and # + # shift the normalized data using gamma and beta. # + # # + # You should store the output in the variable out. Any intermediates # + # that you need for the backward pass should be stored in the cache # + # variable. # + # # + # You should also use your computed sample mean and variance together # + # with the momentum variable to update the running mean and running # + # variance, storing your result in the running_mean and running_var # + # variables. # + # # + # Note that though you should be keeping track of the running # + # variance, you should normalize the data based on the standard # + # deviation (square root of variance) instead! # + # Referencing the original paper (https://arxiv.org/abs/1502.03167) # + # might prove to be helpful. # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + sample_mean = np.mean(x, axis=0) + + sample_var = (x ** 2).sum(axis=0)/N - sample_mean**2 + + std = sample_var ** 0.5 + eps + + x_normalized = (x - sample_mean)/ ((sample_var**0.5)+ eps) + + out = gamma * x_normalized + beta + + running_mean = momentum * running_mean + (1 - momentum) * sample_mean + running_var = momentum * running_var + (1 - momentum) * sample_var + + + + cache = {'mu':sample_mean, 'std':std, 'norm':x_normalized, + 'gamma':gamma, 'eps':eps} + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ####################################################################### + # END OF YOUR CODE # + ####################################################################### + elif mode == 'test': + ####################################################################### + # TODO: Implement the test-time forward pass for batch normalization. # + # Use the running mean and variance to normalize the incoming data, # + # then scale and shift the normalized data using gamma and beta. # + # Store the result in the out variable. # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + x_normalized = (x - running_mean) / (running_var ** 0.5 + eps) + + out = gamma * x_normalized + beta + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ####################################################################### + # END OF YOUR CODE # + ####################################################################### + else: + raise ValueError('Invalid forward batchnorm mode "%s"' % mode) + + # Store the updated running means back into bn_param + bn_param['running_mean'] = running_mean + bn_param['running_var'] = running_var + + return out, cache + + +def batchnorm_backward(dout, cache): + """ + Backward pass for batch normalization. + + For this implementation, you should write out a computation graph for + batch normalization on paper and propagate gradients backward through + intermediate nodes. + + Inputs: + - dout: Upstream derivatives, of shape (N, D) + - cache: Variable of intermediates from batchnorm_forward. + + Returns a tuple of: + - dx: Gradient with respect to inputs x, of shape (N, D) + - dgamma: Gradient with respect to scale parameter gamma, of shape (D,) + - dbeta: Gradient with respect to shift parameter beta, of shape (D,) + """ + dx, dgamma, dbeta = None, None, None + ########################################################################### + # TODO: Implement the backward pass for batch normalization. Store the # + # results in the dx, dgamma, and dbeta variables. # + # Referencing the original paper (https://arxiv.org/abs/1502.03167) # + # might prove to be helpful. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N, D = dout.shape + + dbeta = dout.sum(axis=0) + + dgamma = np.sum(dout*cache['norm'], axis=0) + dx_norm = dout * cache['gamma'] + + dx = (1.0/N) *(1/cache['std']) * (N*dx_norm - np.sum(dx_norm, axis=0) + - cache['norm']*np.sum(dx_norm*cache['norm'], axis=0)) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return dx, dgamma, dbeta + + +def batchnorm_backward_alt(dout, cache): + """ + Alternative backward pass for batch normalization. + + For this implementation you should work out the derivatives for the batch + normalizaton backward pass on paper and simplify as much as possible. You + should be able to derive a simple expression for the backward pass. + See the jupyter notebook for more hints. + + Note: This implementation should expect to receive the same cache variable + as batchnorm_backward, but might not use all of the values in the cache. + + Inputs / outputs: Same as batchnorm_backward + """ + dx, dgamma, dbeta = None, None, None + ########################################################################### + # TODO: Implement the backward pass for batch normalization. Store the # + # results in the dx, dgamma, and dbeta variables. # + # # + # After computing the gradient with respect to the centered inputs, you # + # should be able to compute gradients with respect to the inputs in a # + # single statement; our implementation fits on a single 80-character line.# + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N, D = dout.shape + + dbeta = dout.sum(axis=0) + dgamma = np.sum(dout*cache['norm'], axis=0) + dx_norm = dout * cache['gamma'] + + dx = (1.0/N) *(1/cache['std']) * (N*dx_norm - np.sum(dx_norm, axis=0) + - cache['norm']*np.sum(dx_norm*cache['norm'], axis=0)) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return dx, dgamma, dbeta + + +def layernorm_forward(x, gamma, beta, ln_param): + """ + Forward pass for layer normalization. + + During both training and test-time, the incoming data is normalized per data-point, + before being scaled by gamma and beta parameters identical to that of batch normalization. + + Note that in contrast to batch normalization, the behavior during train and test-time for + layer normalization are identical, and we do not need to keep track of running averages + of any sort. + + Input: + - x: Data of shape (N, D) + - gamma: Scale parameter of shape (D,) + - beta: Shift paremeter of shape (D,) + - ln_param: Dictionary with the following keys: + - eps: Constant for numeric stability + + Returns a tuple of: + - out: of shape (N, D) + - cache: A tuple of values needed in the backward pass + """ + out, cache = None, None + eps = ln_param.get('eps', 1e-5) + ########################################################################### + # TODO: Implement the training-time forward pass for layer norm. # + # Normalize the incoming data, and scale and shift the normalized data # + # using gamma and beta. # + # HINT: this can be done by slightly modifying your training-time # + # implementation of batch normalization, and inserting a line or two of # + # well-placed code. In particular, can you think of any matrix # + # transformations you could perform, that would enable you to copy over # + # the batch norm code and leave it almost unchanged? # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N = x.shape[0] + mean = np.mean(x, axis=1).reshape((N,1)) + var = np.var(x, axis=1).reshape((N,1)) + std = (var ** 0.5) + x_norm = (x-mean)/(std + eps) + out = gamma * x_norm + beta + + cache = {'std' : std, 'norm':x_norm, 'gamma':gamma} + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return out, cache + + +def layernorm_backward(dout, cache): + """ + Backward pass for layer normalization. + + For this implementation, you can heavily rely on the work you've done already + for batch normalization. + + Inputs: + - dout: Upstream derivatives, of shape (N, D) + - cache: Variable of intermediates from layernorm_forward. + + Returns a tuple of: + - dx: Gradient with respect to inputs x, of shape (N, D) + - dgamma: Gradient with respect to scale parameter gamma, of shape (D,) + - dbeta: Gradient with respect to shift parameter beta, of shape (D,) + """ + dx, dgamma, dbeta = None, None, None + ########################################################################### + # TODO: Implement the backward pass for layer norm. # + # # + # HINT: this can be done by slightly modifying your training-time # + # implementation of batch normalization. The hints to the forward pass # + # still apply! # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N = dout.shape[0] + dgamma = np.sum(dout* cache['norm'], axis=0) + dbeta = np.sum(dout, axis=0) + dx_norm = dout * cache['gamma'] + + dx = (1.0/N) *(1/cache['std']) * (N*dx_norm - np.sum(dx_norm, axis=0) + - cache['norm']*np.sum(dx_norm*cache['norm'], axis=0)) + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return dx, dgamma, dbeta + + +def dropout_forward(x, dropout_param): + """ + Performs the forward pass for (inverted) dropout. + + Inputs: + - x: Input data, of any shape + - dropout_param: A dictionary with the following keys: + - p: Dropout parameter. We keep each neuron output with probability p. + - mode: 'test' or 'train'. If the mode is train, then perform dropout; + if the mode is test, then just return the input. + - seed: Seed for the random number generator. Passing seed makes this + function deterministic, which is needed for gradient checking but not + in real networks. + + Outputs: + - out: Array of the same shape as x. + - cache: tuple (dropout_param, mask). In training mode, mask is the dropout + mask that was used to multiply the input; in test mode, mask is None. + + NOTE: Please implement **inverted** dropout, not the vanilla version of dropout. + See http://cs231n.github.io/neural-networks-2/#reg for more details. + + NOTE 2: Keep in mind that p is the probability of **keep** a neuron + output; this might be contrary to some sources, where it is referred to + as the probability of dropping a neuron output. + """ + p, mode = dropout_param['p'], dropout_param['mode'] + if 'seed' in dropout_param: + np.random.seed(dropout_param['seed']) + + mask = None + out = None + + if mode == 'train': + ####################################################################### + # TODO: Implement training phase forward pass for inverted dropout. # + # Store the dropout mask in the mask variable. # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + mask = (np.random.rand(*x.shape) < p) / p + + out = mask * x + + cache = dropout_param, mask + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ####################################################################### + # END OF YOUR CODE # + ####################################################################### + elif mode == 'test': + ####################################################################### + # TODO: Implement the test phase forward pass for inverted dropout. # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + out = x + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ####################################################################### + # END OF YOUR CODE # + ####################################################################### + + cache = (dropout_param, mask) + out = out.astype(x.dtype, copy=False) + + return out, cache + + +def dropout_backward(dout, cache): + """ + Perform the backward pass for (inverted) dropout. + + Inputs: + - dout: Upstream derivatives, of any shape + - cache: (dropout_param, mask) from dropout_forward. + """ + dropout_param, mask = cache + mode = dropout_param['mode'] + + dx = None + if mode == 'train': + ####################################################################### + # TODO: Implement training phase backward pass for inverted dropout # + ####################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + dx = mask * dout + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ####################################################################### + # END OF YOUR CODE # + ####################################################################### + elif mode == 'test': + dx = dout + return dx + + +def conv_forward_naive(x, w, b, conv_param): + """ + A naive implementation of the forward pass for a convolutional layer. + + The input consists of N data points, each with C channels, height H and + width W. We convolve each input with F different filters, where each filter + spans all C channels and has height HH and width WW. + + Input: + - x: Input data of shape (N, C, H, W) + - w: Filter weights of shape (F, C, HH, WW) + - b: Biases, of shape (F,) + - conv_param: A dictionary with the following keys: + - 'stride': The number of pixels between adjacent receptive fields in the + horizontal and vertical directions. + - 'pad': The number of pixels that will be used to zero-pad the input. + + + During padding, 'pad' zeros should be placed symmetrically (i.e equally on both sides) + along the height and width axes of the input. Be careful not to modfiy the original + input x directly. + + Returns a tuple of: + - out: Output data, of shape (N, F, H', W') where H' and W' are given by + H' = 1 + (H + 2 * pad - HH) / stride + W' = 1 + (W + 2 * pad - WW) / stride + - cache: (x, w, b, conv_param) + """ + out = None + ########################################################################### + # TODO: Implement the convolutional forward pass. # + # Hint: you can use the function np.pad for padding. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + stride, pad = conv_param['stride'], conv_param['pad'] + N, C, H, W = x.shape + F, C, HH, WW = w.shape + H_out = int(1 + (H + 2 * pad - HH) / stride) + W_out = int(1 + (W + 2 * pad - WW) / stride) + out = np.zeros((N, F, H_out, W_out)) + + + x_pad = np.pad(x, ((0,0), (0,0),(pad,pad),(pad,pad)), 'constant') + H_pad, W_pad = x_pad.shape[2], x_pad.shape[3] + + for n in range(N): + h_c = 0 + for height in range(0, H_pad-HH+1, stride): + w_c = 0 + for width in range(0, W_pad-WW+1, stride): + + sample = x_pad[n, range(C), + height: HH+height, width: WW+width] #(N, C, H, W) + out[n, range(F), h_c, w_c] = \ + np.sum(sample * w, axis = (1,2,3)) + b + w_c += 1 + + h_c += 1 +# + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + cache = (x_pad, w, b, conv_param) + return out, cache + + +def conv_backward_naive(dout, cache): + """ + A naive implementation of the backward pass for a convolutional layer. + + Inputs: + - dout: Upstream derivatives. + - cache: A tuple of (x, w, b, conv_param) as in conv_forward_naive + + Returns a tuple of: + - dx: Gradient with respect to x + - dw: Gradient with respect to w + - db: Gradient with respect to b + """ + dx, dw, db = None, None, None + ########################################################################### + # TODO: Implement the convolutional backward pass. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + #dout = 4X2X5X5 + x_pad, w, b, conv_param = cache + N, C, H, W = x_pad.shape + + H_pad, W_pad = x_pad.shape[2], x_pad.shape[3] + F, C, HH, WW = w.shape + + N, F, outH, outW = dout.shape + + stride, pad = conv_param['stride'], conv_param['pad'] + + x_summed = x_pad.sum(axis=0) + dout_summed = dout.sum(axis=0) + dw = np.zeros(w.shape) + + db = dout.sum(axis=(0,2,3)) + + dx = np.zeros((N, C, H_pad - 2*pad, W_pad - 2*pad)) + + dz = np.pad(dout, ((0,0), (0,0),(pad,pad),(pad,pad)), 'constant') + w_row = w.reshape(F, C*HH*WW) + + # create x_col matrix with values that each neuron is connected to + x_col = np.zeros((C*HH*WW, outH*outW)) + + for n in range(N): + h_c = 0 + + out_col = dout[n].reshape(F, outH*outW) + w_out = w_row.T.dot(out_col) + + + dx_cur = np.zeros((C, H_pad, W_pad)) + neuron = 0 + + for height in range(0, H_pad-HH+1, stride): + w_c = 0 + for width in range(0, W_pad-WW+1, stride): + + sample = x_pad[n, :,height: HH+height, width: WW+width] + + dout_sample = dout[n, :, h_c, w_c][:,np.newaxis,np.newaxis, np.newaxis] + dw[:] += sample * dout_sample + + dx_cur[:,height:height+HH,width:width+WW] += w_out[:,neuron].reshape(C,HH,WW) + neuron += 1 + + w_c+=1 + h_c += 1 + dx[n] = dx_cur[:,pad:-pad, pad:-pad] + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return dx, dw, db + + +def max_pool_forward_naive(x, pool_param): + """ + A naive implementation of the forward pass for a max-pooling layer. + + Inputs: + - x: Input data, of shape (N, C, H, W) + - pool_param: dictionary with the following keys: + - 'pool_height': The height of each pooling region + - 'pool_width': The width of each pooling region + - 'stride': The distance between adjacent pooling regions + + No padding is necessary here. Output size is given by + + Returns a tuple of: + - out: Output data, of shape (N, C, H', W') where H' and W' are given by + H' = 1 + (H - pool_height) / stride + W' = 1 + (W - pool_width) / stride + - cache: (x, pool_param) + """ + out = None + ########################################################################### + # TODO: Implement the max-pooling forward pass # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + p_h, p_w, stride = pool_param['pool_height'],\ + pool_param['pool_width'],\ + pool_param['stride'] + + N, C, H, W = x.shape + H_out = int(1 + (H - p_h) / stride) + W_out = int(1 + (W - p_w) / stride) + + out = np.zeros((N, C, H_out, W_out)) + + for n in range(N): + h_c = 0 + for height in range(0, H-p_h+1, stride): + w_c = 0 + for width in range(0, W-p_w+1, stride): + + sample = x[n, range(C), + height: p_h+height, width: p_w+width] #(N, C, H, W) + + out[n,:, h_c, w_c] = \ + np.max(sample, axis =(1,2)) + w_c += 1 + + h_c += 1 + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + cache = (x, pool_param) + return out, cache + + +def max_pool_backward_naive(dout, cache): + """ + A naive implementation of the backward pass for a max-pooling layer. + + Inputs: + - dout: Upstream derivatives + - cache: A tuple of (x, pool_param) as in the forward pass. + + Returns: + - dx: Gradient with respect to x + """ + dx = None + ########################################################################### + # TODO: Implement the max-pooling backward pass # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + x, pool_param = cache + p_h, p_w, stride = pool_param['pool_height'],\ + pool_param['pool_width'],\ + pool_param['stride'] + dx = np.zeros(x.shape) + N, C, H, W = x.shape + for n in range(N): + + h_c = 0 + for height in range(0, H-p_h+1, stride): + w_c = 0 + for width in range(0, W-p_w+1, stride): + + sample = x[n, range(C), + height: p_h+height, width: p_w+width] #(N, C, H, W) + sample = sample.reshape(C, p_h *p_w) + idxs = sample.argmax(axis=1) + + dmax = np.zeros(sample.shape) + dmax[range(C), idxs] = dout[n, :, h_c, w_c] + dmax = dmax.reshape(C, p_h, p_w) + + w_c+=1 + dx[n, :,height: p_h+height, width: p_w+width] =dmax + h_c +=1 + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return dx + + +def spatial_batchnorm_forward(x, gamma, beta, bn_param): + """ + Computes the forward pass for spatial batch normalization. + + Inputs: + - x: Input data of shape (N, C, H, W) + - gamma: Scale parameter, of shape (C,) + - beta: Shift parameter, of shape (C,) + - bn_param: Dictionary with the following keys: + - mode: 'train' or 'test'; required + - eps: Constant for numeric stability + - momentum: Constant for running mean / variance. momentum=0 means that + old information is discarded completely at every time step, while + momentum=1 means that new information is never incorporated. The + default of momentum=0.9 should work well in most situations. + - running_mean: Array of shape (D,) giving running mean of features + - running_var Array of shape (D,) giving running variance of features + + Returns a tuple of: + - out: Output data, of shape (N, C, H, W) + - cache: Values needed for the backward pass + """ + out, cache = None, None + + ########################################################################### + # TODO: Implement the forward pass for spatial batch normalization. # + # # + # HINT: You can implement spatial batch normalization by calling the # + # vanilla version of batch normalization you implemented above. # + # Your implementation should be very short; ours is less than five lines. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N, C, H, W = x.shape + + x_row = x.transpose(0,2,3,1).reshape(N*H*W, C) + + out, cache = batchnorm_forward(x_row, gamma, beta, bn_param) + + out = out.reshape(N, H, W, C).transpose(0,3,1,2) + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return out, cache + + +def spatial_batchnorm_backward(dout, cache): + """ + Computes the backward pass for spatial batch normalization. + + Inputs: + - dout: Upstream derivatives, of shape (N, C, H, W) + - cache: Values from the forward pass + + Returns a tuple of: + - dx: Gradient with respect to inputs, of shape (N, C, H, W) + - dgamma: Gradient with respect to scale parameter, of shape (C,) + - dbeta: Gradient with respect to shift parameter, of shape (C,) + """ + dx, dgamma, dbeta = None, None, None + + ########################################################################### + # TODO: Implement the backward pass for spatial batch normalization. # + # # + # HINT: You can implement spatial batch normalization by calling the # + # vanilla version of batch normalization you implemented above. # + # Your implementation should be very short; ours is less than five lines. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N, C, H, W = dout.shape + + dout_row = dout.transpose(0,2,3,1).reshape(N*H*W, C) + + dx, dgamma, dbeta = batchnorm_backward(dout_row, cache) + + dx = dx.reshape(N, H, W, C).transpose(0,3,1,2) + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return dx, dgamma, dbeta + + +def spatial_groupnorm_forward(x, gamma, beta, G, gn_param): + """ + Computes the forward pass for spatial group normalization. + In contrast to layer normalization, group normalization splits each entry + in the data into G contiguous pieces, which it then normalizes independently. + Per feature shifting and scaling are then applied to the data, in a manner identical to that of batch normalization and layer normalization. + + Inputs: + - x: Input data of shape (N, C, H, W) + - gamma: Scale parameter, of shape (C,) + - beta: Shift parameter, of shape (C,) + - G: Integer mumber of groups to split into, should be a divisor of C + - gn_param: Dictionary with the following keys: + - eps: Constant for numeric stability + + Returns a tuple of: + - out: Output data, of shape (N, C, H, W) + - cache: Values needed for the backward pass + """ + out, cache = None, None + eps = gn_param.get('eps',1e-5) + ########################################################################### + # TODO: Implement the forward pass for spatial group normalization. # + # This will be extremely similar to the layer norm implementation. # + # In particular, think about how you could transform the matrix so that # + # the bulk of the code is similar to both train-time batch normalization # + # and layer normalization! # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + N, C, H, W = x.shape + out = np.zeros(x.shape) + mean = np.zeros((N, G)) + var = np.zeros((N, G)) + x_norm = np.zeros(x.shape) + + for n in range(N): + + for g in range(G): + + sample = x[n, g*(C//G): (g+1)*(C//G)] + + mean[n, g] = np.mean(sample) + var[n, g] = np.var(sample) + eps + + x_norm[n, g*(C//G) : (g+1)*(C//G)] = \ + (sample - mean[n, g])/ (var[n, g] ** 0.5) + + out = x_norm * gamma + beta + + cache={'std': (var **0.5), 'gamma':gamma, 'norm':x_norm, 'G': G} + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return out, cache + + +def spatial_groupnorm_backward(dout, cache): + """ + Computes the backward pass for spatial group normalization. + + Inputs: + - dout: Upstream derivatives, of shape (N, C, H, W) + - cache: Values from the forward pass + + Returns a tuple of: + - dx: Gradient with respect to inputs, of shape (N, C, H, W) + - dgamma: Gradient with respect to scale parameter, of shape (C,) + - dbeta: Gradient with respect to shift parameter, of shape (C,) + """ + dx, dgamma, dbeta = None, None, None + + ########################################################################### + # TODO: Implement the backward pass for spatial group normalization. # + # This will be extremely similar to the layer norm implementation. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + N, C, H, W = dout.shape + N = dout.shape[0] + dbeta = dout.sum(axis=(0,2,3), keepdims=True) + dgamma = np.sum(dout * cache['norm'], axis=(0,2,3), keepdims=True) + + size = (N * cache['G'], C//cache['G'] * H * W) + + norm = cache['norm'].reshape(size).T + M = norm.shape[0] + dfdz = dout * cache['gamma'] + dfdz = dfdz.reshape(size).T + + dfdz_sum = np.sum(dfdz,axis=0) + dx = dfdz - dfdz_sum/M - np.sum(dfdz * norm,axis=0) * norm/M + dx /= cache['std'].reshape(cache['std'].shape[0] * cache['std'].shape[1], 1).T + dx = dx.T.reshape(N, C, H, W) + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + return dx, dgamma, dbeta + + +def svm_loss(x, y): + """ + Computes the loss and gradient using for multiclass SVM classification. + + Inputs: + - x: Input data, of shape (N, C) where x[i, j] is the score for the jth + class for the ith input. + - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and + 0 <= y[i] < C + + Returns a tuple of: + - loss: Scalar giving the loss + - dx: Gradient of the loss with respect to x + """ + N = x.shape[0] + correct_class_scores = x[np.arange(N), y] + margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0) + margins[np.arange(N), y] = 0 + loss = np.sum(margins) / N + num_pos = np.sum(margins > 0, axis=1) + dx = np.zeros_like(x) + dx[margins > 0] = 1 + dx[np.arange(N), y] -= num_pos + dx /= N + return loss, dx + + +def softmax_loss(x, y): + """ + Computes the loss and gradient for softmax classification. + + Inputs: + - x: Input data, of shape (N, C) where x[i, j] is the score for the jth + class for the ith input. + - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and + 0 <= y[i] < C + + Returns a tuple of: + - loss: Scalar giving the loss + - dx: Gradient of the loss with respect to x + """ + shifted_logits = x - np.max(x, axis=1, keepdims=True) + Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True) + log_probs = shifted_logits - np.log(Z) + probs = np.exp(log_probs) + N = x.shape[0] + loss = -np.sum(log_probs[np.arange(N), y]) / N + dx = probs.copy() + dx[np.arange(N), y] -= 1 + dx /= N + return loss, dx \ No newline at end of file diff --git a/assignment2/cs231n/optim.py b/assignment2/cs231n/optim.py new file mode 100755 index 0000000..ec9d48f --- /dev/null +++ b/assignment2/cs231n/optim.py @@ -0,0 +1,182 @@ +import numpy as np + +""" +This file implements various first-order update rules that are commonly used +for training neural networks. Each update rule accepts current weights and the +gradient of the loss with respect to those weights and produces the next set of +weights. Each update rule has the same interface: + +def update(w, dw, config=None): + +Inputs: + - w: A numpy array giving the current weights. + - dw: A numpy array of the same shape as w giving the gradient of the + loss with respect to w. + - config: A dictionary containing hyperparameter values such as learning + rate, momentum, etc. If the update rule requires caching values over many + iterations, then config will also hold these cached values. + +Returns: + - next_w: The next point after the update. + - config: The config dictionary to be passed to the next iteration of the + update rule. + +NOTE: For most update rules, the default learning rate will probably not +perform well; however the default values of the other hyperparameters should +work well for a variety of different problems. + +For efficiency, update rules may perform in-place updates, mutating w and +setting next_w equal to w. +""" + + +def sgd(w, dw, config=None): + """ + Performs vanilla stochastic gradient descent. + + config format: + - learning_rate: Scalar learning rate. + """ + if config is None: config = {} + config.setdefault('learning_rate', 1e-2) + + w -= config['learning_rate'] * dw + return w, config + + +def sgd_momentum(w, dw, config=None): + """ + Performs stochastic gradient descent with momentum. + + config format: + - learning_rate: Scalar learning rate. + - momentum: Scalar between 0 and 1 giving the momentum value. + Setting momentum = 0 reduces to sgd. + - velocity: A numpy array of the same shape as w and dw used to store a + moving average of the gradients. + """ + if config is None: config = {} + config.setdefault('learning_rate', 1e-2) + config.setdefault('momentum', 0.9) + config.setdefault('velocity', np.zeros_like(w)) + + + next_w = None + ########################################################################### + # TODO: Implement the momentum update formula. Store the updated value in # + # the next_w variable. You should also use and update the velocity v. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + v = (config['momentum'] *config['velocity']) - (config['learning_rate']* dw) + + next_w = w + v + + config['velocity'] = v + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + + return next_w, config + + + +def rmsprop(w, dw, config=None): + """ + Uses the RMSProp update rule, which uses a moving average of squared + gradient values to set adaptive per-parameter learning rates. + + config format: + - learning_rate: Scalar learning rate. + - decay_rate: Scalar between 0 and 1 giving the decay rate for the squared + gradient cache. + - epsilon: Small scalar used for smoothing to avoid dividing by zero. + - cache: Moving average of second moments of gradients. + """ + if config is None: config = {} + config.setdefault('learning_rate', 1e-2) + config.setdefault('decay_rate', 0.99) + config.setdefault('epsilon', 1e-8) + config.setdefault('cache', np.zeros_like(w)) + + next_w = None + ########################################################################### + # TODO: Implement the RMSprop update formula, storing the next value of w # + # in the next_w variable. Don't forget to update cache value stored in # + # config['cache']. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + sdw = (config['decay_rate'] * config['cache']) + (1-config['decay_rate']) * (dw **2) + + next_w = w - config['learning_rate'] * (dw / ((sdw ** 0.5) + config['epsilon'])) + + config['cache'] = sdw + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return next_w, config + + +def adam(w, dw, config=None): + """ + Uses the Adam update rule, which incorporates moving averages of both the + gradient and its square and a bias correction term. + + config format: + - learning_rate: Scalar learning rate. + - beta1: Decay rate for moving average of first moment of gradient. + - beta2: Decay rate for moving average of second moment of gradient. + - epsilon: Small scalar used for smoothing to avoid dividing by zero. + - m: Moving average of gradient. + - v: Moving average of squared gradient. + - t: Iteration number. + """ + if config is None: config = {} + config.setdefault('learning_rate', 1e-3) + config.setdefault('beta1', 0.9) + config.setdefault('beta2', 0.999) + config.setdefault('epsilon', 1e-8) + config.setdefault('m', np.zeros_like(w)) + config.setdefault('v', np.zeros_like(w)) + config.setdefault('t', 0) + + next_w = None + ########################################################################### + # TODO: Implement the Adam update formula, storing the next value of w in # + # the next_w variable. Don't forget to update the m, v, and t variables # + # stored in config. # + # # + # NOTE: In order to match the reference output, please modify t _before_ # + # using it in any calculations. # + ########################################################################### + # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + + config['t'] = config['t']+1 + m = config['beta1'] * config['m'] + (1-config['beta1']) * dw + + v = config['beta2'] * config['v'] + (1 - config['beta2']) * (dw **2) + + m_cor = m / (1- config['beta1'] ** config['t']) + + v_cor = v / (1- config['beta2'] ** config['t']) + + next_w = w - config['learning_rate'] * m_cor / (np.sqrt(v_cor) + config['epsilon']) + + + config['m'] = m + config['v'] = v + + + + + # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** + ########################################################################### + # END OF YOUR CODE # + ########################################################################### + + return next_w, config diff --git a/assignment2/cs231n/setup.py b/assignment2/cs231n/setup.py new file mode 100755 index 0000000..9a2e6ca --- /dev/null +++ b/assignment2/cs231n/setup.py @@ -0,0 +1,14 @@ +from distutils.core import setup +from distutils.extension import Extension +from Cython.Build import cythonize +import numpy + +extensions = [ + Extension('im2col_cython', ['im2col_cython.pyx'], + include_dirs = [numpy.get_include()] + ), +] + +setup( + ext_modules = cythonize(extensions), +) diff --git a/assignment2/cs231n/solver.py b/assignment2/cs231n/solver.py new file mode 100755 index 0000000..1fab7da --- /dev/null +++ b/assignment2/cs231n/solver.py @@ -0,0 +1,307 @@ +from __future__ import print_function, division +from future import standard_library +standard_library.install_aliases() +from builtins import range +from builtins import object +import os +import pickle as pickle + +import numpy as np + +from cs231n import optim + + +class Solver(object): + """ + A Solver encapsulates all the logic necessary for training classification + models. The Solver performs stochastic gradient descent using different + update rules defined in optim.py. + + The solver accepts both training and validataion data and labels so it can + periodically check classification accuracy on both training and validation + data to watch out for overfitting. + + To train a model, you will first construct a Solver instance, passing the + model, dataset, and various options (learning rate, batch size, etc) to the + constructor. You will then call the train() method to run the optimization + procedure and train the model. + + After the train() method returns, model.params will contain the parameters + that performed best on the validation set over the course of training. + In addition, the instance variable solver.loss_history will contain a list + of all losses encountered during training and the instance variables + solver.train_acc_history and solver.val_acc_history will be lists of the + accuracies of the model on the training and validation set at each epoch. + + Example usage might look something like this: + + data = { + 'X_train': # training data + 'y_train': # training labels + 'X_val': # validation data + 'y_val': # validation labels + } + model = MyAwesomeModel(hidden_size=100, reg=10) + solver = Solver(model, data, + update_rule='sgd', + optim_config={ + 'learning_rate': 1e-3, + }, + lr_decay=0.95, + num_epochs=10, batch_size=100, + print_every=100) + solver.train() + + + A Solver works on a model object that must conform to the following API: + + - model.params must be a dictionary mapping string parameter names to numpy + arrays containing parameter values. + + - model.loss(X, y) must be a function that computes training-time loss and + gradients, and test-time classification scores, with the following inputs + and outputs: + + Inputs: + - X: Array giving a minibatch of input data of shape (N, d_1, ..., d_k) + - y: Array of labels, of shape (N,) giving labels for X where y[i] is the + label for X[i]. + + Returns: + If y is None, run a test-time forward pass and return: + - scores: Array of shape (N, C) giving classification scores for X where + scores[i, c] gives the score of class c for X[i]. + + If y is not None, run a training time forward and backward pass and + return a tuple of: + - loss: Scalar giving the loss + - grads: Dictionary with the same keys as self.params mapping parameter + names to gradients of the loss with respect to those parameters. + """ + + def __init__(self, model, data, **kwargs): + """ + Construct a new Solver instance. + + Required arguments: + - model: A model object conforming to the API described above + - data: A dictionary of training and validation data containing: + 'X_train': Array, shape (N_train, d_1, ..., d_k) of training images + 'X_val': Array, shape (N_val, d_1, ..., d_k) of validation images + 'y_train': Array, shape (N_train,) of labels for training images + 'y_val': Array, shape (N_val,) of labels for validation images + + Optional arguments: + - update_rule: A string giving the name of an update rule in optim.py. + Default is 'sgd'. + - optim_config: A dictionary containing hyperparameters that will be + passed to the chosen update rule. Each update rule requires different + hyperparameters (see optim.py) but all update rules require a + 'learning_rate' parameter so that should always be present. + - lr_decay: A scalar for learning rate decay; after each epoch the + learning rate is multiplied by this value. + - batch_size: Size of minibatches used to compute loss and gradient + during training. + - num_epochs: The number of epochs to run for during training. + - print_every: Integer; training losses will be printed every + print_every iterations. + - verbose: Boolean; if set to false then no output will be printed + during training. + - num_train_samples: Number of training samples used to check training + accuracy; default is 1000; set to None to use entire training set. + - num_val_samples: Number of validation samples to use to check val + accuracy; default is None, which uses the entire validation set. + - checkpoint_name: If not None, then save model checkpoints here every + epoch. + """ + self.model = model + self.X_train = data['X_train'] + self.y_train = data['y_train'] + self.X_val = data['X_val'] + self.y_val = data['y_val'] + + # Unpack keyword arguments + self.update_rule = kwargs.pop('update_rule', 'sgd') + self.optim_config = kwargs.pop('optim_config', {}) + self.lr_decay = kwargs.pop('lr_decay', 1.0) + self.batch_size = kwargs.pop('batch_size', 100) + self.num_epochs = kwargs.pop('num_epochs', 10) + self.num_train_samples = kwargs.pop('num_train_samples', 1000) + self.num_val_samples = kwargs.pop('num_val_samples', None) + + self.checkpoint_name = kwargs.pop('checkpoint_name', None) + self.print_every = kwargs.pop('print_every', 10) + self.verbose = kwargs.pop('verbose', True) + + # Throw an error if there are extra keyword arguments + if len(kwargs) > 0: + extra = ', '.join('"%s"' % k for k in list(kwargs.keys())) + raise ValueError('Unrecognized arguments %s' % extra) + + # Make sure the update rule exists, then replace the string + # name with the actual function + if not hasattr(optim, self.update_rule): + raise ValueError('Invalid update_rule "%s"' % self.update_rule) + self.update_rule = getattr(optim, self.update_rule) + + self._reset() + + + def _reset(self): + """ + Set up some book-keeping variables for optimization. Don't call this + manually. + """ + # Set up some variables for book-keeping + self.epoch = 0 + self.best_val_acc = 0 + self.best_params = {} + self.loss_history = [] + self.train_acc_history = [] + self.val_acc_history = [] + + # Make a deep copy of the optim_config for each parameter + self.optim_configs = {} + for p in self.model.params: + d = {k: v for k, v in self.optim_config.items()} + self.optim_configs[p] = d + + + def _step(self): + """ + Make a single gradient update. This is called by train() and should not + be called manually. + """ + # Make a minibatch of training data + num_train = self.X_train.shape[0] + batch_mask = np.random.choice(num_train, self.batch_size) + X_batch = self.X_train[batch_mask] + y_batch = self.y_train[batch_mask] + + # Compute loss and gradient + loss, grads = self.model.loss(X_batch, y_batch) + self.loss_history.append(loss) + + + # Perform a parameter update + for p, w in self.model.params.items(): + dw = grads[p] + config = self.optim_configs[p] + next_w, next_config = self.update_rule(w, dw, config) + self.model.params[p] = next_w + self.optim_configs[p] = next_config + + + def _save_checkpoint(self): + if self.checkpoint_name is None: return + checkpoint = { + 'model': self.model, + 'update_rule': self.update_rule, + 'lr_decay': self.lr_decay, + 'optim_config': self.optim_config, + 'batch_size': self.batch_size, + 'num_train_samples': self.num_train_samples, + 'num_val_samples': self.num_val_samples, + 'epoch': self.epoch, + 'loss_history': self.loss_history, + 'train_acc_history': self.train_acc_history, + 'val_acc_history': self.val_acc_history, + } + filename = '%s_epoch_%d.pkl' % (self.checkpoint_name, self.epoch) + if self.verbose: + print('Saving checkpoint to "%s"' % filename) + with open(filename, 'wb') as f: + pickle.dump(checkpoint, f) + + + def check_accuracy(self, X, y, num_samples=None, batch_size=100): + """ + Check accuracy of the model on the provided data. + + Inputs: + - X: Array of data, of shape (N, d_1, ..., d_k) + - y: Array of labels, of shape (N,) + - num_samples: If not None, subsample the data and only test the model + on num_samples datapoints. + - batch_size: Split X and y into batches of this size to avoid using + too much memory. + + Returns: + - acc: Scalar giving the fraction of instances that were correctly + classified by the model. + """ + + # Maybe subsample the data + N = X.shape[0] + if num_samples is not None and N > num_samples: + mask = np.random.choice(N, num_samples) + N = num_samples + X = X[mask] + y = y[mask] + + # Compute predictions in batches + num_batches = N // batch_size + if N % batch_size != 0: + num_batches += 1 + y_pred = [] + for i in range(num_batches): + start = i * batch_size + end = (i + 1) * batch_size + scores = self.model.loss(X[start:end]) + y_pred.append(np.argmax(scores, axis=1)) + y_pred = np.hstack(y_pred) + acc = np.mean(y_pred == y) + + return acc + + + def train(self): + """ + Run optimization to train the model. + """ + num_train = self.X_train.shape[0] + iterations_per_epoch = max(num_train // self.batch_size, 1) + num_iterations = self.num_epochs * iterations_per_epoch + + for t in range(num_iterations): + self._step() + + # Maybe print training loss + if self.verbose and t % self.print_every == 0: + print('(Iteration %d / %d) loss: %f' % ( + t + 1, num_iterations, self.loss_history[-1])) + + # At the end of every epoch, increment the epoch counter and decay + # the learning rate. + epoch_end = (t + 1) % iterations_per_epoch == 0 + if epoch_end: + self.epoch += 1 + for k in self.optim_configs: + self.optim_configs[k]['learning_rate'] *= self.lr_decay + + # Check train and val accuracy on the first iteration, the last + # iteration, and at the end of each epoch. + first_it = (t == 0) + last_it = (t == num_iterations - 1) + if first_it or last_it or epoch_end: + train_acc = self.check_accuracy(self.X_train, self.y_train, + num_samples=self.num_train_samples) + val_acc = self.check_accuracy(self.X_val, self.y_val, + num_samples=self.num_val_samples) + self.train_acc_history.append(train_acc) + self.val_acc_history.append(val_acc) + self._save_checkpoint() + + if self.verbose: + print('(Epoch %d / %d) train acc: %f; val_acc: %f' % ( + self.epoch, self.num_epochs, train_acc, val_acc)) + + # Keep track of the best model + if val_acc > self.best_val_acc: + self.best_val_acc = val_acc + self.best_params = {} + for k, v in self.model.params.items(): + self.best_params[k] = v.copy() + + # At the end of training swap the best params into the model + self.model.params = self.best_params diff --git a/assignment2/cs231n/vis_utils.py b/assignment2/cs231n/vis_utils.py new file mode 100755 index 0000000..0aa42c0 --- /dev/null +++ b/assignment2/cs231n/vis_utils.py @@ -0,0 +1,73 @@ +from builtins import range +from past.builtins import xrange + +from math import sqrt, ceil +import numpy as np + +def visualize_grid(Xs, ubound=255.0, padding=1): + """ + Reshape a 4D tensor of image data to a grid for easy visualization. + + Inputs: + - Xs: Data of shape (N, H, W, C) + - ubound: Output grid will have values scaled to the range [0, ubound] + - padding: The number of blank pixels between elements of the grid + """ + (N, H, W, C) = Xs.shape + grid_size = int(ceil(sqrt(N))) + grid_height = H * grid_size + padding * (grid_size - 1) + grid_width = W * grid_size + padding * (grid_size - 1) + grid = np.zeros((grid_height, grid_width, C)) + next_idx = 0 + y0, y1 = 0, H + for y in range(grid_size): + x0, x1 = 0, W + for x in range(grid_size): + if next_idx < N: + img = Xs[next_idx] + low, high = np.min(img), np.max(img) + grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low) + # grid[y0:y1, x0:x1] = Xs[next_idx] + next_idx += 1 + x0 += W + padding + x1 += W + padding + y0 += H + padding + y1 += H + padding + # grid_max = np.max(grid) + # grid_min = np.min(grid) + # grid = ubound * (grid - grid_min) / (grid_max - grid_min) + return grid + +def vis_grid(Xs): + """ visualize a grid of images """ + (N, H, W, C) = Xs.shape + A = int(ceil(sqrt(N))) + G = np.ones((A*H+A, A*W+A, C), Xs.dtype) + G *= np.min(Xs) + n = 0 + for y in range(A): + for x in range(A): + if n < N: + G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = Xs[n,:,:,:] + n += 1 + # normalize to [0,1] + maxg = G.max() + ming = G.min() + G = (G - ming)/(maxg-ming) + return G + +def vis_nn(rows): + """ visualize array of arrays of images """ + N = len(rows) + D = len(rows[0]) + H,W,C = rows[0][0].shape + Xs = rows[0][0] + G = np.ones((N*H+N, D*W+D, C), Xs.dtype) + for y in range(N): + for x in range(D): + G[y*H+y:(y+1)*H+y, x*W+x:(x+1)*W+x, :] = rows[y][x] + # normalize to [0,1] + maxg = G.max() + ming = G.min() + G = (G - ming)/(maxg-ming) + return G diff --git a/assignment2/frameworkpython b/assignment2/frameworkpython new file mode 100755 index 0000000..5ed8ebd --- /dev/null +++ b/assignment2/frameworkpython @@ -0,0 +1,15 @@ +#!/bin/bash + +# what real Python executable to use +#PYVER=2.7 +#PATHTOPYTHON=/usr/local/bin/ +#PYTHON=${PATHTOPYTHON}python${PYVER} + +PYTHON=$(which $(readlink .env/bin/python)) # only works with python3 + +# find the root of the virtualenv, it should be the parent of the dir this script is in +ENV=`$PYTHON -c "import os; print(os.path.abspath(os.path.join(os.path.dirname(\"$0\"), '..')))"` + +# now run Python with the virtualenv set as Python's HOME +export PYTHONHOME=$ENV +exec $PYTHON "$@" diff --git a/assignment2/notebook_images/batchnorm_graph.png b/assignment2/notebook_images/batchnorm_graph.png new file mode 100755 index 0000000..f58b9c6 Binary files /dev/null and b/assignment2/notebook_images/batchnorm_graph.png differ diff --git a/assignment2/notebook_images/kitten.jpg b/assignment2/notebook_images/kitten.jpg new file mode 100755 index 0000000..e421ec1 Binary files /dev/null and b/assignment2/notebook_images/kitten.jpg differ diff --git a/assignment2/notebook_images/normalization.png b/assignment2/notebook_images/normalization.png new file mode 100755 index 0000000..3328f2b Binary files /dev/null and b/assignment2/notebook_images/normalization.png differ diff --git a/assignment2/notebook_images/puppy.jpg b/assignment2/notebook_images/puppy.jpg new file mode 100755 index 0000000..3cc1234 Binary files /dev/null and b/assignment2/notebook_images/puppy.jpg differ diff --git a/assignment2/requirements.txt b/assignment2/requirements.txt new file mode 100755 index 0000000..046234c --- /dev/null +++ b/assignment2/requirements.txt @@ -0,0 +1,73 @@ +absl-py==0.7.1 +astor==0.7.1 +attrs==19.1.0 +backcall==0.1.0 +bleach==3.1.0 +cycler==0.10.0 +Cython==0.29.7 +decorator==4.4.0 +defusedxml==0.6.0 +entrypoints==0.3 +future==0.17.1 +gast==0.2.2 +google-pasta==0.1.5 +grpcio==1.20.0 +h5py==2.9.0 +imageio==2.5.0 +ipykernel==5.1.0 +ipython==7.4.0 +ipython-genutils==0.2.0 +ipywidgets==7.4.2 +jedi==0.13.3 +Jinja2==2.10.1 +jsonschema==3.0.1 +jupyter==1.0.0 +jupyter-client==5.2.4 +jupyter-console==6.0.0 +jupyter-core==4.4.0 +Keras==2.2.4 +Keras-Applications==1.0.7 +Keras-Preprocessing==1.0.9 +kiwisolver==1.0.1 +Markdown==3.1 +MarkupSafe==1.1.1 +matplotlib==3.0.3 +mistune==0.8.4 +nbconvert==5.4.1 +nbformat==4.4.0 +notebook==5.7.8 +numexpr==2.6.9 +numpy==1.16.2 +pandocfilters==1.4.2 +parso==0.4.0 +pexpect==4.7.0 +pickleshare==0.7.5 +Pillow==6.0.0 +prometheus-client==0.6.0 +prompt-toolkit==2.0.9 +protobuf==3.7.1 +ptyprocess==0.6.0 +Pygments==2.3.1 +pyparsing==2.4.0 +pyrsistent==0.14.11 +python-dateutil==2.8.0 +PyYAML==5.1 +pyzmq==18.0.1 +qtconsole==4.4.3 +scipy==1.2.1 +Send2Trash==1.5.0 +six==1.12.0 +# Add this line if you want GPU support for tensorflow! +# tensorflow-gpu==2.0.0a0 +tensorflow==2.0.0a0 +termcolor==1.1.0 +terminado==0.8.2 +testpath==0.4.2 +torch==1.0.1.post2 +torchvision==0.2.2.post3 +tornado==6.0.2 +traitlets==4.3.2 +wcwidth==0.1.7 +webencodings==0.5.1 +Werkzeug==0.15.2 +widgetsnbextension==3.4.2 diff --git a/assignment2/start_ipython_osx.sh b/assignment2/start_ipython_osx.sh new file mode 100755 index 0000000..4815b00 --- /dev/null +++ b/assignment2/start_ipython_osx.sh @@ -0,0 +1,4 @@ +# Assume the virtualenv is called .env + +cp frameworkpython .env/bin +.env/bin/frameworkpython -m IPython notebook diff --git a/assignment3/.gitignore b/assignment3/.gitignore new file mode 100755 index 0000000..a5c5231 --- /dev/null +++ b/assignment3/.gitignore @@ -0,0 +1,7 @@ +*.swp +*.pyc +.env/* +*/.ipynb_checkpoints/* + +# gitignore the built release. +assignment3/* diff --git a/assignment3/.ipynb_checkpoints/Generative_Adversarial_Networks_TF-checkpoint.ipynb b/assignment3/.ipynb_checkpoints/Generative_Adversarial_Networks_TF-checkpoint.ipynb new file mode 100755 index 0000000..ad3724d --- /dev/null +++ b/assignment3/.ipynb_checkpoints/Generative_Adversarial_Networks_TF-checkpoint.ipynb @@ -0,0 +1,5833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Generative Adversarial Networks (GANs)\n", + "So far in CS231N, all the applications of neural networks that we have explored have been **discriminative models** that take an input and are trained to produce a labeled output. This has ranged from straightforward classification of image categories to sentence generation (which was still phrased as a classification problem, our labels were in vocabulary space and we’d learned a recurrence to capture multi-word labels). In this notebook, we will expand our repetoire, and build **generative models** using neural networks. Specifically, we will learn how to build models which generate novel images that resemble a set of training images." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "### What is a GAN?\n", + "\n", + "In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.\n", + "\n", + "We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:\n", + "$$\\underset{G}{\\text{minimize}}\\; \\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", + "where $x \\sim p_\\text{data}$ are samples from the input data, $z \\sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.\n", + "\n", + "To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:\n", + "1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__. \n", + "2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.\n", + "\n", + "While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661). \n", + "\n", + "In this assignment, we will alternate the following updates:\n", + "1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:\n", + "$$\\underset{G}{\\text{maximize}}\\; \\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", + "2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:\n", + "$$\\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", + "\n", + "### What else is there?\n", + "Since 2014, GANs have exploded into a huge research area, with massive [workshops](https://sites.google.com/site/nips2016adversarial/), and [hundreds of new papers](https://github.com/hindupuravinash/the-gan-zoo). Compared to other approaches for generative models, they often produce the highest quality samples but are some of the most difficult and finicky models to train (see [this github repo](https://github.com/soumith/ganhacks) that contains a set of 17 hacks that are useful for getting models working). Improving the stabiilty and robustness of GAN training is an open research question, with new papers coming out every day! For a more recent tutorial on GANs, see [here](https://arxiv.org/abs/1701.00160). There is also some even more recent exciting work that changes the objective function to Wasserstein distance and yields much more stable results across model architectures: [WGAN](https://arxiv.org/abs/1701.07875), [WGAN-GP](https://arxiv.org/abs/1704.00028).\n", + "\n", + "\n", + "GANs are not the only way to train a generative model! For other approaches to generative modeling check out the [deep generative model chapter](http://www.deeplearningbook.org/contents/generative_models.html) of the Deep Learning [book](http://www.deeplearningbook.org). Another popular way of training neural networks as generative models is Variational Autoencoders (co-discovered [here](https://arxiv.org/abs/1312.6114) and [here](https://arxiv.org/abs/1401.4082)). Variational autoencoders combine neural networks with variational inference to train deep generative models. These models tend to be far more stable and easier to train but currently don't produce samples that are as pretty as GANs.\n", + "\n", + "Example pictures of what you should expect (yours might look slightly different):\n", + "\n", + "![caption](gan_outputs_tf.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# A bunch of utility functions\n", + "\n", + "def show_images(images):\n", + " images = np.reshape(images, [images.shape[0], -1]) # images reshape to (batch_size, D)\n", + " sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n", + " sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n", + "\n", + " fig = plt.figure(figsize=(sqrtn, sqrtn))\n", + " gs = gridspec.GridSpec(sqrtn, sqrtn)\n", + " gs.update(wspace=0.05, hspace=0.05)\n", + "\n", + " for i, img in enumerate(images):\n", + " ax = plt.subplot(gs[i])\n", + " plt.axis('off')\n", + " ax.set_xticklabels([])\n", + " ax.set_yticklabels([])\n", + " ax.set_aspect('equal')\n", + " plt.imshow(img.reshape([sqrtimg,sqrtimg]))\n", + " return\n", + "\n", + "def preprocess_img(x):\n", + " return 2 * x - 1.0\n", + "\n", + "def deprocess_img(x):\n", + " return (x + 1.0) / 2.0\n", + "\n", + "def rel_error(x,y):\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", + "\n", + "def count_params(model):\n", + " \"\"\"Count the number of parameters in the current TensorFlow graph \"\"\"\n", + " param_count = np.sum([np.prod(p.shape) for p in model.weights])\n", + " return param_count\n", + "\n", + "answers = np.load('gan-checks-tf.npz')\n", + "\n", + "NOISE_DIM = 96" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "## Dataset\n", + " GANs are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable without a GPU, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n", + " \n", + "\n", + "**Heads-up**: Our MNIST wrapper returns images as vectors. That is, they're size (batch, 784). If you want to treat them as images, we have to resize them to (batch,28,28) or (batch,28,28,1). They are also type np.float32 and bounded [0,1]. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "class MNIST(object):\n", + " def __init__(self, batch_size, shuffle=False):\n", + " \"\"\"\n", + " Construct an iterator object over the MNIST data\n", + " \n", + " Inputs:\n", + " - batch_size: Integer giving number of elements per minibatch\n", + " - shuffle: (optional) Boolean, whether to shuffle the data on each epoch\n", + " \"\"\"\n", + " train, _ = tf.keras.datasets.mnist.load_data()\n", + " X, y = train\n", + " X = X.astype(np.float32)/255\n", + " X = X.reshape((X.shape[0], -1))\n", + " self.X, self.y = X, y\n", + " self.batch_size, self.shuffle = batch_size, shuffle\n", + "\n", + " def __iter__(self):\n", + " N, B = self.X.shape[0], self.batch_size\n", + " idxs = np.arange(N)\n", + " if self.shuffle:\n", + " np.random.shuffle(idxs)\n", + " return iter((self.X[i:i+B], self.y[i:i+B]) for i in range(0, N, B)) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# show a batch\n", + "mnist = MNIST(batch_size=16) \n", + "show_images(mnist.X[:20])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LeakyReLU\n", + "In the cell below, you should implement a LeakyReLU. See the [class notes](http://cs231n.github.io/neural-networks-1/) (where alpha is small number) or equation (3) in [this paper](http://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf). LeakyReLUs keep ReLU units from dying and are often used in GAN methods (as are maxout units, however those increase model size and therefore are not used in this notebook).\n", + "\n", + "HINT: You should be able to use `tf.maximum`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def leaky_relu(x, alpha=0.01):\n", + " \"\"\"Compute the leaky ReLU activation function.\n", + " \n", + " Inputs:\n", + " - x: TensorFlow Tensor with arbitrary shape\n", + " - alpha: leak parameter for leaky ReLU\n", + " \n", + " Returns:\n", + " TensorFlow Tensor with the same shape as x\n", + " \"\"\"\n", + " # TODO: implement leaky ReLU\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " return tf.maximum(alpha * x, x)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your leaky ReLU implementation. You should get errors < 1e-10" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum error: 0\n" + ] + } + ], + "source": [ + "def test_leaky_relu(x, y_true):\n", + " y = leaky_relu(tf.constant(x))\n", + " print('Maximum error: %g'%rel_error(y_true, y))\n", + "\n", + "test_leaky_relu(answers['lrelu_x'], answers['lrelu_y'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Noise\n", + "Generate a TensorFlow `Tensor` containing uniform noise from -1 to 1 with shape `[batch_size, dim]`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_noise(batch_size, dim):\n", + " \"\"\"Generate random uniform noise from -1 to 1.\n", + " \n", + " Inputs:\n", + " - batch_size: integer giving the batch size of noise to generate\n", + " - dim: integer giving the dimension of the noise to generate\n", + " \n", + " Returns:\n", + " TensorFlow Tensor containing uniform noise in [-1, 1] with shape [batch_size, dim]\n", + " \"\"\"\n", + " # TODO: sample and return noise\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return tf.random.uniform([batch_size, dim], minval=-1,maxval=1)\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure noise is the correct shape and type:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed!\n" + ] + } + ], + "source": [ + "def test_sample_noise():\n", + " batch_size = 3\n", + " dim = 4\n", + " z = sample_noise(batch_size, dim)\n", + " # Check z has the correct shape\n", + " assert z.get_shape().as_list() == [batch_size, dim]\n", + " # Make sure z is a Tensor and not a numpy array\n", + " assert isinstance(z, tf.Tensor)\n", + " # Check that we get different noise for different evaluations\n", + " z1 = sample_noise(batch_size, dim)\n", + " z2 = sample_noise(batch_size, dim)\n", + " assert not np.array_equal(z1, z2)\n", + " # Check that we get the correct range\n", + " assert np.all(z1 >= -1.0) and np.all(z1 <= 1.0)\n", + " print(\"All tests passed!\")\n", + " \n", + "test_sample_noise()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discriminator\n", + "Our first step is to build a discriminator. **Hint:** You should use the layers in `tf.keras.layers` to build the model.\n", + "All fully connected layers should include bias terms. For initialization, just use the default initializer used by the `tf.keras.layers` functions.\n", + "\n", + "Architecture:\n", + " * Fully connected layer with input size 784 and output size 256\n", + " * LeakyReLU with alpha 0.01\n", + " * Fully connected layer with output size 256\n", + " * LeakyReLU with alpha 0.01\n", + " * Fully connected layer with output size 1 \n", + " \n", + "The output of the discriminator should thus have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Dense, LeakyReLU, ReLU, Activation\n", + "\n", + "\n", + "def discriminator():\n", + " \"\"\"Compute discriminator score for a batch of input images.\n", + " \n", + " Inputs:\n", + " - x: TensorFlow Tensor of flattened input images, shape [batch_size, 784]\n", + " \n", + " Returns:\n", + " TensorFlow Tensor with shape [batch_size, 1], containing the score \n", + " for an image being real for each input image.\n", + " \"\"\"\n", + " model = tf.keras.models.Sequential([\n", + " # TODO: implement architecture\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + " Dense(256, input_shape=(784,)),\n", + " LeakyReLU(alpha=0.01),\n", + " Dense(256),\n", + " LeakyReLU(alpha=0.01),\n", + " Dense(1) \n", + " ])\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test to make sure the number of parameters in the discriminator is correct:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correct number of parameters in discriminator.\n" + ] + } + ], + "source": [ + "def test_discriminator(true_count=267009):\n", + " model = discriminator()\n", + " cur_count = count_params(model)\n", + " if cur_count != true_count:\n", + " print('Incorrect number of parameters in discriminator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n", + " else:\n", + " print('Correct number of parameters in discriminator.')\n", + " \n", + "test_discriminator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generator\n", + "Now to build a generator. You should use the layers in `tf.keras.layers` to construct the model. All fully connected layers should include bias terms. Note that you can use the tf.nn module to access activation functions. Once again, use the default initializers for parameters.\n", + "\n", + "Architecture:\n", + " * Fully connected layer with inupt size tf.shape(z)[1] (the number of noise dimensions) and output size 1024\n", + " * `ReLU`\n", + " * Fully connected layer with output size 1024 \n", + " * `ReLU`\n", + " * Fully connected layer with output size 784\n", + " * `TanH` (To restrict every element of the output to be in the range [-1,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def generator(noise_dim=NOISE_DIM):\n", + " \"\"\"Generate images from a random noise vector.\n", + " \n", + " Inputs:\n", + " - z: TensorFlow Tensor of random noise with shape [batch_size, noise_dim]\n", + " \n", + " Returns:\n", + " TensorFlow Tensor of generated images, with shape [batch_size, 784].\n", + " \"\"\"\n", + " model = tf.keras.models.Sequential([\n", + " # TODO: implement architecture\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " Dense(1024, input_shape=(noise_dim,)),\n", + " ReLU(),\n", + " Dense(1024),\n", + " ReLU(),\n", + " Dense(784), \n", + " Activation('tanh')\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ])\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test to make sure the number of parameters in the generator is correct:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correct number of parameters in generator.\n" + ] + } + ], + "source": [ + "def test_generator(true_count=1858320):\n", + " model = generator(4)\n", + " cur_count = count_params(model)\n", + " if cur_count != true_count:\n", + " print('Incorrect number of parameters in generator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n", + " else:\n", + " print('Correct number of parameters in generator.')\n", + " \n", + "test_generator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GAN Loss\n", + "\n", + "Compute the generator and discriminator loss. The generator loss is:\n", + "$$\\ell_G = -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", + "and the discriminator loss is:\n", + "$$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", + "Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.\n", + "\n", + "**HINTS**: Use [tf.ones](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/ones) and [tf.zeros](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/zeros) to generate labels for your discriminator. Use [tf.keras.losses.BinaryCrossentropy](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/losses/BinaryCrossentropy) to help compute your loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def discriminator_loss(logits_real, logits_fake):\n", + " \"\"\"\n", + " Computes the discriminator loss described above.\n", + " \n", + " Inputs:\n", + " - logits_real: Tensor of shape (N, 1) giving scores for the real data.\n", + " - logits_fake: Tensor of shape (N, 1) giving scores for the fake data.\n", + " \n", + " Returns:\n", + " - loss: Tensor containing (scalar) the loss for the discriminator.\n", + " \"\"\"\n", + " N,_ = logits_real.shape\n", + " \n", + " loss = None\n", + " \n", + " bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n", + "\n", + " Dx = bce(tf.ones(logits_real.shape), logits_real)\n", + " DGx = bce(tf.zeros(logits_fake.shape), logits_fake)\n", + " \n", + " \n", + " loss = Dx + DGx\n", + " \n", + "\n", + " return loss\n", + " \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + "def generator_loss(logits_fake):\n", + " \"\"\"\n", + " Computes the generator loss described above.\n", + "\n", + " Inputs:\n", + " - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n", + " \n", + " Returns:\n", + " - loss: PyTorch Tensor containing the (scalar) loss for the generator.\n", + " \"\"\"\n", + " \n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n", + " \n", + " loss = bce(tf.ones(logits_fake.shape), logits_fake)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your GAN loss. Make sure both the generator and discriminator loss are correct. You should see errors less than 1e-8." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum error in d_loss: 3.97058e-09\n" + ] + } + ], + "source": [ + "def test_discriminator_loss(logits_real, logits_fake, d_loss_true):\n", + " d_loss = discriminator_loss(tf.constant(logits_real),\n", + " tf.constant(logits_fake))\n", + " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", + "\n", + "test_discriminator_loss(answers['logits_real'], answers['logits_fake'],\n", + " answers['d_loss_true'])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum error in g_loss: 4.4518e-09\n" + ] + } + ], + "source": [ + "def test_generator_loss(logits_fake, g_loss_true):\n", + " g_loss = generator_loss(tf.constant(logits_fake))\n", + " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", + "\n", + "test_generator_loss(answers['logits_fake'], answers['g_loss_true'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimizing our loss\n", + "Make an `Adam` optimizer with a 1e-3 learning rate, beta1=0.5 to mininize G_loss and D_loss separately. The trick of decreasing beta was shown to be effective in helping GANs converge in the [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) paper. In fact, with our current hyperparameters, if you set beta1 to the Tensorflow default of 0.9, there's a good chance your discriminator loss will go to zero and the generator will fail to learn entirely. In fact, this is a common failure mode in GANs; if your D(x) learns too fast (e.g. loss goes near zero), your G(z) is never able to learn. Often D(x) is trained with SGD with Momentum or RMSProp instead of Adam, but here we'll use Adam for both D(x) and G(z). " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: create an AdamOptimizer for D_solver and G_solver\n", + "def get_solvers(learning_rate=1e-3, beta1=0.5):\n", + " \"\"\"Create solvers for GAN training.\n", + " \n", + " Inputs:\n", + " - learning_rate: learning rate to use for both solvers\n", + " - beta1: beta1 parameter for both solvers (first moment decay)\n", + " \n", + " Returns:\n", + " - D_solver: instance of tf.optimizers.Adam with correct learning_rate and beta1\n", + " - G_solver: instance of tf.optimizers.Adam with correct learning_rate and beta1\n", + " \"\"\"\n", + " D_solver = None\n", + " G_solver = None\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " D_solver = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=beta1)\n", + " G_solver = tf.optimizers.Adam(learning_rate=learning_rate, beta_1=beta1)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return D_solver, G_solver" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "source": [ + "# Training a GAN!\n", + "Well that wasn't so hard, was it? After the first epoch, you should see fuzzy outlines, clear shapes as you approach epoch 3, and decent shapes, about half of which will be sharp and clearly recognizable as we pass epoch 5. In our case, we'll simply train D(x) and G(z) with one batch each every iteration. However, papers often experiment with different schedules of training D(x) and G(z), sometimes doing one for more steps than the other, or even training each one until the loss gets \"good enough\" and then switching to training the other. " + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [], + "source": [ + "# a giant helper function\n", + "def run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss,\\\n", + " show_every=20, print_every=20, batch_size=128, num_epochs=10, noise_size=96):\n", + " \"\"\"Train a GAN for a certain number of epochs.\n", + " \n", + " Inputs:\n", + " - D: Discriminator model\n", + " - G: Generator model\n", + " - D_solver: an Optimizer for Discriminator\n", + " - G_solver: an Optimizer for Generator\n", + " - generator_loss: Generator loss\n", + " - discriminator_loss: Discriminator loss\n", + " Returns:\n", + " Nothing\n", + " \"\"\"\n", + " mnist = MNIST(batch_size=batch_size, shuffle=True)\n", + " \n", + " iter_count = 0\n", + " for epoch in range(num_epochs):\n", + " for (x, _) in mnist:\n", + " with tf.GradientTape() as tape:\n", + " real_data = x\n", + " logits_real = D(preprocess_img(real_data))\n", + "\n", + " g_fake_seed = sample_noise(batch_size, noise_size)\n", + " fake_images = G(g_fake_seed)\n", + " print(tf.reshape(fake_images, [batch_size, 784]).shape)\n", + " logits_fake = D(tf.reshape(fake_images, [batch_size, 784]))\n", + "\n", + " d_total_error = discriminator_loss(logits_real, logits_fake)\n", + " d_gradients = tape.gradient(d_total_error, D.trainable_variables) \n", + " D_solver.apply_gradients(zip(d_gradients, D.trainable_variables))\n", + " \n", + " with tf.GradientTape() as tape:\n", + " g_fake_seed = sample_noise(batch_size, noise_size)\n", + " fake_images = G(g_fake_seed)\n", + "\n", + " gen_logits_fake = D(tf.reshape(fake_images, [batch_size, 784]))\n", + " g_error = generator_loss(gen_logits_fake)\n", + " g_gradients = tape.gradient(g_error, G.trainable_variables) \n", + " G_solver.apply_gradients(zip(g_gradients, G.trainable_variables))\n", + "\n", + " if (iter_count % show_every == 0):\n", + " print('Epoch: {}, Iter: {}, D: {:.4}, G:{:.4}'.format(epoch, iter_count,d_total_error,g_error))\n", + " imgs_numpy = fake_images.cpu().numpy()\n", + " show_images(imgs_numpy[0:16])\n", + " plt.show()\n", + " iter_count += 1\n", + " \n", + " # random noise fed into our generator\n", + " z = sample_noise(batch_size, noise_size)\n", + " # generated images\n", + " G_sample = G(z)\n", + " print('Final images')\n", + " show_images(G_sample[:16])\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train your GAN! This should take about 10 minutes on a CPU, or about 2 minutes on GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(128, 784)\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": "input must be 4-dimensional[128,784] [Op:Conv2D]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# Run it!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mrun_a_gan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiscriminator_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_a_gan\u001b[0;34m(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every, print_every, batch_size, num_epochs, noise_size)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mreal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mlogits_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 710\u001b[0m with base_layer_utils.autocast_context_manager(\n\u001b[1;32m 711\u001b[0m input_list, self._mixed_precision_policy.should_cast_variables):\n\u001b[0;32m--> 712\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m 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name=name)\n\u001b[0m\u001b[1;32m 1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[0;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[1;32m 1029\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrides\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrides\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_cudnn_on_gpu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpadding\u001b[0m\u001b[0;34m,\u001b[0m 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\"dilations\", dilations)\n\u001b[1;32m 1129\u001b[0m _result = _execute.execute(b\"Conv2D\", 1, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0;32m-> 1130\u001b[0;31m ctx=_ctx, name=name)\n\u001b[0m\u001b[1;32m 1131\u001b[0m _execute.record_gradient(\n\u001b[1;32m 1132\u001b[0m \"Conv2D\", _inputs_flat, _attrs, _result, name)\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m 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\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: input must be 4-dimensional[128,784] [Op:Conv2D]" + ] + } + ], + "source": [ + "# Make the discriminator\n", + "D = discriminator()\n", + "\n", + "# Make the generator\n", + "G = generator()\n", + "\n", + "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n", + "D_solver, G_solver = get_solvers()\n", + "\n", + "# Run it!\n", + "run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Least Squares GAN\n", + "We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alternative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n", + "$$\\ell_G = \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n", + "and the discriminator loss:\n", + "$$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$\n", + "\n", + "\n", + "**HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`score_real` and `score_fake`)." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "def ls_discriminator_loss(scores_real, scores_fake):\n", + " \"\"\"\n", + " Compute the Least-Squares GAN loss for the discriminator.\n", + " \n", + " Inputs:\n", + " - scores_real: Tensor of shape (N, 1) giving scores for the real data.\n", + " - scores_fake: Tensor of shape (N, 1) giving scores for the fake data.\n", + " \n", + " Outputs:\n", + " - loss: A Tensor containing the loss.\n", + " \"\"\"\n", + " loss = None\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n", + " Gx = tf.reduce_mean(0.5 * ((scores_real -1)** 2))\n", + " Gz = tf.reduce_mean(0.5 * ((scores_fake) ** 2))\n", + " \n", + " loss = Gx + Gz\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return loss\n", + "\n", + "def ls_generator_loss(scores_fake):\n", + " \"\"\"\n", + " Computes the Least-Squares GAN loss for the generator.\n", + " \n", + " Inputs:\n", + " - scores_fake: Tensor of shape (N, 1) giving scores for the fake data.\n", + " \n", + " Outputs:\n", + " - loss: A Tensor containing the loss.\n", + " \"\"\"\n", + " loss = None\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n", + " \n", + " loss = tf.reduce_mean((scores_fake -1)**2 / 2)\n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your LSGAN loss. You should see errors less than 1e-8." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Maximum error in d_loss: 0\n", + "Maximum error in g_loss: 0\n" + ] + } + ], + "source": [ + "def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n", + " \n", + " d_loss = ls_discriminator_loss(tf.constant(score_real), tf.constant(score_fake))\n", + " g_loss = ls_generator_loss(tf.constant(score_fake))\n", + " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", + " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", + "\n", + "test_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n", + " answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create new training steps so we instead minimize the LSGAN loss:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 0, D: 0.8377, G:0.3695\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 20, D: 0.06816, G:1.499\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 40, D: 0.02916, G:0.6735\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 60, D: 0.0283, G:0.5485\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 80, D: 0.03106, G:1.013\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 100, D: 0.1744, G:0.3949\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 120, D: 0.1148, G:1.866\n" + ] + }, + { + "data": { + "image/png": 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W6ueQIUMqDltZZa3dmiLsH//4x1pEcWyFyOXYjDZt2mSEKh99gpP4fxxCG9fPfvaziFhay8L1cGQdKhDE/6slUlA1o4vAixcvTj7jSBp8gnK3ePHihsjFR3wUd4EeK6+8cl5DHVb0hWiUXKqxv+PjpZdemtfnI3vzzTcjomhNkxmI7Gq50OXTTz9N3q1bDO9dno8XXnhhLaJQbUV7z9qlS5fcWua5jbtuLB1dUOj666+PiIiDDz44IpbOoXvzT1YCZWgIkNSmAP5Dlnnz5mVdkw7yP51DmZl1ojWwQ4cOuS5s5DCHNATjXp5D9dJhw4blHNZz43ofmPGTVcg+ZC6zZs3K/gAqMe3Hd+GLL76oELayylq7NUVYW7NsIIeiFMETTjgh65XqWyKniC6y6bgRdfDWuXPnJiejJOK7ojLuiKva5iTy+7tf/epXWd/UUYOrUUd//vOfN0QuWwihB65H8TvxxBOz3qbvVB0YAvz2t7+NiKL3lkJq+9U777yTfEdnGB/V6IybrIF6Cb3xp5///OeJJMzf6Kw54IAD0scddtihYYO+7hwK59FHH53KuuuoH/LbBnZIQc3XHTR//vz0z7ips6rD48eUXjVJc6gOetRRR+UBaNamOVQv/tWvftUwh9tvv33DBnZzSPP43e9+l+hsY4k1xXf91HzEQ437ggULUr1WKy0fJ2QOy/0B1gVfjzzyyGWUfs8lQymvU1YhbGWVtSJrirB9+vSpRRR9oHJzufuAAQOyngSFcVeRFj9Si3INdbCxY8dmJIQ2FObyLg3blyh56ob43tNPP50b6G0WVt9S7xs+fPhyu2SomzIBn//5z3+e0dn2OWhlXAYNGhQRhcqNt+OwEyZMSNSFYvpjcT1qoeyFH3qI3fPhhx9OhK/fvhdRKM1nnnlm+lieQ2PJv0MOOSR7xin3lHXPopZr/GU6+N3DDz+cmQOk0NFV3kmk3k2HoLBS2h977LFUcvnnb/hQnsO+ffvWIor+cbzZOj344IMzA6ClqJm7pvlwDZmOnWNPPPFEKtx8tIvN8/HROJknR+q457hx47JHXIedmrPvySWXXFIhbGWVtXZr2ukEwUQOnMzeweuvvz65ouisrkZRg1gOs4LouO3gwYOTu5YPoRbJREqIJc/3757vwAMPzF5QfAFy4hVlEw1dAx/GMe+4447cJcJHNVSKMlVS9NZ5xJ/BgwfHhhtuGBFFrZCPIjkfHcZFRdTlo7685557Jv+nG4jgspd6M4fUYWq0ObzllluyRumoGXOolxdiyTCYOu0pp5ySGQR1HlelvhpDO3HK46C2e8ABByTi448ylxXNoUxADZ2PuPaoUaMyk4F6dBO9zeYQ0hoTc3jyySfn9RxV43nwXt8Fiu8pp5wSEcX+WGtsn332SX/pIjizDGxF1jQlbteuXcPGYJI8UeCQQw7JRWMxmwgN3IrJBCtOWATXXntttq8ZLCfWWYDSa18iKbSFKmX7+OOPM8WwIP2N1Lh8Vg4ftZHxUbP9QQcdlANOqHCmkokjsPh/ZQlfkjFjxqQQIo2yIIyDVMjE+WkslHJaWloycJbTZqnZ6NGj00f+2c6mbVJp5tBDD83gpqRh7jS5S1/5zQf+3XrrrXlKprUi5VY+MYee2ZdQCkr0WbBgwTLj7ctuzO67777lzqEx9sW4+eabI2IprbEuiF/Gw5xJUfnId5v6b7nllrj00ksb/o25HyHP2hOkpbvWaUtLSwZorarWqVbFBx98sEqJK6ustVtThP3yl79ciyi+9dJHxefRo0cnhJdP+FcakBqLsCKZDcuPPvpoNpkrVktDpDE2AEuBbWhXGNfyddFFFyVa1J1DHBHF+VD/+Mc/GiLXOuusU4soUnVIZ/vV6NGjszThvqK1EpXUUbT0s2fPnjkGTu6H3DZ2l8/2KZcVCBXSvWHDhuWYO7Ve4V5TSv32s7XWWqsWUaAU0cd43H777emfOYJwyhT881l+m8OnnnoqGwA0wkNaqG9MpMAQRSlGpnHOOeckLSn7pxT01FNPNcwhHyEZgVHmdscdd+SYoQjlc6OIp56TiEZoeuyxx7KBaMSIEQ0+ui9q5/l9J6xTgt8FF1yQZSzfJdTBOin7yCqErayyVmRNEXbgwIG1iEI4IrZo29tiiy1y4zERiViCbygB4CwKxhq8n3rqqWwtxBHwCA0SENe1ID7eiQddeuml2U4HDUQ5SFs+l/i4446rRRTij2YIZYbevXsnZy+/hUxk9VkIBRmg4sSJE7PI7hgW0R9qGD+lLT7ik8SzYcOG5RY1JQmoIUo/8MAD6eNJJ51Uiyj4pmNtCEj1c4hj0Rm0hBK5+GcOiVOvvvpqlp1wWXMpU3FNaAnhbGc0X8OHD8+thPziu+d4+OGHG+Zw6NChtYhCONIMoTy0yy67ZPbBRy21fDR2fIR0rvnss89mc40sojyXjjaCpL4DWhld+/zzz08fzaGMw/zfe++9FcJWVllrt6YI279//1pEIfdTGEXr2bNnZ6SmFuObIhYORr4m99t2N2/evGw8oOCJWJRM/AtXJcUrIWgNbNu2baKwiO+ZtUp++umnDZGLjzi3bAI6zpkzJzMAZQa8Bh/zWeotX22S+OSTT1LlhbQQiJKJN1Kp+QjxlVA6dOiQPtp8oEQA2RcuXJg+atujXMoGjOW8efNyqxmeh4vxlx/KVTgvjrt48eI87EDzBj0CGtEjlKeML62h/t02yiUaOCi1/nbBggUNc6iFVsnGHPrcvHnzcj3wiY+aM6CnOeQj5TyiaPahmptDx+34DvCNr+aQEt6pU6fMFpQCPTNkL/vIKoStrLJWZE0RlvqmFrjFFltERMF/pk+fnrk3ddMWPMd1+CnaQU/RqmPHjhmhcEUFavejUkJtm38d56FgPWrUqEQBW7I00Yus06dPb4hc6667bi2i4LrQU2T95z//mfwKv4Bo+A/1WMFfYwfu3bZt29yUwNeyGql1Dv9xLSqqOvL999+fkRwquSYf69/Qxz/z5L6u+/zzzydHlP2YK3NHDdYogpNRSdu1a5c82AF9KgqqAvyTQZhDDTWQ7b777ksV3rZByLo8/yKKg/SsAwhY/w4k/rsGH61tvppb61Rm0759+9QbHMamWYSPMj48XXNGuWlo1KhRWYf1Zr7yOi37yCqErayyVmT/ozewU/7k9WqdEUUEkq+Xa6SijW4c6CnCTJw4MXmPpn/5PQQVrXXJUGfV7nRI7bTTTrnh23OJen5f/xqLeh9FVLVePDSiyAagAsXbhgfIq9FdxwubMmVKRmGdYFRrijsfHcMCvY25SPztb387/aZ42pyg4+bdd99NH++6665aRLH1Dmerf9s9DcGxLtRMfNt8OBrFM0L6Z599NudGtxH/rAPdabImCjCNgcK600475WfNoeqEzrfyHN5xxx21iKK6YD5kDG3btk2FH7LKmvBz8+77QIvRsfXcc8+lVlFep7r8HM4GedWgcXKH3H/3u9/NTIr+QrPg+3vvvVchbGWVtXZr2vxPuRIhRC5Hk1x66aUZMSGITiHb7HTwUJrVadVYjzrqqNzoi8foIYXaro2j4Fz4EKX10ksvTfUZguNfuo7Khnv7yUdc7Morr0xEwf/4+H/+z/9p+L2eYlxcZnLSSSelWqox3d/ykWrJR9FZJFb7PPvss5N/Us/9xIfrDQrY8mcu1WMvvPDCzAjK/lFWcUIdThRgHT833XRTIin/zLeKAo4IJSEc1do1zzvvvMw61KL5R18om3VA45A96Ey6/PLLc2z0tltDNpToeDIv+qXVkQcOHJhojGP7DB/xYWYuZZl4/h/+8IfMHtVqZVQy0BVZhbCVVdaKrCnCUq5EAdEJGo0dOzYjtg4jvEOEws30rjpSBhJedNFFGcHtclHHEuGhHaUPH9W/ilO3a9cu/0YWIOpC3BX56HMiMaR98sknU7kTFfEtKjAU1ycskqojnnrqqRm51WPxUB1VfHRfNV7HkuBSO+20U3bH8FGEpyTXG7THr9VO8d1x48alf2qCuq7sZKGgX3vttRFRvGYEEv7hD3/IGrpaI+Q0BurgkARXUwGo10Wgn44lf2urYdmMt3mQrchOxo0bl3zWmoKcjswxpmr/atqymQsuuCAzP3oCJJehyIJkR7IacwiZP//88+zO00cPhVe0TlmFsJVV1oqsKcKq7+Fw1LD6DiQ8V1SjHNqNoFYnkqph1R+noifT4WIiPeQVfR0FQzWETvUveqZk6k7Si0vFLJvaGlUQEuLFS5Ysydqybh5REApCImjND6i9cOHCRF+oiPNRyKnSfLR7xFi419ChQzM6QwvK5/J8hD7u4/p+v2TJkuzrhqzMHOqd9uzqolDp448/zl5d/+bYUhmLuZSlQB1zCK1OOOGEvC7/zKG5KRs0pHVQ763fWq2Wc+hAORmLrjE+mkP81+b4GTNm5KtjoKI1Zt+r+3pOdVhdVOb45JNPzizC0ae+J1B8RVYhbGWVtSJrirB6VSl86pDy7c033zyRSKTAI/w/RLXrgbIo8j7zzDOp4FKJRTA8QzSk0uF7upFE0i5dumTPqnqga+NSZdtvv/2W6yN1dcstt0z0cCAXhBNhReWyau3eL774YnJ5PAxaUcupq7g9pNKLXe8jTcGxn/bOQuN684xe/qyDyrP26dMnFexf/OIXEVHUW+kS/MNLPbP+5BdffDF5nDqm/lnzLfNycDceDJXco0uXLvms/INg0Lhs5hBPl83ROrbYYot8DrVQCOcwdGtOPdY+Vdnlk08+meNc9pGOIxOUGZhDWQ39pmvXrumj0yso4Nb4iqxp48S+++5biygWmcUM2vfYY4+cRJK2iZPyKlsoGdjQ7kEHDhyYX3Jpn0ll0idFZl8YjQPSnBtvvDHTJ8JAufTx0UcfNRSkf/SjH9UiijRXC51m/F133TVTbj66vzTaae18NJFEqWOPPTa/BCaVj0oX0idnOllQymB8HTlyZAp6fscIOvWNBT/4wQ8aJthYCo7f/va3c2FrbpEGlt/La/FqRql/Num/zdsCNv80dzjvSOnI3GlOufHGG3NzPwGTX8vzLyJir732avDROvUsu+++e4qPfNQcoozi+ZQhCV62BR5//PG5Tol0AK0cKLTFmkONPdbv6NGjl1mnTDAu+8iqlLiyylqRNUXYVVddtRZRIIko6OS7Tp06ZQpHVEGwRTkpmeYIUbu+oYFoA5mkS64FlUUwsrloRDDo379/po7uL1q79h//+MeGyNW1a9eG7XXKEjaJr7LKKpmmSHVtR/M8Bx10UEQUAhaBR/vd2muvnagISZUANKgT0iCgjAAyQ+ttttkmx4ePSkUa1+vf7ubtfMQMCD5gwICIWEoF+GcOpama/QlInlmzjOaHnj17JhJBX+mf9kYIxk9zB5mV9L72ta+lf5Bd66Ts54wzzljuGwiJPVCLryuvvHL6xEfil3togiEsQVqZwJprrpnPYY7QPaUyz+ffNU7w0ee/8pWvJK2Tnck0UJZzzz23QtjKKmvt1hRhr7vuulpEcRSGQrCcfOTIkSlAibZ4B9TUCO04EZHVMZM33nhjFpQJUTgVqR9SadjAsaCW0tINN9yQ/EIjNi5t21398SkREXfffXfDEZlEByWOG2+8MX3EXWUa3tejsR1f56PWxOX56D7ELT4qM/ART6QV3HTTTVlucS1j7PdjxoxJH6+//vpaRCH+mEMlqpEjR6YApZyCg9mgzT/lH8gxfPjwfCacn0BkrspzyA/+OaqFTnD11VenlsE/KKf54ZFHHmmYw1tvvbUWUWxrtBHf9rwbbrgh/bYeoLBNHzhteeukMbjtttuSExtvPvJJNkSE8ty+L3y84YYb0hdCFu1H88udd95ZIWxllbV2a4qwuIGSB24mOsydOzfVRu1rricqU+E0fUMaUek3v/lNcjGog6Mq0WgMENlFSfzZ80yaNCkP9/K30ENZ5e23326IXF26dKn998+IKEoxEKOlpSVVcj6KgiIojq+Fzr9rGfztb3+bPsoeII7nFNn5zkeHBdhmN3HixBxjpQFtjjKAeh/5Zw4p2cZs3rx5qeRSwW1fhDqyFa1+xpi/Rx11VG6TM8/QmsmwzKlnVbaSHbzwwgv5rMZGSdCYvPXWW+h2anoAACAASURBVE3n0PNaazNnzsw5NO4Q1HPSVmRrfOTziSeemDqIdep+xsuxO1Rj60XDkTGfPn16lir9jexFpvrOO+9UCFtZZa3dmiJsZZVV9r/LKoStrLJWZE1bE3v16lWLKHJ2qhvVdPLkyamMlTkKPqQ2qdm83JLWo0ePbIHTAC3XxwXwDZzE79WubHfr3r17/k6zt3qg+l/fvn2X+5oH3VsUUgrgc889l77gip7Hc9h2Zrufeq36Zvfu3bPO51l1PFGU1V35iL9RGKmpXbp0SSXePKgt4sm9e/dOH707lT8+i19PmTIl/SjzaByNkqqGbPOHGvIGG2yQbaLUf0qpa1sXlFa/Vw/W+datW7dUcI2R8VX3/OpXv9owhxtssEEtolCHdRTRLyZOnJjjiZOaQ3NrA72NGzYU8Kt79+5ZmzeGeLfxck1tmO6pqmAOu3btmi2S1rptkGrfm222WcVhK6ustVtTDjtmzJhaRNHZpJNEPalXr16pjKlz2uSuk4MKJ+pQNuvfK6tGRkGFOpRRR3E41tNPXVS6UzbeeONsYHfYGZUagk6ePLkhct177721iOKoEEebQK8+ffokWlN/qcIQVZZB+Suj5j777JNKt2zF34jSlE31a88DmSHAxhtvnJGej+qCNhDUH+V622231SKKKE+xrH/pFv+MkW16GvIp/ioCnp06uu+++6ZKbC7NrzmkTuv2cSjagQceGBHFgeObb7559lk7OE5nFQR9/fXXG+bwnnvuqUUU3Wk6j/z9hhtumD5ah1RhXUm6kVY0h9/4xjdy3VunkNV91FTNoY0FMkgbCnr16rWMj6oTMqzycbysQtjKKmtF1hRhu3XrVosotjXpe8TN7rzzzoxAopocXMQ87LDDGq4JjdQXv//972c000eJZ4hkjiLRf6zDSFeTeujqq6+eKKCuhSPp83zttdcaIlfnzp1r9c+jW0kP7L333pvcxAZpWYXtf3ivsSxvT9ttt92Ss9mYjg9DoPLWOC/Ahno6iDp06JBIKguw7Q9Prz+EerXVVqtFFNmIcXCg2v3335/+2QbmuFPb2/BuGYZ6qJ7q/v37Z+aE5+rgwllpFtZL2T+ZUPv27RMFZSW0E+vvueeeW+4c6j03hw4JuPPOOxPxbQmUETi6CG/mI/7uEPB99tkn16mdPOU5dJyMe+Hz1qRupk6dOuV9XEMmpbe5XGtmFcJWVlkrsqYIu+2229Yiin5WCPPss89GxNIopc/YdXBYh3yJVLibjiMK4JAhQ7LPVbTVlYIrQtryMZd4s66Uo446Kne1QD2Hg+EV48aNa4hcffr0qUUUCjW+JFN49dVXMwozOzk8FwVUD6x9kPjIkCFD4vLLL1+uj5BOpIVeZdONdMwxx+TOGLt0+Ghn0pNPPpk+elEUZRNSy2ImTZqU2RBOSjPQySQ7KL/4Gf8+8cQTU0fA5yAI3cP9dPaUlVVZw8EHH5w92DigMbLWyq9i3HLLLWsRRW+5fbsQeerUqZkNWSvGTLYkK7JOPacMYfDgwTmHntX6M15UaV1c1oW587khQ4bkXmZcnrYgmylnEaxC2Moqa0XWFGHnz5/fgLDyfn3Ae+21V0bfuhpZRBSHWInaOAxUsiulR48eWYuyJ9IODxFVf6WIrofY/6sftrS0pMqHE4luIvnEiRMbItenn35aiyh4EjVRpN1vv/0yKvNRpoGPiqSiZN3rFiJiafSGvlAAx1PTFOEhMD4mE4EE8+fPz+ehDuN8OFY9/1m0aFEtohhbY0Ul3nfffZNX4mYUVPuQcTI83xxShDt37pzIaq14bjt6zCHdQQ0XEtefEuF3+KZsxHO8/PLLy51DmYfnNub77bdfIrr6qnXqaBjrgxZjfGQVnTp1WuZQOn8jA6M046x89Py4dUtLS84hddg6ZSvisE2/sOPGjatFFFK89IDNnj07t6FJIQkFJlCKafA0WPhC33bbbZm+SksVlTVqW1CcJGQpa/i5zTbbrDC1lK58/PHHDQPx1FNP1SKK7XQmiIjy0Ucf5UJQAiiXQZRkfEHdy+n6F198cW5nk/LYKO/LLgWTNiml+ZytY/37989meO8xEhQdgzJ79uz08cEHH6xFFG86t+jM+5w5c5Z5d5A59OVSkvElJIzYlH7NNdekPxogvGHcsynvuaZ03hfce3G+9rWvZXDyGV82c1s+PsU61WRiDn3B58yZk/NL2ETZ+GQOlW6Aki//n//853xmc8JnmxU8r40jUnpB2hnIffv2TYpgDpkgMGfOnColrqyy1m5NWxNFOluOoCQxaKWVVsq0jFAAUaW8hBdbk8qbzrt165bpoCKylMPfQlqR1qZiaY1IOmLEiERfKZhouKJMgoCmRCLikuA7duyY6TWUkjKK5HyU8ni7mTcY9OzZM8U34wRZlE4grGgtldTEQOj505/+lCUIKRihDZrVmzkU0aX65rB9+/Y5vrIf2yihi0xCI4Vnrt+6KCOR7ThkDLIZT5TFHCufeYYRI0bkAW4+I8OTjZTNc6Mi5VJYu3btUohSTiIcQlTz7loyIdnUeuutl/fXqqkN1vtybcp3oB8xlcAFTc8444xs/vE741ROjctWIWxllbUia8phN91004bGeJuNHai27bbbZnTBb0VYRWQCBYGG6IEvTZs2LcUcUY0QgOdCbZyQzI8rOCf3s88+S6HKJgPoDcFeeeWVBhhaf/31G3zUFFJ/b40axBARn5CGF8oiICtfX3/99WzuxtVsSvCz/pzmeh9lKs4xbmlpyTH2O+imnFTfONGvX79aRNFkbg5txthqq61SO1CmgO6QgQhYPmSMn2+88Ua2ouL7xk8zB3HFNYg7eL+2vZkzZy7T7A99lQSfeeaZhjns2bNnLaKYB4021kCvXr2yrGOdWmuagmRLDtaDrObwlVdeyfZQZTAH/pXfPMBnJS3fAQfUffzxx1lmlMWUj9Att9CyCmErq6wVWVMOqxBPpsYxRfgvvvgiSxkQTJucxnjbuCAXNdm///vf/848XoSiUoq6+Bt+CqU0Ujiysl+/folYMgeoWP92tHqDCCKc/+frkiVL8tn9jeMzlUFEUK16yl8Q96233kp+Q3H17I4OpQUonUC5DTbYICKKNrt+/frl2PHRz7KKH1HwLM+Oh8uMPvvss3wrgjmkhhpLJSjlNMgKMd57772cQ9yYouuIVO2j+Deeb2MBVXmzzTbLE/P5hRNC+rLhw/7O/+Or8+bNy2yoPIfupUQlA8H9bbebPn16ZkE0DXMoW6E0azOVmckuzOHWW2+dSM5we5neiqxC2Moqa0XWlMN26tSpFlGgjVYvtaKpU6emciZf12Rusy70E42gj0g2d+7crB+K8DiAuqvoSJ1Vh1QHVFNbd911U/V1GJjXKdjOdPLJJzdwg/bt29ciCuVZjdE1p02btkxjhG1cNkqLqCIs/uP3b775ZqrQlE88ndKL00Fg98f5fH611VbLjQ1UWqjsHTNnnXXWMgeJy4qMNW42ffr0bLjAm6FieQ49u4zCnM+dOzfXBHVevdO1vb1OJkG1NlYQuUePHg0thRHFmpIJnHbaacudQ8o0HcCY1c+h7ZPGSosgH2V51GK/f++99/J6sgVzSCexPoyTOfQ8fOzevXuuYRqFz8pQTzrppIrDVlZZa7emCHvuuefWIoo8X1RwbMYaa6yR29fk8SI3vqneKlLhFbqbLr744uSCIqRtdCITHgod3F/Eswl64cKF+TyUTGiIO3/xxRcNkevss8+uRRSKImRWH+zRo0fWTCmNUIGPOB4EwKGoySeddFKOC/5t3KFI+WhUbW3GT/fXjBkzssNGNgERIe+SJUvSx/POO68WUaifPoOPduvWLccIZ/UMtgeq3Xom7ZR457Bhw5LP8o/iTAXWPglRNMyrg1ofixYtynHDH3V9WQ+LFy9umMOTTjqpFlFUIspzuOqqq+Y1ZVJqthCNj6obMhLdU0OHDs2tg7ip+j80VkO1TmUV1octdEuWLMmXgvksxLdO6+ew3iqErayyVmRNEbZ///61iIJv4rJU22OPPTY5lqiCB4m4IhtU0kUj8q600kpZs6Om6e5RO7SxGgfQcaPzigJ81FFHJdJr5qb+6U7Zf//9l7u9zkZlr+xQDzvrrLOy3kalxN3UQSnLEE4GoLY3Y8aM/Fv8x5jyCRqoJaot68mGUMcdd1x20JSP0qH8DhgwIH3cfvvtaxHFO0t1KVFnTzzxxFSd3QuPk/nIJPBUfNt4vPfee8k7zaEaLp7NP59xcJzeafc+/PDDs64JwawVVYsjjzyyYQ579+5diyh0CjXO+m43NV1rhg9+79qyC2q3DGDhwoXZB0ARxzutMVkLn205ND+qLYMGDcr1iFvLPFQHDj744AphK6ustVtThLUxWH8rdVC96+CDD06EVYPUOWLngh5JER0S47BPPvlkdujgEeUeYshFhVUH05/rno888kiqgCKj/mMHZJ1//vnL7XTyvJ4Pp/zFL36RqrUtb3pd7QiCQGqtdnrYnHzPPfdkxIYkuouoxNRz0ZtabPui7p3HHnssEd7OF+Pgs5dffvkyx5zWdUFFRMG3Dj744ORmUIeyTu1Uy5UdQBScbOzYsVmjhKB4vy4syKUSoA/cHOPHTzzxRHI/cwBp+XfRRRct95hTWRFtQ23z0EMPzYxPJoWr8snhff5fNmGdPvzww3k4gzHEc80R7qwjS1XDGFg/TzzxRL7e1Bx6Vir6lVdeWSFsZZW1dmva6ST6iRx6d/X2jho1KqOfWineQ+0q111FdlF7yJAhGZ3Lh2zb/SLPF4WpsPgPBNxzzz0z+on0UBlylk1EVTNzTZHu1ltvTUUXkvPRfaEHpU+fNDV38ODBycshDy5F+RSlKb4Uc37ovNlvv/3y+jh++TWd9Sbam0PdUHar3H777VnvxsFcn+Irw5FFuS9fTjjhhBxnqIO34WZUY0im0wlnx+X333//VE7LO6co5mXDVSnu7iUTu/HGG3Od4tbUcjudXFvNV1bpaJ2zzz47szSH45vD8lFB++67b0QUmo+x0MH3wx/+MOfdfMgeZIYrsqYpcdu2bRveu6msg0z/7Gc/y7RFOuVvTQjotyvfAPgy3nPPPfkZ/0YwIodzxmAbTL/XwLBgwYIcAGUDqaAv4qOPPtqQarRp06bBR89FvDrggANy4RCT/C15371MENFDsBo+fHiOGREOrfBFshWM4EO8sQikZi0tLRn0LEiFe9esP9OpXbt2DU0FAprn2X///fNEBGmq0hs/BGiCiNMrLMBHHnkkmyyIWp7RHBIqbXNDQcyPbZDz5s1LgU1qLKUUWMpnOplDz6t1UqnuV7/6VaaefBSMjMv2228fEZHnNqEs5vCSSy7J5n/P6mAFIhMfBUPzY70q5SxcuDCDChEWOPJ9/PjxVUpcWWWt3ZoirPfOiJaEI5L0mDFjMnJJ6URrBF2rn2hDfBFRHn/88Syb2LZX3nQAdQg2Iplyj3T33HPPzTRFdBUh694/0xC5nNvLR8LRLbfcEhFLkUhaSiBi7q+Bn4+QQeP+ww8/nIjtb21gJor5rEyAOKN5QVp66qmnJjWxuUAbINSsP3uZIAOd+AdJ7rnnnkzDoJ1x55/7SAkhiGuNGzcuyyNQSIal9OSzSjQQFlUyTxdddFHe35ZElMP6eOqpp5rOoWt7pquvvjqFsvKbKJRmlN08p8yQkHX//ffnOlVWROEcHFB+9y5ahS6iXUOGDEkfbUvko62F5XOrWIWwlVXWiqwpwg4dOrQWUfA67X7ayHbZZZeM1Fq45Pd4KNGB+CE6EVHGjh2bErtmASem24yuMQES4M0OtYK4l112WbYp4n4iKp7x9NNPN0SugQMH1iIKBCOWibxf//rX00fPgcOJrIQTLZUQiSg1YcKEREqcCRLhi4Q0Udi8ONSMsHLzzTcnKon+fCRuPPLII+nj73//+1pEwZXNoY0J/fr1y1ZDQiAhBjoaO4gKFQgnEydOzK1jI0eOjIiCI9IuNNlDOlkCHQC6Dx8+PMtFsjAcnX8PPfRQwxwec8wxNc8RUWyYsK769euX4+3AOT5apzIz97RO8fTx48en6EbD0OCBt1uPeDuhS2mTwHnttdfm+PNbaU456R//+EeFsJVV1tqtKcIqulPWSO9+zps3L9FWhKKYyuNFOQomlHTNzz//PFVeXBV/9J5Vzyii42yO19C40Llz54zCSiwipGi4cOHChsjlHbhUQhI9pFuwYEE29ePQfBRJ3av8zhvNDp988kk+o/IOLouziMZKRmW0oDAuXrw4nwMSGQ8tnosWLVrm5H+8WvnC9efOnZulJHqErZB4lnKPOcZDVQY+/fTTzDrwOIcPmEPzr/Qik4Fkmky6du2a95HllN/1u2DBgoY5tE61xfKNry0tLbkFEbLjqhBfZka9xSmNzWeffZab7yndOK0jYzQ/UKmVx2wDtQbatm2ba+eggw6KiGITgPH47LPPKoStrLLWbk0Rdp111qlFFHwQskDVKVOmpHIKVURDn8G9FKopmY7o6NChQ26jwqUoyep/OKEaItSBbKL33XffnRESVxHpqYJvv/12Q+T60pe+1OCjuhhUff7555Mryhr4oj7o3/0eb9M40aZNm9zszH+I53R7vJDiDWm9Jxba3XXXXanEuz4FGkLWH7RtDj0r9R7nfPXVV5MjGiOIgFfxD5I7qhUPbNeuXarwMhkIRv22OUDbpjk0zpDvoYceyuewVZLa62+mTJnSMIdrrLFGwxxqCdQE8dJLL6UPEF7mxUeqvG2QUBPyt2vXLpVk6rVauezMFkbXsB6sUz0C999/fx6Nq74OWf3+3//+d4WwlVXW2q0pwt5xxx21iELJFP2poiuttFK2p8nBNcRTMnEEn9FeCA2eeeaZjPbqWtAad8IZtOBR9vBh3GCnnXZKRRPfgArQufyqDm8o1yWDH+puatOmTaqnOImsgY/lWnT5LXtPP/10bkqgohtLv4fsEFd3jLEQifv165ebJGQRtn1Bj/pXdYwaNarhCBxZAq7ftm3bRGqoQ7m0Jc6YOMAMsuDdr732Wv6OOoyjaXg3Jn5CNuMATb/1rW+lz/wzhyoL7733XsMc3nXXXbWIQlHHsSFymzZtcm1BeutUu6l7ykRkd/XvHbJOPZf1Z027phqucTSuKilbb711+u25ZJHW76xZsyqErayy1m5Nm/8ppSK36EydvfDCC7MGCOXUsWxjovzqjtEvrOY6cODA5MT4p57hMs9Q9xTpRVwdMBdddFEqsziJ2mgZ9Zj6pEzAMZPqlWeeeWbyMPxP3Vd0xpmgJTTDLa+66qrcQI6jQFJqpHGkNPKR4un/R4wYkT7KjnT4qPPVm0jOP+iDjw0bNiw7ssodTFRoaIOrUfEdmXPUUUelz/yjd8i8+IWf6gNX49dQf8EFF2RmxaCccS6bdVrO5nTZDRkyJDMA2gEfZQB81N1Fh6ivWODEfLC2rUPbHnFYiEs1dq3rrrsuFXB6hKxxRe8HzrFo+q+VVVbZ/yprirBqhfJ66i2lb9y4cRmx1ZNwGNusqHM6TSjC8v5zzjknEdUB1iKmGi40oAJCDRuvqW+77757Kp7uA6FWxNWhM86oX9rOpKeeeio5o0PHvEsWqnguiEMt5OOwYcOy/moXCGS1dczzQhP3p6ZSiffaa698Vuqp7hhqeb2pFZpDY+0z48ePz8xJJ48dRfQJXWoUYD8pvEOHDs2dTHbt4IQyA2PCD37pBqt/WzmNwKZzlQf90GWDXK4BLekWf//735OHewWkbX6O1YFsMj+cUq354osvTkTVtWUd6mKjlOsQ06/AV9nbj370o5wP1QP1/xX5yCqErayyVmRNEdbxprgDxMA3vvjii+SAOJkIoVtFfyUEUW/Etz766KPkC+6H74mUojLVUuSFwKLnoEGDsi6MX+ITlLuyQW8KNG4h0kYUyp0uHnyMEkpFx+1lJvj8Bx98kHtJ77777ogoIr2shdJMeTZOeBMdod5HiKj32hzUm2ehtOsw0zUUUaC9DMccQgS9s57dT0i4aNGirHfrtjL+1G+qO//MIT6Od55yyimprkJwfd74ZdnKO23sjtEDvmTJkux6okcw61SnlqNSZV6Q9913380sUb8A5d664INsgw6iW0o2etxxx+Uc0nr0I+P+K7IKYSurrBXZ/+gQNrl6+aiOvn37ZrTRzYGDQRsRRFeMn/WKnyimzkmFxUHszrHHVV+taF3fzYTfiZwUUL2iQ4YMaahvbbTRRrWIoi5JrRUNe/bsmSjlcDKKngjKR/wNx+FjmzZtMnJCNv+GY1PIZRmuCalE/AULFiT/gYAUV/XU4447bplD2PhjrGUHffv2zQwBN6c6Q3l8DwLjfbKkzp0757jJaGgJ9A5qqMzFvdQ2jfGiRYuW2Z3jNA5r7JRTTlluPzgf6Sb+f/3118+swBxap2rYfu+YW2uM9hBRIDhOqu5uv7T5MIfWnCyirp89Eba8Th3qXl6nrOkXdrfddqvVO2cy6tM3qZovii+1NIHYICWzyG1wHjhwYH5W6ltuOTRA2sR8oW1b0iRx9913p+jltHjpHR/KjRN77LFHrf5emtJJ97vsskuWC4gJFqovHeFEeitF80U49NBDUxDxxbS9ypffwuGLRcpHRferrroqfdTe6Qvm+ebMmZM+7rvvvrX6f5PyEeq+973vZUA2D77UxpkAxl9CiWaTAQMGZBnNAnSivvsQCPmHLghymlNuvvnmDOoCX3kOP/zww4Y53HXXXWsRhbCoJdBY77jjjvnl1eyhzOcsKmDhSwlw0I3DDjtsmXUq1VVu9BnrlFgn0BHxrrrqqtxmqWHENayHuXPnVo0TlVXW2q0pwnbu3LkWUZQCREfo1alTpyw8axKwjQkqOo1dykPcgcQ9e/bMzdvSRShQPoFei5ooJOJ552n//v0zHRHtNGqIoGeccUZD5OrYsWMtohC4IJmUqEOHDpm2eT6igjY3aKmBW9QWYTfZZJN8RtI/lJLWl09ihCZSJ0i0++67Jw1xf4IJn+vPXu7WrVvD1jPRv94/abfN51I4qOgUQP4TtzzbxhtvvMyb171NgADk2Wws4J+mGOWuLbfcMlNxYpP0WZnk97///XLn0Hoxh4S9Dh065DijFta0pgYCHh8d76LxZosttsh02TyjaHzks3GD5o4BIsDuvPPOuQ7MO9FJpnX22WdXCFtZZa3dmiLsjTfe2HAEKHItJ7/xxhuzQA5NCB8OaiOIaFHD5RwNcv311ycn1vqnVOR4GTI/AYPA4bA2PGnEiBH5jBAc38QRxowZ0xC5br/99lpEIYbhYJr0r7jiimycEG39rfu7l/KIDEAp5/rrr08BBbdXKnI/13YvnFBUx6NHjBiRZSw8FxrgbH/729/SR5sbzIeGCZxx9OjROd7uKStxb2OojRBy4Og33XRTcn481zU9qxIQvzWo4Og2/48YMWKZQ8xkZcaqfMzpyJEja/XP6/N8HDlyZHJY3FlGAPWsU007fDSH11xzTbZdei7XtC41iZR9xld9V6666qplzlz20xyOHTu2QtjKKmvt1hRhvb27vDEYorW0tGTUwSeVHKAgPqcUJBKLTieccELm8aK/a0EqKqVrQwL8WcH6pZdeys+4P1QQWd9///3l8h88Q/S0NWvOnDnZKOG5GCneuEBYPvJn8ODB2UYIQcvXws88h+fdbbfdIqKI2pMnT87ob16Uv6D0Rx99lD527dq1FlFEfyqoEkRLS0s+r3KKNQEFNczje1Rp/g4cODD5dPlN7BReGZZSiH/XjGNNmeuIgt+W38vz1ltvNZ3DciPDnDlzsgpgHKjmSkDGRbmrPIeDBg1K3cHa5Qs+rmLi2uaQBmLup06dusxGFp/1POV1yiqErayyVmRNEbayyir732UVwlZWWSuyps3/m266aS2i4E+analikyZNylqc7hI8Av/QgK37Rz0Wd1l77bWXecM7RbXMEfAc96BWU2tXWWWV3ObnmbWkqZ3tsMMOy323aNlHnGLKlCl5PzyzXF+zvcs9HNKFf6622mp5mBrlki/lDhdqKY7LR1veVl111VRxcTu1Q3Xq3r17L3MIGyVeZw219rXXXsu5opB6Fr9Xn+WfWqb6Y9euXXOzO3WdBoDPuWZ5DnUzqSV37949Kw2UZvdVy912220b5nC99darRRScVQeYz7/xxhvL+GgOrTHtre5lndpe96UvfSnfC6zOa0z5wug6ru3vKdOrrrpq/s4608egDr/VVltVHLayylq7NeWwN998cy2iqMupk4qem2yySaqw+it1kIjC6luQQ9ShHu61114Z7aloUEc9jUoJHbxIytYzR4duscUW2Yesc0RXjvps+SVDd955Zy2i6H3VESSL6NOnTx5V45mNg64eWQY1WfTmx6677prX89M4QHZquo4fvupXdSD32muvnV1K6o4+K+LXq6j33XdfLaI4LtUxJvqH+/btmz3iGvQ1vNvETe2ktEJN/u21116p7qqdGwOoR4VV99Tk7rkgXM+ePfN35pDqW/c+1uXW0o2LrZTGeosttkgf1TnLPlKgqcNlH/fcc8/cqGFd8tFzqTCcfvrpEVEcmwSZ+bjeeutlt6B+7LJ6X/aRVQhbWWWtyJoibPfu3WsRxe4ZO1kc1oWXRhSby9Xb8FFHQopYaqn6VfWxRhS9sRBXJMMFfdZB1riCeuHqq6+e6O9Z8Rm9zfWvYvzva9Yiip5eGYMo/Ze//CU5tM3f/gZ3xd+YeqYe43322SfRSy0TaqtP62VVn3TEjsxFzbdLly7pN04tkuN406dPTx/V0h0KYIxxs3r/HLLGP1vhIBceah6g/w9+8IOs3eqFxRUhrvvj5rrnynO48sorJ5K7hl5i6+2f//zncufQWtI3br4efPDBREM+Qlz83Bjyg4+yuN133z1/ZwcaDUFmaEuk8bSrquxj586dc8uoO6gYkAAAIABJREFUOZRR6aOfNm1ahbCVVdbarSnC7rjjjrWIQqm0h9ChWFOnTs1XQrqOqCb64He6QyAIvjVkyJAYMWJERBTcGI+ActQ+fLjceSRa/fSnP80eZZzUxnEHpD344IMNkatfv361iGKvIo4HZSZMmJDPp8PHsSd6Q/FRXAbC4u2DBg3KHTY4nWfWJ6tXWE+2LiaoBmkHDhyYqjgeBFkcSl3/qkL+2e2Du8k4Jk+enGPDdG45GEw3Dh1AbzfkOO2007Ln1nPy3fi7lvnnPz+tnyOPPDL3SlPsHdvDh/KrGDfffPNaRJGV0C1kEy+88EK+dMwc8hGXpyLz0Rzy57TTTsuKh7UrE3C/cl99+btFAxk4cGD2o9sBR3HH8ceNG1chbGWVtXZrirDz589veI0FbmCnwa677prqmjqSUxccsgV99P2WD+5ebbXVMqqpNeI9oqJDn3FafLnMCz/++OOMoHg2VRCHLqvEn332WYMS7kQMUfQnP/lJflZtDg+y/xEKlhVmCvCqq66az67uKIsQtUV0qCW78Px8nD9//jK9vlBKxK9/4dcnn3xSiyi0BNeR8ey7777J7+xDdToIFOa/fcC4Jf+6du2a4wX96RDUbf6pw+LQsioqc0tLS/onozMG/Kzn6P/978v1UR/wfvvtl3NkD2vZR9f2XNan3uLu3btnBqWeak5lYL4LlF5zaE7rfbROKcuyRr9/5513/r8fEfPQQw/VIooN4r6o0pmWlpZ8t6tBIn37Eko9fNZp7Bb71VdfnV8WcjhZ3xElFpTJNTE2vkuFtttuu1xM0hKyvAL1jBkzGgbi0UcfrUUUwcIidM/Zs2cnJSCoESxMmIVlMXoGX/5LLrkkN45rENBAUX5TvFRSA0jZxy222CKb/X1GUJSS1b9bZ9y4cQ20xhfLvM+bNy+bHcyhTeYWnpMGjQ264yTMq6++Ov2zlaz+nb31P/kn3ZUaWmNf+cpXcmFrhNeYIAjU+1fvo3IfH+vn0HijOk7r55MvDspCDLJOL7nkklyngpL7GUtH67g/wVOKD/i22mqrBK7yW+aJkGUfWZUSV1ZZK7KmrYnEJRFdquoojoULF+Y7S7xPkyAjlZAOKFCLpKL0hhtumNFXKQjSlsUukczBWFJQCHPJJZfksSAEKimotG5FPops5beQRxQIoxRBSPH2Mimb7EHbGaRdbbXVcutb/TuFIormAOktMxZKWlDmsssuS4FMCmy8lmcQhejDP2JXu3btciO8e0oXUaDyQWqaO2QcPXv2TLQ3vzIWzR0yHn4QK60lwtall16aaG1dSEGVS8pmDlERmYGGkrZt2+a6tJFfM4Y1bZ0+88wzEVG0WFqnG2ywQYpthE3zQChC3axH9yJkKgsNHz48S2VEOILa/2szToWwlVXWiqwph1USkOcTjryvZdttt01uJfcn72tRRNTxOVK7SDZ9+vS8Hm5go7AiMn5RLvoTTrR8ffjhh1mi0ESAI4hkEyZMaOAGG264YS2i4NgQDT/bcsstM6Li0HioxgF8hICBg9XzJWfSisY2tGvch+gOHoPixtXhcnPmzMlzgT0jdML5Jk+enD727NmzFlGgJOHI9eoPF8MvzSlxjx6B2+LweOCECRNSzNEswF/tm7QMGxSUl+gAxmPWrFn5PLIypUEIV34De3mTCh/de8stt8xMhrgD0fytcdb4wTfzMWHChDw4TeOOdUus46PMREuttWWuW1pa8nnMobLSinxkFcJWVlkrsqYclgKMf4m8IvxKK62U7/KEZA7VcuwLZc+7UyErNJo2bVpyKDwDJ4HWog4+CokdDuZk+l69eiWykseVfkTysnlrt0wDP6tHHagrCuIfIqziu7EQnZWBZs6cmYq3jQOQBhLhW96Ehi87flV2s+WWW2YGwqAGlKg3/M+za66gAyxYsCDfzwupy62A/NPAoK0Sqv7rX//KQ8RwYxmDjRn80+qHw+LukG6HHXbIkiDD1fHvFfmIO+KW9ZmRVljjoNIAHfnomF56BJR+//33M9ODpHy2nVIDvxZFbzc0FtbR1ltvncjKzCE0XpFVCFtZZa3ImnLYLl26NDROUD2h5qRJk1K5E31FMsil5QqyuAaFd/bs2RndICmuhHdSojVlqBdS8KiDXbt2Tf5A2XYIti1O5UOo27dvX4sojuzU2K1wPmnSpKzneXualkBtZTZHqMO5N98XLVqUdT1qqsjOZ9EX4vANEvv8l7/85fSfwgmdcOr6Q6g7dOhQiyjqufg1lHzrrbdSuTWH6uBUepkDZZWi7nCxDz74IJsUNA9AZ/VF802B5p9Mi39rr712cmTagLcU2kxRf1B6RDGH1qnDFDzn9OnTE33xcnVu9VnjL8szxjYtfPzxx8u80xfvhtoaJfjIJ2vKOu/evXtDe29E8b2xTssH3rMKYSurrBVZU4Q977zzahGF4op/ijrt27fPjhbvjNVtY7sYpMINcDaodNFFF+WxGOqAIr4oB+FEOEjgXnjf7Nmzk8846gNKUJwXL17cELnOPPPMWkTBx6AjlXuNNdZIRRGnphxCWJHTuFAR/fsZZ5yRSMJHrXLQhI8yEOMFJYzXggULskvIFkJZBNRcsmRJ+jh06NBaRIHosiNH9HTq1Cn9UyOmbtvyZZxtQcQN1SoHDx6cmYBMhXbg+WkKnhFvxvfda8aMGamdqPtCneX5FxHxxz/+sRZRbHfkIzTt0qVLrgutoDINm0TUqfFPHVvqyYMGDcrsjb7B9BzgoebUGBuDww8/PCKWVjMgu++UNWxtlX1kFcJWVlkrsqYIu/POO9ciCo6kxmYz9bHHHpu9lhQ8PEfdFRqIQuqLapoffvhh/i0lVXO9jcG4IM4GzfU0U4APPvjgPJpGj65rULgPP/zwhsjVu3fvWkTBkyAE/nzCCSckOrs/Pi7zUJcVWSni9YdSuwYlEYJDdNEYH7eZgm/qqL///e8TCXUCQXjocdBBBy2zvc4WPOqzmu1JJ52UqqzD74w/NZqiCpVweXXPhQsXZl1XRxM05g9+Zz5sHOeXbWyHHnpoZmGyDs9l/R122GHLnUNZnb/DpwcPHpy+uL/xV+vlowyHjzLEzz//PHk5nYFP/sb/l/sD+KgD68gjj0z13Do1z3WvS60QtrLKWrs1Rditt966FlGgTR2HiIilOzKgDAWP2uYzamJ6e6GOnt/x48enCiwC4Q36f0V2yK5LRu0XOk2YMCERXNcOFFbPPPfccxsil04gSl99RI1YqphSrfEbaiy1EMcqv5YEf37ssceyzxRXgnTua2zVll1Llw/ePmHChFQhoZKN0bYUXn755ct0Ohkr48+/Qw89NLt7oDA/jTfVGHpCKf3Q9913X2YOuqIotvQIaiglnQ+yBPd+5JFHcq5kYXpw3eOSSy5Z7hwaK6iOUx5++OG5Tine3o5OtccvzZM+cUeuPvDAA1mHtUb4SIGmsUBgc6gSIFMcO3ZsZmnmsPyy8CuvvLJC2Moqa+3WtNNJpKAa2nQtOt5yyy2537R8gBcFEzpSBaGBPYLHH398Ri59tiKYSC4LoKzZnWKHBe64++67535cnFpUpnSWTSYAASAz1B89enSiBB/wIb3NPqu2B72oxGeddVbuSvE7vIwqyCiblEjdS/w66KCDEvFxSJ00kKXeZDaiv51FussoqxFFXRVX4x+eTammCON9gwcPzjUhOzKHFFtIimfquJKtueaPf/zjXDt4ncxBXbZsUNs8WKfW1fXXX598mJKvMqEjz2c9n1ouDWbIkCFZo6WOez7jbm3/6Ec/iohiry+kp3rvv//+ObY6m6xTyL4ia5oSt2vXrhZRSNwGTCvWwQcfnAvAWU7asXwhiVCEEvK6a1133XX5GQ6axPI5R1rCpC0Wo7Rl7ty5+YXTeG2i2AMPPNCQarRt27YWUbRbKiVp9D7ggAMyrVb8J0z5KQ2X5mpSULK44oor0n9Bp5wqSs08t0XIRynbvHnzcsylVVoKpVdPPvlk+sg/wVDBXlvlAQcckEFBKUljujFxH+UcAdOivv3223PThdZUz+R+/JHaE4SIX/xbuHBhfuGsHc0Xfl8+04mPng+9IPoceOCBmaqjbP5Wi6aAyncB07VGjhyZJ0vYyA6sBCXr0jpBQ4ipSqDz5s3LNWVNGw9z+PTTT1cpcWWVtXZrirDeO6MILtpr3P7rX/+a0QVZFiFI3AQNKZhIJmo+8cQTKa3bBF//rpyIol2sfjNxRCEgSLsvvvjiTIWdJaRlTrStR5+IiDXWWKMWUaQtkA4CXXvttZn6SJ8hkm1+0itpjTIY9Bg9enQ2LmjoIMZ5Xp+Rfmqil4bz8eSTT04qoJQC6ZzMWL+9ztnS0ADS+dvbb789/bPJw5qQOWhUt5Ff2i7zuffee3MOldUgrWyJkERYtF7Kc3j22Wen2KSZpZz1vPDCCw1zyEfPDdGU/2655Zb00fyiJFJk1ED6rezjWmPGjMk51JIosyM6ScGlue5FxIPWZ511VpZ6bC4obwYpnz3GKoStrLJWZE1FJ3K+qK9JAufcaqutMvpBAvm9zbqK+gQsDQGizfPPPx+HHHJIRBSHVuEXOIMyCqRVbMalcIdx48bl1jecSoSE1mVTXsI3tRA65mXXXXfNN+Dh5fXljIgioiorGAuo88wzz2T7nzZCaEEf8K4ZY611UZsetJ4yZUqWB2QiMh8lonqzVU5bHbHD8+y55545JzQEghCEgkYQQwlGZvHiiy/mfQgusjDtnMQ2GRnEJ1xZHy+//HKOkSYCvpffWs+sG74RrazFPfbYI5GLjzQFCMdH9/r+978fEQ1vjMgynS2CfJMZmitZHT0FErv2yy+/nOKW0pDsDVqvyCqErayyVmRNOezXvva1WkShelF2ccnZs2cnByi3JkIdLX8iGOVX69rChQuzFIA/iH7at/AgaO7wLxyO8tqjR49sWqB4Ugevu+66iIiYP39+Azfo1atXLaLgZcosykCffPJJ8ku8Aw/XgibiKufwUYvi559/niig/IVj+1mngEZEUR6DGvvvv39ELEUZHFoZCaJAyk8//TR9dCq+OcQV+bdw4cJ8TlqFZzSW3h3kvubWmC1ZsiS3HDoFXyWBf7ZhKqN4DiUqGUaHDh2ynCfDM+8OBFi0aNFyWxNVKrQXev758+cvcxSMv1VGgXg4tmfwuYgCya1THFsmSPmn2yg3mlMNNl26dMnrK5VZd65Z9pFVCFtZZa3ImiLs+uuvX4soFF2RhTr66quvJvdw2Br0xVHwKiigQR2/aNeuXUZjCipFmUopKitQi1jQW03rL3/5yzIHVbum37/++usNkWv11VevRRStYWqOWi1feumlZQrztkThxVoUoWF9jTNiaeQVdfkPiUR6Siw0wTkhK2X4zjvvTH9xIyimPvv++++nj2uvvXYtouDEsgMZyAsvvJDcCtqI9uaOeiyz0biPs7Vp0yY1C+oz/6A3HQDq2fABWT37bbfdlpmK61OejcF//vOfhjlcc801a56j/t648Pjx43OdygQhuzmkA5hDftj217Zt29Rw/BvlW/YiA5CJWifmUGPF6NGj00dqOr1AplX2kVUIW1llrciaIuwdd9xRiygORKb41h8U5WA0+branMOT5eo6jkR4KPHiiy/m0S/U4fLRIKKjrhCftd0N0uy+++6pslIfy0ePfvjhhw2Ra9SoUQ1dMprzcZm2bdsmWru2qIz3aBFU69PNo57497//PX0RscscSbSmAZgX16Sy9u/fP1VIarXIb35mzZqVPo4ZM6bhdSt4t+u2adMmD+5Wz9XqaIuZcaZg4rgQ76WXXsrfqS/bZCCDkLloYax7T05EFHO43Xbb5f08FzUa7545c2bDHN5zzz0NR8QY2/pD6axT7YFUWZmGcZFp6SOwDp5//vnk7LJDWoXNJ5r9jYXavszQmtx2221zDnV4yRqtv7KPrELYyiprRda0Div64R0UV0rWyJEjk9/J06EhBROfUENVO3SMynXXXZecgAoLnUUd0U7erzFfNMKPLrjggjwOFIfGa3TplA2aOzBLRMU5zz777PRB/VGPqGZ5GYLnpiZSsQ8//PDsdsHVIA3OKrKX33IO9aDbyJEjE8GhsH9Tj643vLvuRVIRURyZc9ZZZyWiGmd9rTgg/yEM5FPbHDBgQHYKGT9jIiuxxcy8yFJkZt5UOGLEiKwgyMqMiQ63suHcUB2y4snnnHNOdiFBfJzVvWQ4xkBGY60fccQReVAankljMYfq8OaMNmDjBp56xRVX5H2tT/NND1mRVQhbWWWtyJoirLoelVAkxtkef/zx/G+1SB00kFRkFYH9xP8uvvji3KECmcqHmkEqkVb+r0sFV9x1113zfngdhMF7y+Z5dNHocYVITzzxRN5PrdFBanaz6KbSAUaBFM3PPffc5DUOKqemu58sAbpBfNFa9tGuXbuM5Hz0t8vTI9Sf1QghH07/j3/8IzMnqrYOHl1YntHvbS+EuJdcckn6qkMMJ4Rk/IOssiNKKvW2c+fO+TcUfutBPb5s1ilEpnXInh5//PHkm2q7ds7g9uU+ecfmqIgMHz48D07Q8eS5dEUZF/ODe1OerdO99torFXgb640H3rsiqxC2sspakTVFWPUvuTqOJqJ98cUXGcWgr3onHuHwM9ESdxK1P/jgg6wjimq4onyeWolfQFYIV3+0DITSf6zfGAKUTRRU+/V3epEjikgKUURlvnpVCN5rZ1D9kSrQt1yjtSvELie+Ggv1YKru0UcfndFZ3679tzKVeiu/jAr/pRssWbIkswlcFZKZQz3UeqPVjHHmN954I7MQ/dWOYHFfz09RdS3jQAEeOHBgagS6gIy3dVg2a4pKq/arxzei0GFoKfQQPsoMrYdyxvPmm29mDV3GR7ehj8iCZIbWvDl0r+OPPz5RmP8O3l/eHNZbhbCVVdaKrGkddvvtt69FFPk1xBAN+/btm1EHv6MCiqAOSFYHs5Ofmty9e/fkD9Q2z4Qfy+spiXaGQCOotWTJkuSianFQGncZNGhQQ31ro402qkUUXT6eBWL07NkzEQVX4aMXJqmxQgL10fpo7ro4G66N61GPdQw54BrXv/rqqyNiqUKphimiG0vq7THHHJM+6rNVC6Tq869Xr17ZaQaxGe5VPolCHVJNfcmSJZlh4WTGXV+vOdQNZD6o+n7/ySefJIeFSPzzmdNPP325r5tUv1VjNbabbrpp+ijzkkWYD4o4jUHNlJrfpk2bVJpX5COzHu0Es+e3viONKoy7Wx/m/dRTT11uHbbpF/YnP/lJwz9K+aSg3/rWt/KLAeKlSX7atqYUQ2onSh1zzDH5JZMWErA4JU0RFNzLecBa2EaPHp0DTvzQ0E6QmDFjRsNA7LLLLg1tbUoEBIJvfvObGaAINzaqKzP44mrd1ErpyJgBAwYsc4yLspegZOH4MqAMCut8HDVqVKbXtiVafAJJS0tL+sg/ZSP3UZr45je/mV9e/hHRbKL3xdXUQezyBf7d736Xc6iZwhfDM0lbBVuL3cYNguOoUaOy5GerpDXqWvPmzWuYw912260WUbT+aaHk44477pgBy3MKAnzy/htpN0qCbhx77LG5HlEhqS8x1PfDXJlDX/76VlJ0jt9seXNYb1VKXFllrciaImzXrl1rEUXqI3Jo1evcuXM2DygKl0+lk8b4dxGs/s3oRA2fUawm8vi9NkONAlCTsLTNNtuk6CHKSk+VWsrnEnfq1KlW/1yiodSkY8eOuSGdCEfqdw9oQqiAwNLe7bbbLg9Bk/L6SciDyppF+MZXkX7HHXfM8eGTTdkEposuuih95J+ShPFQ1ujYsWOKNFJ/whA0liq7r9RQGrzOOuvk8zlGhTCj9dJJjDY1QCX+oUrbbrttNmT4W3QKgtX7V++jOZRdyUDat2+fc+Y5lNlkgAQk95ZdEJ369euXwpCU13igDOXTJstNMPWH2knRzaFrQ/o//OEPFcJWVllrt6YIe9tttzW8O5Uwg4fccsstye/wHv/meA9cRQsaLoWH3XDDDSm4+Fub3evfpxlRtJ7hjqR4/OSaa67JbX4EH3+Lk95xxx0Nkeumm25qOAbU53HJ66+/fpn314rg5cYJR3qKrLjYVVddldxOC5yWPWPKR0KJtjaRV+H/hhtuSN6LIxEs6hoO0kcbOKCw1kSZxFVXXZU6BDTBK80h/UFGAf0JjldeeWVyc3PIj7LGYW4JQ7QMPo0YMSKfEc+E6OZw7NixDXN48803N2xwUELi4zXXXJPPY6uo43Z9xnOX35iovHfllVemQGQOiU3EMfyYLwQ+69TfDR8+PHUg9zXGK/KRVQhbWWWtyJoi7KqrrlqLKCI3bqacMGvWrCxAQx8GMcjilEhRCmIcf/zxyU0hqPIRVNJGR8X077iWyPXGG2+koijaabyGWG+//fZy+Y+ISjV0zQULFmRbIy7ibyEbvgM1PTefBw0alHzHv1Gly8+pZKMdVMmo/iR9yrcxF+n9f/0WQv55dhmHOZw/f35mOBoyZAiU/vI7g8w5/44++uhUgT2n+fe3WkT5599xS4jzxhtv5P2hofn3/x9//HHDHHbs2LH23z8jouDWxrqlpSV9tJY9B6TXNOJwQH/PxyOPPDLbRK3T8povr1PPy0fXfPnll3MN+Ztmc1hvFcJWVlkrsqYIW1lllf3vsgphK6usFVnT5v/11luvFlHwOZ0a1NDJkycnF/E7uTnOZPuaGqUDtNVv11xzzWy/81YwiiMu45o4AP5BrXXw9corr5zqLuVYOxie3Ldv3+Uec2qLmVozTvnOO+8kz6R0ex58SMuk7VU2R2i0X3PNNbOtsfzmPd056pG4nHuqeatzdunSJTcbUGDVntVht9tuu/Rxww03rEUU6retYfybNm1ajqc5pOR7JhsF+GcOaQhrrLFGHqZmTii1roWz49+urdNHLb5bt275O3OiZmkO+/Tp0zCH22yzTS2iqCpQuW1MmTZtWqrU6tzub/16KZZOPK2S+Of666+fbZSOE/J8fGSUXr9X06e69+jRI+u+5qX8Kpetttqq4rCVVdbarSmHdbiVjpHyC5v69euXqCNy2h4k6lO9KK3l1z/uscceGRkpciKTCKZHV+1Ml46tSTZp9+7dOzcb2HxNFdVXOn369IbIde2119YiCmTQXaVut/nmm2f9ERo6UIzyy0f/zkd9oXvvvXeOGeRfkY/QQXePrYYOVd9ss82yUd3heOqC6pevvPJK+uiQORsDdDWJ7Ouss06qm+bQJndzCIXKarke5t122y236EF9Smn5Ha8OaTOHuqhsSdx8881zDiGSOqzne+211xrmcPTo0bWIItOCiu693nrrpfrLB51fMkDrpNx7zscf//jH6aMDA6jq1q1r+r7odbfV0AH522yzTXYEQmt9A565/EpNViFsZZW1ImuKsKusskototjipe/RdqE77rgjUURPrFdM2Dwskvo79UUoscceeySHwh/U+2zrgzJ4nc4rNV3dIp06dUqeq+6HD7l2/asYIyJ69OhRiyg2Getf1Zf60EMPJVroP5VVODIFz8S5IZJDu77xjW9kvc3OHh1iorW/1TWl8wof0gnTtWvX3KqGj+kP1odaj7D6wY2h/mBbE8eMGZP80oHYun34h9dbK/yDHNtuu22+KEvftyNw1GWtGdmIDAiXV+vt3Llzahh4tjnUS1yewy996Uu1iGKXlzmUTdx5552Z0fidcbDjiH7CR8/psLQdd9wxNQufhayyJJoF8woPmY/N/J06dcpszHfKGOtDnjBhQoWwlVXW2q0pwvbr168WUfRk4qdeuzFp0qTcWC3Xl8fr3IAsekj1q+q0GTp0aPZrikS6Y3Sf6CzRWQQR3BPiHHPMMdm3SUm1k0JP7rhx4xoi1w477FCLKPadOiLGLo5JkyZlz6z76d6yYRoCi7Q6sajrgwYNSrUad8SpZSaQVv+sTAEy4F5HHHFEKsb21OLdepvHjx+fPlJQ8Tv8VCY0efLkZfyDtBBOVlTuc4aOp5xySl7D78pcEf/jH4Rh/B04cGAq6TIXnFTWUe6ztR/WIe34MtX71VdfTZ3DfSEt/o6P0hh0ecnizjjjjMw46juoIopsxXjxUVbpnub8sMMOy4P8vGjM8UfG8e67764QtrLKWrs1RdiFCxfWIoqdK9RSSuCPf/zj5G1qgCK4UwnUu+T3kATiduvWLZUxKrD/h7wiumjnhcYQGV+aM2dOoi3O5/89xxtvvNEQuT7//PNaRKHo4ZjQcs8998xdFxRFKOwYHGMg0uLgduKsvfbayeXcx/9TDnFXCKzmZ5xE71mzZqVPOBEzlxMnTkwfv/jiiwYVHEcyxnvvvXdyUv6V944ytfOyIty1a9d8TrtvZBd2ZeG47ouz+zsnfcyZMycRSV835OfflClTGubwk08+qUUUu6P0NVsv3/nOd3L+7UOmUeD9zHNR9WUVXbt2zfmk5ENYeo3Mio94uxMqaA6zZ8/OLNJe83LWuCKVuOkXduzYsbWIgpAbADZz5swkyxqjfSH9rUbs8tvtfMGvueaaDAjeE+soGCRfGu2aRBZfcKlg//79M8Wx9UxqJtWZPXt2w0A8+OCDtYjidEf3MHAfffRRCmeEIiUC26j4KK0zMXy84YYbUjhxbAzpn5DmS89sU/RcGgt22GGHvL9xUWbxfO+99176+PDDDzdsrys3NMycOTNLPq7rp4Boc4MvlzG26f7qq6/Ok/Kls8ZMw4Jndc1yw4R0t3///pnKasxXYlH+Kzf/P/LII7WIZU/RNPdz5szJNUNQEwAJR77A5bcGOL/pzjvvzDOalGIcXGAOBT5jLEgKYstbp8DAHPp+vPPOO1VKXFllrd2atiYSJkRu0UfE7dChQ6ZlJGz/L11SiNZWRvwR4dZaa62MUNInAgnJXaohFRThpG6QePjw4SlUEKig94oyCZkBHwlJ/Gjfvn3+t2MLO19lAAAWQUlEQVRuiE7EMM+l/VJErd/cT9QgNmggIJLxEZpAHFSC0HT11VcnIkIvGRCRo97K79YR/c1hu3btsqTi1HtpGpSBWNDGM0PiTTbZJEtzSm6yJWOh9GasNEUQzIiTw4YNS7Q2h5pIVmRaEMulJMLS/PnzkyJJX82p8SEkSZk9H/F0nXXWyayknNnJhpxtLbtzDeIpFL3qqqsyAyHooUr/L6sQtrLKWpH9j97ALvooIkOSPn36JNH3NzgXZCDeEDBwCKWh119/PQ+18n5P/48j2ThPEFIqgPgapufOnZtN3NrqIFvdZu8GGOrTp0/NZyOKiF/XhJ1Hp0IHyE/8wo/5hMN63smTJ+fz4GwOKSNcQQDXIGwZP4eDzZw5M310FCd04uNLL72UPtrcgDvutttuEVE0m2+99daJFEQUWZC/pT9AWHPIz1deeSXnDMLyz0YIHFHzgzKGjMbnZ86cmRmUsTGHxMf6xpCIojRHICL2uPcOO+yQHN79bCjw5gXjbM6sV3z6tddey/Uoo/F85gFqa4awOQJPtklk1qxZKfB5RlwWOv/tb3+rOGxllbV2a8ph8Sb8RJ4NYRYtWpRRWBRUvBZ98DitcXiPrVr/+c9/srlbwRtv0PqmEE2lU+6BSpCuT58+GdnxOfJ5WeFmVG5lFZEY15wxY0a2ZkKwcqucEoCSgNY1iDRjxox8Vj7SB/B2/Ad/w3HxH0etbrfddhnR+aggvzwOa87MIf+o1YsWLcrx4x+ObCxlEMZBeU/p7t13302O6j0z0FDDAs5sTmUa9ABj17dv31wb5S15sqCy0S3wf5sirNOIAnWVl/DOcjus0hUf69sQrVObLfyNjMcc2sKo4cjn8NbNN988s1QZrgqDsV6RVQhbWWWtyJpyWIdbicaaqiHKP//5z1TObDnTaiWSi5wiL2SBuO+++25GFfk7rgAx8Awcwf3xDf+/1lprJQeE7A4xs2F46NChDTC08sor1yIK3uZaUH7atGnpo5Y9b1WDEhBKhFVgx9tqtVpyJ0oh9FW7Kx/LStXFFymga665ZvqIX3keb3mvfy+LOaQPqP8Zn0mTJiX62tzgetRQY6iRwTWg1qxZs1KphbSyAAq+TAHa4HXWlOdZZZVVsonCGNiKBp1PPPHEhjl0WCBFGlqq206aNCkzQNy5/L4gyCoTUsWQQc6bNy+vKxvTdKHKYXz8tIb0JlhbX/7ylxPZZZxaaW1COeGEEyoOW1llrd2aIuyf/vSnWkShUKpZUYC7d+8eRx99dEQUCpimec30IqfoT0WW559++umJlOpWTISS34uYNiGo9XodyPz585ObaMGz5UrU/uKLLxoi14UXXliLKJRvqOVIl3bt2iVHGjlyZEQU9VfN96Kxjdc2RECqyy67LBGV/5BH9MW1IaqIz0f8ccGCBdlBA8VwI+rk4sWL08dhw4bVIopjXSA1BbNz586JpBR/SGJcZQfGkqqM65555pl5b9xfVmI8tRtCZ2uIssu/JUuWJI9/6qmnIiLy+BlzWp7D888/vxZRtCTillC0R48e2T3n6Bdr2TxT0WVzVFwdaRdffHFmfrYG8tG6NIfWvI0dtony8f333886O7XenFKn6+ew3iqErayyVmRNEbZv3761iGKzuXxfj+fxxx+fUUfzt/qh6K/TCJLgump7s2bNSrVVt48OFdEXjxDJHCeibujngAEDEknwX/2+Iv8vfvGLhsi144471iKK428ofrjYgAEDMgOAdjqsdMXozIJMOoX8/fvvv5/IiaPi49Rrn9GJBAmMk7/75S9/mfe1rQ8flKEcdNBB6aM5xI3oEfVdRKK6LY+4LJWYf5CC4mt+WlpakovqmsLfrQtjVq6D+ndz+Nvf/jbRDYLpXKKoH3jggQ1zuPvuu9ciij4B2oJn+fWvf52c0Tji1JRumZl7Le9AeZvz8XC6g89AZxoGtZuPUPyAAw7IsaUfGEsvBdt///0rhK2sstZuTRHWEZmicflVDT/5yU9SsaQYQmOKKS6rk0OE+7/t3VuIVmUXB/BnDo1mgYkhdJUg0pUhpBcaKGVBF3aReGMNXtRVkGEaeIpIJVAvNDrqhZg6Os10EKNUIklqwA4y4eiApiAGQjIlJp4Qcnfxfb+197t1Xr7L74Vn3Vgzs/e7n8O7/uv/X2s9Wy7zwIED4bF4IHWo1De8mDonl0lxpWJ+8803gcZ4prYqbX8bN2686zGnnhfnMsbu7u473rRO+dbk7PhX/8+jyhv//PPPgay4E9VW7tDcmieIq15YBdZPP/0UCFjPS1LZ33333Rijt5PLi+JXxvfiiy9GrpHaDak8s7USdUAUqHT48OFYQ+hCsTX/1tAzyr+qpRXhDAwM3HFQHLNG77zzzl2jJHNVf+v74sWLg//SBnBoKKybx2eIWhz0Nzg4GOtrn9Jh6sfguqd1kgnw2YcPHw4dRlQjInRt/bWoLCNstmwtZE0rnSAa/qleWHy/b9++QAgcCR/itSEKxRSXoLyuXLky+ASl7KWXXkoplXkrXlouVW2vw7qg4oIFCyLPC8nl1YxhtDHKh6kaggCOMk2p5GG8NY8KccwBRVpXy+uvvx6cleLuGmgs0qGIQig1rpDxhRdeiA4j/BwquFfV6A2uxxWt4Z49e8K762U1h17F6FoqMd4lQ7B69ergcxRnz2YNRUOQVISBs1OPn3vuueDDkAzq4HmjjdE+cB2E6+/vj33qkAFKsuNjRWSiJ/3Jqv1WrVoVFXe0Ap8jSyCbIVsgUqwfKjd//vyYF9eIPEYbI2saEnd0dBQplTK2lABRYNGiRXecR2wwxA2hqKJwgoAF6u3tjWtscIMgUAjJlYQZnFI6aYBr167Ftb4gwhHhUf1Mp3vuuadIqXQCHIxi9O7u7rgnQc3ZPsr+JOMVrdu0nMAnn3wSY5TWsYEsnHBLgYkvoY2uxeyvv/6K5/G5SvicbFA986izs7NIqVwX4pczphYvXhzXKQyxVp7Vzwl3NjEnuG3btmjNs4acgE1qzTRu2PxCYtTp5s2bsac4cg7bGn777bcNa9jV1VWkVAo29o3zkbq7u+OeUi3a7IiivtzSk0Jk+2Hfvn2RirGHHXJADK2H++iN5gxNAzdv3gw6Y26l+Xy5q+/4rVoOibNlayFrirATJ04sUio9CY9Bkt+9e3eEYQg/7189uzilUijifSDJ119/HX/z1VdfpZRKbws5eFphrjBWyZrwe9OmTeG5JKTriFJtPfvvvRrGKLzSkN/f3x/CANQTxhGGpKXqKQ2RwYEDB6JwQjG84n5IBL2Fn0QiIh7RZtOmTZFqkBrQ5ibdMzAwEGOcNGlSkVIZcnum/v7+lNJ/QsL6G9iFzYQZqQ9H4UBJc3Xo0KGgQvZGHYVQE3NH0HJ2ldLGdevWxXxrcUQ9rOEvv/zSsIbEUfsFeot4Pv/88xCwoKCQHeLaQyIde06E9sMPP0SaRgSiyUD4bH6EteaTmGeM77//fswDiiRlZoz1KIJlhM2WrYWsqeikLAvflDhWKD979uxowCWSiNN5e54MYvCaStmGhoZCvIAmeASuKG0CffARpXG84q+//hrvapFkxx/woLop6+P9HJymgGLWrFmBtpAFN1KkQbCAwDg+rz08PBxRg3I/3hmXUrYmLYKvQSrF6999912URHpGPNdYqyYlQ0iyhjj7o48+GmkrPJlYolzTMylsERFVz6fGBT2bcXlG8wzp8Gb7xf4YHByMw82UQGqJG20NpV6kYqC9Ustp06ZF9KYIRPmon9vjxDiRkJ+fOXMm1lA6B8cnXBHlIKw19Hciw4MHD8a6ODVT5GOdR7OMsNmytZD9Tyf/U4DrrWdXrlyJVAYuy0Phm5CDZ+WtJZFv3LgRCIrnUmjF8zyVlBCvp/zR+zfvvffe4Hf4Lf7jiJYbN240cIMZM2YUKZUckhJO3bx69Wrci4KHjxmT5nJpL1GF4vORkZFALXMJtR3s5lpI77nxVFFIR0dHfK65rarRKaV0/fr1GONjjz1WpFRGB9bQHF65ciUiG7wKB6e2WivFFhBEA0FbW1uoqj4HQkEf10Jl0QfEh15dXV3BqUVLogNRW30NH3/88SKlsrhEeqd6BrY1xM9lEaTNcGlNCp7BeP75559oGbVXZELMu3ubY2MVqcgetLW1xfdAUwwOKzK5fPly5rDZsrW6NUVYCiOOBAEhx9DQUPAmSWRJb3yDsuznuIPT09vb24NH8MaS8JQ6x83IiTkyVTEGz/bll1+GSg2R6t7v999/b/BcDpqTF+VRKaSDg4OBqLyxpLtrKIkKJryxHE/p6uoKrm+MPLw8nCYKJYoQSAQDoT777LOIOKASFBYBVA+ae+ihh4qUyjyfNdRWduzYsVAzRUkKM6y7XCnUx7tERl1dXaGg4u9KFUUUlHSqPd7sOajJ+/fvj3G4P86M99bf8fvII4807FP7R5nq8ePHgz8qWBFh2P90EHtL3hqqdnZ2xrriyEo17VN7XHQGYUVCrK+vL5RwRxTRTvy8ns1gGWGzZWsha4qwPT09DUfEQBg8sb29PZASz1B9pDxLPK8VTK6SR/3xxx+jYkduktoH5fBi1SjyXtRZlU4zZ86Mnyl99FzymPVXdezatatIqTzYDXcxxra2tlBPeUyqpTI3nNVcyjVD+9OnTwcvVCVkjO6hZc2/UBRfNM9z584NtZx3prhX3mQfY9y7d29DpZPx0Rg6OjqiJNT4IC7086zWTMUZ1D958mQgKZ7mGjlIuVERDDS0Pyiv06dPjyOD7Afog+f98ccfDWu4f//+IqVSF/B3uH57e3usoX1AjVVuKkKUF7eGtIXh4eEYo4ovEZ9yRvPm2nqLZ/UYGFpF/TUpnu/s2bMZYbNla3VrmoflBXhLXgjn3Lx5c3hbzbuqX7Si+T20pB7yhjt37gxPDpF4YWof/sWjQzjoyVt+8MEHUWGl8kr1iwqXuuE0xojHyRd/+OGHERWoisHbqZjqpc0BFDXW1atXR27ZGM0Lj+p5GQSidKog2rBhQxxgJ2rxPFCsahR9iq8oCafcsGFD8DvtcnKi+LPowOEDcu6OzFmyZElUTtXrgI3PM9bfG1yvYtqyZUvsB5wVhx5tDR3WZq5EGsb40UcfReQnVwoNoZ7nxX9lKjRArFmzJqIhDSLQGTqaJ5+vSULUh4uvX78+9qloUfTi0PzRLCNstmwtZE0RlpeBYDwcjjEwMBAeW7M2ZVT7lOoY+SX5UNzm7bffDs+NZ+AgEIwCySvL3Wlzwx2vXbsWaKwtrn5AVt1UKzmOBIpCpCNHjkTOTAWOsaj4glB+7rNxmzfffDO8MeUTkptT3tcY5YONlXK6YMGCQAtziUvdzURDVHv1z5TMI0eOxBqab503ul8gHJXU51Jht2zZEmo3dd68mxtKtrWiFhufyOPZZ5+NOZBnh5x4fd3kZ41RdAd5Dx06FNxddCI6wJPNqRp02QyIvHbt2tgb1HnIKYeLa0NWeW0ZALpBZ2dnVDYZIy4PvUezjLDZsrWQNUVYXRqUS0goD1cURXh3PI8XpLKqWVWlpOoDh7h69Wrk7vAgNcxylXJTKorcQ0O73OUbb7wRXFD1jnpUvLhuvDNOqaoHpyqKIjw1rgqtoR5FWr64PsaRkZHgs3J4ns/nQlyoBRmNWR755ZdfDp3AwdrmGAJUTR4aR3P8i8gipZJXehZrCHmhkIOz8WlawoULF2IN3deB8taQYivqgCxylPbL0qVLA+38Ti2uLEHd5LaNkV4i93379u3g0mqejdG82iciHWgpQrxw4UJEnNZGPYKcs8+1DqIHTfvmYPny5aETVA5/TymV1VqjWUbYbNlayJoiLO8uJqfo4XeTJ08OVFR1hEPqSvF7KIg7qBL5/vvvA50r3fYppfKlxrwhVIKK/p9XHzt2bCin8o44FFWubjwc/qMCR65z2rRp0VmDn+N06lM9j2jDMazQ5dSpU4FGxugaPZtUQnOO9+BH0GLChAmR38Oh9ZZCz6pRXM0Lr48zTZ06NeqZrRkOaU3xOuOjeKuaOnr0aKwnjkp30DVF8bVm9gUUtS/uu+++UPYptMZXrxhi9BOHoanUogPMnDkz+LiaZ9Vp1l80KVtgvkWXv/32W/Ra49vWyN/6vWyBOaDX0DbGjx8fkaDogcJ8tzWsWtPCiSeffLJIqZSeNUqb/Dlz5oS4YLKEax5AGCWZ7HR2x8688sor8WVBuG2y+ntopB18CRUTSD739PTEfRUVGJ8ywkuXLjUkpBcuXFhUx6S8zOQ//fTTIYIZIwdDOFCqqClfuKeIYMWKFRECCTOJH778ih8UgdhQ2sEUN3z88cfRckfAYsb6559/xhifeeaZhuZuDklBwLx580JEtM7aBIXI0iMatJ3I7ziY5cuXhzApxPTF9HNUQJudFJT2Omu5a9eu2MT1NSQMjoyMNKzh888/X6RUOnZzSfSZP39+7FPP4/ONVSmoL6g1NMbXXnst1kT4rpVQKss9vMGRk66/ze7TTz+NZhC/Y/bp+fPnc+FEtmytbk0R1pvdoCUP4T0lY8aMiZANIQf/0FjIIQSsvz/04YcfjtBH6CXUIMAQcaCfkJC3dBjaE088EeGdIm7F265dt25dg+e6//77i5RKQYNXJ92PGTMmxqDYQ9gCrZRhCo2Iczzy1KlTAymFXsZKuCBYiUh4WlEGVJ0xY0aEVgpHiF6Qr3r28rhx44rqMxlftTEB2ps7wps1ND5hrGjJs02fPj3Ccykfz2h/QCxoDc2F3+bnqaeeinm0ZtDX87z11lsNa+goIyWuQmRjfeCBB0IQEnqLAKCx0yrNAYERSk6ZMiX2v4hPyCulZc+hHSJHkVn10ADfF8VB9psijFWrVmWEzZat1a0pwvb39xcpld5d6gGf2rFjR/AfXAzaQU0eSuqAZ5WYfu+99yJtA30VvLu39AixCzrzWEomt27dGrwF38JJJK0PHjzY4LkUxzvKRIK9yhnrp/Lj0gQVxQ+4rDEqpNi2bVu6detWw/MQfZRGQmNjJLTg58bV29sb/y2d4F9jrB6RqbnB+NxfeqOnpyc4unnFvZVY+rlSVeivYWLHjh2xBho1CFSe1T5QbECHwOXsre3bt0exvLZG5Xrm8IsvvmhYw76+viKlMt1IODJ3e/bsifmmseCZEJcegYcSWo1x9+7dITbZW+5pz1hDn0+Is5cImX19fXecuWw+fEZvb29G2GzZWt2aIiz+w6PyLBTVv//+OxSyevF6/fBvCMtL8kJLly4N3sYDQVSfq4yO14Pm1GQK4PDwcPwN3qCszTUXL15s8Fzjx48vUir5BmTj6S5duhTPzINCUPeU3qgfEscDL1myJDy3cUt/UT41v7u3+cPjJd1PnDgR82KMeJAI4Ny5czFGHN3nWI/qW/Eci4KTUpTrij+ENTfG++qrrwZ/hjbu5V982/oojlBUIN11/PjxGJ+/9bmep34IwYMPPtjQwF4vuKnuU2tGB9G4Ds1FdZ5HpLBs2bJYQ+M2BnOLc1tbkYk1FG2cOXMm5lDkobXRup86dSojbLZsrW5NETZbtmz/X5YRNlu2FrL8hc2WrYUsf2GzZWshy1/YbNlayPIXNlu2FrL8hc2WrYXsX1iMRozbySZQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 140, D: 0.03162, G:0.7134\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 160, D: 0.07158, G:0.6837\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 180, D: 0.05088, G:1.206\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 200, D: 0.1465, G:0.5892\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 220, D: 0.1839, G:0.2881\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 240, D: 0.1571, G:0.2371\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 260, D: 0.2313, G:0.1217\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 280, D: 0.1052, G:0.313\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 300, D: 0.1108, G:0.5719\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 320, D: 0.1427, G:0.483\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 340, D: 0.06388, G:0.5528\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 360, D: 0.1384, G:0.6742\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 380, D: 0.1249, G:0.5619\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 400, D: 0.1144, G:0.8899\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 420, D: 0.09295, G:0.5572\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 440, D: 0.4251, G:0.446\n" + ] + }, + { + "data": { + "image/png": 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UKVPSk3vuuedGRMXzNTjQOkgBhyIIPgb8c+nSpYmUsn+cN1x6zZo1bXu42WabtSKqBguEFdHX15cWi8/hQ+HxZsFATeOvx6i1zIG0xol/a1rHapADLzPMXg4MDLQ1SDDG+v+b8rpGGnkJSE+EnTp1aiui4hI4GXTo7+/P6g/PwdW0gGHvf/7zn4+IStuIw+24447JcyEnL6FsH95hmVC4lKwZ8c577rknOZoMkrLo+M8pYC9zWEueDF3EX2lx1se4ceMyS4YGF5/mVfcsP8WroZkxlNk8g0ldO2+44YatiCoezbLBnV544YWMoVo7r+ErOPTQQyOi8qxCHfHpD37wg7n/UI5HGaL+3d/9XURU+8GDit996lOfioi1Hmh8l8fWekLMcg9ZSfZ4sLxcUQp/g7D2yBmTccTC4XN41atelRyaZ18eNF6umoz84he/iIiq3I4/4r777st9LJGVNAjbSCMvAemJsOutt14rIjLGJluJdp4wYUJyRXme+CQNRWQjyeyhcefMmRMPPvhgRFT8TXG1+kxZQNBcnAtH4NkbN25cjoe3jSarNZUeFGHXpTidlsaHy2dDKMijcuXII4/saCDHe6pqhjaGsOaKL61LU3IymJf461//ekREfO5zn2sb84wZMxIVefLx3PPPP7/t/8cff3xEVHxYzuzee++dVUasHVlT+DCklUMLrfxdbHPs2LGJft2K9/v7+wfdQ9bc7bff3vb6kSNH5l5pHMe/YO7OixxjqCkCccABByTfZr2ZMx7sbENaechqsflc+vr6OlrU9Grz0zbXXl/Y0aNHtyIqE1XHOZPz4RGRjiM/OTCYVRwxJiOdb/jw4WniSprff//9I6IywX2RBdJ9+aUsMtn33HPPdK1zBPmCOASl08lmlx3vBpMyicGiCwlI+JDIwWTq6+vLMI6C7k9+8pNt68Rk9TpK0iGxsZtttlketm4lWfXNZi5KrpecQvr7+3M+aIovE4VZJor4YjiII0aMyOQB5rOkizI0w3Ekrc8httfnnXdemqU+n+OQw648zPZQ6Kq8baLeU0sxgj1T2KJQw3llCkusmDhxYiaWABRJQPZQ+JOS9aXntDWGj370o7l2CgYap1MjjbwEpSfCTp8+vS3lS0Abaj333HPpCICcXNfKp5gPfi8oL0Tz0EMPJVIKxDO9/J5JxmTzLGalMrPHH388kymEHMpUvm4Iuy5Smi80LEeatYBmwiXLli1LExGy3nvvvRFRmfPGC6F67cuL3btT186vec1rWhGdXftZFCtWrEj0g1BohbFAWKEP5rmSyblz56b5zMRk4aA7Ppf1JMwFjfQgvvLKK9NSgbTlnTll10S3G/TquO/f5f1NSjW9RzME4Sitjh599NEMTQnFoYicjc4By6q85QJdWLZsWYaqUKXG6dRIIy9B6VnAXt5IJnwBHephHQQcgtBkwhdlMa90t2HDhiXqIu3Ku5Qx4bucPcYlVALxzzzzzBxbyTM5lV5MBuvWX/ILIQtdBBUj4J3GKSwRUZVW4WzCNUrWjLdETc/g8Pr973//JyVRlJ+HX0tkmT9/fiZtCCVxAOF5HIpK5KCjOS1dujSTCvghONwUguPd1pVF4bxw1F122WWZKFPeOFDesUPK7oTG5zPXrFmT/BJX/sxnPhMR1Z6yiqC7dbfXa9asSWcnp1a9e2hE5X/wTBapFEV+nMsuuyy5fNkXufx/KQ3CNtLIEJKeHNa9m+VdIbT9448/nrwCjyzvTuHp8zpBZzxpzJgxGTSn9SC6oDrPHQTTA5lWpvlf//rXd/SypWUh2JNPPvl/jcMS1gUU50Wst93ElSSJ6JkL+XgppdDhmEIDUPxPDQlMnjy5LfnFe/Dr22+/PS0I641XshS03rTH5iINddttt82wjfRF/gfN5HhdRREkwhsPpN1tt93yNTyp0MjalE0Ihg8f3rYA5X2yq1at6uCw9bt8IirE55HGySXzTJ8+PT772c9GRMW3+W2Mnf+EtcEnoJ+xBnzTpk3r+Px6z+KIzvAjaRC2kUaGkPTksLyBEJVHS9zp7LPPTs8tPofT8pgqlaPBaDjxsKuvvjo5CGSV9A+VeThxJtpcaiKude+993bc1VImff+pcsIJJ2RapXHS3FDrIx/5SNsczcNaXHrppam5xZS1iGG9QBEcExeXOkfq3sR14bJ4H14n7odfzZ8/P9Ee38W58DhWivg3dICEl112Wb6XPwFClZ32RQDwZq1YoPc111zTkWIIqaBdKdbBmRNFMMef//znmeYqscce4b3GWzach5rnnXdenn8Wn2R/c5aWC1HFcPlanJsnnniiwzrytxe7a6lB2EYaGULSE2HFjaCUbCHo9YlPfCJbPtJuNG2ZPUNr0tq8lB/72MfytVBFNg6upFSPVhQjE/OjpQ488MDU2GJiOO26Nmou5ZBDDunwVpY3kEMovIxHUvbP2WefnY3TjUdxhOwhyCtBnNYuZdNNN+2KNIOJfYHcSgCh5Fve8pZEQVli4q2sIYjL7yATStre5z73ufQriE3yFovhQlbj8JksCwhz+eWXZ8M0+8sq6CasqrIsz7nYc8898wzbQ/HW+v1H9Tnyj4i5zp49O19jfVgJ5sxrrA0Oy69MK/3MZz6TnvBuDc67znWdXtVII438j5CeXuLx48e3/utnRFQeYF6x733ve1keRjPJVsK9PJ8m4fXUQuQDH/hAajHPlx0jH5U2FiOTLaOxGM/rpZde2lFkzkqoea/XyUvsHttrr7020c4zoVM3L614G5SfNWtWFnZDZeNkLfCu4jt+LxkdQkCEXlL3Eo8aNaoVUfFNvgYcevbs2Zm8bkyKLcyL11NeM04vt/aMM87IYg48Xwke7zbrSAN5ewjVNfP7whe+0HE7Xj2zLqLTg6pFDKQ1BleMfO9730vEdA6dMRYIsT+KBOSvH3nkkZnzzh+CK/s9y4OvRTmiNWBVXX311S96X3CT6dRIIy8B6Ymwb3jDG1oRlfdL9QHv13PPPZdIipvS0jx7NDokZu/zEv/bv/1bZsVAUlf94XUySbSXgTL4Ms6y3XbbJW9U/UJjQdoyhle2iIFEuF5dSi5L88tXVqgst9SaPP744xm7kzmjFap4KE1unMZTxutGjRr1ony8rp21ORX/1ppF5tmTTz6ZXIsnmWWgjaf5QEXclSUxZ86cvE4DyqhKwuegjM/l+Tcu8z3ssMOy7aq1YL2xMsoWoJtsskkrokJ1jf7kIvf392eMvGzUVnJ7n82P4hzfeOONWQopH0AWnxgziw+f51VWdukzt9566/RdsArsb+1yrAZhG2lkqMs6XdWhHlZlSf22atxAHiUuQqMpMhfPov0g7L333puvlXfKYyb+i/+ox8UdoA9eMm3atNRqxuhzoWK3ax7Ky5nrUuYS82p7DwTweyhGe59//vn5b1y1jL951p8bL67Psd5IXBMCxea4a13e//73R0TFSdWq4qx4tFxy3nARgrPPPjtjj6yw8ppJuePf+ta32v5ube3pNttsky1iiDPGWrrnnnsG5bBl3W5dnCXWGb+HNXN+xYNlz8lm+sUvfpHvUYnmnIqxs55UKpGyjeyIESM64q2eAWGfe+65BmEbaWSoS884LG3Ji4uTQYvRo0d3tOPAadnrEEW+rRYluM3uu++eKAipiKwUcTgIhvfxNEPr/v7+5D3GWr/seTChUcvYZr3xWlmr6NnGo+0JnuZiJ/HYXXbZJblbadHQtGUc7sVqXgeTwV4LDcWyeXz5FCIqvmkM9tD/oSKLhkdYnPbf//3fk1/ydkIy1pNKLEiH64qb+//8+fMzKoEjOzvd2uSIh6pXhVI4eV9fX8Z7y+iBvTQn3SF4eMXFDznkkPSxGIdnscpwfZ/Px+Ec173f9rdW4xsR1Rp3k54mcXnzWVl029/f3+F6d9A5fTgwpHM5/Ij7nDlz0iHjyyUw7cATJhplwIxw6DfZZJP8vLKsziIvWbLk/3rn/9qz2v7vEN588815uMu0ufK9L/YFrbc76SZ1pxNzkcntAEpKWL58eT7PWlk7ilQxAJPTl5Oz6oorrsgb8BR7cBSWifBl6p8vp/2ZOXNmfmnKbvjG+WIFHL3a/ZR7VKYz2ifiXuGrr746iyM4qKyhZ5QUiZSOpQkTJmQSkt+VZZ11WtP2rMF+2UgjjfzPlJ4Iq2yJ6Uez0gIjRoxItGUWSqL2Ho4JKXmK1QXMx40b12Gu0vTlzV40lxACc48M1hwOsktgX7Ro0Z+NsOZEG3cTWvq/40BCO4SuekmJzoMhrLGzkuqIq4j+vPPOi4gqxQ6CQmUteXyecMWkSZPS5CwTEer9j+s/OYaUFZL6HkJ6FEM6azeELc1M7x8+fHiarRIiNEUoy+4gPVpgjiNHjuxwSJb9quvO2IiqSEUYrFyT+ntYpJC3Ka9rpJGXgKwTwuKnNAyOMDAwkG5v6CcFkJbBUTh3aCzketSoUanVOBmgts/z+fUb1yIqDcf+HzNmTKIxjYUXcy78dwrYX0z+HEfR/wupI6z5QQwoac1arVZaIdbVa0rtX5a52fP+/v7cb5xPv2F7BME8Cw8sS+PqbWAgO6eT9z7xxBM9EdYz69yxfmYjOn0IXssv4kyy8uq+A3ORMFN+Xi35oe0zBkPW0mqrpSg2CNtII0NdeoZ1aF6oyBVOCyxbtiz/Jo0QHy3bSUpvxItw2s033zy9bdICaXjax3vY96XnV1hn4cKF2ShLqZ6Ea3fMlvKnoOI6tBft+r4y9ezFCpV7SekF7TUu85eMwPNubRcvXpx7w9/g+da55J32CW/dddddsyUMhJWSai+V6EHL8kZAHul7770391CCvnarpc+ClOP0bOuyZs2aXKvSP1KuHb9DmcI4ceLEPNvWSYucMlW1277Ub6Mom8I5w9armzQI20gjQ0h6cthGGmnkf5Y0CNtII0NIenJYyf88ut3uSY2o4nvirBKg8Qr2PG9xvb3LYFcrRHQm5Jde4fKOzVar1VEIXEpZGGyO+HA5x3pDcTxdOZWMLN5C4998880jovKmDgwMdMyR4JLl55eexjrnK1uh9JrjNtts06qPxed53pIlS3JMYpTSRn/yk59ERGepocw0nuDnn3++IxHe+vM/2O+y2TcfSP1mQH+T2VYWCpQe1G9+85utiKp4wVxlWd1///35zB//+McREfHKV74yIqoyR80Eeb4VuvzoRz+KiLUZXMr3xI5lgNXvfY2ooh1uYpf15Q7avr6+XGNNGpx1e/vss882XuJGGhnq0pPDim+V5WX1thY0qYR8nrMyf3JdPKjdvJ3dbqkuZb311nvR5OluVxWWuZyDzbHMPio9nesyx25zWddnjR07tuOCr15zFEvnlRQn5y0dN25ceib/9m//NiKqKzt4LllJrABjtFajR4/uyN02Rq8RYfCscp2t8bbbbpvIVSIrWb16ddseuhZVZhG0hO7bb799NkOHqFrVQFqlcRL4y+Z9Y8eOzTiwddFYryy39JNX2fg969RTT80SPe8trcdnnnmmQdhGGhnqsk7VOrQhLYiHLFu2rAN11hUN5SUrPeol3dCv19i7xTtLhKWdPQsXq89Rhk85xzq/HUzwpmeeeabrmLs14eo1xzJntqxKqc9xiy22aEVU+4EzQpYHH3yw42IuY+lmrRhTvXlB2QLW2Pxepll5daQ1rP/EgyGkMZtDibAnnHBCq/5sV2JoJHDAAQfkDfQHHXRQ27P87DYe18eceeaZORc81/8hqnNi3WTZsTpYN61WK/0gGuAddthhEdHWhrZB2EYaGeqyTrnE3dCyF//sdm1e2cZj1KhRqc0gF7teZtOfk43kGXhDt6voyyZsvebYjcuXnw29tB2p5zhDL1kxUKGbdEPkiEqzQ+Xahcb5oqlTp7YiKu0+WD1ouWYQoqxDLS/oUjA+Y8aMDi83D60rOsqrKDyztN5GjRqViASVZR9BthJhd9ppp1ZEld1WWlWbbbZZvPe97436Olx++eVt45SBNXPmzIhY21ghoqq0WX/99fNyZxEQRe8nn3xyRFR+HOtm7uZj/FOmTEmvuIiCs44XNwjbSCMvAekZhy3te5pLJ4Vbb701tbMYJb7RDRXFYSHs6NGjs3GbnFHtTD1D+0iNvp+GEuMAACAASURBVMpmaPVYb9m4nODapdDwZcyXNr355ps7UMMcS8T12eJw9SsMxTgPPvjgiKg6bhCtY12sVffA1udY/5yyLpeGrwtEocnVBbsc6txzz811hi7iq6WV4qeYO2th/fXXTx6sibb5QVZorG4a2qgiMs5Ro0ald5Wn2fpqBVOKPHHcVV2v1jc77LBD1tJaf50v8E75wVdddVVEVF5k1sbee++dbWO8tuzAoYk+j7P1kiddtya0edUhxVzViXeTnl9YUjpCTPKWW25J54ygMuH6Lu9O0YHvpJNOioi1gWn3bpqoBbFYuilyRvh9mVAxduzYNC1KsUDdpHyWMbRarQxJWNxyjn7vgF9//fURUZlK5557bva20rOWgqNY3KPKVGYGlskhEyZM6DCj7YuE+8FEuM3hqn++Ig+9kR0sY1XszYnmy+d+mEsvvTRDQpIIKDzJD/pFlz2/zMXebrXVVh3jcHbc11OKveMg8mWzb6tXr85xSYRwpoWCjF/RikIDfZPPPPPMDuBy99NXvvKViKiAxffDnITNOOl23XXX7GLpi2qsiie69SBrTOJGGhlC0hNhy073TDFdFCM6zRZCqzCfSm35i1/8IiLWmma6M7rvlRbmCODEkS52xhlnRERnD9znn38+x7quoZfy9Z4FETx3sGdx2ws/lM4ZCQizZs3KW+a57xX+K3uDANBZV3k3ufns5557ruscB3P0cXhAbtYJ9G+1Wlm4LsTmuZBuhx12iIgK7f2UZPDGN74x0YYlAamgEXQmrCbrwkS+//77k56U9yLVi9vrgu6gIsbF7G+1Wh0mp72yz9oOuXWCA0ta4bRp0+Jf/uVfImJt4kN9fUrHJmS1FtaGpXL//ffnfqCIzkG3WwtJg7CNNDKEZJ04bImeNPCwYcM6/kZj4RFQqLyXh61+zjnn5P2qOBMeihNCVkFvt5LRkgqqFyxYkMXPHESawkG0blKipzn29fV1/M0zzQlCuTmes4MT54ILLsgQgO7xND4EVADubhd8rHRkLVy4MG/Wg+zGg4sOJjiR/YJe48aNSwsCR8NRjQUqcQq6iR06XnLJJbl3noHXmx8u+8///M8RUd34RnDI66+/PteRj0BoxbxL4XyDitbWTQWTJk3K1qmsEI4q4/YMyC+BQT/p97znPek41H9aYgZuClndsuhuImcR5z/mmGPyGfYXl3ZDQjdpELaRRoaQrFPyP8Eh6uVtXO20m3ANW5yrW0K2Tu/CDLfffnvyG6VGNDsthANIDaTZhTHcV/vCCy90ePJKHt7f39+zCZv3m+OqVavy7hmJEG4cMEdBeDeeSTQ3xxtvvDGT7918QKPyfEIRnl5z5APwvlWrVnWU1w1SEphzHDlyZFuDMl5iHHPRokXJzQTthUCMWdka68m+QP2zzz47kcq9PO5Q8h5WAOS1ptYIt3/ssceS1/Fsi0SYZ3l73YQJE1r19WC9iTpcfPHFyWv5BvBQz9QmRxmg8JRy0S9+8Yt5e527pqAvSwc688Bri6MBOZ58zjnnpA+BJQWN7f9VV13VJE400shQl54ctkSrsqxq2LBhyW+gnybUeBAex74/5ZRTIqLiUJtttlkG2cVjBbVpRV5D96+6IRzHpZFpyYjOggFcal3nWEdo/EcsV2qeIDtuA7XcS8p62XLLLTM5RPzNnUS8wuYoacQczaM+x24JLYN5GPF7Rd34neZfo0aNSmSVcsdHIAEB/8bFjM0zdt9990zlk8wOKcTdjdUeOgfmDY0WLVqUsemyQIQnvRQ38ontm4fzM27cuLR6eO7FTHFp624v+U1EM4477rjk1goJIKm7n/B0lsfxxx8fEdUeWrcLL7wwrQFr6swreu8mDcI20sgQkj+pgL187WDvLVtwQlK8T0wVP3nPe96TKXw77rhjRFRaWRaM90JiqCE1TCuTwcZT92hHdLYXMceyvKrXHMuUPYXN7q/lCVaIvd9+++UctB297rrrIqLyovMBSL+kpXl+xQPr4ynjsaQ+R+WD0KrMBFu5cmUHz4ccstess+Lvd7zjHRFRleMdc8wx6XXFSfkuWD1iljib8jJrdcwxx0TEWguntCBYR7js/Pnz2/Zw2rRprfq4nB+IfMcdd+S+Qm/8Ez/nY+Gt/uu//uuIqGKnBx10UCbsQ1+5Bni5lFXxV+mazq29b7Va6ZVW4OB7g/sfffTRDYdtpJGhLj0RduLEia2I7tfn9fX1pfbDo2gOWUvXXHNNRFToUzYnHz58eNrxZdMtPAen4onkrcVtypKttgn2uCgqImLSpEmtiM7cTbyjr68v+TergZbGuUs+CpHwxuHDhydq1RuzRVQxTTFmiAOZZPnUOXa3UsbB5viGN7yhFVHdewpp6tlZ1t8a2BveY/ngGphpJsZv0d/fnwnucm8hh0wtXlEc0ZrZc2ds+fLlHQUPZeP40tN/zjnntCIqjmiNcchf/vKXHYURvMSy9mRLQVheYpbQsGHD0ttrLpBT7rAsKXvK86/gwvifeOKJRFTz9r1geT7//PMNwjbSyFCXngjrqsJerUNpUugjVxVnwgVoNBqFRt5kk03Sw4h7fPWrX42IyAwoHJGmxTu8zxgef/zxF20jUyKsIv1anLZjjnh4PWc5omrNKe6nEgQiyYDZaKONks/ceuutEVFlDYm/eRY056HFac1x0aJFL9pGpj5HFgSNzi9QLyDnV4AAYpGQQ1tTHmAILB65/fbbZ+tPGUK84hBWSaazZK1k9uC+t956a0drlTLeXBawb7XVVq2IKu6KW0PqZ599Ns+h88enAHl5mKEmlObVPuigg9InYRz8AfIEDjnkkPqw4txzz42IyiKBzF/+8pfTS826qXvtIzovHicNwjbSyBCSdfISly1MSV9fX6KOjBC8kib3d15E2SL15lO0MiQT76SdoQ3eJV9YnK1eNP9ibWS6tTntNsfhw4fnHHj5zJGXECpDT1k8OO6xxx6bc2J58IzXm3xFVJlNPkuM9M+d46hRo1oRFVKLQ5Lx48cnd333u98dERWXFUs1P7mzuKKMtKOPPjoz3GSt4XNnn312RFTF5dDcHvJt4P9jxozpaFxfWk0lh91ss81aEVUVl3Yw3rfDDjskF5Uf4KyZiz1V4QNNZaTtsssuuR7i7ZCcBbD//vtHRJW1xWrgVVZjO3HixPx+sJbKhvLdrptcpy+sYm6m3mB9m0zCa0E8Ms8s8HcpYuPHj88EBAuuK4BJMRd9IaS/OSTkrW99ayZyIPov5nQyR6ZJWYhfF5vsS8Uk4jBwCH35JXuvt956mZIpqeLzn/98RHSW6LlNQLol0808dt111yzFYrr2miOTn9nO2VMPBXm/ZHlhM2soBMMs93cHdtiwYTnXc845JyKq0IYvgn3nMHSofWGN5xOf+EQmOVDIZa+pgYGBtj2cPHlyK6JSDpSggo9HH300FYUvJBOeErKH/s8Utv5Lly5NysZs1nRACqfzKoFFgk3ZYOC6665Lx5Qx10JWEdF5TkljEjfSyBCSdXI6devcV/89BwHnk7YZzBKIK82Nybdw4cJMJoe6Ss2gjfI1QeUyRbKerEG7lbdjk25Op7JMsNccmTHCHsYBAZj9nE6PPfZYRxGE/9Pa0IwJVnYTrIc6yjBHrzlOmTKlVV+Pcs3qBRNQ3jyE5jjCoCWrSbnbfffdl2ioEEIIiMXDOnMuzAsqSRRZsGBBhsXQBvPslvyy//77tyKqNEJnjWW2aNGinC/qZo5ogJ9SBKUbSl382te+lufUGUdXnE8WmNJS68QSkUJ53HHHJbLOmjUrIqrwHcr01FNPNQjbSCNDXXom/5ctT2haYY2BgYEOREP48TvBa90A8SCEvNVq5Xs4pmgopWeSuUtkpcFwyptuuinH1q0v8ovN0figycDAQPJzz1ZSSJMLWSl8ILjfwMBAOn1wSOEcZWfmWHaeF1qBfjfccEMH4vSykiAJDol/4sHDhg3Lz+Bw4ywzb2WP5R7uueeeOVZppdJEFTlIfjFvyRDOi5ARhD311FMzTbPsi+z/pcydOzciKt9GmeCwePHi5NhQjvPNukrK8TrhlXqTAq8VkhOaYmlJAnEulBZK6cSjDzvssA5r0dxYUt2kQdhGGhlCsk4clvS6gQ2vKnvl4jC0Mg2Ms/X19WW5Ek6kJA8qCYhrziZ0IM0NSo0bNy75BfQp76ctPYzd5liW50VU2s9neC30kODB211fC8ntEMUcJRhsu+22EVF5mFkNkkO0nRk/fnxH69OyB3N9ju6/Lfe53uDMc6RPQgg8Eze07hJXJCGMGzcuC+ylkUIMXtky2R8K4vnS+F75yld2oDBPOtRbunRp2x66P4i153WSVX7wgx/k/rOCWBjOIe+9hB7zEE7bcsstc4zf+ta3IqLyKEu/Zal4hv7OohksyQ9+8IN5DvgJhNJYnnfccUfDYRtpZKjLOnHYbne9zpkzJ3kDbQhJ/cRhcTReMF7i888/P2NQirTF6GhpSQdS4XADiAwNccy6vBjP6zZH1sOZZ56ZMVF80NzMBVcSJ4ZE4po//elP2xplR1RpbMYnFU6MTzyTFWGOgzVK73VjIJQ0ZuhprX76059mTJi3E8+F8rycvPe4rf269NJLE229x5p5La8rxMUlxUxZJ7/97W877oxlDXS7Tc85KducQq0FCxak30Hc11mChqwUiRTGw6s8d+7cjHBAQV5jVoM5SvSxFtYeT77gggsyNdH3RtxX9KCbNAjbSCNDSNapkXi328FnzZqV3LVMiKY5eMbEVPEkidGzZ89Ojywt9653vSsiqtImpU40v8yS8l7UvffeO9G3V8ldXYy7vGvV+z784Q8nOlqPco7ihkrLaFalW7Nnz87X0vr4jDgs7QuB8fVyju94xzsyE8jfet3Fa3+gVO0GuIhY6602Xs/h7cQd8S0iK4w/4nOf+1xaSXwWCjg0pjvqqKMioioq1xJGeif5zGc+k2mazoX17tYU3hxlmuGO3v/Zz342W6+IA5tb6T03dz4F8/jsZz+bhQoynMRO7a10TGvB2y67i0V24403ZrtXXFq2XzdPOGkQtpFGhpCsUy5xyQNl8vzsZz/LWChEk8gvrlmiMiShWfbff//00OEotL+GzDypbj7DJWglY5D3WZdBbpgb1Etc8yJHRIWAF1xwQXpuzUFOKK9x7V7WQef48Y9/PON70ELC+Ec/+tGIqAqncS1zlYNsDGX+9IvNUYsYVgKElbj/3e9+N+OpeC3Ux+OIMbN4cMUvfOELmdhufryvvONyd90A56fP4kW+7LLLOnLVeX1r2Vptezhu3LhWRHUu8EOx4C9+8YvJN80fd4aSvPayl7xXrPWCCy7IEj0ZVKwJ6+DMa1HL/+Az5HP/4z/+Y6IwjzgPNwts5cqVjZe4kUaGuvREWFUQvvVQQFyu1WqlVsFzPY8HUfWBmCo7H2e47bbbkhuIt8qzxHd5mFVUQGL5qRB5xowZXe+nrZVqtWkuubbmiK/V50jDl5k3ZYUP5PFef7/tttvSO2qOcm2tg2ZfOCw082xz3HDDDbtW6dQaDeQcd9hhh1ZEVS6mFQ3P9ooVK9InAIVZS8aqXY058ILylp577rmZA81jKkvNfMRutROF5sZlT//yL/8y16/M3eZxfvbZZ9v28MADD2xFVBYNDzvEfeCBB5Kr2hNZXco8efhZjxDWZ5911ln5XGjMo+x7oYyOD4aVwUoyn9NOOy3XAa+1z64nefLJJxuEbaSRoS7rxGHVFfKg1aXMJcYn8SnxRVocp/G6q6++OpFT7A6a0Ejeix+TwaqIyt+xAGpZQT3rYWnFupS5xKwHyIvTyjWltVkIt956a3JQ/AaCWj+eWjHnkvuTetOAco4QdsWKFR0F7OJ8OGP9uWKNEEqM0vNkcvHw2icIe9111yWyii9DTDFp8U6fX3q46y1jywurWSH2cN68eYM20pMNxg9Qv55SCxjx3y996UsRUXlwxT/FznmHeesvvPDC9ELjv2VLHT4XZ9zfnR9W3L777ptREghuzM74jTfe2CBsI40MdekZ9CnjWjScmN6aNWs6sov8jdbXSA1HE3+CsO985zvTg0jL4mb+T+v4PeSF6oM1Ove7F4vH6oRBw3ofrdjX19eh8c0R/1BLaY40LQTae++9E4HK3Gb/1zLU7yGv9a3PsWz8bY5lzDai4p1ih+Zl/UePHp1NvgkeRcQT8U0IA3E/8pGPpDVR1t1CFdlS/ADWxqXdkLi/vz+5tDUQq+2W6STWq0ULNHf5VF9fXyK/9RRnFY/1DKjI58LDe++99+YeGLv9dg7UOrOmeMKhuqs+7r777rTGND/H+cuzVsqfdXvdi91qPthrbQITSFrhFVdckY4JiyN9zpe+/AJ3k1GjRnUkPrxYWOfPmWOZUFI6RfyfG//aa6/NOTp8gvrlHMviidKxNHr06I51KF9TT/5XoG8dfDGscX9/f9uz689xEI1Z6p95c75cfPHF6eDxBSx7PFGylG9Z1mh/Ntpoo+ykT8qklrrJ/19/b0VUCQvCasJ8ixcvzuejHvbXHnGG+gKhgdI1f/nLX2bfKcn+s2fPjojqxgrmNGeUz6AAKbPddtstQdAX1546Q7fddltjEjfSyFCXPwlhyztcIirNyDFQOoZoUKhDw9FcgzlRSinHqCuh8EIvgexMnbK8rhvC1oP3NCVTiJOJlGV3HBUcWMOHD+9A7B49hSOiPTnlxYTpOFhnfPOzD1DImo8ZMyb3QrKGlErzKhvrMYWZyGPGjEn0IFCxvA0Qkki0YC52uycoojJxOXfKnr3uwLUOzHPv22OPPTKcpTieo00yhPcwmb2OyTx16tS0HjhByz2VlGEfWFEcmtZg1KhRHUX5mrK5Y7Ypr2ukkZeA/EkIW/t9RKxFgxKRujUzEwKhYdfl5rvyGWX64GDj6laA3q2AvVv6Zf3/JVp0m2N5J0z9Wf5drkP5jLIX7393jubH0vC59ZCHFETjrrcAqr+X5SDIX38W9MBzJbCU/Nj8oQ8xnhEjRuTnsgY4YvDgZcuWDZpeqmQTOlqPESNGJMdWIgitfRYuyYEoecMcFy5cmAguMULjBOuv0MUzNZEr937kyJHJVTlcFbqwVB944IEGYRtpZKhLz7BOiXglv2i1WqldSnQpEcvvyxDM6NGj8280Pa9k6Ukc7G6f+jMHBgayWFg4oUzsKKVEvMHmOFir0focCe5SIt+oUaPyPfXWLIONo9sc68/keYVSZWJHXSR5QEVoWUuySATjzRQ+gWjmIQmG99jrZsyYkTwY3zU/68p34bPK2+Prew91cGRtZAYr7oioQleSMnjkjfOJJ57I5H/c1XjL9FIN09wFJHTz7ne/O4tNpKA6Y1ISJfsL0dStGM+IWBti82+FA8pCtQTqJg3CNtLIEJKeHLaRRhr5nyUNwjbSyBCSnhz25S9/eSuik/9A5WXLlqWdLnNJyhfOUHoLpTsq/l2+fHlypLKcildS3NPn41qKEfDWNWvWJBcyZs82zvJu0UMPPbQVUd2ehgfimg888EB+nhQ9HEu8UlNq/Eymi9/ffvvtGZ+WHaRkTYqgz1fILXtGBo7snREjRuS64Fvmhss+9thjOce99tqrFVGtd+mtnT9/fvoI8CreVtduaH2Ds+OM+N1jjz3WcekVn4HsH95ZMVyZRNIiccunn34619ut8fi/sZdeYrfMP/jggxFReZfJU089lb4JqbJ4rji3/eCv0Q5VVtPTTz+dc8LHcdiaZzciqvOp0F1BhzmvWrUq+TWezrNsX5ob2Btp5CUgPTmsPFSaQqZLvWEzzSpjBCJ4T3lR1GAXWXVrBeK1kIP2g8Blgv/mm2+eiF6+xjzLi5Re9rKXtSKqLB/3h0rg3nTTTdOT50oGSAuJeBqValmT+m301gwCQQ/oKA5IrLXxy6L5/Oc/n3etskjKxl0LFy7saBFTxihp+GnTpiUyaFejnQ1LQtsXY+IN9/sZM2bkXO27DCGvYUmUTcrtsfnttdde2WSuNoeIqPbwmWeeadtDJYSQToxVRtLYsWPTwlEgcNppp0VEtR/OoL0zPj/Hjx+fn8+iK69f5b1nEVgn++P3f/VXf5VWSxlTtx5lvjRpELaRRoaQ9ERYHJaWUUXB/p83b15H46uy3Wm3LCVZIfPmzUstA0HLCpayAJjGgtL1DCTvpTmN2WesWrWqTXMdfvjhrYhKs1500UUREXn1xIknnpjlZK4UoQ29h2VA8GAXHl1xxRX5HvE/6Oi9uCsE8mxzrbcDldPMKlAMjbfX24u8/e1vb0VUawvZtf289tprk7tqW8pXUK5daa3UywrND1KyKKAjK4TfoYyP+/nCCy9k7JiV45K0WqvYtj3UBsc51dAb7//1r3+dBeJa1VhX692tZFHh/69//ev8m3HUM5ciKu7KuigtwLqFiGfbd1y2dk1Jg7CNNDLUpSfCTp06tRXRqYVInX+WReVlzSih6aDCRhttlFqZpuLp5R32+7LAHcLSXKNHj06th6N5dk2Dt01it912a0VUVzjUuG5ErOVePJg+D+ejJfFmVgP0UhS95ZZbpmfRnHjT5ayyCIxfZYh1hZ4bb7xx8kQeWFlFWnQ+/vjjOcftt9++FVHx6zL3e+rUqfm78uLm0muPu+2xxx4RUbUA3WqrrToytLT9kX1krcpcctzXPk2ZMiXnxxvOupBpVVbruAwLepdZdhMmTEi0MydWkPV2hkQHXKQlErDRRht11FbzO7AAWBP+Xl4qjtOOHTs2LQzPYD1C5wZhG2nkJSA947A0N86kvpOn7ayzzkrU4YUUb4OKJQrSUrT6jBkzMualXaSqf8/gudXcm3ZUGcJbN3LkyNTUNDft5tKuUrS3dL2gvNAjjjgi34cT4evifeJquItnaXxO02633XaJuniZig+eRbW96mChnIZoELqvry/REqLwF6geqYvGajorGLv5nnbaabnOUOXSSy+NiMrLKr5sbSEttNpiiy0y99Z4xTuhjmbxn/70pyOisiB4onl2x40bl3/D9yGT9kKleK/YNT6oWdy3vvWtHCteqyUQ/wjkN1fn03n9i7/4i+SzfmojY/20rlWv7ZmsDVbSyJEj02qwd74fIhDdZJ3u1tHD1UYx31auXJkOAonZPtji6SnMzHUYfvCDH0TE2kRoSRcm5u5Q5qPgts/1BWU+MC+23HLLDMTXw0YRVUe97373u21z9MVWOExZ+IIsW7YsQxJKoDgGfOn0DmKGc1xJbJg7d27OnwnGISVRXDhM0bWDZJMlHOywww7Z/4niojAcaAc4olJYxi6Rvm5uWm+H2EHTO9ddN8bAwaiH0Y033pi0gTkrzGf8QmOUnDX0pTSHV7/61ensMj/rSqn5XOKcUpg+wx6uXLkyx+d8WBc9vZi+TFTr9Z3vfCciIq688spULp5vjkx1CfycskJJzql5bLXVVh1rbb/RKAX+pTQmcSONDCHpibDMCD+ZJm6gbrVaaUIw4ZgQzDYmEhOAc+KKK66IiLVmGI3J1C4dFMxIcsopp0REdT+MMdxzzz2JYOVdJaXzi+huT7NCL6g5MDCQpiCUMEfmv2545ow6cLhsuummeVs3k7DsBOiZTMgPf/jDEVFpeKmLjzzySJqqEgWgRpmSV58XtGGdeO7AwEAiGvPZfvtM4RtIYV8kYWy99daJfkJN0IR1RqChsJlQGavg+eefT2sEWrO0WHil+Cwoxcx0N3Gr1UrHoM9xHuyhGx/sCzrj7zvttFNaYSwZ6+18lJaYJm3u32VtLF68OB18KJEz49x2kwZhG2lkCElPhMUDaZAyNWvVqlXJMwSi8RsIAZU+9rGPRUTlfHDL+umnn54cFt+kDWlOHIETQeMuWrAewP/IRz4SEZV2loiPp5XC9c5RQTtryrXhhhsmchofTu2zIDxHlbuBONG+/vWvp1NO4sLcuXMjouLtENZczBHa6CZ/4YUXJkpDK2l2OFVd7CGkwx1x2A022CARFmLoYWw+uBmrQ8OwfffdNyLWIgreZj19HgTxewiMqzk37lf6z//8z0Qw1oY7bRSOdJsjPuz/zumECRPSOmCBcWRqzqZvsv7FfCx6TF900UXpuDOXMtlfkr91gbDQnDPquuuuy57FrBkWD79BN2kQtpFGhpD0TJzQPpKUTaGfeuqpRErBY+hT3kIOUXhFeUPPOuus7IqP1+CyNDuvK40mJAGdaOQnn3wyPclCHlCDllu8eHFbQHrjjTduS93beeedI6IKadxyyy0dPJLXlAWglJCXEgIZ30c/+tHk29qICP1AGOtjHMrwIDM+dskll6S1AgWEjFgHP//5z3OOEydObNU/BxJal0ceeSSRU/iKd9ta4m44qz0UITj88MOz5E5ShTtnWS7mB+H5NnwGjn/77bfnnEUYcHUWxb333tu2h2PHjm1rpOf1EHr+/Pl53xG+bw48vhJ5+FaEyKQOfv3rX891njVrVkRUZYaSbpwDfFRoyxzdQv/www9nko01hdKsuPL+INIgbCONDCHpyWHLuCcNXk9MpwV5GHEAGlOygSQH927ibG9+85vTtsfbaOXy7pK3ve1tEVF5FmlQqHjZZZclf8H9eImhXSk4FQRQ9gTpJk+eHLfccktEVHHK4447LiIqpJX4wePs7lVF48cff3zGbHnYzV/htLUVJ5WIz7qo80UoJaEFL4ZadcHdoX/pEV5//fWTb0qTxNv8H8/jBfV3iTVf/OIX87PtDZ7Hy8rfYH54MqR/1ateFRFr0yvL6AAvcTcOWy9GiagQrn67uXG4iU9BBwtQUo5z+pWvfCUiKv/Evvvum1aD/ceZpYSao7VgmbFIndNHH300x8YSxbGtTzdpELaRRoaQ9OSwip9528oi9BdeeKGjLAkq0xiS3HmHIY0i5WOPPTYRgnbxN2l0vLS8sjJOtttuu4io7vLs7+/PseGs4lo0+R//+Mc2brDVVlu1Iqq2syV45wAAIABJREFUlpAHX3/wwQcTtWlF3l9ojEPTwNIgFYKfeuqp6U2HSj4HCvMBiEmb4w477BARVXH5mjVr0muPZ4s/8hJ/+9vfzjmuv/76rYgqSwnCQvinn346/20Pt99++4io1h2y8VccfPDBEVHt6UknnZTzYg2JDvC+2mMWhr1jJeCYq1atylh9Gav086abbhr0flglfIpVyJIlSzrmaD1kI/Et4Jl4Ko//d77znXwtD7s5mpuffAIsEtaDc7pq1aqOtkjOJ8vwvvvuazhsI40MdemJsJpb0RxeSwv09fW15WtGVDHUY489Nurv5ZUTc8WbxowZk8+g0WkdsVwxO9koeIisFTx1+fLlHc2+aWnWQf2iqIiI4447rhVRcVjZKHjp73//+/QkQkmocfTRR0dEpR3xHvmp9cwh2pY3EqLzFuJYuCpvO0SEqvUrI/gScGfrV/eE77///q2IqiEcfug9EZVVZM1wMtyV4J0QV3z8ueeeS08/nwa0Y41IjIdkLAp7iA/Onz+/o1E6Xum1ZfuUd73rXa2I6h5jey0SsWLFio5ziu/aQ+cWokrwr5c5Ggd/AGsCGvOum7NMN+PG65csWZLn0u/KBvhlGShpELaRRoaQ9ETYCRMmtF1VyJPpPf39/ckFeFV56FRHiCfSZDSprJGdd9450YfW47H70Ic+FBFV9gd+euqpp0ZExPve976IqGJlN910U0fxdXkFR9nmVHsRXArSkaeffrqj5I2GxS/FjV3hwPNr/HvvvXd6G42HhhVTtT64t0ZrEBfi//jHP86KJ+hcZir94Q9/yDluu+22bXHYMrd3xYoVeT2E+am8Mj+IIRvJ50GWN73pTR0XZfFHGLd5GAee96UvfSkiqmL8X/3qVxkrhWjlBd9lE7YNNtigFVEhHrR0XlatWpU+Ab9j/eDrrDdZZHLK+Vze8pa3ZNxZvNrZlhdgHfkW8HKWos+6+uqrkw+zJny3zLFpc9pIIy8BWadMJ95PjaVpqYkTJ6bNLQdWtQOUkbsp31V+pbzg4447LjOY5P+K/3mP6hw8SZaQOC1v7ciRI5OHlY3aatdhtmmuV77yla2ItbHEiCpODAm32267rL6Q8cOLaT1wJu1vcEnx5cMOOyyzYXApVgLEM0fWBauDF5nHeYMNNuhorwmV8bO6dtbmh5bH0WjyKVOmZFaY15gflKH9eVDxUVlN55xzTnJSfJjfgSdVLjRU4iWGdOLOU6dOzTNkjNaXlPyOJWjtxK6d0+nTpyfyizTwCcg0IiwdfF2c/pRTTsnX4ufWUkbeSSedFBGVfwKiWgPndfz48fm96XbFamkJkp6JEyZsAZmVfq5YsSK/kDZMsNuXz6DL27ls6EYbbZQHQHE5M0Eog/lkwXypmCY+a999902Ttv4ljui8j5UIRzBZmTOcVwsWLMjkA+a8QD1nlJ82hLODWTtx4sT8Ekuot+nMWkrIl86cbSxz7Pjjj88UOB0A9SGiJOvieZ5jv/xctmxZOhHrt43Xn4tycFzZU1+ycePGpdNOah/zlIOK85ETR0MB5jcn2Ic+9KE0ic2PaSyBohSHnUlfdt1cunRpzlGoEAVydigQnf51pvDFnTp1aibOSAryXoqaaSzxxzn1f+v6sY99LJ/lp/GV9+aW0pjEjTQyhKSnSawrPpOovP9j5cqVaY7RHpAMQnCuMLtoZ6j14IMP5r2bEKrUvrQOjcXMhYo08IIFCzJRg6kFYUgZ1tlnn31aEVWoyGdB+T/+8Y/pzOGE4YrX04cJJIWxNJEvvfTSnBuko6U5HzjJzJHpzGJgml144YWZuCD1zTPswaOPPtrhdGIloQz2cMmSJTk/KGcNtLEp+xFDT0n68+bNy/WDpNL3hIiIwgjnhIOI0+mRRx7JFD7OPY5MNKe8W0fXRNaSOQqVLF68OC0mTlLngsOQZcgCUF7J4XbnnXemheE11h+l87moAzF+Z/O3v/1tniXrVfaSajr/N9LIS0B6cliJAvVi7ohKw7RardRMEPQLX/jC2gf/lzbktpcwDgWg0wsvvJCOCJxF5zikHh/lDKEdlTvhnXPmzIn77rsvIjpbwnRrEePZ3Pe4ou6JS5cuzdALZ4YEdxwGd1H4YHzmvmLFiuSDnG34H2cXbsUZ4v/WAuc6/fTTU7NbD2uNu9UF6uCh+DfrZfjw4ZkkAM2VOUJeDjFhHWhlfv39/flcCTMKI5wHn8da4zhTgiYsdOedd2YZo88h3dqn4N4KPFhcfB7Lly/P0CB+fOihh0ZEZUkJwUgrdU45qVauXJkWiPRKqZkcqdav7LUstOX7c/fdd6cTsrReBtvDujQI20gjQ0h6cthx48a1IirOSOrcgCcOCgtyew/uJkGdF7HeyZ4WE7SGmFqO0t4l/4UMNP3222+fn48j0eS0dXkvy2tf+9pWRMUhaTyc6vLLL8/3Ku8TivFZ2tBAiTPOOCMiKq9yfY6KzqEIzipBnNUgDIJPQu9Zs2YlgmjHI0UQCv/qV7/KOW6++eZt987Yb5YEJP+vtYiIam+sIbSERjz+9RsCcUHFC5BUqMXYhP+kbyo2N99dd921rTAhokqoYaU89NBDbXs4ffr0Qc8pP8AjjzySyMUrj1eaoyZyPoO3GMeePn16Ri2cN95pc/Js55LVZu647Rve8IZEYWfIePh8Fi1a1HDYRhoZ6tKTw9KgvILlnZ/nn39+2v44bBnfknQAUSAte/7MM89MJBKLUq7m/xBN4yqF7tpHQuS77rqr48YwGhMfL6X0cvPa4aG33HJLfh7uDom8l+dXyiCtzTK46aabEkHMX4Aef8e33EHr99aNVTF37ty0BngWeVVLj2xExafEisUEWQ1z587NptXQREySJSV5Q5wXkmmhesstt+Sc8V58F8qJmUNiaaV4IMS/5ZZb0mqzB6w356TbHH1W2cp07ty5ud6sEamKZRNyXJsXvX5Tu/NvD6Vb8uOUCTOsIvF4ltHNN9+cfgdz9H3wGd2kQdhGGhlC0pPDjh8/vhVRaS7choYdO3Zsasby5nMePTFDXlcaS5vJo446KrUZLYe38RJLjcR3IJkkdPHhf/3Xf830wfImba9ptVpt3EAJIZTSMoR2njlzZnpY65k9ERXC4LZiv9IKeXM//elPp4fRHK0pJOKJZREor6PxWTnf//7300sN8Wjl2r21Ocett966FVEhrOdBlmnTpuXf8ChiX/BvFoaxSCH90Ic+lNZQPYIQUaEMbzeeb35iyMZ+xBFH5NnAryGoOOeaNWsGbaTn2dDc3q+//vq5J/5m/iwCyK880DnVjO2ggw7KebM4jNl+KEfEQ60PDu71s2fPzv12hiGt71aZL0AahG2kkSEkPTksL5hYHp6C0x177LFpe+OAZQ6v9pxloS7euXz58oxr4SgQAz92FYafvHWQFjoec8wxiaSlBu3GYWlciAcZaMvZs2fnrW6f+tSnIqLKecYHaU4Iy1uIC86cOTPbqxAJ4WK7OKuYngwbyAzl3/e+9yWaQRDciAVUF3tXz06rz+873/lO5ndDFVaGvXUPLK6Oo0Gcl73sZbkmzoyYJCTBWa2hLDCIL4Z6zDHH5B7geTgsP0kp4p1eD5Gh+0knnZSFCixCZ4fPg/feHM3dZ48fPz7z30vvNatO3rTvR705ekSV6cRPEVGdNz4f+9VNGoRtpJEhJD057MyZM1sRkdlDMnqUhq1cuTJjc+UFVlAGp+FRU5kjznn++efnc1VI0H44Ir6DK9K05fUTO+64Y+Yl4xFQyPjKRuIHHnhgK6JCS9U00P3+++9P7kzb4l0KuFUqiS2K6eG6Rx55ZKKWdZGXqrIHd5IBxifAYoEeBx54YPItaIHLypN98MEHc4577LFHK6IqleNTYPk8/vjj+T5WBv7PM80Ksf5i6ZD98MMPz/xvfFh0AKrgpTKOIL3zIfLwpje9KcvUPEus0ufV51efIw8/ZBUTXrJkST6Lh53F58xZQ5lXWho532eddVZ6+usx+ohqH8SntcExbt5j5/Rtb3tb5pKzUoyH5blgwYKGwzbSyFCXngg7atSoVkSVucEWxxMjKu4B7TRZw+sghMoOFSB42N13350IBt3EF71G3FP7U58PPfGmKVOmdLS4xEVw6nolS0TEJpts0lbAjn9Yl76+vszAkvEjLxrSqtKRrSRejNdfddVVecE1vs4SgSw4q6J9Gpe3nTdxr732SovD/GXUeE/9qg5tTnFGc6jHq/E7+4uzW381m6wDKATxbrnllvTCQkdjgVD+roEd3lmuwzbbbJPVL/ZZ+1avvf7669v2UDRDZly5PhFVc2/cWnYS34bf20O5AMZ/xx13JJLKl2YlQG3nFPI6l2UO/EYbbZRWonPGR+Gc3nPPPQ3CNtLIUJeeXmJVKvhJmaU0ZsyY5LeEt8treSMhBM3Fgzpr1qzMjuIhKzUSXgptaH68zP9Xr16d3NVPaF3WxRJcVbaPcdHWrVYrvaRQwefhH3ghjapCxTxOPvnkjovEaFjPcqWirBn1kj6bZ/ahhx5KzmmsKj8GywRyCZcWnKwZTdJGjx6dfJq2x6txWfPjBbdWOP1+++2XrWHqDfoiOlsGlder2FvoPTAwkL4LcVXx2NJ6Is6UOeKBspkiqhaoxsWn4ZywonS1cJWHs3nAAQckwrIGID7/gnUUS2eBsDL9/4UXXsh9dy54+LtFM0hPk3j48OFtt4IJyTi4AwMDXcuCLIgvjM31OmbOnDlz0sRiViuGNikL4ktffqZN2HDDDfPQek0Zzli5cmWbqbHeeuu1Ijo32QGbP39+Pt+C1/s9RVTmlDYvlJbxXXnllWkqCudoqcIUcji1JPGZ1s0hefOb35wmuPUh9unGG2/MObrZzRfFF6NezmetOJ0oGoeViapQ39+ZgJdddll2iOSgYl5TEJ7t98QcOGRe/epXd9zH6ywxs0ta44YK58Re1gvaracvE0pAOaA3TGJryfF27rnnprPPOdV3uA5gEZWT1rpae+v2ile8IlMkmf3e65yWnSFJYxI30sgQkp4I29fX16a5aCfaqq+vL80mJptULu+hfcpCalp0woQJ+QxarUROYxQQV5Lmp/cNHz48yvkwH4VJli5dOqjDgnnL7KH5X/WqV2WCutCMuXKkQRHWBDRk9k6dOjXNSZ9DrJP1UQ6o0FqIwFqgGvV5SygRbrn55ptzjjpfmg/TlZWw+eabZ0iJWViGVawJ6wUKSbMcP358/g1yQRU0ATraa10gFctDHxZRRLXvHJnWsAzrjBgxoq1E0lw5PidMmJDhIzSPcw/NsoeSdlAT1sWECRNyj+yZn8ZpTSVfSMZAS6zNqFGj2hy3EZW1Ar0XLlzYIGwjjQx16Ymww4YNa+M/ZUuSgYGBNodPRKVdCFJf3oNZ7x9MM+FxpfPE53lW6Xzw92HDhqW2w7fLmwDKBl54OicIbkc7Dx8+PFPO8NqyzKx0FOEwUOXJJ59M5MBzJYYTloA54vE0cd1JUvJRvBhK3HbbbTlHewglrQdkWb16dSY38BEoArCnUJl1ZP6eNXz48NxD62j8zgyuaOy4LrE/EyZMSB6HQ/s8voGHH364bQ9ZgqwXlljdUuML4NThDLOHnD/1hn4R1R4OGzYs5yKFExo6c9bUHK0ncQZGjBiRn+t74bWs2Mcff7xB2EYaGerSM6xDK9KktD9ZuXJlImo9mT+i0ia0Eg5ZpmJtsskmqdGhAC5YaqFSc0IAWmnx4sXJryRmQyGph6VISocIQhoQbeHChemW92wWgPVhNWiHg9vilPvss0+m6vFCsxLwMr/HmawfhMXjLrroovRW4rlS5soCg4gq6cVzS6R7+umncwxQHvpYd+gpfAT5eDp33XXXTOKH8sIjzociBhYMLu4csFLuuuuuvKPInihO+I//+I+O+UVU6Y8K151FZ2/p0qUZNjF2Z4wlZo7Q3Dm1T69//etz7M6IvzmX0LlsS+TZ/v7b3/4201g1LrC/zkk3aRC2kUaGkPTksI000sj/LGkQtpFGhpD05LA77bRTK6LiHbgjVF66dGl6aP/u7/4uIqokdoXLPKa4mKJeifyLFy9OGx+f4EHmncSHcFU8ybh4l1evXp38BS/zuca5atWqNu+bOZatNMnTTz+d8U5z8/mS4sX4fJbkeZzvueeeS76NG/u/Z+GAOKb4tXi1/9dLGq0LDsXHUI81f+ADH2hFVOlxxFo//PDDySeVOUpb1NidR9uYtXtRBvmHP/wh+VzpjVYcImaLx/EHiAHjoStXrkye6YzwZXRrVTtjxoxWROVLKPn/qlWrcg/5CrTMVUaH0xKvc8ZWrVrVcZb83/qVF47ZD783/oGBgY6/+U4ZZ3nLImkQtpFGhpCsUy4x7yGNTqOMGTMmsz80jFa8DfU8v2wwXteaXuO5cpVpdLFe8bmyDYzX7bTTTuk59RrZJaS8d1MuMc1fXiw1ceLEnLecZ3OEBGJ7kM54WA6TJ0/uKKQvG55DUFlSnlG7ACoi1nqgaX3rQaNb03qsWS5xecGU+U2dOjU96FrvKAGUfWMMvLDm6eeECRPS+rJX4pjl/Mr3indbw3322Sfb5pT54Pa0RFhxWKjl2d43fPjw/J2CdfnYUM9nlQ3X63fwljns5laO0z6U59T/Z8yYkZ7k8jWkQdhGGnkJSE+E1QKUdqeJaanbbrstqzQURtPG4msE0tE+YpX3339/2wXREZUGo31luEClslk4NGq1Wqkxxcpw2Vq+56Actsz/xWHuueeeLHYWV/UZEKeey1wfl3Yo+GlEhSTQgFbGvcWky1hzfa4sEbm+Yoy1i41zju9///tbEZVfQDmb29bnzJmTBfnastpD6+3/xOeIl9Zjh6wjmUvWBLc11nKtvK+/vz+tsze+8Y0RURW9W7syH9wt885W2Uht4cKFWcCuMbvPLy/6tpfllSYLFizIv9Vz6esi06meHVV/Vv2n9/KZmL/XNAjbSCMvAemJsBtuuGErosrcKGXs2LGpSWkTWrDeYiWiQqX99tsvIiJbRm666aap/dnz+KSrJ6BQHUnrr6clR44cmejjGVBCrmbJYbfaaqtWRIWsNG+dV/sc2rD0+hHv5U1W3fOKV7witS60wiF50a0jK4NAIOs6adKkfK38WPvD01lH2B133LGtkV7JpzbYYINENHslK83/61w8ovL0n3zyyRGxtv2L17JUeH1lf1m7sjmBPfW+KVOmpBec70RmmYYFS5YsGdQP4Zklso0YMaLjPHbjjv6ubc7FF18cEWvX3Vi9hqed1VB+Lqk1sc/3Oyue4XzU/DQNwjbSyFCXnnFYmpu2FKvURvLEE0/suHJP7I49z96HHPXKmoi1nlatPSGThmV4nHinfEsxPlUTEGHkyJH5NxqbVqs3b64LHmqOPMKup5gzZ06OFe8uPYylV5gnuH6lBy+t3F6WButBM2qtSVgEuLRY75gxY1KjQ1QaHC+ti1xViCE/V2ucI444IhFMozZXkrAC6lVH9XnZy2222Saf/5rXvCYiIt7+9re3rQWkck7MQTzUHo4fPz4rebwGKh933HEd84uoUAla2XvN6S655JJERTnN5cXfpdVGvG/y5MnJ2ffZZ5+IqBqHOx/i1dbCeeCDsV99fX0dTfqJ70I36fmFJSbHBFNGtnLlyjRbOGQMUgjk3HPPbRu0+3Lcbfr1r389v6A21106gv1ln2KLakM5cLbffvu815Oi8IWw2YreiQ1zsM2RWbZkyZKcryQGc/R7bU/K29u/8pWvRMTaUIo5Wi9fXF8CJXKS4B0CCfbCJdtss01b65OIytzUdkYX/vo66CToC2Itn3nmmWz9wiFFufpy6e9rDPpMuQf34osvznHbQ7eOWwv7Yo2Mi2Jibu+yyy455zKhxnq6PYBYqzJxwZdz7ty5mYQjJOYMKRlUHAFomPLm+v3vfz+TgwCFFkHGW551+2RcFNz06dMzfFia6vpCdZPGJG6kkSEkPRGW6UP7MTelsLVardQeZYsQGnWvvfaKiMqcVrqllczMmTOzPOyTn/xkRHQmDTA9oaHAPk3LsXT99dcn2kId6WqlC554XWlO0aytViuRnLlMgwsZKUpnmpV37ey4446pfWlpCE47S1HU0M1t3X7S1r/+9a9zzBCPOVmGXyKqBBY/0RyleP39/blGxouKQFw3HNhDr9eJcIsttsh2KAcccEBEdIb1yrS+Qw89NCKqHr/W9tlnn835GbOSt3p7nLpY7zIU9o1vfCNfU3bPhMqK0FEP59MzWYJ77rlndsMUBkMZoGVpPThDWviwGBYsWJDr4CyxakonWCkNwjbSyBCSnghLK9LKNAc0Gjt2bMfNYRxEyLP7RvAPN2Hr+3vcccfFW9/61oiotEzZpoW21p3fzetQkwa7+OKLs4cuLqWHroLhUmhjXMocacv1118/rQeaH5q4FxTSlP1xccCbbropHVZQgiY3TkkpHBucUBwuirhvv/32RGk8VIiME6Qu5gMdIYz5TZ06tcMXwHkCWd2Hag8hl3075ZRT0mkHbaRAsiSgn0QbvgR7bN8uvPDC3F9/sxbu9ikFD8Tpy1TBkSNHJro5M5CTA8n9SNDT/VGspzPOOCOdotadw9K68fUYv57cxiOR5vbbb8/PlTCjWEY6aDdpELaRRoaQrNPdOuxq3i+cdv78+fHVr341Iqqwg/aRNKsSKW59KYxSB08//fREH2023XRHs9M+NJrwA+0HYX//+9+nF1ZogDY0z7J95MSJE9sWAPKwGJ544okMY0F+VoPSMUjLs6jBtOKAY445JkMMWqT66bVS5iCt11tHvH3evHkd5YVauUCkefPm5RwnT57ciqiQGhLay9/85jfZ1lQ5IAvCa3iYjdFNEDzDhx12WHqFWQh+Skhxex2LjMXDe+5epRtvvDGRG9LzxrLA7rzzzkGT/51TiSV8H0uXLk3ktGbazZS3yZt7uceXXHJJ3g+kNavziutLJIG0/s6rzPezdOnStLT4TFgD5rBixYomcaKRRoa69OSw9QT4iEo74l+jR4/OOzkhBY4qFqiYGyrgIzyNu+++e/KaL3/5yxFRxVfxDBrTtQm0oJguTvfd7363wxrAP6F4KVCQt5AnnPd73LhxmbwvVqcZNasBd4Xm/g7V9t9//0QNFgmvr1gzDSs+yxLxe3eS/vGPf0xroWzJKcZaF+tvf3BZHuEJEybkHvLssxjsIS4Onb/5zW9GRGVVHX744Ymo7p3l/xCHtS8KR8yP99xn3HXXXfle1o51l8BfCt9CWZpYL+FTdgn9NPcWS5aUA3EVQvABvPa1r82Ih3PqmYrwcVlc11rYQ2eQnyei84oOZ6ibNAjbSCNDSHpy2DFjxrQiqm99mQq3cuXKjtYWkLTU+mJSPH20+eGHH55xRFwMGvg9hJfO5xn4D4Tr7+/viPfRbkrRHnjggTZu8LKXvaznHJ977rl8Fo6IJ+PUMp5KLzZP49lnn52ZVNquKkljRUARGVlKw3AoPH716tWJ3DyvPI20df1uUffDasnCi2tOTz75ZKKIPVTWpviCR1/apH1Q3PCNb3wjrS4CfWSH+Qmd7B1+yirp7+9Pj66sIwjK4rv44osHbQZv/mWRiPnV58g7r6iCpSVbDMfm7T7kkEPyHGqhgwcrieRz4Z+QRWWuJ554YkS0XyLHa20/7GlZQkgahG2kkSEkPRH2ne98ZyuiQkOoA0UHBgaSR+F+slJwNdoH2ogVQs/hw4en9pdHCV1wU3yHJxI6QTjaffny5amhypYstQLpNs311re+tRVRaUexPDnFw4YNy/F4BuuBZxMflE9NO1uLESNG5DNwZYK7QSsczzNlQkH+J554IjOc/E7GjXh5vUj/wAMPbEVUXk9caebMmRGxFmF4e81PXrMMtPo9txHVVaHWf9WqVbmHCgfwXWjNkuDhx+MgPl/DU089lYjqTJWNy5cvXz5oiaRsqbI52rBhw3LNoDcfgtiyDDN7yGqyXxMmTMjMK3sDFcWQcX/RC4UWvgM+e/Xq1WmtlRZhzTpoELaRRoa69ERY/A4PLEuB1qxZk4hAu9OCKiUgiFimjCJ2/Vve8pZEMzFIWSAybXjw8A0xPc+GaBdddFE+v7wEmAZ74YUX2jTXxhtv3IqoeBLkhypr1qxJbUtDlq1ylM4pyYMM4skzZ87MWBwUMGZ8hzXB0yhLRlkghL7rrrsyxgxZeUfNsc5/tt5661Z9PaA+Lrd8+fL0JPPc49PmxzuqSRsLA7fdfffd07Ixd3F2ucXey3MrXo8Pq9A699xzE5nMmSUhrrp48eK2PZQvYAxlE4BWq5XoaF9ZZdDbfuCZ5mjvX/e61+V6GJ852Xf+BvvAAy7rj+/jnnvu6WhCV/oRmgL2Rhp5Ccg6ZTrJJMIl2d2TJ09O1BXXUjmDExCxUzE8ntTPfe5z6UGUoyyupRhexYq6TV5izblkmowZMyYRtZvmKjnspEmTWhGVhi+vgZwyZUqihuwjSM+a8FnyY8X25P9eeumlGcuFGjQ5a4EXnVcdJ8SX1ARPmjSpA3HwbnOtt8HZYIMNWhFVdo71t+8bbrhhou6XvvSliKhQCM/Eez3DWJyLI444In0AMrNwct5i8XlrArl4YeVfT5kypaNixnkzv7KRHi8xvuz8kFGjRqV1pLDeGpbXXuKwKsFUFR111FG5F+YoMwzXd8ZlRImp87nUc+Stcbe2Mt047Dr1JWai2igfMmzYsFxE5gLTz5dQwJ9ZrSOBZ02ePDlDFqeffnpEVKYGAi6YzVSTfC78YzOOOOKITIXkwOLqr/WaalsIfXttJIdLvQct1zuTRxjFM/3el9GGOjjjx4/P0BOFIFmEs4XjRzeLc845p208Du9ee+2VISLOQGY0xVI/0OPGjWtFVEntvkj1L7vwCeohHOHLhrJ4vgPJNBw5cmQWuVNEQkDW0T4wu3W8kHxhD4866qh0UOn/bP85b0ql6w5DDzJdAAAM0klEQVRcISLJEfXQiX8zjcub5iTdc5bZS0608ePHJ2BxoFJgkmxQFOumHI9SonhmzZqVCTNl54taX+TGJG6kkaEuPRF2+vTprYhKs9IQ9Z6uCDbyTtswY8tuetCJWXvPPffE5ZdfHhFVGptkcwgF0Wltn09bQ4SHHnoow0aQCQp6xpo1a9o01xZbbNE2R6jOPH/yySfTYQMFOQxYAhCVtmZV0NaLFi1Kk9i4FJCzRIyTtQFxoAonzrx583K+zLnyXph64vjMmTNbEVXHQWOHeEuWLEmE5ii0V//0T/8UERXCogD2lNPppptuSrRnBUkbZQ04J2gDi8scpB3ecMMNadFBN/tcuwVh0K6JZSf+ei/sem+m+jo4p/bYOjlH9RCdcylE5ie0tA/OKUHTnJNnn302E3WcGchKGoRtpJGXgPRM/hdI5wChIZDnxYsXZ3hG2paWH4LWtI2fUgQ1aVu5cmXyXrxTKENhtuA1vgwN3Z5OW91///3pKIHCpPw/8SzJIDiOz1yzZk3ybuEcDjYaH2/Trxdi4TirV69OhxnnCweF5AuOCSgB5YR7BP5/97vf5Th8vnXBResCMRROSI2EiPPmzUtnmvJACQCsDA4YoSYNzYTqli1blr9TMCBsIzGBw4YDy/q6TV7I48orr0zuChXLsrlSyr7Jxi3Et2rVqhwfn4E99GzOJVaT4hG+mdWrV2cBurUz9tLBVvbLlqTCUqmHH7v1Re4mDcI20sgQkp4cdvz48W2FwV7LGzZ//vz8G14lXY1NLrwDcXEbr9tiiy3Sg0qTQxf3kHo2bqjvK6/g4YcfHhFVMkZEpV2V6tVaswx6t2jJf2jYhQsXZkAeOkmuwHeFJKAgngpNRowYkUkUkvtZK6wXc5SkIMG8ROadd945LR9WA8SF7AsWLMg5Sgwp0Urp2t13351/g8JK8PBqiMeSUcAuhDdt2rRMkDBOfBdysaJwVUjGpwG9d99990RI/N54rNlDDz3UtocjR45sO8Rli5gVK1bkHMsewV6rON94NAm019OnT89QJMvOGtozPgD+Bv/HgzUh2HDDDTva2dR7Fkd0hq5Ig7CNNDKEpCeH9e2ndaCiZIk77rgjNStvH6SAwjxokhvwLc8877zzUhvTrEqwaHQIghOKKYr91eNfZTyLRuW9LgUK1q2GiIq/n3766ck3pRVCWu8RO5WAIMaJr99www0dPFPBN+SXBC853roqouCFv/3221ML8z4qS+w2x4iKP4n7ecbNN9+c8UK8EjLghMY2Z86ciKg4mvnfcMMNacmYH+6Km/Ies4YkqvAEi2Vfc801adGYn79Z/1KMB6KW9/b87Gc/yxa6vLL2pryZgA+EteI8n3rqqXkmxHDNxXitn5RZzRpwXc9cuHBhxx1Hxl56i0tpELaRRoaQ9OSw0vbY6hDEeyZNmpTaBsLSHLQOb5zUNZxNVtOhhx6aHjxxV3xSTJI29t4f//jHEVHF53zmKaeckm1NxS9p2doNYm3cYNNNN21FdDatrmdZib/yMEJL6CheyUvIMlDg/IlPfCLfw2uLp0sV9AwWgSwurzf+ww8/PJPK8avy5vd6JpDkf55usW6y1VZbZeaS9eeNhT4S9O2h19vDgw8+OH8n3mwsrCLF/ZCeZxU/dV5OOumkbM/iTEHabqmJo0ePbitwKPnpiBEjOrir9WQNGadYq8wnt8EfddRRec7sMz+JzCxFIObIm2yvycEHH5xedHMr7/Rp4rCNNPISkJ4IS3NBEtqJR+/kk0/O2CPbm0Zir+M03vsP//APEVFxha997Wsd3mFIJqaHU+F9OAF+LD7GC1cX4+hWwK6FijnSmhDhhBNOSITHjaAUTVreeSrBXZ7sHnvskVkxOJQMoNmzZ0dEe8vQiKq0kJbWHuUnP/lJIg2+Dlk8e+XKlTlHVlLZ/hXHPOKIIzJGDql5vaFemZWjnYvii8MPPzzHzToSl1cIIc7N/2B+uC2/wIknntiR9F9eu/L8888PmvzvfdBdccLpp5+exQa4uzxo58Nnee/f/M3fRER70zYF/Lzj0BGXVbghIuC7wX+jCdtll13W0VpJTL0WrWgQtpFGhrr0RNg3v/nNrYiKl8hAUk60bNmy1L48irSgNhneCxXxUhrvtNNOy4bLkJJWFmfFd+SYQi78TkzrHe94R5apQczy7tCy+Hm33XZrRVSZOO5YNYalS5cm+rEWaGEeRDxMK1MxPZ7db3zjG+nRxpVZBzyteJB1g/TlFRuvf/3rsyTPe8t7SOuNxPfZZ59WRNVQTSaaUroFCxakH4LnFDdmKeCu733veyOiQmC+jaOPPjp5J2tIVRLk1NxMDF0JXcl599lnn4wSWCuvNb6HH364bQ/xdF5krYT4OtasWZPngdXASrGHPLwaClhj8eMrr7wy/QyQlC/B+ZATL55d5gI4p9tuu23y4NKaqHm6G4RtpJGhLutUwE5jlXG4YcOGJVJqRi3rw2t4O6ElNMX/Lr744iw8FivDCXkrFQLTmOVt2V4/bdq01GqEhsQVyiwZHA860pL1AmO5oFCqvJTJ73kWcV6x5oceeigtkB/+8IcRUaGGuks/yywpPAkH33zzzRMNaGdVQSyAm2++uaPNKd4p77le0SKvGyrK5IFC8pyhp1pe1tV1112Xcxc3Nn7CcoDO5d/Nb+bMmZkdZ39lCtUK6wflsDgiflo/22UusQwy1gmuiuvj5KyYW2+9NS0mHuWyHasMJ+Mv26z6/+jRoxNtCQugVjfcIGwjjQx16YmwRx11VCuiihXSJDT6yJEj09tLi7DzeRyhJ76pWp8mO/LIIxNdZIjQXDxnclp59HAGLTq8f/ny5R2cleb2+9JL/PnPf74VUaE3jgOZIzqrMCCQ15b1orynNP7OO+/c0TDb3KwpzS8uCJ3xR3NctWpVIo330tYy0Or854QTTvj/2rt3nEaCIAzAFiNASAiZEyAkMiI4AWfhIhyHAxD4BITcwDkIIRmRIB7Cm+xXFGXjYKMdqf5kZXamH9U99e7q5WTyfW0EurCzdnd3I28W3eXMku7sUGOU+UVruLq6ChqIuxuLMfrNo20POaFFU3t6eor1ZiOzTflHqn3HTmcn8wSzgZfLZWSw2e/O8votD52mqHIJG/fy8jKkMg9ylpiZXta2nhry3NfX10p5U2uapPG/l4ihelErEO79/X1l0AZCXZLAQJ1ATMnts9ks6rr6MIRNbAipd9QWsBlstJOTk1AXawJ4Orb2gxBKqFCZzRGRc6V9C1ATyVXYozJaBB/F9fV1OHDQjtpkU2mzJkqgKxwdHQVjMGZ0sNEfHx9jjkJzmJ0PRT+LxWLlwIP5SoYQerFhjYmzajabxTFB6qgwFsaMrpJJzE+fGPz5+Xn0Z9/V2lXZqfaXdj82cb2vJu/xenzNs/p3wEDfDrLf3NyEWi9Vk/MTbWvlyNqncUyn00gj9bdag6wWWoi21v2x0Wj8n9goYXEunAP3wQX29vZCujDEqV6MaOoriUWi4PD7+/sRiAb9GVtNTFAMS6gIhmFYqT5H1cK1n5+f1x7NwiWpotnxhaMKhDtuRrKRROZIe6CyHxwcROKIdkmNqgpRHUksh8lx6Z2dnZXjjqSmtMqHh4eYI8eh5AOqtf6Oj48jOUOiCucfldT86k30+ptOpyH1aRckF0lKgllryQSclFkjqonxTCXm13w+X1uEzXtV2xuGIfp3awFnqHdoT9RadLK/c+XFWoLG7+pk4rgSSsvFCytokZyRufJlRkvYRmNE2ChhcS6OAhIu31/DNsGZ6O/axY25x0kQ9tYwDBE0l6Bej4CxQ3FBgehcilRbv9nQ5rBYLNaGBGrpUhrC29tbOFRwaf3Xo4K0CU4SbW1vb8e79QYA3JaE1y+JBflWcZweVzZH9Mm3zNOS+BCkF+Ybv9ne5sVBU2lZS+XkJHtj4mSUGALaMA9tWMMc1sjSfzL53g+0s98kbE1CyBJQv9quYRWaQb1rlga0tbUV7+ZiamiY/7Ufa+gqFxGovh/PpvTKlrCNxtix8QB7LbZc07teXl7i/3B7YRScg/QkMUhk3Pn09DRKgeCopA+uTSqxEeshdRLu/v4+wie8xRIiFBirIDX0iXvCx8dHzFECO85KKpojO5k0YfOdnZ1FiiG7kLe43qau7Xp/KbrN5/PwODs4z3srBJQh6QSNjS2HHIzb2NjmnqEd3N7eTiaT1btmLy4uom8JFNIbzVNoyLpU771D6nd3d1EUTkKKUKHSNBVVKtqf8Pn5GRIMXa1ZtUPrrQqeOzw8DO3QWtjr3v2t7erxf319Da3NnvabtvQbWsI2GiPCRhu20Wj8X2gJ22iMCP3BNhojQn+wjcaI0B9sozEi9AfbaIwI/cE2GiPCH/SlnNefCskRAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, Iter: 460, D: 0.05643, G:0.741\n" + ] + }, + { + "data": { + "image/png": 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9syl4SDTsD3sKovXp08cVJmBtSHLAncW1sM4yX4JOCDIIqdQ9tB3pgrKo7rfoiAT7c9bYQ+wjjJc9DDvO4fHAWs6eoevbM7/rrrtK8umizc3NDllBdFveJo0iwkaKVEbUJh22Xbt2jnOBdvhZt9xyS0k+fQnLow0ba2xsdDoHPjCscHBtPsf6ZpGuUG+YkNqiw+KrAyXQO/E5Mh6LDE1NTW5soBdcGQs53BjO3pYucKUUYQv3cKuttpLk9VD0Tsq6ID3ZLg6rV692qMIeskacCz5n3tbWgeTRmj3s3bt31qS4B9fs0aOH+5vCAdhJsOgjzYF4tijBsmXLXNDP9ttvL8mXl8HSz/lk7syZPUaCmT9/fmoR/OB8RB02UqRypzXeqiNM0g3fEy0Dxw1LZKLHXHjhhZI8omJ9JcwN61xa0nwxtCb7w6aVBcV/iaQwdOhQ7b777pJ8UXTbTgK/HwH1baFwjqUkN9hEduZjy6psttlmkjySbLTRRq6o2bRp0yTl6rlTp06VJJ1//vmSsltzhO+TxlOokF6PHj0y4T35HWvcvn37nH6snEdsKdgdsGITkYU1u0+fPq4w2x//+EdJ3h7DHCk+fuSRR0ry55h7YZmuqqpyY0MftskQsURMpEhrAeW1ElsOh+6CTL5w4cIcRIVTYRUFPf/6179K8jrulVdeKaklfckmgJP0TPoSPkusdIwHq9ya7AzHfPD5osNIudZhLI0kAZAyiGX09ttvl9SCqtY3SGQT6YfoUHBc5mhTCVs7H64HwuA7nD9/vvuOTXFjXlg5KfuCZZWys5dddpmzeqKbUXiMNLuLLrpIko9HZq9J+EjqblisZZ85sVckj7CHM2bMcMiJHYJzCBqiv1M8jjlSNPzhhx9264Lkce6552atCzHEzJlzgE8aifGTTz7JKcVq26GkUUTYSJHKiErSYYtBMtuSEK4NIc+j502fPt0hEwnf6BXI/DYaBp+VtRqXakFNmmMppVisrgfxntjTv//97zrrrLMk+cJd6D3oLmnWwrSeufkoX6RTWp/ekOwe2pQzpCmkp9NPP91JE5Q1JcIN/3FYilbyCE+qHlJLayKdKCRuC6GDoplMxiEX3+E9KExZU4j1J8pr7NixrmDBddddJ8mnzxEnjY+X9SG7jOyzsIRQWnMunq2Ghoaow0aKVO7UKitxMShks3fI5kAvpRzm0KFDnf4G6sL9LRqkleMspbxLU1PTGm/obFsX4ttDTx84cKDuvfdeST5yyVq612RxtWJiiUuRlpAYQC6K5mFb6N27t0PYQnsYjIuxZr3mo7SGZt26dcuyEnMtysy+//77Wd3YJY/kJKjjP6aEEd4MfK319fVOV6WcDJlJ7DuReLbsLRZq9PxMJuPWg/sjaSBdfvjhhxFhI0Uqd2qTHzasDmB1Llt60kbJ4KPs2bOn0yOsPleMvhVSp06dsqy6SdQWP6xFJWs1tpFOWHg7deqUU2QruD/jKmoM7du3L1gSp9RIp7AdheT3wSIE79lLspF69uzpbBOgCNcsdQ/XXXfdgrnAdg/r6uqymmGxPty7W7duDn2xKxC1hC5NHjJWZFATC++YMWOcBR//M/tLLivrYb0erBfnZty4cS7WOi0+3UqCUFEPbGvENit6QLwPg9qLdcvYcdjQxEwmU7C64JoMnCi0LjCipB6o3yYlPbClVtOX/ANqDUJhyJ/UIs61dg+5Bw90c3NzwWvZPezSpUsmHA9BOTDHDh06uOvb5BQ6UPA5wffsGeGxs2bNcqmCPJi4eWz4KGIuIjPGOUBq8eLFTlwnMMOOy4r9UBSJI0UqI1rjbp1CRG1WTO6hWG0RE/q2DDLf3L/Vbp00gnuSqvX/TfmMTpaTt4Zs0kNSUbGEnq5Z79uyznYPq6urM+E9OWNhfyLuY7vJ8xvOdljpUvIF12pqapyoTeAQ6he/Adm5ti1Vw70aGxud9MVn9pmKCeyRIq0F1CajU2VlpVP0Ueq/61SqDltRURG6E77Fka05KqVETE1NjUMCXAvFUoisaeeoWCQtBXELIawtJjd8+HAn9fzXf/2XpPRyouwx1wj7PVm3EdcnNBIE5tV2eyA8ctmyZW6+fNemGcbAiUiR1gJqFcIWY3EslmPW1NQk9p39tmhNWomLpWK6iq9JKgZh8+myNkCl0Nhra2tbFULZWkpDWM4aUh+oOnfuXPc/5oQLC3cUQQ+2GBo0cuRIzZ07V5LXg0FQ3D24ebgXFnDb1SB0zVlE57dLly6NCBspUrlTXoSNFCnSd4siwkaKVEaUN4G9UHmRysrKnGJaazKJPC1sC92gNbqv1X9sD1yru4T+N9s/Nc89ct6nFQOw723pEFIIk8iuS6BztqqQ+P8nrUk/bKFzWlVVlaMrBm0/Wj0uG+EUJJhIKq1vsSU7R3ePkq8UKVKkfxmVVCLGUpcuXVzxZstdkr4rJZc8sVzP3tdyMtv/M7RiWvQtFNljkc6Oob6+3gV72xKt1ppKIDkRL2FSAOOySG4D1bkWAfgW1SsqKhz6svZYPAslPiRRdXW1W5tC+11qIH8+KuCdyPpOoXEV+rx79+4urpfzkJaEgU/aFkWT0vfftoyxReCs9FlRUZGTDFKs1BgRNlKkMqI2lzm1MZhwEYu0aVwzLGRt09SgNeHDTNLvvvl/VkaSRfPm5mbnq+MVvxrc0OraVq+vrq52c7D6sI1xTSscntTwy0omgR7cKh3220im/zaoNb501sbqlexVGponFYe3+2v/X0rx97T7paXXRYSNFKmMqE06rJSLqJa7UIyLAlUUuyKBOMykSCsJ0xoq1mptUQoKERdE5dXm+tKAmiJdlMx86qmnJLWsH98l7hTdxUoTSRJIeK9wTRiH1UFbS8Xm9VJOheigkP4VKF3I5hGWZLFni9+QuE6uqz2nYVYZenCaLl+K5bmQ7Sfn+0V9K1KkSN8JarMOC/fYdNNNJfnWj9baBZe27RQqKyudlTOtXaC1xlnLb6jTWU5lS9dY/aeysjLLh2d1iRABKfr97LPPSvLVCWwRNvv/2tpa9xkWZ1silfuwboyb9YKbJ5WIAfGCkqGt0mFteVl7NtKkloqKipxC8pZKtfyGZKWMUv2w4TXI5aU0ado5tfG/1dXVTjqyLSHDsyz5vbJrku+cWvtImh+2zcH/xS58Wve6qqoqtxAYBCjbQYdra4K3KX2hOGsDwNkQ7m97lqSVUAndMGGf0W9+k/VdG7jNb8M54oohXYt6t3QAQNy07gUORxiUzmbzHdaBTV+yZMm3GjhhCw20a9fOMRheqR1NZX/rVrMPf9L5KdYgk1bKKFR3CiUy8DnnxJbHqa6udusLwMDgEJtxq3Etvo+LqJhzCgNfsWJFNDpFilTuVCg0UVJ6ITUpHVltbVZeqdVLD5PjjjtO//7v/y7Jc2GCLAhEOPXUU7Ne6VHy9ttv54wL7mZROU2ptxXtrGhaUVGRalCz1ePhvPRjoZvZwQcf7HoLhcXGJKlfv36SpBNOOEGSdOKJJ0ryNYBfeOEFSdnuH5AD9LUItKaJ64I+9D6iPvFxxx2nP/3pT5K8JEDVe4JIzjnnHEm+ux17m69Coj1baeiYZpgL9zzNEGQ7ASAhXHLJJZJ8v5wjjzxSf/7zn7PuQw1mVBJ+c/TRR0vy55QeO1AotVnRu5DhMCJspEhlREXpsGlI26FDh5yOXlYBtwEBgwYNkuRLQG644YauJuzDDz8syXOsPffcU5Lvz8Ir17aB8RUVFanFvwIDQKLBwuoSUNeuXd19bI8ZuDE6CutEVzvQY9CgQQ4N6XIw8JsubgcffLAkH7JJ4TDGDUKFnNd277aJ1KXosGFyveXuaW4jSp2wLv3793fzQyKgUBlIy+e82tDMYqiQDpsW2F9XV+f0fJvIbu0h/J9Cbpy5wYMHu7Hfddddkvx5mDhxYtZ6UM403zm1urut7x112EiR1gIqqZC4dfJWVFTkICo9Sd54442s70J073733XclSY8//rjeeustSb6vJ24FSnLwSi8XSk/iBtpmm20ktSCPtRAmcLJEt44NsQwDt60VGNfArFmzJOWa7X/0ox9J8qVc77rrLoecW2+9tSSPvug3WFPp/oaeSMDJXnvt5e4VWi6Zd/jaWrcOe0kle8Zo9xD9mrHNmzfPufNwfbE3IC5BJOzhzJkzJXkE23nnnSW1rggbUlIx55Q1w0rPObVSxA9+8IOsz2fOnOnO7BZbbCHJB47Mnj1bku9SR29c+kcRjLH77rtLatkn62GwRdpjel2kSGsBlYSwtit5yLks0qLn4GOFG6PbgFadOnVy/U4efPBBSdLkyZMl+ZKUcEOQFz8tHeHwYVZWVqaGnkFpgeOMG+sl96qqqsqZG/owYZfoHbRdAKGwdldUVDhr8JNPPinJd+mmd4vVewmNoyscQRHt27fPaRFhLdyhjmcRtpgUObvfzBs9G90NP3BFRYXTyQkqoYMfaAMqY7vAgoqvPUS4QiibtoeMm3UP9WV7TlkH9pu5gpKEKuJLrampcfv92GOPSZKOOOIISdJtt90myfftAXkJJkLnZf1CX7r1D6clqUARYSNFKiPK64eF4Hg2QTosfWI5BboJkT1wPUL06Be77777umgf9LbTTz9dkkcMuDI6LihkLYwVFRU5YYvFRmLxOcgaoheIatEJ/RO0YG68h3vvsssuDnnwQ9N1nmtiDR47dmzWe9u5vF27djnB+NaXnI+KQVaI/bbRP+wlqLn//vvrnnvukSQ98MADkqRDDjkk65rMB3sD75PG09YEAhs6mAmapFmdEf2Sc4pkyHukpUmTJjkvBnr4BRdckHVN7mttMTYmoLKy0q0pn6UloViKCBspUhlRUQibj6x1y3bthutYdAI133zzTU2ZMkVSLkKARkTRYIl87rnnJOXGY65cudL9DVlfoqViinLxW175Dfo5iG+DvdHxFi5cqD322CPrPlyLqKgbb7xRkrdKouuyXqDa4sWLXZyu9TG3tZi3nbuNoWZ/QEcswXPmzNGhhx4qSTnIQQfzqVOnSvIWfyzs9l5htBrU1hIxmUwmJw2U+2F/4Jzij2X8oOTbb7+t8ePHZ92P7+Bbv+yyyyR5yStMrwzv9fXXX7uzkzT/fBQRNlKkMqK8VmI6W9t4x6wLGIsiHIpWhPis8E0SQQI3qq+vz7F6HnDAAZK8r5ZUKD638aegT1JJUMt9rYWxZ8+eGcmjRVIaGP9bZ511JHmLLRx0hx12kCSHoujnSAYDBw50vwGd999/f0neKon0AOGbhsgMWbBgQQ4Xtj7n0MKIn7mYMjtWCkLvZg/xsaOHIgHV1dXlNH4izph0wmeeeUaSRxLbPK2UNph2Dzt16pSR0htnh4jNWUE/Z47bbrtt1iueiL/85S+SWvbetoZEMsSHjlSExINnBCKC79NPP02VZqIfNlKktYjy6rAW+ZL0O/4H94FLo9PYxkRwNvxbu+yyi+NEcCpKrGCVg6OhZ+DTJGYzTBgvNUEaZE1Lzg5jbUFw5nTKKadI8n5JLIz44fj/T3/6UzdWfLXDhg2TJD3yyCOS/FqDxKAZ3w9LrdrsnHwldUopYGevQ3QV9yH6Bw8AqDhp0iSnk2J13XXXXSX5zBWIc2ITxdvSYNrmEuc7pzam+LDDDpPkzyPIyl5jh5g4caKbN9IPcdL40pkDe8iZZ++w27Rln0py61gKHcAQIX0MElcHDxkiCK6PV1991R0UDBKI0/Pnz5fkk7x32mknSX4RbWJBPqd7WtoS/0879NXV1TnGHIL92UDEVZztiPAEE8yaNcttyBNPPJE1R1IE2XQMG7bWcJg6Z8eYVB2jLcQawnh4uDCUEIqHeDljxgwnBsKYeIAJFsAQwwPBubBi45oYt6XKysochsCDi1GJ8WDQ4/8kqzz33HPuN4RbMkfO+qOPPirJh2dyLVS7pHNaah2uKBJHilRGVFKJmHxiJiIGYhLiAOiEiIEIAirtvffeDplIs8OoQUA4JTjg1qCTFSPC4H8oof5T3ppOtoRNU1NTTtkWxCfEVtw4cFpcGBgZdt55Z7cecO5x48ZJ8gHhzJH7IlXYAJBVq1blJDTYULxMCR3YQ7LB84TiIXKC9qg7oNLJJ5/s0IQxbLjhhoiOvTcAACAASURBVJJ8CObjjz8uyYuLIGsxKkwhw6Gdoy1hE14T1woSAePkLGHQJO0RyXD8+PFuPew5xYDIuUQyIezShh02NjbmnN1Ctcfc3JL+GSlSpO8mtSmBPZS/bQ8YjE/oLKATuiLGlBtuuMGFIhL8f99990nywdP77befJI9YpK1ZvSQcX5oSn5aalVZrtqKiws2fcDWkBZD2hz/8YdYawL3Rdc4//3znxqJUDPoOKH388cdL8pIH3Bm9iX0K55xWE7q16XVppVZI2ECvhrAxTJ8+Xeeee64kb6OYPn26pBajouRdX+jDGGDS+inlo0IJHDYIJnTN2eqE7DtGQNYfYyTn9E9/+pP++Mc/SpLuvPNOST4JgD2cNGmSJB+SitRkXV6h3SctYCcibKRIawEVZSXOg1bubziW7VIHQiDX20Jr3bt3d9wN7vf8889L8kHWXItXrjFmzBhJ3ikfjimtK50lOG8xvWbRvwigQB8FLbgGKB1ayEm9Yt5Yhwmah6MzR9bzJz/5iSSfahiWF8Fqa8MdW0s2qZr7cF30U3RZ9NbNNtvMdT9g3ZGCrr322qxrMU/uhQRmC5W1htKSH8JzynmknA5zYs9ARfYSV93AgQOdGwdpEQmK4ArWCf2c97gpkarCUkYgPFSoM2BE2EiRyojy6rDod2n9P6qrqx33Ii2MwPyf/exnkqRbbrlFkrciW+7d0NDgrJAgF+VU0GGxnIFkFkXhVsUEv1vdoLa2Niv5Ga7INdG5JK+HobtgAb3pppuyvmvX6+uvv3YWZLg0vtqf/vSn7juSDzyA0yJ1hAiVZP2UsirPF63Dhj5BpAD0NsJJQQbbwza0pGO7ALmQqPBjgsq8pvkhiwkgSLP02xTPMDmE/1Gi5+mnn5bkrfT4wW1BdJB2xYoVznaBZZlw0u23316St6bjn0/rcpcWQmnmEHXYSJHKnfIibCmB49aHh35l05gowoacP3LkSBcZctZZZ0nyXBj94cgjj5Qk/du//ZukXKtoGKVkw/YSyp4mIqz1d9o0LCm33QhWa5sAQbkXUHXEiBHOkoylEQQiUYCEh9/+9rcKx8P9wzI4jC2ta3drrcSWLKIiJYEw6H9jx451SdtY/NHB2cPddttNktdpW9NjB7J7WFVVlZHSddgQxW25HyzfVqJCuiNya9iwYU6KPPvssyX5PSGOgLI4FEvnXNri9GG4a1rJnmgljhRpLaCSWnVg4STiY86cOY5T2eZN6D9w3l/96leSfPwlbQ3uvPNOp3uiB6H3kqJFkveoUaMkee5NTGtSEfBiYzVtWVMiW4gDfvzxx931bZwpFlwspKAjus3vfvc7SdLdd9/t7gNXxrJI5Ncf/vAHST6VjbVAX+beS5cuzZEskrqEQxbBGAcWbOwCUm5UGJICeh/pgvhfKXNz/fXXO6mIcWPVBlmvu+46SR5xGTNrWWwCdxJZuwhzA/lmzZrlkIwzbEvkck5//etfS/K9fmkjw/jDMVKSlnI4v/zlLyX5FFK+h70Ce0Roa7HSYqHzGhE2UqQyopLKnAZtBMLvZP0GbgdHw2oIB0VHoIDXmWeeqf/4j/+Q5Muk4JvDV8n9uDaZPrbQdTFFyNL0H4jxYhEMC3jZ8jfokKAGSMwrzZKOP/54Z4W86qqrJOX2KYXjc230RHR9JICGhobU1hqBzzlVhy0Fyex8rQ6P/3z69OmuURR7yR5Ziylks5HaosPizYCwn4TWWGtjsc3aGI+1EnMWp02bpptvvlmSR1s+IxuHubA+FHBjDVjzMOa92HK8bq5J/4wUKdJ3k1plJQ6tk1b2tj5S0AbdjKgPSpoOGzZMF198sSQfk4mMb62glksmxWZCaQ2abTOsmpqaLAsj40cnX758eY5l0RbV4rtEXuGXY46DBw/WaaedJsnrvSA4OhU5tralBHqqbQcSfof1CaSDglbiYqyzdt5YtPfZZx9JXg/fbLPNXIw0koG1sqfFoZeCrGk+yjQrcb5zCllpafPNN5fkY6CxpwwYMMCVNcWCjORn45MtilsJMJyz1b9jM6xIkdYiKirSKa2dQGVlpftfWiYLXAcdAa6DpbF3797On4lOyjXtNaA0+b9v377O95lGVjeorq7OyvSwltf27ds7qyk6CfdAdyU/kkgtuPZLL70kqSXaZ7311pPki66Biqwb1nVbNtZy4NGjR+vVV1/Nuw6tzYdFGgLNrdXY6rBhtYawhGdIpVp/O3fu7KSNNErTYdOyjaqqqnIsyPjBQ0mK+0secYkt7tu3r7PUU+bVomKQy5r1avenf//+7vwXO0eoTaGJ+Uqy8LDxua1tGx5+DooNC7RkDy8GLA7Y6tWrcxbPUtoDy+HkWuEDY2suMycYDe9tfaIwDBHRl4OR1gPIGvZgCrz/+uuvcwxjCSF5ayRwIl8ieDi2zz77rNXV+pMMZ0XUGU4MTbRMN3xg0tx8hc4pIv4777zjvmvPSDh2KTcYgnMaJofYfS6UpA9FkThSpDKikkrEYC6HK7Sm94lNEggRDM6UFnoIpfUhaQ13Rorg+7a7eFiJPo2j2uJnBAvcf//9klrEK1AXbosEwLWt2G+Dz8OQSWuQsnNdU26dNKKboK2lHI6zNWejWCpUIiYpQCH4beI17bgxHIaJD/ac2jW06Y32PIdjKCS9RISNFGktoJIQNvi/pBZOhj6Fc7hYCsPpknTj8DtpZTSgULdtK8Lae9fX1zvDEEnWaeFj9p4YLioqKnIC9NPcN2lz5VqhtJFWXqWU4P927do5G0K+Dg/fJSqEsMH/JbVINRiXbNeIQhSmxiWVnpHSdf209McQmYs9p1BE2EiRyohaVeY07BZnCS5iq9Kn3ad79+5ZYYAhpZnpIfv9YgqJW6e7RdikZAabgGyd7bgyrO7NNfv06ZPTDzVMrpZyi9fZ9QvGm5U4Ht6PcTY2Nq6RwIliqaqqqmBpkzVJhRA2KZQ2rfi6tSynSS09e/bMKW8DWe+FDdLIV7ww6Qx/85uIsJEilTvlRdhIkSJ9tygibKRIZUR5E9jXX3/9jOQta+gE6FmjRo1yBbsIfKe4FTpCGIUk5fqqkiy7JB6TXE2wNdc++eSTJfkes2FQtdVfGEeQJJ83cDwpEsYGeVvdvYhUKVmyvjwbaUVB63xWzWJSs2wCh51fVVWV25O0YuppFN7XjsX6pq1Ojo2AlLQkKjb1rBhLuI1Wa4sfOm189tqFou7yUbQSR4q0FlBehCXJHGsovlZ8ittss42LWLK+KCynJGrTS/UXv/iFJB8l0759e9dMiP8Ro0uANEnc+HxpFEXZUZLAq6urXfraDTfcIMmXYCFNKmcBChQc79mzp7s/CGT9lcydkp4k3oeoBjpbNGOdrGUR6cK2QKmoqHB+YQqbE9COFTOktPRHqEePHi6I3wb7299yX3ufioqKHImBNbGJA1yLeSfdk7XiO4WQqhBqdu3aNacPcNp3bSH8pPsUW0CO8SaNz6aOFtuBPiJspEhlRHmtxJ07d87yUcIV4JZLly51xcooF/Lyyy9L8jGzcDayHmjDgZ46ZswYl55GN3I6fcNt3njjDUk+ztd2uoYrdujQwXVxp2AbqE1i8rPPPluUDy/0oTFvW3rE+kOtP473NTU1OcXOQRHIxhZDSdzc+oy5X5DQnuqHTbo+vmD2Fa6flHAdUhitZtei2BYUSVk0aQgWZI2VpMOGv7VrlhaVFFw7Z3xWirD/bwtFP2ykSGsR5UXYTTbZJCP51g1wJ9Dzyy+/dHoGn6Gz0i6RcqYU0OY9Ou3SpUsduqADkiCOLmubTcGFQFwQb9WqVTmFoEFnOOr777+fmA9r45lDvcOukc13pEg4zZEojP7QQw+5a3F9m1OZlmucNA5LIL8tOh5GOlkrsUWFELHT7sm+UGyM4nIkd4fFzW1uc3if8LWQJMF1pVyJJZzfN78pOZjAojflbZHQbEZSKEWkRTa1JqYhLZovImykSGsB5UXYYcOGZSRvHcZyFraqgNtS5vL666+X5EtBwnEpavX3v/9dki+nUl9f7/RLykiik+LbhcvBhfBRwuEp9zFp0iR3fYgi5L/5zW8kSStXrkysVpCGABUVFW6+xxxzjCTp1ltvleSRn+8yJ+KG+V1tba2bk/Wrsj42xjiUYsK519XV5ZRhoeEy9w19eLYEaNL8IKQj1ttaw/Gd2v/X1NQ4yYG48OD+WfexRfGQrsJcU6vvYjsImluXjLDcH+mHAu02i8pa5aGqqio3DmtBTrPEW986FPqFIRtT3qYSMXSUxjhEeZehQ4e6ukXU4UkrcYLRiZpGuGgymYyrxsfh5KGnozUbxYLtvffekryBC7G3Z8+ebpFgENTJ5QFIMzrZxeZAhSJxmgjEobTlRsIUOh5Y/rfnnntK8rWYbdkR1g2jXVJXA74Dw2LMS5cuzTE6pR2m1atX54jl9rs26SNcG6llz1lf9hV1hr2xhxxmxgOb1JXcqg+8D+cXztFSOI9ixVbrfgrHa4NxUIWeeOKJrHFyD8toktLqLIMIOlFEkThSpHKnvAi7zTbbZCSfuA2HAAlramoc54czwTF23XVXSd4gBJc59thjJfkeJnvttZcTwRAx6ZnK2CZOnChJOuiggyT5PqYYeXDvrF692omlID73R3R/7rnnEo1ONjk5aV2s6APSICoyd8RvgkT23HNPJ6rzXUqQ0B3t8MMPl+TFbqQOqw6EJWKsYQcEWr58uZsjoZdp5W2SyKbrcV0qCtJDiG6CBx54oOtowHfoVUOFR3rpYnzEHUin9rD4WZohLq22tE2RLIW4F+cXVLz88sslSUcccYSklnPKHjKObbfdVpKXGgmZPe644yR5SZTPwzOWhvhJPX6zxlvyDCNFivQvo7wI26VLl4zk0RJZHf10u+2203333SfJGyxAXBvuBueFYz377LOSWlCC74K+cF+C/QmRfOCBB7JeQU+4Yvfu3Z0LyiYdoG/MnDkzkTtbXSLUE0F+5mCTzwlgh/NuueWWkjw6Dhw40Omi6OcDBgyQ5HuKsm5z587NuibSQ4h61ogBEuWr/J9WEKxz585Ov0QPBQm4ng3rI0iGbvGjRo1yBi9CQnGT0OGca/A9zgV2kaT5FDO/cI5pLrDa2lqnK3Nfa6uwYYT0zSEsddCgQW4OnD9qTVN0j71iXRh3YCxz9yhUCshKEVBE2EiRyojyBv+jD4J0hBMS3P7BBx+4juroYlhlJ0+eLMnrm6CoLV26ww47uH6zuGu4Pn14sAbzOfoSHdJOOOEESS2V9uFQmO9BFrrHWbJB6jYoe8mSJe5/0F577ZV1Te4Bgfig9rnnnuvQCWRljj/72c8keWSFG9OXFF0cq3fY68cmFCQFrFtdySLtsmXLcsIIKYDOGG2xMdxI7OlFF13kgirQ20Amki94T9/Yq6++WpJ3c2GnCPVyW5oH20kapaW5rVq1KidghHK72EFsPxw8I4zvyiuvdCjMuWN9CAJiD2+77TZJ3k3JvtAzOZQgbL8m62ayFBE2UqQyoqL8sOh/yOwzZsyQ1OJbBRHg9nAMfKhwYyzN+COfeuopSS1B+fgR4a50sIaDYzHFp4cejLWShIINNtjAcUSuiS4I2i1btixR/wF5QHHCI2tqanL8kXwXJIIbo5ejv3Hvfv36ufUhFRCpAC6MzkRoHFbka6+9VpLX9cKEesh2iguD460f1rb5qKyszEEfEBeJConhrbfekiQnEYW+dP6HnQOLKV3tsEOg51npjfFUVVU5lEnzD9uwPeuHtcnxdn7htRgH9gksuqAoe7/eeuu53jqcXeaIxIc0yRnkubn77rslKSuMt1CRgJjAHinSWkB5dVjQEv0EhAvbTWDRtdEvhO9NnTpVkresHXLIIZK8X/SII45w3BWrKtFT3Bd0OfTQQyVl61+SR+3+/fs7LoeuBAoQFZRGjBsLX6hr2/QzuC7jJIqL78G1GcvkyZPdXA444ABJ3poKWr733nuSvE7LnG2zpMrKSocG6HRWD8pHSQ20rOTAdbC4s3bMi4R59n6rrbZy0sVFF10kyYeqhqmYkkcdrMVJBdbTuugVW7rGlp1pbm7OSa9jjoTdss5IE8wRqXLSpElOOuIcIuGxfkgPSJfsYdIc04oFFNrDiLCRIpUR5dVhKcKGPgi3JE50+fLljnvAfW3QOmgI2qAXoWd069ZN++67rySvG/BbrHLolXBBuDklZEj/uv/++7Xzzju7vyXv57zrrrskSfPmzUsM/s9X7NlGOMGl4ZI2aRtdh9fKykoX9QRHpTs73HjmzJmSvG+ZtQCZkBQ+/PBDh3j4h/kMZAmjZNLmF87Ncncb4WSj2UB49rBLly5u3Sk+8Lvf/U6SRyH0PO5BaSHmi4T25ZdfuutjIQdxGUchHbYYsntpG11xjrF9dOzY0UUwcYb/8Ic/SJLOPPNMSdJNN92UNW5sK5zbsMSQLXtjUzajDhsp0lpAeRH2sssuy0jSaaedJsnL9+iMq1atcuiCz5TYUfSf0aNHS/LxtXyOTrvffvu5pPbvfe97kuS4NRbSadOmSfKpeY8//rgkz/0uvvhiSS36MuOBU6Fnoat8/vnnWZyrb9++Gcn7kS2FCIQ1mIgX0vuYI75EJALiUTfeeGNn/QWVxo8fL8kjDeNGumD9IKyw7733Xmr8KdcIEahTp04ZKbczemj5Rr+zTafxBuyxxx6SpJ122kmS3yf01fr6erfevBL3zVrde++9kjxysR4gG/f+4osvcpK5rbXYog9zZNz5iPuDepxHUjzxlWK1RzJYd9113VyQBog1eO211yT588A4kYAg5pjkT7ZSTkTYSJHWAsprJT7jjDMk5eoQ6FU9evRw/kp0LnyoWImJ0MGiSumNe+65R1JLtgbZObNmzZLkdQDuj5yPZfGkk06S5EuXnnvuuZJa9GiQFGmA6BPmYAlkzZc9AcfECsi1TjzxRLcO4f9BfnTATTfd1PlsidraZJNNsu6Pj5Px830s8yBSOB4bNZZEtoxqEnoxZ1AHfernP/951rzQ3RgjiLHvvvu6c4BkQGw52TlIAfwWyYf5h9Zrm/SeVj4HKoSsYXkX5sh48GKwV9hiOONhZg7nnz3kLON7Zm1BYrteXDOpaVuxmUZ5H1iCHggrYyNRnocMGeI+46HDuLPNNttI8gvBtaj1xGSHDx/uRF3CGlHWqRaBYQYzP6IG92bxu3TpklNDl81Pe2ALdRirrq52D4QN9udhgjkgRj3zzDOSvKFt6dKlmj17tiTPlAhrxDgGgzv44IMleSbAAcvn/LcHPInS0usqKyudISwM5ZN84ApryWFmLuzpk08+6c4GgRCE/BG+hwqEKoB6AaMK52dTHJPmXgqFNbVsKCrjg/kQMIGojCoyd+5c98AxZgyFuCMffvhhSV51wHjGOU3an7RiAalzKTzdSJEifVcor9GpX79+WW4dxJwXX3xRUgsnBm1x+CNCIBbyOdwIpzMBCtttt51zTiMW4QogNYuAcq4FR4M7IjK//PLLDn0RqzEE4UZJ6w8LJZjXHdfDYAE3JmACzsvrkUce6dZHkvbZZx+Xkga3RVUgsRvkZf3uvPNOxivJc96VK1c6dGDvbLX6pJpO1mUTdjqw/YiQJIYOHSrJi9W8YmwBQcaPH59TAmjChAlZ82R+XAMpJKlTgE09K7WmU74gBNaXswSCcm2kJ5IWWJOjjz7anVNUM1xYU6ZMyZoT+8BzYqmpqSlnbDaQIhqdIkVaCygvwvbo0SMj+R42VOYHfdq1a+d0LXQTWxWRNDd0FtCRgP6pU6c6t8HZZ58tyXNUDBKgEn1zcCdgHod7V1VVufQ1givCsUrZ5VO++X9Gyq3xG+pP/JaAEVv0jHHBJRkvQeB33HGHC8lEx0eXt2VlQGL6B9nk58bGxrBQl6T8NW1BH4usSW4dJAiMOCAtBjIbIogOf9555zkXHMkM6KygNCVwuAfGKbs/YRgf80rQv4sqpGe+I8nbVKzxiU4RYYCK5NH0nnvucahLveknn3xSkrc38DmBPBhabUXMUKpJC2iJCBsp0lpAea3EoA2oYMO5JM8x77jjDkk+5Qi0BEn/8Y9/SPIchYCB/v37O31zxx13lOT1YfQiOBmWPpAO1wdB2UuWLHGovMUWW0jyFk5QOI1s2RUo1DfgtrZKPHobbh/GCQI3NTW5MEUkDXRq7otbBG4M0lDo68EHH5TUwqXZF9uvN6m7WyHXSFj5n32wSMv/uQbv0f822mgjt94gJZIUydzMk2syT+aX1CUBhGd8RaSkFfwMicWGP7K37CnrH+r1NhUPOwPSAqhNKinjtb2Tw/HYJP1C/XkiwkaKVEaUV4edMmVKRvK6IkgH51h//fWd38qWhbzwwgsl+URtfHagEKjUrl07l4pFgPx//ud/SvKlUNEnQBDe47NEp7z99ttzggl4xUf66quvZukGtbW1WQnscMmwAj8cn1IgdH5HmkCnBpkYA9dqaGhwyI9Ohw5LGCacnrW2SMAYlixZkup0TyqRSZnToC9N1m87duzo0A+0Q/+nuJgNK2StuNaKFStcKhmJ6mnzI4jAWoJZs+XLlxfslme712GHSCuPWlVV5a6JvYTigUhzBO6DqIyHOTY2NjoLMqGnoDGhmmFZoXCONm0xTZoLKeqwkSKtBZQXYQcPHpyFsOgUIFrfvn0dx4SLIOcTeoalFOsbqXREwlRUVDikwv8KhySoGn0IayVcEGvco48+KqmFCzIfOD1ckLEvXLgwi3O1b98+w28Zj5TtH7QIxitz4jfoqQSQo1sPGDDAcfLjjz9eUm73uiuuuEKSXDd6G10FQoUlMm2iNxRaiWtqajLh9aCkyBr20FqNWW/mR/gpPu9hw4a5boHnn3++JC9JEe1FyZvp06dnjZm1DK22FpmsldWiTymFxG2qpO3Ty3uSWSgsMHz4cOctIZ0OfRw/OJF5FINP6Lrn7p1WMA6KCBsp0lpAea3ENrKDxk3ostddd11WTLDkfbVENO2zzz6SfFQIgdJw4tdff91xWxCTEpQUr0K3IlaToH8SiEHeN9980+kRNumea1qy3A9/HPTaa685hMHfhlRBjDOvRFWxPui6Dz30UE4iOvo5UVGsFyVUsWoT08rvvvrqqxz9Ksl6D9k4XCzsWOBffPHFnMR0dDBK2BIXThojyIHN4YknnnASAEhJmdajjz5akrekssfcE18laYdhGqBNckiLsw1096w54iF45513cnobsx9EYhELj82FeHb8y3fffbeTaKxUhN0G6QlpA72e8kC2hK6Uv3BCEkWEjRSpjCivDktiMHopWQggbVNTk+P8Vu8AnWlbAQcFnSjG9fHHH+eUDeEzskJI3SMCCm6IfgEnfumllxxKoEMxHjj3ihUrEkvEwNlA0bCFhM2GsSVikDLg8CAfCLXTTju5lCy4MhIJ8dChjir5InXMkfVdtmxZwS7moQ6LfmfjobF4ZjKZHN3czg/d1TY8w/J+yimnuAg3EImILTJbmB/XJj4cG0do1beRW5YKlYix0Uzmu6xL1muhOR5zzDEuw4rzjz8eH7otmGcl1GL9yd98N+qwkSKVO+VF2IEDB2Ykj1bI3sjqt9xyi7OUJTUPkjwnJRkatMTy26tXL2dVO/300yV5/Q1EJYsH3QoUBYXwyzY0NGQ1aJJ8dAnfnTt3bhbnot2k9d3ZZslSbpsLdBqkDCK1iGwhmqlr164OWdFvWDc4O3ML83Alj6z4oMP94jvsS1KzqLR2k6GF2eq5ttkX+4C/keZolJLt06ePi4Um4gm0sVFTIKzNSw1Rx7YV4TPGkRYPntZYKtQZrVWWe/BdpDv8x9dcc42kFjTlb866jYZij9I6sidFm9nxBHsaETZSpHKnvAjbs2fPjOT1OgjOMnDgQMeZ0FmIHcUPSsNefFiUzcBnddhhhzkrGtwX9AWp0IOwJqMPgbT4waZPn+6yhqAwy0XKjZKxkUA2e6Surs7NF5RmTtw/LJkjeeskEUNDhgxx6ACSWh8ec7IRQJYD77bbbqmNvYI55yAs17Hzq6mpcWuDNERUFoiNH573rBV6+bBhw5xl1rZatLmsNgPHzm/IkCEuCwgqVKAsrd1kaHPgf0g0SI1W32W8nGtsDH369HH7jcRnY8Y5J4X8wRtvvLGLN06jNB027wM7aNCgjOTDuQgZxCDTr18/Z7xAhGSxzzrrLEl+k//yl79I8uIDk25ubnZGJKohosTbxGor5nJwqen0zDPPuJBIxHaC7XHyW1GDwAI2ivmETu+0hwfmw0PAwxiGbjIfHmIebuuSsXPlgMMkeIiWLVvmwjwx2Nhx5Xtgg8qK7r7WiMUraxJ2fw/nh7Ft1qxZ7rswVSv+cQ8rxmOM5PsrV650a2CNOIUCJ2zIZDHEuO3cmHN4fop9MG0VS5gye7p69eocMdpSNDpFirQWUF6Era+vzwpNpAIexdJWrlzpkJLvwDmsUQETOKVDqJrYvn17h6gEm1PflSBrCpVhBMGgBTdkDAsXLnTJCKCdNWrYztbWYME9kSLC9bGO/DRk2nvvvSX59Kva2lrHlS23hRtbtw7c3IblNTU15STbW0pKYIdAtKRKg2lnwbpXMDqRMlldXZ2YUhhe0441rSN8JpPJF67Ha163DqhpEzlKIcJLH3vsMUnZIao2kT+teiXzsEUDwt+kjS0ibKRIawEVFTgBdwQd+M0555zj9DSC1m1fFrgkCIzSjz66cOFCF0xhi4qRiob+Ri8drglqEc739NNPOwQBta0+9OWXX+ZFWIuW/fr1c6hLYEceh37W59y7qakpB3Hgvrb4VlpZFNvnhusmjScfwgbzltSCRtgfWN80smvEXldUVOTMzwYoWJSx8wNxm5ubE8vYpM0v3xyhLl26OIMRRrFiKXR5pSWX50PSpO8VSlL/5hoRYSNFKnfKi7AEFaA7EmpFQa1p06Y5NITbEoqIJZPSH2GHbclb8iGcYwAAGTxJREFUj6dNm+Y6ndkAchIGSIZGPwVFbef2sWPHulIq6FK8glALFizIi7C24Nqnn36ag7qsB9cOrX8h8f3Bgwc715VFImt5tIEF9vvV1dWJheLC+4UJ7GkSRBCEkINgjIG9SrOK8r5Xr17OTWLdUjb4wY41X/C7lViCcjdFIWxa+mE4DuvOS3seunXr5qRE+500fTTf/wvp1RFhI0VaCygvwkaKFOm7RRFhI0UqI8qbwE6BMlvOI0zVsiF2vLfWzkKFk6XcoHMb3kaUENE/NoQtbKuRxzqZpRt069YtI/kIJ6y2fH+dddZxPlkbqmfHbQt8Q83NzTk6KetFpAuWWizmzJEQNqsbhutk9a/GxsaCVuLg8xz99tuQuuy1rW5bCln9jhBazgf2Cu5VX1/v7B3YJsJIu/C7aUXawzREbAhEr/FdG55JGV5KHYUWcs4DngSuERTwizpspEjlTnkR1hZyttEqQ4YMcaU00qyBWEEp0UIccIjaBM2DznBIuKL1+1FYDY4WJqtjySbNj3jftA7rIB3jtrGdQ4YMcT5d/LEgLNwXCYDULGKiw/kQk8q8maMNJAfh4dIUDyAWun379m6OJEWQ7kXp2JAKoWbHjh2ddJFWJhRKSldLu0+h+9ooofB7aWcpTTojhY9xs+5ce8SIEa51CB6GsECB5PeDogP43Nnrjh07ur2wiQIUGSDGgHXinBLlxxmsqalxJXpAX2IOkvYwpIiwkSKVEeW1EttWjLasxurVq51eB5fDl2qzNkAO/o/sHqav8VuuyW9tF3GbKhY2erL3s8nPX331VWK2TvDeXUtqQROQnGgZi7BwXOYEh4eb19TUuPHAZeHWXAMpwcbiMn5+H96PNec90sLnn39etA4r5e5rWrRVGoW+00Lla9YEWR2WUrU2egxatWpVjg+fPeTsccbYQ6QOzmJtba2bA1lStCpB8rSF/8L7h9duampye8aZ4sygFy9atCjqsJEilTsVpcNaC1bIkW22jo15JaGc1gjE/VLCtKmpyXEouJlFVMjqVklZE2GMrOQ5V5pexnhtNExYouXjjz+W5PVIdBWsknvuuWfWHCntSmGyhoYGtx7o1BTmslZpxmGjqsLoJlu6pVAMayEqhKhwffYaXR6kCe9bSrxsIUrTiy0hWdgWkmFeKnom681Z43yQRUYG0kEHHSTJl9RtbGx05xJkRQ+25XxskfgkSzCSFAhbqPAcFBE2UqQyorw6LPqdrTgQcjDkdYop05rSVo2wljJ+V1dX5+KO8avyGejHNeBQ6G62OFzPnj1zLMuUqqHUh41DxQ+LdZhrhcW50HvgulS6sFzb+t1Aws6dOztUonQr3Jdr2zliOWcdQYL11lsvp9LE5ptvLskX5S7FDxuSLduZllFjpZV27drlVOywZNEyzSKdFGdrKzhYHZbG40ghVh9t37692yOaLlOUnnI/jI8ieXPnzs2ac/fu3Z3dgSKBzMFmsXGW8Axg8ee8brDBBs6CzPXJXqM8Upoftiijky29wQPT3NzsFpHP7OKyUHZSfK+mpsYdTg4IIuatt94qyYtiLCo9Z0kHY4PCvjO27i2ihw3+J8HBGowIZAjr5PKZdTvwYPIbe7Bra2sdU2KTt9tuO0leNbCOfJL0eXj4PFw7+hfhCkBUDytDpj2wrQmSsPWKGWtlZaVjruwza4Lobx9kW+AgHIcNrgjvI+WW+cHoxD05D6x5Q0ODux9qDN/l4YLh8FBaw1Vtba17AGGyPNx09+PajNfWuObzqqqqnG4X7DN7+Morr0SjU6RI5U55jU5wErgQXCc0CqVxaFwhcC4MF/SUmTZtmiRp8uTJTrEHsTAeUAeXzm/ULbbFwUJRBGWeXi6FDBZwNCtGIb6EqG2NbqC4rd5IDWbSBidMmOAMUBbJqTaJoYr6vqwXojxI1NDQEHLhrDkm9dZJQ9JSOr3ZogT0zaHr4EEHHaSrr77ajU/yEgQqCuWF2HekA4I/kgxnaR33LHHWQHEkQO5dWVnppB2b3ocRkL1DSqIXMP2bJk2a5CS+sO+v5IvRUeLovPPOk+TREzWQ+SxbtszdD/HaBomkUUTYSJHKiPLqsPSdsYH9/KZ///5Of0K+txXV4Xo2RBFFfOONN3YciFKo6B704SSgAqMUnBPdIExGRhqAo/IKGr7//vtZUEuCA+O3idgjRoxwSIbuDNqBOOgmID/d9jBcbLTRRq4oHB3fMPCcffbZkjzCo8swDmtgqqysdOsRdgeXfEe6efPm5eiwaUhbVVXluDt7xnfZM1tuFLcG8x48eLDbEwoIsIdbb721JL9XzAtpLezxY8n+L60qPqWMQEfbAX3EiBGuhjJGSOaMJMO6856OfSRfbLzxxu66SFAEYyA9gLg8E+wp78MAH9bU2keQKl944YWow0aKVO6UV4eFI2DRslXcFy1a5PRcOBbF2O644w5JHoVAOjpyk0Z2/PHHO26DVQ29hqCLwCUjyXfxhgNTVvSLL77I6YYO10sL/rclQpAm4Pzvvvuu47rcD50aF5ZNB+QazH3q1KluXOg1mO/Re1gPkiPg2iASfUs///xzNw70L8aKmymJ0oqhNzU15ej3ICjobnV3dEaCZW6//XYXLA+ygkbYLJCO6DFEgALIS1+iTCaTmkCQVtbVFrILe+lKLXuIW816Iiihy28gG3I7ZcoUJ1EgyZBgQqF93iPloeNj4T/uuOMktewpc2ItuQ9upjSKCBspUhlRXh0W/QfL5oABAyR5ub5jx445Vi3QBlmc3xIwwP/hfoMGDXK/efHFFyVJBxxwgCQf2kcxNrgPfWrpRYo/tq6uzl0XsoEQYVCB5H3NcFT6tpI2WF9fn9PeAqmCdDo4PN3SQSjm1b17d2dJJrhhv/32kyTNmDFDktetsG5vv/32knyvIvThXr16OTRm7dF/Aj9kqh/WFiYL0dUiKZIF88Oniq4YphsSvAFyHnvssZL8HjE/G95I/6EwbK9QWKMNnMDWwnjxj9Ipsb6+Pkt/lHLT6TinBL0w3jBwgn3nbEycOFGS9Oijj0ryaY5IDSAxRfORHHv06OH2kLVlXQK7SNRhI0Uqdyop0sl2HKuoqHAcAYsinAy9Cp2V91jf0IcnTpzo9BusbldeeaUkH2xP9Mkuu+wiyXOjyy+/POt9ly5dHMeyHCzos5oY6WTT2sIEB/RhuDD6LjrN6NGjJXmpYZNNNpHk/bPbbrutQyeuhe4EN+baSA+M4/zzz8+6dvfu3R0S2vA2xhX2Ty0mNNEiK8SascfsGQhLIv2ee+7pesWit5144omSvD6HboulnT174oknssYe+pLTWqNYhMXSbxNPmE9VVZVDX2tzQdcGlbkH55a5brbZZs5GQPkepB/mgg0GLwE6NX5r7tW1a1c3NvbQNseyBe+hiLCRIpUR5bUSW+sbFOo9WHh5Bangxlh40RHxh8G5Lr30Umd1Ra8CmSZPnizJR/SgX9CgCG4I9/7ggw9cBA3owH1AOEs2hdB2ws5kMs7HyP/g0lj2iFaBizNO0HTBggXOkm0bJx188MGSvP6ODotuy7jQi19//XXH/UEgrMWkAZZKYaOtcIzMF67P/rDXrOn8+fNdZBP7z29uuOEGSdKvfvUrSV7yQd+3DaYaGxvd32kIa8l6L/gde9vc3OykIdCQvWKPsI/w3vppFy1a5Hocc0aY66GHHirJl5WhaDwJBoybWIDZs2e76CjbLI71SaOIsJEilRHl1WFHjhyZkbyFMilFysbVgkag4hZbbCHJW0VBz7/+9a+SWqJQQquz1BK3KXmLKpwdnRadl/F8//vfl9TS0DkNJUAqm143duzYjOSRHx0qzBBBv8ByiN7xyCOPSPK+VMYNqqNjjxgxwuk/oMHuu+8uyftjsfQyTqyn6KdYyp966qmcDBd0N64RttRERy+mnKhtYzlz5kxJcg23x4wZk/X65z//WVKL9GTbeiI5cM1rrrlGUm6stp0DKJmPrA673XbbZSTphRdekJScMQba4gXAZ0pLUM4nHgoi36666ipJLXorEgxzYt+JkiKiibXAmsx4sHU888wzTkqxBRewVYSW/pAiwkaKVEaUV4e1SbyWS1dWVjpuAvqBoBdeeKEk729D7wPxQNNRo0Y56zDIhU4G8oIcWFSJT0XvgLNWV1fnZK6gS6VlQYCsFonDMq1wQ1CSOYIwRKtYCyT6z/Dhw52OwjVstAxzRP8BxdBpQ8S1ebjoZTaHMxxLMWQLFBx44IFZa4HPEukJtNxvv/3cXqGDIfUQOw0xVizM6MHFIGsaof9blA5LtLCv+OzZK3RsxsPecq753qhRo9w5YE84t0h8rBtrgZ8e/ys6bvv27XOK61k7SRrlfWCZsK1Dy/87duzoNoAHELEBowIGISaOCIgRZfHixW4RETH/+c9/SvLGpeuvv16Sr2rBvWASMJTKysqclDDEl7T0On5r6wHx4Hft2tUdQgwSPHQwDERgDgwbRBji4sWLXbIDyc4YlZjrJZdcIkk67bTTstbnySeflOQ3MqzIx9+sh63WVwyFKZI2XRFXnDXQcKhxYzz22GNuf3mYOcSELyIeoiIxZh6MpMqLxRJrgzhp1ZpOnTq5MEvui/gKgwRYEE0BD/ZhwYIF7qyTDopRiQANQmbZQ84+3+N8hPNlrISzxvS6SJHWIiqqP6ytcB+msMER4ESISdQ3QjwBRUkOgMOMHz8+xxDAdwiqxiSPoYLwN1vpcOnSpTnuGcRrOGaa0511sBURGxsbHRfE6MQ6MG5QhAQDjBGsxbhx41zYGutAgjQGHcR91pY5Ijoxry+//DKnEiTXRoUJDWul1HSyfWFxkXFvHP/URUKCmDBhgkMovkPwCAkc9Phlnrj77PlLCkssVNOpc+fOWYY1m9C+YsUKJ2nhEkMiYJyMGxEdIxT7scsuu+SUJgJJcfdwfpkDwTE21XPJkiU5SfAEYyCBhj1+Q4oIGylSGVFepQfuhw5jA+urq6sd9wNN4DLI+yjevKKToetutdVWLtAd0z/og8xPyQ0SB2wplDBwHB0FZGc8hXQDm8AeuhtAHOaIjopuSzghrq158+ZJ8og7duxY7bvvvpKk3//+95K8+4CAEvQcUM0G9ofGNJveZo0epZINTWQ9QUGkFBCGPjXof19//bVuvPFGSdJDDz0kSTrppJMkST/60Y8keb2P/bFpfknBETagI424BnuIdBSmO3J9kBXDEYj/4x//WJJHZwyZoOmwYcNcyR8S2An+x2CFYZBr2PVEX6+urs7prYR0hCSQRhFhI0UqIyrKrAjqQHCrpqYmxwWxuiGLgzJwO6yk6AphTxGsrLxiOQWh4EKE/KFX7LzzzpKkm2++2Y3NJsMzVisd2LnYkLCQO8IN4YKUucFMz9wYF3MM3Txcg3HBwRkvnBy9HK5MYjflUBsbG91nWCFZN7tPxZJNELchqSAY82Ke6INbbrmlk2QYGxZ8ajlzDxAN9MP9gxU5pLQev5bYQ86LLZO6atWqHC+FreWMWwXJjHOLRFBXV+fOLEjOeUBq5Awh+aBTI4HddNNNbnzsO7Yf1pj7plFE2EiRyojyWom7du2alV4HSoU+SjgDJUgvvvhiSdJZZ50lSbrooosk5fYQgZOtWrXK6WAgF+iz6667SvIcCwTB+oeuACp99NFHqcnPQUGxLOvbBhtskNX5DE4LAvTq1cvNl5QxSnoeeeSRknwABbo+6wW3XLFihXPuM6fbb79dktf1mCPIxG/RH0OnvU0+t4n1S5YsKboIW2VlpfsfBQpAeytJEKjB/MLysrbaPb5q0tYYM/7QNEorAxOStRL3798/K0WSNWQPBwwY4JB/ypQpkjzaHXPMMZJ86RqbtM+ZX7ZsmSvfgwUZvfzUU0+V5M80yMtc8DywT/PmzcuJbWAtgxKt0UocKVK5U16Exb9lw/tCXce25IALYzmzhbHwWWGlW2eddZyFjkRfdBH0vgsuuECS14fgTvhDw05w6A2gjW0RYf1bvXv3zkiek9pi6bW1te6aWIkZF6Fp1v+HXoYlvEuXLm7eoDGICiqQfnbUUUdlrRvzYE3atWvn5m/LekJh4DhFCIqJHrKWdOYLorK3hFViwd58882d7eLSSy/NGhMIS4QT0UA2ZDK0Fhfq4m4Rtm/fvhnJrynWcvawrq7OnVPWk7ODBGOL8JFsgc7dt29fF5p52WWXSfJIjlRBmiheDRspRshibW2tk5g4M9b+YPsYQxFhI0UqI8prJYbDwg3QaeBCDz30kPsbtAE5sYzRDxa/I76qc889V1JLWc+gfIskj6jojKAO+gf+QbrIUUJmyZIlDiXQmSw6WGL86IwkFsCBZ8yY4TgoAfwkrNNTdKuttpIkHX300ZI8F0evx4ooeUT97W9/K0n65S9/KUk644wzJHkujV5PtAz65YcffugQxCIKYw7JohT3D4qO5/QxBZnoqIb/G18r+ilIctNNNzmpAlTBp86eEWPMXrEf7CXjCnW7YmOKsY/gTaAMERb5e++910l8vOJxOOSQQyT5yLxzzjlHkt9jSpO+8sorbl2sVMReIl2MGzdOkpf8KCZIZNiHH37o9oX1skkxaRQRNlKkMqKiYonh4PiM4LDNzc05WSRwTlAGXyGcA10CHWHLLbd0VmEsp/jIKI2K1RLOZsuegqZLly7NiZyxllSrw9LmgTmCPEFcrkMukMh28R46dKikXN0ahBo5cqTzU5O4jz7EHEiCBuVIryOaCE68aNGiHNS0HdTDBHYbS5xW9idcq7AtiOTXnT1mb0H9K664wqEMxdhYR+Ztu5KDfratRjGoanVYevwyXmKrQcmmpiY3Zhu1xh4yF+bK51xrzJgxbq+wMHMOkR5sUTz0ds4Sa/DJJ5+4sxKWIpL8voSF9EKKCBspUhlRXoQFfdAt+S5Iu3DhQscxbTEzOBf6BUXIQFx0s0GDBrl2DugeyP5cA13N5hBy79A/DIJzX3TToBhbFufq1atXRvIIFjZQklp0Se4L17UWR9ACSzARQJRYqa+vd+U/WQeiYrBSgrC2ZAjjYY6rVq1y92dOWKOJZZ4/f35BK7GN5ZVy9V2bvUO2EqVwkIiGDx/u2jKi74IUtvhBmuSTdA7teNKKwa+//voZKbehMq0833jjjZzigJxpG2WHDQN9E99r7969dfjhh0vyei+IyrpjyWfceBOYOz7+xsbGLD+/5P3TnJ3Zs2dHhI0UqdwpL8LSit4WOYbzduzY0cnl6GvkPWINxFcFKoNKZKdsttlmTo6Hy2CVBo3hTHAq2x4Rjjp+/HjXoMpNMIh7lnL1H6QIuCH35n2nTp3cvPEpEveKxRHOihWZyJa//e1vklqaG+OLQ3pgTpY72xaSrD3rvP/++7vY6bQmzaGeDsLa7wSfu//ZcqG8Z014z/eJoe7Vq5ebH35LyJbeKUSdO3dOjfsO5pC1hz179syE4+P3Yfw2/7NZYyAatgLeMx9aoI4bN85JQ2Rr4TtlD1kP9s42kWMNJk6c6JpDQ0g8Qf5zIsLmfWDr6uqy+s5gIAgVZh4EK/ogUvAwheKAlN2nh+RnzPK2vg+TYRxcCyaBSPjZZ5+5++KkRhRi8Zqbm7MWor6+PiN5BoNoiojcuXNnN19bcxdXBmvAhjF+RLLXXnstpyNc2MU+nCPiNXPCAMcD/cknn+QYxuxDHYqMhUIT85ENQbRGLhL458yZ474bMMbEa6aJuaHhzCasW7IP7LrrrpuRvHhp97BLly5ubazqRiVD9pTfoIaxx88++6x7MGG6BI7YgBPOEgkRGJ84Hx999JEzVGKwsoBhO1RAUSSOFKmMqKgO7HwHjoqpvqmpKavPqJRcu/iba0nyCjtO+JqaGsftECWtkQtuB2FQsuVgGhoacjgoFHDwLM7VoUOHjOSRH0QLe9LahAVbqI33vO6zzz6SpPvuu09SC4owHgJMkFYQGeHGvGctbDX7FStWuGAPODzEd0Jxyrp10ipglkJbbrmlJG+QCQuopaXE2XOWD/ELSQMWYZEEmRNiL8EnSXtox8m54d5Tp06V5AP8w3NqJUK7h7aPLueZ8a1YscKdYcYTqDN8JyJspEjlTiUhrEXLYcOGOVN/mrEHsp3vwhAsixTWuQ3XQQ+xug26baj7gphWv7AuAYqw2Zq86BSjR492OglBAbacKL+1rgx0qoaGhpwkBMYFWobphuG1WDd0fox4kufcVqoI9Z9CRdiqqqrcXAsZeywVU5q0NbpzIUozHNr154xtuummzogUSj1Sboka1pBzwB5KHlEhvgNasnecZ8bD+cAouXjxYnc/9t3aAGLgRKRIawGVhLDI6pi+58yZk1OpHMSAY8CVQEmrO4wdO9aV6bA6KdeC84MkNsA/LJgG6lhEDzoBZHEuXFc2GAKr7uzZsx0CwRVtkjM6tu2XwrjC8EvGzHrgugI5bQqf1WG7du3q1jTs/xP+NimB3VI+S2xacEMaheterK7aFuS1CNuxY8csHZY9JKzwhRdecChoCwvyXWv5tXaKsWPHuuL4FkGRwLgG12QPrQ2mU6dObl+tlMQe2gAfKCJspEhlRHkRNlKkSN8tiggbKVIZUXxgI0UqI4oPbKRIZUTxgY0UqYwoPrCRIpURxQc2UqQyov8DaFMoQa3j3+wAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 480, D: 0.3287, G:0.5938\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 500, D: 0.2266, G:0.4387\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 520, D: 0.1223, G:0.4337\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 540, D: 0.1005, G:0.4029\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 560, D: 0.05293, G:0.4737\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 580, D: 0.1088, G:0.5858\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 600, D: 0.06165, G:0.4433\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 620, D: 0.5439, G:0.9013\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 640, D: 0.1375, G:0.2611\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 660, D: 0.1605, G:0.5257\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 680, D: 0.1279, G:0.461\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 700, D: 0.2016, G:0.008294\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 720, D: 0.0939, G:0.4389\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 740, D: 0.0786, G:0.586\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 760, D: 0.03781, G:0.5937\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 780, D: 0.154, G:2.624\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 800, D: 0.1702, G:0.3581\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 820, D: 0.1251, G:0.4706\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 840, D: 0.08812, G:0.4353\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 860, D: 0.09223, G:0.3789\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 880, D: 0.8718, G:0.4795\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 900, D: 0.1292, G:0.3515\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 1, Iter: 920, D: 0.1492, G:0.3195\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 940, D: 0.1683, G:0.293\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 960, D: 0.1513, G:0.2309\n" + ] + }, + { + "data": { + "image/png": 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w320pI9QcktAJEkG622mnnZzUxfOIikaYJdIcrIk6ANMfeeSRkhpcjrZIg63xlFbTKTBsQEALQtkZFn0HvQ699KWXXuKckvxO1759e+eQ/sMf/iApN2HdpqARhMA5bChaylwkFU5+LgYEN7CD4pTHUW6li0wm45KbCcVjhwd2jjAyUkRaV7j4d/m7ZMmSkgInLJMRoGKT6LFD8Jr73rt3bydBIQlst912krxeTSJBmq5uA26SEGOjvDpsUmVO2zkOdoThkd74iyGOsNl11lnHSXIktxNmynWQCLk/jLdr166S/BpOmzYt5xlh7ISFTp06NTBsQEBLR16GffPNNyPJ70b5QPD2008/LSnXSmtLhUyePNl9bs3ytkAWVmksywRuwz5xy3SpdYnr6uqi+DjzgcD2WB9WSX5HZ47oI3Fzftq4rMTBOGwBMvrdvvLKKyXNccstt4wkr2/mgy0Fg96JrgzzrrfeepK8rta1a1fHFITYofshWRDGh7sKnZ0AGvTjadOmFSw5a9fwZz/7WSR5F03suJzvWr0f3RWLPimG6LJYpvfff39nMSbpAT0XOwTdDAlRxP6Afk946RVXXJHjZrQej6DDBgS0AjSbldiCgAnCDeM6GzsXARKU1LB6XqHC3UlhhID0vqOPPjpvicxiAhTSrJdYOrF2x4FORHI798HOyfolLdt06dLFSRYW+BQ7d+7sTkpgvPUVxueQVp3fJjUgQaDLondWVVXplFNOkeRDEm+66SZJnqFs4ba0wIHq6urUYtrowS+++GLWTautrY0kr58m6bJILhxj4wNgWOaE75zXkpcOeT7xnVJmBikBvRcJi/nwN14Gx95zLNGvv/56YNiAgJaOZvfD2uJithhZRUWFC1tkB0MPpvg0VuNSevA01odXCtKYiYQHkhnq6uqcjxgLNzs7+jj3p5QercXM0VpQS4G1XGNpRVqAlTp16qSDDz5Yko9koqwMuiCpkgTVExjPOWM9ZQomOtg1tFJEUi+gtJYl2D9gQ+aEvQKr9/z587XJJptI8uGVSEm//e1vJXk7DhIWEhdM/NRTT0nKjhizfWFDEbaAgFaE5abDYlnDskg005w5c3JSywimho3SmjIljDd1V2YnX7hwYdkY1uKCCy6Q5C2h7KRLly51cafoP7AI48JK3JjC4SAphZD5WYZpTBMwWnVgCabZ16xZs1zQOqmH6OrMm+goW27Hom3btjmd8AD65IIFCxLtEDEfpqTshle2+ZX1XSPhMDcSHyi9+80337joLZ5lfM/YRygCR+IDPmUrgfXq1ct5Saw0gZQ5ffr0wLABAS0dzcawq622miRvMSPDBr8i5V0mT57sdFN2LCyL+B7ZHcGNN94oybdMKAXl1GFhE1iD3ZFyNOzasKfko2NIQ4MVrbUYHQ+drxTE51ioyLaUbpkmcosyP8cdd5wkn4FDWZtevXq5EiswOLHCpKLBfoASoOi8SSjWDoGV2Dbz4rmKosj5X+NSj+QzgpgTPmbsJvhre/To4TJ8sDDfdtttkvyzzVoiGbKGnDM+L2vbsZJV8MMGBLQCLDcdFuCrolTpxIkTnZ5gc2YZG9a3N954Q5Jn7VL0L1BOhk2D1RcnTpyYVeIyCWT60E7SNm0qBU1t1QHS9Fys+CR0T5gwwelkJOLbsjNEEBFZFLcKLxtz6jhsAbtC7Satrzf+nl0bLPlEgjFnouv4/LXXXnPNzih3Q2F1pAXmakuqMu589glYmsyfYCUOCGgFaDLD2h0LmRy/Gxk4tnwHxcVqa2udrw4GBeh75B+mjZXvf/XVV1k5sUloDMOmzZHSMBTjAjZCqKKiwvnisCAC4k3Jf02bI2VHRowYkdOk2qJUhsX6ii4Oo1G4Hda3jbm4D8cdd5xjJAqzcc+23nprSd43neZDx+axyiqrOCZLKwye1gzLxqLH7QLYG7DcEheNZRd7iW2Die779ttvO28GbAyTYo+h6B4SoB0PkXyzZ892sdbE6fPsx/yyiQxb1A/W1ofN59gu5PTGgEFANMm9kq+0iGM6LVAi3ttUKk40LpReR1kPqrZj6EpKays0Rx4wjBPxBHO7YfEgISoC63aIG64KISlwAoMJAQHxuSR1A5By7yvrwWZMeB0ispQbgmoNb/belRIME0uXSwycQO2YMGGCJL9pVFZWuvvO9dOuS+AE7kcC/I899lj3XdvLh7I7rKENqeWcBPzX19fnlAaKj1XKdT+6e5D0ZkBAwIqJRtUlxpyNyb4UYC6PlxuJlz1Juh5oTHidRbHlRQikR3QqBRheEHOvueYa5z6wsEHxTQmcAPlEYq5D6CBsGYdVAexrxEbcWDNmzIgbhJo09iTGt31oLMOmGZ2uv/56Scnuv3h3dilXZEYCoqLnE088kSNt2YAIKwLb8fB+bW1tTqICojfnmjVrVmDYgICWjrK5ddgZYF3CtSxIpaNsRvv27VPLgmCIINWJsSaV6kwbT0JyfF6GzaefstOjn1HuNKF7dtb7q666alap0zgIMMCoBPbYYw9JvmhdEixLxFwkqQybLySRMEnK+zBPC+pCE6LXqVOn1LrPMJUtMlZMJ4M06SNtDTneVtXPZDI5zw6lUzEC2ueF9EBKm/bo0cM9u7Y/kk1gsOO1r+PjYaz8hcVD4ERAQCvAcg+cADjSZ86cWRbd1GJ5lDktBHTYUaNGpeo3TYF1x4ByBU4USvVDl128eHFZdO9ir5/m1rFrXYw3I5ZQkPg5c6yoqEjVYe04k3pQ2XnwfHJ92x0gMGxAQCtAXoYNCAhYsRAYNiCgBSFvbU/0H1uEmcDuuXPn5sjt/KWYFB2+bGRH/JzxxlhSriXR+mOB9YPV1dW5Y/GjEuZG1/A0HdZGvsQbQqX51QiJtAXVklKnbKsOCzvHpGLk8deSj5xhrqS4lUuHXVFRTjtEKYn8jUn6L4RXXnlFku+WRxmZEPwfENAKUJSV2O4s8dSptHaJ9jWw7Nm2bVtnIUsqnpX0Hfs6zmRplkKsffPmzStqd477eguNoxDipTtL/S6I3+e078baQgaGbeEIDBsQ0AqQ9wdbVVXlWmlQcLqiokKdOnVSp06dVFlZqbq6uqJ8cJzDnvv+++9XZWWlKisr3fn5Z48FHJ/JZFzUiD2/ve78+fMTM284hwVzJLY1fu60a6Wd++WXX86Zk4X93N6L+vr6vOwqNejfxWS9/K+g0D0vBdgHfmgEhg0IaEEoSodNKxQVz/vDYkpDIqvbpllaO3bs6PII7bG0Zvj73/+edX2+mxanKvlcVJKJY60a8sahJunAXI8YW6zZxVoNV1555Zy4Z8ZK/ijJz/ZcSYXc4ueVfB5mUr7o/4J+tzzm2K5du9SWn/lypxuLoMMGBLQCFO6xKM9sVIkgtzPOuJR0tFkPtjUkn8MOffv2dWVFeI8KDWS4cB18o2Rh4BemvOR3333nrktBN1i4kJ4Ns9GImDaIcX0VZrWWXuKiqZ7B54y3a9eurnJB3L8rKUe6sMXTYVZb1Cz+XSvNBJQfbdq0SWXQcjJrQcQNNvZfJpOJliUHZ/2rqKhw/ziG11VVVVn/ampqopqamqi6ujqqrq6OKisro8rKymjevHnRvHnzov322y+qr6+P6uvrIzBo0KBo0KBB0cyZM6OZM2dGffv2jfr27Ru1a9cuateuXc41GVd1dXXOe1yP9+0c7fFJ/+wxXJ9/XMNea+nSpdHSpUujIUOGRBYDBw6MBg4cGM2ZMyeaM2dO1LNnz6hnz57uvqWNa1mge97xGSNc3rmV6x9gLXn/pZdeil566aVyX8saGpttXp999ln02WefRWussUbOGrL+8+fPj+bPnx+l/V7KMUf3m1RAQECLQV6R2AYwJAVF2BQvRDhEUV6/9NJLknznbUIW7777bo0cOVKStNtuu0nySdt813Z6QzSnOBydrefMmeOCB6hkR3W6NONaMe4ZDD+2lq5Nm6IzHzVmEaFHjhypUaNGSZLrz/LMM89knct2TzvooIMk+SR+QtcWLFjg1qFQuGNzgTrS1FJeuHChmweJ39RVPvXUU7O+29jAkeUFShcR0krVz6+//tqFgFojrO2IFw/dlfxzkuRyKzXcMTBsQEALQqOKsMVZFYMPCcDsIuwyuFUwKJ1++umSpAsvvFCSdMYZZ7i6vtTupV/sjBkzJHl2YayHHXaYJB/Qj/GpqqoqtRxojCWLcgnEkwFsXxZ2VFveEoPReeedJ8kXODvqqKNcZztKZFJulF2Y77DjUuoViSQu1aStWVLy8/Jwebzzzjvq2bOnJH/fYBXKq9IVLg2lMG9T3DrFMpp11QwfPtz1fDr77LMleWmCcq877rijJD93ng/69pSC4NYJCGgFyMuwhbp3ZzKZHD0XJoMV6flKsbGTTz5Zkt9xp06d6tiWdLEHH3xQktcJ6WFClXS6YtNz9L333pPUUIH/rrvukpQcTLHsdUm7c75C24wbPWfIkCGSGqQGyXex+9e//uXmDaM+9thjknyhaqrrk5YIqzNH+soeeuihrltAGks0d3rdE088Icn31omPBVbhObD9ksqBcgZO0IGd+4yEwByPPPJISQ2uQ9x2lOWlM8KAAQMk+Wea0qg8N1bHzdcDFwSGDQhoBcjLsJtvvnkk+XBD2z6iT58+jt2sdY1jCBG85pprJPk+NMOHD5fUsCMj+9OljlKpp512miTPvOgMsChMiw6x7rrrOqswsClxtrjV+uuvH0ly3ddAvFs2VliKoFPuMl7GVPI9X9E/KWS91lpr6Wc/+5kkH5jBfTjzzDMleV3puuuuk+R74NJXlTXo3r27a5mShuZi2I022kiSl2jAtGnTXNnaNGmEIBjuVVNg2aeQJEiSipSuK/M+kg4tW5B0xo4d61qBjB8/XpIvc4u9BlvG2muvLSlXuogXIyikQweGDQhoBcjLsJSPtP5GvlNdXe3es77Jq6++WpI0dOhQSdJDDz0kyXf1hqX22msv17nr2muvlST93//9nySvG+B/RWfEgmctzvHrx7qAZc0pTf+xIZXx89l5c+zdd98tSTrllFMk+fIe6HZYBwcMGOCagDFHpIeBAwdK8skTzIkSOjDwxRdfHB+znVPqHJvCsLQpwVpvy+jEO6/Fy/TEYdmnHCinDksp2nvvvVeS91RgY+BeL1261EmLSHz4mlmzJ598UlJuT99C5WKTEBg2IKAVIC/DVldXR1Ius8aBH5Yd9IorrpAkPf7445J8j1eY9s9//nPWcR9//LEuv/xySd6SzHcobkZa2/bbby/JJx9gtdt///0lNeiMNp0Ov2xMN0lsVZhv97MB+VdddVXWHGFBxsH4uOann37qWpfsvffekvxOTt9U9EMY9+GHH5Yk1++WdpgXX3yx28GtTxHJ5LLLLlsuflieh1mzZrkEb5I8AFZxWjNip2jidZvMsMQF2LYbSA+bbLKJJK/L3nPPPe67NINDR6WxGHo6khYSFs8W+nta2xbJ68xz584NDBsQ0NJRUqQTrBVPNLclUNmpaEUI46GHwsjEHs+ePduljMEmhx9+uCTv54Kd0CFgcyzN6Fhrr722O4fdOTfbbDNJ0oQJE4pqN8l9iaIoNX2NHRMWwWrNHNmB58yZ4+4DqYO//e1vJUl33nmnJG9hxo8JU2ELYFfu1KmTm7+N6iK2+vPPPy+JYW1bycaA9eR5AFbvLwcaw7BpTccsbHEEnuvq6mrnI6c0KdZ6nu3u3btL8muIBMjaF4PQqiMgoBWhqEinnC/FrJTWcojsj1WUWFKuQ5QI7HTQQQc5C+ivf/1rST55nHhbLMxjxozJ+hymQe5fuHChYwlrqUsqn7JsLnnnGEWRmyOMZrNkmCM6LlkdZN4cfPDBrkUjOirZSuzCzA0df+zYsZK8pBAvh8Mc0+KmmzvSKeF6Oe+hq48ePVrSD8+wxSLNojt79myn9yLtIFGhq/LslyN7Ks1KXFTFiXhIlZT9Y+Ch4RjcNYivhGtZN8sHH3wgqeFhJ5SL2k38+Akb47q4Swhg4Noc9+mnn7obvs8++0jyDwzjKQTb7TtpjiwYSRB2jhxHOGHXrl116KGHSvJBHjwQuD1QNwiF4+HgnFS1mDZtmnsP4xw9Ttm4lhdY62WFALI+s31XWwrsD6kxLMkAACAASURBVJVN+E9/+pMLPf3LX/4iyW+muBkBIjHqjw3eiT9bPMts8oVCOFvW3QwI+B9HXoa1oo7tQ1pTU5OzI8E+MMazzz4rSdp3330lefGB8LaKigrXyRsRF9aBadmFCNPjGhid2On2339/N55HH33UnV/yBqBCsPOpqqrKYU4bcjZhwgRJPtADMRcpYtGiRTrwwAPd/6XcXRjDBe9TMZLvca0999zTjYM5As5VbqRVBUQkjN8zpI6Whh122EGSl/IAYbNbb721kyxPPPHEvOciTJMw2c0331ySl4CoYSZ5ZgWF0gsDwwYEtCAUpcMCK18vWrTIGY8I16IECgyKOd2Wx8AVs3jxYldKhR2Ic6LEoyNSOoZdCQMWLiS6B0i5eifHlDrHpUuXZvXZkXwAPwkD7M52d4T56+rq3G6L8QiDFHWJP/roI0neQQ+Lk34XZy4bTA7Qc8uNtKqAJN3HgQSxopeCscYlXDUWt99+u6SGdSFggrnhwmLOzJU1A0hg2GKagsCwAQEtCHndOquuumok5ep/casX/7dBFTAKvV9xjRCiCJP8+9//dsEDFB6zoZDoqhRyIwUNd8qbb74pqSFpHqswsFZf69ZZZZVVIim73m8abCggzI+bh2sxR3bnL7/80o0Ly67Vi3HnYF0dMWKEJC9tsGsfeuihztIMLJuVy60Dc9hgCOwQ6NAnnHCCY2HGYINcyolyunUIUMGGwL0kkB8p4sMPP3Q2AgrjIVUyZ2uXoBgfkhjhpx988EFIrwsI+F9AUcH/aQ76ysrKHDke/yGMRTgXOhq6GMdNnz7dnZ9dmtA+WJlyMzbImh2N8h7nnXeehg0bJim3RCvHzp49u6Tk50wmE++7KsnrIoyXOSJVwLTcm++//z6nkj+WV3RaUvWOP/74rDlynwi5PP/883XJJZcoacxJgePNGThBKZ9hw4bldH5oTjRH4IQNAOK5wZ//wgsvOG8FjGklm1IKvKXZBUJoYkBAK0JRCez52Mf2w2F3odg3+i+6GLoB35M8M+DXg4UpCbP11ltL8iyNjxLd9uCDD5bUsDuiS9siV0n63bI55J1jRUWFY0pbSJwSpLAfwfcUCYeJ6+rq3Bw5BoaFWTfccENJ0n/+8x9J3nfHHEm0XrJkidvJ03rBNndoYr7IHYq6n3POOeW+bPz6ZWNYOwdbupbn6aijjnJ6LvYFiuylheNSLsl2GSwGQYcNCGgFKIphSRwnCiQebE/iMrsJFl6YBLmfpG50OfyikyZNcrHEsAoWU4KoSQJAhyUZ2uoOm222mStWBqwV2xYSh2Ep8M25YfnFixdnWffi52RuxDJTnAtdl4it6dOnu3NQoA1LN1FRMCuJ6iQHAJigV69eTtKwaO7+sNxDYmmZS/wZSuslXE6kMWxjSrFYIC3hJ+c5WLp0qXs2WCNiDWBU7BMwb9o48hWDB4FhAwJaAfIybE1NTSTl6kpYNqMocroYzIkearumY4WDWWGWLl26OD8mei5+WazFsDU7OlZjykkm+YWt9TrN+pZWIgamiM+RWGp8jPhG2VmtBRxLeceOHV18KdFIlJdhJ2eupKWRtJ+vLGiaVbJcOmxatBISBT7Mvn37OitxUkZXuVEOHZa5URSPNcR3uvvuu2cdV1NTk2PJx6fO83HzzTdnfYcidVZaKgaBYQMCWgGKKhGT1m5S8kwE6/IZVkJ2MKygtChkB7vhhhsc62L9pU0CWTxECcWZXVJOdE0SOCdRUoV25yRWgclsCRUimY477jhJ3jJOMjp68ejRox2TUowafYjGShRHtxFjaZbgOLjuCy+8kDPH5WElXmeddVzJz3K25Mhz/UYzrF1fWwomdg1JPopt6NChLqeZc2BTQeJDAmtMHHVCKeHAsAEBLR2NaoYVL1RmWQd5njhbWlRgCaaoGAWrPvzwQ5dfCGOy21EKxhb4oqgVLIQuO2PGDHfsHnvsIcmzHWhMiRgbvYOOSsUF4qVpR0gDMCSHqVOnOr0HXZ77Q1ww47Z+ZObI/frqq6+cvkUhN5pjgeXNsMcee6wrB7o8UE4dFlsLnhArRbJes2fPdh4RKonEy/YsG1fWOYph2LRj0xi2KLcOPwgesninOlst0T7cTBwXByZxavhuuummzk3ED5KwPF7zMNvePhhqCEaP/7i4btx4JKW7dTDu2F6e8cr/IK3yPpsVLhsegvXXX9/NhbkyRxsSZ6+BgSuegGHnmC/BoTlDE0vtHl4upP1g0+5lKeC+YzwjGYO00eWFIBIHBLQCNKpqIsXQ6D8SB7scuy6pSDAEux+i8oQJEwoGT+cLG4x/3qZNm5zEdcSWWBB9UeLU4MGDJeWK1FIuo9nwTMZAh/i4Wb+pid1VVVU5jM94YIV4gsPyqJq4vFGsSMyz15gO6MC6B5cXAsMGBLQCFOXWgTWTeuzYzusYYAiEtozC55SF6dChg/suCcFpneTQESnVYhk5k8nkKP7WbJ9mdEqq+G/nyDE9e/aU5JMR0uZIKONKK63kvksAeNocbSqfPXc8OMSOK1ZKNjBsAdjeSxb2uSkmnLCcCAwbENAKUJJbJ5/+ZYMrYIo02T9eyCrN4WydydY6mxTIYS3JVt+zoYlNYaBirZJVVVWpO3khnTbf580dmriiojkr/68oCAwbENAKkJdhAwICViwEhg0IaEHIW0i8JesGxeh3UnryM5FPs2bNSo10omwLJVutjh3XcW1KWjmA7m+blSXpsI0pGFbIkhpHoeTxxvifSV974403JGU13yopvDTfdUndpNerBZFms2bNyrH+lwN2XLH7FHTYgICWjpI6sLcGpO3OlgFIxJ86dWpqylixbBG3EjdnC4tSSsSsKK00GhOPXOwalmOOnKOystKtYXPGUMd6/waGDQho6fif/8FWVVWpqqpKURRl7cSDBg3SoEGDXIRL/F+pIDJLUuo5Kisrs1IVKyoqsv4Vg/r6+qJ3/VLOm8lkEguEcw7K35R6Xqm0Maehc+fO6ty5c8697dChgzp06NCk4uacM76GxY457b7lQ11dXd4icv/zP9iAgJaEoMOm6D+xz91nJ510kiSf71qsjlRbW5sT8cV3aC1C0rtFvmtQdoZMqKRSrstjDddcc03XLsWiHBkzFqWWOc1kMi7r5vXXX5fk23yWQ8/t1auXJF+WtxSJYY011pDk26+S4/3tt98GHTYgoMXD6mdGV4ta279Cc9xtt92i3XbbLe85qqqqoqqqqiiTyUSZTCbq3r171L17d/d5RUVFVFFREbVr1y5q165d1Ldv36i6ujqqrq6OampqomXlYyNJUf/+/aP+/fvnnNtes23btlHbtm0Tx8M4kuZoj2Vs5biXnKtbt24lf6eUcdhjC63hlClToilTpmR9P+3c3Lu+fftGffv2Tb326NGj3f/tWjz22GPRY489VnANu3XrlnqvCs2Rfz+YSBwXRfI4j7NelwNWnCrUWycJto5VWsI9olmPHj1cVUHQp08fSb4IwI477ijJdwCw/WPj104S+cx3miQSFysmUu7nwQcfdBXzbYc/aiYxv3K4QsoZ/G/TQ20BBP6OHTtWO++8c9Z342mdki/KwBqiBti01EwmE/rDBgT8LyBvaGJzgN3GllmJIymJvLlQzDUSkuAleRahwiGGJY6nIuInn3ziKso//fTTknyCP+fi3OzW9Bdi16ZD2rx58xwr/PKXv5Tk6zg3FpZR0+4JYXrM4YQTTpDUUPmS0D56G+EGoecS1+jWrZsk33/GJvLX1dXlJOaXo09PmtRAtwZbpdIev/POO7vKn0hF1mVjK17SE2qttdaSlFs+SfK1q3lWCiEwbEBAC8JyZ1ir/y1dutTtTBYw2A+BuF5IfWFYwSbps6NSyhXmwf1zzTXXuBrLP/nJTyR5BmIHP/LIIyX5XXq33XaT5M388Z2f71hmbWyAQLyYgOSTCuzn9AraYYcdJPnE/REjRrjCfAToMxY6LnCO6dOnS8otcMC16+vrc2wAjUXczZOW/ECSB2tLGSIK6nGOwYMH629/+1vW2G3hgpdfflmSX1vODYsnlfmxzFrIXhMYNiCgBWG5W4nZwajyLylHN8CC+s9//pNxlO365bAwoqMSsEBo3pAhQyT57gaw4tdff+2YFSskgQYce9VVV2WdC4skuzXlVseMGaM999yTuRScY6HAkHzzoxs5Rc9hG7q40ReoQ4cOrgsBHdhhXPrOnHbaaZJ8n2DuDWzO65qamoKd78ppJaaLBAEMSEvMGXb87rvvXEF9wNjpSEiBPQJarr/+ekn+WacH01dffeXWuZg1jCMwbEBAC8IPHppY4PpZx5SDae3OtcyRralTpyYef+qpp7qO4xQGR5eBiQ444ABJ0m233SbJM9OUKVMkNeh+dEGj6DjJ0HTxRl8jGf6CCy6QJF1xxRWSpIMPPliSdPnll+vMM88seo60W0mzfsYLk6dZZdGr9913X0nSfffdJ8nrnc8884z69esnyfedwTIK2yAtbb311pJy+/ZyzcYwbI8ePSLJW6QB5x4wYICee+45SQ39iCUv4dj7QaldWBO9fdy4cY5h11tvPUne4k0LG87B+JGK1l9/fUk+HHL11Vd3unyxcwSBYQMCWhBWiEinpHKlkt+ZJk+eXLbrlhr836ZNm5wdHyshPsdddtlFkt9p1113XUneYvrYY4+5zur0GL3jjjsk+eD/I444QpJnAHQnIqR+9KMfNWqOTYl0sn1nt912W0nSpEmTJHkr6BdffOES/vHHEsyO9PHKK69IUk4SOH+LKUMDStVhk4qv85qC9vi7KeGDZMBxixYtcsfAwkQ0YQnfbrvtJHmJhHuCJRiGLqYoeWDYgIBWgB+MYePXTWs4xK6L/sMO18TrZu1cy4K08/r88IWiz7ALozORXjVw4EBJPn4WfbWurs71cKUjPO0+mCMsPWrUKEnShRdeKMlborHC7rPPPs7qaCOwYr1Om8SwrAdsxBiRgLCooss/++yzrm0J8+RePfzww5J81M+dd94pSS722EaNxb0Hlg2xDcyZMydrDdu0aRNJuV3U4+B+o2Nj4d1ss80kNTRlk7wXg8+5x0uXLnVjZv5YzeMWbsn7dLkHBx54oCQfIdazZ8/UdMPRo0dLkvbee+/AsAEBLR1NjnRqrAU3fjw+Lwt0gXIwaxrSSpjGJQB0Fj5DnzzmmGMkNWSrSH6nhXFhhEWLFjkrLxE07MacGz2Ra+DPxLoM8/bq1ctZny2jfPzxx5J8I+licdddd0nyfmQkHRvna/VNrObt2rXTzTffnHVOGASpAwspDMa9wQ8aZ9a08qrcO4tCVmXJJ5cD5kKZU+wS6OmMj4iz2tpaF/HG/Yh3Z5e8D5cIKHRWfPBIagsWLEiN4iuk2waGDQhoQVgh/bDLMx+2GAuj3fGPP/54SdItt9wiSdpiiy2yxvvmm29K8pbeHj16aOLEiZL8zs25+Asj4ZflGlgx43GxhcpsljsfltewI0zC53PnznUSA5+hq+OL3HTTTbPm2ZRMrEJrmCQlsRbopljwiThCojnnnHOyvgsDT506NSejCth14DjuF59jl7jgggtKWsM4lnvwPyhmwcr5Qy0WiC2IqlEU5dQUJryOY8ePHy8pV7wmGGO11VZzLirETTYBDDwYP8466yxJ/sFmYTfaaCNJPi1P8oYbRDX+5kMxIYr2M15bkRQR/N1339XGG28syRvJcP10795dkn8wuWeIwmnXknLrQXGvCiGpIILtkEjoJ6I6YZYWGBYzmUxOYAZgjdhUTzzxREm5nSFwg8XHMWLECEnSUUcdVdTcgkgcENCCsEKJxMuDUUsViTOZTE5gh02whjUJmCCAAvGwbdu2OWI1uy2MhFh90003SfKBBxgnOPfaa6+dc58SWKqsVRMJoCBAAEmCMY0aNcqFJhJwwD2xIh8iJveIEMCVVlpJUnqPmzgas4Y27NLeQ4xeJOkzfltwwVyX8UiSfv/730vyYjbuHNxAnBPxPB9C4ERAQCvAD6bDlhJq90Oivr5eu+66qyTv+L7ooosk+dBEAt1t4necLWBWdldcL7feeqskudDFP/zhD5J82Btd72DaODNYZiU4oLGARaxEQWK2ZcuRI0e6OTFXxsT6/ve//5Xk528ZlM/zBUw0FfX19a7kDqGS2AKoNQ3TE+wCG+KWSgKsTZe93XffXZKXIk4++WRJcskaBNzkC03s379/3rkEhg0IaEFoNh02zSVjnc51dXVu58YSiiujOWB1gz59+kSSt2rmAy4ZksrRUdF7mBv6KHra1KlTXeodScz2vmM1HTp0qCSf2G4Le40aNUq/+93vsr5r73F9fb17ozFlXAFV8kmJw3Xz0EMPSfKhkGPHjnXSxiWXXCJJuvHGGyV5S7Z1CdniZ9zDOXPmlFwCdFmd55ySLSCprCj3zOquuGS23HJLSb43rZReGsbaMtB7sWrbjgSLFy/OsXjbcwcdNiCgFaDZdNg0i69NWF577bXd/5uTWdNQ6JrxwAn8kOiK6JWUIGWHhW3i54BZmesee+yRdSy6HQH1hDvix4SRxo0blzNG2JOE6qTPGgOY1e7+6GynnHKKpAbJg8ADdHBbWA8mTXuNhFFVVZXKsGnF+tKYFdTX12cF8UteCsI6/fbbb0vy4ZZY8fl83rx5OdexaZaMDz0Umwbnzqebc+5QhC0goBVhufth0xKJ4+81JxpTwAt9A/2GMZ966qmSpKeeekqSDxQfPny4JL8Dx0u5srNjrSSBALaGxYmMIswxntLFfbK7cqwQeFn8sDZ8Dh2eYHfY5/XXX3f+5Pvvv1+SdPvtt0vyTIUua+0A3Jd4O4tCVuJiixDwurKy0iVRIMkgDVGqhZQ4QChl0jPJfTn99NMlSVdeeWXi3EjSoKRMPGkfvdaGOya1W8m6dtKbAQEBKyaWG8MSKE8DpRWNYW05lLjuQiMnrKKwAvolPjysquhJMPOsWbMc05CgjA7LTg8DDRgwQJJcQrhdn379+uXoyCAWzZPDsGm9U0sBeuo+++wjKTugHx2UzyicbtPI0iKg4nMo1UrMHGFJ/OIUdl+wYIGLRyblDekHGwZWYnTd2LklNUgzfIf7QPyxXSN86LYsKjj//PN18cUXlzRHEBg2IKAFodkY1pYvIYIE/QesscYaLm52RdJh44zE7svYeU2Ujm07aAtuDxkyxM0NizJsjPWX+4XuR7G2fO030nS9fGVO84Hz2SJsMBdWY2JmsZb36NHDlYIlCR4pBFaizA8ggiutc3s+FLuGcb2Q66GzEqV1+OGH573WBx98IEnacMMN3X0hyR2Ji/cpoEdEkx1HKRb7wLABAa0AP7iVOO295kJjrMRpOyRRPeeff74kb9mFTcj9/PTTT53lcPDgwZKkf/zjH5I8ixFpZa2mtjBdEoh8gjUaayW2eq6dd7zkp+RLn+y3337OUoqkwGcUPeOczK+Q7zTfuBqzhlhwkfgoQYolFxsD5YqYOwntF198sXsP+0Lv3r0leaswUiTH2YZf+YCkgSQQGDYgoBXgB2fY+vr6RrdJbOT1S96d08pZEodK3CnF4ihIxve+//5713qD72JJpHQMZUC5P1yLv1gmr7rqKlcNgzYe6JRJc2zMGrIeWLIfffRRziXJW8GxAD/++OM67LDDJHn9LR4rHp8XSNOp8xX9Bk1phmVzmZkL60OkFsdhr5g1a5YrVkeeK5ZxrMGci4gzKo7wPllfzzzzjJsT5+A6aXN04/+hEthR3BENlxfSFpuep9aQkMlkiqqsKPkfKD9GHto11ljDuXhYbBY/qZsf15V8QkS+5ATOHasTlfODTbtOKWA+zPOggw6S5EXxZdeTVLyBpTGpdGlrSHAJRkxQjKvIAtEZET6Tybi0Oe43onFalUfmRrfBRx55JPV69hxBJA4IaAX4wUvELG8UK05RwZ56wklgB4V52HkxQlCM64Ybbih4jlIYKY3ZMXLNmDGjoEhsi80lnc8anyxL4ZJatGiRG1M5AjTSEFOjilpD1o61TALMxrl/+tOfSvLVK0l8p/NCc4HrE0I5ZcqUwLABAS0dgWGXzTEpCN0CpsHIc/nllydeg4AJDDE1NTXOQIORwQYpcL1S3B581yaDN9boFA+Wl9I7ytkav1VVVanHlqPcS77khmWfZ61hMZ3wCDel/I1FUgI7pV6uuuqqxHEBirBR+7gYhAT2gIBWiMCwTZhjsexRW1ubU6ANFKoAny9JwhZLA+VKryt3MbRyoRxrWOrc8lmam+M+BYYNCGgFyMuwAQEBKxYCwwYEtCDkLcKWFiVDgeRbbrklx5qJhZFi2+eee27W+7YIm+QtZLB9LNpD8eujB1pdIsn3R6gZ0TiU6SinDlsI8c7yhXTVxiCp6dOy93N0WGvZpZzqJZdckjMmwicp90LRNe6zDeurqKhwa2h7tTJGrOR8bv20SWtIQgFRaBRIK+calrIupVihi8XkyZMl+bTKa665RlLQYQMCWgXy6rBVVVVZyc8cG+/YDYuw+7CDsiu++OKLip8jZwCxyB2726UVbo6NT5Lf8SorK3PGWqwPrzUhKYHdpsjF+6HCftw71tD6EwtZSc0YJBVmJbvmlZWVqbHboNhIpxXVyl0MAsMGBLQC5GVY2jzYVn2UiiR7QfIlPinLib5r/Y/2evG2BWmMSkE0YnRBvrhV+1laHGpTWlkUi3jpzuWB+O7M/KzPliR7GhpLfj0pCAcrxqJvEq+3YMECx9JxvT0OPud5KIaBLfuWGktcTrvBoEGD9NhjjzX5PMUiMGxAQGtAFEWp/yoqKqKKioook8lEy3bqSFLE+/F/tbW1UW1trXvNsZWVldEyPSqSFLVv3z5q3769e33CCSe4/7dp0yZq06aNO8ekSZOiSZMmuc/tP3ut+P+rq6uj6urqaOWVV45WXnllNw47x7RzJ/1bvHhxtHjx4qKPL+Zf165do65du5blXElztOuUdg8rKiqidu3aRe3atYvsuttzcG85xwEHHOA+q6qqipbZPiJJ0XPPPRc999xzqWO2z1b8X01NTVRTU+OeGa7RlDW0z2Ox/9q0aZP6WceOHaOOHTuW9blI+00Ghg0IaEEoKpYYfTBfmQ/0BcqjkNmAbmL1CIpezZgxwxXktk2Brd5li4JZ3bpt27Y5OlKhLAir/9AqA725GNgWDRb5dFh0Pdt+0KIUi2eSHxZd0ZagiY+NvxR1u/feexO/w3EUDJs2bZpr4Iz+a3Np03Jrbfmddu3aOftHWo5toTUs5F1IQqH727t3b+cztWgO/2yaDps3cILK6dTjzVd7iRv+2WefSfI3mYfY1inGUTxo0CDX3Yt6QFQf5Cba7wIWkM/r6upyfuy2a1khlPJDBfl+qJI0fvz4gj807m2844CU61IrFfZ8XCfpAbU/Iu6dXX+Ou+666yQ1VPtnDfmxrbvuuonnsl3rbC/VxYsX58yVOcyZM6eoOZfyQwVp9/fzzz+XJO2yyy45x7BRU5+aqonl/OFaBJE4IKAFIa9IbKvG2x02k8m4sEHLMuw+iKiUIuEcFMx66qmntMsuu2R9FxEDhmdnZXfeYIMNJEkDBw6U5HvbXHDBBW53u++++yT5LnGgnIETuCpiieOpxxYqnYIYx71lZ6ciX5JIXUxVQbuGSe4uysVQk9fOD7Gd/jSwJeVM3nzzTVcsjs9Yf1QfJC/Wsl+/fpJ8L1Wkp+uvv96NdXmsoQXFAUlsJ8DkzDPPdP1wzjrrLEmeUXnGkQRYS8JBqYFMF4H4eqW5noJbJyCgFSCvDmt/9dboIPlCVxgoYBt2TNhwtdVWk+Tl+1tvvVWS1KtXL/3yl7+U5DtV77333pJ81XX0IatTX3vttbkTWsbOdleGWcoJGxxgGY/XW221VU4Ah2VF9C56v2DEyTfuYvRau4Z2HBUVFfrFL34hyXd9t0kYdIPjL2t73nnnSWpgFpiSnkFHHHGEJOmEE06Q5Iubde7cOesc1FaOA9Yp5xpa+4t9lpkzZXfpXEDJ2sWLF+vII4+U5KWCCRMmSPLlTpEmsBfQ+zdpjnYcRc+jpKMDAgJ+UBQVmmgZIx/Gjh0ryRe52n///SVJDz/8sKRcC96iRYtc5zM6idFnBFbmO3QHQ/+FAb744gtJDS6l4447Lu/4yqn/pCUYYDVEt66rq3PMX+i7vEbXQyfEilpMp4R8JWKKWUOkJYqew3SE5tli5N99953++te/SpK7/xTzRipifjCtDTeFya688kpX5K45Ezg4J72Nnn/+eUl+zR566CFJPv3tvffec54NJKtXXnlFknTHHXdIkm688UZJnml5fulOj67btWvXggXagg4bENAKkJdh27ZtG0l+Z7AWxmOOOUa/+c1vJPkdld0YyxjBEOxo6Et//OMfJTV0wN55550leasbuy96Djs6VkkYld4v7PwLFy7M6aBtUeruPGbMGNdxrlC7C+6LDTRZtGiRmz9Maa2Ddh0oh4rFsRT/cHyOP/7xjyPJ93i1wSe77767k2zwg6N30u8FvZp7u/3220vyCe5PPPGEszugo7L+MAm2Ddbs6aefliTnIaDz/TfffONYrpj5LZtTliRosdJKKzm9Mi3IgfuChHDTTTdJ8vrq+eef73onMX+6zdO9kN8J7z/33HOMV5KXFJNSCAvN0Y0z77cCAgJWKJQUmmjDy7p16+b6f/LeSy+9JMkXYkY3oHPXxx9/LMlHM+2zzz5OX7jwwguz/sLWlJvBsox1GL8hTBDXz9KsscWmZhUDOp3xN08nMtfRDKbBp5lW5gVp47XXXpMkbbzxxkWPK19oIszCenXp0iVHnxo+fLgkb+UcM2aMJDlpCqmJDux77bWXRo8eLckXT6c9xlFHHSXJW/6RrDgn9yxJgijWR9mUNUR6YjyXXnqpJF9Ch/vVuXNnt3bcrwEDBkjyza6wsr/11luMS5L3Z5fSiCwwbEBAK0BJVuKkdhbo0FMC0gAADBNJREFUZMjn6ChYha3ugAUYP5fkWyrCvhx76KGHSpJOPfVUSXJJAvgoKdJFwsHkyZOd3jtu3DhJnunRL9q0aVPy7tynTx9J0jvvvOPmvexcWXPCOmiTE6IocvcBPTjNWmv9g1hZsTS2bds2J6YakID++eefp0Y6Jfkjud8cs++++0ryjaBsJBdjYT2iKHKJ8B9++GHW+Y899lhJ0tFHH501RnTd0047TZJnuPfff989SzA9x2AnWX311UteQytxYa1GP4dJsVrjR6ZMzo033uh02GeffVaS92qMHz9ekrT55ptL8tITxRxIiEDKQipNAhFic+bMCQwbENDSkZdha2pqIsnvPuz26B1xfxJ6LtZBGiOj72AN3GKLLSRlN3Jm96MUKX6ud999V5J0xhlnSPIlIBkPfi4sqXFrrE3rIv7z8ssvL5v+A8NgtWY87LCM4Z577nERYWm+xbSyn0gI7Nb5UvXQkdq2besOWJZo7s5v/Z9t27Z1DMo1kWCI5WUegwYNkuQtp7BinKWJxaWj/JQpUyR5izIWVMaKbz2+hjajiLFjvb7xxhtLXkObtQSwfMctuPFr86zV1ta655LxYLsgIg9JDOkRXzrPOmtdU1OTKiXFGpEFhg0IaOkoKVvHfSnWlpDPsISxQxBnirz/wAMPSPI6AbvVsGHDXLI7DIUuSMYKVjdYhp2eTJb4HAolIpfDwmhZ8pBDDpHkI4SK+Y59H7B728go4qeRcgqML6cIW5o1Ou4ThG24Nv5x2BJ/d+/evbPGevTRR+v666+X5CUsGHPq1KmSfLlb2AaLM96CpGIIaXG25VhDm2PNc8tf3uc+XXTRRU5KI7LplFNOkeT1YLwbRDw1JS82zUpcUve6Rx55RJKvBC/ldh/fb7/9JPnQLpR7AvsxupAUMG7cuJw+p4jC/KgJRQT86Hmw+Dt37tycqvS8RjTv3r172UTibbbZRpJ3vVjEf6RsXKQEpoGNDzEb4wxiNpua5IPPMXbErpv6g6W3KZuk5O8794pwPdQaXEr82LjfbFR33HFHzo+d7+Ke4voERSAKc03+LliwwP2fHw3nZL79+vUr2xoW2uARoTfYYAO36WBgQ+S1mzGEw+d2HvEf8mabbSbJ/z6CSBwQ0IpQEsPa3ai6ujrHWMIuws5KIPyoUaMk+V0Zo8Mqq6ySVRtI8oYKnPG4bxCvrr76akk+JAyXAYEVktSxY0dJfocstqZTY1BI3C0m4B6jFAY9xCrEflDMuYoJ/o/XS4oF1bv3JC82cg9J4KZvEe6dzp07O4MhSdrvv/++JLmkgNtuu02SX9thw4ZJ8mt54oknSmoIioFhbQGDYms6NQY8O8cff7yk3PrNgwcPdmoeaXaWMXnN84y7D2mGBAd+C/kQAicCAloBigqcAEkuCeR1wgTRTWEMdmd2Us4RdytgxHj00Ucl+YR1G9oF0LHYxVH6410G4iZ0yTPu9OnTS96dYWfbZY8dl3A29Pi0butJIEgBtwdzRpqYOHGiJO8WwwaQD/kYNva+m0u8AJrUUFRAkissQHkfxohtAXdQ+/bttemmm0ryBhmeA66DWwWWxHUEkyFZxN0uNgAFvXfq1KlNDpwgfJS1ZVy8Zlwkn19yySXOloI9hHOwJgRIAFssgHuAATEfAsMGBLQC5GXYmTNnRpK30iYdi77z5JNPSvLpc+wu7D7sjlgk0Q1qa2sdI1lzPjoMIWmwJDorehEMN2zYsBzHeEKN47InsNuUNYqawUzx6+dxNyW+n/S9QnpsfHeeOHFiJPmiZ0nXYw0J8SToPx40IPkQUhiCUM2VV17Z6ZmsHSxMWRnWECs3wQboxeiFo0aNSu3HlLaGTemP9Mknn0jyiQxcA92bXsizZ8/OKSrAusPK2223nSTvfrQhnjyn1157rbsfaQgMGxDQCpCXYZf1wHE7CDpOvHC3TcDGOsxuxC5EIDfO5niSN4W7sCBjabTV8HkN85Jwza64ySabuF3fTXDZZ6+++qokaZtttmkywyZUos+6FlLGpEmTssaZNC7YxO7atnh2nGlLYdgOHTpEkmc8GygQHz9riSSDFZhjsR3gU46fA6s264wvnWB2a/llPEhE8YJ11q/Nd5944glJ0q677lp2KzEMS7AO9gqSWcaMGePmi4fDhjUSlmlDEa01uX///s6na5EmRYDAsAEBLQiNCk0sBkS0EKFDxA560pAhQyQ17LAkNRNRw66MDxJLNKlZhMrZnjZxn6K12DalgBeRXZT84HrWB239mPmYEBamVEqhQuPFJOfHxpGaXlcKuL+wIK/R0Ug3W7hwodPP0ONgFQLhsQpvsskmkrwNA4kM1spkMjmldmCmpqxhmo8cDwTX5BmjdCnz6Nmzp4spsNF0PL9E+aGzkh5KaSPOWQyCDhsQ0ApQVKST3dHjXbbTynTa81L+9KSTTpLkk9WnTp3qfIzs3FiWibMkhY+/tkVG3F/LTm3HQfznxIkTE3dnm+Adt1jbHd/u/GlI0jttmldCCZusa1gccMABLu0tDUl+WMvgjVlDYslJKyNZffr06S7lDv0T5qRgGUnbtOywel0xa4iV+t13301cw3xdBNMSCrgvPFP4gwF66syZM11kHZIWJVBJ+yOKizYfaf74UIQtIOB/BCWViHFfiu18SWVjJK/LsLOSuE5RNnaub775xu1+lNxgJ8OHZ0uXYskjgdyOK47m8MMmtSxJAjr5e++9594jhhY/JEhjgGJihy2K0WGTIp1slM9BBx0kyUtDRKSRiYOdYsKECe45+PWvfy1JOvvss7O+gy+X68K06IzxcaUxfTnXELsIHgqkN5LQ8aXjL+3SpYt7Hpk3OipWdfuaGHgK0ZWCwLABAa0AjYolbgqw/MK8EydOdGyMzI8PjNfEsI4cOVKS153IhojrQXaM7Oywh80zLGZ3Jv/WRqfYJGd7bayob7zxhmNQCmbj07TRUo1BAiPl5MPaY4tB2rzQJZEcxo8fr1/96leSvFfAtiGFbWjLgqUfXT6fPYBzYbNoCsNaScbaFHheeA2bXnrppU7iIIYYdt5pp52yxlcoAT8JXDfm1QgMGxDQ0lEUwyY0IxLvE2dMpgLyOxUn2HnZOdi52Nk6dOig3XffXVJ2yw3JR82QT0h0jNXryPJ56qmnXHyxjUKJRWSVPZbYxjaDuCWUuZEJQyUOO5d77rlHkteHYuPMuqbk74eNCCuFYSsqKpw+Bzuiv8GG6KVYb7ku93iTTTZx/lW7hjajyVr2AWv4wAMPuOsmMGvO/Jadq+Q15Po8t8S6s2axgnaSGizhW221lSTvT0XfJaoryQJfCGnHNqpEDAYLqhYi1lBf9csvv8wxT1sRw1bAQ4yNp8ixECj+3DwMEqQzYYLnGkwWUbWioiLnweb6iBxz585NXOzGiDFpsEapiooKV8aFgIFi6/2Qpkgl/WKQZHQiUIX0N8S6uXPn5qyhfYiYB6oKidjxEEzuN+mAuNFYS8Iz+UGwlrhiSM+Mu8DsGjKu+fPnJ65hWt+cxgDVjTDDbt265SR5lENFTEMQiQMCWgFKCk3EGU6AAxXvsk6Ysjuyo9Jjh0D/m2++2Tma7VgIFOec1qkdG6f7vmVIdl3GsWjRoqLEKZvEUAwIICdl69xzz5XkXTnNDYLPX3311dQibLAUyRAYxuKw0obtDEDxOULxrrvuOuemsWsIK7OGafV449e2a4jozftLliwpag1R1wqlsuVDKeJtORCrpx0YNiCgpaOo0ER2Sf7GQ8dsWh0uGMqL8B30inhZEalBT8XEb4GOREc8drmtt95akjerx41gNomc68ZKUjbZYGFN8BZJfWSL3e3TKtSXgqTQRFsSlrEnrSG9UTF8IcHAcKTdcY/XW289F5Zn2ZHAd0resIa4ytBtk9aQe8Yzg4uoHGtYiDlt3eIkl2FzIuiwAQGtAI0KTXRfzrPrpAV38xc9aNKkSW63j5UilZTel9bqVvFr8B1KkVCgDTRHicwVDUluncasIdIR6xL/juR15kmTJjlpwoao2jW0Ng7LZPHvYMOIl9qx81t2zla9hnEEhg0IaEHIy7ABAQErFgLDBgS0IIQfbEBAC0L4wQYEtCCEH2xAQAtC+MEGBLQghB9sQEALwv8Dz0Cg3Ew4Sg4AAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 980, D: 0.08527, G:0.4496\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1000, D: 0.07626, G:0.4684\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1020, D: 0.1024, G:0.336\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1040, D: 0.09701, G:0.4975\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1060, D: 0.08243, G:0.5578\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1080, D: 0.09536, G:0.476\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1100, D: 0.06933, G:0.3392\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1120, D: 0.1317, G:0.429\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1140, D: 0.0534, G:0.6257\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1160, D: 0.1577, G:0.05612\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1180, D: 0.09224, G:0.5291\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1200, D: 0.1026, G:0.3152\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1220, D: 0.09343, G:0.5961\n" + ] + }, + { + "data": { + "image/png": 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ZsE1SSpzKeU6fPj2yM9R7fsuPkYNiC7TO8fHHH0fSmaLmZMGX718J60oHFbMqBuCee+6JLPUqX6vnNAz2V6Tez3/+c4BIgtRzOmrUqChcMezymDRHwRjWYGgiVNQMSxEdCsJff/31gXxRaPmetLopllTJvEoEVpnPsHzGWWedFTGrrHCyKKq4lfaVDqBzyeKoqBo/RlOrrD57McY1Z1hJHYqF9q9xo3TYanRKSVRKRg+htiJf+9rXgHxg/AEHHKDzFWwr74AksDR02ySGHTFiBOAK8PlRdnruFB9w5plnAi4wX/EB6l6nayD2HDNmTORXlU6qlE0lBUj/VUJMWDyhmrkbwxoMLYCSDLvaaqvloDjJ2i/OrdVDuuvXv/51wGVraCVTkrr0UmX37L333hEbh42rxIrydz300EOA6xcr+L1HNTaNK4xt/eijj2rOsFqlv/vd7wKFfWtrYTUN0VkrccgEYqdTTjkFcEwitspkMpFUJDuH9tU2sjsogiiugFulCNlHMe+h5dVn+zCyac899wRcuV0VWJBXQwn2snpvtdVW0XOq6KeQSXVdxNrS68Mx+PuEfuGkOUb7xX1pMBi6JioqJC5rpyxtfqlOrWJJPrpzzjkHcLqAfK1i04022iiy7CqRXS0uVBJGscTSWeVr1covf6e/ooZd4rVtLcuchqtlKX01XPGFcsniFY4jVT9saDMIu9qXQpgLKntFUivJSlAuWydOVwzLyoYNyK+99log3/QKXOsUWYlXXnnlqIyMYgmUH6soNHkmZIHW9QotwZlMpqykZQxrMLQASjLsyiuvXFAiRlZDfT7wwAMjdhQLS46Xb08rmPywssapvUYmk4mseorrlP6gxlGKA5Y+JAbWscUAcXGYfsE4qK0fNmTWSipN1CIiqFaRTqXmF7b11DZqV3HXXXcl7tuBccRGOoVj0bmGDRvGc889V3AMxQmokZpYMJQelAP7xhtvRPYXxT0rLztsZRLaYkIpY8mSJWWvQxLDVhSaqIddAxPVK/QLnAIu44NM33IiRycMqtW999570QQ1FqXVSYyVmKJ0ujCAXMf84he/GL3MYeK8XvZGlIipN+qVwF4NKkkmrxTlROLwXN26dSsKVdU2CuBR4H4IGVPff//9gnrc4N4LPX/aVu9C0rvVp0+fiPzCThWeK8hEYoOh2VGSYfv165cDJwqH5Sx69eoVrVRiPR0vXNHCUhxarZYuXRop6VLwlSwcMnpSOQ1h6dKlEcPLaCPRRqLO4sWLjWGbHCHDKsAnKVXRN+wluVGS4LtftE+YKtgRMT8sXBBKBWZ0MhhaABWFJgphcHW3bt2KViw/HQmc4i1HeejGyGQyRcfVNkl9cpLKavTq1auo8Jl0g0YE/zcKnzWGrcStE36nz3I/hgnlcUhi0pAdy5X98fvVlujEbgxrMDQ7SjLsBhtskAMXqC+dMq4EilafsHyLdFY/fBBcGY25c+dGeqZ0gtAKHIZx6dhhKc3FixcXddqWBVrBFmYlbn6E7NO3b98cOJYMw1OhmB2r1WH9MjhJrFgOpZhf75akSbMSGwwtgJIMazAYuhaMYQ2GJkLJMqefBf0naY7Sgz788MMoIL8roNrA8c/yPZQF+KOPPqq60VejYVZig6EFUFKH/SyvzmmiElYsBz89rFw8rjFs88MY1mBoAVTWE9DQKaRhiW82HcxQGxjDGgxNBHthuzieeuopnnrqqUYPw9BFYC+swdBEMCtxCnMMy6OE8K3EfoOmYFzRtp1FV7QSKyOrM+VNhVrcQxUJP/744zt8jCeeeAJwjcc7gw6ViGn0zfY7VqeFNG92WMunVCKzahqpPpX6raj2lXoQKfhbCfeqOnndddcBrm+pf56YzmwNf2FVLkVBJyr7kwbSvIflUuFKQfXJVK8sqQeREPd8qF9P2P/X3DoGQwugS7l1tPJopepISKAYTOVmagmlAUoU1op73333AXDVVVcB+ZIi6k8UruQ77bQT4KpNKiRSVf1Umd5P2q+2E149oOutipevvPIKAKNHj676WGHhgloiiVmVnumXQ1IRwDvvvBNwz6cK/+nZS5Im4tLqQmYtB2NYg6GJ0KV0WK06qk9c7epTCZL0n1AfDAttgTOcyMgk1lNPUXXoVse2OOj40lEVoK7v1dVbc9cKXw3bxOmwKiurRP7OYODAgYDrvdu9e/fommi8sjvMnz+/0+cLkXQPVZl/8ODBVR9TBRXUR0rGMc3r/fffj7qy33LLLYCrm+0VRyjYpzMwHdZgaAHUnWHVJ0dlZ6CY3WbMmAHApptuWvB9GkjTwhj2Z/nb3/4G5CvN+5g2bVrUU3fVVVcFXK9d9WeZPn064FwDKkgnPamcBTIYVypW4iSrt8a43XbbRb+H5Ww17ldffRVw3QvTQJr3UIXuNb6w0J8koVGjRkUBLJqjpAjptvIaCJKWOhL4YgxrMLQAupQOmzSWrsKwIeNcf/31ABx88MGA00e1Oo8bNw6AG2+8sWhuYsywkJx0JxWPC3XXbDZb1mfYSD+sytqqzK0QlrLtDGoROLHjjjsC8OijjwJwyCGHAHDzzTcXbSv7Ruh/l91hs802A6qTikIYwxoMLYAuzbC18DOmybCCVlxBOo301VwuV2Q5lM6kaC79LoYVW6uxUjVoJMOKmdQVLilcszOoZQK7uquvs846gGNN//5pTrJZyNZSqw6EPoxhDYYmQpdiWLHLrFmzANcFO010ZnVWh21FwUhf0zWUZVSWRfUTHTVqVHSM559/HnARTldccQUA//znPwG49957AXjmmWcKzl1NzGvaDKt2n7Nnzy74Pk7iUFF4+aKPPPLIzp6+CLVgWEUnyR+r673GGmsALpoJnESleygpSN6NNCK0jGENhhZAzRlWrSPV4DnEZZddxvjx48Pzdva0iUhzdf7+978PwGmnnQY4vVQ6rB/zK4uhomTUQFgW5SlTpgDFHcDD9hA/+9nPGDt2bOx4tK3f8CuNeyifuXzoYdTUyJEjI1+jpKRQr08TtWBYWa9lzVY21W9/+1sAJk6cyOTJkwG4+uqrAZdpJTaeM2cOkE4stDGswdACaLgOG3f+sIFWyudLfXV+7bXXAFh33XULvvfja6UDiYVDKUIru/4qLlXXpxId1mt4XVMrcTiWt956i5tuugnIMxE4SUI+6jRRjzKn0l0lCS1YsICpU6cCcPrppwPuOsjfrpzlNNBlE9jLnL8W56v6ZsuZLud6CLlgLrzwQgCOPvpoAM477zwATjjhhEhMCg01Sqs7++yzAWdokxFKqKZiQ73dOsuWLUu8V7o2lST5V4qO3MOw2EASwsVI1/2ll16Kgv31Yko1mTZtGgA77LAD4JICtJB3BCYSGwwtgC7JsBIbJY6kfL6ai1PHHHMM4OoEZbNZtt9+ewD+/e9/Ay7h++WXXwYcA2ywwQYAPPfccwBcc801AImGpjjUm2Hj7uHMmTMB2GijjWpxvprfQ4WMqtTNe++9V1RgQSpPGPQfoiNShTGswdAC6FIMK1eA9LSwa3tK50t9dQ47xAuazwcffBDNQS6ftdZaC3DuBK3CcgUohU3F2e6//37AGeRKoZEM+8ADDwCw++67Ay6oQEyV0vnq3lvHn6P0W93f8B7qXodGU0lVKiVT5nzGsAZDs6PuRdhKMbrKsYQrVSPRu3fvspbF0NWiFVdhhiNHjozC1oYPHw7AWWedBbhgc+lDsqr6pWnABSu0t7cXpW0pZFLlTWqNUq6l3XbbDYC1114bSJdZ6wndcwWj5HK5KOVRYYxiSj2vSSVLhUqYtRyMYQ2GJkLdKayUxawrle3UWMqxq7+tyr2E5Vmz2Wy0jay/SkNbtGgRUD4NrdQ46sWsmoN09ksvvRQorJbfle5hR6CAfll+NZ/rrrsuKhguO0J4n2tRNDCEMazB0ERomJVYQe7S4Zafr6J9O9OHJsnCmMT8uVwu+k0Fu5R0rlBE6WmhbqlV+qOPPioqUrb55psD8OyzzwLuOsiqKp1Wfkz5b30rcVK5lXpZiW+88UYADj/88Oi7JIt5CBVI33PPPas+b7VW4p/+9KdR+KCg66j7kXQtZR8YOHAgL7zwAuCS25UGmgRtP2TIkJLbxcGsxAZDC6BL+WHrof8krc5f//rXAfjNb34DuBU4rpCW2FJWQfnjpMOoXYXigtva2iKfnOZ4xBFHAPl0OSCKhBLjyh/bkZjbevthFy1aFEkTvlRRK9TDDxtKXL169YrsCCpvKilIz4hYOo1SMcawBkMLoGEMm2Y/1CrPG7s6y0pbSZsFseMNN9wAwKRJkwDnW9UxZEXMZDKRniursLJAlAk0YsQIwEVCdabwdr0YVvPs0aNHUVRaLVIjhVoybFgwQPerT58+kdSl4gKhdb6aZ6gcjGENhhZA3f2wamwszJ49O8ofbKQPr5pVUdbRM888E4AJEyYAzkKqCKe9994byEe4KEomhOasvFjt05Xx4IMPAs43vPbaa0dWbF2DZkP47EnikS4+duzYqNnZ7bffDjiJSr51sbLK2/rNt9NCc15dg+EzirrpsPXQbSpBtfpPnz59Ij1GCNs4iFXE0lqt9Xnp0qXssccegIuG0j4qRq32Dmlcp1rpsCqkp8J6gt8+RC065s6dm9Zpi5CGDptUckex3mJN+drb2tqidpayM6gkapqtPIUuWyKm3kjTYCHDkV6uJLHaf6CTXkipCtWIxCWCPRpW+b8eaER6Xb1hRieDoQVgDNvic2z1+cFnY46CMazB0ESwF7YTmDlzZlRszGCoB+yFNRiaCKbDNtkcM5lM2eDyeumw1XTUSxP1vIcK9FdKZb1gOqzB0AIoybAGg6FrwRjWYGgilAz+T9INGqW7pIFm12ErgT/Htra2HFBUomabbbYB8p3YDjjgAMCVVo0rk+OjkkJ6YTB9eP7w2Hqm1lxzzaiJVHgsbet351v+e0vfQx/GsAZDE8GsxC0+x6Qic4ppXrZsWaKkVGmsdM+ePYuKwCf1tQ1/j+uTq1YtSUxuDGswGJoCxrAtPsdu3brloDj1z9s2KrGqhHQ9EyFLqgSKio75hcVDlta+KpOjz+XSBksV5fPG9Zm6hz6MYQ2GJkLju00ZaoowuT6UqLLZLAsXLgQKGz9BsQ4pZg23W7ZsWWL0Va9evQCic4THVEEzFZaP2yb8/rMMY1iDoYnQFDqsirS9/vrrnT7WZ03/SbISx7FYkoX3ggsuAOCUU04p+F0F1Pv16xeVRxk0aBBA5EuVzvriiy8Crrh6OA7pwL4+rLKiYQnVTz/9NPV7WKpwfKVQK5fOlKgVknTYmonEpZzrPuIMFqpOp3o66ijWikiqk5Q2yizMBZ8l8o4ePRqAk08+OfZYcuV88sknkXFJ91KVBU899VTABWoId9xxBwBHHnkkAPvuuy8AP//5z6PjH3XUUQBcccUVBceuBSp5UeXmUoX/cDwdEdnvvPNOAPbbb7+KtjeR2GBoInQJkViGB4m8Wqn22WcfgKgvZyW47bbbADjwwANjf2+0SKxV+Utf+hIAr776atXHKMfKlYjEPpKYQWLiF77wBQDefPNNwEk+qnzfu3fvqEfsN77xDcB145M688wzzwAwbNgwwHX8E5u/8847QN44ForCYm8vdLLm91DP4tSpUwG45JJLovOLaUOomIE6DpaCjIBJfYHNrWMwtAAqYlj1MtUq2RloZVUf1O7du0dsI8aQ3qO/5VBNMkKjGTYMaK+FXhbHsHKbSJqJc6f4vWfBMZwMSarHqyr/0vv69evHnDlzAPesDB48GIAtt9wScN0Bv//97xcc+6WXXtI4NfbE5APvmsXew8suuwwo7AhfKcK+ODqngkl69uwZPWcnnXQSABdffDEAjz/+OOA6EKYBY1iDoQVQMx1WJn/J/cLRRx8NwLXXXgvkTfWyummVO+644wr2ldVY6Ex1/HowbJx5P2QJdfaWVJFmT5o4hk0Kws9ms0VB/bq+Yk3NY7311gNc/9vnn38egIkTJ0aWXG0zfvx4AP74xz8C7nn45z//CcD8+fMBOOaYYwC49dZbgTxra4xJ0kea9zBJl9T92m677QAYN24cY8aM0fkKttE4w2OE0mQ1MIY1GFoAXcJKrJUp7J0q5lUhrL0ICoEAABWKSURBVDS6ejdah0263mmG3flzzGazubjz+rq02FfXXbqpwgolDchyqn5AV155JZBvM/Luu+/q3IDzs0qHVf8Z+WflY9U932CDDYA8E0tXVve3kNG66j2Ubq+5dPIcxrAGQ7OjYcH/fiiYVl91B5Pvrk+fPo0ZXA3x0EMPAbDLLrsA9QtolxQTdtbzE9hDm4C+17YDBw4EXOd5WVAXLlwYsbTYRxZ+saWi1dTxbY011gDglVdeARw7+WMsl+zeKPzlL38BYOuttwbqOy5jWIOhiVBzhlWUjFZawV+VxKRvv/024FbdZoHGH/aRjYOit6ZMmVLTMQlip9AS7DOirMKah+6NrJvST9daay0ANt10U8B1Gv/1r38d7SO9Vxbm0Boue4QYVftpDIsXL46kgZDxa1mSV1FKSk6Ig8YlZhW23XZbAKZNm1aj0TkYwxoMTYROM2y5rBz5XSdPngy41UiWxosvvpgTTzwRcH5XrdIhump5VTFRUlzommuuyezZs4Hi1LVao5K0OjGZUuTEdmJDXe/DDz8ccDYGZZjssssukTX4uuuuKziWWFsdzeVvvemmm2LHO3z48Gjb8H7L1lELaPzCrFmzABexFYdJkyYB9WFWwRjWYGgi1N0PK1+fSlnOmjUrytTYYostAFh99dUB54dLE43w4eVyuUiiGDFiBJDX+wAOPfTQWpyvbDMs32oshg1ZX8+GrMPaR5/FhBdffHEUI6z5SLKSPeL8888HnK44atQowDGuIqFkeY6DPAu1SGAvh7j3RNdNY08qA9vB88Wanjv9wlaaqJ6ExYsXRyJYWFlPIpACJqox7iShUS9sEmrhEogLnAjH4rtOfvjDHwJEf/Xi6r7IjaNQxAsvvBBwLpp11103cleF90ii5cSJEwH49re/DTjjm86lFLoPP/wwMYzSc0l9pu6hDxOJDYYmQqeNTrUwtW+yySZAcShiZ5i1q2HevHl1PV+S8WnZsmWRQTAMZpeIF4YR3nzzzQD85je/AWDAgAE88sgjgFNrnn76aQB23nlnIJ/kDjB9+nTAMbGSvf/6178CecYNuxOENZ3qiXHjxiX+1ohADmNYg6GJ0PDgf//8chcoPW2vvfYC4O677y55jAEDBgAwd+7cSs7XUP1HaYUyytQi7K6U0alUtcQwvFBGJiW7q5DaAw88AED//v2BfFKAWPmSSy4B3D1Tcre2VeVFbaf9VAZok0028Y1LZecXN8dawL9eYZC/EvzlukvCaqutBrhyOGXOZzqswdDsqBnDhiU3hFAvlUkc4jurQboBE/VYnSvR6+W6CkM2Uzp/WbeOrLJtbW1Fned0nRWw/+yzzxbsK4aRVfnUU09l//33B+Dee+8F4MknnwRcSZinnnoKcAXkpJfqnNJply1bVtSnNnQF1qN7nVIMfb1Zz6Ge2TTSPZNgDGswtABqZnYLmVV6icIO/eJfQlK4XlcLRSwHscYvfvELAL75zW8W/VYvJAVDyF7wyiuvFOmu+vzYY48BLjzv8ssvB2DIkCGAY6HHH3+cc845B3DlZGQtlh9WiQI6tny4Yk8lfrS1tRV1zxPj1iucE9zzus466wD565TURb6eMIY1GJoInWLYuDYb/m/g2DFshRDXGqFSXfX6668HXNRMV4XCEX2GrRRp9WkRS4XFuRUyuGjRoqhY2BFHHAE4HU3RSQr6l56qY4olp0yZwp577gm4Up8KMZRuKkvqCy+8ABAlfPzgBz8A3L3fdtttI2aX/ujprh2/EB3EjBkzgLwVe4cddqhq31p4AIxhDYYmQt39sFpJ1crhf//7X7QC6btaRjQ1woe3ZMmSyGquv7Vkizgr8dChQwH4xz/+AbgSLW+88UakR4qFVc5T/tZdd90VgHvuuQdw91DJGYcffnhUxFsMq8IFI0eOBFzanSzMYRSTb6FOik/3Ettrfg/D4vmlOsPXAmYlNhhaAHVn2LAI2JgxY/jVr34FOIthWHw8TdSTYcs1PKoV/Dn26dMnB85nGFqC/dYYYr2vfOUrADz44IMAHHzwwYDzy0rH/PGPfwzkUwbVGEsRTSoJI9Y+66yzALj66qsBZz0Ofb+ZTKaIwUJ/fD2ydbbaaivAFVzLZDKp9JCtFMawBkMLIDU/bLm8WBXjUqsGrZp+q46VVlqpomN1dRxyyCGA0/GGDh0atbeotw8vTAiP06HFXPorZhWjqFmZytBOmDABcBLEokWLoraZ6tYu/Vc6q2KHxU4ah6Qqf5yNvO86d5hNVU0+rCz7svSnCWNYg6GJUDcdVlE/hx12GOAingYMGBBlL2hFj4uCSguNbvOgedcrDrV79+4FrTrCCLSNN944shyLKXSPVChNzPuvf/0LcDmsahS1dOnSqFzM3nvvDbjG2opwUvMrxTCrSJvOqe9LXZd6tOooVawuRNjYTHp8SuOoTYmYZkOjX9h6oFRvnVAkbm9vLyrNo4XTd735xxD8cjDaRw+xgh30WeV+VGEyya210korRS932AGxnm6dRsOMTgZDC8AYtsXn2K1btxwkV/TLZrOJ3eDLddrz0yC1r0RbMWyl4aa+CJqUSO/1A/pM3UMfxrAGQxPBGLbF5xjOL6mE6PL9CraRDisjYKlnJWTFsOyM2DGm12vRccIxhtsYwxoMhqZAwxg2LPtRL3zWGLZ///45cG6VsPxOHMLfkljZZ2BZicMSqeUQMrP//7DIuIIuzEpsMBiaAiUZ1mAwdC0YwxoMzQSlV8X9A3Kt9q/SOU6ePDk3efLk3HIdsKn++fPLZDK55Tpewb9x48blxo0bl1u2bFmuvb09197enstmswX/ko5f7vdK/iWN6+qrry57vkrv4dChQ3NDhw7NjR8/PvF8afyr9NjarlevXlU/p/pnDGswNBHMD9vic9T8kpphxX3nxewCxeVcQrS1taXWG7VHjx5FnoMY321F97CaAP5ytpxSBQerRSXXy6zEBkMLwBi2xeeoEjGlOptX6hNP2i6TySQyVFj8u1HtVspJCUmohqW98VV1jjgYwxoMLQBj2BafY1tbWw4qY7akiKZyWTuVsI9aVi5cuLDgHGr0XEoCCBHewzDnNw1UU6YozDEO9+lI0QJjWIOhBWAMm8Iczz77bMC1VuwI9ttvPwDuvPPOzg6nonaTlUB63+TJkwHXVkPwW1Z++ctfBlzxMrUp0THEoGoLEja48lk9KQ/Wy7VN/R6qcN7NN98cjcNHe3t7FMusYoGqxKGxq8DgxhtvDFTGzknW+zAjKdq+K7yw5VKuatWdfPmxOzzHAw44AIDbb7+96n2T+ucKoZFkxIgRgHsRSiGtF/a1114DYK211or93X/ZNB9di3322QdwFRf32GMPAK666irABfKrX80tt9wCwPrrrx/Ve1JvH9U/TnqYOzNHdVFXvaqkFyiTyRRVelQ3g5NPPhlwndV1z/baay/AVaHcdNNNAZg+fXp0ftV6fuSRR0rOUTCR2GBoInQJhhXKGTeqOUbSPo0yOqnP6Ne+9jXArc6qkJ9mumElgROVQPuoA/vw4cMBWG+99QB4+eWXgby4qDrE6vMqEVNpd+qXJCOTxNuwyqLfW0eQtCFWTvMe6lwrrrgi4MRcGcmEnj17RlUkVXxOkodY8vTTTwfgqKOOiubiw+8jFM4plKjM6GQwtAAa1r3ONzIkuRxqUSU/aXUux8zVQCwzd+5cADbccMOom1uatWuTEFfmNHTVlCqOFrJNeE1ef/11IN8XFmCbbbaJetGoZvEqq6wCuNrG6tejGsg6x/rrrw8QdX/wz5f0bCbdQ0krOndcLxzpoTq25j9w4EAA3n33XQBmz54NEHU06N+/f9TJTn10NVf1yZWdIdTT9bea3jzGsAZDC6Bh3et8033YI1S/3X///YCzMKaBNPWfsKuad46C39va2hIZXMz71ltvFXz/1FNPAa6LWjWoxkqczWYTrfPq0r7hhhsCbj4KAFCnukWLFkXsJrYJAyLWXHNNAN58803AWcels//kJz8BnAvJH09Hg/+93xM9D7rO6mMrt5P6Il177bUA/OpXv+LRRx8tmJvcN9tssw0Av/zlLwHnspLlVxbzckXUfRjDGgwtgC5hJU4qZB3+ngZqYSUOGUABFAqoiIN60QwbNqxgX7Wn8HU6H74DPwnV+mFVTE3n1N8kPfeaa64BXL+kefPmMXPmzIJ5qFu7fKmyND/88MOA0wOl78tnuc8++0R6rXr3lJpfpXP0ti34LJ1Weq/GM3XqVADOOOOMaDtJFrpH6syn3joKGjnmmGMAuPvuu4HiwI++fftGIZpJXgFjWIOhBdAlGFZ9NNdYY43w/Kmfq5Z+2M033xxwup/0IVlVfWy77bYATJs2reD7NHrjVlNIvCM4+uijAadvrr766pEFVOwcWqOl46pPrKJ+FJLpz7fcGMvdw2quYZi0L/aUf1jRS4899lg0R/3VPZTUoPstvV2SUJK0VArGsAZDC6BLMKxk/NDa2iwMG1oz1VpRfjvF01aCWjOs4LOY/i8Lr3zFoSVXv0tXkzXUjwpSxI4YS/sqkkvJDaNHjy4Yjx/p01E/bCUIC6nrr3RXdU+X3/jggw8GYOzYsdG+ug6DBw8GnL9deqmum6QKb5zRvKqdo2AMazA0EbrV+4RajeWTikMtmLUekE9VOozPrGH0UBJ0XcJY1rTh64f6v3yPgphX92P//fcHnI4u3+WAAQMiC+icOXOA4lIskjrkUxcDh82ynnnmmcgWEOLAAw+sYoaF0BzCcUkf1fjXXXddAI488kgATjrpJACOP/54TjzxRAC++93vAk53lV9WTHv99dcDzhIeom/fvlGDsRChlFn0e8lfDQZDl0LDddi489eSYeuRrbPnnnsCzhcZF2mjnE9ldigfVNvtu+++APzud7+r+vz+HJMaOodWXB9hqRMxiLJUNOZzzjkHyPswxc7SVRUdJcvp9ttvD8C9994LwNNPP63xFfwt5WP2yq+mVuZU0Li1z9ChQwvG+fDDD0eM+fvf/x5wUpOS3mU11xzWXnttwEkklYzHkzi6ZgJ7NdXoUjpf1S/smDFjABfIHkIB4zJYxNXuSQqzE+T6UQhfZ+YeF/xf6jorLFJuNW2roI4nnngCcOKukr71Yn/uc5+LFiCNWwnhUgt22203wIWb6gXVYqDggzfeeKPTbp04hIuoty8AW265JeBEZM1ZaZETJkzg2GOPBdx1kjtSCQ1Kxr/ooosA+Na3vlVwrnJFC3yY0clgaAF0SYZtdpFYjCUj1CqrrBIZJBQwoNDEWksRnZmfGEFjFDPor4xDzz33HH/6058A2HnnnQEXGP/iiy8CTjwUa991112Aqygol0gul4sMVGLtUvMrNceOBFBoWwV43HrrrUA+ZW7AgAEAnHvuuYBL0lcIpa6X/mpOqgEld5ivIiWN0RjWYGgB1N2tUwrN6s4RNH4xq+C7S5588knAuQC0skpv87ua+xg7dizgSsukibCuroxL0sVlkJHLSWO97777ANh9992L+vB89atfBeD5558HXHC9GPixxx4r2N4/tsrJdBZxzJqkH2uOShmcMGEC4AxLm2yySbStSsKoCJ+YVK4hSRdKDtDvChrZb7/9OhwgYwxrMDQR6s6wpVYUpTL96Ec/qtdwOgUxkUL0pNtpjtJZwDnRb7jhBsBZR6XDhSVcQqTNrGKa7t27R+PXucVwKqymv9K/NVZJErNnz+bGG28EnGQgd86OO+4IuHBHzVvnDMfTr1+/IulCjKW6v5XClxzCogKCmH/+/PmAu2cKfpELZ+HChXznO98B4MILLwTgtNNOKziGypnuuuuuseMRI8e5+SpNyjCGNRiaCA2zEtfbOuydtyILo3yq8rVVgqSSMRMnTuS8887T+Qr+ptkPRojrrROWgdF4Jk2aVBQ0IElh1VVXBVwivvzRCpS//PLLgXzBcbHNkCFDAFdcbdasWYALTRQ7yQqrsiti5Gw2W3Xv1KR7qPDOBQsWlL3eSquTVfjQQw8F3DU47LDDolRAlXuV1VgpeEllgMJ+u3EM680FsELiBkNLoCkZtjMlSav1w/qpUIIYSbqeVucwsFwr69y5c6NIHhXqOuyww0qOU/7Kv//97wXfl1qdvTEX+WGlK4u9lFTtz2/GjBmAi2TSPprXBRdcALgg/EmTJgF5VtKYpM9p/LKKS8eV73LQoEGAKycqzJs3L7pW8t2q1Gjc/Pw5hvdJY5ozZw6rr7464EIOxY6KRjrllFMAF7Wm67TZZpsB+SLhKqampAdZtlVQXRZ++Y8VqqhysJK8+vTpE1mOw+SHpDkKxrAGQxOhS0U6dSUdNk1ss802UWyqErrFXrXWYTU/pYZdeumlgGOvWbNmRau8rL6KwpJFVxKELL0KZt99992j78XYCvqXFKLAeLGSAuM70zokqRmWr7P6Y/jkk0+KSsGEyfj6rPRH+WOlnx577LGR3i0JRNdFUsXjjz8OFEovceOvZM7GsAZDC6DhOuwHH3wQWRDrgXoybDWtGdJEnJW4mg7sYhlZehUbrXsmKUEMs9122xUVkwuhkqjS3U444QQg/tpUm63T3t5ekEJYSQaYpAllIEmnlu6t6DQVAx80aFCkm4rJJXGItSVFKCWvkuJrIeualdhgaCHUjWGr0VWkBymnMk3Ug2FlNVRX8v/+979RlI4aKNUScTps2M4wrgib31rEh5hCFl0VH/POwWWXXQY4a6vud1j2NIydDv3D1VrB/TlWgnCu+qyia9J7zzzzTMAx74Ybbhg9j+PHjweIiqfLuq59xcCyOHektKzpsAZDC6DuDFvKUvbQQw8BbrWrBdJgWGVldKRAtJoAv/baa1Xt11ELY1Kkk3D//fdH1l4hzOcVQt1VTNze3h5l9iTpZGGBOsXuin0UWRb6ZcvNr5I5fv7zn4/YTlDGjXRU4fbbbwfgoIMOAlxc8LvvvhsxrArJ3XHHHdHxwem0mlPY8iRpfJXMUWi4W6feaIRbp94olcAeLjalevX4rh8fcYtHuQVFD3X44oTIZrOR6Bh2pU+qd1RJ9zohXEjiwgb97fyyLkmBGYKurdcpPnY8/jVPKhtjIrHB0AIwhm3xOYZF2OIYJDSGVGogDA1Zy88HFBuwKik8loRQKujIPUwS1cvN1U/WSHNO5cZjDGswtACMYVt8jh1hH0EB8ipnUw9kMpmo1KtKv5YrUNaZe9iI4JZMJuMHSMRuYwxrMLQAjGFrOEelxincrV7w5zhgwIAcuFC7kFE6o8P6+motE/IVbC+3SngPe/bsmYPkbuY+ksaZ9L1S/eSyKbVtZxC6fiw00WBoAZRkWIPB0LVgDGswNBHshTUYmgj2whoMTQR7YQ2GJoK9sAZDE8FeWIOhifD/cQDp3ne2dfAAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1240, D: 0.107, G:0.2593\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1260, D: 0.2479, G:0.05076\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1280, D: 0.6698, G:0.0403\n" + ] + }, + { + "data": { + "image/png": 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kc845R5IXmxFB+Qsb/fa3v5UkfepTn5IkzZ8/vyBsj3EEjF+W0alYUIB1g9hrwbyhS4d5z5o1K2/Otj8s58ZlRRXCtWvXurmloVYMC0uypnSgW7ZsmTM2jh49Om+8FqUCRf7+97+nus1ALRh28uTJkryaY9HU1OQ68Q0ZMqTouXBD8ttIUFlKFrKLDBsR0QAoyrDvddkq0Av5O3/+fBfEz44R7iKSNySh2/z4xz+WlK/LwiIci6EF3ZZzwj70GKUO8J133ilJmjp1qlasWKFwzADDwBtvvFHW7hwaTZjjV77yFUnSnnvuKckHSmBwY+5XXHGFpE5DB+fBcIULhnOeddZZkqTLL78877oYsNB56TlbDNVm2FtvvVWST9h49NFHJXnJa8KECfrRj34kSdpll10kSR/60IckdfaOlbwR7aijjpLk1wwwb+5DMZTLsM3NzZmT++mt8+c//zn1GCuN8Wxjw+AZ5PkNxyFlKzQQGTYiogGQyUps0+tgvLa2Nvd/dg2YDJP3/fffL0n6+Mc/nvc+8v2f//xnF5pImtrBBx8sqbNjmCRngbz++usleeswus6bb74pSTryyCPdsTYlD8ve22+/XXX9Bx2VznNICltvvbWkzm5w6PC4Br797W9L8nYB2Jo54xbDZRXqPOWkZlVjfrhCvv/970vy9/TZZ5+VJI0ZM8ZZRpnX+eefn3dsNcvXdKeVOGRT/n/33XdLkg477LDM35Wkk08+WZLvdFEMkWEjIhoARRPYsXbaDmuEy61cudIFU9NTFF8VOyvsid6z9957S/K785w5c5z/in4mpOwBWPq0006TJF188cWSpG222UaSD2CfOXOmC8C30gCMbkG/1jlz5uS9z1x32mmngr4xgFBJUvfQ0xg/elBra6v+9re/SZLGjx8vyVu8sbziS0YX5FwwLPdziy22cL19agXLDJtttlnea2wISELr16/Xn/70J0mdUo7U6beU/LOCvaGUdHDIIYc4XTAr0voVgWOPPdbdx3LB3FesWOFsFSeccEKm79q53nXXXZI6bQH0By4XkWEjIuoIRXXY5ubmPB8l/qUwEB32RYfBKgiTYOXE+veRj3xEko9a+tvf/ubYBnZGB0TvQZ+Dadm9sV4ec8wxkjp3cyy1llGZw/r16yvWf6xexrk/97nPSfIsib6ey+Uca2BR5F4yXhgIyyKWRj4nxTELKtVh0yKoXnnlFUm+8zro6OhwVuE//OEPkqQtt9xSkg/Ls13iK0E1dNiTTjpJknTzzTdnOn7Dhg3uWWYtbHGGaiLqsBERDYBMkU4wSFIRND5Dj2DXId4UHWDGjBmSpNNPPz3vHO3t7S7KhIghYomxurLT0wGbQH4sq4wr3MWxvhKBg1Swdu3aLu/O5557bt5fJAFYECnj3nvvleT11PHjxxf4smExxs7c0A8ZN3owx0v+3uEP/s53vpM3zkoZFrvEU089Jclb/pFa8L8y5iFDhrg2otwbm9yAnm9jpYshLfqsmlbitMZbvMYvO3LkSM2fP19SehFA1pLnYY899pCUHAcO0ooGRIaNiGgAFGXYfv365ZWIYScJmYyMDazEMO2kSZMk+ail559/XlKhxXnAgAGOqR544AFJ3pKMNe7CCy+U5KN8aNE3ceJESfkF3YizRUdEhyL++IUXXqh4d4b9uA9IE0Rskc3DHF977TUX2USjYHZdSueQtcN9JF6Vc6DfZ0mGr5RhkXhgFJtpA8PCPosWLXLjx/qPLo7P/Atf+IKk5NTDctEdflieU8a7cuVKx5yM3bIythUs5TfeeGOXrx8ZNiKiAZAp0gkd0pYubW9vd3qMjQIhsgnGRc8iMwef1NChQ51uBCvC6JQ5xfpKqZhPfOITkjxLYbVMKnBuc3krsRLjByayiTkQcYWuRxZLFushrIxvde7cuZJ8VExPWImD83AOSYXWY/yezz//vCtMYME64Msm1rgSy2p36LCMm7+9evVykXg8p0h2+Jop3HfJJZdI8nHjSCpljiuRYYsGTuBOQQTFILJs2TJJnTfdKtz8mBCBR44cKckbaPiBDxs2jIG5NDqMSByLyEltJ8QtHmLCELlh7e3tbgGCKomSpL/85S+Jc+S7WdwOP/zhD/NeE9yAuMpGRkhiCML7SAjgWM6JCEmYJcY5myzB/UsCqkS1YB9iG+6JuLjrrrsWBC/YCpisYSU/1FJpbV0B62DBONl8Bw8e7J5/3uPZ4ZmzIbxd+aFiqExDFIkjIuoImQInMLKwW8JGHR0dLqwQw0vY5yb8TihGS561Z86c6ZiSQAMYnHPwl+ti4EA05dx9+vRx4+A6OPIxkGStS2yOkVRo8OEahENOmDAh73MYac2aNW4OiFXs1gQjkOwOUC0IWXzsscckdYZjkkgNUAk4V7WD/xHXCcUEuGoWLlzopAoMVKwp9w4DIaI97NwV1MLoxLPEs56UYA6DIgUxB6QLnk+MUml1q5KKIiQUgIhGp4iIekdRhn3nnXdykjdXuy/5Lmnu/5dddpkk6fjjj5fk2ebDH/6wJK/TUtaF7y1ZssQF7xO2CGN+85vflOQNVtOmTZPkDTPsZOEcijjb+Zu3c1nDWjEwPvRhXEY2kAAWQe9sa2tzlfa5D6RmIWkwF3ZpEggoIUPyQBbUumriAQccIMkbAd966y1nANxvv/0keRsC7jTYn0R8ysxkue8Wdg3/+te/5sJzFgMsicuFEEVbYHDq1KmSvJE0DBJCikDCsmxcje6FkWEjIhoAmUrE2E5zYXgd740bNy7vu6QzURKEoAh0BFKznn76aWch/clPfiLJ60roxzYBGEYmKCO0VNuAfL4La1cSmoj0QBAErMg1mRv6ULGOc4TwkSpoy+9Yy2Mw3polsBNK9+tf/zrxcxv8njQOLKdIGwTXw1gUaQvrUpeLSnRYrLB0m8Alh9TEayzBSAhPPvmkSyE88cQTJXmbC3YGAnyqkbQfGTYiogGQiWFhJ9v7VfKWMHZfLGjovfjlrHWQoPYPfvCDLvCZ86MbUG6Egl3sWIRD0uWd7w0cONDtegB2Y2d/5plnqmYlZidFEiBFjvdJMdt9992d1RcLN/eUsppIGfgaqRrP/cyCcjqwVxO9e/d2Uhh629e//nVJ/h6QqFANdIVh06Qd1gPdGlx66aWS5JIaQiAJfvGLX5Tkn0vKAAXjkuRZvavBLyEiw0ZE1BGKRjoRamd9rGFJU8IKSZtC/4Qh8MOS2I6etGDBAkmdjIJugCWRVDz0CHyQWGU5HsbDQh3uhowRxsI62xVwHXRWrMK8D7NiccTSi657+OGHO4a1OznMz/hsGRZbPtbqtCHQH2sFpCmYAmbZsGGDGx/3mwR85oNFGeaCeUGte9OmWaNhXpiWIngAa/Ipp5ziJCnWDP80VmOY1NohymHWUogMGxFRRyjKsDZKCdYkEinUGYm2YackBpb0MViaXQpmXrNmjUu9QgcgJYvAeKvbwjokx5Mc8MILL7jka6yUr776qqTqlCixyQncH6zDzInoKvS5bbbZpiANkfvC3JBQ6GrPfcT/Gloe00qTVJLOFSJNZ7dFzsKIHtaGe0AaISmSBP1/73vfS7wmzNra2lqVZlJZAUvauSVZenmGsShzn0q1TrEoZunff//9i343MmxERB0hk5XY6ifolpLXcynrQhQMugHsg++Uc+FTnT9/vmNFdFhbEhNmB2TrwPRh2w9rDUzIOEm0MMJattWI5HUTGMXqkVyTOaJrwxpbb721y06iCdZVV10lSXrooYck+SgqdnpKuMI2STt+WkRNV/2w3INyGa6lpcVFtlEeh3ti48CrUbCslgnsSIDEa4f3mP/Xoji6RbQSR0Q0ADIlsMOs6JjocjCh5HcbyrhceeWVkqQ77rhDkrf8soPhp21tbdXHPvYxSd6SDKORYUMeLKAoOaVUQXNzc4FVNfxMSk9gt3pbsU7sXPe//uu/JPlMGzJx0EuRBLIARuJ62ASsVTkLahVLbONtuae33HKLKxgP0MHJ68XOUA10R4kYYrmRGPv161eWT7xSRIaNiGgAFGXYlpaWPP2OndXG60p+J0VvI1YUFraRO/hSX3rpJedHhbF+85vfSPLRUDRWQq+w7Mkc2tranM6ERQ8WqEYhcay/6NyUwSFuGr08qTg117e5qwDJhPtVTD9KY/+PfvSjkqQFCxZ0iWHTzmurcmAVxf/Yt29fp0fDwlhfkajKtaQWQy0YljXl2atGxk0lSGPYoj/YPn365CR/03GGhwtnDRT8mAi1w4iCuZ8fLA/HlltuWWDw4UHgIca4ZMt5cDwienNzc161fck/QHQNOOCAA6q22NagZdUCDHETJ050YrLdbDjHddddJ8l3k0+7VlNTU+qPmfuzbt26mojEuJiSSuCUgk2MqAS1FInpeUMv3J5CFIkjIhoARRm2d+/eOamwix1i5rvvvuvSkSzTcl7rkOYcuINWrVrlzo/TmKRuxGiMXWkpc4hsa9asSQ0f5G9XSsRUEza8s4vpZXnnSPi8W4P/qw0MbYQLWnRnf9ha4dBDD5Uk/fznP0/8PDJsREQDIFN/WKt/8P6AAQOcbkp3bhu4T38cjFEwK7rskCFDnG5EEAHMgd7H9dGhbfV49MLevXu7zzBywGCnnnpq0RtRzI0DbBlTazhKQ5hYbxm/nOtL+UUDLC644IJM49nYkcasPDuNgDRmLYXIsBERdYRMbh2rb6HDJlmJsdhirU3T0bjukCFDnBUY5ub8NqWMc6MXJ7EWDEuoHw7wUoETtUSWspal3k86V1qIXL3rsKXQCDpsKUQdNiKiAVCUYSMiIjYuRIaNiKgjFLUSW93AWifD4sqWqa1lN61gWZ8+fVxkk00ls1FBQQe6vPGEkVKUMSGBAGs0769cufKfSv9JW0Pb6zT8jLWkjAt+cGtLIGwvfC7SkifSosKsX7qlpaWgc5wt/ldNOwRJKBQjALa5VzXR3NycWug+eB112IiIekcmK3Ga77ClpSWMIEq+QAIrS3733HfffV1anb0Ou5zt1m3jhJMCtRkPxb/xe7399tv/VAxLPDhMYdejvb09NbkcJsXXnobwHPYZsfHgFkSpJSXNcy6kNaQyW4SA5zTtGQz94EmfFftuFuAftiV2y4F9hiPDRkQ0AIoybBr7kEY2d+5ctzORYmYTmQHNn2jZgb+0f//+rlBa1vhauyuGpU1sU117zn82H17a/C666CJJvmWI5DNVpkyZwncleVsCdgHYlM/79+/vyuKk2RlgUuwVtpRPuIY2UT6YV8H8is3xpptukuSfV8kX5Su3OfT73//+kgyaNVotCyLDRkQ0ADIxLHG5Vs8Id0BbmCxN7wWh7lJqZ7I6KyVZeE12R2hhtCxAAfMnnnjin4phbSG9hGML3ktra5EWwxzm6KZlVPHs8NquE2hubi5oDcOxWLZXrVq1Ua9hFqZNOyYppzlEZNiIiDpCJoZlh2PnDXdFdgrKXGLRpWUhflf8oVjDsDyGOypWSYqZwcLoTugQFPiirUKor7LTM1a7W/d0PixI80+WynXNeO4CHTbLrs8aIlE9/PDDkgrX3eqyUmFTKdYXSz/XJ+ML3fn666+X5Bk49DwEc5CUV4K2y2s4Z84cSb7cEK0iLZKkjMsvv1ySdNZZZzGOvPEB3qfFDE2uy9Ft03TYUoETeRdKqp7Pj5lEdioaAhaCPjN0Vaea4j333KPTTjtNkl9kOgH87ne/kyQdffTRkvzicxOpw0P9qHB8jMcGbvQEpk+f7sb2wx/+UFJx8bKaKEc8Y5Nlzey6U/aHIAM23+23396pKRgT+YxeOqw7LhrOzXF0U1i/fn2qwbAaa2h/bBZpVTf79evnKj8yR46FSIYPH573PkiridYVRJE4IqKOkKkusXWjhDue3ZHYTagpjBmd8DYS26nwt3jxYnd+CqXBuAcddFDeePbdd19Jvi8nRghYdPHixS653BZ2C8ZZM5G4GJvxHuIk7o1ysWHDBieupaGYW8e6vUJYZrDSEzV6bQf6OXPm6MADD5QkfeMb35DkJSi6GcKgEydOlORdLrA2LPX666+766WhFmoNfZyQHmywzt57761//dd/leS7JFKZkznwHQrpXXHFFZJ8pUx+C6H7MQ3RrRP3vSAAAB2ISURBVBMR0QAoyrDNzc2JDAt79e7duyDU8NOf/rQkz7CEBrJroqdinHj55ZddZ7Mvf/nLknzn9R122EGS7/TGrg2LotOi6+60004FvXESetrWjGGzGIzsMXTgC7soJKGcYPRwd7Zhe1YK2Gabbdy1bZA96/yhD31IkjcQYZxCbz3wwAP1+OOPS5IOOeQQSXKvMS4dddRRkqRf/epXkqSRI0fmjYMSQ2eddVZqmGOg05a1hiNGjHA1rtOed57fq6++WpIcm3LN2bNnO9sKHQaR7GBSui6GnfhCfPWrX5XU2V+plAEqMmxERAMgE8NyDOb+sOypTYmzOhI7LUXQ0GnQYX/605+6rm/0DmVX5nq2+BY7Fz1Vcf/kcrmSlrieDk2ks3rYAbDaCOfIGgafSSpemJzysuhzZ555piTpBz/4gSSvy5533nmSOvsoUYqH7g2w8tChQyV5N8mSJUskeSkJC3/o7ktLKOkqwybBShoUC3zuueck+VBaulKce+65LqwWiYQ5YTVmrvSP3WuvvST5MkVIH/fdd1/J8UWGjYhoABT1w6KjUIbUhpdtuummTo4P9VrJ76B0Yuc11uIxY8ZIkr71rW9p7ty5krwTG/0XneqII46Q5HcmduOXX35ZkmfYUaNGud4oFhzTU7CSjA0gqRVOOeUUSb47u+01NGHCBMcAjDEMapF8vxlrDeX+33XXXS7AHtZFKrI9dzgn0hEgGH/69Ok644wzypojNoyxY8cmfn7bbbfpE5/4RN57dj2wqWDFJ+WTdZo/f75j0IMPPjhvTljACeRh7gROAJ7BhQsXFnRezIrIsBERdYSyEtittTiXy7n32D2CUix5r7G+0YmO3XnlypUuCgbr8N577513TnZQdjv8X7aLWkdHh9v1gnIibqzv/e12HTaXyzm9B6twWgpZla6XqsMmld+x64ofEemIz2+44QZJPlqIlLq1a9e6jvHogFiBYR+kJZgUuwUSWWj9tpZ9+4xWQ4elHQwWb3DVVVdJ8r5+xt/R0eGkSCQMnkeePz7fbrvtJPk5piVTFEPUYSMiGgBlFRJPSrfiM6KQPv/5z0vyOxeB+uxKJ5xwgiSvF8+ePdsxKZEgu+++uyRvUb3tttsk+eR4dnz0YfSOXC7nzjVs2DBJfsdnntUs4GV1+mJgp4ZRqhHkn4auBv9z7yi+hhSE3xFGnTp1qiTfL3f8+PFuPWFhPhsxYoQkv4b//u//LslHNuEfDb0L/P/f/u3fJPmAfcZeDV+6jVPmGYINYVquOXz4cBdTgKWbVi1EMqHrX3jhhZJ8rDv+2STgl0aqBJFhIyIaAGX5Ydl9wuR0dmV2RaKS2J2xvmFxXrBggSQv5z/88MOOfbAG47vFWow/a+bMmZKkadOmSfLpeKTuoSeFsBE+PZVeh95NWc1aMCsopsNaZmlqanJ6JSlnMAPrzFpSBIC1Y93uuecet75PPfWUJJ9uR2NkMoFgEqLWsKwGYy/Q7xOyd8peQ9tADf2T9UB6+OQnPynJW7G5NzfccIN71letWiXJszLS3JFHHilJLq6amHgkx3IQGTYiogFQVhG2JFbg+xTugkGJ5CFHcPTo0ZJ81Af+W0mp2Sf4EE8//XRJPgKKc6APwqytra2J5TKl2jTDKqWHhnprqfzLaiJLIfHwtU36hzmw9PMaXZa1Rc8bMGCAs2HAZIA48TvvvFOSdMwxx0iS/vSnP0nyln5YKyxJmlaMvBpSEte18dP4UInmI6prxowZLrYdyY5xEhm2dOlSSV4ihKV59sIyvKXQpQR2bn5QrUFSvmjMRH/yk59I8oYLRCJbPWLcuHF555C8O+dLX/qSJJ/kTC1hQuNw7+B0fv755yX5G7hixYoCFwXjs6l61UCpH1toSEl7+BA/0wI+KoV1KdhxhGPjGDYafmyABxIgAra3tztRkiAYRORnn31Wkjc2EhjPDwaDXSgGJ20qkg+eL4UsBjZcMR/72Mfyrs894Ac7f/58d05EXfoY28R0VAXUQNCnT59M45b8xpaGKBJHRNQRMhmd3MEJLIFYyo7FDoVST/1bW5oDk/iAAQPcsTAmRqXrrrtOkherEXcnTZokyQdhE0A+fPhwvfDCC4xdkk82wO1Qi8AJEhkeeeSRxM//8pe/aPvtt+f6kryrgvvCfYOZcFl1BUlGp6SgF2BVi7DPjeSlJNw9nAPx9sADD3RqCwkDGNkIHsBVB/vQv5c1DaU32C2t431X1rAU69oyNBxP54LtttuuoIQOYN15DlD3uF9JY7HjsMkH0egUEdEAKMqwffv2zUneIGBDxsKwOnaXp59+WpIPNSNxnR2X1CN2oQMOOMAFZl955ZWSfHU6KtFjoIA9Z8+eLcmXKlm0aJGkzt0xqZJ8iJ4ITRw0aJCrFgisPaCaCOe4++6756RCHTmJcbAdwLj0w0FKwdjE/Q8DWHD5kEJGYARJAQ8++KCkzmSP8G9YfM2OLUnftvOTpHnz5uUkFQT4JwE3Du4mUgZ5n3HbRPvXX3/dhZVSCRTgqnryyScleePoxRdfnDfuchAZNiKiAVCRWyc0wWP+ZpexPV/RQ9HNsBIuX77c7UBYktndTjrpJEmeWdn90HUJfwz7tdiK/6Ang/9DwKjo3VhNs7BDVhRz6wTvc6yTRmBHrKCwJuGDhJ3OmjUrby7vvPOOu79Y9Cn3g+TFPDknLg9bGifU79L0zkrWkPBWrL/MieAGiihgvUcybG5uds/nE088IckHTODW4fm0qZORYSMi/klREcM2NTU5pkQXAbxP0jM+PJzw6AyjRo1yqU6Uj0RXwJKIP5ZiV+jFhJOBsLO17aIWdE/rdob9wAc+4KzUwXVrdr1iwf/2umFKoi3NwhoCm+yw5557SupMBoApCaBBN0TvJSkACcj2/g11+jSfcRDsUPYa2tJF/E2zdXD8rbfeKqmziBwWcPz+9957ryRp3rx5knwsArC9b7MwbZD+GBk2IqLekamQuI0aArlczu0a1157raTCxF8+J7D/mWeekeQD9nfeeWdnwbRJ5+im6BuU5iDtDks0Ota2225bkNQO2BV7ogP7wIEDnS6UoI8xjqpdL2RYutfZfq32+pLXyWzpHb4LexK+B7PccsstLm3uwx/+sCT/PCBZkBSCvodPEzYNo4jSdNc09ilnDfHpMi7rg0Y/JQyT9NBFixY5iQ8rOVFS6KpYj61vOe3axRB12IiIBkCmZlhp74c7IPrn3XffnfcX3yoRLvj6sETuvvvuzkJ68803S/INlPDZcj30HXY6IkyIW37ppZfcjmh3Z2JZuxNJ7En5FYpy11KXldJ7vIb3h/dIdcPOQAws0hD+biQirKLHH3+889lSvAwrMcyKFdbaOmBxOuWNHz/encOuIYxWCSy7Wf0d5kc/5/1nn33WxQOQWD958mRJXnpLk5Z4zbVHjBjhPB8W6P5piAwbEVFHyKTDWj9T2A6DjB4svcSMcl52a2JN8b8hz7/xxhsuS4fz41+lCBhWY4pUX3PNNZK83oHVeNWqVc4KactsdkerjizgvuCPpglTla9RoMNay2s4Ht4jfczu/uiOpEgStUWM8Y033ujivmlxAVMgBfEcWKtwWoG/cKwJzdi6vIY2kd0CvzFSG5LQW2+95dYKe8ztt99eMOZqIeqwERENgEx+WPQMm9Gwbt26gh2bYss0LsayiB8OXYVCXjvssENe6w/JMy3MSQlN2wkclkralYHdwXuCYcMY51rGEIOkbB3b4CrJkmkzVWyndazIvI/kdf755zs9F78lzEpxb16zHtgyYOtQ167USpwlHzbtWPRRIp2QIr773e+6AueU0gFpvlwYmXOUg8iwERENgC75YZNkdtrJU86SPEL0UCy76LrolltvvbXbufHF3XLLLZL8Do4+wS5ufazhHCwrAKonPPvssz2iw1oLIuVvsMw++uij1bxWahG2Ynmx+Le5zzAGWSf42GmpQovIYcOGuWOwYaD7cX4sq2nxteH9scXOE6KzuryG+OmthIEVm6JrlDrimezdu7e7P/j9iZ8nHoACdGllispBGsNmqkucVmMnfM8CMQElH5M4PXVI1O7fv78zpaeNBeWeHqO2Qx0ie3t7u7t5dqxBR4Ka/WDZhAj+7imEi82ma8MOk4L/bXCFFZEJBWXtMBwuWrSoYAOwGxTXsOOw6ZAtLS1uQ7bPA2L0smXL8tbQVvesBIyL55bCAySW1xo8OzbAB0SROCKijpDJ6JSmVEuFPWNhMhzkNtwQwIobNmwo6C3LZ9YFkDC+gvdssD8IdvYedetUA6X6jBYTiZNS12xgfJKLJem7BFaEwRC2t1JSL5+kc4XqFgbKT3/603nH9kSKZNK9KPZ7yAq6AdAlwCIanSIiGgBFGfZ973tfntMd3SLcFfk+Mj9pc5iycV/w1+onm222WUFHa7sb2wBtWxmev7179y5gdq5DicyLL7647hm2FIrpsEnpZdxv2BFpibUqlSY2cODAglRHYKUme05rQAyLIgDWm3DBxx57rKw1DJ9TniUbIpkmVYBiNa/TzlGOewkQWvvcc89Fho2IqHeU1VunmJU4bTchRJBgCBAmmrPDW6ukZVprkrfj2mSTTQr6ntjSnY2gw5ZCkg5r1yVcr7RgE2sttuxszxUijansuYp1k7DV+ZPm99456nYNs5bBAZFhIyLqCEUZNiIiYuNCZNiIiDpCqQT2hqPffzb9Jy0wvliYKcBiSYlS9E88AZS9aWlpSY2kCsaUOGZbNE/y9gf8ndglOKYrRdjKRSmrcSUYNWqUK9WbhqjDRkQ0AiiklvRPUq7W/y644IKaXyP81xNz7N+/f03P39TUlHuPZQrm2NramnvPF5t3LP969+6da25uzr1nTc50fvt68803Lzgvn/Xt2zf3XsuXxO+2tLTkWlpacu3t7bn29vZcc3Oze49jJk2alJs0aVKura0t19bW1iNrWOt/vXr1yvXq1Sv1OeVfZNiIiDpCWYXEAalzFN6SfMsFyrlYkJJFca6eQlYd1vpxJZ+MTWNpi29+85uSpG984xtVGGnXUUyHBaS7XXnllU6PpMQnTbet/kaaI8XB8ZNuuummrsBYqdQ4q7MmNS/L0IQ60xom6fgUkCNB3SJNd21vby+Iyqolog4bEdEA6BLDbizoSqxmvViJuzK3pGgu5meZI0vRszSGA2FMd1rkkm2ZwmskGK4fRqTZSDdekxz/61//ui7WsCugrMzSpUsjw0ZE1Ds2SoZN262LxZ+Wce4u785ZWS/LcZbpSr1fDpJ02LTrhDjxxBMl+UyWO++8U5IKGp5RuocMnaamJld9ArsG+bB8l3tCQ6nPfvazknzTbjKEwoZmtloFOmQl8eAwPU2qbaNtcNhhh0nyTcPfu64bY9JrYG0CNHwuB2k6bNHAiXKAM50FSRiApEJjwyOPPOJqwQJ6p9o+OWkV1ZN+GKRv2dpOlSAtGZv3Dz30UEm+2x7XXr16tavGx3sEA1A7iKr2dvG7IhoX+36xwP3HH39ckq8KaH/UkyZNkiT99re/lSTNmDFDUqcRCoMbNY0prUKAAPMjGYRxWENWUvVEm1ZZCfjxgzQj0wMPPJD3etttt3XH3nTTTXnfZS703EkjlErXUooicUREXaFmIjH1h6ksZ6+T5Dahv2ZaH5wRI0ZIKqxMDzo6OkqKktU0OpHwTeE3xC36CO28886SOtmUcXEMner/4z/+Q5LvNbrddttJ8pX5LF555RVtu+22RceVlF6XxiRhwriVgqxUcP/990uSvvOd70jynRiWLVvmJBr6I1ECBamDc++4446S5LrdIZq+8sorkjrZnffSmKoWKZKkfyIBWDQ3N7s1QXoIP3tvXJJ8Te7TTjtNkjR9+nRJ0tVXXy1JiSmNFtGtExHRAKg6w2KYgEFLYeXKlW4np9I/5WUo58FuxE5vC6yVY6ipBsMyLua4cOFCSb43KuOEZW666SZXfxjYHkDMkdq21HEGfB8GLoYkhk3Tn7bYYgvHLtzHLbfcMm9sX/jCFyRJxx57rCTPok8//bSkTokIhiQw4aCDDpLkGXa//faT5FmaABvG9dhjj0mS9t1337zeTeExgZRS1hoWK2FjgU6NlBRcI1X3pAMFtbmpq21ZtBy7SmTYiIgGQM3dOpz/oosuyvvLjhda7dh1CXOsBWoRODFo0CBJvmJ+WOBM6tRbeY/q8UuWLJFU+85ntkSM1bfa2toKdnzGDdNRipO/I0eOlOSr4v/xj390VlVsFxMnTpTkLf24fSi2jlfhD3/4gyQvtaxbt86NNa23bU+X+aHw/R577FH0OLoI0FWgHLdkZNiIiAZA1fywAH3029/+dt77l112mSRvJfzVr37lPkNnJSyLHYjX+LeOP/54Sb6ANb5MEBZ0qxaK6S6wFRKBZTF08uXLl7txYWmEYQ8//HB3HcnPqVg4YDlzpIcPxbkB+tSYMWOcX9Umt7OWd9xxhyTPktgp0FMvu+wy10PpqquukuT1YO4F30XXxbLO57SoGD16tB588MHEuaTZKAjwoJWLxfr16wv8r1nBc/u5z31Oo0aNkuSTI9KAlR1mBfiir7nmGtdHt1xEho2IqCN0W2hisXDDCy64QJL0rW99S5LfSbG6wbBVGkfN9J+0aK/169c7RoFJjj76aEneaprWEbwrCOdIQzPrYw2LYtuIHYLsSSOEjT/zmc9I8lFAL774oqROXzptNejEDhtRRgZLNPdoxYoVkvz9QLfN5XIF/YgTGm1VvIY0Z0Mft7CW6XXr1jmJKSyNE46rGi08QNRhIyIaAFVjWOJPsYwB2+bBBlX/4x//cG0+LAuX8r92BZXszuyon/rUpyR5izcJ/ZbFYIbXXnvNtUoMrpt3LMnx+DGzFAGz7ASKBf/b84USD/OAYQnM32abbSR5q+0nP/nJvONPP/1054tEnzz33HPzxvjQQw9Jkk499VRJ3iOALxd2ampqcv8/6aSTJEk/+tGP8q5fTStxmuQ3cOBASd7m0KdPHzcXLN9IRXyXyLfjjjtOUmdxgPD9JKDT2+i1yLAREQ2AquuwWHyHDx8uyadZAfyv6APhe+xqCeOQ5GONp0yZUu6wHKoZ6WQbKgFSzGCR5cuXO4aF6dADaRmJNZJmyViRu4IkPyxIsj4j4cAYWHRhOqyde+21lyRvU9hhhx0kSWeffbabH53YTz75ZEmd7Cv5kjSw86xZsyR53Rb2DEvEpJVOraUflmsMGzZMkvSzn/1MUmcUG3O0jd4AUsX5558vqTizlkJk2IiIBkDVGNZmO1h9bvz48ZI681/Dz4vh8ssvl+R3Z/yyaUnHWVAJw9rGXjaG2eb6hjmopXIkKU5H9k4lEVBZComb4yX5+8v4KaxGJBe6LLmrfN7S0lKQqA4oRj5//nxJ3i9Mcjf3FMt6mD2Ulv+8YcOGihnWNtqy12KuSH9Tp0510gMx5NgOmMM999wjSTrmmGMk+ftEXPjLL7+ceXw1T2C3aUn2BhAgTyhaiFJ9NW+99VZJ/odqE9uzAFGzEtgOfNaRb7vwhQYNghN23XXXvGOo3mB/qFbszpL8nJRwYXvr2rGH57OiHumBiMq4YhhzKPKdd955knzlSIIuHn30UUk+jJGgetaDcYVieZqBjAT6aoBnh2QKrslfjKjhmjIHDGiWnOw9ZaNjrlnW0BonLaJIHBFRR6h54ATBEARHgPC67DzW+JQ2Nur+4hLAcJMFtQicIOF66dKlee8/88wzkqRddtnFGXBgFNiQ8jiUisE9gtEDaYJ7k9Sh3CJJJLYGkjAYAXeZFeVhZ+7v/vvvn/dd3t9qq600ZsyYvPHPnDlTkjcQ4ioijHOfffaR5Fkd4+Tmm2/u3oOxhg4dKkl66aWXCuYXzrES2LrarMsvf/lLSZ3GMD4jVJPaxjApwTGWcS122203574DVt2KRqeIiAZAt1dN5HrnnHOOpM5yI4G5XpLXlWDnK664Iu9zgGHABlmXuH6Xd+esCciUxWFHnjdvntPDCUlED7R6MYY2XAToj+UYocI5Dh8+PCdJixYtyjsmSZ8iyRxjEvomehUshGSD3jllyhTHpFRDvOuuuyR5nRaDy9y5cyX55BB0RQw4oQRhjXhJ8+OtcE7FwNqgh9pEB5JTYFbmGIKQTNxa9rqWLbuCyLAREQ2AqjEs5VF+//vfVzwoxkRwAcnQtlRnV2r3VsKwpazTNiQRvTWJkQmC5xjYC2mBUpqUmSkHWXrrJLER95nUR+4v+iXlXii+RprjihUrXOgpoakEuRx55JGSvIWfhALCHum5FFrU07wFSfMrNsckwH5IOIzve9/7Xt41YXWsyIsXL3bBLBQhYM3OPPPMvDmX6sGUBZFhIyIaABtV5X+b5AxjUfSLcLdKUAsLow3tAwTH9+rVyznR2dEpNj5t2jRJXi+kCFslRaeLlYix521qanLWYDt+3keyQM/kfSy9J598sgv8gDF/8YtfSPI+SCyrFBaH4WxZnaTK/9Y3vXr16rLXkK4GMDsSARZpLP1cG6koKfGdeSMNEWZLAXzOxTUo20v4aTEExeojw0ZE1DuqxrCEX8EQ1UDa2CrpuVMJw5YKzGdnhTXAjBkzXFsLG3HE2D/+8Y9L8uFtsAn6MsXTKaZeDEkJ7KV6+UjSV77yFUmFLSdgCgL3mQPhhZdeeqkreYJei0TBsRQjwEp+xBFHSPKsjRX9uOOOS4zCkryV/t133+3yGtrWIKTPkTpnI43CZwsmxe+K5T0saCcVWrW7gqjDRkQ0AKquw3I+0uus/w9gQRs7dqwr2EWUTFrwN2DXhoXCa6d9h91v7dq1Xd6dOTdlRSjuje4HM6DTEtC+4447uphgfJz4/fBTkqxNlBdxutbCfOaZZ2rOnDlFxxemn7W2tiYybGhx5//cI/Q9/OG0ESGBg7ho9L/Jkye7ccOUzAfGuu222yR5PY5EdhgWVpo/f74mT54sqVAaOfvssyVJl156adXsEOjU6JlEpxHzzVzfu46k5HtYDvbZZx9XON0iiF2ODBsRUe+oGsOWapdhC5SF1sqsOxQslRR9khVpOmwWqyyxrEgPFjAUzE+h6RdffNFZumHjgw8+WJLX6XbZZRdJPmvHlnAtdj3LwuEcYVhr7eQ7YUkWfJGkM9oUSZLR0dGJUvra177mWlUiLRFny3e4J+j/SFOMA0v0+vXrC3RCW4StkvS6tEwg9FJr6Q2f2yw2kmoh6rAREQ2AquuwtnAau/eNN94oyScw48MiaqQYKBBN/GcW9klDNf2wtqA5c6aVJJ23k6QOysjgq02zjHYFSX7YtEbR69evL2AOWOWss86S5Fkf3ZnMITwDH/nIR1ymEsxJ3jM5rBQZY37W556UvRXMh7lIqk4Cexqs75esoyVLlriiallRSVPxyLAREQ2AqjGsbbbMTkVmA01w0f+IVx00aJA7Fv3O6qjV0B3IP12zZk3VSsSg202YMEGStziyExNf3bdvX8ek+HKRMJgbOh4+z64wbRC7nFpI3Orq4XWIwiJWGMsvUVlIDFg4iRvffPPNXYE2mJN7gW46evRoSd6GYS3AINSpbUFuLOcrV66seiE9mw9LthH3JAuylKbNijSG7bbQRMRGxEiwdu1at6hpKCcwohRqWfmfBGYerLAmc6kf4PXXXy/J1zxOQ7FePyDJ6GQfovAcaeI4GxI/IAL4SSjnR7pgwYKCCpJ2zUjIx3BnDUncq+bmZhcsYkVhRPU333wzbw1tcEglYKOhc0FPIYrEERENgI0q+L8aKMXGtWTY7kI5c7Qd2JNYyCaK224Nth+P7VoQnjPs8xp+lhaul8TutoyORU/3h60GUIlspwwQGTYiogFQ9f6wPY001glDzCpFucaFLHpnOUibY1KJzDAgIQmtra2O/QiAtylmaSF5nLN///7OmIQeD/iOLbcauGgKxmtrHHO9tP6v5aASV0s1YXvpgFJ9bCPDRkTUERpOhy2FRtBhSyFLiZgQtlugTb4oUgxNUidrWf02LTAeBkmTTpqbm911AlecHd8/1RqGiAwbEVFHKMqwERERGxciw0ZE1BHiDzYioo4Qf7AREXWE+IONiKgjxB9sREQdIf5gIyLqCP8P+l7l0YHF7RUAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1300, D: 0.1459, G:0.3828\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1320, D: 0.1896, G:0.2598\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1340, D: 0.1976, G:0.03838\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1360, D: 0.1504, G:0.3492\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1380, D: 0.1029, G:0.3657\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 2, Iter: 1400, D: 0.1181, G:0.2838\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1420, D: 0.201, G:0.3022\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1440, D: 0.1646, G:0.4351\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1460, D: 0.1744, G:0.2898\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1480, D: 0.4752, G:0.09396\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1500, D: 0.37, G:0.7185\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1520, D: 0.1632, G:0.2291\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1540, D: 0.1835, G:0.1948\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1560, D: 0.1289, G:0.7358\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1580, D: 0.1241, G:0.2657\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1600, D: 0.1741, G:0.2302\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1620, D: 0.1434, G:0.4208\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1640, D: 0.1762, G:0.2012\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1660, D: 0.1336, G:0.2763\n" + ] + }, + { + "data": { + "image/png": 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AeO655wDzhVXNK3tDmzVr5o0r9cI+8JI2OunB016GwZ6v5vTJJ58AyXQYjPuF9ffntbsnar4iCSW42IY8/3OgsdRbR/dFvZ/UC0qwVae6urqM74kOMD2vCxYscCKxg0O5o2CGtYMFbNfH6quvDphaw+uttx4AV199NVDfS3P69OmAqdl74403AvD+++8DJo1PlfTtHiZ212zI7Kgt5DqdbfHXH5oYJEZJRFbI4AYbbACYnisnnXQSn376KQDXX389AMOGDQNg3rx5AFxzzTWAEYV1nyZOnAgYRvCvR6eyrX6UKr1O96NJkyY89NBDgAlBVSE63TuFqN57770FX7cYRqeg50WM/Kc//cnrRnjRRRcBsPvuuwMmRDVJOKOTg0MFIFfwf9rv2dg4iHX0XgUyiGH69u0LwL777utdo9PvPWSlR0g3u/nmmwHTW8e+pnrQSuetqanxWMZ2/QQhSELwr8PWZ7JV9geju4pZe/bs6c1Lnb632267tDFkQBND6e8yznzwwQeA6YS2yiqreJ3AbT2sGPVxw6DrzZkzxytfq07m9lzuueeetJ/2fW9oBLkKe/ToAcADDzzgdVdUT6fzzz8/7TOywajLon5Kbx8+fLh3Pfs6Bx10EGCehyA4hnVwKCPkpcP6LbwydduFpHWC6ORQGUyF3m288cZAvR4qHUyn8ymnnAIYS6o6osk1In1Yna+vu+46oJ7ZbF3E7odi6wZB4Zd+F40q+KvSu83aOll79eqV9ruYf8aMGR6jqIv9XnvtBZhO9JIyZE2eOXMmAN26dQPgL3/5C1BfZd+f3giZroqGCE2UrWLu3LmAWZfsE/vttx9gujtI7y9mj99CgiAEVfkfMWKEJx3Iop9LelMv3Dlz5gDGAv39999n9E/O0pvK6bAODuWOgqzErVq1ytBhdeqvuuqqgGEK9Xp94oknABOqdtddd/HGG2+kjaHTZuTIkUC9JRmMJU+9R2UlVYuMRYsW5eztE9fCuOqqq3osIGaT7qjynmJ+6Sh//vOfAfjPf/7jreeYY45JG1drkaV7tdVWA4yvWf2GpM/r/v366685k97jWImXL19eFN1Xc9RPSSddunRJ+932k0dB3D2cO3euJ+3I6is7gCCJ74svvgAyiwN8/vnnng99ypQpWa8jxt1kk00As8e9e/dO+/sxxxyTk/0dwzo4VADyYljplEuWLAlMPte4OqGkl4qFdLJuvvnmnu5qQ6eQ+m/qVJb1VTqB2Oibb77hzTffBIyv1kau01nXFHsvWbLE07d0KmttYg+t8bjjjgNMyRgxcM+ePT0LsmBHhillTr9vu+22gLlPmtesWbO88juSPGypotRlTidMmOBZU8eNGwdA9+7d0+YvSUvSkMI680ESfthcDDdt2jTA6OKLFi1i6NChgIlok6SlvZTnY/PNNwfMc5DNkyJJVCxswzGsg0MFIJRhlXoWlPLlD4y3rV4KiNYJtv766wPGEizrcV1dHV9//XXaewT1Ve3Tpw9gTqO11loLMAWzxKbvv/9+zmT4oPQ6MYHt26yrq/Oua+s/0rHlY1WygmJLDzjgAG9MMYyswcJll10GGAlE+yFdVrrXzjvvDNQzWJw1lspKHGRlt5McHnjgAQCOPPLIQq6VWKST9lSMJ+nRbzOA+lhorUWSntC1a1cA2rVrB8A+++wDGBuG7Vv1l1YKgmNYB4cKQCwd1pb7/QXKxEy2z1RQbKxOoaeeegqoZ9rbb78dgIEDB6ZdR35WZetstdVWgNFZFa8qxk2lUt64Yjc72yYovS4o66i6utpjNMX9SjdVGVPdD+nU0nsUvXTQQQd50U+33HILYBhd9+Wdd94BYLPNNgOMf1Kxxeuuu643nyuuuAKAc845J23O+rlkyZKSMmxNTU2GZGIXMEgShWTrTJgwATCpcJLSZKeYPXs2YGK7Fc1UU1PjPY/ad1u6lH6uyDxbIpQkCUbHf+211yKtUXAM6+BQRghl2KZNm6Yg08qlE6Vt27YZ0T86VU466STAyPPSWcU+0un8+pgYVjrgs88+CxjmELPKgis290eLxNUNpMMGlX9ZaaWVPF+o1ij9sn///oDxoUofPfDAAwGTF7l48WJvPFmxVXT80UcfBUwJHeWNaj7yW+o+LVu2LGcpnVKXiGnbtq3HTLo3xYwVtvewW7duKTBx10KUSCdJAg8//DAAf/3rXwGzDj3HytABs8/K3rGlCj2/imqT1OTPAMuV6B/EsKFf2FatWqXAmOBtMSeVSnkbI5FCzuXnn38+bfIKxXvhhReA9C+IDBL6IsrQopCu9u3bA0aMlpKvIOzDDz8czVPz0RztsD37RrRs2TIFmSZ6v+tG4qu+VFrjY489BhgjlAxHSqGTu6dTp07e9SVOaxOlOui6OuBkcFPYpVIOf/75Z+9Q1L7ovvn2paRf2FQqxd/+9jfAuO3swISErxcrRTIMOvS1VxJ7pabJhZitQoaST6Tm6ZnWGArC0J7rC+xXJYPgRGIHhwpAaHqd7dwWC/mDnm2Di8RABcwreFrOZDGsn/nEDGJwO6RLovGJJ54IwOuvvw6YJAC5dfr06cPpp5+eNkcxm1jbhsz29nr8TCtxVPNSzWD9XaevnQaov8+cOdMbV/WOpRKoxrPcHXfffTcAd9xxB2ASIHQfu3bt6gUnSBQTO8i9VGpUVVV591nBBZJKcgXIFwNhzKqAFAVG2EZJ7U+/fv0AvF5NnTt39kILFRorkVhqjMRoJcLod6VGag/nz5+fYbCKqjo4hnVwKCPkFZro1xHERCrUpZNLjKuQO8n5MjrJnP7uu+96zK2/qcyIqtNJ9r/vvvsAOPfccwE49NBDAcNKP/74o8dIMoLYc7ar0dlGp2xJ+9KxpUvLzaTE5K+++gowgR/Se6S///TTT55JXylXKkon3VTlb5TgLxbVqa4E8e+++87TiaRD+V1QUDqjkz+QxJZoiplMb+t3zZo1S0FwvyI/dP+VhCJ7iPbfdk9JSmrVqpUncUoqkoQnA6JSCDWWQkdVmEGSYW1tbc6yQ06HdXCoAITqsN6bftfdVJJTJ4vfPC2XiwIB5L7QZ6QrKvhALOXHI488AhintaxsCvHSaScWuvzyywFj6UulUl4CtZDLYqi/S+eSrisd0192RrqJWG/AgAFpa5QurtRBvb9t27beGApT008xp6QG6Tk6zaXza41gmFUodbkV3TMFe0B9nyUwIYilhM2sdoK/P4RWngcFTuiZkp1Ee6rnSIH+q666qrcncsXJdqKCBSp7KolLqZO6ph9xugD44RjWwaGMECn4P1u/Gf2UhUwlQqSr6qSSHqrPqASoSodsuOGG3v8F6WgKL5T/VSFhsuBJX5beOnny5LRTNRuCutfZSeH+wmZKZ9MJqlQyWWXtUDXp87Im7r777l5InMZXqKGkF3U3ULihrMayAej+fvXVVxlpfnaKY7F1WDuFsrq6OtAaXIyeqvkE/+u50PMqdpQfXEXAFQikEruyU8yZM8eTvrR3gwYNAszzoJK0kopeeeWVtDmoqIMKE2ZDUIcK7/XwZTo4ODQmxLISywoqi6c/FFC6gKzAigJSBIzKwEgP0Im3dOlSj5Vl2dWpN2TIEADOPvtswDCuIN1F+meXLl28YPmgTuZBRdgEJSHLSltVVZXWJgNMKKLmddtttwFGl5R1WFbluXPnenqvwucmT54M4JWOUbSWSrtqLFkYZUFv0aJFRlSWrcMVuwhbtmcmX3/r008/DdQX6o5x/VCGtQvv+SEbiyy2suhLkpGks//++wNGsnnggQc8m8ngwYMBE1sgXf7MM88EYPTo0YDxj0vHVUuabCG9WcrrOoZ1cCh35JVepwiORYsWefG10jvVxEk+OlnMZOXUGLIAf/DBB14BLDGWHckjNpZlWWwk32Uc/SgXw0pS0Lrmz5/v6R52kXLdB7GmdBwx8auvvgrU6zzSVaX3yKes6ylVUNZJxSXr/llzTpuPrcsmzbAa/8ILLwRMmxW/7zqXrqrkftvCned88i5zqph3tdmQbUH6pwq9yy4iO8Sbb77pFRHQ3/RcaA+lH6sdiWLK8ym36hjWwaECEKlVh31qKt0MjI9UVjb5W6WPim10uuh1MU5NTY2npykOWcW01ZpS+oV0ErG5zT7V1dU5y5zasPvBij39RdGUdK74Xlm15WeT9KD5ScfXKf3LL79440pnU3SMWpi89957gGEA6fV2REyTJk1irzFf2ONfeeWVgJEshMWLF3sSSRCSYNa48DOanjH5ueV3lS4py60swGrMJmvxP//5T6+AgiLxFFNul4wRguwoUdLrguAY1sGhjBBJh9WJKv+fXXANDBPoVBFLyrKmRkKy/CoqaNmyZR77KI9QDG7PzY5PDWtSFdTIK0iHtaOFsp2OYhHNXdFcKhwuy6ISnP3WSo23ww47AMZ3a/t/da91T3QtSSR1dXUZGUUa2+eDLkiHte+n1i0Lv83wYU2vi4EgHdbOC84G24IcNHftgyy9Xbp08WwoUaOUCmkV4nRYB4cKQCjDtm3bNgVG/vc+lKXMqX5KR5ClV7qgKk6IWeR/7NChg+ejlX6rk0ljKU5Zfw8rSSoEnW72ybX11lunwFigbdbwZyTp1NVr8uUpM0lWYlkLZQkfNWqU9x4VZtNY8s9KV/Lr9pBZuSGVSnnX13tttqitrS2KH1b3RlZxRQu1bNkysMKEyuXIB5nQPAoucyoLr+wPsvzKd27rpdn0TrvxWq7WpXGQV4kY+0bYolGY8qwbojq8EhsVGCCDUSEKeJb5ev+PGppou3Xs2sP+ch7aEG2UjF9KmteXUi4bBXEsXrw4YxNtV4wt3ttryuZCsetQFbsDeylS56IgaA91T3OlrmVDMUIoC4ETiR0cKgCRGDbKiZXrhMpHAQ+6bpT5+I00/usvW7Ys6+lsi5f2vLPBZkeJ6r4+rUC6GGszq65rd2+zxS3/NW1jkFQDSQeLFi0qeeX/UiLJyv++MTR2oUMlAsewDg4VgIJKxEBmH037pAoKCvdfNx+dI2heYc7q3/8eqsPaetDv7wGCjQsRdMqclfCz6ar++Qg1NTUZ+q593WIZnRoLKoFhgyQowTGsg0MFIBLDKiVJaWN+S2rQ54NOkDBdVjqZ3Ds2S9vXkqNc7J5tLmEW1N9fT4HpwK2wQ3+hM5v1NKYdsCDYEoNfD5Y7xy4hG7RGpdX570ku90Kx0+saGkEMawdFKMBfgf1xkIsBsyFJlnYM6+BQAQhlWAcHh8YFx7AODuUE6UTZ/gGpbP+qq6tTv+t+jerfoYcemvM9UdfYWP9dfvnlsdbY0PON+69NmzYVv4fLli2LvUb9cwzr4FBGyMsPG/BegEYTKRKEYvjwGhuKFUvcWFDIHpbrcyo4hnVwKCNEatURBY39xHJwgPJ/Th3DOjiUEUr2hfXH+eZC69atvYbQfjRp0iRnLmac6zgUD02bNvWihfyorq7OWVKmXPawWbNmXoaWH8Wcv2NYB4cyQmI6bC6E6Q5qfaFSNCpGrvYNKg2q5rtTpkzJ6zoNCZXP1MmrdpZ2c2iVndltt91KPcWCYLeZVDyv2lmo+sZ1110HmKZf2dBY9/Doo48GoHv37gCcfPLJgJmv9lax7cVovZmYW8eGJquK9qpdlOUaOTcoV/pdmKnefq0Ybh0F5tv1euOgWIHjSaxP9aa6detW6FAFIaizWyFrlEirtMcgUdZfKijXWHYxgiiwUzbtNFDBicQODmWEojGsbwwgkzl22WUXAKZOncpBBx0EwMMPP5z2mUIT2rOhoQIn1LPFFueLIf6VKnBCvX5nzJjBxRdfDMDQoUM1h2JdtsH2cNtttwVMlwbffBK/lguccHCoABTd6GRXtJe+p8Jh8+fPz+i/o/4yCxcuBAzj2jVj5fqx6yb7oUT0H3/8MYnlxIJ0rt9++y3DALHrrrsCMGnSpLS/29XroxSvU33gqVOnJjDrYNi2hO+++w6o19m0h9pvGRLVb1UuHl+hOCBatX4VUJChrpTQc7tw4cIMl6KkRPWXEmxdNsozqJ7I6t8TBMewDg5lhIKKsEWR3W0neVChsmyvBUEnmFwHcXSIhg7+z1WcTiikMF2xdNggtm/SpInfuqnrJnXZDJTLHghOsXQAABWHSURBVNr9eeKUm3E6rINDBSAvHVYnzN///nevO539mnDNNdcAMHjwYMCc0upO3rNnz4zu7EEMLtbRiaUTTB3B1TIjSUTpfRo0X+ngrVu39gqCRWUeuzi5CuBJN0wK06dPZ8MNNwSCGbRfv36A6SyuNain6iabbOJZjPVaVAlB11RQgroeJom6urpI4ZCQuYf+9iTq9RR1D2Wv2XHHHQHT7bAQ6cMxrINDGSFxP+z6668PmC7kNvr27QvAVVddBdSfPrKiqSuaIKuwOp+px2xYN2+xmizNNpLQf3KVwLS77t1zzz1emKXWaut6Ck2UtTAMEYq0F6TDav5B1veBAwcC0L9/fwBuueUWb1+POOIIwOyDLLzq3j5o0KDQsQHmzp0LmA72NpLYw1wSwKabbgrAp59+CsCsWbM86UaWbhuK5lPXwrDvVpw99MMxrINDGaFokU6XXnopYLqR220l2rVrBxgWygYFWR955JEA7LPPPgCce+65ADz55JOx55WkhTEX00aJLbV1PjHQmDFjgMw+pVGQlJU4yFcsiIn9BeXtZmJjx44FTCvOtddeO23s2bNnx55XknuYS5oI6hebDXoe9HxKhx0+fHjseTmGdXCoACTGsI888ghgdBg1dFZjZEXF6KSVxfG4447L6dfVZ5R2J9jNkaOgkNP5qaeeAowubcO27IpZ/Yn3xYiPtpEvww4YMACAkSNHArD66qsDJkJn3rx5aX+fPHkyUB9ppUguSRu2RBUloikqCtlDuyl1lmwuwOipisjzJ6XHjR3O53OOYR0cKgChDGu3ovc+FCH/dO+99wZgyJAhgGlMJH+YfK+KKQ0bd8011wSMzrrFFlsA5PSPZoN9ctXU1KQgXhSKoFNazKusI1svjTK2PvPGG28AJvE7W6mcXPCvsUmTJimI10hbkGRz++23A8bCLzY99NBDAXjwwQe9z9gJ64KkjlNOOQWADh06AHDRRRfFnpe9hxtssEEK4Kuvvoo9lvZIzC891LbeRmFHfVasbLN4HAQxbNGMTkEd3Hr06AHAK6+8EvhZJUxvs802aT91I/Qw5NPVvRRhbRIpJWJmg50Ubndkt403cVCs0ETt6csvvwyYvfQ/kDqIZUyUKKzP6nnL1Qc3DEnuYZCqokCVddZZJ/Cz77zzDgDbb789YPburLPOAkx1jXzgRGIHhwpAYgwrMVXKusQTiU8yPvkNMVAf+mefcjJmaCylHIlhlUgscVqO9qVLl3ontm00EAo5nXv16gUYsVVrk0vA7k962GGHAfD4449npBc+8cQTgOlHq9BNMU/Hjh2BTHdCXV1dRp9aW8LIl2GlYmgf7BQ/va7ryfg0ffp0LxDm2muvBWDOnDmAuRfqc6uaVX369AGMIcufPKD1BbFvIXuovZoxYwZgAjzkOpTb6e233wZg9OjRQL1x1IZUghEjRgBGzBfGjRsHwP777w8YV+a8efO8tUnlyWJQdQzr4FDuSIxhZVTQiSXdRSfn+PHjAXNqSzdQSJ7/MzJuqNyIjE46+adNmwbUJx9APYP5rxWGJPqyiLU1X91DSRGSEBT44Q88l46nMRSKKAlFmDVrFgAHH3wwEC85PV+G1Tyfe+45APbdd1/A7JlYU0wh9lS4JcAll1wCGAnrxhtvBAz7aA9ljLztttsA89xEQSF7GFR0zf4eiPnlngwzHC1evBgILsI3f/58wNy3KHAM6+BQAUiMYXW62CZt6Wx2GRgFV3/++efeGC+99BJgXEIqwdGzZ0/AhDtKlxKjxUEhp7N0VLGE1mIHcut3BZMcddRR3t8UWKLXZFlU0oSkB7vsZRwkbSW2LaZaiwrLffjhhx7biikV0icpRM+FrMdByRlRUMgebrzxxoDZwy+//FJjAplWY81X64HMBBdJD3p+VT/7mGOOAdLdXlHhGNbBoQJQMMPKJyqLqE4snbh2ORfpZM888wxQn1731ltvAUaHUiicWFsso7HEsLZVNkoB53xOZ522YnzpndJdbL1UhceUjjV79mzWWmstXQ8wOpL0GgVXaA35BIUIcRn2hRdeAEzIpW3Rl8Vd+2AnBYwbN87Td8U+jz76KGB0QD0XKlgmack3T8091vp+/2zOD2nczz77DDDBILfccgtg7CEqyKD9kAT00UcfpdlbIHc5HNuaH6d8r2NYB4cKQMFlThWKptNDllGF6Qk6raVD6NR56623PIZSgoD8e9KHVMZTzKWxFFGiMjTrrLOOp28lCek10sdefPFFADbbbDPAWBx1ikvaEKuutdZadOnSBTDsrJNbn1Gal13KREwrNm/WrFlerSDCoLRF7aF0VVvCkb97jz32AIweuv/++3tlUCZMmAAYv6t0QD0ntk7euXNnwFiVq6ur89Lbc0Frk+3kk08+AUxxhBtuuAEw+zFs2DDA6Kdbbrmll6CupHbtlT6j+AC7L9IJJ5wAGH/tiSee6P0/LhzDOjiUERKPJZbOIh1NPlT58ASdUptvvrlXGkYW5U6dOgEm2kdQZJNO9oaKQ91hhx0AEw2jk9dv8fbPq3nz5t7avv3227S5q6udWEXlb4LKo0RBoVZi21IalBonpl955ZW91+ygf/mX9br8yTvttFPcaXlIYg9ty7fifhVx5rsWUH8P9PxJx7efQxu5IrbC4HRYB4cKQMEMe+yxxwKZ5SnFGPq7YjGznTayOsqCp9M4TvRLVORzOksPsyNVtAbFjO63335AdqvgBRdcAMCtt94KpKcVJo24DKvUx4kTJ6b9XXG3so4q+spGVVUVZ599NmAszmLaUjSKimMlDkqbk1VYngdJONLbwdgupJNKWiqGzu0Y1sGhAlD0dpONDQ3d5qEUKFW7yYbCiraHfjiGdXAoIyT+hfUXHMsXLVq0SMt8aNq0qee3zAZ/gaxSIIk1Vjp69+5N7969vd9LvUe50Lp167zK74Qh13OaBJxIXOFrrPT1wYqxRsGJxA4OZYSSfWEbm0jk4FCOcAzr4FBGKK6GTH5hWeV0vRUB7p42HjiGdXAoI+TFsDGTjfO5RN5I6nqVzipROusJ5XoPKnEPHcM6OJQRQv2wDg4OjQuOYR0cygihOmySESSNRZ+wI0hWWmmlFCTTt1T64NVXXw2Ywl7ZStfEbeSVb5GyQvZQ7SMUwqe2ISocnmSvW7voWRhKEemUZD9bQaWQopTndZFODg4VABdLHGGNKihWzKTzJFGpscRiu2bNmrlYYgcHh8YPx7AlWqN0s1zd2OPockGIwrAqDr5s2bJEddFSIOoexvE1J4mbb74ZgDPPPDPvMRzDOjhUABo1wwZZRuNaWP2Iy7DFKmxdTJRKh7WbQpUKLh/WwcGhLNDgDDtt2jSvfUcuH6OshPKR5YPGfjqrNI4Kj+eDMIYthj88lUp54+byNW6wwQaAaY6V5/Ua9R4mcY+DGLbo6XUzZ84EYL311sv6+kYbbeT9X0q6lHYt+OOPPwYK+6ImCXtDkvwSFPJFjYJ85mh/RhXvX331VaD+INV71B9JPVHV/0iwewdlQ5wAg6Rgd0IsBMUMDnIisYNDGSFxkTgq2+j1l19+mb322gvIDIWzu2Sr81iSokYxxSmxyfLlyzM6ndmYPHkyYHrOFGLoKtToZIfl2ayofr7bb7995DGHDBkCwIEHHgjg9ZPVnpe6P1IuqCrmTz/9BMAjjzzCGWecAZg+wMWEMzo5OFQAEtdhc52UOq3tvqdgenHK0S22llFKrJOLrRoL/CwZxJh2oISkCv1daw2rgxw1KCMqxKy6/9IlFYgQhuuvvx6AK664AoD58+cDmXulv6t7nPrUNDTU+0h9Y/VM1tbW8uabbwIwevRowASfyO6gvZs2bRpgOvepN666NBYCx7AODmWEoluJBTvYQadSVVWV938xq3rMPv7444DpEF6M7upJQFbNOBZesdXFF18MmI71kyZNAuC0004DDLP26NEDgPHjx2eMVSiz2v1gBdtK361bNyCzA9ykSZO8DoSavxhUCLJtiFnFVqXQD7PNR71+u3btCsBVV10FmM6F22+/vWdLsaWfU089FTBph5JIZHFOglkFx7AODmWEgqzEb7/9tteNPF9UV1d7bKPTVXNSOpv0CfWiTcqCCsFr1AlcV1eXswC6fQ91ouq09o9nSxpB91+vn3/++YDRDaOgUCux5ijGkG9VVnr18ZUdIkyykP9d4YtiHendO++8M2Asz1EQ10q8xhpreEwpKU73V9KbevzOmTMHMLYV6bBnnXUW9913n64PmEIF/fv3B6BPnz4AtGnTBoCxY8dGXpMNZyV2cKgANFhoohhn1KhRnHfeeQD88MMPgGHWG2+8ETCnr5jr+++/Dxw3V2JAPj68bIxpjRn6+cGDB2cw5BdffAGkR3pZ8wLMaa1u6FGQL8Pauqxfygi4DhAtekkMJ0aTzijoms2bN/es1GJh+/4m4Yc96qijAHj44YcBs0ZFcUlfXXfddYHspWJkVxg5ciRgwi61lqRSJP1wDOvgUEYompU4V8STXn/ooYe8aBLhtttuA+CCCy4AYNtttwVMqZYwhi1GKlwQs6oomdYoFmzbti2Q7i+2g/o7deoUek1bX4yCMF07yBLsh17bZZddAGPxtWOmTz75ZAAGDRoE1LOi7A9ilXbt2gEmUkh798wzzwCw2mqrAfXSBxjdsra2tiS9dx966CHArPmOO+4AzFpXX331tN8hc0+k34pZBc1fUV2ywSQBx7AODmWEkumwOmFlJdTpPGzYsLRu6wBPP/00AIccckjs6+Ri9kL0H1k85Q8OigDS69JPs+k/QfOTDhVW8G3NNdcEjEUzy9h56bA2k06cOBGA3XbbLe19djbNrbfe6vmNg1je1nc7duwIGAu0WKmuro5rrrkGMBbyLGPlvYd2QT1JPFOmTAFg+PDhADz66KM5x1KcwCeffAIYaUESViE+ZafDOjhUAEoW6aQTTTqNrJ/ZLGnSDfNBMeOLldsrnHPOOYDRf3RaS/9ZuHAhkM7Aueanz0yfPh0wcdR+i20QsxYKmx2lg0k3t6WXLbfcEoBnn32Wrbfe2psfmH3V7/qsHSudjZGDmDUJ6DmUVDdv3jzARCt9+OGHgMmPFVv++OOP3hj6v2KEdR/WWGMNAD799FMAZs2aBUD37t0Tm3/JRGIFWMicL6e7X/STuKzQRD28Qcgn6L0YqVn6IssFIGgdeijCkGR3ev8aW7dunYLwe6lrS0ydPXs2YAxDgtxutlGoX79+XpCARF7bkChRX2NIrNbzJ7dP+/btY63v9/kn9pzKkDRgwADAhCg2bdqUF198EcBLArjwwgsBoxocccQRQKaRKZ8CB04kdnCoAJRMJBaz6rSR+Ljtttvy7rvvAkYMGTFiBABHH3106Jg2szZt2jSxFLM4UPXAG264ATBlUcSsVVVVOU9XW1qwDUDCggULvAT/oDH8yCWl+K8ld9n777/vXQuM8SwoBPHee+/NeQ2xkFhbLhJBwTH+wImGgJheoYmSGGpra70AicsuuyztM0pcUOiiJBWtVYY1qQNDhgxh2LBhec3PMayDQxmh5KGJMjZJ3h8xYoRXkuTzzz8HgoMKkugolqT+4y8BkwsKDAhKAr/pppsAY3AppBhYXLeO3FXSxfVM2MEeYvtCis7ZxibdQwXHfPvtt56eG4Qk9lDXkztHz56kOv1dv0+fPt2TBLUGvysK4J133gFg9913BzL30OmwDg4rGErOsDqNFHZ45ZVXss022wCw1VZbAaYERzFgn1yDBg1KAZx77rmAKVkSpRyKDekuc+fOBYxOO3jwYF566SUAevXqlfYZ+5ROAvkGTijgXaF2cq9FsdxGmFPWv6uQm8qqZEtyyJKSmLiV2JbeJFVIymjWrJk3D+njsoSfcMIJgAn4KWZdYsewDg5lhJIz7Ouvvw6YspevvvpqYLC3bTktpKeOkM/pLKupSqTkgh06mEqlvDA1WRSVHG77K4Ogkity5IchLsNKqpAOpsCPJIprK7lb+925c+e012VBXXvttYFo9oB89jBoLZJ4JAH5xtS1gHppSb5m7Z1S9A4//PC0z0iHnTBhQtqYURIwBMewDg4VgJIxrE4fRdEorO/+++/3mDNutzqlOdk+vTDYJ1ezZs1SYE7eXNbcMJx44omACRxXiNp5550X6KvMFeEk5olTBjQuw+rk7927N2AieRRap6ifUaNG5by22ENjikHlk1Q433vvvQcY3dFvgY5QhL5okU561uTNkGTTvHlzL8Tw+eefB4zk1bNnTyC4KLrSM+MUY3MM6+BQASgaw8q6pogRJT3bicJVVVXea/Jz6adO3SeeeAKAQw89NN/peCjG6aziW2JWsYtYUTohGMurYqml0yaJuN3rZN22/Z8zZswATCSXXcBdwe5z5871pB3ttyz/QVByvJ26FwWlbLeidVRVVXk6qe6HvBq6f0r8Hzp0KBDdPpENjmEdHCoAoQxbXV2dgnB/UpDlSzGsOpXFuErDmjp1KhpbJ1TQiZRkK4p8TudchdCk68kCKsuoSngef/zx3nulGyfR1lAI81NGWZ/0dllSC+msHtUHKauxWnoILVu2TGvfkg1JMqyKwok9dS/s+e+555689tprQGa0VjFSOh3DOjhUABLXYW02Uo6oWMi+XseOHT2Zf+DAgYBhDJ34SWZvFHI6R2V6sah/3io9ctFFFwHBJWB0aouV77777qjT81BoIXFZu2VvkG4rSSgKtN9K7pYFNQmUQofVHkpyTKVS3t/0DOs50HOaZJsRx7AODhWAovths1WWAKNv/fzzz17kiKCsf53SdpG2QpDP6awWhCo05htLY6T9bmfxVFdXe6exYpavvfZaINPfa7fhVGRYHGuqf421tbUpMCwQBvlEpcPKWq/CZTbs9p9gLOLKrVVBOkVTJYFcexhHt9xwww0BU5LHbsql3OMFCxZ4VTIU8SZLd5RC6nERxLAlC5yQkSXKgxMXcUIWgzY7TthYljEBEzCujdWD0717d6+8iEILFUCeZGkY33wKEol1gMoFpd9lkNHfi2l0CUMxRGKtRUZRpdvpua2trfWCKJT+ecoppwDknYweBicSOzhUABqst05DoRQGC5utFy5c6DFrPgweF9kY1u7fWs4oZeCEmLdt27Ze+ZhijJ+rf5DgGNbBoYzgGDaBNfoNEwHXLIquKkOW3E0+Y1dBOmwQwnTWUuqzxdhDu5uBjRYtWoT2wU0ajmEdHCoAJStz2thx8MEHA6a7WlCZ0WzIxSrFYFfILGsaZiWX+0iW3nwQts5SW4qjwHaRhSGIWYVSsmsYHMM6OJQRQnVYBweHxgXHsA4OZQT3hXVwKCO4L6yDQxnBfWEdHMoI7gvr4FBGcF9YB4cywv8D7XmE8MmKeaUAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1680, D: 0.1704, G:0.2932\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1700, D: 0.1669, G:0.2332\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1720, D: 0.1436, G:0.3316\n" + ] + }, + { + "data": { + "image/png": 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lAog41lmJHY56x+3DZlA/tRxqPJPXnYRNQyPvpes56ejocBLW4ah33D5sAllKV4cjiaTnzUlYh6OOcBK2BtTLXncaK6ZNpYnQ87Ssh5FnFFEtcBLW4agjamYlzqPoUyXkaWEsJ45XhbNOP/10AC644ALAT8lpZ6YoRzrHpcishQU1WDDMbq/apmRnSslSDVmMYVSitnIygyh9zO9+9ztdP+3lE6l5ipikAVKKmGXLlqU+t5ZPWk6lIe1gDxo0yMsHZC/f7HoxUQS3hgJm+6K/becPfX7AAQcAfvWDMJQzSJXx2tvbK/rBKu+uMltGtTXNEv+EE04A4O677w49h53jq5xzZznpqtKgKgEo57TS4uiebt682cv3pBzM9g9WdYxVuSKNimFPIM5xwuFoAHJfEtuzs/4NVkiTocKuVSOqMYaEnCt0ds57ya5UI8qAL4mpvquqgZJ/SYrb96+cXLtZVa+TdKmmClwU1YxlHmqNjFFp7vvatWsB+MUvfgH4as5ll10GlCblS3MfnYR1OBqAmhmdlN1/+fLlRe8bY0py8pY7+yrHrHTMcugKt7a2tjbOP/98AL73ve8VfSYJqxlekv74448H4LbbblM7ATj11FO5+eabgVLjVkAPrEjCSm/beeedAZg1axbQWcO3XGbMmAHAF77wBaDU3pDnKqkaVCnx8ccfB2DEiBGAPz6yMcShZ1v2GRmwtFIJSms7yOFXv/oV4NcmchLW4WgAqpKwYYmpVLVbepD0Us20qiWzePHi4HWKzhG1NZAFaWfnBQsWpJIwAHvuuScQXj3NloZnnXUW4FtRpf9IQmn1oNm6qakpuH0DlG4nBR3Hk/pnjIm8/+U6Q7z11lsMHDgw9hiFBUp6lxNGF0XaMQzro2o/qaq67r9q3sZJVDsUUM+2EspfeumlgF9FTxI2qXKA1WYnYR2Oeqcq18SwgHFJVs3S0g2iXNCCM58tfWR9y9JKnBbttUKptS+qZor264JoI16SUn2RvqO6PKoMJ0eE8847r+j4MEtjNQnCwu6pxkiSNWkFFKxXE7U6WrVqFeDfw0r2dCsd/7DvrVy5suj69u6FPaZr1qzxxkjf0apiypQpAMyfPx+Ayy+/HPDvXyX+AlE4Cetw1BGZOf+3trYCeFXaNEurYJIswHblM4iuSn7iiScCvgSphaS1Z/6gRLOlmz0Law9Ve67BzzVzy/IqVOlOHl/aB5bHjYpMqXL5N77xDaZPn17UxqyRpVrn19+S+hpLrRbAH2d7bKSjDxgwoOic9nFBPTyqX1n2167Ed/TRRwN+ZXY9z1dddRUAt956qzdGdttvueWWor8lWbWqktefJPSmTZsqfoadhHU46ojc9mGD1ceD/z7yyCMAHHHEEQAccsghJX6yUW2SriR9qBIPnCgLY5pEbjaPPvoo4PfJtrKec8453v+vvfbasq6javQvv/xy0fE77LCDp39FUa2nk9of5aETp39qVSH9vhaO8erj2LFjgXi/65hzAvCtb30L8G0HWhkNGzYsUsJGIQmrPV3bByGhPc5K7HDUO5lL2FNOOQXwPXQkUY888kidM/K7DzzwAADHHHNM7DXsc0RVPg/Dnrmam5sNVOcvK08tzahvvfUW4FsRJ06c6Ok1Tz75JOBLzKVLlwKw++67A/4+5b/+9a+iawT3ROWjqn0/m2olrB1pVYnuqHsh7yl5QE2bNg0ovd/VpADNwtPpvvvuA+BDH/oQUOzrrvZF2Vq0EjnnnHMA+OEPfwj4uqyez6ClXNfRdW2chHU4GgFjTOQLMHGvXXfdNfKz/v37m/79+5skevXq5X0nivHjx5vx48d7fye1K+5l93Hx4sVm8eLFJcf17NnT9OzZ0xx55JGJ52xtbTWtra2mvb3dtLe3m46ODtPR0WHWrVtn1q1bV3Ts9ttvb7bffnvTq1cv06tXL7NlyxazZcsW7/MxY8aYMWPGmOuuu85cd911pqmpyTQ1NZnm5mbT3NxsZsyYkaqP1dwrvfr06WP69OmTOJZxpBif1GOYdLw9BmGvq6++2lx99dWmUCiYQqHgtfvGG280N954Y+x3b7rpJnPTTTd5f+sceg6y6KNeuRmd4s5rXSPVsdViMsxLrGWqHPtfeuklwF/ezpkzB+jc5rGdLGQYGTp0aNG/WkZrWR3GPvvsA8ALL7wAwO233w7AySefXNLHasbw61//OgAXX3xx2d+JcrKQA41tuNES+dvf/rb3nSSXyLRjGLye2iO1QkYmqW6TJk0qunbwe1Htso1dv/3tbwF/qygMGQ61HRaihrglscNR7+QmYSV1rrnmGqDUYVqO0Ntvv70nfRIb28WhWXJ6mDp1KlC67aGtIZnvtSVz0kkneQ4RS5YsAXwnBIXVafZWH6PCuoIpa8rpYxYGGbVJRj25Xgaljy3B7IAEGdn0fiVB3aKaMdT911ZL1BZV3Dafxk7O/fbWW6CdRddIg5OwDkcDkJmEtZNWCc1UafPVxlGNU7U9c6XZ1rEz6mnm1FbWj3/8Y8DXXaTDNjc3c9JJJwF+0i/pKnKBk86qLYAoKdra2uq1w976CetjmjFUm6RPJVGJA7+O1b92mN1RRx3Fww8/nHSOiiXsQw89BMDkyZPL/UpqJk6cCMAf/vCH0M9XrFjhJSh47LHHQo9xEtbhaAByTxGj82vdLycDzeKrVq3yap9GtUU6rjaxRVi607QWxoEDBxog0d0P/M10OfkrHahWEbb+KQvolVde6bVL0mPevHkAniP/gQceCPihWZql5Y6pe/PWW29FrmbC+phmDJNqtNoSVXqg9MI4stwJqEbCanX06quvAjBq1CggeQXY0tKSeIzdR0nzK6+8EvBdWAuFQqIO7ySsw9EA5C5hZQVVeNVrr71W9HmfPn28FJ9K1BbSjthrpHH6Tjs7jx49mhdffBHwg9l1HbVLIVlKO/Kud70L8PX6tWvXevdB7peqACB9VOF01QSjBwIuypaw5aR3ERpDudiVg201Vv+kLwdd/1Kcs+K9dO27fuUrXyk5Jqzd4ogjjvAkZNj5g9+xE6pVgpOwDkcDUPMK7NLlJA02b97s6WmahSWFo8o6xJF0bNLsXE6YnXRneSfJwnv44YcDvr4uR/+mpibvfFGB2/pulGUxyJAhQwA/gZg9hml1WI3JrrvuCvhJzTNOfgf4we+yZei+BJPIR41hIG1o1c7/uoY8z8qp+XPTTTcBnalmreuHtlt770oAEHb9cp9T4SSsw1FH1Kw+rCRqmKVNe5Tjxo0rev/+++8HomehsPIa1UqFMMmqc0pyqi/Sv/r37w/4kkkhc5pFwyyB2qv997//DfjSzSasj2+88UbZ/SkHjYmsvbbEsKVi3D1O0kUlJW3dNkjU+eMqyiWh+6u9a6XxufDCC4uuaUs+/btx48ZQSRn8jo3u1/777190zeB50+IkrMNRR6TSYfMuGGXPbnbboix5aWarJB32mWeeAXxLL/jlAy+66CIg2vqn+3PmmWcC8N3vfhfo9C3WPZMlXFZj27c2qRJ5WPJ2m2Afzz//fAN+apqwe2aXlAhLlBf8O24PManCub6jJOujR4+O7UsY9hg2NTWZYPvC+minL416ZtQ+7cvLgr5582Zvv1+lUhYsWADAd77znaJz6NxaealoltrVu3fvxNWC02Edjgag5lZim7BIj5B2ZHm9UAlbTvSIbUFWe1VASQWRZT1W3Orjjz/uWUHld6zk0/IptanET1dUG62j2V86WJZ0dcRVyLmAUi8vO5oqjlokmhNOwjocdUSXS9ggtu6h/VntN3bV7Gx7+GgWlsSVJD3ssMMAPwlXMDVpXAaJ4Hf/+Mc/AqUzfqXFgHv27GmgOruDxkVW/GeffbbkGMU9X3DBBUDpCiFsv7WK9sTqsBWeE6CkuHgwbWvS82dnkaiGKAnbZT/Y4I9T/9cycfbs2XldtuRGtLS0GKjOJVDbOKpap2yGqog2b948L22Mtn6qIWm5XO2S2A6gSDIGgr90tCeGapb2UWS5JLbzZys4X66iClLZa6+9PKeWvffeu9LLlY1bEjscDUC3WhILJTN773vfC/i1RbMganbOUhKU496o5b6WUdXkRbYJk7DVpCupsA25XS9qDFUHR2GNeWMnTssSJ2EdjgagyyXsoEGDPINMlnpOFFnqP11BU1NTojTOOglbOSQFv2dJV41hLSW4k7AORwPQ5RK21kTNzrbemYd1M44sr9cVEraW1PsqqRychHU4GoBYCetwOLoXTsI6HHVErJ/Y20E3SNPHJDfBPPVeBV6rMntce5wOWzkKoUsKc6wEJYfXHnwcTod1OBoAZyVu8D42ev/g7dFH4SSsw1FHuB+sw1FHuB+sw1FHdOsf7IgRI7yCS478GTBggBes7+iedOsfrMPhKKZbWolVkvGoo47K/NxZWBij9j+TSl0GmTp1KgCzZs1Ke/lEuoOVOCndaTW8na3E3fIHmxT8rCrlq1evruTcmQ12NQ9lUh/VN+XFjQuGV41TBf7X6ger3EVhtXV32mknAF555ZXMr9tdfrBJY5gm5FCV2FVjyW3rOBwNQLeQsH/9618Bv/6qUOoNVbNT9vZq6IrZedSoUV4Nl0996lNqh64P+LPxjjvuCPjZ4pW1L2wZHuUKmbWEPeOMMwC4/vrri66r8Vi/fn051dgAv4au3PQqoaskbFLlC/VRdXxOPvlkAK644orU13IS1uFoALqFhNVWgiq52Umtomq9qBaMKp2XQ56zc4wzfkkFOButIrSNpbosy5cvVzuBTsmrz3Q/vve97wHwuc99Tu/XRIdVO8JsCXkme+sKCWuMKTvpmoymhx56KODXLgquiOz7YwcGOAnrcDQCxpjIF2Dyes2cOdPMnDnT9O3b14h169aZdevWGZtddtnF7LLLLua1114zr732mneOpqYmszXre9mvtH1csWJFyXsDBw40AwcO9P7u0aOH6dGjh5kzZ46ZM2dOSfvLYfXq1Wb16tXe3zr3mjVrzJo1ayruY9KxYffPboPus/1+kOHDh5vhw4eblStXmpUrV3rvb9myxWzZsiXTZyftGN5www1ln3vkyJFm5MiRFY2hTdT9rKSPejkJ63DUEV2mw2p/8aCDDmLy5MmAr4OpnIUqnMt6HFUlOw0mQ/1HpRsmTpwIwA033ADAZz7zmZJjdYy+M2TIEMCvpq79StWlle6qvgerdydhMtJho+6z/cw0NTWV1Gb9xCc+AcA999wD+BXxskiYnuUYPvXUU4BfedAOXA/WArrpppsAOO2003RdtafoOyrzIR0/0M6y22X3UTgJ63DUE1Fr5XJ0gyxe8+bN89b4CxcuNAsXLvQ+6+joMB0dHd7fEyZMMBMmTDAtLS1maxGr3PUfwBQKBbN1Fg99TZ061UydOtU89dRT5qmnnjJjx441Y8eONXfccYe54447zIYNGzzdyP7ubrvtZnbbbTfvGqKtrc20tbWZFStWFOnR5ehjWY2h2m+/v+2225ptt93WvmaonpaFPpfFGCa9ZIfQM9fc3Gyam5u9cYhr87Rp08y0adNK+hil07766qup++h0WIejDqm5DqsyByp7UCgUSvQaJcIqx4k+LVH6TzUJ1OTn+453vAPw95N1zu22287Ty6W76jPty8ovWZ5NqhN7yCGHpG5PVjps4HyA7+EzbNgwgNCat1F1YZWovbtVYA+cU+cK/Rvg1ltvBXz9XKhvQh5R2p8up4p7SHucDutw1DvVl8MuE81CkqzHH388AJMmTYo8VjN6ngWWkiRrXPEpfff3v/894KciVaVyfa9nz56cf/75oeeQ5JWPrbxibMk6fPhwAJYtW8bQoUMB3zsqb+wyJorOCd67Aw880Pt/EFlZlyxZEnsN9VvW5FqzcOFCwG+/rPNBSWtLVmGvBHVf4iRrUsrcyO+lOtrhcHQpsRJW+0jaV6oGzc72DDxgwAAvBlCl6OV3W4k+mZY4yQpw/vnnM2PGjNBjJD2OPvpowJd48+fPB/wojba2Nn7+858DcOaZZwK+HiTJqVn5pJNOAuC2224DfH1Ikmfy5Mk89NBDKXtZHZI+Iuye7b///qHfLXfPPE/J2q9fP882YKPVwz777AP47VUEma2fgh9//OKLLxa9rzJy77sAAA/qSURBVD5K4tp6u3ze+/bty5tvvllRX7rMcUI/iFmzZrHvvvsCcO655wLw29/+Nq/LZmqw0DJOwQvadJdh6bDDDgM6f3xaEuozOXsrCFyDWk719tbWVgBef/11wP9BjR49GsjO6FTNhCmDi5INqF/dzXEiCv24FGACMHPmTADOOusstaPoO2kManaonn7kEgLO6ORwNAA1l7CSrLvssgsAS5cujbt+1pevanYu11Agt0IFo/fv399b5gdnbPBnWNtAYacMKYdAMHym2zp/+tOfAPjqV78K+O6VlWyFdddtHRslENhuu+2AznGYO3cukFxbSWh7TwHtaXAS1uFoAGq2raOgXwUBS7K2t7eXKPZ5Bj9Xg2ZbO2Bb7T3nnHMA34Hi8ssvBzq3suTkL2l06qmnAnjGKJs4yRqTGqbMnqRj/PjxsdfbtGmTN4ZBZ3nwVxBabeg7UUkJuppLL70U8G0LsjXcc889PProo0XHymYhfdSmEsmahJOwDkcdkbsOG0zUFSR4XVlZZS2WW14e2LrBkiVLDMAee+wBlGfFLFd3k7Rpa2srSacinVZuftoauvvuu4vaITdNbTs899xzidfP2jUxiaBzSVSbtMKQhbsaQvS7IvfSLNH937x5c2RKolrYWoSTsA5HHZG7hJVuI8upJIrW/4VCwdP55NqXJ2ktjIVCIdIx3MZ2NAlupD/77LMAvPzyywB8+MMfjj1XJeRlJU5i5MiR3j6z9hO//e1vA3jpXbOUQnlaiX/0ox8BcPrppwP+XnvQuq//Z5F2NwonYR2OBqDm+7C2W9fSpUv5z3/+A/i1dKZMmQLAL3/5y6wvXzJzNTc3G/B1xqRk0dCZ1gbg6aefjr2WSmeMGjUqUpJK8qRxfp89ezYAxxxzTOjntdZhgwT19q3XV5uK/o4j6dgkCatUoXHJyssdw2BbksYwDUoof/PNN0dd10lYh6PeyV3CyoNnwYIFQKe+A8X7cJqhlHj5iSeeqPaykVSj/8jjRz7CdliV+iSr91577QWE90eSVJZx6e+2lbqSMKyukLBqny1tupu3msasX79+gG+tF7K5KJBDdglZi4M8+OCDQPRKpxqchHU4GoDcPZ1UilHSRhLd9njpKjRzSudSvVbVbwV/1pX+HZW6Rt4xKrUoyWqM8SzHCkxftGgRAOeddx7gWydtX+MsoltqgZ3WR6lvqoloqRRF0yi6JojG0pas4p3vfCfQmSgAfH0Y/LEIC7mrFU7COhx1RO467He/+10A3v/+9wO+pA16xtizrpJmRwVFV0OS/hNm1YzxKAJg7NixgB+4rhhQpVJpaWnxLOGKUlq8eDEAu+++e9E5y4mHTaLWOqwxxtP5lL7Gprvuw2oM7eTfYWl47MTxSrig3YAscTqsw9EAdItyk7WkktlZ5SbknSS0d2qnH1H6T0na5cuXe5LULgUhVLokLHVoWmotYXfffXcvmZyep/322w+Aiy66CPCziNxyyy1VX6+SMYza25WVXrsYwi4POmXKFG//Oy3am06TtjdKwtYs839S9vxavfLoo1iyZIlZsmSJsfnsZz/r/b/Wfcz5Ol6fRK9evUyvXr3qdgyjOPTQQ7tsDIMvtyR2OOoItyTe2sdKli1RyLAkQ1MYCuiXY0mcG11aar0kDuNnP/sZAB//+MczP3eWRqcoFA6opHlxaAy1hZkFzujkcDQATsLWoI+FQsFzV9RWT1bnBcJSt3S5hM2TrhjDWuMkrMPRADgJm4MO29U4Ceu9r+Nr0KpscRLW4WgAYiWsw+HoXjgJ63DUEbHhdfWi/2iVUGb6kbeVhbHR+wf108fjjjsOgHvvvTfxWKfDOhwNgLMSN3gfG71/8Pboo3AS1uGoI9wP1uGoI9wP1uGoI9wPtouZMWMGM2bM6OpmvG2YMGECEyZM6OpmVIz7wTocdUTNrMSVJMTOg+5iYbTTq2aJsxJXjl0yJSwpnlKfKmZWz3YWCfRElJW4ZhXYU2auB2Jrq8R+HsTOdJclyvheSa6fpCCDqFqk9UJ3maCTUN1iVaLTD1XVHVSFb9y4cV5+JwWs232zf6j2c5rFJO2WxA5HHVEzCZsGW3JqplIWwnHjxpV9rjwkq7Alq6SmQvXilkghQeeA3/d6k6x2Shhb+nTX8EVJVuWJHjFiBOA/Y8p2efrpp3t90rJZfRL6XFk2NZb6Nwv1x0lYh6OOqJmEjZI2xhivOoDqzEQhKaSM66pqHgxUtqWzdJG5c+dW0/yysGfcMMm68847A6UJ2q677rrQc6bR17uKsWPH8olPfALw26v6utL3uptkFdK1Zeu44IILAL9+8bRp04DOOq5y2v/a174GwLnnngvA97///aJz2dg1c+PakaTzOwnrcNQRVW3rLFq0yKuVE/gOEK2jZUElVb0D7chtS0Azqaq4ywIZ1r6k+6HvKDP93/72t7LbUattHfVv/vz5fPGLXwRg/PjxAFxzzTVqC5BtlcK0Y/jII48wadKkovcuvPDConZqRfCxj30M8PVQVXXo1auXt2JSn1QdQOMuu4MkaTUWcuf873A0ALk5TtjSL+46tn6bJKX1ufSisOrYMe3KvPJZlIS3N+EBjj/+eABeeOEFwNeVxKpVqwC/inslumveEvbxxx8HoLW1FehcBciq+uc//xmA2267DfAllOwUWVR6y2IMJfHtZ07IWjxlyhQArr32Wu8zfUfJ4CVpJVn1PFfjSOEkrMPRAOTummifX1bDb37zmwCMGTOGo446KvS70t9GjhwJwH333RfVTu9aSRIpTx02TVrNo48+GoDf/OY3gK/3SpfSOfRvmqrfXeGaqHarwt+DDz4I+FJHqyBVqa8moXo1YyhvJUnFY489FvCfrTibgaqzz5s3D/DHTCU6NP7Lly8H/JItuua6devKbaaTsA5HI1AzCRtn2T3xxBMBuOuuu4q+Kz3jkUceAXyr5Ac/+MGSc6RoT+YS9qCDDgLg6aefBqBfv35Aad3YILIgqg+vvPIK4O/TVuOLm7WETbIx9O3b16tULr1O7ZakknSSJK6GLHVYtVPtkjedJPBPf/pTAPbYYw8vidrrr78O+PdDe+iXXnopkH2N36J2V31mh8NRM3KTsJrBpMPob83Oa9euBWC77baLPIfdNukC1ZT16+rwOluyBtoB+GUnFcJVCXnpsGeddRYAM2fOLHq/ubmZXXfdFfB9cjXusiTL+p0FeYyhbCt2WJ3G4e9//7ung2oVJN773vcC8PDDDwO+5LXtDs899xzQ6RmWhJOwDkcjEFWa3ZRRin7x4sWRnw0ZMsQMGTLE+/vee+819957b1ll51etWmVWrVpVcqyN3p84caKZOHGiOeOMM1KXom9qajJNTU2Rx++5556RnzU3N5vm5uaS99vb2017e7u5+uqrzdVXX1302RtvvGHeeOONyL7Mnz/fzJ8/3xQKBbNVcqR+pRnDNK+WlhbT0tJi1q9fb9avX++1vampyWuv6OjoMB0dHWWNdzX9M8YQNQ56vfDCC4nn1DlWrlxpVq5cWTI+y5YtM5MmTTKTJk3y+rZ582azefPmkj7qPkVd6/e//33qPuqV25I4aosj7P01a9YA0L9//9jvlIMdJKxzBa6b+XJKTg5a9u20006Ab0gCfwtABjQFJdjty4K8lsRCqsmiRYuAzq0QqThC9+Tll18GfGNUFmQ5hpdccgkA06dPB2D16tWAv6yVw8Rll13G3XffDcCpp54K+M+t+n7aaacBpcbTSnBLYoejAah55n85SUhBDxLVFlv66LtRDhdx5CFhbWRY0ia83BDDkAEjKI2rJW8Ja7tPrlixgoEDB4Yeq2O0TaJ/q6GaMbTr28gwNGTIEMB3etCzuN9++wGwYMECz03xgQceKDrn8OHDAX81oWAAGbIqMZI6CetwNACZSdikjf7DDz8cgMcee0znBjo33aNctvII2s5Twkp6aGtAm/BbtmwpCW4/55xzAPjBD35Q9H5YwEBa8pKwcuP73e9+B/iOIW1tbZ6OLiRxq3FBjKKaMdSW1Nlnn61zAf7zu2TJEsBvt4IampubPQk7atQoAK644grAHzONf9IKa8OGDZ5zTdTvxUlYh6MBSKrAbiAfSadzdnR0eI7icjkUsvgmpRexHbrjyFLCqn1ya5M+pFkzeG8V3CzHAuk3zz//PJAuWVxS2FbWEjZq9STL79q1a0us/1/96lcB36kgy2R41YyhHDlkDZZ++cQTTwBw//33A3DVVVcBfp/XrFnjrZh0/y+++GLAtz/I0izXxYMPPhjwVyJKUHfXXXd5K5AoN0YnYR2OBqDL68MGr689Mc1cOV0vcx320UcfBXw9Xft1J5xwgneMdCLbmiqdSm5/WZC3lTjkep7bnSTSgQceCJAoSSq8XqoxHDx4MG+++WboZ3YKUmXzV2icOPvssxMT5cniLyu6glXsayWsanWMk7AOR72TSsJmWX4h6OFT7v5rOUQ5Xgeum/s+rN2fIUOGeFZf6TO2h9OyZcsAf0+vnPPHlDKpiYRVmtlx48ZxxBFHAL6Du6RMJWOYRkffeo3UfRw6dCjgl0TR+EjSyhPt3e9+NwCXX355yTmkq0ov1rhoP1ZSXasNqw9qe2j7nIR1OBqAVInE4ySrPpPlNGp2VLC39iXDpGs5a/wo0qRSSYv2GmW1Vp/t9JdClsjjjjvOC4QWmlm1ailHstrf7WoOOOAAoDNEUom4FR4oK3glZFH9LYr/+7//K7qGUtZ8/vOfB3zpKP9g9RF8H+Inn3wSgBtuuAHwx13/Sv+dP38+EF40rdIxdBLW4agjUumwWufLsyNI0ppc7+u7QQtaVLrSPEpF1kKHDZxb10w8VnqQ9KJqqLWVOIiikezE3VmSNIZKkqa0NEGS7DDljJnKrEyePBno9DMOflfX0DnkeyxPqXJwOqzD0QhEBcpWG/xsB/VG/W2MMW1tbaatrc37e/r06Wb69OkVXzuhXZn1Ua++ffuavn37mh49epgePXqUfP7JT34y8f7k1cc8zm+/Vq5cWRLMX6v+VdvHQYMGmUGDBpnbb7/d3H777Wb06NFm9OjRoeOlZAcbNmwwGzZs8Po4a9YsM2vWrKqSDiT1US8nYR2OOqJqT6co3TWqrKJNe3t7SSRLntRSh+0qulKHrQW1GEO7APWIESO8SB4lZpNFXGU9VHalGgu5iNJha57532bhwoWMHj1a16v2cuW0J/fBDtv4T2OAqpau+MHKyV+hd3lSy0k3D9fKcnBGJ4ejAai587/ctQYPHpx4rJ2KJAu625LYrqmTBW/XJXGWrrPloKAPbTtmuXpyEtbhaAC6PLxu63WA2ut3W6/d0BKo0fsHb4/nVDgJ63DUEbXbT4mhFjOWw1Et3eE5dRLW4agjkpKwORyOboSTsA5HHeF+sA5HHeF+sA5HHeF+sA5HHeF+sA5HHeF+sA5HHfH/zN5/HrEoLtYAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1740, D: 0.2372, G:0.777\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1760, D: 0.2156, G:0.2283\n" + ] + }, + { + "data": { + "image/png": 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TX/0t3XTkyJGAsYirYqFqJF922WWFmunjJKzDUQfEkrD5Kq8XQrOyKnLLkhacreyZy56FV1llFcBULZfzv/Rl7eOOGDHC3/sK6QtQPSuxrbsqgFtePGkmBUjLShzVphNOOAEwulyS79pU25dYemlUmqF87RsyZAhg/MNtL7Wk/gXB69nPqXAS1uGoIYqyEmedwNLNpItIKks30HHSk7RHFaa7SEeyw+dsySsrZdAPM4a/Z1UkrPb3pIfZVFOHtbEryNvEaWshCav78OOPPyZsXTpjeNNNNwHw+uuvA7nJELQzoXEbOnRozjmUsnerrbbKej9tb7UgTsI6HDVEXgnb0NDgQe5sqegTScDQEy+YZZZaainAeDjJaizL7i+//OJLTklb21vprrvuAozlTt4w2ttTsPciiyxSMN1qJSSsookkRVpbW/17KL1b+9aySq699tqpXb9UCXvnnXcCcPHFFwMwdepUwNgS3n//fSDam2lBG/JeY/HFFwfiJ96zzp36GEa1d7nllgOMtRuMVV0edjZaochDTP7CDz74IAB77LFHnPaUJ4Ddrp6uH+ruu+8OGIdnLYXlkrbMMssA7Xlv9EDIBXLevHl2O4BcJ/WwicM+pqMEsNsOBDKglelaZTE62fdWuYpWXHFF/1g9rLYzi56PQln9Y7YrtTG0+/jcc88BsPnmm2e9H8w9poAGBTgEj0kLtyR2OOqAWBLWlqJxkFFJ0nLhhRcGYPr06YCRjplMpmAgtD1zaVP+008/BeJtISgUbP3116+YhJV0b25ujgzbKkd+pFIlrG30C5xX5wTgiy++AODmm2/2U91EOe/bhsNiEh8ceOCBAIwbNy61MbQdPOzQzSBS5+wVoDjggAMA+Pvf/w6Y7ZxvvvkmdnukDi688MJOwjoctU5ehUKzYTF1czQbSSeQ8i69RzlbZ82alSNZNUtH5R2W40TY7Gy71QmF5KXpoFAI6c0PPPBARSRrWujeRdknxJJLLgm0r4wKhcXZ3y0UZhiGnGLGjRsX+zuFsFdpUcEILS0tkZJV39WKQ5x//vmACRKIg9IeRT2nTsI6HDVEXh127bXX9gCmTZsGGAtgvlnRTumoY7Welxn/mWeeAWCLLbaIPNduu+0GwOOPPw7kJoGLkqb5qKSVOOzeVkKy5tNhX3nlFcCEfsVBqyIFZm+88caA2b6KSpkSpJwWVLuPpYQmaiUmm8fNN98MwOGHH17wu85K7HA4svE8L/IFePleY8eO9RoaGryGhgYvk8l4C2a6rJfef/vtt723337b69Kli9elSxdvm2228bbZZhuvubnZmzdvnjdv3jxvzpw53pw5c/JeM+zV2NjoLXCjjHypnUn7mOTVtWtXr2vXrl5LS4vX0tLiBRk6dKg3dOjQ1K6V71Wu/kW9MpmMZ7Plllt6W265ZeQ42O+3trZ6ra2tifsXp4+bb755or6EPcedO3f22yiijo16DRgwwBswYEBRfdTLSViHo4bIq8MutNBCHuS62skhOoi9vybLojxedtxxR8Ak4VJKkfXWW893wLbRPqa9HxgngXWedJVl12Hz6a4nn3wykCyYuYjrlzWRuPo3Y8YMoN3iH/UcnXXWWQCcd955aV4/awyjXGiLQeOkhOdKi3PaaadFnl8helFW5GJwOqzDUQeklkhc0lCWXHk2KbGanNzXXHNNIDfdCBiJKY8RlWpIk2pI2ErvuZZbwtrpXjzP8wuTKfmeUgIV8h0uxuOpEmNoJ2sr8DvJe64kJU2Ek7AORx1QVHhdEBWuUjkHSUd5I8njSYHK+VKTnnnmmYDxEIlLvv1Y21unnLNzKft/aVJuCWszcuRIPxmf0oZKqrz55psADB48OLXrVXIvPV9aWn1WyMc4yXX0DDkJ63DUAXkVDFtSSB8JSjIF5x511FGA8acMzBRZ3wkr0SEr24UXXph1XVuyK+mV0ndoRsvn6VSMH3RSFMcr4khaHSMLeFQ6lrTRvZblPw2uu+46fwdB0kb/SuIKFeOOSpWTBkn0Yrs9tr4ZfLYUYaZAdDFz5kzAJBJXgLus6HF017g6vJOwDkcNkVfC2jNDmCRTAjTN3MLOQCB/Uzux2scff+zvyR533HGh7dCsp5lMktVOgF0tFKkSJVFbW1tzokCqpeeGSdarr74agGOPPTbr/bg6uRJlA7zzzjsArLzyylnfraR+HyatlHTNTkWqtESKktGuxq677pp1rtbW1pxyHnouJVmjClAXYwmPIpbRqZgwu3333RcwAyfnaeV/VXjdBhtswH/+8x8gu95OEoLLmCgTeiXyEitIQZW3q0W5jU52gHsmk/GXlFpiDhs2DICXXnop7cuXxegkgaJnUM/vPffcA8C2227rq2LKIaY0MgohTRNndHI46oCyVWCPSiujTHKapcBs8cjZQoasqNzCcbAlrdrT0tJS1ep1laBS2zrBexy17CsmBLIQldzWyUeaS10bJ2EdjjqgbBK2GjQ0NPgSVQ4dktaio8zO5SRtCVsOKRmHagZw2PTs2TOy/k4pyNhl5+J2EtbhqANiSVhbWilLvUz41neAXDN+Kev8uGlQ41zDSdjaJ+4YVmtlkAZOwjocdUBeCetwODoWTsI6HDVEIef/uhO/tm4QpwZuqXTp0qWo6vVh9O3b13efi8LpsB2bYGGtuJZw4SSsw1FD1NU+bBxqfXaOg5OwtY+TsA5HHeB+sB2MpqamrMR0DkcQ94N1OGqI0uvXV4F8nla1jrxyRo0aBcBtt91WsWsXk46zWKqVsK6cETaVuJaTsA5HDVExK7FiXcPKfMRl1VVXBUwStmIop4XRTrBdLcplJc6XzUPP0bPPPguYbAy2BFWKGjulUBLSGMNlllkGMAWdhdIVKYFaGEqYZ++tK0mbXVqmGKKsxB1qW0fZ6PbYY4+8x5WynOooWwIKp1J4lU1afUyjf/ZENH78eAB23nlnoD1JwRVXXAHAiSeemPXdiy66CIBTTz016xwdbQyTZJNUbjHVhNp7770Bk1VUiReUi1t5o5LgtnUcjjqgYhI2rB7LgmtEfkdpXXRMKSljRDUkbCaT8WdZJSkLaRcAd9xxBwCHHHJIwfPa1cID50pFwmqJZy/9lPbHzgQZB2XZVN7iYogaw6i0RKWgKnaNjY1+dkTlx542bRoAkydPBuD4448H4NZbbwVMNs1ijE5OwjocdUDFJKzW99988w2QncvWJsQRGkjHmJOmhN1+++0BmDBhgs4NwKxZswBTyc3zvJy8uK+++ipgpKRWEbpPkhJXXnklYGbvTp06FVxppCVhl1xySQA+//xzwBj7ZPwLViCUoUW6YFTQ+O9+9zsgOpd0t27dfCkcRZpjqPut+//Xv/4VgLPPPhsw/fjiiy9Yb731AHjxxRcBswKRTr/RRhsBxqCl/Nm2MerYY4/lxhtvzHqvUB+Fk7AORw3RoazEIqpNaWyyl0OHVQJxSdwwNNsW0t2iau0k0c/SkrBKri0dVo4qa6yxBhAuLe2xs/XdPffcE4CJEycCuUny4pDGGNpODaoisf/++wNmu+f000/3P58+fToAK620Uta59F3psrJXLLroogB89dVXQDLHFCdhHY46oGyuiZtuuikAL7zwQuLvPvTQQwAMHz4cMHqGLHaa4Z9//vlSm5mIqKBjzaz5kG4nfUbnUGmIbbfdFoATTjgh9FqVqMJn90urAr2/5pprFjzHaqutBuCXX9E5JHUeeeQRwPS70oEOtvPNWWedBcC5554LGNvKVVddBcCHH34IwEEHHeT7CQwcOBCAESNGALDOOusAZoy1OurZsydgJGwaLp9OwjocNUSH1GG1V6lZOU0H8WL0n6ik5IFzZn2u4/O1W3t6X375JWCKhGlWVhEmWSRlVYxDNQPYoyqW6/00AgzKYYfQ3uobb7wBwNChQwF47bXXAHj33Xd9/VZeavKK0mpBnk3ScT/++GPA9DXJfqzTYR2OOqBDSlhVc5eUKZR0LAlpzs5R9y7OvnHc2bYavsTFhIVpz1l+tmnundukOYbq61577QXAE088ARir9rfffusfq8CGrbfeGsiVnDqXqs5/8MEHgNHjdXznzp0LBgg4Cetw1AEdSsKqLfJ00T5gtXXYkHMAuXpn4Jyxz2GjMMQon+OY7UtVh9U4SHLYRZrD+nLSSScBcPnllwOmpGga6V7TlLC63/fddx8AgwYNAkyYnQqSNzc3+x5ms2fPBozl/rPPPgOMNVj6cD4KrWKchHU46oCKS9jTTjsNMHGSkGs5tCVtKZEdNmnOzoEi0Tq3zgmYPcag7690l0L7j9rL0/HFesmUYwzzPTMXX3wxEB3/mtL1U7cSy1tJXlsXXnghAGPHjvWP0ZhplSBfa0Xt9O/fHzBW4169egFmtaTURpMnTy44jh06gN12fC9ntbFyGp3GjRsHwMiRI7PeD2ZkiAorHDBgAJBdmb6EdpW1PqxUAD2Iwb5UIkdTMWNoZzxRO/Wj03JWk6s+lwPQpEmTfCeKFVZYATA/UBmmtEQOGpeC50yyleWWxA5HHVBxCWtvUHueF6mAl2O2TlPCyviw+OKLh34eDNq304dEUUqf5aq56aablnVJLPc9Gd1+/fXXnDGslW2dqHGRuqN+DBkyxHeiUGCApPM+++wDwJNPPgmQU6ldz4ekeL7fXEDNchLW4ah1KiZh5aYl5V7Jx7799lt/xpEyry2APO0C0km9kUYf5ZJmb++I1tbWgulUNMNLX+xISdjiMGfOHAD69euX9zj185ZbbgFgzJgxia9VjjGMk/Hw5JNPBkxAi1LzKAnbU089BRjXRem6evaT4HRYh6MOKLuE1Ywlh2jNqJqtOpqFMZj2JCl2UjQ5HEyfPt138o/aVC+Xc0gabntRz8ibb77pf17IWUD3VZZVJZvbbbfdErer0BjGCfZPGoSw4YYb+uljtG1z5JFHAiZUT2llFKghh4piQiOdhHU46oCyS1jNLqNHjwbguuuuA4x1rrGxseDmusLspCuWQlL9p6GhIZXAY7tvSfddFSQfJ4g8bR1WupgkiZLMadU0ePBgX9pGoYB2BZArBajOkaQiRCVT1erZ7NKli/8sa7WgFYj+VVilglcUHK+VVqHkckGchHU46oCyS1h5w8hNy75er169fMlpJxvXzCWdxPa0iVNWwaYSs3OYDhV1n3VsmhXjipWwtkeTsEtOKPhe+4pBbL1XElShZlHHJ6GUMYzSy22d1v47k8n46W122WUXwFQWPOOMM4Dc+6FVx+DBg+M2z8dJWIejDih7fVglX7b3ITWLB/VS2ytGs2BwlgPjWXL//feXocWlo0Rql112GdDeD/VFe3TyFiokWdOsiFYIW7KqzXrflkrHHnssANdcc03Od2SjsBOGV7I+axiFJKtQwIm8mX7++eec9DFrrbVW1jkUiqc+FyNZC+EkrMNRQ1TM00nr/EsuuQRIp7BVMVSjGFbYPbYlTUfYh5VUlG520EEH2efN+lspUzbddNOcfWvbMqrvyiouK3kx2GPY0NDghbUvH5KgSt8iVBpFqySl1n399df9xODyaFJSdHl3ydIdtRpKsrpwOqzDUQdUTMIm8atM6luchDQlrNKJKIDZJugvbc+udgkLfa4iWRtuuGGxzSp5H1bWYKVtjeP1deihhwKm1KKNpLdtlyiGNK3EKr+hZOC6//LIO//88wHYbLPN/MThKlqt1YI8m+RjLN210N50PpyEdTjqgA6RcaKSFDM7R8VM2vqnjlMqkFdeecV/f+rUqQCsssoqgIkHXn/99YFcS3gppO3ppLZpVaB+KtNCnz59/HbLj/roo4/O+rvaifSi9MfVV18dMGP7hz/8ATAeWV988QUAhx12mF/8Sz7CSyyxBGDiY0eNGgUYG0ApdOgUMZWkEkYnBTprS2v69Ol+3l6Z/qUaFFPFvBBp/WDtLaVybjGpbrAMO/lIcwy1vabJR045CkKX0Wnu3LkV3ZJyS2KHow5wErYCfezZs2dO2pByUs3aOkJZH9PIQ2xTjTEMo5wS10lYh6MOKLtroqN8NVDtvLcdiXJI1o5GNdwrnYR1OGoIp8PWeR/rvX/w2+ijcBLW4agh8kpYh8PRsXAS1uGoJRRcHfYCvEKvzp07e507d/mxiYgAABEOSURBVPY6derkderUKfK4fv36ef369ct5v1evXgWvEfe1IMwq76uYPjY1NXlNTU2ptbOU1+WXX56oj3HOudpqq3mrrbaat9BCC3kLLbSQP5ZdunTxunTpkjPWuh+NjY1eY2Ojt0CHzHplMpnQ96NeuubYsWP970ado5gxjPvq37+/179/f79ver9bt26pXaNv376Jn1O9nIR1OGoIZyWu8z4W6l9jY2OOU3+hwlD2M+N5nv9dYZdtLMVWonPLe+qnn35KNIbBwIM0bDZJk5AnPR5yn1P/XLHP4HA4qk6HkLB2hEQ5+a1J2AV2hZxyEcFUrFEpPoUd+heVyCx4rF193k66HRXInslkIktXhvVvwXcqMoZp+kcXKifiJKzDUQeUJGG7deuWqPxAWsQtjhxGKbNzOSNQbEpZdSTRYTt16uSn63z//feB3P7ZvtB2OlQofG9sCat/wySMLeHlM60UsUnHsHfv3v53K8m1114LmGD+JDgJ63DUAanrsKutthpgih8J2xJZSurPJJa+EH2oZP3n8MMPB/DLD4q33noLgHXXXTfyu3aGCd2XNGMr80nYsOvoPUk2FWdWNgah1dRWW20FmBQ4QT1UScVPOeUUwKRNkRQfO3Zs1jVsGhsbcyS4kvAp00Vra2vJYxh1v3UNpUGNs4qzz6WiWHPnzg29RhxqIkWMHl67ts52220HwNNPPw2Ym6h6nOeee67/PduIYdeLKYfBYumllwbiVaJTpQP1QT8K2/CjcbEr+2UymYIPQJwlcTAPldoyfPhwACZOnJh1beWoUhbAfNsUdtt0HWXDV/U91UU6/fTTAbj00kuB9jQ0ao/GbOGFFwZM3t9qGw412eiHqT7qR65nTsvwjTbaCDAZGcPG0Fbz3JLY4agDYklYVSBTDtc0WX311f3q5Jq5tKR8/fXXgehtBi0vg4myRFTCsKjZOWopnw+1y17mSzJIeh566KF+XuYjjjgi6xy28eXggw8GYNy4cVnnDENLRVUEF2ESVpn+lllmmazzNjY2+ln61W4ZjpQVUONjqyr6d+LEiX4+YkmX0047LetvXVe5jt9++23A1ElafPHFAZg6dap/HeVHljQOrD5Cx7AYB4W4NDY2+v21l8m6b1HqntrVo0cPILvqYtTS3ElYh6MOiJUiJg3JGjX7vffee4nPFWWc8jzPr9GpGTsuSSSruPzyywFTj2XGjBkAfPjhh0C2fnbTTTdltVlSS7O17sujjz6adZy9Urjqqqs488wzgXgV6XVvomrZtLa2Rm4dqU2SHPfddx9gau4888wzQHtuZUmPfAY3MKsh1eWRbqv329ra/Pu44oorAmYlUWg7rRySVbS0tPDkk08CsO222+Y9NsrxQ++3tbUxfvx4AHbeeedE7XAS1uGoISpmJVb9GSXSjoO2EaRjCdUlvfrqq4FsSZDEggrF9dFeLSgJtqSE9NGgw4Ey/Q8ZMiTrXMomrwrldo1RYVtK8xHHSqw+dO3a1deBoySUpLMcAXbaaScge4sqaitO2znvvPMOYHRp9Uc1fjfddFOg3ZagVYXaJQmr9v3yyy+hY2hXzCsG2RBuv/32os8h1Dfp6Vol9ezZM9T5BLKksNNhHY5apygJG2bZUg0SVay2JYaOVW0Z1ZoBMwt/8skngEnbqe8MHDgQgJkzZwJGOhRT3a4UCSu9U/px0NIKRrKqXXLXa25u9md/7SnLgnzXXXcBRleVLqi9z5deeilu83ziSNgw5/Pnn38eaK/UBtm1cxacFzCVCDWGTU1NfimSzz//HDB6ryTqYostBsCpp56a9a+qmQefB61MonTWpGMYDCHUsyV3R+nhW265ZVYfw55x9cl2p7R3B2xXTttpKA7OSuxw1AEl67C33HIL0F7dKwxZ/MRyyy0Xdp38jQxY14LY3kB33XVXTtVwm2IkbKH9PVXxlhTRLC6pOmPGDH/lIXSsqqc999xzgLEa26uGJAWo8klYSUtZhltbW319UhLV5uKLL9Z5AeN2GKxqJ4vu119/ndVee89Weqmq2kkiq2rc+PHjfdfHqP3MYlwTpXc/9thjoZ/rHvzrX/8CzApHY9m5c2d/X9hmr732AuCRRx4BCldg79OnT+S9Fk7COhx1QCwJK2mgPc4kRJ0/Sb1Q6XWSCpJCkmzSqWbMmOHrJnnak5ofqlYP9qpBwQFafUCulJYX0ezZswG44YYbAGOllEVW0iwJwT4uSEznl75UKcyg76o9FhozSXl55kg6Sv8NrgLs/kQhnVZ7+/KzlY/xlClTfH0+Tv8gHU8n9Vkhhlr5JHlON954YwAmTZqU1Q6V0JRv8SWXXMLJJ59cqD1OwjoctU5eCavZOUl4kCIY5AdsSx/bkpZvBouK/BB2dEnwmKg2J5WwYZEVmkG1t3jUUUdlfS7rcO/evYH2mdXWayTh7P04OzqnGIJ9XJCeM8ffONgOO42LfMefeuopwPgBC3n8DBs2DID+/fv739WqJ9AWwEgb6YF6Tvbee28AbrzxRqBdemtFJau87on+nT9/fsmrJK0Odt11V8B4cam9xx13HAC77747YJ61IFpp2L7F9nNaTOIDJ2EdjjqgKCtxkhQtWrfLChc4d+R3otokKSXpLL1MXkSLLrpoZGB04Nwlz84hMbaAsRY+8MADgNG9f/311xxJY+tZ0s8l3XS/5CEmCbDffvsV1NHi7MPq/D///HOO9Lf9l+WzrOB06V86LiyNqPZ05cGk50AWYFnFR48eDZj96Ntuu40xY8aE9ivKCyiJv0DUZ1pFyEosD7Qkz6l8EWRHkQTWvdZ97NatW2KPPL/dpW7ryIlAyxe558lcr0G95557ANhll10AszRqaGjIWSZHLYXtPEcHHnggAHfffbd/rO0QoOvoZhXzg5VTgBw7hO3cveGGGwImE4Pa3dTUlLPstJ3+tY0wZcoUwEyKO+ywAwD//Oc/CzUz2C6/jwsqFuRMrsGQRT2kupZC32RI0vLxnHPOAczyWvf4mmuu8bdl7rzzTsA8Dwqzk2OEDHJ6qPWD1g/kl19+4aKLLgLg+OOPB3LDO4sZQ/sHqv7LzVVLc21Pyjlf22C9evXKcYgppLKJMCcVe5lcKNGCcEtih6OGKLkCu2ZSoSByhU8pEFvpM/bdd18g2xgR19AiySppHeYkYbuN2SlZisGWrGLrrbcG4KGHHgKMG6b6Iwm1zTbb5ASq6xjNvjLSaRmteyHDVhzCDCNRBpHgslpueVoNrbPOOgDMmjULMJJORjQtdw844AAAVl11VV+yKl2O+mEHtktlkeFKDhRrrLEG0B5uedJJJ2W1WZJVKkcx2M+W+q/tpO233x4wKxsFPAQlc9K8W/lSB9kGKEnWqGdNOAnrcNQQJUvYKDRT3HvvvYCZlTTjykFghRVWyJmxJLWjNtAVthQWohQ1C+bL1lgs0v0UMid97cEHHwSM7jVhwgTfgCMJo8Dll19+GTAzriSTVgZ2lsV8BLe3RKEs+sH3bKd1uSTK2CTDl0LPlJRtypQp/nhLJ5WOqtBD3QsFhSy11FIAbL755oBxqPA8L8fuIGTMS5PLLrsMMKlrtPJRiJ50zc8//zzn3kkvl0uiTT4HkCgnD9lLop5TJ2Edjhoir5V4wIABHpg1eKF8wRCdk9WeUTT75NtMVvoYzWCaDaXLBpzBI89hU8jCKJ1m8uTJsc8pora7evbs6UsNmf7VZklQOYPLiirJq+OTEGYl1n2XBXuDDTbIuj60O0CACem7+eabgXZXOsi1lksiDx482LeuDho0CDDpcyRZL7jgAsA4VmjFoXBLbfvMnz+/YD5qewxHjx7tgVm92bme86Ex0/2RS6LGXxZxSdowrrvuOsBYnG2pWaiOThjOSuxw1ANeRKVnL0Zl6+uvv97/f6GK28OHD/eGDx+e8/6OO+7offnll96XX37ptbS0eC0tLZHn6NWrV2jFdrtSeL5X0j527tw51nkLvfr06eP16dPHu+KKK7wrrrjC69atm9etWzdv2WWX9ZZddlmva9euXteuXb3m5mavubnZv5/q88CBA72BAwcm7mOhcRkzZozftn322cfbZ599ciqgqzp6v379vH79+vnfVZXynj17+u0fNmyYN2zYMK979+5e9+7dvYaGBq+hocEbP368N378eG/+/Pne/Pnz/f7PnDnTmzlzpte3b1+vb9++XiaT8b9jt1XXSzqG5557bsF7pr7OmjXLmzVrll9l/phjjvGOOeYYr7W11Wtra/Pa2tr85zXpM2BXdQ97qe9Rv0knYR2OGiKvDrtgNsip+RmVQCr0Agv0iH79+gH4XiwjR44E2nXZqDZIz7WvF5UkPA6luCZGWfZsS2yYzmLXWNXf0svPOOOMrO/Y6VmSEOyjxjAs1aaNLNcqKSGLtspoyH1QYyhL/5w5c3L6p34oJFP7mjpOnnCyFuer+WM/H/YY2gEOxTjbK4RUoXB2+t0lllgiMnRQ411MWJ9LJO5w1DGppTmNsgJLCioQWxbHqGrfYGYb24JX6JpxSDOAPWp2tIO59913X9/zy04CICup/KS1t6lzyptL0i0O+Zz/7ZQtnuf5XmgKhZRlWgmztZcuT64JEyYARio1NTX5CfLkE6xVkSzmWlE9/PDDWW3db7/9AOO91tbWFnlftcL79ddfix5DO5GanRxB6WWVFCEMSXKNXSHvpGJwEtbhqAMSSdiwme+OO+4ATKCvvGJUTsLeK41KBQm5Ra7i6iJJyj2mEekRxf777w8YaaHjm5ubcyI81EdJJnlLRYUHRnn/hBHs4wIrt3/f99xzTwD+8Y9/+MdLX1ZaGgXk676q7dpb17/yLQ6ukjSGivBROtPzzz8/61g73FBeTGPGjPHPH7VyKmYMpUOrpu8hhxwCmKRxdthjmM+B3lM5kiR+3hBdvCwMJ2EdjjogNR1WXjKasZL47Mo7RsHaUR4qOrdicItMtpWaDitppwBlRXw88cQTgIni2WWXXXL6KJ1JqVClW6mQlnTBYgjTYWX9lAQPrnz0fzvdaXCFAEZCSC8NJsbeYostAFN0W3q8+infXOnHknh29FKnTp18u0eUjSJqDPXc5POAi/KttiVqmITV/5UiaJNNNsk5P5jYWvmUJ/FwEk7COhx1QMnROravcDHIBzNKsqq8haySaUbcxCFqprelpLJGXHHFFVnfA2Ml1TlGjBgBGImbz2peCtKVFTUjgv620r21IpC+LL1XqT+lu9kRLU1NTb51W7sD9r1SQm677IfQ8cE997gZHezjwyRaoftbyE8+6NMdJVl1r3Uf1Q47U0oplK16nZZROr8CtJWHScure++9l1GjRgGlVR2LS5pLYmXz1/aN+iojjd4fNGiQH74VaAdQnpqm+VLEhKVKiWqDjH2qfSRnB/1wtQy+7bbb/M+KWf6BSS00d+7cyCCKQjmdkgaYgwmRVJZE1efV2OoHvuyyy/p9LJYk7XNLYoejDqhYfVihRGVyf7OuB5gQvUIZEIshTQlbiHwz6uDBgwHjKJEmYUYnLVXt5G/5sFUB9ScqH2+QF154ATBpawq5c4ZhO87ouqU4TthEtSOs1qzuoY61+1+MqhZl5HL1YR2OOqDiErZSROlBlZSwlSZsdi5UH7atrS3HQaWQpJD9IXh8KXVtgjQ0NIQ5wvufQXHV6zoqco1U/SLhdFiHow6IJWFt179iUl50FOpZwoowHVbhjbJWB3WmqO0TBQXYs39YOKEkq3Q/u8ZO1DOTTy+2K5brs0KOE2qXtpAK1WLtiDgJ63DUAXklrMPh6Fg4Cetw1BDuB+tw1BDuB+tw1BDuB+tw1BDuB+tw1BDuB+tw1BD/D/5eqbm7nV9DAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1780, D: 0.3536, G:0.07158\n" + ] + }, + { + "data": { + "image/png": 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trN+K7ov+HjVqVKCo2p0+bPfxKLbwKMY91H5J8jn89a9/DWx3/7rEYWubwhpGBVB0hdXI9dNPPwHQs2fPYPfuiRMnAs6u2XfffaM+fVFG56FDhwJux75U17icIp18T3Y2XtxslVR2sY7dvHnzjNemGPfwscceA+Dggw8GYNasWTz77LMAnH322YCzt7UyESWmsIZRARRdYVONvPr/L7/8EoC9994bgDlz5iR9118Hy4dijM5PPPEEAAMHDgRg0qRJ9O/fH3Btj3MNNV+FVYSO2vj9998Dbt/e/fbbD4BtttkGcB7gX8+p82VqW8rXtf6sGViGYxQ9ljhxW1StXmhmqM9EuYWqKaxhVABFL8KWTtGXLFkCOGX11Vgjf6l3x87EiSeeCMAZZ5wBwIABA4I+KIa6EDp27AjAggULCj5WIn6EzsiRIwG4/vrrARedJc9pPoTZtrIDlyxZEkS8lSJpXTMfeYITZw76/9atWwMwefLkpO8W4/doCmsYZUTRbNgwVVywYEEQMzxp0qSCzpE4CoYRp/2zaNEiANq1a+efk1atWgG1ETNxk6sN68dfxxHLG6Zc+l0o8uuGG24oqZdYs7hOnToB7jcpuz0RXa+NN94YgBUrVkTVjFAbtmhTYv9B3XnnnQGYPn06Tz31VG1jvAX6MMKC30td98d/UEePHg3UBhEsXboUyP4hKGYA/H/+8x8Axo8fn/L9ddddF4Affvgh73NoGuknuj/88MMAHHLIIUDtcp+cXQpf3GijjQAXUBMnfrBOqgdVv2UN0Gqvj0y7bbfdNrL22ZTYMMqI2BS2UaNGQHgwt9KYnn/+eY488kggdaoVwNtvvw3ArrvuCsSfVpYtw4YNA+CBBx4AnCL4I251dXVGpdRUTLWeiqGsom/fvknnvOuuu4BoVf6tt94C6obznXzyyYBTWD91D4qjrMJ36ClZYebMmXUcZptvvnnSd/3rFaWyClNYwygjInc65Rrcvf766wcGvoLl47RF61vFvVGjRgEuhVAUEiSfb+CEfy4FsLz00kt5tyXbc6m/9913X1DkLM13Y7+HfuXImpqawEcxduxYgCAYJg4scMIwKoCSp9eB8/oqLM0PMg8rapVPqGKco3M6u1196dKlC+CS9YXsd6Wn+csfuZCvwuq6+zZrRKVJARgyZAjgEj3yOUepZklafrz11luTXlcie2JCf6GYwhpGBVAyhdXaWrdu3YJkbq3V+UEWN910EwCffvopALfccgvgUvRy2RemmKPzc889B9Tagn7yt2YLShpXXw866CCAYG06Hwq1YaV2p556KlBXUVKhviol8oILLgDgsssuSzp22Lnqu8JWVVXVmYE8+OCDAAwfPhyItviaKaxhVACxK2zPnj0Btw2HUuhE48aNgxAvrX1p/UpFr7bccksAPv74Y6CwguJxjM5Kdh40aJCOmfT+hhtuGKyv6npffPHFAFxyySUpv1MIUSWwq3Spwim1RqqZTSKaFcmf4Kuz7r/WOQtZ4y21p199VJ+0HqukBSWxFIIprGFUAJErbJjnVio5b948AJo2bQrUrj+qYJdsAT9AXqPy9OnTgboxu7kQxegsW8bftKtfv36AS7tK5XWV91ceZR8Fwctuz4d0CqvrrtlLIn57M6lfquRu3Rvdf5U/DftuPsSpsGHr382bNw9+l1JSeY1lp4vDDjsMcHHS+WAKaxgVQOQKK6+n1EebQKmYlew90bBhwyDrQWlKGrG32morwNmuURDF6ByWKqgC6L6d3qBBg0BZ/RE87hKZfv/SJaH7BQLURsX/6n1lWmnX98TrEKZQ8uyfdtppadveoUOHYDUgjFLZsP4909+zZ88GoHPnzlGeyxTWMMqd2L3EilKSJ03ri5tuuilQ64lUrmiLFi0Al50hpVLx7XIpQu3nc86ZMyew4aVWUjFdhyjLvUTlJfazUj755JOcj+GrdZj9n2O7SqqwyjTTTCOhHVGeyxTWMMqdokU6yXs8ePBgwI20Y8eODezeXXbZBXCxmXFsGRnF6Jyr0r/55puBJ1ylMeMksY+NGjWqAeedToV8B2Hrh9pCRTOhdISpjNZusyljmoliKqzs0vfee6/Oe6rAoZlUlIQpbL0I/i8mpV50LwZRTYmLtQ9trtSXexhFnewwbEpsGBVA/Ro6jXpFfVPW+kYcypoJU1jDKCPsgTXo1asXvXr1KnUzjCywB9YwygjzEld4Hyu9f7B29FGYwhpGGZFWYQ3DqF+YwhpGGZF2oW1tsA0qvY+V3j8onz4q0V0F6tJhNqxhVABrjZdYUTurV68uy9E5F0xhy5ejjjoKgHHjxpnCGka5s9YorKjk0VmYwpY/ZsMaRgVQLx7Yqqqq2DYwXrNmTZ1iaUbpefjhhwsqA5orN910U0GlY6E2PzhdIYBCqKmpyaogQr14YA3DyI7IbdgddtgBcGVeciFsu8Yoiq+Jtc3+KXb/VFxNW3SccsopSe+r7E/jxo3zPkcU91C5rKoaUd8oixIxass+++wDwAsvvAC4KvWqWl/gOUr6wKoSvqrHx0HUD6xfh3nVqlVAbTVIqB2k/eqIeiD1YERplsRxD/29gHyROPzww4Pd6hLO67cr5ev5YE4nw6gASq6wa9asCUZjVaHT1HinnXYCXGV1Vd7zR/NcKIXCHn744Tz00EM6f9J7/mgctqtALkStsH6btbvgzJkzgfQzn++++w5w9amjUKFi3MPNNtsMcPvedurUiW7dugHw9ttvJ312//33B2DSpElqn9qV9/lNYQ2jAih5la0hQ4bUUU6hEXzKlCmAUx3Zf3fddRcABxxwQPCdOPesyZZHHnkEgKFDhwavab+YTA60+rQEpTA51eTt2rUrUPca//jjj8HesX7/1ltvvZTfqS906dIFgDFjxgBuT1zt65SIr6zi6aefBuCrr74C3D66ItWsKd/rYQprGGVEyWzYM844A4AbbrihznsrVqwAoHfv3gDMmjULgFatWgHU2c1cCquRLh2lsGGHDBnCBx98ADjPqj/qRmG7ivoUmqhlE3/3vkKI4x5usMEGgNttMV2RcM04ttlmG4BgVwftnOD38eijjwZg3LhxWbfHbFjDqAAiU9hMtpm8hN9++23o53SMPn36ADB16lSgrurceeedAMG62LPPPpttM0uisLvtthuvvvoqAC+++CLg+ljs/WHjQvdIsx/txyN7TrMm/V3ILnaF3EP/d6qdEjfaaKOk1/U57QW0fPnyUDs97nuYiCmsYZQRkXmJM62J+sqqHetWrlzJHnvskfRephFr+PDhAJx33nmAWys77rjjuPvuu/NpfqyMHj06iNrae++9AVi4cGEpmxQ5Uh95/P3Xwzynxcb/nUpZRZh6brrppoEaa4Ynr3kmtAOeoqjWWWcdevbsmU/zTWENo5woupc4lYrqNdk9soN8XnrpJQAOOuggANq1awfAhx9+mHScDOcvug3bsGHDIKFB3mJFzcS9B24x+jdixIhAPXyKYd/l00fZpoqLVnSdvMI9evQA3G7rv55X58vUPoBgtnf88ccDrvjaFVdckbF9ZsMaRgVQNIXdcsstAWjbti3gopeaNGkSjHK+nTNw4EAAnnjiiaTXFeepFL727dsDtXayonPGjx+fsh2lytbRdZ4/fz5QG5sa47mKqrCdOnVi3rx5aT8TpdLGeQ/99L/E9fFcvcJhz9aRRx4ZJO+HJcSbwhpGJaDSFKn+A2py/a+qqqrm1xEv6T/RtGnTmqZNmwavH3PMMaHHqq6urqmurq5p0qRJTZMmTYLXGzduXNO4cePg70aNGtU0atQo7bES2lFwH3P9b+nSpTXTp0+vmT59euzn8vsY5XFbtmxZ07Jly5pciLt/2faxQYMGNQ0aNAj+XrNmTc2aNWuyPmebNm1ybueyZctqli1bFvzdsWPHmo4dO2Z1XcKeyciD//1pgKYUHTt2BFwy+oEHHgjUJqs3a9YMgNtuuw2AZ555BoABAwYAzjEgtHTQoUMHAD777DOgdhrsh5RpKp5pyhYHCq2cOXMm++67b07fVarhuuuuG3m78mXZsmVAdmGU9S3IXyGHrVu3Buq279hjjwXg3nvvTfn9xYsX51z5RIkEupf6TU6YMIEvv/wScCZiWLUVH5sSG0YZEVt6nQIlFJLYvXt3wAUMaLS67LLLAvWTOvbv3z/pWFq+0YJ1LknfpVBWMW3aNKB2mUpt9kPhxPfffw8QzDbqk7L6XHPNNXVey6Q+pU6v0+/QJ5d2ZVJWOayk5v4xFZ4px2gimZRVmMIaRhkR+bLOoEGDAHj88ceB9GlKUKuOCt5XqQ2NVLJhVXpDaJ8cLecsWLAg6/aVYlmnc+fOQZmbhHbEdr64l3XU9lQzm0xKVUjQf8L5876H+h3q/HEqvn+Pda5mzZoFM6o037VlHcModyJTWHm5Nt10U8DZZPKGhXnBqqurg1FPtqpsAHmDly5dCji7TyGMKrviB1yko9SBE5MnTwZcUEg5hSaqD1LJXr16AbWlUxQQomTubNE9lMc/y3bkfQ/937s/WygEHWvGjBmA89uIXNTcFNYwKoDIFLZfv36AUxCfjTfeGHBlNJQa949//KOOPaFiVwqQv/322wH4+OOPAbj66qsBeO211wDYdddds21mURVW/WrVqhWff/454ELeZIcrNG3PPfcE4OWXX1Y78z5v3Ap75plnAnDjjTcCtbMmzaB8EvblBdzsSNcjH294FAqr31oua6tDhgwBYOLEiYDri9anMx3DFNYw1jJiD/6XvaPzyGusgmpLly4N/l9rlCnakfLvfFSoFDZsu3btWLRoEeA82orAkp0uJYrCexy1wl5++eUAQbRWuuRrf1XA788mm2wCuJKgYasH6YjjHuazHuv3Vb91vV7I2rMprGFUAJFFOikG85hjjgFcIWbZcf5IqhE21egTNiLJ/ovDsxoFYcp/ySWXBAnRm2++edJn/N3TZOPL5i8lUoy5c+cCbosOP4otEf8+65r4NvtFF10EwN///veom50WlczV71PlTdVO2dSK/xU1NTXB9ZA9PnLkyKTP+FvIxLHGawprGGVEZArrF9/ab7/9AOfZ1eij7Su0ViXlATciKcJJ5VQUf1xflTUTJ598ch1b3ldYvV8flFWojIoURW1Opaz+7MJXmcGDBwNuG5M4lVXx4/ITJLLFFlsArm/33HMPAMOGDQNc/ECqonL6Dfs2qnjyyScBt8YeRvv27YMMs1wxhTWMMiI2L7HyXmW76F8hW6Zhw4Z11CasTb4dlA/F8BL7a86/nlfnA5ytp7+jLPsZtZdYbZ8wYQJQW+Lk12PX+UwY9bVEjNqtTBrZtLmg6D5FbUVhw5qX2DAqgIJtWHkOtcmTcllbtGgBOCXxY4ilplOmTAmKa2capQtR1mLibzu4cuXK4L3DDjsMcHag742sj/hKIYUVqe6b+llfCrv7a6b6W33bcccdgfAtJROR/X3xxRcnvV6MnN/YpsSqU3vqqafme4hYKFXwfzGJO72u1ER5D6NI94sDmxIbRgVQsv1hS4UpbPmztt3DRExhDaOMKPiBra6urnfzf8PwadGiReAILWfsSTOMMsJs2ArvY6X3D9aOPgpTWMMoI9IqrGEY9QtTWMMoI9KGJq4NtkEcfcxUPD0fst0sCcyGzYVsNvaKit///vcA/Otf/8r4WbNhDaMCMC9xifpYSCG5TJjCli9vvvkmAD169DCFNYxyxxQ2pI9RK6B/vLDjq3SJX3InF0xhyx+zYQ2jAohtQ+dyJ2rbMmwTJp98lHW33XYD3NYlRl1GjRoFwJVXXpn3MVQMXpu2RUm2ye+msIZRRpgNG2Ef4/T85oLZsJlJVwwd0q97H3XUUQC88cYbAHz44YcZv5MrYTZsyabEqv/arFmz4DXtu/LFF1+UpE3ZErbYXowHtaqqquQDQjmje+c/qO+88w4AO+20E+D2uV28eDEAkyZNCnYYFPvssw/gam+rbrYG7jgCaGxKbBhlROwKqwqCG264YdK/Y8aMAWpVStUQtXdOmIIl7niXiTZt2gBuhIySTGFsqpa4atUqAJ5++mn233//tN/RaHzbbbcBtbsFpCJRXRNrOxvZ4d87/dbGjh0LuOu70UYbAbX3DqwnTeoAAAo8SURBVGqrg/oF255//nkAvvvuOwDuvPPOpGNkswdtripsCmsYZURsQ7NGE6mADHKNUrJdq6qqWL58OVB3R2ufbJRVxKGsPlJxORk08srt/+677wJwxBFHBDML9U0jqr5z6KGHAm6vF3H99dcDcNZZZwWvjR49GohfWcOUYfbs2QBsv/32db6j/vg7HNRXjjvuOACuvvpqALp37w7A0UcfXeezfikkXZf11lsPgBNOOAGAyZMnA+4a6H05ulq1ahXshZxrDWNTWMMoIyJf1gmzP8Nc3uuvv34w8vg0bdoUcLZg1LuTQ7TLHlKT5557DnA7+AF88803AJx00kmAG9G1m5ro27dv0jG0X8tpp50GwOOPP56xHcVe1mnUqFHoUoY8p9pvSLOpQsj1HjZo0KCO0vu/0yFDhgDw4IMPAm53g4cffrjO8RTcIiXVTnQqmj98+HDA+Wnat28POFt3+fLlGZcALTTRMCqAyBQ2bMQI25VaHt9vv/02sOtk50lR49ijpBCFPeOMMwC48cYbAdc32cvyLKayO5955hkADjjgAMCt3Wm9T/a79m3p1q0bAPvuu6/anW0zY1NYefGlmpttthlQu3+vVEfJC9pPqHnz5lGdPqCQe6j2aFb36quvAi68U9dZO7G/8sorAHTu3DmID9Du7drxbq+99gJcalxCu5KOmQumsIZRARQ9NFFqKkUBt26pPWV9z3KU5DM6+2tlHTt2BGDBggUA3HfffQD85S9/AZxNkxjx4nuFNVrL09y2bVsALr/8csCVEXn00UcBp8Sp7DGxzTbbADBnzpyCFLYQZXj//fcBF0mkvVOjJA4/hPqs1QspsGYMI0aM4JZbbgFg6NChgPtdjB8/Pu2xFcE3ceJEoPZe6zcUhimsYVQAkSusVHG77bYD3Fqkj7yeAwcODPaHlQ0o+0frjNqzs3fv3knv50MUo7NUUjaMRlqp5LRp0wDo2rVr8B1fpeWdvOKKKwA4//zzk87xySefAHW9yNkQlQ1bSIEy/a40k5oyZQrg9mEtZA057B5mk/yvPmn/2nvvvRdw/pOXX34ZgCVLlgDO5wDuOmhWpPvvn0+/19NPPx2AJ554AoBzzz0XcN7jXPoYtD/jNw3DqDcUzYZNZxf59p3/Hd87GaX3LVMfq6ur6yiM7I8//elPgEuzeuCBBwDn5U6HRvDWrVsD7hrsvvvugPM8XnvttYCz67Ppc31Ir1PKmeJrlTiumYYiux566KGcjx3FLEkzPG08rqgtqaP8KkqlGz9+fJ2INt0LxQtorXnhwoWAmwn6mUFXXXUVf/vb3wD44YcfUrbPFNYwKoDIFTZM/fS3bLVddtkFqI0w+e9//wvArrvumvI7UW5nGcXoLE+oFL9nz56A8/xqpJVqJuawPvXUUwBB9o6ul1RcccJ33HEH4DzRIpXi+xSqsGH3cIMNNgCc7ZYO2d4zZ84E4MILLwTcGnYh5HMPdU80s5k6dSpAkOMq5Vekk66BZgK//PJL4G9QX3yllf9Gv4vzzjsPcPdUkW/PPPNMxhiDoiWw+zdZDZPz4a233gJg8803Dz4f9qCq4z169Ej6rp/mVGw6d+4MOHe9grvlNFO7FCzRv3//jFPZU045BXBTyJ133hmo+8Cmelg1YLz++uu5dSSEsLb6D2qiqeJ/R+3U/fenhQpRjGLP1mycYxo4FPQgh6ACKTSN/cMf/gDA/fffDzgH06WXXho81MLvox+eqSW6yy67DIAzzzwTqE2Sf+yxxwA4+OCDAejQoQPgQlFD+5r2XcMw6hWxOZ1kxMvlPW/ePMAFQF9zzTUAXHzxxXWmYJq+6G8lwSvYun///vk2K5IpsaaGUgmNypr6aPagUbpdu3aBCaDgEF0XLXupNIlmImEJEdkQldPJL+MzY8YMwKWgiU8//TRQCKmMUvC0jKN7qVnHgAED8m1WJPdQS2+9evXSMQCXInf33Xfn3C6ZRFrCOuaYYwA3VdY5/RllKszpZBgVQNFDE3U+2TQrV64MUskmTJiQ9K8WsxVEryUAOXNUOCvH8+c9OmvJRbai1ESqoZA12Xpy9//444+hToZJkyYB1CkhoyAA2Ym5KG7Uyzqy77ScpSUm2XQrVqyoE+Tfp08fAGbNmgW42UgUu8QVcg+VqKHrqiSUbNoVFpjhF15Q4oD+VpCI/m3SpEnGpT9TWMOoAIqmsBqdtSA9Z86c4D15+cKC2sPUKTG9K1v8katRo0Y14FQjn6AM2SiyvT///HMALrroIqDWq3z44YenPUZYH6XqSvPKhqgVVstYe+yxB+CWPo444gig1kucaQYQZapkIQor1ZMfQmWHdO8SZ0U+WSSdA+EecP32s9ndwRTWMCqAelH5X2FsW2+9NeBGKv0rm1HrslqzTFTpbCnmzmey8ebPnx+kmck7rj7465P+mqIfnpnr6FyM/ml28uv51IakvzXbUIJ+IRRyDzMl1qdTUX8PI9mk+o7WzFWETzNGXadcZhmmsIZRAdQLhRWJoXyJ/ya0p+BzxKGwsmEVKK6i1B999BFQuyat2YGPvJZKzYuCqBXWT+qWYuh1ra3/eu6Ux6gvNqwKoqnMqLy1mrloxpPqfvlhpL4PRceQrZrtjnSpMIU1jAqgaAqbrki4RmyNXIqw0XqWRiiN5IkbaOVKnDasbBrNFHxbHFxC9AsvvADUTcXzt/nIh2Kn1/3yyy+89NJLgCsaF4Yii6ZPn573+Qq5h0pYHzduHABnn3024NbB5RFXvLhmTytXrmT+/PmAK87ge4N9JZU/Qr/9XArhm8IaRgVQL2xYP/vGb5MfaVMIhYzO2ZQgSUWfPn2C+FKpsJRI0VuKuImCUiSw+95teY6VwRRFAXGR6R6m8wRr5qJ7qYwrpd1pdqTEdmXPKPMGwjewUvyxivHpmELn7NSpUxBrnW0fhSmsYZQR9UJh1QaNfrLjhGxWxRYrkijPc6UdnXOJdArLw9QxpC7nnXceI0aMAFymy6JFiwCnPDqWRnIl+ufSnoR10ZKXiFE+r8rn1BcvcYpjJf2bTzuVw6wtQqPI1zaFNYwKoOQ7AavoFdT1kCoqRiNWIcqaLamUTHm4/fr1S3pdebA333wz4GwUFWeTSg4aNCjwRl533XWAW7PVKKwi4H6501ximku5taN/77RtRRzbrfhkU3FC7dP6qjKr5OHVxs2KZkrXbl1nP9JJ69RxVkKpF1Nin2HDhgGuCmGUxLms4/9wlHY3cOBABg8eDMAjjzwCwNy5cwFXsiRK6kPVxDiJ8h764YM+6ZIB4sSmxIZRAdRLhY2TYgT/+46iBg0aBKrrX+9zzjkHcEkBhZxHrK0Kq4SK22+/vSjtUBhjIeV8wjCFNYwKwBS2jPsYtoC/tiqsT2IZVu3LpEJo+VDIjhO5YgprGBWAKWwEfcxmWaGQneByZW1V2HwUsJj3JRdMYQ2jAkirsIZh1C9MYQ2jjLAH1jDKCHtgDaOMsAfWMMoIe2ANo4ywB9Ywyoj/B1n+/HDCWDRSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1800, D: 0.2403, G:0.148\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1820, D: 0.1791, G:0.2377\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1840, D: 0.1682, G:0.298\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 3, Iter: 1860, D: 0.1691, G:0.3033\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1880, D: 0.1683, G:0.2076\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1900, D: 0.1457, G:0.2317\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1920, D: 0.1975, G:0.3545\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1940, D: 0.1557, G:0.3842\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1960, D: 0.2047, G:0.2279\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 1980, D: 0.2014, G:0.2365\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2000, D: 0.189, G:0.2885\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2020, D: 0.2248, G:0.2686\n" + ] + }, + { + "data": { + "image/png": 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45S9/CcC7774LwE477QQU1pXR6bAORwUQuYRVhXt14M45gABO5CiJo3udLL1y3ajwtvSzb7/91nttv/32A8yKLo477jgA7r33Xo0r47UCjq8ogRMnn3wy0FBQW7qfJFahc1pdXZ0zISKKOZTbRp3hZVNYunQp0NBVEUw/34ULF7LzzjsD8PHHH6f8Ta43ueL0WWjnWIh+7iSsw1EBRC5hbWuoX0lS9U6VRTUTfulr0iVklc1zfHmvzk2bNgWMLqJ71MopSZp0TsD0pNlnn308vfy1114DjHN99uzZgNF77f65+SDp9uOPP4aSsEFDQ5OfHQVCHHHEEYGuYT8n+jyCdICLcpek5/Pll18GYOzYsYBpu6Id4+jRo7336nORLUO7J3Wxe/311wGYPHkyAKeddhpgYg8kxXOMz0lYh6PciT3SSb6p9u3bZ/z7sGHDGD9+PACtWrUC4umZKsKszpJg2i20a9cOMNbhbJZRvVfSS35KWY/nzp3r+958CaLD5lPGVbqbope0K5CV+KfrALmLvIcpAi+CzmE+dgCNR++xfzZt2jTts9LfZC3WTiyK6DUnYR2OCqBRptcJv+ZDYQgjYSX9pGdJZ5FuG0RqSdLax0gfynVcEOKyEqsfbNu2bYHU+ck3iP/UU08F4NZbb817HFH6YW0pLBvDbrvtBpjor0zeDMWD33777QAMHz4cMJFOs2bNAox/Nh+J6ySsw1EBRCZhZV37z3/+A5jYUdvSq2RerTpffPGF7zkVUaMMEGVOhCHM6qzPasqUKQAce+yxANxzzz2A6biu40455RQAJk2a5Nv0WDqtdhH33XcfYBoJF0KhElZzJR+kYmVzSYaamhpfndS2ttoRRIUQZg5nzJgBwPPPPw+YCDM9r9LL5YH47rvvAOjVq5eXlWPvenTvy5YtA6BLly4p7w3rS0/GSViHo4yILFvnggsuAIy00Yql2FmtrLKOZtNL77//fgAOPPDAlGOvuuoqAC688MKohh0I6TnKPHn00UcB428bMWJExuPF7Nmz6d27d8prkjiyLEpXktSW5fm8884LNfZu3boB8Oabb+Y8VrsgOxbaD0mQb775xrOU2gwePBiAww8/HEj3fxabfv36AcYTsWDBAsDo1PKpag4nTJgANPjU5UcVo0aNAsy9SPKuWrUKMDaPKCP5YjM6ZeianfJ3/b569Wr22GMPAObNmweYYGoFSOhhsB36eshl7AlSjSHMdkpbRG3j7XQ0vwT2d955x9sm6QFRMrTeY4cm2vdoh/5lI6zR6a9//StgFotcxr8FCxawww476Nop7zn66KMBeOCBBzQeID1EU6ln2qpmIwqjk5+BUC4ZzbFCGN9++21v7HpPixYtAGN07NWrF2CCgfr06QPAddddB5jnety4cTnH57bEDkcFEJuEVUC0QtUU2pUt1UiKvrbN2p5oZVdwvZ8L4aKLLuLKK6/MOq58V+dBgwZ5dX3sz0pqgAxuwq9aXqZj/EIRw9Q6CithJWW0swki1W2UPii1xkaB87qW5vjbb7/NuV0uRML6BWxo+y8VRNjhh8mSuE2bNoAxnCroRbshGZ90j/mgea+vr3cS1uEod2KTsOeeey4A11xzTcrrKrnRt2/fbNcFjEST/vb1118DcMABBwDwv//9L+Px2Ygj+Vkrq1Zju9dN8+bNPYOOn+6ksWulVwrXPvvsA6TvNrIRVeCEKlzaxpYgBJ0TSThJpSDEMYcar+bOHvcGG2zgfQ7qJqGfmlMZspTMr+qSktZy1dlVFDPhdFiHowKIXMLaEkQ6g0zdCvCX7qbwLoC///3vui5gTO2SsDqH6hHbeknTpk1zluOIYnVW8LvtqhEK6H///feBhtQxv7Sx7t27A8btpfmQtJYlMvnzzOceC7k/XVvSR0XHks6vc3uv6Rjp7dLz/PT5RYsWAeb+9f7333+frbbaKuv4wsyhxqPr2TsW3ZOe42x2CD9kv9EOUHTq1AloCExxlf8djp8BsQf/2z48Xe/mm28GGpJ7JWXtshw6VmlsWu0knQupgh+l/uNneVQ6mqyHOcYD4KUYDhs2DDDF2Y466ijABCl07do1Z8mRfCWsfJ+qVC+efPJJwPhMVbX/X//6F2CCPO677760cqV+OmwUlf4LmUPthrQ7EpJ6kvjCDqlt0qSJp99KF504cSJgfLYdO3YETEqpdke2tG7ZsqVnkwh6j8JJWIejjIgsNNFvRZUUGjhwIJDeCiE5YdsOrtY5ly9fDpjoIDv6p0ePHoBJjYoLu1uZX8B7Nslq6/jaPSh1TUiKKepIEUGPPPJIQWPPhiSrPYeyZqqcz0svvZTxuOrqas+CL+znwE5ysEvF5urmFxZJVrvcjy1ZtduzyxIlW/5tv7SKrencKk7npwfnkq7ZcBLW4SgjIpOwuaxeCrpW+p2SfsGsWPJBavWVDqVkYumutg8zjGRVDKvaKWQjl84s6SFJbPtjwYxdf9N7pAcpuuiWW24B0iVOnGVhNQ+K4Jk0aRJgIrpk+bWvn63ci+5TkkvH2pbuMJJV4870edvY17UbgOkZs33syfesnYd2O/K3KqFDaaHyiPj53AvBSViHo4woeYmYDTfc0Fv1JMHsVn+K+pHVrXXr1gVfLworsUqkBC221aZNm7QmULneI5+z7nXmzJmAv+/XOncoP6x2P8oykeVXOprmST9zPEMpvyslbcyYMfkOyyPMHGpHo7FLn5Q0VNkfoV3G4MGDvfdot6g0UNkZ9t9/f8CUTFW2UyFFBZ2V2OGoAEouYbNhF5eOogxqlJkeNrYVObnQuPyU0ndsZCWWRC0kQ0bkK2Glm0qqF9J20s/PGQdRFIMXmhd5IrQTsuPDR40a5RVQsK3kKuErS7k607/11lt53FUqTsI6HBVAo5Cw+Vj5wpJrddZqqWiVbCjXV4XGbORX7tKli2cpVNNq7RZUiUGfgVZxtfYIYr22KVYzrGTs3UWcRBmtJsmqthqqGiH/uK3TgqmEoufVjrUWQZ5rv/gFPwnbKL6wuVDiuow8YYhisoOmjt1xxx1AQ71av56y559/PmC+qHZ94kIoxRe2mEQxh7Y7R65BJR5INbFDRsFs/6UiycgUJW5L7HBUAI1CwirZ3S5OpU7WqrhXiBJvr6RRbqdyoUTmV1991Tf0Tq8rYEJB/kHwS12LWsKG6RYowvTUsTsdFnMOk3nwwQcBOPLII1NeV49ZFaLLB30u+un3nHrH530Fh8NRMhqFhC0mcazOkuLSbWRYynRMIS6TfCmFDhtFV7qglErCltI4KpyEdTjKCCdhK/we/e7vkEMOAUygej7kU/QuDn5uc5iMk7AORxmRVcI6HI7GhZOwDkcZkTWs5uegGxTjHqurqyO1nubSIV2kU/njdFiHowKIrETMz5GgvsdMf1dKm53YHoRysDuo2LtS9hzR4CSsw1FGVKwfVgXd7G7iPzf9p9LvD8r7HrPEmDsd1uEodypWwvrRWFZnFVZXSw4bu6GWaNasWVrZTJtykLBhEt4byxzaZWSCEsRr4CSsw1EBFN1KHCZrRe+xcwgbG2r8u3TpUt9j/CSrioX5lXLNJV3LhWKUkgmD3cQtE7kkq59+GsYnX/ItcfL2QA+pXB36QDS5v//974HUrgH5EmeJGHU8mzFjBmAS2E8++WT++c9/AqbfypAhQwC49957M54rDHFvif0qECZz2223AfCHP/wh6svHmiIpoaBa2MmdFJWgroR1myj7ArktscNRAZQ8cGL16tVeqROhRGHVI1avF3UpV3EzSV4ZMBKJRFGCCuytjn5XJcQ+ffoA8OijjwIwZ84c7xj18rnnnnsA03NGvXTU+UwUM/E9KJKso0ePBuDSSy9NCyLRnKhnquYyru50YdHnq/Gre3ry82RL1uT+uMnH2veoHjyDBw8OPU4nYR2OMqLoOuy8efMA01Ozrq4upUesdX0gXb9TBzxJ2nx6pkah/0iaSKIuWLAAMD1dhw8frnMDDbVtX3/9dcDouTqHKv6rlKskkgxWhewYotZhNT9+Un7NmjVpu6RcqLfOhRdemPd4wsyhnzsp1z1mQzq9OlJssskmQLg+sE6HdTgqgKJLWLt3alVVlbeqydWh8iXCHqOfHlSIQ7qQe5Q+pl4qBx98MACffPIJYEqVSoouWbLE6x5/2WWXAXD11VcDMGDAAMB0Dzj66KMBeOyxx4DCXABhJezEiRMBo0/rPtQ7NROaE+m3kjp+z1cYXTaKObTdNkEkbK4xa8clG4x6/haCk7AORwUQuYT1s5T5HZdIJHyd09tuuy1gCorbY5UOe8wxxwCmGHeO64ZenTUOWXAlWbVKqz+P9KRevXp5EkdtHaTLyho5duxYwKzwOpe6fQcpsSkf75o1a0JJWHsOVahc57f9sKeccornZ/Y7l6SzrOJJ48t3eJHOoa6vPkna8Zx22mkZj8vE6tWrATOn6mus16O4R+EkrMNRRhQkYRX9oWiQbBx22GGA8UlKGmm/37Jly8ArkDpdS8eSXzYfwqzOttVaEsfW7bRjkOU0kUh4ktH+vNX2QfrOCy+8AIQLX4vaShzUhpAJWfAV2TVlypSU37NFS2UZT6A5lF1A0jPHOVN+1z1uuummQMMuKWhJ2KC7zBzncBLW4Sh3imYltq8j6di9e/fAK5GOk/RR706loNXW1ub0o0Wh/2h3IMkqKTFhwgQARo4cCaSWf7HvsX///oDZeUhfP+GEEwDjpz7xxBPzHV5kEnbzzTcH4NNPP9V5Adh///0BeOqpp3KeQz5zWcm32GKLlL+XSof1Q75TtV1RdFN9fb2vrcVPosovq+clH5yEdTgqgMglrHQ1WTVzRerU1tb6plrpve+99x5gpJB0x0JSzcKszmr6+8orrwAwYsQIAGbOnAmYrumSuI8//jgAhx56aFqEjb1b0Otq7Wi3ncynPUahEta2BtvX9LNUZ5OSYfTfLOcseA79xpPrc91pp50466yzgNTmztnOGQYnYR2OCiDybB2twk8//XTGv6sV41577QU0SEk7EVgrlO3vi8L6FoZevXoBsGLFCsDon7/+9a8BM15JUzUAXrJkiadv2xLVbkolHUrn0PHFyEKSVVtWeLvZtHJcbamUSCR8G1VLaidbzJPPUWx0XXk4tJsQfrHG8+bN871HmzjvMfItsf3AKSBeIXc+1wHSPwC/LZp+6lp9+/YFgrl54jBY2N2zNW4l2p9xxhlcddVVAFx88cUAHHjggQBMnToVMF3mFSSin0raHz9+fMq5sxG1W8fevmfCDkm0kTtPLsEwRDmHN910E9AwR5D9HhUQ8f333wMm6KVnz54ah8aX8Xf9DFIpxW2JHY4KIHIJ2717d8BsgWbNmgXA3nvvDZjAAPHZZ5/Rr18/wBiXJF1kZBIqpyJXhx3AHSTYIN/VuUmTJr7hgDIy6R71Wcq9o1S5E044wUuj23333QHj7tCqLWTAktsnn9pH+hzq6uoikbBz5swBzJhFNiNLrq17qYxOMlBqt2aHDy5evBiATp066Rpp57AlpgyEK1euTHnd3gkKBWEMGTKEG2+8Met4nYR1OCqA2AInpBucfvrpgElFW758OQDdunUDGnQfv5XJb2xaHaXP5WOQiVL/kZ4ux/h5550HwCWXXJIyrldeecULq7zrrrtS3jN06FDAfF6qRyyjiHYN6mCQrRKjiKsIm18gQH19vRdEornRMXpP0nhCjyPKOXzzzTcBU0BAFS+F3GstWrRIe6/9nNoli2xuvfVWwCQWZMNJWIejAohMwialdqX8rn2+CpOdcsopgEkKuOeeezjzzDMBk9p09tlnAyY8TGO0XR1Cr/fv35/p06dnHWcUq7Od9mdjd6bbeeedvdI4WpW32247wJSI0c7Dr6tdcsnXXDuKsBI2nyANP2x3TpREMYeym6gkrW0J32+//YDU8MtcuwOFX9rF2jTH7dq1Axp0Xr+608JJWIejAggtYXNVN1dZT1mHZVGT5E0kEgwaNAgwgQj2Cn/99dcDcM4556S8XgiFrM5BCmcns+WWWwKmlMxP1wFg7ty5gNHh9bmocJsssiqDatOqVas0vdAm7kLiOa6t6wLQtm1bwKQPKslb1tkCr5H3HGaaE0gvDaNxq7Su/Pf2HHwAABEvSURBVMfJwSHyu9qlUO3n9sUXXwTMd0CvF+LNEE7COhxlRGQ6rC1hpRsceuihgAlvW7JkCZC68kqC2cH8WpW19/djt912A0z5lWzEmZoVJLFfUTLSYfW5aKWXP9AO/s+HuCTscccdBxh/uM+1AZMgMWnSJACuu+66qIYRag5tn70dGiqpqOi5hx9+GDCRaD9dH0gPs/XDLwIqG07COhwVQGQSVsW15C+UdFy1ahVgfJTyR7766qtAw6r99ttvp5xLPj358rIVHssXe+WqqalJQGElWfwirFRIWkkCyZ+x3qMkd/mn5Z/VvcofqORxUVVVlVcp11L1Tg3T/zUXUe6SZJ3XnGk3p7lbtmwZ0DAfsmHIAp7JNwvQuXNnwBRWsGOJg+AkrMNRAYSWsJIY8qHaZUP0d+mnWnmvvfZaoCExWCVHMlw/1+Xzxl65qqurEz+9Hvgc8hsrG8cvg0b3vt1223m+2+eeew4wklWd2C+44ALASFhl89jWyzgT2JPen3LNXDRv3tzTufWZKMuoGHOYzz2qBK0k60knnQSYHU6XLl0AeOedd4D0mONMKPrMjpKSnWLhwoVBh+fhJKzDUQGETmDXCipJod8VSSKU2P7ZZ58BxqdaU1PjSRVF8xQzubmQJkg33HBDxtfPPfdcwGQmadfRqVMn7r//fsD4dCWRJHm1y1DxNXs8xUhgF36fv98YpKuBkayNDflCtcPxu0dJQz+pmYkNN9ww5ZzKyy5EsubCSViHo4yIzEos6WhXX7jyyisB01ZQDa9U3Pm7775LK2IWJ7n0n3x0RVUpUBaGxv/ll18Cxi88d+5cLy5apXMUU61IGr8i5WELzYWxoNrW6Hx2PnHukqK0EvtZ+uVHVn53TU2Nb55rHPjpsLHXJZbSrqACKfOlIorJtid53333BYxL6+677wZg0aJFQMOWWJOsTusKfZPjXgEUYXqKimJV/tfryZUvlQCuAJA4tvJxBL+ot5GMf6XGGZ0cjgqg6P1hi41tpo9ydc61fU5OlbPrNQsFicgJn0+ggbbNkvRJ/XuKEjiRvG3PlQQSpPBYUOIML80H7az8KoTmg90Bz0lYh6MCqHgJa+O3OocJpZNZX2GYNtXV1V7om9xacVKK0EQVHYgyyN+POCWsQmwzdZuPIrE/KE7COhwVQEESVmFchXRWKzWNRf+Jk8YQ/B8n5T6HG220kVfIwQ8nYR2OCiCrhHU4HI0LJ2EdjjIia/B/HLpBGEtbrmZDQSh3/ScIToctf5wO63BUAJH1h1Uki6SdupXvsssuKceFLFEa+Fx+3cIdjnLGSViHo4yo2Egnv9jWStJ/1M5DMcvC6bDlg9JSMxQscDqsw1HuZP3CVldXp2RZ1NbWeitCoay//vpevKZ93t69e9O7d28SiQSJRILtt9/eazAEDdKyqqqKcePGMW7cODbbbDMvBxUaCru1bduWtm3besc2NjSu4447zivMnUzTpk29MjLJx2eiTZs2tGnTJu095USm+23saE569OhBjx49fI9r1qwZzZo145lnnuGZZ57xXlfuMzRI1nzKEzkJ63CUEUXXYYOU0MygdwLwxBNPAKYEaJBzZLAsx67/qHWDWjkkI+u1VtUtttgCgI8++ggwDYZ32GEHwGQR5coISiZuHVatQh977LG836v7UVmd3/3ud3mfoxhzqKLhQXYualx2/PHHA6ZqiHaShexK/XRYb/uZ6R+QyPSvefPmiebNm2f8W21tbaK2tjbt9ZEjRyZGjhyZWLduXWLdunWJoUOHJoYOHZpINFwoASTat2+faN++fULMnz8/MX/+/IzXSf5XU1OT+KmCf85/Qe9R/356GLLeW65/Y8aM8f4/bdq0xLRp07x77NChQ6JDhw4p10u+ZiH/8rm/IP/Wrl2bWLt2bWLp0qWJpUuXemPXWBOJhDevfkQxjkLnsJB/zZo1SzRr1sz7ffDgwd7/ly9fnli+fHliyZIliSVLliS6du2a6Nq1a2xzmPzPbYkdjjIi9i2xtkAysCg17/XXXwdg3rx53lZC1dg33XTTlPdqjHZlO1UtvPHGG5PHnPIeG7/tVJzJyT/88ENaJ3JtiadMmQKYYmyqg6ttlbq8q0PfypUr87rHKLeLKmOi69vGw0zYfVbtAJtyCi+1y/zYfYPte1VPWn1uqqbp+sM6HD8TskrYn/S1QGVT7NKfkhRaVVRCZMyYMakDqKrypMkxxxwDmA536k0iiatOYmEoxuqsUp/vvfdezmMlYYYNGwbAxIkTAdMDRt3r8jH9h5Ww6pF61VVXATB16tSUsYbZhagvsJ6LQiimhE3uAKD71nOprov6XNRjVrulMDgJ63BUAIF0WDt8KlNgvQowS4Jq1VFgg3Qw9ZIZN24cOrd0APVlUQe0oGQaj/rc6DoaT319fdFWZ/XATXau33zzzYDpgJdvX9rq6urY+8PaNgPblpD0WXp/19/0jGjH5aezhiEKCSv9U+PN9ZluueWWXHrppYDpeHf55ZcDcNlllwHQunVrwHQmDIOTsA5HBRC5ldheSdWjpFevXinH2c5mMMW01W9V+oP0Oa3WspxKV5w3bx7Q0Gs2wPhCr87qd6NC2lmulfaaCkV///33Wd8ru4E+k2OPPRaACRMm5BxfVBLWlobqSq75ELW1tV7XPQV8SBcXGvfQoUPzHU6m8YWeQ9lDZB/xQ0EwU6ZM8UrVqgWL7jVoCudLL70EQJ8+fXIe6ySsw1EBFCRhW7VqBcA333zjvab+pk8++WTKsTq//q4O7Vq9165d6+nICvRfsGBByjkkjbU6S+fNhPydktJ2gfCgq3M+oYBKb5PlU8XCkxMTJCl13+oPWwjF9sNeffXVAPzpT39Kub4488wzvf6rgwYNAszn/eCDDwJw9NFHhx2GRxxWYu3eZAGeP39+yut1dXVp+rlsJ9J/o/TlOwnrcFQAsUU6+bU8sKNDxowZw6hRowCzcmml0opmr+ha2fR6NokrpG+uWbMmNiuxxiupnoxtLc2FguPlC/3444+BhnvOdY64Ip0mTZoEGH+5qK+vT5uj4cOHAzB58mQgvY1mmBI+xfDDag4lPevr673/S5f94osvor6sh5OwDkcFECobvbq62lvtpbNoFZKeqzhK6baDBw8GoHPnzgAsWbLEO5/tC7vzzjtTXpeUKmRVLqSTuY1W3Z49ewIwZ84cILfUnDlzpu8xfr7nU089NePxccQ6i1wNwSRZs41hxowZAIwfPz7lWElgu61isbElp8Zn37t2S8lFBBWzHqdkzYWTsA5HGZFVwsqyKYumHS+cLBFl6dUKdcABBwDGWnv44YcD6Vbc999/3zuH9FvpN/37908Zj1bD3r17A0bCJSNdVXptLol00EEHASY5Phu6t5dffjnnscnsueeevn/r2LEjkD7OUpRKSbKkZxyD5v+hhx4C4IgjjvD+pl3P3nvvDZjPVd6BOHcG9nOZDd2j4n0V/5vr808kEl7UnB+Kn1fz8ORSMFERyuhUW1vrbR3kBpFZX186Oczt66gaw1577ZVmDn/rrbcA4wIaPXo0YBYOP/N5kIc8CoOFAjratWsH5K4o8Pnnn3vBBhqz6jZLRVDFCSHnvIJDpGKUsuKE/XknV2Xw++z1Hj0fCttTZQ1tOfNZoOI0Ol1xxRUAniFUc1tfX5+WuGCPWel0Sq/Td0NCSotakGQaZ3RyOCqAUBK2qqqKBx54AICjjjoKgM033xwwIV1KWNd2QVtgbbdramq8GkHaAm+wwQaACcd79tlnAVNLSCubtr/77LMPAA888IC3kuu+dC6FAkaxOisIfMSIEYCRtDJGaCVORqutjC5bb7014J9mpnpJ+gy0Oh9++OGeq8SPfCTssmXLcobn2diqUo6xZHw9zJY/Dglruxvt3++++24v+MMOSe3SpQtggv+ff/55gLSiBfngJKzDUQEUJGHtkCxI35fLiLPHHnsAZvWRbrbNNtvoGpmum3J+Bfd369YNMKv2+eefD5hSMVtttZUnyRQuqPBAhZpFuTp3794dMOVuhFxIMs4ce+yxPPLII4AxdmiV9tNntLIrVC7ZOJeLuHTYMNjPmdIL5SqRDi8jXI5zRS5hbWOoTXLgxAcffAAY16QdwKPnUcEvheAkrMNRAUQemigJJx1WKUXJHQQg1XWg/ys9buHChYCRPl27dgWMlJQ+rLA9BaWvWLEiY1igdd3Qq7Mst4sXLwaMxVfJ+TvvvHPK66+99prn3pB+K+mrRHuVZREqSKcdgu6rX79+PPfcc1nHF1bCSieTeyIMKnEj24afZT+fBPco5lBBIAq3tMeh0Mo77rgDaNg1KZ1OElbPp3YF2iX49cvJBydhHY4KoCAJm7waXXPNNQCcd955KccoRU46mG2tTS4JqdVs5MiRgFnh7VVXARMqWHb66acDJgm5bdu2nhS2kw5Evqtz06ZNvXA7XV9ce+21AJx22mmA0ctl1dbOoK6uLi2gw8+XbIfOFUJj0mH9ni870CbPc+YtYf2CK2QNlhVe4z3xxBMB4+Woq6vzitHJLqPdkt6jhBfNcVRzmHIfBZ/R4XAUnch02NmzZwNm9ZHPVBZUpVcp0kWrT/PmzT2/nlY1Fdf+8MMPAbMKT58+HTCSzva5Jus/cfTW0Tn1UzqrdL1Zs2aljE8J/q1atfJKqMjibaMIpo033hhI13/0e5A+LXFJWOnTStAPgt/zZadS5iONilnm9KKLLgLgb3/7GytXrgTMs2yHHuZKYB84cCAA06ZNy3ldJ2Edjgogq4TdfffdE5B/sDuY1UbRITfccANgCnhJajZp0sTzTcr6KWkja6z8rbfddhtg0tukX0j6ZNKH7FUvzOos/VKr7p///GcgJTk+5XjFRLdo0cKLM7XRvW600UYp4wxDVBJWVm6Vac3TkpvxdbudRSERT/YcNm3aNAGFpV3aSIpKespzsWDBAjp06AAYy3fSMxX6ujZOwjocFUBoHdaWYCrhePHFFwNGl5XFVO0NJK0+//xzT7IeeeSRgCmROWTIEMBITl1LRcFUvFw9VpOT4W1kpV69enVo/Uf3qlYcsoTvt99+gLEaKxMn28pvS5hcSeQBxxdJmVPp4JL+Qp/72LFjgQY/pHyQQXVSSe9dd9013+FFosPKlqBoJUU6yUYgX6uilrbZZhvPJ6t4cPnKZXcQksQq61MITsI6HBVAVgnbsmXLBGTPylDOqqSLH3b0Rya/mHRWxRtLOksCaxVXXHIh2CtXdXV14qfXfd8jH7P0GVmzbbRKK3tHxarr6uo8y6LuQecKI0n9yFfC+hVEU95vcrnWQpHkVZ6p3RQtHwqRsEFLkGZLhpftRMXx4ywy4CSsw1EBBNJh42x2DCY2WCu6HypWrUgnNYlOHlehDZ2DEEa/9IsIi4OorMRh5ls+cru8aRRE6YdVVtdXX32V8e/Juw9loMnW4hdNFwV+Ejby4P986uv4oVBDKfXaqmlb7felbNKkSVoXNZtiOt1LRdSBEzIkKpEj6Tq6hveaannZPWWjpNRzqC9xrmctDG5L7HBUAHlJWFuyNWnSxNdlkc8qpC2mnRqnIGqV3Mhl2Mp0fZnWJbXjWJ3z6cMTByo3oyT3UgT/qxq+dkVC20YFxkdBqSSsXR9b7LvvvgA8/fTTeZ8zaAitcBLW4SgjAklYv/6rQbqBK7lXrhpJPp/rAbmNHQrK+Mtf/pL1uEyUanUOaliLglJI2LgNk8mUWocNSphEdidhHY4KoCArsVLFFNwe6EIRrMC5zpFskQzr1lEq34ABAwoYaXwkF2/3ozElsMdB0DnMpxxrY8NJWIejAsgqYR0OR+PCSViHo4xwX1iHo4xwX1iHo4xwX1iHo4xwX1iHo4xwX1iHo4z4f/WwN6PwYpdcAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2040, D: 0.1778, G:0.2352\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2060, D: 0.1892, G:0.2207\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2080, D: 0.1577, G:0.2527\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2100, D: 0.1743, G:0.2201\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2120, D: 0.1665, G:0.2535\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2140, D: 0.117, G:0.3337\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2160, D: 0.2224, G:0.2309\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2180, D: 0.1703, G:0.4239\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2200, D: 0.2084, G:0.2636\n" + ] + }, + { + "data": { + "image/png": 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tnGeffbbk9bUFtGDBAiC/ol2mQqdTpcENep2yZowbN44ePXoALnBCE7KCSarJ3VSk/djvocZY7L2xbR3DMIoTZtzGbczr38SJEwPjfeDAgZmBAwfG2l41YyzlBCn1+iOOOCKzaNGizKJFiwInUt++fTN9+/aNfYxxvpeDBg3KDBo0KNOvX7/gHnbv3j3TvXv3hruHEbUbm5Op1BjN6WQYKaRuD6xmjEmTJgU/69KlS8EN6nqTNZNX9Hr9u+eee2jfvj3t27fPu9aSJUtYsmQJffr0oU+fPnF0PzamTZvGtGnTAlsaYOrUqUydOjUYzzPPPMMzzzxDmzZtAidZuTRCPaFC997/2V133cVdd92VSH9MYQ0jRdTteJ28hdmB/UoXmbYjWfL4Kt2LPKIPPPBA8Bp5jjXujz76CAjf5mkktL2iUEtVI1h55ZWB1nuoezVu3DjAVTDYfPPNgeo8qo1w/wutCnQ0VDmXu3Xrllx/EmvJMIyaqZvCnnTSSQDceuutwc/WXnttAJ566qm69KlapkyZArj9UG30//WvfwVgzpw5QTC/jgwqwZm/76rEc/5h9IkTJwJOsZJE49ExyNGjRwPuqODLL78cKOjhhx8OuFWHkFp27doVcNnzGw2lfZkzZw7g+p2t9kqkF7ZqiHOFaAprGCki8UgnHS/zy3OAC3krJyyvWqKMklHyb5XhUCmHzz//HHBjPeGEE1hvvfUAOOusswAXqqdUKkpa9vHHHwNOgaRIYr311guO4qldn0ojncpFKVFmzZoFtB4bhNbIK//I3TbbbJPT/1JV5++++27AKXQx4ox0Kufguv/acvGjp0q81iKdDCPtFFXYjh07ZiDaFC333HMPAGeeeSbQ6i2VF1LFo2TLxoE/c914440ZcAnTCqHY3bAiVNdccw0Au+yyC+DipRXIf8wxxwTpTOVh7d+/P+C8xT4rrrgi4BS4kljc7DHOnz8/A7V54H1F2HXXXQEYP348AM8991ywgpAnWUH/8hqXopKEAQXUJ1Oon7UwYsQIwCUUKHTvLZbYMIyiJG7D/vrXvwbg9ttvB1pPnPTq1UvtRd1cHmH2TznKo5WG1E/eUyUBv//++wGCtKhaMQwaNChQEH1VO/fddx/gil1JqXSaqRoK2bDljK9U8SuhEzlaJbVv357zzjsPcHuzKjUSh8c/idM6xZLHWakOwzDKoug+bJT7SdqjlF0nu3DhwoWBwmo2qzTmtBYqGaOikvwUnrJL/eJgKjzdp0+foISmf5D7oIMOyvn+7bffBnITuIHzquvcablU4n0NU1aN17flR40aBbRWXdf7p7KT2m9Ogkq8r5VeU/c2O8Y9jvbKxRTWMFJEbDbslVdeCTg75+KLLwbgD3/4A+DK+HXq1CmYuTVj+SlBoiRK+0fv3eWXXw64ol6//OUvgdYIIIBtt902GK9Spxx22GGAS7MSJVHtw2pFIeXVePfff38Axo4dG/xe5Sa1j1xq37UW6pFxAtxn+oorrgDcWOOgYVLE1JskHRZa1taSY7ga4gqcKIco8jmXIsmcTvXCnE6G0QTYAxsDOuD87bfflq2uOsieBtq1axcEu4DbyoFWZS2krtUcYK8nnTt3DravIFlHaDEaoxeGYZSF2bBNPsZGHV+pcM9iLG/3MBtTWMNIEfbAJkBYZYPlmaVLlyYeUN8M2ANrGCmiqA1rGEZjYQprGCmiVPB/08nv8uZhrGZ8srmVxlMHEBqFWu6h9lajiD6rpNJgpZiX2DCagLqlOV3eaZRk6ZlMJuiLvs6dO7eeXQpl4403rurv2rVrF0SRRRnXHWeywDBMYQ0jRVikU4OMUaoWxZ5tGiKdaqFR76FftLsWzIY1jCbAFLZOY1TKHCUfj5JGUNjTTz8dgD/96U+AO9T/9NNP13ztRrmHcWIKaxhNQOwKW8+EVYWIY3b+73//C7gUMfUmaoUNS3+qIsZHHHFE3t+oOHUcaVSWZ4Wt+5I4e1shCdJys8O2fVZYYYWSlRiifmDL2YI6+eSTAZfvaNVVVwVclcLtttsOgP322y/nmj4dO3YM8l+FkZZ7eOqppwJw7bXX5vz8hx9+KFkX2JbEhtEExBY48a9//QuAPffcE3ApNiZNmgTAZZddFrxWSy4ph2Zf5fVVBv20oTH37duXadOmAbn5bcHVbrn55ptzfh6mZlHWOSqXsL4o42OXLl148803AZdpUTWBhLIo7rzzzoAzI2bOnAm4/MwKh2wUiq0uVJ1h/fXXz3mtKkNIWY866ijAVSTMTj1TKaawhpEiEnM6+Wgmbd++faA6/mvfeOMNwIWkRWHrlltbRw6WDh06sOOOOwLwwgsvAOEpPP0Kc6oP+8UXXwSzrarkSX1vueUWwOVt/uCDD6of3I/Eva0jO/Sxxx4DClcMUH2hRx99VH0qeC2litF7+txzzwXvdxjl2rDKAa1qe1A6DauSy/kJ8YYOHRqskjTeUult9PvshHU/9rdkSKrZsIbRBMSmsDNmzABcgMAll1wCOC+iZtYOHTqE1nRRTVPVUC3lWSuHWjyMvo2i2Vq23GmnnQbAVVddBRTf2vBrtvziF78A4LbbbgMqr6GTTVwKO3z4cMD17cILLwRa6/nqfme1G9Y3ADbccEPA1Qs+//zzARgzZgynnHIKANddd13YNWLzEvv+lGKKnL0KK4RsV1U11JhfeeWVkv0whTWMJiA2hfXra/o2YjX2aBRBGNXMzmGzrPojRbjggguAfHVcunRpcA3Nyv5qYYcddgDgkUceAdzKxKeY/aMD1QsXLozVhi3Uvn9P+vXrBxBU7ROTJ08GnO1+/fXXA+596devX1Cpr0j7kSmsr+aqulfo0L7uv1aH/vvgV1/Ue/Lhhx8CsOaaawLQq1evkrWHTGENowmIbB/WV9AwFSimjl27ds356ntM6xXe6HsO/VWDInO0vybvoN6D//znP0Gkj5TV9zCqUvnrr79etC/FVkT+3mdUqO8vvvhi2X/jK4gin2THyZehgmFS2KTvsW8n//SnPy34uvvvvz/Yd/WPQMoPIb+EkNLKZtXraqnsZwprGCkichtW6pNd/xXcbFNNSpQoDxDUYv8MGzYMcF4/9UepQlQUSl5kHZ3L7rd+p1n2wQcfBJytd8455+T9TaWU4yXu3bs3APPmzQu9jlYDZcT2Brafb6P73latLF566SXAraIUzSbPc4n2IrNhfZtVX/W5zWoj+L/GsmDBAgCGDBkCwPTp0wHnNfbvYSVpgcyGNYwmIDKF9VVQnrG11loLcDOrn/Aru33/Gv4MX8uMldVG5Ht422yzDeAqrhfC9yyGRcn4Y6ymQHK1+7D++ynbS9XVi+2Dl9p31e+z44+z8SOeihHlPRw0aBAA7733HgB33HEHAPfddx8Ajz/+eLF+FPy5It7k+5BqV7JqMoU1jCYgMi+xTuUIf39Ne4SF7FF/ptKMpJjcUrN3vQlT1lmzZgHQv3//QFH9GFUpjexg2T96fSXKWiv++zllyhTArZIUeeaXiswudiwP/9SpU3Oupfuv+OqzzjoLcDHU2od+/vnnoxhK2ShCy7exjzvuOMApcHYkl39P9L08zLrH+hql5zu2wAllAVR4nv/wZQdEhy2T9VWudx0I3mCDDQB3vKkSqllOVVrLVP3O3kDXTdVkJGeHUPaGI488sqw2ID+8Mav9mgInBgwYAMCECRMA9/ApMERBEQorzP5AnnjiiQDcdNNN6kvOtbVc1IdZE7p+3tLSEkz+Y8eOBdxnRe9hlEviMLNKx0P32GMPAEaOHMmxxx4LwOeffw64rSqhCS0s8OO1114DWg8ShAUWCVsSG0YTULPC9u/fH3DLP6HtC2Va1wyi18uNX0kO13pv6/jH56pBrn8dzpeavPrqq4CbvSuhwMqkJoXVslTL1N/+9reAW+n87W9/A+DQQw/N+9t///vfgFNlLZF1QF19lRLriJ4OeGSHcYaZA3E4DhUMoaWvAjx0wL5t27ZBYIwcZ0Irrzlz5gAuBLEWTGENowlILAmbb9OKmTNnBtkG/XW9tnP0tVu3bkB1Wx0iyQReCrAYNmxYSQdZlI6JahV2/PjxgFPWrbbaCnBbG35qE9lwCxYsCPo/ZswYwB1g1z2SzSrFVQIDHZSopE5NEjbsuuuuC8A777wDtDoD9TnMdrIVulYUmMIaRhOQeJrTsBy3kD/LacaSHRyWvOrGG28EXErNYsShsLLXDz/8cCA/oRo4hZHHWcjDq8D9KLaqqlVYf8ttl112AZztrjQ3a6+9dt7f+kEuGufWW28NtKZ+AXf/1ZbCORUKOG/evJJKFcc9VL+UbEDbT167Od/7x+3KpWvXrkFCtjBMYQ2jCUi8PqyfyjQbX32VYiNMWS+99FKgPGWNE6ljIWWFVs9p2CxcjxqjPjp44CcL05E/IV+Df1D7+++/zwtblOf0iSeeAJxKa5/Zv/977713wT5kE2Y7loNs5zBl02dutdVWA5yaajdDXv0o+uN7mSu5pimsYaSIupfqqAT1VYp6zTXXAOFJsEKukZiX2Pd6Z6P9VtlKlXgYK9mnrCQFjpA9WUr9ix17lML6Kws/0s0P1Zw+fXrgoS3SbmT3UJ5uvZeKHlO/io0xirRHYZgNaxhNQEMrbBx7l/7M1bZt2wzEE2Tfq1cvoNUTXG4a0Cio1ksspVUMr+Jos64FuFVBqWRp4NKXlnMwvVyiVNj9998fgIceeijn5++//z7g4qoLkdQ9zMYU1jBSREMorOwGHcEKO8ngz2hhdlAxkrBhdRLns88+A1ptbN8DfsYZZwBwww03RN18ZPuwSh8jD68izYqh42gqdhUHtdxDxQireJdPVqrY0GskkSjOFNYwmoDE9mHDvIaQr6zlRvtUoqxJogghncwZOnRo8Dspra+s1aS78VFSsEKUE3/t90H3pRxvsf723HPPBVzRL0WplSKK8ZeD7om/l6xotbAE7kBJ77VPNWOS3yMMU1jDSBGxKaw/u/jKWsgL1ygpX6pFcbEqtyFaWloCj2pYRokoxq49xULXKscL/rOf/QwIT0mrCB2NUzZ6nz598vwOlXrd4773+vxpVeanMT366KOB8AJcI0aMCBK1Cdn0YUWoqxlTqSTjiTudlA/3k08+URuB80h1PHWAutzlVCVEUb1O71lYLRX/w9qpUycuuugiAM4888yc14qf//zngNtSqYWoqtcpNFFLfKFlWy0Z7IuhkFPlaPaJ0nHoO9p8R6dMuV69ehU1OWrFd0qa08kwmoCG2NZJkji3dSZNmgS4PL5KFTJr1ixGjRoFuCVxFIEaYU6NuOrD1pusZW1k99BXNjnNtNzViiibwYMHAwQV2WtBByq0DabV5uLFi01hDSPtmMImMMaWIjVda8GfnUXcCqtAit122w2Ixu6uhCTuYaWpbaslzHFlNqxhNAOZTCb0H5Bptn9pH+OyZcsqGmMSfXrnnXca8h62tLRkflTfhvo3fvz4iseof6awhpEiitqwhmE0FqawhpEiioYmmpc4nTTrPqxY3u5hNqawhpEi7IE1ImXZsmWJ1rRd3rAH1jBSROKJxI30oSLU/pG0QoQlwlYy+DhOYDUKSg2kciVxYAprGCnCYombfIxJjS/s5JBKZBxyyCGAK1xWSyKz5e0eZmMKaxgpInYbVsmtVDBK6MTH4sWL4+5CKmkkm0+ZH5QJQtx7770AHHzwwaGnkVR86tZbbwVcphEVz1JxNKM8Yn9ge/bsCeQ/sHpQu3btypdffgnkL6fExhtvDMAbb7wBuJxJqvLdDPipSvR+JZEDtxT+gyp034qhB1bZCGfNmgXAgw8+CMDLL78MwLbbbgvACy+8EOSWSjtxHNGzJbFhpIi6OZ1U0+T//u//gll4++23L/haKemECRMAl71P7vNKKgDUy2Gh2rEnnHCC+pHzeympxupnXqyERghNrPZQSTkrinrdQ38ZH3YPo8CcTobRBFSlsOVkkS9wLcCpoOqj9uzZkzXWWAOAOXPm5PzNk08+Cbj0p+qrHBerr7562e2Lcmfncmp+1pKtPqnZuZbxKVBCgROVoORmcppdddVVAPzud7/LeZ3ffo8ePUpWxSv3Hj777LMA7LTTTmX3uxL8dLeq3q58zTVe2+HIstQAAAZ1SURBVBTWMNJO3WzYAw88EICHH36Yww8/HIAtttgCgOOOOw6AiRMnArDlllsCTtnDko+VQ9jsHHdtl8022wyA1157reDv41bYLl26AMWrstWCwvLC1FhJuFWdwB9vJWqehA3r9+err74K3kPhJ5Jfe+21Afc5Hj16dNXtm8IaRhOQuMJqT0qzVbHAAM3CCrLQ3qS+r4Rqk1AvXLgwb2Ytl0LBIWHvd9I2bHZ/4tjrrXSclaxwolTYsAAVvx+77747jz/+OOBWC6pEL4YMGQK42rO17L+awhpGE5CYwsprqL1T2TxLly7Nm4nuvvtuAI444gjAKZXKKdRCPfbwJkyYENg1PmuttRYAH3zwQWTtJb0Pu2zZsqCGbLmrkUYP/tdzkf3ZLFTbGNznUxF5U6ZMyfvbKto3hTWMtBO7wobFU44cORJorWp90003AfDqq68CMG/ePMDFoUpZpdK1UK8omSRs16y2Siqs9sMVJVYN2fvxxx9/PAC33HJLzmt87/CFF14IwPnnn191u0ncw759+wIwe/ZstZHdfs5X/9D+5ptvDrhdjmowhTWMJiD20zpSVu1ZaYaVfXrKKafw6KOPAq4gkPa+Bg0aBOQrq18h3JKhV04tyip0H4rZavKcSqmEDrTXslcZJx999BGQq6xa+fkrPn3+tMcuhY0DU1jDSBGJ2bCaabWul+JmMpk875vsK51/HTp0aK3dCKiHDVviPY6jvUS8xPI5lKMolYxTr1155ZWB/HO3Sd7DcePGAbDnnnvmxQ5ntR95u2E2bOxLYn8bRxvU2rAGeOWVVwDYeuutgZwgBwDuvPNOwFUvbwYa4WB6tei+aEm80047BYH2cibq4IaCDR566CHAHav0Ofjgg4HWLBZ6MOR0rMd7pYdyxx13BFrDZfX5DHttWMbIYijnlcZaClsSG0aKSCxwQgo7adKknO832mijwPWf1S4ABx10EOCWJVHkN6r3kjgJtYh6SazjjDo+puOQCnBZvHhxUI1d44uzgnm9t+amTp0KtH52f2w/jrZsW8cw0k5imf+1Ea0QvOxkY0q9IUaNGgW0HmYG53xohAyCldAs201SViF7VOGWe+21V6CwQskIpMZxKG0SFDroL2Ut9pq4MIU1jBSR+PG6YgnTFDiuQHJtRMuD+O6779bcfpL2T6H3Nk02rGxX5ZZeaaWVAJcCZfz48QDsu+++QRrTAlswgEtcJt9FLcR5D6+44gogP5XNj+3mfC/vcNihgFowG9YwmoDEFbacSmg68K2ZXd/7fa0mGVwSCqs9Zj95+o/tRd1cHpUqrFKq7rXXXgD07t0bIEiGpj5rT10heVKWjh07BkEuCiet1K6r5PVJ3MOnnnoKcIE+Z599dt5rRowYAcDw4cOjbt4U1jCagdgUVsm+ZJcqwkUexnLwo0AGDBgAuEDyajyPSR5+LoR/cCEOorJhfdUr1udDDz0UgPfffx9w9q3+Rr6LUvds+PDh/P3vfwfcYZAC/Yr9HmrVVk6a27DaQ7VgCmsYTUDsNuw666wDwHvvvQcUn6W136dyFcOGDQPgsMMOA+Dcc88F4PTTTy95rTDqpbCajVUcLIpk00Xar0hhpXq+t9M/Pucr7oYbbgjAtGnTgr+ZMWMG4GzZUujeau+9HJK4h5MnTwZgk002AVrtd9/DHac/whTWMJqAyBU2zN5RoqrzzjsPgCuvvBJoTdAsb6pmMH0dM2YMAHvssUfOtWuh3jZsI3qJs/4OcIrqH3Lv168f4EpGSoEXLVoUjCuKg/Fl9HO5uofZmMIaRoqIfDpU3K+8w3PnzgVcWg0pq2yEXXfdNdiT9aOgXnrpJSA/lrWS8pJJINsvrJp8S0tL4NnWQX6Nv5EIWx1Jcf3znor1zk7srmRr/gmsZqAR7pkprGGkiNi8xIopDUssnW0HqECz1Fl7uPo+SqK0f8rdp0w6Y0Kt+7AqDXnaaafl/NwfZ72yZsRpwyrm2U/8V897mNOPpEITNWAtGz/++GPAOTKSIgmHhT7oV199ddSXLou4czo10wPrH0bPakPXrvbSNWFOJ8NoAupWH7Ze1Cu9SJJEpbDKIa2tuFqQeSPzpxaSuIeFKg8miSmsYTQBprBNPsZmHx8sH2MUprCGkSLsgTWMhKmlCqM9sIaRIorasIZhNBamsIaRIuyBNYwUYQ+sYaQIe2ANI0XYA2sYKcIeWMNIEf8PL22FWZB6kGoAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2220, D: 0.1886, G:0.2538\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2240, D: 0.1771, G:0.2678\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2260, D: 0.2133, G:0.247\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2280, D: 0.2268, G:0.1851\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2300, D: 0.2013, G:0.179\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2320, D: 0.1905, G:0.2206\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 4, Iter: 2340, D: 0.1666, G:0.3342\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2360, D: 0.1991, G:0.1745\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2380, D: 0.2024, G:0.1965\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2400, D: 0.1809, G:0.2847\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2420, D: 0.168, G:0.2554\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2440, D: 0.2107, G:0.2214\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2460, D: 0.1934, G:0.2252\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2480, D: 0.1958, G:0.1886\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2500, D: 0.196, G:0.1868\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2520, D: 0.2405, G:0.1642\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2540, D: 0.2122, G:0.1578\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2560, D: 0.2309, G:0.1885\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2580, D: 0.2105, G:0.2102\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2600, D: 0.2442, G:0.1503\n" + ] + }, + { + "data": { + "image/png": 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88cQTAWNZk39u2rRpXssL7b7a5XK0aADg1ltvBeDhhx8GTLuFYlCK/hOkO7788ssAPPDAA4Dpo7ryyisDjQHlKjotphUrt23bFsju6q1OeeoKp/6rxfiFm4IOaz9Xkko0f0knUQJEKq3DHnTQQUB2b99S5mTD6bAODjWA2Bg2qHyM5Hrpd7kgv5UYSeew+7Bql1ZESaV3ZzV+GjJkCADvvvsuAKussgoA7dq1A+Diiy/mb3/7G2B0OzGmHdFkdy6XlTiMX7lcDKux+yN8pHOrO7sNsZKkkCioNMMGPXdxht86hnVwqAHEZiW2LaZ6LR+rInlU5Ezw70pBO5fO1b17dyC7W3bSWG211QD49NNPM8Yji6h0WhVuU1TS7NmzgUbLuZhHx7z//vsAzJkzBzBW4KOPPhowndqDLNJNAX5mld0hiFk1bjWXqkY0hYQMx7AODlWE2K3E6kIu/a0YeV/tJc8991zAdDqXX3aXXXYBjK9S+rBYKwzi0H809u+//x6A/v37AzB+/HgApk+fnvF+Op327oP09DXXXBOAV155JeOcum+KIRbDqiO4/1xBqKSVWK1NgjJXkogCKscc6+rqAqUbSQ1jxoyJ7XpOh3VwqAHErgR27NgRMI2JbN0r1w6rRs6CmNXO8JD+Z2dJxImbbroJME1780HjkUTQq1cvAD777DPASAILFizwLN7Sgy+55BLAzFGMJF/0oEGDSpxJeaGsI83nxx9/BIxOW60FDIRcUk0l5hS70UkPsf1DlQiYD7op9r+6MTJYJZncbf9Ql19++awuAHoIFUihIAf92CQqH3nkkUBjgIcCxvXDPOusswCT8KD7dd111wGV708aBg0NDZ4RzUa1/1CFprIOTiR2cKgiJBaaKEf/2muvDcDHH38MGNb0G6fkntFnQUECtiEjSpJ6nAYLSQ92YH/fvn0BI9qff/75nmtI4ZQSIRVAsMYaawBw9dVXZ1zDZqgC66VxlcXo9PrrrwONoXozZswAzJooYKZ169axX7ecRicZEBUiuux6SV3OgzM6OTjUABJjWJn3l1tuOSA7oEKv11prLS/AYJtttgHg+eeft8cRdRhZiHN3HjhwIACPP/44YAL5xS4yvK288sp89dVXgJm/jtF3991336KvK9ePpBQb5XLr6NlZd911vVBL262ThL2hnAzr/31IgpJkkSQcwzo41AAST6+zwwjFlmKaJUuWFNTTZKGTm0TMFQVxBk7Y45ReKolB1uO6ujrvWDGrdCOVgtE5pQ+XUkSu3Azrh9Imw6Q8RrhuRRh2rbXWAkw4aZJwDOvgUANIPHo+KEQtVyBFENtPnDgRMH7PJErFhKm4H3TMhRdeCJiEds39yy+/9HR5fXf//ffPec5KlmctFrvuuivQWN4HGkviKFAiSWatBLQezZo18/T0SsIxrINDFaFJFBIv1I9ThbOTQBy9bFTiRiU8pXOfcsopANx5550cd9xxgGlZIT2olLS5rl27AskXp7Pv0cUXXwxAjx49gEYJSGtY7b2BfL7sjPfr6+ubRGqjY1gHhypC2Yqw5cMXX3wBmMRvQfGp/iiTUhHWwti6deuC5SttVpGlV+VOBw8e7JWIUXlTJXwnwURJW4ntMS9atCirnE+SKIeVWHNUWuPAgQN57bXX8n5HBQ1UBrWUtXVWYgeHGkDFddiFCxd6upD0OqWWBaXPlTOTxc+uinSxd1pZg2VRlDVbifnHH3+8N1al1xVqK6kY7FmzZpU+CUyZWTtWuRiozI/dVkR44YUXog8sRqhUrGK4w2Do0KEA/OMf/8h4X4X2irET6FihUERaFDiGdXCoIoTSYZNmNpWAUbRMkFVOOqMYb8qUKUVfI6z+449SUmK6Womo4ZMswDfffDNgWikqb3bq1KlsvfXWQPTY2pB+4pJ02G233RYwDbskKailigrqiXl79uzJ1KlTw14mA1Hnt+y7semwuv4tt9wCmPIv5WjWbI3D6bAODtWOJmElFjSW448/HsBrbxFUpDziNSLvziqgZrfVEDvceOONgNmV+/TpA8Bzzz3nFVWL0yocxEpJWYnt60jC2HTTTb0yOeVAORhWUPZRmzZtvNhw33VzfiemceRk2Cbxg9UY5N5R5XyVT1l33XXjvFbJi60ww8svvxwwBh3hgAMOAEyN4QEDBngV/yVWx1FnWMY6ub8kbpfLrRNnL5lc0H22fyjlcOvo3r799ttxnzoDclnaHQmdSOzgUANIjGHD9Nu0i60liTh3Z7u3jMReBcKrf+iTTz7J+uuvDxjDlHreFoJYRqJZOUvEFFoXv6oig5SKy0VxrYQYV2xrWCjRXiVuFi5c6AX2jBo1CoCDDz446mULwjGsg0MNIHEd9qKLLgLg7LPPzvl5Op1OlFlV01glWpLQfwrt0i1btvRYtxxoCv1hk0Slu9eVA45hHRxqAE3CSmxDYXtJJHPbO9dKK62UhmxXTblgB6MUchWkUqmCluVqZthWrVp5hdiD4BjWwcGhKpCXYR0cHJoWHMM6OFQR8qbXSTdQO4k777wzkUHYHdxkbfWXQgW8aCFFRAl+KUHFvBUdo5KouXyUy95P+6+l45JKcIgjwkk6vsYYJjSxKXZyD4tCOmynTp0A0x6mGuF0WAeHWoA6euf6A9JAOpVKpVOpVFqv9deyZcus95o1a5Zu1qxZ1vv6q6urSy/bEdNAulWrVulC12nVqlXGcfY5/O8HXVd/QXMM+jv00EMLnjPMX9DY4/wLM79q/Au7hvX19RUfc6lz1J9jWAeHKkIoP2ybNm0A07DYrwfZmRv2ebfbbjsAnnnmmYzP/QniQosWLbzPwDSJ1uugUpTW2HOOI6wPb7nllsvKFgmCjlP8bz6UKzUrTh+l4pnLWXAtF8qRXlfpRtROh3VwqAEUVYRNu02+CBSb7ezmToolVitJsVGvXr2YNm0aAGPHjgXg0EMPzXlO7X7LL788QM743LitoMWyK2QzT67dWg29CkXz5EOlLL0q+VkMVCZHSf/VAsWcay21hv44cbthWRQ21u9ALVaLhWNYB4cqQlE6rHIbFW+rHeWXX37x9E17t//hhx8A4xcVK6622moAnHDCCUBj8fADDzzQOx+YCg4q7KWWjPLTXnbZZaEn6tsVQ+k/+bKJgkrXqByo2GX+/PmsueaaGZ+dfPLJgCnp+vTTTwPGh2hXsQiDcscSz5o1i5deegkw0lESjZyFsDrsSy+95D1Dduz24MGDAXjggQeA7FgAP5sqHkHSkdqLXnPNNYCR+GzJKoqdItYSMf7gfP0QbcOQJj5p0iTAVBp85ZVXADJuoKr0nXrqqQDsvvvuAGy22WaAqZZ/zjnnAHDuuecCMGzYMCC7X00+JGGwuPbaawFTi0q1jlZddVWgMflZCd73339/xpiPPfZYAE8tsB+YKPWsyvWD1Wa8ePFi7yHeaKONgODSKvbzEgVJGp0OP/xwAN58803AVOTs0qWLV4xBZX70g12wYAFg1kgVF4866qiM98OoMM7o5OBQAwjFsPk6pUtJl3irEjF6X+yo8hrq7NaxY0fmzZsHmCr3qnrfuXNnwIgl2pXtCn0q3bFw4UJPrLYNVApRDApNzAebFU466STA1OtVGRFV/FdI3AYbbAA0du7+z3/+A2S7fiSaqeK8REob6kCga+VDPoYNI6b169cPMK44QWrQyJEjgcYiAUcccQRgpC8VDrDXSqVyVDguCqIwrGpHqxKnoLWSKiL8/e9/B+Cggw4CMkvezJ49G8BTc8SgmrvqNdshtL7xFrz/jmEdHGoAkXRYv+Jum78l34sl1YdmnXXWAYzz/ZNPPgEaGfaf//wnYNhZvUikAwrS81TATLpDroJv6hxnG6iiMKxg3ytbX9fc7OO/+eYbzyUihhk/fjwAe+yxB2D0XrG09J0oyfxJ6bBaQ5XklJSQSqU8+4NKv2r80gllmFGhOuH1118HGt17vjFrHjnHUYoOK+PYjjvuCGTrn3bSxwcffADkLrVr9xzS+tuBM7lKpoado+AY1sGhipCXYZcF4hel76iPzOabbw7Aww8/DBhLonYhMYxcREcddZRXKV+9cuTi2GmnnQCzM2288caZg49gNi9ld5aeIybX7qw5SmdRhzj1hF2wYIHXj0YMatsD4gyFi5thxYrSw6XXyZI9ZcoUb17CpptuChhra5CFNIrVOA4rsdhRz63ca1dddZV9LV3Dey533XVXwLh3dExQyGaUnlSOYR0cagChdFjJ5n4foZjhr3/9KwDnnXceAHvvvTcAw4cPB4z+I1aS1fiee+7xLLiyxL344ouAsezquxdeeCFgSqcKxeh5vn6ssfnwHnnkEcBIC3LCy4oqvWjixIle8kPQ/T7zzDMBowOWgqgMG+QvlKQja7FdFKBTp04Fk8U1b4Vm6rWkJ9k8ikGcfti+ffsCplOfoHvh94sHSUV2woueNb1WO45333236HE5hnVwqAHkZdjmzZunITvaxh+2pc/Evgqpu/TSS71jAHbYYQcAHn/8cQCuuOIKAE4//fSsnUrWNrsnpwLK1Swr19jtc9k6UpTdWd/Vuezr2mlnuj/SjxYvXsyYMWMAuOOOOwBzH6Tb6zu2PzAK4tJhgxI6nnjiCcDofQpD9UNMKjZW2J7WXRKWgt+lHxaDOBm2kP1DHoyhQ4d6PnKtpaRIRa3JVy5JUXMXgizRAeNyDOvgUO0oqQN7fX19VpC0nWSuY8U+tq8SzM6jf7WD67uKZNJOLj9sGKtboSJsQejXrx977bUXYKKzxDzqB6soGv99saH7JMtiu3btMo612Ux+zdGjRwMwcOBAAB577LFQUTLFsI8tldhWbvmIJ0+eDMBWW20F5I+RlX9Z/mZBCREjRowAzD0Ng7AMW1dX57GeJBoVY5g4cSKQ6QfWdwph5syZAHTt2jXjffldFYcsyUzW9mJSKx3DOjjUAEIxrKyfws8//+zFwA4dOhQwPkn5HbWjSv+Ur1URUCuuuKJnZZXvViysXU87mFha1mB77Outtx4zZszIeM9mgUK7s/SScePGEQTbKqjdWNdSOVYx0zrrrOPprJ9++ilgpATt9IrAUfyxf06AN690Op2lK+cYX0k6rNZUkWc25MPM1d5k//33Bxqt//4xynKqddA8tbbyHshDkA9RGNZ+VjQOm0n1vuwnxWTYSCJRyqikJ/0G9LwqCrBjx47e/QiCY1gHhxpAUQybr8i2LKE+K2zOc9ltH/26W58+fQCzC999990ArLHGGoDZhXV9WaRz+bWSiENV0r3Y0bb2yYenOSonVEilUt6xiilWJpLipm3pxYbm3KxZMy/jJ8jvXKofVjqWdC9lU8lnma9Jt3zmOkZSk/zzL7/8MmCsxWeccUaxw/MQh5VYko1YULAbaOeCnl1JOjqHXp9++umAiWMPk8ssxJrAnsuApJBEJagLdg0jpVUpjK9Lly7e+fTgDxkyBDBikh5QuXN0U3PVW7JvTo66UJEX+6OPPgJg6623Boz4JleMNgslLivB4YEHHvA2NkEGnb/85S85r6VO7TJcqCaWUrryId8P1lYRxo4d63USnz59OmAc/ar4sckmm2R8194UO3fu7IUgSiVS0LzmedtttwHmh6INXpuxDDjFhJmGXcM111zTK26gDVPPo1737t07Y265oO7yCtzRWPUDto1K1113HWCemz/84Q/eeArBicQODjWAUAybq/awXB4SfQTtOjK2KI1KBgsZqXr37u05om3TugwtYgOFyEm8FIMpGP36668PTPCOWtMJTE8h1fSxIalByQkaz6233qpreGOW6JtP5IJgplmyZEkog0UUcVEO/6D6ym+99RaAl7Q+ZcoUb7wSnyV1iF2k3sjYpqqBmouuaQfL5EIcIrHWw5bIxI5Sb/zrJAOaVBGpDGLlF154ATDBILqGnUI4fvx49txzz7zjcwzr4FADKIph85WGCYJ0tosvvhgw7hLpONJtFy5c6LGiAsFltBEbyxw+YMAAwOxkuobC3lKplKc7KY3P7vBW7O4cJXVPc1TghySHVVZZxWNW6fpBkDvkyCOPBMyOL/39888/98YmY4fcCEJYhhXL6TxiEhnENJ/tt99e5wRMiZjRo0d7xypMT8cq8EOhiJJ0ZDUQ5xoAABQySURBVH/Ilbih5HJd10YUhg1aT81VbKnnRbr1hx9+6H1fkp7uk6QBuSwFnUuBPjqnju/atavnxlNQUKE5Co5hHRyqCKF0WDtt6JtvvvEYU+/JrC+rnMp73nvvvYCxHvphJxdot9XuJzO5LIna9eRmUMcA/zh23nlnoDGUD6LrsD/88ENWWROlyonplVYnd5TS0GQVnDdvnseUQTu9v5AcZCd0+x36+q5KtUiaEErVYW2JSkXndtllF8DodSpP27p1a6+EisYkt40sq7a1Xix03333ASaRoJhSOGEZ9q233vKkNtvDoGdahdWke0un1lxbt27trY2kJZUhuvLKKzUuwOjttvdAkqD/eQpKbncM6+BQAyiKYe1dIBdLKBBAu7ESsmUNlqXV/u7IkSPp3r07APvssw9g2EVy/r777gsYfU47nP6VTzedTnt6QjFBBf452pBFTxY+/5h1bl1LwSDyMdq+Z7/lU6VT5OOU/q7d27ZeihHEUIsWLcqy1uv+KZAkrvQ6SQ79+/cHzHOgZAfp1V988YU3bpU1lU4rnV2J+SoHpALqt99+e+hxlWIllv6p8FGFX5522mmAWTsFeug52HPPPbOef7G2vqPnVXYIxRPYluj58+dnFRgsNEfBMayDQxUhlJVYx6qVxsSJEz0WkZ9N7SqUYqRjd9ttNwD+9a9/Aaag87Bhw7yd6KabbgKMhVRB6NLvdH2lSKm8qYLQ27Zt6zGVGF6RNmGtxLl8r7pODqssYNhTvmf/LhoUQWMHx4slxZqCGLa+vt7bsVUwzO7DExfDakxiVLUkydVVUO9JT5PkYCdIhOnxm6t87bJzlmzpV8D+0UcfDRhJRr5+WeDFmtb1gezUSL1vR/3lC03U/bIj4RzDOjjUACKVOdWOccghh3glTxQPrILRKhEjy65fB4NM/U9RJTbkl5WOoHNKZ1WUlXx8c+bM8a4TlDAfVv9p2bJlhp8Xsi160nc0DhuLFi3y5qsEAiUUKF5a+q8gHVBsLqumP0nA9kPnmmOh+bVr1867VtiSo35dWvNSo7IbbrgBMPdf98xmkigIu4Zz5szJaquhVLgNN9wQMI3VFJ2m9D81ZPv2228z2nXkgyQT+znRWup+54NjWAeHGkCkbJ1c0K4h/6cSmRXppJKg2rFyQdkN8m9JD9LuqAZFitkV2+TL3vHNBSitVYddbE33ThFZ8scFxRyDkTA0V/WJLTTuMMWok2rVEYSuXbt6ifZiVlnOFVcry7rWSL5KeRfCoBQrsa3X2q9lL5G1O1/2jp4DWYula8vWoTnHmV7nGNbBoYoQiWG167Rq1cqzEit29KmnngKCC3QpEmrChAlAY4tJWepUjFo6gPRhFeySJVolMcVWKlh2zz33eGxrl5gUCu3OYWKIg45V7qr8mD179vQYRX49xUcHtXewmVQ6cNh8UXt+duMxP6I0HhZ03+WHV1uPoMbOQr4i8EGfFVrDfPOI0lxs2TW9Z0proDUq1AI0n3QUNFbHsA4ONYBIflghlUp5O1ah/E5BVjnpCA899JCXIyldVUxrR5aonIjiT/OVKik2RjMJHU/M+txzzwGNZVHWWmstwDRJVnUN3VMVq1MFBI27UO5rLpRbhy03yrGGNlZddVWvqF4hiDXtrJ0wCGJY0ul04B+QLvTXokWLdIsWLdJ1dXXpZTcuDaRbt26dbt26ddbxw4cPTw8fPtz7Xq5zduzYMd2xY0fvtX3uFVdcMb3iiivmHVcqlUqnUqn04MGD04MHD/bOEWWOpf6dc845BY/p0aNHukePHt7rMWPGpMeMGRPpeuWeX1J/Q4YMSQ8ZMiTv/Kp9jsWsof/PicQODlWEvCJxQ0NDGrLN034FWaKnjA5y4isQQIEBKlw2adIkwBhbfvnlF69Lt4qX6VhBNYxV01iQ0Uni5qeffhrYD6bY0ESVe7FT1pKG7WYQ7NTDIs/1mxaJ/UkJ5YRcliqcJ+iZV8eEYuCMTg4ONYCSAic6dOjgGY+C4O+vGXANj12UDC8lXcyuAAnbVSOnu1wmK6ywQkEFP6zBIp1OZ6WzBSHIRO+fo51e5z+mmGsUg986w9po27ZtVv3hICgp3+4XW244hnVwqAHkZdhVV101DQSyaENDg8ecURkiV98TQYwqhpUOq6Ryu1BWu3btvPS6IF12yZIlZXcJ5IOtu9rJ7xHPWRaGLWbN45QchHK4dcKkAarsq93xoRQ4hnVwqAHkZVgHB4emBcewDg5VhLxxb9INlBqnci/VjEqEtZUb/jmqCIH85GpKFdT7tdzI17ys2MD4alzDoOIDgtNhHRxqACX5Yf1tNqoFtbA7F0LSVmIlpweV9kkav7U19MMxrINDFSG2EjGFEBQrW2781nbnQvPz+8Ftn2kpCe1B383VsjTXtfOhHGuo6DnFuatoQrngGNbBoQYQPjs6ImxmVZywP0E7avkOh2DYrUBs+Nub2PHeNjvmWrMgBLFy0Pt+Zo3S3jQu2Ixvl+WtNBzDOjhUEYpi2CB9ZPHixYG7bVAepwqmKQ44nU5n7WZB5yilbEol0dDQ4I09ij5YCoKYVdh+++0D9TPdZ425lPuu8j9qQ5oPcTNq8+bNA0sYHXrooYBpvq1n8eCDDwZyM2tQw2kdq9K1agAWJxI3OqlK3zHHHAOYvqF6XVdXl9WxzYZEMd1Mu0/tm2++CcAmm2xScDzlNDopaWHrrbemZ8+egJl/kmJeUm4d3d9p06ZFPoe9+ds1noPSMP2IYw1VO1o9lDQePWN65lRjq0OHDl6xhnzBHrkQxXjnjE4ODjWAsrl18iFoDOqEpyr5Os42TmkHW2WVVbwEdv2rioyqYFhOhlVHgIsuusjrPK+eLZpbvm4FUVGJBHb1ClKPnaCiA0HQmrZr1461114byO7+5usSV/Ia6lmS+0ZliT755BPAPFvqh/P999971x8/fjxgKmAqzLCQYUprrTDRAuNzDOvgUO0oG8PaHb103Z133tnTibS72X0384wv41x+d4DdlVwoB8PawQFt2rTxKuHvuuuuGZ9JEhAbFyq5UwzCMqztRpFEow5vWrNOnToBMG/ePKCxJA80sunEiRMBUzNafZDUn1flfPbcc0/AuJv23ntvAMaOHauxF3TvlbKGtr1ErKeOFKqJnasGtwymo0aNArJ7Ldn3MaiDYjFwDOvgUANIjGG122iX0a6Zq0iaXSJVu6/KpEiX2WKLLTK+9+WXXwKman4xqIQOe+yxx3L//fcDxs2iAHp1aRdrxYGwDCvdS6yjUievvvoqYNZH0oH0viOPPBJo7NOr9VX3N9slt8suuwCmO71Y/PrrrwdgxowZ3lhUGreY+eWaYz6Gtp93PafqOJevB6wkiqDyPUcccQQAt99+e57RFwfHsA4ONYDYGFYdrK+99tqijtcuuGjRosCEAP8x1riA7B2vmMSCJBjW1qXtQtbt27enS5cugOnmJr1G3+nYsSNgrKylIB/D5vIJimGff/55wCS3z5o1CzDsoyLrKogtC3BDQ0PBsEXphtKDH3zwQQD69u0LGB130aJFBX3Uca6hvXZB3f38SRJB45O1uNgO9vngGNbBoQYQW4yfzay25dbe2dX7NVd00ymnnALA6NGjM97fb7/9cp670il70vHE+NJTzz33XKDRUnrAAQdkfCadVVZKRdQkjVzlXxWepzKyM2fOBAzDKmxPUpT0T7/fUb5TfUdrqHVWb1+xsiQOP7NC/giwKJ3MBd3fgQMHAqbs0W677QaYdjB9+vQBzFqqfcwNN9yQV78FOPHEEwHz3BYTtSUUGw3lGNbBoYpQsg5rF/sOi/r6em83k3VQjKkoE1lU9b50BelcakJUDJLQYe1etBqn4oeHDRvGt99+C8CZZ54JmB1c97+UwuE2wliJGxoavJ69Yn9FZckqLB+y5iWJYssttwRg9uzZ3prIK6C44xdffBEwuqvmO3LkSAC22WYbAC688EIA/vznP4eaXzFzzFWsPqigwtFHHw0YH7R02XfffbfouIA44HRYB4caQNljidWGUozia8rrWRjFmHotJtXrDh06ANkW1VGjRnHaaaflvX4SDKuoKrGndBhlEc2dO9djoOOOOw4wVlKxm6zHcSBqiZj33nsPMJKOoo9uuukmwOhZkqr0unfv3p7P1u54rwgi+d/lO9eaiqXlsx43bhw77LADYPT7JGOJdW5JPBrvG2+8AZiIrZNOOinwXJqzpIs4Uigdwzo41ABCMWwphdRs36TOVV9f78XPKqNG+p4iR3r37g2YyBs7LjkM4mRYW3e1I2xkRV2yZIm3Y3/22WeAsU4mUQ4nH8Pmim+113XChAkZY7QT2MOMWefUverRowdgdFpZa4uBzy8feg1lJVYklv0M+5qlAXDXXXcBcNBBBxU9PkkEUe05fgQxbGwisf3wBr2v6+2xxx4APProo94DocVUN7AbbrgBMGJkHB3eovxgw3Zg0zyUJD1o0CCvwobug14rzC+p7m5B8/O72/RD0FjU0V7HFOq5m+u8mo8eXrm1FIKoxHHdB7lErrvuuoIbcZQ1LNSVXYY1/eiUpOCHVAWpdb7xACZgQq4jPfvPPPNMxnHFwInEDg41gMSNTjq/dk0p9bNnz9Y1smo2BUHuBIlktnJ/6KGHek7+POOJ3egkMdNO3ZKksHTpUi/0cPnllwfgvPPOA0zJmCgpWEEIG5ooqDv8O++8A4Rz/NuQS8hWd2R8840PMMa36dOnx86wHTt29FSRQhDTyvAlqaNHjx7e/SgU5KHkCbmE5KoKk27nGNbBoQYQO8NqB5depJQ5yfOS88U0YaCQObF0LhQyjCWZXte2bVvAsIjC70455RT22WcfwOjdSvg+7LDDAAqmlIVB1BIxQYYYO1XOLliWK+m8W7dugNEZ5fp64YUXsr5rQ+4lncM+Nsk1FLvbJW7WX399Pvzww5zf0fj69+8PwFNPPZXxfi4EFXQQHMM6ONQASgr+b9euneeCEWwXx+abbw6Y0phR3BiXXHIJYAKz86GSiQBiz++++w4wdWn32msvtttuO8AEDMjSqHBA6edJFGWDYEZr3769F/ppM6dea8xah0cffRSAjTfeGIDJkyd7nexk7VUZFZ1jxIgRAFx55ZWACZhYd911AVPKZ9ttt/Ws7JLKCqW1xQmth/RUu6RuLtghqYLmrkR/f2J7VHeeY1gHhypCyTrsXnvtBcDDDz8MGIbQjmvvWCqHqRC1pUuXFmTFMEXZypn8XAh+fV76txz4Kqup+5VLL4yKMH5Y/3WkM0pq0liLCVDR+VQNf8CAAYBZf1nFJ0+eDMCkSZMCz5PkGhaSZOz7Ikkon41B9hiFosrGIqlBFnBh9OjRXkG3IDgd1sGhBhCJYZXIq9IhYKI5lAStaCQxh62P2CVD/NB3tAvavru7774bMNEzuSCWUIqeUE6GFXr06OHpag899JCuC5TWgzUIxTCsgt3zRTEVYnt/OVeto3rn6LxiGdv+kK/QuF2QW/fGZ1FtUj1+dX/0vKqIoGwY0uvtXkUFzukY1sGh2hHJSixmTafTzJ07FzA6q5hVO6gKdmkXUhSIooByQTu3SmLaeoeukQ82s1YSb7/9tqe7iWmlLyq2tpT46CgQA+bSGbV2+tf2BAhap8WLF3uphdJZlfqo1DzZLhRxlK+Fh+wdSUgfgqy0N998M2Di1SUhKllDXfdU+sYP3RdJTYMGDQJMJJNsALbVvRg9PQiOYR0cqgixRToF+crUS1MF1FTUKheUxKydK2hsYnP5D8OgEvpPv379vILZ0uVVPkZMF5TtFAVxNcMSw8nqqdIxWhf1QV1nnXU8SUr2BtsyqvmKtSVp3XjjjYApL5rPLiGUsoa2Xi42lIRjx/n6j1dbEdlflO5Z7DXDSAxOh3VwqAHEzrD2DnbGGWcAJitFUFHqbt26eRkdylksFmJku0hbgXGWnWFTqRRXXXUVYDI31DRKuZNxIirDSq+eOnUqYKydaoViZ7xIKqirq8uK3NFaqDWj8p/FwNL3xNK77767d41C1uk411A6qhqMX3311YDRxb/++mugUUJUhFcxJVn9kL4eJn7eMayDQw0gtkLisq4JKsWhiCZB8r8aGqfT6dDMKmj3q3Qh8SDI7zZz5kyGDh0KGL+zLN4qKGeXzikX6urqvDhfFQiXnqn7Kma1W6SokdfJJ5/s+Rrt8jhizltuuQWAe++9FzA6qwq9+VHOeyC/sYqtzZ8/HzBx0rKXXH755V6GlT/+AILtD7qvF1xwQWzjjT29rpBDXgtZSnJ0KaiU013B/1OmTAHMD3f69OkaV2zXCiMSp1KprAqCej1kyBAgO+xUP1ylSo4bN459990XMGqNxGoZl5QQYYu7hTrC5fpOOdbQdiXuvPPOXsKCjHBKUNcGp24OIqtS4ERiB4caQOL9YcMwh3ZllSophChVHCsd1tarVy8AXn/99cSu4Z9jKpVKL3sP/7/F3DMZBtdff33A9MdRsIfEx+7du3sMqRQyVdBXaRyJ/LbYqA4Aqvzv7/wW9AzFuYaS9OxwyFwo5HoLurdRpErHsA4OtQBV3s/1B6Rr7e+3NsdCx7Zo0SLrvbq6uvQy1koD6fbt26fbt2/vvZ4wYUJ6woQJ3uuXX3453dDQkG5oaEinUql0KpVK19fXp+vr671zNWvWLN2sWbPAcfiPsz+bM2dOes6cOZHXcMSIESXf0wULFlRsDf1/jmEdHKoIeXVYW/8pN+wQRFuHkDUuTG/VSuuw5YB/jscdd1waTFH2KIhij1CC+ksvvQTg9Tx67LHHgEarK+AljyiAoVevXl6v1iA0tTVU8IeCY+znVMEiKkhYDJwO6+BQA8jLsA4ODk0LjmEdHKoI7gfr4FBFcD9YB4cqgvvBOjhUEdwP1sGhiuB+sA4OVYT/B4WTV7/SUrvpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2620, D: 0.22, G:0.1813\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2640, D: 0.1998, G:0.2331\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2660, D: 0.197, G:0.1851\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2680, D: 0.2489, G:0.1699\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2700, D: 0.2472, G:0.1607\n" + ] + }, + { + "data": { + "image/png": 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aGCX1bXQqBE6HdTjKACdh01xjRUVF1hZkSVN1bJ85c2agFVivK4E9qLdoULpgMooh+D9KnIR1OBwlQUoJ63A4igsnYR2OEiJl1LKtGyg1TInZhUYFx2fNmpXwujpgf/nll2mP8WfTf4r9+hSiqqiwTAiawzDagIRBPk2whNNhHY4yoKisxGHG04ZZIiYdSn1TKlx9E7aE7dKlCwDvvvtuvocKhSjmUEUT1KYklU+9EDgJ63CUAXlJ2O7du3vNrkoFp8Nmzl577QXAyy+/HMKowqPY5zCMnaKTsA5HGRBZQ2e7vIuKcKm5U9S0atUKMAnTothX5zAoBitxNpZ7m6ZNmwJ123eKQs5hFDnZmeAkrMNRBmQlYYOkVioeffRRwLSxUMzsvffem/ExVJRN5UPzwUnYYLItsie/5/HHH8+ee+4JwFVXXQUYq2sUlMMcprvXBSsRkw5tMVRFf8SIEWkfRAW8qy/NbbfdBpgK+6oEmAmFmOxkDnz1uu3cuXPYp6tD1Fti9Q6SUWrRokU0b94cMD/EKIMXCjGH2SRbRIHbEjscZUBeEraiosLr7Kbq/epvaqPzvPHGG4CRiqtXr/YCDlSoTB0A1Ffz2WefBaBXr14Jn1N9Xhkpfvjhh7Td4utjO7Xxxht7Rcq23XZbwPS+jYKwJKwdCqqO8qotfd111wEwb948tttuO50PyLxmcy6Uw5Y4HU7COhxlQN46rL5v9ypVzdz//Oc/gEkYUAKBPrfrrrvy+uuvA6Z8ikLgVNtWPWr69u0LGAl70EEHAXDBBRekG6Z/vFmvzulKtwYhKf/xxx97upDuk7p5yxgXJmHrsEFJH3LZtG3bNq8a0tmSyxwGldOpL7dNOpyEdTjKgMgCJ9TPVFIwKPVp2bJlXn8Vuzu3+r/YVuCzzz4bMNXmU/HNN98AsMEGGwCF0X90jfZqDub+SO+W5FWweRipi1FZie3fiizf0l8LRRhzqGtRcL86LWjO9Ldr165A7byox5LCcWV7mTBhAgCHHHJIwnf1+mGHHZbt8JyEdTjKgdAl7JFHHgmYfi9CAeTdu3cHjBTq168f48ePB5L34PxjHEBdaaSOaPIBZpIKVQgJKz1O17hkyRLatGkDmEAS3acodL+wJKx2NrK8a4y2rzUT/S+XzodB5DOHCxYsAEyfWtlP1BMoyF7RsGFDL1RSuzbZVmRDUY9b/Q51rFyu3UlYh6MMyLuxpTpsS4+RZNWqoi5yioqx9bs/2mV4/w94YW6vvvpqwrkkgVV6I5lEDXMlDyLIsijrtr8JFtTqz1qNpc/IH/vBBx8AMGjQIADuvPPOyMadLbIh2HqebWvIBPnpP//8cyDa+UnF888/D8AJJ5wAwM477wzAGWecAZg51Y5I19qsWTPv+hVhpzlVooP9e9Qz8d5774U2fidhHY4SIiMdNsjCG4vF6kgZW4LOnTsXMK36ZFnzF9265pprALjwwgsBs8op2UCWUxW30uqspAB1BG/ZsmXaYl5R6LA9evQA4O233waM1Nx4440B6NatGzNmzACMtVp6UDJLcr6EpcO2a9cOMFJR86L56NevH1C7y7ITQmwprN9FPpZTEeYcyg4yceJEILeWkUHRXfnMrdNhHY4yIC8rcTIJa//7559/BkzHbTvipLq62luxs12RzjvvPACOOOIIoDa6SvHHQVkWUVqJpcMoxlnXWlVV5eny+oz0celFYWa3hCVhZY1XkTnbR2mdE4Ddd98dMBk9P/74IwCjR48Gai3mYLK1hHZgst6mIp851NzYDahlrdfOQPPlt1dMmTIFMDHtvvEA5hqHDRsGwLfffguYnUo2OAnrcJQBefthFckkS5isxlpBbf+WVpuWLVsC8Nlnn3nHWrp0aUaDtldBv18wSIf16RmRSVjbp6rV/JRTTmHkyJHeGAFatGgBGCkm/TwMcpWw2gXJ+qk5lS4ua+hXX30FmNhuvzVfn5W/U8g6q12RfOeXX345YCRzJuQiYRWXrgZlL774IpDfzka/wyCLt+w38ghkk1sbJGHzduv07t0bgCeeeCLhdZnJNfn68aovqv5mg27QJZdckvC6fuydOnUK3PIUIrjbDn4YO3YsAOPGjfN+MK+88gpgfqB6fdGiRZGPLx0ywGhxk1tC1yVDmXra6qHcZJNNvIX6/vvvB8yDIIPVvvvuCxjD3IEHHgjA1VdfHdXlJKD+uwpc0YIpckkCuPjii5O+rqAYnStM3JbY4Sgh8pawgwcPBsyKqgT1Y489Fkjc8iajWbNm3sqeDq2CMmTYAQrz5s3LZuiRc+ONNwImtA9MUMicOXMAs60vBmzpot2Kggjsbfsmm2zifU8uuTFjxgBGPbj00ksBM0cqeDBq1CjA7NCiRr+VvffeO+n7uvag6p7J+gRrOy86dOiQcIwocBLW4SghcjI6JdvvS6HWynruuecCcO2116YcQMOGDTOWMlrx5WaQnipDQoMGDbyghSAKWV5E6VZKNUxGMQdOKGHj8MMPB+pWy5TUatKkiRf4vtNOOwHGgCgjo+ZKIYoKpFeaoXZimRiBwpjD/fbbDzAB+0HBQTIode3a1TPC6fem0jmXXXZZwl+56vIp4ObcOg5HGZC3W2fcuHGAWYUlMWbOnAmYFdcfKAFGEsuRngy71KQd9K+xq0xq//796d+/P2DCHLVyKoCjEBJ2iy22AExJ07lz5ybosQB/+9vfALjvvvuA4nDr2Ng7KbtEraz13333nTdHmjO78r/sFH369AGMK8ifvgaZ3YegObzyyisBM/d+5Eb8/vvvE15X8QTZRRQ6Kd1aOvmYMWO8zyisUp+10bXbri3Rpk2btF4BJ2EdjjIgbwm79dZbAzB79mzArFRyjOfjmLYDEeyx2oXdkumDdjBAIXVYBUXYPmHr/KGfN+wSMeqZqmr+2rUoCGLAgAFe+RQlbEhHlZ950qRJABx11FGA8c8qGSKf5O4o59AfbnrTTTcBMGTIkITP2CmdYcypk7AORxmQk4SV5Ntxxx29Ehv2qqKC4rISduzYMedBBklWJRL7A7fTFbKujyLUlZWVgSVgSkHCCumZsupL0r799ttss802Oh9gdFmVmdHvQL13VNwsFz90fRUSV3SaXVjBN47QzuUkrMNRBuStwyoiRFY4STvpJscffzxg/LFahVIFTsvfKv3BlpYas8qcSpfKRA+qj9V5zZo1dRK67cLrYVKo/rCK1vrpp5+8RmXaSUjf09zYcxjk98yEQs6hf5zaNSi9TuVO5YEIEydhHY4yIPQEdvkbbZ1VK68ksQp8tWvXzlvFZFVV5odStcQxxxwDGP1IPsxu3boBJo45FfUhYVetWuXtPHznjex8hZKwkqrrrLMOU6dOBYzFVL8LfxI/hFPOtb502KCMHu0ScilOF4STsA5HGZC3DqsCaueffz5grH7KnFHJFu37bT1zxYoVnrXXbqyVYlyB7+k8KudhU8jVWXmjY8aM8SKbhPyVduRNGBRKwvo55ZRTABPBpbzYm2++GTAJ4/vss4/GmPaYQSVri7XdpHzP9u9Z5W+7dOnifVY2nf/7v/9LeiwnYR2OMiClhK2oqIhDbpZMtZlUZIv2+cOHDwdMLuWQIUPqRDTp31pZVb1Cq9E999wDwIgRIwLPbx9LFHJ11rmffvppevbsqfMDprB6kE8vH+pDwhaSXOYwH6t0kvPrvHkfK8U5kh489N466dCNU4pco0aNvO20ugaoZlAuN9dOGLCpjy2xwiLBdJdXmJ+2xDLGhUExPbBhPiginzmUqqYEjSCSuR1vueUWAIYOHQqYxAH1+lVt4zvuuCPT4QTitsQORxmQUYmYbDqs+QtzJUMrrVw4ych1y9GoUaNQ09TyRSllVVVVde6dDCphStZi4uCDDwbg8ccfr+eRJJJOsgoloyuIp6KiwtsVChVLkFvRLitjE0a3dydhHY4SIiMJa0sHrRS77bYbr732WsJ7m266adaD0PFUtsOWrJmuTMUkXf0k25nUV/e2TFBAhIoQZIPmqtgkazrsxAb9jpN1SFSJ1qBdZBCpfr9y+cgFFISTsA5HCRGZlTidtTYbVNBN5TpssukJW6xO9zApJitxFBTLHA4YMACIpmC4sxI7HGVASgnrcDiKCydhHY4SIqWV+M+g/1RWViZ0mbdDGmtqajKO0gnDz6ZWEtOmTQNgjz32AEwIo9/XbPvHpcuvXr3a6bAljtNhHY4yoOCxxGGSi0SzV66gBAd/DKxthbbLu6jhk92qQT69qqqqOtZye+yZWrobNGiQNuIsbCuxCrgXi587FwmrJlzq2VrsOAnrcJQBJS1hcyHd6mxHWcXj8cCWFYoDVuMnWzpm4x8WdvK+PT/+f9uZMMm6zP8Z51BRSiqeUIo4CetwlAF/egkrK3E2+Zp2QTFbAuuvdL9Vq1Z5x5fUtaVjisLndV5Lp1O6SKfSx0lYh6MMyChbp5zJR7IKuw2mKhHIMrx69Wqvadj8+fMBIx1TWachuR6s4wbp1o7ypSgeWPuHJ2OOqs+pl47+Fho9RHpQmjRpApgHWInqQqVh1MmvT58+Xp9UHcN+UO2HzQ6K8D/ItpGp0Oy2224AXl+lZGRT9MCROW5L7HCUEEVhdJKkuOuuuwATjid3yc477wzA/vvvD8Dzzz+f8L18Aifsa0xWNMwOlNB5lXSsrfCgQYMAU9VR5UUqKyu9rgS2Ycrekks621I72RiDXD5hG510vb7Qx4T3q6urvUJzSupWYTJJ4zApFqNT0C7ihRdeAEwN5lxwRieHowwoCgl7xBFHAKY3zrBhwwDTf+fss88GTK/R7bffHjDlNOxQwFQErc5B0joWi3njufHGGwEj4aTDdu/eHYAZM2YARnc94IADAJg+fbp3fOnhQWMOI4EgWwkrndwuMqbr0y7ADgBRjelbb73Vu2a7P2+Y/WZELhJWpUcHDx6c1bm23XZboLbgmnY91dXVgLk2FXaTIVGdG/V7ENmEeDoJ63CUAQWXsFqBVea0pqbG038kUVUq9dBDD034jiRWUN+cTMh0dU4m6aTDqWq/dOzNNtsMgCOPPBIwRcxeeeUVAM466yxvVZWU0vH1t3PnzgC88847gNEF5QYS/uB/rfA6ho4dlg6rMWg+hO6J3FexWMzbOag7g3rHaAchKR5G8bkoddggC7w6zIPZHS1ZsgQwIZD6fVx55ZUAXHjhhUmP5SeolJKTsA5HGVBvOqz65VxyySXe6quO6ldddRVgLKValSV9lOQtnUudsKVLpiIXK7F2A+o4p/FIt1533XUBOO200wBjNVS63fXXX+8dSxJH97158+aAkUDt27cH4LnnngPq9gZauXJloJ4blZV47NixAJx55pkJr0sqVFVV1RmLevmqx1KYRCFhZ8+eDdT+HsF0Y9Ruqaqqyptn/T4HDhwImN+p4gaEfieSovl4M4STsA5HCVFwCStr3Z577gnU+ljVV3PSpEmAsT6qm7tWqqZNmwJG+uWSUB2UwG5bN6WP1NTUeO+1bdsWMJJTkqdTp04AjBw5EoBdd90VMDpOw4YNPV+tUr/UzW78+PGAkV7y4U2fPj3pNVZUVHhjzET/yWUO01mqk72v12Txlx82imisMCXs5ptvDpjmZPZ4NW/yqYO57/pdaj7s+7Vs2TIAWrRokfB+48aNkxYo9+MkrMNRBhQ8llh64MYbbwzU6mwdOnQA4KCDDgLMiiXfnqyxixYtAox1MkzsiCP/v7UyKh54yJAhgJGkinBS5JP0N1mzV65c6UnbX3/9FYDHHnsMMKVL5INWJ3O7BYo9Fvv/ITxpZh/XluTJJK/09pNPPhkw7SyKHVm3kxUuAGOf8KP7IP+8LOL6vcpPq12S3boynXRNhZOwDkcJUTAdVr6rjh07AsYKuskmm3grjyTn4sWLAWOxe++99wB48skngfyaA2dahE0rblVVlWfZ1TVIv9V4t9tuOwA22mgjAO677z7A6Lxffvmld03ybervFVdcAZgIHH1HK7/8sLJU//bbb2nT6uqjRIzGoutUtI8dOx3SuULTYfV7VOtI3UvZS/zRX9r93H333YC51qCWoUHpmJngdFiHowwomISVlS1ZU9yLLroIgIcffjjhs9IVZD0+/fTTAfj2229zHjcRDfgAABKcSURBVEcuq7N8ocoiUoyovwQMGB+r/MHSuT/88MM6EkYNj48//njAWIMlcbU6S5orbjqT3UWhS8TEYjFat24NmPEpei2K0qhhSliNU7/LDTfcEDA7wN69ewOJeme6fGTtjtZbb71chxUoYQtmdFJggAwxvXr1AmoD+rUlVnrdqaeeCpjJVoBCPg9qptiBEw0aNPC2PArcEHKYn3DCCQA88cQTgAnk0OT7Ky/qAVQanQxqMtborwIo5Nryu5nSjb3QxONxb3HSPMvwplTDYkNd0zW3CoN9+umnARNiKSPhYYcdxkMPPQQEP6g///wzYFSjKHBbYoejhCjYllirkqSAVqNGjRp526nvvvsu4Tvff/89YFxBycIGsyVbo1ODBg3qBNnb1RLthAbtDLStvfjii73UPLl3FCihYAut+Np+azehv/7gCI016H4Uekuc7DeUj8Elg/PlvSW2A2WUIqfAls8//xwwgf01NTV1ehjZkjbMIBFndHI4yoDIddj1118fgE8++QQwLhAZaFauXBnY70Srnb0ahknQDkOSzi/Z5NaRJNVKKxeADBj6znHHHQfUdqKTZJUklcvqkEMOAWDEiBGA0YvPO++8hPH5paldRiaMnUcu+MP1ZIeQFLIrOxYbuq/qEihJ+tVXXwF1kzRmzZrFDjvskPRYheww4CSsw1FCRKbD2iVQJI1k8paeN23aNM9Cp1Va0k1Jw3PmzMl1GHUI0n+kw8ybNw9IlFqS9F9//bX3GhiLqJDFUSGLcqy/+OKL3msKAtF9UHK4dCalGKqEqBIjfMnpdYL9U11jFDrsU089BRiXh98KLgmlpP5C6rBHH300kF1Knx2EMnnyZMD8blUAcPLkyZx00kkA9OjRA4CpU6cCxgX07LPPAmYO88HpsA5HGZC3hM1Uf7JXMkmtiRMnemVLzznnHMAUO9O/wygrIoJ669ghdH4LsCS+Vl27vGVQaVKFHy5fvtwLIpcO//jjjwNGEt16662AuWb5YbWKa1y///57HX9rfXWvS/XbUTK/0ilDPm/BypxqRzRw4EBefvllwOweZbPwl8r5Y3x5n9dJWIejDKi3EjGKAjrnnHO8sK+zzjoLMNZH23cbhhU029VZujfULaBmS+Oge1lRUeFZkv/73/8Cpii6CrUpYUBB6PprWytXr16d9nyF8sMqsqtv375eKpmkj8I4iz00MRvstE9J1kKUchVOwjocJUS9SdgHH3wQqI3D1SqsAPfDDjsMiMa/la6QuO91IFGqt2nTBjBB/fZn7Xsp3XblypVeQWpZxBVTLevxuHHjABNbrLSvWbNmAXXLwEBwh/dCSVhZzbVbAiNh01my86E+JOyZZ57JzJkzAbxOhNJv33zzzdDP5ySsw1EGhC5hg6SNooNUsEyr0ueff+6lqcmqGiVhxqGm02H9Orh8yv7YVDDx0spu0aqtxHVFiulzqdqRFMpKrGwUjfGWW26hb9++AGy55ZYJn5U9QlkvYVAfEnbttdf2otXkb48y0sxJWIejDAg9ltiWMrIAS8+SNNWqtNlmm3krtvINJW3qg2R6ocpUaoWVxVcZR0HZM/6Wkopkkj9WcalKcr7//vsBk/+q70pvXrhwoTceu6C59N6o43Z1fNkc5I8ePHiw15bCbvhkS1YV5pY+mIwg3TxTgpp7pULXouKAGoPm59VXX/UimZQzq4L2hYzhdhLW4SghIrMSK5JJDaJUNkWlISVJOnXq5BVxLgTp9J9UBc7se5Wp7lJZWel9RpUMFO+q1yWJJLXtItXJ9GRJM0Vchd0My0bRV5ovv5VY41KhMsXX2o20wiBKHdaOG5fN4Pfff/fiv+V/lX6ejw573XXXASbCTQTpsAV36+y7775AbUA8FD4lzL4RDRo0iEPw9svvFNd2VlvQIPRwde3aFag1HB111FEAjB49GjA/iH79+gG1Wy4wSQCaF3t7GIvF0oa+Re3WkevGHxShzoOqzRwlYT6wQQ/bueeeC5jAltdff91bZFUSKIk7Lddh1MEZnRyOMqAoOrAXEnvlqqqqikN2aWDpjBp2qJp/SyykItg9dIL6tKjkSkVFRdpwv0IFTvi36bkYenKlvkITbex61ZlSVVWVVYqkHydhHY4S4k8vYXO5Rulp0tuSlUYFE55XUVHhSUwFFiiAIkhaBvV68R9Lf9WXVMf6/fffC175v5AUi4SNEidhHY4y4E8vYYPKnPreT+la8b9uW0/9yQK2xJT11y6daVseJa2TWYnrO70uCjbaaCPPgh6Ek7AOh6MkSClhHQ5HceEkrMNRQqQM/v8z6AbFdo0XXHABAFdffXXOxyhmHVbpglF2IGzevDmQPiKtmHE6rMNRBvzprcTlfo3lfn3w57hG4SSsw1FCuAfWkTWxWKxom1yVO+6BdThKCPfARsjixYtZvHixV1IGykM6xeNx4vE4jRo18pLoS53WrVt7jcWLGffAOhwlRFFbiVV82y5Mlg/pLIy5lPtQrqqycxT3u2zZMsCUxfzj/IAp+6pmWAceeCAQfqvCQs+hXUYmCv7MVuKieGCV9Kygebs6voLqw9hKFmKy7733XsCUEmnSpIlXs0nd5rWVbNeuHWA6oamKvt0JLxsK9cCqiuAWW2zh1ZtWfWL1EBJKSVR3B1Uj1KKWDcX6wNqLvd0jORucW8fhKANCr0ucCyovImmvAm02qjh48803A9F0RkvFGWecAZj+tbvtthtguqVr/Noaa6Vdvnx5Qhc8gEceeSThM5K4uazGYWP3v1WCvKT+ww8/DJi+QGuvvTYff/wxAG3btgXgscceA8xuSbuoZ555Bqhbe9pWK0oB1dPW7km/R+2WVFVRlRHvvvtuoFbF02eqq6uzOqeTsA5HCVEUElY9dSZMmACY7nXSWbUa/eMf/wBMYrgkbtQkq1EM8NprryV9XbqLvtezZ0+vk/p5550HmHKmKn968cUXA6Yu7sCBA9OOKx8dyY+dmC/JqkRyBdMHla358ccfPcmqncSAAQMAOPzwwwHTAULGNnt3FLVkzbWbgP/e6Pd27bXXAub+6JqeeuqphO9o13D77bcD8Mknn3jvZytZhZOwDkcpISd4sv+AeFT/1dTUxGtqauKDBw+Oi/bt28fbt28fX7FiRXzFihXxysrKeGVlpff+8OHD48OHD8/rvFFco7D/3apVq3irVq3iQHzhwoXxhQsXeu+tWbMmvmbNmjrf0X3J5vwVFRXxP0rd1LnGXK6nQYMG8T8KrCeMraqqKl5VVeWN0X/eLl26xLt06RJfvnx5fPny5d53YrFYPBaLef8+6aST4ieddFKdMdf3HNr/VVdXx6urq+NNmzaNN23aNF5RUREfOnRofOjQofFff/01/uuvv8a7d+8e7969e3zVqlXxVatWede4dOnS+NKlS71/N2/ePN68efN4o0aN4o0aNYr37dvXO4/uabpr1H9OwjocJUS96bDyWfn9wNKdpJPJ+iadQLqCTT4+vXzQ2GUlFhrvZZdd5v1VgIR0Ofv69R1ZFrMh7HYnmgeNTVZif9lWSNRp5V/VZ3Udsj+UWjimxt2tWzcApk6d6und6hv00ksvAbU6PBhvh1q6iIkTJwKw1VZbAcYGA9nr7k7COhwlRMEjndQBTAHxsViM++67D6iNmAHTxkKoCdHRRx+d9/nDiJLRNdgrqaSI/LOyIvvZY489ANNkSd8JKqGaCbqXv/zyi46RV6STLKpBbUNSIem7ePFiIDEs03/sbKy19neijHSSv1g7BO2Ihg8fztixYxM+I4kqdK26ByqDo53hTTfdBNT6sYNiDYSLdHI4yoDIJawtOey/S5cu9XQDRY7YY9Ixvv/+e6Duqp0NYa7OdnSOJK4kgT8eWHqPOnx36NAh6THT6XrrrLOOpzMFka+ETSftkxVSs3cKl19+OWD8y9o1TZs2Ldvh1CEKCRt0rbrXCxYsoHPnzgnvSQrrdyC9d+uttwZg0qRJgPGtZ9M0y0lYh6MMKJgOq329VlytwDvttBNvvvkmYCxyb7zxhs4f1uk9olidg+Jg/dZWxefa+s/48eMB6N+/f77D8MhXwgrFNwfFbCuyp3fv3oHHUNbOEUcckesw6hDmHMrSq91Qr169AGPRHTx4MAAXXnihZyvQZ2WrUIbVkiVLALjnnnsAk4mVC07COhxlQMEkrLI0tIKJlStXev5WSSH58iSV7faNYeYZZnONQRZO6a6yHtv3NB6Pp90tBL0vfWj27NkpP2edL9R82NNOOw0wMbH5WLKDGj7bGUKpCEPCKovommuuAUzivX57Pos7APPnz6dfv34AzJo1K2HMmhONvWPHjoBJ4m/RogVgfh+ZWMiDJGzBHliZtmVY0sX4u7HpwmXyVvpWUMBELuQz2RtssAEA33zzTcLrcowPGzYMgC+//FLn8p8XMIvQggULANh8880BM4l2EIR9bzIh7AdWaMy6D3bKIBhjmrbJ8+fPB2Dy5MlAOCmR+cyh7r/689pJCxIKb731FlCrskHtoqwHTshFp2vTfZEAStf1MBVuS+xwlAEFC01UEIEUc610c+fO9VYgGZv2339/wEhYMWrUKAAOOeQQgDpm9qj46KOPALPVsdF4hKSIv2SItlqSpMmkUzK07d9ll12A2u1YJtvGTPHvcNIhaZQsFFJhoXPmzAHqptEVW2iidja6v1KzdJ/t1Ml4PO5thbU7VGqktsbaRmvexaBBgwD45z//mfe4nYR1OEqIyHXYvfbaCzAO81atWgEmCAJqgwHAmMu1KstdIj1IZvJ8wviiDGsTBxxwAACjR48GancCmRqdpAPKyCRjyLx58+p8R3V0FQYootJhlbAtN4b//kuq2GGbthFH72vOcyGfOVSSwg477ACY35qkpG1YVNjs+PHjvbI3utYwg0FsnA7rcJQBkemwdjhhKgkjy5xWbJn+9W/bKpuLZM2H999/HzA68+677w7ULb4mZDWUXrRq1aq0QQg6hnRA8dlnnwGJ+rAtzcImaKwKftfOQUEH++yzj/cZOyFCPPjgg4BJhYyCTBILVGZWc3PiiScmvC/7icqy7rzzzgAMHTq0TlphfejlTsI6HCVE3jpsuo7aqmivotOpVj/bByYrsayi0mmVpiS/Zzbkov8oZPKdd97RdwCTemUzcuRIAC666CKg1j9n65lByNoaJKkqKirS7jCy1WFztQlkkyonq7GKsqkYnUqmSnrLh5mKQtghRLNmzYDa+y7917alBKHw06BgkVQ4HdbhKAMisxLbq7ascPLh6W+zZs0867AdGaJoFKUnKUpq4cKFuQ4rp9VZeo0KiWvXIKuhyrKefvrpANx2220J13HDDTdw1llnJRzTThgImodjjz0WgAceeACo9Rum6xiXrYRVyJ3SwaKwEeiYigpTGVTNeTYJ7dnOYSwWY5tttgHwCp5nej6/7cC2gOfjrfCPLdkxnIR1OMqAyCSs/IfTp08HjO/OLuQVj8c9HdWOECm29DpZTSXhJGklWceMGQOY61BM9DbbbOP5MLWbUPC5pHS6VboQscR+q3YmY8oGHUu2DO1aVFA7m+ZfhdRhdd8bNWoUaLOIAidhHY4yIHQJK31TljH5YaUzSHdLFg+rWOKTTz4ZMJE88veFQRir84wZMwATd5ouo6aystIrJ6KIH994kv61s0d03zIhrEgnWUhzaX2pgnp2n1jNrZK87V1LJoQxh3ZryHHjxgHBifYNGzYMTOfMpadwOpyEdTjKgLwlbJCVSw2UlJ2T1aAijCDJZXW220oGoV2DnYkzbdo0T+LIzygd386T9Y0r8DyKz/3555+Tvp+thLUlhD2nPXr0AOCFF14ATBMyJbY/8cQTDBkyBKi7E1DhAsVXa6clr0EuRFmE7auvvgKMzUWsWbPGsw736dMHMFI5CpyEdTjKgLxjiYMktIp/Czv+UlFKao5czKSTrNJtdtxxR8BkhAh/iRgVXVNWjn3/VMI1Vd6sJKtdQDxXgqpcaIySrNotafekGOIvvvgisAStrMEjRowAYObMmXmNNWzkl5UFWPaSDz74ADAtTv2ljDSHYZCtDl/wyv/1TSFcAtpiqkzKlClTCtpZPKr0OqHetTIoPf74414wi/rGaPtYCINMLteYroaUrRa0bdvWK/1TCNyW2OEoA5yEzeEacymMFtYxKioq0kqrfCXslClTgLoVLrNBY7SNaWFQyMCJVEgFynabv/baawcaDIWTsA5HGeAkbIGvUV3r9txzz8jOEbUOm4pcutNlS33PYSFwEtbhKANKQsLmU+nfplhX5zB1vvqUsIWgWOcwTJyEdTjKgJQS1uFwFBdOwjocJYR7YB2OEsI9sA5HCeEeWIejhHAPrMNRQrgH1uEoIf4fJ5RvpWFaAOwAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2720, D: 0.2277, G:0.1659\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2740, D: 0.2324, G:0.1894\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2760, D: 0.2143, G:0.1693\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2780, D: 0.2346, G:0.1734\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5, Iter: 2800, D: 0.2397, G:0.1752\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2820, D: 0.208, G:0.1755\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2840, D: 0.2243, G:0.1881\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2860, D: 0.2219, G:0.187\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2880, D: 0.2212, G:0.1651\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2900, D: 0.2187, G:0.1826\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2920, D: 0.2584, G:0.1633\n" + ] + }, + { + "data": { + "image/png": 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S/+QqWLFihWeECzNxIQj5NDrZrrdswF7DrbbaqgLiJxDY4Zwap5g3yK3k/50q/Mtg1rt3b8AUr7O7zgetdar9kfxwDOvgUEBwDJujOaqTukIys4mqFJqYDaS6hjVr1vQknEygkqwqlaMwRn0OI/3T1xHCMayDQ6HDMWwOCs3lGo5hg+Evffr7uZK+rs2gciNJHxaL29ewkcw1nQ7r4FAEiMuwDg4OVQuOYR0cCghx0+vyrf9MnjwZgP79+4d2znxZiXOJqqTDypKqMM4w8EdbQz8cwzo4FBCclbjI51js84M/xhwFx7AODgUE98I6OBQQ3Avr4FBAyPsL62+wVKtWLWrVqkXjxo1p3LgxFRUVEVEhe+65p9dEKAjbbrutlxDv4BAWDjzwwKSPLS0tDWzgnSny/sI6ODgkj9CaYalMiRjw3XffjXu8SnYsWbKEvn37AqYE6hFHHAGYahRqiaASKHY7RD9i/a0qQvdLZUQGDx4MwAcffACEU0ol21D2ytKlS704W63VwIEDAZg+fToAy5YtA3KTD5wNqAB6Mshmob28uXVUEbFBgwZepzBBlepULV+lN1TpTjfP7gSQDPLhEti4cWNUjxklMNtd3GykU14mbLeOqv2ptvKjjz6q6yT8reat8j6DBg2KONf++++f8nhyuYb+jgGff/45YOpVZRPOrePgUATIW39YdaqbM2eOV6ZD3d5Uua5fv34A3HPPPYBJX0qHWfMJFf4CU8P25ptvjjhGlfAFpWzNnDkTMH1c8gFVv1fpG7vLYDxIPLQlBbG2JA0V2st1wbog2IX/Nm7cyLx58yKOSae3U6ZwDOvgUEDIOcPKUPHDDz94n9VvRb04paOqs1iyyFafmEzRs2dPz6BmdwVQWVDt5NrZxTw248Tq9ZNtqATKNddcA8TXXR988EHAdGXfd999AbzeNqNHjwaMfULGR823RYsWzJ07N9TxJwO7f6zqFcsAumjRIoYNGxbxG9Ujfuihh3I1TMewDg6FhJxZieW+qFevXuAx0gnCKGIVpFfk0sKosdSqVctLL5PbSR3wfOOKeQ7t/P5eRImQqZVY7CdXjFgnaIwbNmzwxlW3bl3AVMwPwpdffglUHUv/jjvuCJg5y+4gi3jfvn3ZZ599AKPT27CfPbk41f0gFTgrsYNDESBnDGvraosWLQIq+3BKX5BlNMY4ALPDX3XVVYBpryC9ePny5Z71UYEJNvKVmhVUAtO2otrHKQDlkEMOifg+HsLyw2o9fv75Z8AU3dbftabJjMmeh9hHkpdsD7E6/9kIcw3FrJIqPvvsM8CwZaKCan7I/qD+Sb169Up3WI5hHRyKAVlnWO1Mdmc6YcuWLcyYMQOATp06pXUNPyspcijIV5hLhu3QoQNQuUu/9dZbgGEp6Xh16tTRuADTJCoTv2tYDLvffvsBhnUEWXzVrXz33Xf3LMk27JBFsbU8AmpnkgrCXMNmzZoBREXbqTPd8OHDgUrGtVlWc5O1+M9//jNgurpnYotxDOvgUATIuh82iFmFhx9+2IsvTdTqIOh7/+dUonCyjVdffRWo1G1sPU/MKvbS7t2yZcscjjASsozKIh1UOG3SpElAcl3ltTarVq0Cop8H6YqyQ6irea5gW6kVE6A4AUlEJSUlXtKJ1kptJmXxl+SRzQQHx7AODgWErOmwYhT/DuWHrltaWhrInHZHbX0vPVUWZ18DIS8eWWlqNnKpw86ePRuIzZpqVSkWk46nucRrnZgImeqwsgInklYuvfRSAM477zzPvy6WFuvqHFozraUsqppnKnG5YayhHdkkqWLx4sUAns/VP57atWsDJntMz6MdlRZGUyynwzo4FAGypsMm2in9jBu0E2k3tqFdW5bJY489FoD3338/kFlzCUXLSE+NBWXnKMdXyGXmRxDkmwyCLL233HILAM2bN/esrGLnXXbZBYjO97UZWHqg/PK5aj526KGHAvDmm28ChiV32mknILrhc1lZGR07dgRgypQpgJnDaaedBhgpwf43zNjvrInEElftULorrrgCgFGjRgGVjnQ51ZW+NHbsWAB+/PHHiGPlbPenq0FkSGOiDuC5EIllYJGYC+YlVuheNtPIMhWJtQ4K3F+7di1gxHg9qBL5582b5z20CuUbMGAAYNZf51J6oV0jyX8/2rZtCxgDkI0w1lDj7dmzJwAnnHACAAsXLgTgyiuvBOCxxx7zfiOi0Fjt+6L1tokmnU3YicQODkWA0BnWbwCKBYkHMjqAUfybN28OwPjx4wEj6oqdBIlkSs2SUadGjRoJReJEu/P1118PmFSyTCCxt2nTpt6urLpUMtJkQ/xLl2El8imYQWPbe++9ASO2ijHiVQa0jU061/LlywEjMutcSoro0qVLILMKYUpJCklUSKtEYgU/yO02b948jj76aMDcp5EjRwImkUFSg72m6SS6O4Z1cCgCpMSw22+/PWDM2hBZOQ/MjiWDkIpsKeQuVtEx7WraZaX/Cgofu+SSSwBTyGvBggWAKV1SrVo1z4ktd4m9u2VThx0zZgwAF198ccQ1N2/e7LHW4Ycf7v3t9+tHHBsG4jHs448/DhidzQ+NRcEN/vHHg79wgF1axUa3bt0A+PrrrwHDbH6XSCKpI8w1tMd7ww03AKZInMY5depUhg4dCpiqnRqnEtj1XAZB9zWWy862vTiGdXAoAoSmw9ohZ2eccQZgSm0I0le1o/bp04dp06ZFHCM9R+Uk7RA57fwKH2vSpAlgrLPxkE2GVbC8XayrrKwsiqWUVubvfBAWMrUSN2jQADDsYhcWyETv1voHBYakmj74+29CW0M7WV8S24svvujpqKtXrwaM+0v3R96LMMoUOYZ1cCgCZMSwPXv25Pnnn4/5XaJdWDtpvXr1vIBv7VBKPpdPLAjplJTJxu4stpDP0Q4O8O+4Tz/9NIBncZTV3PYtZ4JUGTYolE56lSylYt4MxwYYvVjzVinbfffdl/bt2yc6R+hrKKu1fP82ysvLPT1X/tcePXoAxqbz6aefZjoMD45hHRyKABkxrL+ch62jXXDBBYCxnArvvfceAG3atAEqE7ll7Z04cSIQGV0CxqenVgn6bSLrZSykszvbkT/Sa6TLiKGkS2uc1nUAE4po/zZMpMqwGtOKFSsAw3oh62SA8SbIu6D5P/vsswAcddRRyZwr52V+ysvLPWlIz53K8A4ZMiT06zmGdXAoAmQU/O9nOFsPUgFmoV27doApfSIL8Msvv8zpp58OmOgSGzvssAMQ7NvLNmT9tZOxbSim1L4XDRs29PQcWbYFHSNftKK4cgm1QJHuqDQyWec1xiBpYPPmzVHF0+xkeEHMKmlFOOWUU4BKu0RVSICwsWnTpqhGZrKi6z5pbtks9O4Y1sGhgBB6LHFQNEe8GOOgHVwlNyZPnmyPK+J3qSBV/eeaa67x4ou7d+8OwAsvvBD3GnZpzE8++YQWLVoAxu8qvT1Ru8l0kIoOW1FREZV4reifESNGAEZa6tq1K1Dpk/QfX79+fY9dzj//fMBk5QRBcePpWFZzqcMqim7VqlXeM6wkd0HeDNuKHuZzKjiGdXAoIGScwG4zalAES6KC2WCyIGRJDrIC57KL92GHHeb9XzqLYp7VJb1Vq1YRv5EO06dPH6CywZPGrLYNVaWtYklJSVQpHvkZVQJGOaOKr1U8cCzYzOpPANf1/P9Wdfgt/rZurTmIWRU3L791Np5Tx7AODgWEnLXqqCoIU/9JVI5V2LhxY6iRTIkQViFxVQKxI49UNUStUmLBzpLKhFFtaSyXOqxai7z33ntexRPh2muvBUwGlHzJYSBIh3UvbAhzPOeccwBTJV6GpD322CPTU6eFsF5YG6pRpSR8vZTl5eWe0UlJDdlELl9YFUlYsWIFL730EmACd+T+kgoUL30uVTijk4NDEcAxbJHPsdjnB7mZY6wUyTDqDwfBMayDQxEg6711comqGtbmUPjYvHlzVNpkOsknQqJSOkFwDOvgUEDIuQ6rgmphJvumAqfDFj7+aGvoh2NYB4cCQlyGdXBwqFpwDOvgUECIayX+I+gG+Z5jNnx5VUmH9UcKhYVEaxgv4ijV+20nRgSMJ+Lc6V7LOqfTYR0cCh3uhc0zKioqcpoumA5KSkrSDt5fsWJFqOyaDDZt2hTBrv6mXfb9TjS3zZs3J/S32ucoLS2ltLQ0cG3ffvvthOcIgnthHRwKCC6WuIrNMQydtirpsNlAVV9DlRDq1asX4Bo6Ozj8YZF3ho2VBeG7PpA9C+rv16hSu3MYyJRhE7WtEJQPOnfuXO//ahj18ccfaywRv+ncuTMAb7zxRsxz+p+HoELm+VrDIOuz5qK52c3EBDV9U6NyP2xrtGNYB4ciQN4ZFsyOo+Li2llvueUWIHqH1W6uptGpoKowrIqRqzmw5mw3es7Uh6f5pRLDbVsrNbbTTjsNMIXWVFB7/fr1XksWFUpv2LAhAPvssw9gCrkFIZVqDUFrGC8Dxr6fKkWrahHJ+FtPOukkwLSS0VhVSFySia6/3XbbRRwXb02TZdi8pddpctWqVePBBx8EYOeddwaCU47Ugf3WW2/NwQhTR7wFUb1eiZlaTEFzVnkZ1QmKJT6lA/tFVTXIN998M+pYPcwa00UXXQSY+sTaWDXf2rVre50NtInqJVdHg0QIo6xKvFQ11bjWy2ZX57dfVHsDGTx4sNdpXS+o3EW6X8I333wDGKLRuR555BEABg4c6H8xgeQNU04kdnAoIORMJPbvxmB6aq5YscLrXWMr6dlAJiJxJmKqfquav+q9qnOpp46kjEyQjNEpmbnIiCI2VgcEMU3//v2ByuqKnTp1ivitGEPsk0myt42gNdQ9lajuF+3DMFyqp++TTz4JGObU5969ewOmW4DYXGqBJJTPP//cY2cxvbq56zlwRicHhyJA1hlWu552Wu1K6o8aRu/RVJAvo9OECRMAOPPMM3XdiO91n6SHhW10Sgbqvqd+vNJVv/rqK8Doc2+99RZQ2XkQKvU7GZnkznn++ecBU8O40F1zv/32m2dXeOCBBwC8sqeSJrR2Yk31EZZhUV0Cf/jhB+rWrRvx20S1lwXHsA4OBYSc6bC6znfffQfAK6+8AuD1hk0F6sHzzjvvAKmxUT5256ZNmzJ//vy4x8yZMweAli1bZny9eAwb716NGzcOMBXtVRxcrCnmlTRw/PHHAzBlyhT/tQETlvfUU08BJs1OOnwmSHYN0ynsbUs+0kN79uzpSSD2+SQlau4KpFA3eemn6tg+duxYXn311bjX37Jli2NYB4dCR9YZVvK7Or6lA7tPy8iRIwEYNWoUAHfccQcAF154YcJz5UuHte+zPstvKetwUA+edKWIMOangIR+/foBRofTmtavX59ffvkFwNPNli1bBhiWe/311wE4/PDDNcbA6yUKYkh2DZMJhgiC+gbNmDEDqOw6+MknnwCGUZUmd8MNNwDGij5s2DDASE02fv75Z3bddde4Y3M6rINDESDrDCtWVGMo6bDJQFY26T3q/P3ll18CcPbZZ6c8nqDdOcxEA53rmWeeASpZRSwlSGqoV68eYKJnFAGlz7GQSDdLl2G16yu80B6rrJ9t27YFYNasWVHnkC9dvkeNUfPXWsbrPC9dUccmCtvTHL///nvANO1KBa1btwZg5syZgPHlqtdrq1atvLnoPpx77rmA0U317Og43TedS4zcq1cvL8zznnvuiTkex7AODkWArMcSi23kh3z66acBGD9+fMLf7rXXXgAsX74cwOvP2aVLl7CHGbafEDCMVaNGDU/v0f2YPXs2YFhEf4/HrEImcbeKwvF3FhdsZlUk06RJkwC48847gcpeqX5UVFR4PkallsmCrzX7y1/+AsRnVsG2JCerg8qqLc/DvffeG3WMYqcVvSVoboceeihg9NR999038DqK1tPaqZ/uwoULAWjevDlg4sdlY6moqAhk1kRwDOvgUEDImg6r9DHpZLIoasdVzGQ8aGzSYaQP21bjVJALK7GtY27ZsiXKv9etWzfARAulglSsqGHMT9fr0KEDYFjKXo940D25/PLLAbjxxhvTHk/QGgYlvMeC/QxpfNdddx1gbC2yEl999dUeK0+dOhWAoUOHAqbB9SmnnALAa/DjWtcAABGeSURBVK+9BphIqBEjRgDQqFEjoFL3VxPwK6+8Mqk5euNOODMHB4cqg6wxrNhl6dKlADRp0gQwMcWS/3+/DhARRwkYHVA6gHYy7VRimFRa9uWCYeUnFousWbPG80dL8tD9kGUzTITFsK1atQKMzqaopVjlOGUJVRSUoPjbiRMnAsYfK113wYIFKY8r0RpqfL6ooahzXHzxxYBhycGDBwNGz1Q0nSTEwYMHM3DgQABOPfVUIDq2QMyuZ1u5wFpzWcq33nrrhNJAEMNm9MJWr1496mWRqCvFX4ssQ4Ue0OOOOw6oFA/0YNgJvxKfZUa3H26Z03WzAY455hgApk+fHnPMuXhhZWA766yzor7TXPQCZyP5Id0XVm40PWC22Kh/tRn7gzyCqt8L2lz1wir4QqmV2uSgUvwEE5BgI5M1lMtHz5KMb7vvvjtgXEpSA7SxLl261EtYsVUSVdOQG+/SSy8F4Lbbbos4p0I7AXbYYQcAL+BEm4CKOTiR2MGhCJA1kVjugy+++AIwu7NdNS9W1cSHH34YgEGDBkX83T5OO5fEsJUrVyZ0ASTanSUCaTdNBWIXiezz5s0DKudoG2jETmGURrERlkissYpRFEYpdlDq3O233+4FuCskUQiqs6Rz6ngFkJSVlXnidRASraGYWUwN0ZUMtVZ6XuTOuf322wFo165d4PWl1okxdW65c4QhQ4YApgaW/9m0g0MSzVFwDOvgUEDIWuCEClHZCr+YdfHixUBlcMTw4cMjvpNynwjSA/0Of4XE6fqJdCsb6TCr2HLy5MmA2VnFImDYSjq+WES/zVV/HUkvckHEu67069GjRwPGQNS+fXvABLc3a9bMC4xQuKIdgioDjZIDdH0xsz7Pnz/fCx4RY6UavO9nVkGB+2I2pQTKpiBpKBl3o8JsJY3pN9KLZbgSm+seK2hk3bp1EfpsKnAM6+BQQMhZArt2Mu1O6mi2bt26pNlPx2lXlvnctmLGQ5hWYo3nggsuAODmm28GjMXRH2Z48MEHA3DVVVcBxjqq3VgWxnSCQWykq8PaqZC2nqexSe9SUvqECRM8KUk6uSzO+qwCZbKcK+xROq6stTVq1PAYSfqkyqzGml+ycxTbaW0kHWiO8lTIWqvEdaG0tNSTmPQbsaSkJ0kkkpqUUNC1a9eI+STjGXA6rINDESDrDGuXhpE1ULtU06ZNYwaix4N8abJaKlUpVrB3jPGE7odVwTGVTFFivZIUOnfu7IW41a9fHzD+PelWsrz6xqXxpjyeVBi2devWXkqZDTGBGET/akzSBzds2OD9zV6L5557DjCWVBV2E+SHPPLII4FKvVWJ8gq2iDe/ZOZYrVo1737q/isJxR6PYgDs+15eXu4VAj/hhBPiXY4nnngCMBKgbBqxELTOjmEdHIoAoTFs0E6h8Czpc9KH/PpAumySTgmQdBjW7uYW1JfFbrsgq2Ht2rW9vykhWoyj+2P7HsNKcEhHgtB6yM8qplDZFM13++23Byot6/pOPtD//Oc/gGFczUdrpc+6liKfdtppJy9cMKjYQapruHLlSk+ntiGdWmt39913R3wvfXT9+vXeWG1dVb/de++9AfOs6/lIJ5rNMayDQxEgazqsfFPSzbQ7LVmyBDC6bOfOnT0dStEwsiyKdWwfnix9ixYtSnlcqe7O/kisFi1aAIb15LtTRJZYQvdU8/nggw+8YPIBAwYAJvpFvjlZjXXOTBBWpJMYVHG+KomiTnWyeINhEc1Z96hmzZqAYWXFkKvwuHy8+r68vNyTTILalqS6hueeey5t2rQBTNy5/dyrSILWR2VI9YztueeeUT59fVaCgyQxteSIh1TK/PjhGNbBoYCQMz+sdjCVEpG1rlatWhGpdmB0RUUtKfpIJTJVjM1u85cMUt2dS0tLo+KAJTVoTr5zR/yrnfjXX3/1sjBUAtNu6RiG/9U3jqQZdsWKFZ4/1febiH81NsXX2iViFi1a5MVz694oK0eF0dUETUyn4uS6ht3+MR4SraGt/2/atMmTZPRsSXrQ/dczKFa3I9Pk+/efV3NKpqyPNV7/XGIe4xjWwaEIEDrDylooC2Nagwqx5KiNVBm2Q4cOng81CNqNpZcoAkY5uUOGDPEYVTt8mO0XbaTCsDVq1Eg5rlWFBOTDbNeuHe+//z5grOA9evQAjA9SJVEUTeUba+B1gizlqa7hn/70J48pVbZFZVzUHFxFAYNK5/7yyy9eVJaagWluuh9hwjGsg0MRIHSGzSY7hoFsRDrZRchsnbBRo0Z88MEHmV4maYRdhC0Rvv32W686yNy5cwFjUQ/K+0zlOUm2kHgQjj76aC+yyV4rWcBVMkb2EkU1iT07dOjAiSeeCAQXXE8WderUSVgaKCslYgoR2SwRYwdU5AvZfmHTCViR+KyCBpkgm2soUblv376ACa1USmFYSLRhOZHYwaEI4Bg2xDlWFXUgWwybSbhkmMhkDYMCFiQdyS2lxJJ8wTGsg0MRoOgYNhEL5Ks/bC6Ra6NT2EhVv8vGHPUcKQx2yZIlgeNKVadPJqXUMayDQxGg6Bg2ERzDZo5s6+rpMmwyTGcn4fvOmfT4gvRg/d3nfor4XokQa9asiZqjXcTdMayDQxEgLsM6ODhULTiGdXAoIMQtJC7d4JJLLgFMoHQho6rpsNnQB2PpsAqNVLnVQkbQGp5//vkA3HXXXTkdj/TOoDI06cDpsA4ORQBnJS7yOWYyP7FxLhMXksEfbQ39cAzr4FBAKCiGDSMb5o+2O1e1+anMj0rIpIOqvoapNmALOIdjWAeHQkdBMWwYyMfu3KVLF69spgprP/TQQ4ApTaKC1mqVeN1116V9varMsGFYxdNZwzBYL5dwDOvgUARwDJuFMjj29yUlJVGxoiqbKX3cbiStsqF2C49kUJUZNgxUdR02DLgSMb8jl4utl/TXX3/1XkxVvFffmKDK75nAvbBVE3ZfqXhwIrGDQxGgSjJsPusSh1EGxT7HyJEjvY4HCpvr2bMnYHrLhomqwLAqtaL+r2GiUBk2FTiGdXAoAlRJhrVr2apavLrYNW3aFCDlzu2Qm9350ksvBYzLZtWqVVFSg3qLptM7NBFyzbC77LKL19HANwZdP+ZvZHRTRf5UEOYaJpIEpG927NiRl19+GTABPHafWEF1i6Wzao6pSIyOYR0cigBVkmFtqEucdi5ZVlu3bg2YKu1NmjRJeK5sMuz8+fMBePvttwFT/b558+aBndaDkAkDp8qw6lWroI4Y5wNMpXv10TnmmGOAyvRLVdZfunQpAN988w1gmNTuiKdzisFSsRmEsYbqWqd+ORqPWFHj1Pj870mQ9PDtt98C5vls0KBBzOOSgWNYB4ciQEEwrMYoHVY67axZswDT+Vr9XRKcK2sMqx6kYlr1ty0rK/MYJKhAmG1ZVu9RFbT2fz9hwgQAhg4dGvNcqTKsnVShzwryEAuJOcS0e+yxhzenESNGAMbqrbWRPSIIK1asAIwumQwSraG68Wk94mGvvfYCYPbs2SmPQ0jVs5AM4zqGdXAoAmSdYaWD1atXD4DHH38cgE6dOiV9DnUQ026t7tl2J7JkkA2GtcdnW7f/9re/eU2V1D/V3mWzXSImFQR1Whekj/utn5rPwoULAcNcQlCUTxj6XSZrGKeUKhBZFlVjFXOLyW093GbaJ598EoB+/fqlMi7HsA4OhY64RdhCucDvxZUXLVoEGP1IKCkpCdzl5BtTTK50rHSYNRvQzlqnTh3AsIX6wy5YsAConPO8efMAY1mVVVX3QwyUDb9sshAz6P7ahbk1P1l+BT9LNmrUKOK3soqHwaxhwLbw2mwp3H///RGf/QXW7GM1Rz3rgu3jldShdyEdVI0n38HBISlkXYe1z6+dTZZH+cPi/faAAw4AjOXRjixJcTyhpdcpBW7lypUR4xJTffjhh0BlM+DtttsOMLuwfjNq1CgALrvsMgCOPfZYAN544w3AWFH9SFQqJxUdtnHjxl6T5aC1CkIyMd/6TlKH5qN7F2/945wz7TXUdcWSQf7hFMcT93udu1mzZgB8+umnyZzT6bAODoWO0Bk26HzK/xw7dixgipJXVFTw8ccfA9CyZUsATjrpJMCUUYkxrlSH5R9faAxrWwNt/Uh+Y7GrH4sXLwaM71h6o7J69O9BBx0EpKb3ZGolttdQ1m6NKRXoHtlrlu811By///57wPiY69evn8544n4fZqRT1l9Y25yvh1hiQfv27T0xSeKRQhAF1ceViJkJUl3satWqRYmecmvoJZI7Ry/dTz/9BJjgh2OOOcYzMkkU07EKjDj11FMBc5/OOeccwKTh6aXfuHFjVPWKeHNMZQ01NomNNhR6Z7tsYkEGFzu4X6K/PzEiVWTywmpNNC7dV33u378/AJMnTw48h9S5oDDTkMRsJxI7OBQ6QmfYo446CjDheV9//TUAy5YtA8zu5A8B099ktJEoLGOTgqhtw0U6CEOckuHIdllol5YIqdDENWvWsPPOOwPQo0cPwATa33vvvYBREZQyqPsop7t+t3bt2oRlZTIViR988EEAzjvvPMDcb7HhlClTADj99NOBSteI7epQL1RJVBqz7l2+RWJBz16QESoWtN6ak60KnXHGGYBZ23TgGNbBoQgQGsOKMbUba8c68MADAROSJ31P7DRr1ix69+4NGBaW+0aVBFUtPh3Yzv9Eu3Msd1NQZ2+5Zl544QUAevXqBZh7MWDAAACmTp3KzTffDMDcuXMBk9ImfVEGHd2fNm3aAOaeKPxxzZo1HnN/9dVXQGVyNcA777wTNcdk1vCjjz4CoFWrVoAJjHjrrbcijttvv/2A6GCDWGxkP1cyOqZj1LHdWOkwbBilf4RE1TLDgGNYB4ciQOg6rB1UIKaSvnPPPfcAZsdr27atZzEePnw4YKreJ1MOMlWEqf9IT5szZw5gXDSHHHIIYJIAnnjiCQYNGgSYNC4lPyhAYubMmQA8++yzAF45EjsII5myqGGViDnyyCMjxiL9U5JG3bp1gUpJSLqqbwwRnzXuTIJefOcOPYHDfl7t8ZeWlgamRsZz36ULx7AODkWArIcmvvvuu4BhnVh49NFHARMwkU2EuTt37doVgJdeegkwzCPpQbpX06ZNvdIp0vu0k/v90WD0X+mP0ivl200GYRdhS8afaOuI9rEKAJGEkQlyWeY0KPADjDVY9gghjFRJx7AODkWAKlEiJplEgLCQjd1ZCd8zZswAjLVWEUGlpaWeX1oRTfJljhw5EjCF26TjJdMDN4j58llI3A7bVGsS+eOTgYrqnXzyyTG/zyXD3nTTTYBZp9+vl/F57VRGG45hHRyKAFlPYE+EkpIS/v73vwNw9tlne3+D7LTqCBMap6KQ5EOV/3XMmDEAdO/e3dPlp02bBpgoLkXDKPFbzJpK6lpVgMYrn64gX7F9XDwEMWs+sNtuu3n/V3GBMJ7PdIswOIZ1cCggVAkdNpfIpv6jnXf69OkA9OnTB6iM8lGkjzKR5GPu1q0bgJdErnOo0Fw6cdP51GH1PNnW1TDL+qS6hjVr1kwrNfD3a3n/t/3Q2YTTYR0cigA5Z1i7FGiuke9WhWIa/VssDZ1VVFxtPBQF1b17d8BICvnIaU7zGlF/85c8zTYcwzo4FAFybiUWs0of2LRpU6iZFFUdQcWmCx1LliwBDLPmko0S4b777vOYX5UubJ1a6zFu3DggOkNo1apVUXmv+Zhbzl5YOyXLLwoW28NbrLA3VgXM//bbb1GivRL21XlQv1HCRLpGoHQQqyufKhgq3VEJDkp4sJ9JGQEhv5uQE4kdHAoIzq1T5HPMxfymTZvm1VOWy+Of//wnAMOGDQv9emGuYaI+QtZ1ABNMoYSObMAZnRwcigCOYQtwjuqH++OPP8b8Ph9uHT1HSuDQGFXmJ+Rr5WUNgzqvZ+lajmEdHAodjmGzMMegom25Qj5DE3OBqiYlJZJ40oFjWAeHIkBchnVwcKhacAzr4FBAcC+sg0MBwb2wDg4FBPfCOjgUENwL6+BQQHAvrINDAeH/ASWxsh6zK9jzAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2940, D: 0.2235, G:0.1825\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2960, D: 0.2384, G:0.1677\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 2980, D: 0.2515, G:0.2396\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3000, D: 0.2293, G:0.1948\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3020, D: 0.2517, G:0.1495\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3040, D: 0.2512, G:0.139\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3060, D: 0.2474, G:0.1234\n" + ] + }, + { + "data": { + "image/png": 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vv/wyYObGPqaub+TIkQBceeWVUa9nuy5xsmhcsYIkpEUsXLgQgObNm2t8QMVCeyoX26hRI69nrN/3z9mwDkcBELoNa4fiqfjYoYceCsC4ceO8ivJabQ4++GDAVFIX2tqRnaT3yz5au3YtdevWDedC4qDzq9C2ioWrywGYsEr12LHJZRc73UdpAWeccQZgfAcam/7epk0bAGrWrAnAtGnTOOusswCTPqfuAP369Ys6lzSLN998EzAd3nSOPn36MGnSpOAuLk369OkDxJeskpgdO3YE4IEHHgBMxwftWuj9ul9Lly5N2x53EtbhyCOybsMef/zxAJ7Ns2nTJho0aADgdfHW3xo1ahTzGFqxZP/IW1etWrUKHmabMOwfrcIaj+w4tXsYNWpUwnElaoeRCqnasBr3YYcdBhg7W5L2kksuAaBly5aASb5o164dALNnz+aggw4CYMyYMQDstttugNmzfO211wCYPn06AK+//joAn3/+OVB5bdjI74c0PmmANkrJO+SQQwDo27cvYBIeVGg+yfM6G9bhyHdCk7Bq9jRq1CjAdDrbd999AaJCDc8//3wAHnrooaSObY9Zx0pmlQ5ydbbPK6+w7LK2bdsCFW3xWEhaffrpp+kOxyNdL3HE56N+1q5dG4Abb7wRMLbZ3//+d6A88kxaxb/+9S+gYifCwYMHA0Y76tSpE1De2Q+MJzoZwpSw0gAOPPDAqNfr1avnaUlxPLuA8WHII/78888DRutIBidhHY4CIDQJqxU3yG7qfmOVhKhTp07ClTrM1TnI9phBlcFJdH3FxcUV9rElBVUuZcWKFVFjibXHanua5Y+45pprgPIesmBsWVvixivOZu/VhjmHtnahay0qKvJs0okTJ8b87JlnngnAU089FXUMaWLp9viNxElYhyOPCHwfVjGlivM9/fTTAWP/pCMx1BXbr7yKnc2Tbbp16wbAXXfdBRjbVZSUlFTw/vpFAmV7P7ZOnTpe/LL2UjV39957b9TY5B2VFzlS+mjcuk5F/+y1116AkaRPP/00ABdccAFgOp/HQ15Wu+F3mMSy69UiVRL24osvBuDuu+8GTBbZ/vvvDxiNS151aR2Z4CSsw5FHBGbDalVW/KT2pDKRGPLKaeVXBIwKeClyJBUbIQz7R/a6YkllCyox+913363wGXvvVhIoCNLNh5VEULaJbcfZc6n7Hm/sOsYnn3wCGMk6ZMgQwMxtKoRpw955550ADBw4EDA52tttt5039ltvvRUwczd58mTAeLyDyC7ys2EDdzrZdV1nzpwJ4G2sp9NxXGPUF0Phh+moSGFMthwtcrApkEMLS+Rruv7WrVsDZrtLX/Z58+ZlOpzAE9j1YOpBTGZxUSqk0gqlImtR0L2yn7/i4uKEi3yYX1g9Yxq35nbFihXeF1UBEHqmFy1aBFQsTpCJsHJOJ4ejAAhMwnbu3Bkw5TCU5JxmB/GYr9tOp3TqwoapEktqKBxPoYl77723J5UU/G87XRRs8corr2Q6nNAkrJ0uFgsld8gxJW1In1VwiRwzSjBIhTAlbO/evQF44YUXAKNNxHrWbIdhkNt6TsI6HAVAYNs6WlG1wqo8SirIrlPwv5IC2rdvD5RLqsqIJKucMFdffTUA119/PVCeqvXkk09GfUYS55xzzgHgxBNPBMw2SC7T7WwkSeJJVkkZlUPZcccdAZMMcNxxxwHlqXgAb731VjiDTZGhQ4cCZpwXXnghYMIJx44dC8Cpp55a4bMKBhHaVpRmpd+1/RMETsI6HHlE4F5i2Sxr164FoFmzZoDxpMXigAMOAEyQvGxVv3A1bREocTiV8Mcw0+vsRHbZtqWlpV5a4YgRIwCzJTVnzhzASFxtKyhpIh2CtmGV+mdv90SioAAFCch3obnRzw8//BCAXr16pT2eIOdw9OjRAPTv3z/m33UdJSUlvnWdn3jiCcBIYaUlKhEiHZwN63AUAElJ2GQ2yO3NdXtV9mPy5MkcffTRgCn61bBhw6hjzp07FzD2XSaEKWHtAHjdg19//dW7d40bNwZM8bX7778fMPaOJFRQm+66Pkl07S/GI9XEjY4dO3phewo0UNmfI488EjCe01hBJKkSxBxqzlQwT/d9v/32A8xzHClVO3ToABgtQX/TtekeB1GO1UlYh6MACNyGTdQ7U6t306ZNgXLpqfKZCkKXV01pSopokiRWmc10yEZ5kVihabLLtc937rnnAiZRWpJPdnAmZGrDah85UaqiNK+lS5fyzjvvAHDMMccAJkzviy++AIyHWfckyCigTJ5T+RukDelZvO+++wBTjnXw4MFeIovfsy0tQ0kAmeAkrMNRAISWwB7LBogk8rwqKSnJGUSkiB+56i0q2062rDzissslWe2Y43QIyktse/xttEc5duxYr32K9t9VzlQSTPadStSmQqYJ7CtXrvRsU8W4K2BfWptQ2xdFpMmv0r17d1/tUR7yTNqqqAyP4s+dhHU4CoDAIp2UnSFbRuUy7PKd+qnY4x49enhpc+nEBucDRUVFnl2oNhj6qX1q7dMqwiZb1KxZs4LnWBLNTxoqoVxlXjp37uxpCrJh7SLa6UhWkWnUl3YdwNjdtmTVOJWR8+ijjwLw5Zdfeu+xfRPplH7xIzKzKx5OwjoceUTgNqydkC1Po3T0HXbYATArV9OmTb09u0xW4WQJo8ypbBg7mkslXefMmeN5TZWNY+9pB9lSMV0b1rYVJf2///57wHi01YJE192kSROvTI/8ESLC7kzxKioSsdef9hyq5aO89H7jevDBBwGTVXXsscd6cdCya8PE2bAORwEQuISVd037rSrkpXYPkfZELggySkbxv7YNqL+rKdKECRM8ySMvZZC5kzapSNiePXt6pUdtqa9WKRqzvMbypGovffTo0V4hOhVZkw8jVcma7YoTihWWxqcqEsqiUlvIyZMn++54JELxBdK4kiFrJWJSpWrVqoHWLk5ELrZ1atWq5T38UqO/+eYboGJJnSAIOvhfaOy22bN06VKOPfZYAD777LOoz6ji5RFHHBH12VwHTtjoWiI7UkTSokWLpPoPB4VTiR2OAiBrEnbAgAGASWdKFMIYFrkKnMgmkddYXFxc9sdrQGrOLUlDdQYQCphXoMDLL7/sSVi7jEwmzjS/z25rcxiJk7AORx6RNQkrZ4SSe3PFtrY6Z3J9tr2pn7Ytu3XrVi8wRKmFYVbp39bmMBInYR2OPCLnXuJss62tzqlcXzKFCoImHRt3W5vDSJyEdTjyiLgS1uFwVC6chHU48oi46XXbgm1QWa5RkU9BFEuPZcOqb282e6yGRWWdwyBxNqzDUQA4L3GA+5SJXk8FNfxS8bZUCCuWuLJQWSWsHY9sF29IBSdhHY4CoFJJ2GuuuQaA4cOHh3aOXK/OycZQp1rMO/KYkQne2b6+RMX3gqCyzKGS9u+4444wzuEkrMOR71QqCeuHX7J3OjZCrlfnbOBs2NDOq/NFvR5keZ+IczkJ63DkO4GVOU3EsGHDALjnnnsAUzB7+fLlXqaHciinT58OQNu2bQHTWMkmHe9bNlAZUOWNQvqrcKxjpYOyaBI1J8s2yp9VqZZMiKdx2e1HNB8qcK7PyGegeTrppJOA8jI/Knek92y//faA0fx0b3VsFXK7/PLLAVi3bl2ml5g7lVhf2FdffZWrrroKgPfeey/qPbvtthtgOpqr0l0mhKlOaaLUfVwTWa9evbj9cYPGqcRpHRMwz2Dr1q0B2HPPPYFyJ5q6KKqLg+bbrtqvLTnVnk6ni4NTiR2OAiAwCZuqyhdZfEzqntSPF198ETBqTJCqb5gSVlUEV61aBRhnWXFxsXd/VqxYEfOzUlltlSwd4knYIHr32HNdUlJSYY5sFVzaxgsvvABAv379oo6RConmUPdQW2PxUC1sqcYKelD1RNVgnjp1Kt27d080rqjfVUJH9ZtTwUlYh6MACMzplOpKqbKePXv29BwOqoov476yOpUkQeVMkHNMfVnsDgY9evTgL3/5C2AqyfvdL9lDiXqzpksmklXS54EHHgDg2muvBcodYrZU0++StOo8b9+bli1bAjB79uy0x2WTjGQV8jfIsSkNSPMjyZuMU8wvGEaa1+LFi5Melx9OwjoceUTOAyfWr1/v2aoKnpa9I2kTJJnYsFqN58+fD5jxqiK+7EN5B4877jigPORSHm4Vo1u4cCEAe+yxB2BC+bSSa8tA3QPkodx5551ZtmxZ0teYSWcDPRv6ed111wHwz3/+E4D27dsD5VJp6tSpcY8h6dKkSRMATj31VAAefvhhwHhekyGdOVQn9ZEjRwLQoUMHAM477zwAhg4dCgSj2fh1d9ccJ6ONOhvW4SgAMpawkjp+3s9ExDq/bKVM+4L6nC/l1blv374A3HfffQCMGjUKgPPPPx8wKXCyYWXbSuKuWLHCk8KSynaY29lnnw0YyfrFF18A8Oabb6Z0fX8cOy0Ja3cR1Dxof1E9gN94442o37ds2eIrNSTJ5G0Vzz77LGB2BlIhnTmUdPvwww8Bo9mUlpYCFUu5poLsdNm78gpLK9IxYxW3S7ZYuncdKY/O4XDkjIy9xOlK1o4dO1Z47ZNPPgHCkayZMHHiRAD+8Y9/ACbk7PrrrweMdFSRdI1fXe0aN27s2Wi33HILAIMGDQKMLSfbTg2XhgwZApjuadloxGR7cO1Qu3nz5gGm3UoyqX+SYEIedu0EZAvNiTqvq1SOui2ms1cqdJ8UHTV58mTAPM/aRYh1jpS7+6U9SofDkXVy7iWOdf4wG2Slav80a9aMr776CjCB+Brz+PHjAbjtttsA0wc03p6dPIaHH344YPaeFX8qe2jEiBEAPPTQQ1HHjCXVLrzwQgDuv//+CteYyRxKoioS7eSTTwaMlzwZ6SDpo5+y46RhjBkzJuVxBdnjN8jO8EpkkK9Dmpf24OULmDJlSoVjxGhW5mxYhyPfyVp6nc2CBQtydeqU2HfffT0P748//ggY2/X000+Pem8yNp325mTn9OzZEzD7f3PmzAGgV69egFmlJWnvvvvuCsf897//neTVxEeeVO2Nyv7UvrLs8GSk0uDBgwETdSQJq3vZoEEDIJy0v2Skpxpsywv/7bffAnhtM5NB8cbyhL///vsATJs2DYBHHnkEMN3c42mOyWqVTsI6HHlE1m1YvyiayP9X1gJeinuVFNS+WybxuWvXrgWMfaxjf//994DZp5Q3fpdddvGyQPzI1IaVFJeUfPvtt6N+3nTTTQDst99+AMyaNavCvNox0drjlec8k4ZbmcyhoupkT9rj1es29erV82x5zbtsfF2L7QXWPZG3WB7qrVu3et5yP/xs2ErldMpGN/ZkJzvyAZT6JpXYTlFLp+ubXUnixhtvBExAvV09QQ/S6tWrE25BZPqF/emnn6LGou2slStXAtCqVSsgeqGSit+7d2/AhJdKBVbAQps2bTTGVIflkc4X1q8DvUJFZ82aBRiHmm1mzJ0710tmt48hZ5O2xQYOHAgYh5reH1m3ONH1O6eTw1EA5EzCKrk7cgvkrrvuAuCSSy4J67QZqVNaKdX/RsEMqWwRKM1MKu8OO+wAmJXdduxo9ZZTJlZQid1pIKhtHYVkyuElCSKHTWQwuxxwb731FgAzZswATNim7lEQXREymUO/OZLzS87QP//5z4DRKkaNGsV//vMfoLysERjtR5+VBJWjzZbq0jI6duzoHf/zzz9P6hqFk7AORx6R0bbOww8/7KUnpYpsnB133NFzqGRaGfDiiy/2pHQYaMWUZJV0TORAiEQpd0uWLAGM7dewYcOo98le1ip+2WWXAeVVJ+36zGGFcipgX/OisX/55ZeAqXx/zz33ePaspIk0KCFbUeGb2UZagZxeCna5+OKLAaPpyT9h3+OrrrrKm2cVavNLlJcDSyGt4rnnnvP+b4eaJqt5OAnrcOQRWbNhmzdvDhhXuFaUWrVq8cEHHwAmIdpe3YKsXRtGETa/1TGyXIoCIbp16waYhGqlJ2qrRvWbJd20LSIvZklJSYUgg3ipWWH4IWwvefXq1b05s8ciW13BBdIsMiGdOZQnX4ERkyZNAkwpXdnnslmlKXz88ccAHHDAAcycORMw9q2S3pXIorrDCpSIGF/Uzx133DmPj7oAABE5SURBVNErkZTsNQonYR2OPCJnXmJ5Q5cuXeqtNgqe115eGPitzql4L8eOHQvAaaedpmNG/d3el926dau3tynppGB/aRUKoNBnTjnlFACeeeYZwOx9zpgxo0IV+3jXqOtLFBgQFEpqVwBI586dATwtKgj85jBeZ0Cl0R1//PEAfPrpp4CZd91Tza2SMiRxJ0yYwFNPPQXAgAEDAPOs2BJUe+jSOhTqqYL5Su2LPL/93DkJ63AUADmTsLIlVFgaTDK3yodkEr7mRxA2rPYaVVjavofynCvEr6SkxFtJtZc3YcIEwOzDKQVLnlgFlseKokrUtS+XrTp0LxQppOuSDS+veIbnSLtEjCSZJO67774LmBTFo446CjCajfwFderU8bQghSaqGF2fPn0AI0llw8ozLa1GxfOTKfrgJKzDUQDElbB16tQpA7NCBBGlIhRRsn79ei/6R4XHtEf22muvZXwemyC9xOoUr87xQva5VuTzzjuPiy66CIB99tkHMDalVmlFFWn/Ut5jSYJ486SVXZI9FxJW+5kqIqfEAZVp1f5nEKQzh7qv8ugqpln79vLoa79bCQ5i06ZNXrke+S7skkaaI3VfVKE37dfHQ5JcUVNOwjocBUDWbFh50LSCPP7440B0Ergk6pNPPgnAuHHjgjq9R6qrc4MGDbyoIz/sWGLt6cnWXLFihSdpFE+qiB+1oQwyWikXElZ75JJYKnaucilBko6EVUlaaXaaM8UFSwL3798fML6GJ554Aii3U+14br/4AEVEqXi69m3jebETXaNwEtbhyCOy7iWWraMmzWvXrvXKdKQbl5wKQWTr6J4pgVuNnCRRteJKm6hSpYpX6lKRM7anV7af9mszIVsSVnvKixcv9nKG5UFXsrYdtRYEQfghbJsxnZxmG7vczWOPPQaYRuSpaFFOwjochUBZWZnvP6As6H8NGzYsa9iwYVnt2rXLateuXXbMMccEfo54/1K9xkGDBiV97KKiorI/VvsKr+v/nTt3LuvcuXPa4/c7fuTrYc9hxHnKysrKyjp06FC2devWsq1bt5Z17dq1rGvXrmXVq1cvq169eqWYw1j/7rrrrrK77rorK8/cvffeW3bvvfdmdI3etWb7C2v/a9euXVZuWpCTneq/Y489tsJr+pJpAdPrNWrUKKtRo0Zg15iN6+vcubP3hd20aVPZpk2bvL8tXry4bPHixXk/h+n881vA07lG/XMqscORR+S8amK2SeSwiDW+RGNW3V5VOHz55ZcBEwhSq1atqEQAMEEoQdbjFdne1tlhhx1SSuLPlDBSJLOB3Z0+Hs7p5HAUADkvc5ptglyd7a2YeBvjKqeiwAJJXJWGUXBGEJpHLoP/s0G+SthUcBLW4SgAnITN8TUGmVAhnIT1RxqNEtP9OOKII4BwElCSwUlYh6MAiCthHQ5H5cJJWIcjj4hbSLyy2D9B2nmVzYYVanClhPVMcDZs/uNsWIejAMh6Anus82Uz+ilfVmf1U1UyfCo4CZv/OAnrcBQAlWofNpUSGhmcI6erc7avsdCv749zFfw1CidhHY48IqV2k0Hamipg9dtvv3mlYVSA2T5PKlkO2SCT+xDmqlwZKPTrg9xeo5OwDkcekbEN61e8yu91FchWTGfr1q29EpMqKSmJqvYOL774Ysxzz5gxA4D9998/5rlikQ37R+NVO5K//vWvvPHGG4ApfelHEFqM8xJnjq3V1apVy8td9sth1jOvfOgjjzwy5t/TeU69cSX8ZAL8Tq7XVQ9W1fEPPvhgAN5//32gvGeJ3cl66tSpgOndqR6j6vytAIrWrVtnOvyksLdY7Ml8/fXXAejZsydggiD0e/v27b06zIm+kPbrYSQHbIuoc5xd0V/Y9/mGG24AoF27dkB5Fz51a9BCrC9m/fr1ARP0InNP6ZeqRa2uD5ngVGKHI48IbFvHFvcql6IuZurYJQlTr169CsdQSRWpuKpXrL6cUoG/+uqrZIdVgSDVKa3KO++8M2AqvKua/LRp04DyLgcPPvigzh/1Wa3odp3iTCSrU4lTx3YkqYugOhDuuuuunuakeVZd6ttvvx3Am+O99toLgC5dugDmu5GKueO2dRyOAiDwwImPPvoIgG7dugGmCr56rKgPp857wgkneB3cvvvuO8DYhOqA1qRJE8D0a0kGe8XU+TJZnXVMjWv8+PEArFmzBjDahardy+b5/fffvb6jkrr6TBi2aaYSVl3ZOnToAFT0U6inquZ4y5YtFYIJZO/L/pfdLzuvtLQ01WF5hOl0kjSUY6lZs2ZAeQkfdZXXa7qm//u//wNMPx5pS+ryoO51KlRXVFSUUMo6CetwFACBSdgzzzwTMP1OtVWj4mJaac866yzASM2uXbty/PHHA6YPrVayK6+8MuocM2fOBGD+/PlAelsfmazOkpzqwK1eKhqnPIs2a9as8Wx2HaNOnTqAWck7deoEwKeffgoYW9a+xlRX5yDtO/u80g42bNjgSR2/cqf9+vUD4Nlnn406ZjpkMofyL9hbLvIZCNmneuY2btzozbts1Llz50Z9RnOmHZBDDjkEMJ3XV69eDZR/FxIldTgJ63AUAKEF/9slQNX5euPGjYDpzP3SSy95n1HncnXr1gouySuplEmIYjqrs+waeQ6ff/55HQswXuIffvhBx4z6fLVq1SpITN0X3ScFkjRt2hSAzz//POr9qRC2l1g2mrSFLVu2ePacxnvqqacCMHbs2JjHyJWE9UMSVprf8uXLAbOnun79eu+5u+666wC8YBhpVuq4Lm/yFVdcAcAdd9yR8nichHU4CoCkJKyfxzUS2abaM23Tpg1g9kybN28OmL1USVbZNpFo/1UrmewjRYyoi7bPmKPGaI8jndVZx1TE1d577w0YyW9L/AkTJgAwcOBAoFzytmjRAoB58+YBxv6dMmUKYCLCVH5TEjkdgpKwkqDyR8RLKxs6dCgA99xzD2B2Cw466KCoz+gY6h/7yiuvpDwuvzmMlySi+y2fSt26daP+Lq/1I488AsAxxxwDwLfffgtA48aNPa+/vOaNGjUCzO6GziutSTsk6fSedRLW4SgAMrZhtfom6rT96KOPAqbLuuIxtXcF0fYCmNVacciybdX5W/ZwPGQLKjolSPtHXkONy5Yi8mYDnH322YDRQKSRLFy4EDA2vbSIVPZnJcW0x5uqhE02fbFv376AseXPOOOMCu/R3q0krI3s30yagAU5h/Kt7LnnngAsWbIEMJF50giXL19O//79ATOHjRs31vkBEwMvX4zikO1rLS4uTji/TsI6HAVA6BJWtoOkkGxZ2YNlZWVeFsPVV18NmJVcjaMmTpwIGC/sokWLAOPR06oYC7vlQhCRTrpn8lprtdS9kAdYe6/r1q3z/ia7/G9/+xtgPIu6tsrYDEvXvW7dOsDsqWtuN2/e7O1XtmrVSmOIeSx5Y4NKH/xjfGkfTNFIkrC6NmlPel6//fZb73oVI6xnWte+YcMGwDwP9jVqP3bFihUu0snh2BYIvQibVlT9PPzwwwF4++23gfJVWjZr586dAXjuuecAeOCBB6I++9hjjwGZFdsOUsL6IckrW3vVqlWeh7B9+/YATJ8+HTDxxvI4JzpHzZo1vZXdj2xl69g7A9YYon4PsqxKJnPYtm1bAGbPnh01LmmIdiaOns1JkyZ5x7Azq2x0TElk+QjkcyktLfU0TD/8JGxaX9hIN7XUPgWz+yEVQyFhCj5o3769t8Ujp4lc6kpg181VorBuVKapZ5DcZMu58vTTTwMV3fRylul1ffkiSXSf/L6oel3BGUcddRSjR4+OO97KkF5nbwGNHDkSMGZPhsdOeQ7lXJJTT0EPMpnkcNP86FlLBTmhli5dGvW6vrg65i677MJnn30W91hOJXY4CoDQVGKpsVpdbr31VsCssDLQN2/e7K3CUh30N41NRr6CH7QhLUdOKiS7OkeqcIlU4FSqOjZo0AAwG/jCltpSxa699lrAOOCSIRcSVtqOrfrq98rSH0nj0U85lU488UTAv4RMKsgk0hxqTqVKJ3MPnIR1OAqAjIuw+aE0KxUfU8CEtjPE6NGjOeCAAwC8n1qhFPKl7ZtZs2YBZntBRKaqJQrgSJZkegDpd1WClGYgjWDy5MlAufSU9JVk9au9LEnUo0cPwNj1SoJet25dRkEHYWFLVrvYQGUpImc7QbUlKGdUEJqAJKnmVv6LIO6Bk7AORx4Rmg2rsKxBgwYBJhDeZt26dd72h51ErBVJ0kc2QcuWLQGTEBwrRNFvpQwjNUv2p7zFuh4F8ieDrkG1mBWErg190bx584QJ/GHZsLYXVPe/T58+Xlie0JylEvCeLEHOoZIA1q5dm+GozHzY9vyYMWMAU+QhyWM5G9bhyHdSSq+L915JNEkZJWRrpVVamewvHeuXX37x0pGUojV8+PCo98hGVUC57MDevXsD0L17d8Ck5cUjk9VZHl6l7Cn4I6LAG1DR41u/fn1PGvmhe6DOAApVVCqfQhjnz5/vBVvIZrYJ20sse9z2AANeGuE333wT9Gk9MplDBeUoyXz33XcH4NxzzwVMWd500LOtZ15+HD03IpPACSdhHY48IikvcTKB2tLbR4wYAZgEZdk9CsVTaJ7SyZo2beql3qkUqiSqzitvq8LGlNBup6r9+uuvnoQPA0l2Re0MGDAAMJFYiuaSZNXP3377zSvUdvPNN0cdU5E2SkvTvvWNN94ImHugpP2ysjJfyZotJEk057Vq1QrVZg0SzZ1KuWonQr+ngySp3XJGBQltYknXZMNenYR1OPKI0LrXHXrooQC88847gLF75I176KGHABOfCybZXJJKqPSn7CPteyoeVCVCY6H9QJXzyKREjL3/qsRxpWgp8kraw9dffw2U2/W2BNJ9GzJkCGDaPMhmlV0YUdrGdzzaq9V4shXppPP36NEjKf9BgOdNeQ6lDcljq3slP4S9d6r5kb/A3vsHM9+KvJMPRiWCpJElo3UooUPPvrNhHY4CIPB9WO27jho1CjBJu5IYdgTMhg0bPLvTjkJRorpKf9r7tEo21h5mtvvDyhOuSCxlYOgeyC767LPPuP766wFTXkWx1Wr3oZX/0ksvBYydo1Xcr7B4LMKWsBqDyqj06tXL04KyQZBzKM1PElXPmJ4pRZht3LjRN15aNqzeq8imTPZ2nYR1OAqAjCRsvXr1vPxBNS9W2U55O9WKb9iwYYBpX6AC07feeqtn68mekFTx84amUzZSBLk6yy6V51sS1baHfvnlF+9+SFLqd+3PSio/88wzgInASZRnHIugJKzibCXtFet9zjnnACZH9/XXX88bCWsXQ5Btm4iffvrJ2yuXP0RtP1QWR89lGBlJwklYhyOPCL1EjLC9pLIDbrjhBq+MaaKqC/brKkHZunXrpMcRZqtCv7zYGjVqeJJVe8qqYiFPoqK7dC8yISgJ6/dsKCNK+7HFxcWeNzUbBDGHkpKKKLMLp6mFpArdFxUVeZqfKqEoKk3x3noO9VyKdDKAAi0REySNGzeuUFJDdV0V7B9EJUER5hfWj8aNG3u9WuywPm1R6e/aovrggw/SPl+mX1g505S8oPuv7Qs5yqROzps3z0sEtwkycV2EOYd+wmH16tVe2qTSPMPEqcQORwEQmITt06cPEF1dLhJJEm0FRBJZ3zZo7BUzFxI2chwiSK3BJiiVWCqfVD1Vq5Tqr03+ICsiJkOu5jCbOAnrcBQAWbNhTzrpJMCEbeWKIFdnW3rL5lZAR6z36meYJVPCCpyI170umwQ5h3aN4VNOOQWAcePGpT2+IHAS1uEoAHLuJc4225r9U+jXB9vGNQonYR2OPCKuhHU4HJULJ2EdjjzCfWEdjjzCfWEdjjzCfWEdjjzCfWEdjjzCfWEdjjzi/wH9tM8po2dY1AAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3080, D: 0.2379, G:0.1687\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3100, D: 0.2156, G:0.1553\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3120, D: 0.2255, G:0.145\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3140, D: 0.2321, G:0.1662\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3160, D: 0.221, G:0.1718\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3180, D: 0.2353, G:0.1754\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3200, D: 0.2327, G:0.169\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3220, D: 0.2467, G:0.1608\n" + ] + }, + { + "data": { + "image/png": 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7PhnM5/LUU09lvmg8O9VECK2N119/HQjzhvSlZdhHHnmk6vU08ww/8IEPAPDKK68AYXrgUUcdVfHvRo4cWTUlU9/ez7ZinX+nIFcLEsMmJHQAWp6aGCeIy6xxYH3ttdcG+nYu36P/JsvYJvSUU04B4Jhjjmn15daE2P+Ju8gXoRZmFa5R7LjjjkBQL6slhTeDag3P9ZmffvrpkmtyavmECRPK1HU/BzKrf3PZZZcBsPvuuwNBOZ05c2arllMRMmuRZVnk69ZS8CCzCi2v6667ruzYjUYjEsMmJAwjtNyHdSfVfp84cSIQfBd3VhMVpk6dWrab2UR7iy22AML82Oeffx5oLkbaCh/2qquuAuDFF18E4GMf+xgQ1GEthEbK0B599FEgxCfje1NvDG+nnXbqBbJRGv3Nlqk1wC9rmigg4x500EGZP2tSQdHfyqQvvfQSEHxKVeOpU6eWpf5VWh/AqFGjeqG+1EDnvv7lL38BYNlll634/noSLOJnmI8xQ9B1KiH5sAkJHYCWM6w70X333QeEaeTHHXccEOJ0xuPyZVnusvq37rSytXG/ZtAIw8alX0X3TOaP1eJKOPnkkwHYbLPNgMDW8bzSepBf4zXXXNMLsM0229R9nFpRC0t7D51eHq+rHgZrhZXk9frMilivkdRFY+xGOTx2mg+bkPA+Q9vjsOZkVvJLZFb9Cn0Ad2V3LBPKW5lUXc8aJ0yYAAR/fOONNwbgxhtvBMI66oFs7GgO807vvfdeIFgVZst89atfrXrMRuOwsp5ZVbWiu7u7UFeQXV599VUgWEv6vPp3X/jCF0reVwmtZNgY6iTmPNfyTF2jaytSmi0sWX755Wu5vsSwCQnDHW1vEXPxxRcD8PLLLwN9uaJ5LLTQQpm/Nm3aNCDsTLKOyuJgNdm68MILgfLqD7N0GomRmkNsTrVwjZ///OeBsMP/7//+b93nqBXeb5m1qDzM1ydNmgSEbKZKqr3KqNEClVxV4qWWWgqojVmbgffz17/+dcX3uZZKzGoZX5GyXOT3Wo7XDBLDJiQMI7Tch9WeHz9+PBDY0WyaO+64Awj+6j//+c8sFifbuAsaI7MIuhVoxP9xx1TRjrO2jDHWYgHE2VJFMIbbiF9crw8bq5hWncSsF9d1qvRPnjyZm266qeRYxuPNkor9PGPW5inX0jigv/VBfZ/TZZZZBgjxYmOlf/zjHwG45JJLADjwwAOB0Ezu85//fKZZ1Iujjz4aCNlsKuWVMGgF7Ao1b7zxBhBS1fzw9/b2Zh94F/Ttb38bgIceeggIxQCt6PDQzMP2A+wHWvjhcxPq55x88pOfBEL6mvehqEugH3jN1Homm7cq+d8vmRvq4YcfDoQySLHUUktlJr4bktPaP/vZz/Z77G233RaobqL2h3aITnFIrpbNt1oZ5YorrggEsdSSve7u7rqL9EUyiRMShhHaJjq5O8dJ3e5KsuY777yTCVKm+llq5Y40VHonFe2KilIWoT/88MNASAAB+OUvfwnA17/+daC8ON/7ZFpmjHZONi/q3SSzKhCdf/75Jb/XnH344YdrFgQ1SXfbbTegMYZtBj7DrbbaCgiJ+d4Df++9yCfBuEZfi92aotd1azS7+zOtay0GSAybkDCMMGgtYrT7V1lllSy5YueddwaCzb/TTjsBMH36dGDwC9i9V0svvTQQhIt4520GjaTCxWjWh/3BD34AwAsvvADAmWeeCZQnjojNN9+cW265Baje1qeav18LmnmGMXPGKbKGIb/yla8AwRI8+uijs+dt8YdN6GxF8+CDD9a8hmpzipIPm5DQARj0Nqfd3d1Z060Pf/jDnhcI7Tu++93vAq3prN5KhbGIDeMUv/nz52eF06a8xTj22GMBOPLII0uO7blsqVKpAVuusL5mhp00aVJm0dgYzJY9Jjvoq9p8bY011gBCq5j8dRbBdDz9esv96oHK8pVXXtn0MyzqQx0XAXgrx48fn+kQNpYzEcKJAP/xH/9R8ZyyqezaH7RqllhiicSwCQnDHYM+H3bkyJGsssoqQPBdZVwDzDLrUJmwHTNrrLLKrDZSy8fdJk+eDARfyfQ2g+v9teiEyswqKpVxFd27GTNmZBaBiqnN5YQ+q+ViXqPHiifX5bHffvsB8MQTTwDNlUjqOxahns9HUaKG8W/TYldffXWgb+1TpkwB4Fe/+hUQlG7TL4uOZeqqzFppBq5WZtEaEsMmJAwjDLoPO2bMGBZffHEgtBzRh9Jvs5lZK5h1MCaf5Qu8zWzSP5Q5LfT/05/+1PT5alGJVWlnzZqVqcDGJi11k0l9LlpC11xzDVCaxfTb3/4WgE033bTkPFpHlkq2As2kl9b7GTKaMW/evLIij3g0R3zsRssV3ztW8mETEoY7Bt2HnTVrFqeeeioQ/IoDDjgACG00B9tnbRZdXV1ZPE+F1V25FXHXRpAf5OT91+cyI0dWVD02htrfNcuscUza/8tUHqNaHLLVqPczdO655wIhM60/WOBSdGwLItZbb73CY1heWOv83MSwCQnDCIPuw3Z1dWUxOuNcZtR8/OMfB4JaPFx92JEjR5bFkK1msfmabWaKqlvqQb2ZTuYxyxhC5XrdddcF4DOf+QwQfDOZtru7O1OBbecT4/rrrwdCC9iBblUbx1f9f8zwtXzGLMkz80kftZmZszGSD5uQ0AEYdIaFEM/63ve+BwQF0x3fChZrapvBYDBsHu7sq666KhCK9FuJehh21KhRfOMb3wDghz/8IRDaqZiV9aMf/ajkb2ycZuvU8ePHZ/5vPMy4GmT3hRdeuOa/acUzPOGEEwA4+OCDS17Xb+9P2VUl9nPZisy7IiSGTUjoAAw6w1599dVMnToVgPPOOw8IrWL0bVupJA42wwqZ1ookW5O0AvUw7OjRo7O6V33WH//4x0AYVGxc1q4Zhx56KFA+9HigMBDP0Hi5tayjRo1qK6PGGLQWMUXwRsydOzf7QvqB0Exsh+Q/WF9Y17LCCisAxTNoWoFmy+sUTfzpBhp3bjShopY+u80gDgHV+wwr9U0ugufKl0zaRECzuZGpDLUimcQJCR2AQTeJR48enZkf8c7ejtYwQ8Ukrobjjz8eCLOI6kF+jY1MditCK2fVKiAawlNYNOm+EuJneNxxx/UCHHHEEU1fVxHGjRtXUwFGHs2EeRLDJiR0AAadYfOlRgNRPjeQDDtY5YCtanM6VFH0DGu530UN54qQb2VajxXQLBLDJiR0AAadYQca7WTYgU5oL0KjDBsn7DeTPhij1jaetaCdz7Cehu3tRGLYhIQOQEWGTUhIGFpIDJuQMIxQsYC9mel1rfR/WonYNxg5cmQvlE+LyyvXcVG2GS7GI1UPbSLn+/w5Z86cwgl3/j++b15HnCI3Z86cLFHe5mdeq6/PmDHjfakSDzbieGsz1mvyYRMSOgBDSiW2YbWjOdqBot1ZBpPpzAzq6ekpayQmC8psMQP7usfq6urKdl8n0D///PMlf2OOsazpWJK4OBpCuxWP73s879y5cweNYW3bGudKt1JBHyyGrbWpWr2x3v6QGDYhoQMwpBh2IBDvXOPGjeuF4I+6O8oEEyZMyBjV98T+Zq6KpOSn4yD+9re/Zb5oPKHeTJqYeeKhYV7X7NmzC/OCtQDeeuut5MO2CM20Km0GiWETEjoATTNsO3YgC6YdidBKFKnEuUFSQGC0ESNGlFWnmA0jrOLQT1Ottf3Jaqutlo0i9H5b+SLzev4ifzg/HtHB0V6rf5trLNaSelgRf0ZifzSvpA8EqjFsK3K48/XaHtMxnFZQadH4PLz/PqsiS6iW+5UYNiGhA9B0I/FWMKttIx9//HEArr32WiDUSprXKdu0MsYbxz3zyi70rU9/03ar8Q76zW9+E4Cf/OQnAGy55ZZAGPC79tprM2nSJCAMlnrmmWeAwLAq5P6NQ6NVk127u3n+Wt2tW9UBIY4NazFYpZJnVuhrb7PDDjsA4T4O5uCyes7pGh3SdffddwPhGfv56OnpyZrB2/Dev7FTig3YjcvHg69bcU8GXHSKQxHz58+vee6KN08T1BkvX/7yl4G+7vPOeClCbGr09PT0QhCB/GLZ/qS3t7csKT4utP/Yxz4GkHXQd4NxA3r55Zez2TkmV9gh0ulozhdygpwPW9NZcWrUqFHZzCG/QLEZP2vWrEKTuL8i9Nj8K4KbhpuIU+723Xff7D1OHnTCwZe+9CUg9Imq9mG96KKLsunnRahmEtsbzK79/Z0zNuv9XDrj9dJLLwWC+/PII49kX0Sfxc9+9jMAjjrqqJLXq6GWYvhkEickdAAGjGE1G88++2ygXJipBe7wspTTwzQrp0+fngXui1DEsCIO2XR1dbH33nsDgSU01Z3l+cUvfhGAJZZYAggzgeznO27cuGyKvMeSFddZZx0gzF71/mh+25nQrvvPPvssL774Ysn90ELxPsybN68u0akonVQz3e6J9ot++eWXgTAt/phjjqmakiqTuY6iKX21NExrJKxjeEx3xSkTiy22GBASVfz8uGaZdrnllsvm4Pg3v/71r4HwvO0+2Y4JFSIxbELCMELbGVYW0u9pBrEPGTPMhAkTMvatcIySnWvEiBG9UF68nS8G8N/6m86RkTV32WUXIHTO17d05z3yyCMzMcl5QbK04tL2228PhN7AXo/ilD7P/PnzM19TJtLP0i+t5MNWQiyK+P949s4LL7wAwLRp04A+v88pAbU2HKunMVnM3s0kTsRhSP8f31MtH5n5gx/8YCYyOb/YdrxbbLEFAC+99BLQmuaBiWETEjoAbZsPO2bMGKC2WSubb745AN/5zneA4OfEvky8K9vYOl8KFyca1Io4rVBFcObMmWywwQYA7L777gDccsstAGy77bZAYNarrroKIPMx9WFHjRqVMam+kezr9HPDNRtttBEQVFanoN9xxx0A3HnnnRmj6ufKrM0m1uuDe/3CcFVR284DDjggs3Kc7XvMMccAQY2N4Twfj10JrQzjxUq4TKtve+eddwLw8MMPA8GHnT59eqbOm3JqkcqJJ54IwH777QcEH78dSAybkDCMUNGHXXHFFXshsEI9MEa16667lrwe79Kvv/56Fvssuhb9DEvT3J2//e1vl7zvrrvuynyPIsS+wdixY3sh+MGylswwf/78zI/xp+qpa3vggQeAwPgqkCaEPPPMMxn7xQkU++yzDwD3338/EMZiyHLu4iq0M2bMyErw4mL7XDFCW5L/nVqnBWTCwP3331/IgqeffjoQ1ukzXmWVVYAQq64H8TPs7u7uzR+7GagE+7weffRRAC644AIuu+wyAL72ta8BYTK9VqS+tmp9M0g+bEJCB6BplVjl0HihzBSnzQn/r+JWFI/LIy6KLrrmPffcM4uVFiHeuTbddNNegJtuugkITJZnMZndndW1yvzPPfdcyU+Z1vf19PRkWTD+zVZbbQWEzBr/5g9/+AMQMq3c8W+99Vagb+cv8lX1zxptc2oMeM899wRg0UUXBULc24wuWdHz5SfMy8L6gHvssUfJOW644QYgqN/f+ta3ar28DEUFHHFDgVoQK9BxBpTT5++4446yKe4OAVPBVyVuBRLDJiR0AFoWh600uRrCDuZOrIpcy27orufure/UH6rF94r8n9gf1F+dNWtWWWxOVXi77bYDQu6w8Vf/VjZ99913s6nmFjaYHaXSrNI7ZcoUAF599VUgTAo31rvKKqtkKqRr9d7LCs36sHGscplllgHgnnvuAULZo9fa3zMseg76rmZ2NdK0rx0F7F6vFoHqtVPmr7766uw++3n02WmdvfLKK81eRobEsAkJHYCGGLZSmZC7v3mVMaxKMXezpot873zVppXPmTOnaolZ0e5sbNMdX59z3rx5mX+p362fefPNNwMhdmec7ne/+x0QfNqurq4s/1S/0CypiRMnAvCf//mfJWs1pmfM94orrgD6LAF9ptgq8HnMnj27LoZdddVVgb6KFAiagT74+eefDwSrQOY11r3JJptk12cml5ltqu7eX7OjtBhaMYqxljWaiVUtRnrfffcBIXtJNX/ixIkZs5r5FZdmqudo6WhNxmV2tSAxbEJCB6ChTKc8s7rbx21ThLuOmUP1MKvw2NZaFl3P6NGjs4wiWXwkpr4AABizSURBVLEa4kycmL26uro4+OCDgZD/a6zULBhjdf5tXN+7/vrrZ6/JnFoJV199NUBWHL3++usDQbE1pmfbnJtuuim7Zu+p6mSjQ5s9pwxrfNmh0hanx886n2vs3xo/1hry2LL4zjvvDNSfiVYLKvnD1ZjVtXz6058Ggnpv7BWCWh5XWPmZ3nTTTYFgiTUyjLsaWiY6eRwlfz+AcbF3M1h77bWB4OT7wTEMccQRR2TFBhWus+RCxo8fX7JGvyB+Gbq7u7MvyEknnQQEUcb3+IGORSc/4LfeemuWSGLRvckXhrU0M9dbbz0gdC8wDGKa4+KLL56ZqHFvJ59BveV1IjaNFVU07TX9f/GLXwCw2267ZX8bp3b65dEF8P8KdCa/nHPOOUDtxd/vnaPifNj4mmqBhf0+B6/TL+eMGTOypBfL6vxcGAL02fk3buymn9aDZBInJHQAmk7+d1fW1LNNiqglMaJWKGgoXLkraz42kkIZs5RQyJk5c2YmJl188cVAMBVvv/12IAhHb7zxBhB2XE3mqVOnZuEMxSZT9lyDSQmKTIZ5ZDXf9/Of/7yswEFT3fM3CpnVazEEle8cmIfrHDt2LMsttxwQitpt86K5aMK89/K0004DQgKJ52oGMaPW00NJl0UrTuvJoosNN9wwExu14kxJvO2224BQdrnmmmsC4X62YhKASAybkDCM0DTDViufs/lYM9BnNO1Qv0c04x/HU+XyyQ7Qxy76pMceeywAv/nNb4DAegoaK664IhBCAl7XF7/4xSxRxF3WvzH1zV1ai0VrQd9KvPPOO1l4xYZthhmaTX63vM5r0/pQK3C9WgvelzfeeCNjZS0I4T1QMJR1TH7ZZJNNgFKG1VLQcmgU3o+FFlqopNtkHqYXKlKaEmoYzkKOuXPnZi2J1Bfixn1aXIpOl19+OVDaeRH6PgNxV8Zan11i2ISEYYSmGdY2Ge6wIk6EjiX3WlLSlNRVVotmuJoSVpSsAcUsHCfS5ybAAaVtTlVHTcjXZ9Ff23DDDYHQ9kVl+Pzzz8/K51S49YMMCU2ePBkIyRimxNkyRgaaOXNmliKoKp1ThwvXXwtsvWqhvvdGtVsm/+xnPwvAueeeC/T5e7E67E/9OZVm2VnLwRapeRYqYtZ6LQifeZ5dtXT0v7Um/ByrufgMP/e5zwF999bwnlajBQ6GgPzMGP7x86I1YTLJyy+/XPisTHstQmLYhIRhhKbjsNV2vdh+Nyalnb/DDjtk/o27sm1GbBmjrxRDdtLvO++887ISuArX228Be64JNxB257lz52Z+joqmDdUMshu7NYYnq+gHPf3001mMTnVYP9g0QFP4LFj32LZFveCCC4A+y0XrRSVZhdP/N1peF8NnK/PK/saBLTs89thjs2YCxx13XMnPQw45BAgF7P6NWoGfA/2/SZMmZQxV9NlqJvm/qJxOJpX5taJcx9ixY7MkFwsDfv7zn5esJZ6LlNcwICTJzJkzp6qFmeKwCQkdgKYYdoUVVshUYncVU9PM9lDtVFGLJ5HHk7rz8NocCSHTmlmUT9CvFfHOtcACC5S0F9EPyU+XM6NHFjALRma3YZpjQvTXbBGz9957Z6yoj+J90s9xrIRsLsNauC4DnHjiidm99jpk2EaT/0U8szY/TiV/LSbw68MeffTRWfw11gpizcIGAZ7DazY+W22ExXt/07LyOp+z6rD+u43vtfK23377bI1xmWes+BrLtYGez94ChDSqIyHhfYKmyuvyYxViW9zjnnLKKUBQGPUx3b1nzpyZtfxUlVTVk6mcFmYieaxI5q+r3rmbiy66aC+UZ/OoeuuvQkjA1+c2kd08X3/KPPqljz/+eLZui7/NjrrxxhuB4OeodHt+2cxE87feeiuzKPSd9F09X6O5xLK66rxKaZxZZTxSZfXiiy/OSvG0EFyPz9Z1ag2YY6xy+uSTTwJ9Odaepyhzq5n5sPm5vxBi5p5fBdifKsO/+93vsriqzQjUJTyPvrdZXcav/Tz7HSmKCVdao0gMm5AwjNCyah13F5lDlU2WLMKsWbMy31QVbeutt+73vbZNUR027lkPilrEuPPGCt/s2bOzf5svq7KpzyqbyLwWsPv+M844I5vera80depUAA466CAgZIzZlEw233///YGQR/3AAw9k1kAcQ5a9mh3VIXIT3UvWE+sOF1xwQfbM/uVf/gUIVoa+t58zY7jeB8d+1DNnuBkfNmZfn7c+9KGHHgqErLYLL7wQ6Ps8a3nYGkir0aocowLqM7EFmB+wliawJyS8D9A0w9raxBpBs47iQbmtqIe17rVaK9NKiHeuSZMm9ULIGhJe94ILLpjFZuM16dvZpExVOM64+u1vf5upjsZfDzzwQCDk3loJoh+smmxWl0pjd3d3fqwkEJhVtnj77bcbYtg4Niirm2frfVDhVem8/vrrs2yfoqHXvu56mhkr0o4mbK5d31XdRGvmxhtvzNTfuA1Rfjh2f2ikWicxbEJCB6DpXGIrZ/RVtedtPKWyq6Io3HFfeumlrErE3VimsnuF743zla2SUKHMIx5yXA1W2pgfrML35ptvZoqiP90pHT/50EMPAcEf82/NFx47dmymitpA24wmY3krrLACEIYyqZB6Ln28BRdcsMw3cq21qI+V4DX67FxfXimHEJs0/3nHHXfMfif7eN9lZ5+tg8WseLGtinrAYEGrwjxtn6EMO2XKlOxzWI1RRTwWtRVo23xYTQol7tg0yptOrSqnqgXVJrDHrULyiG+816tpmJ+iB6UhDUvhDAloNmsa6kqYWBBP0fMezZkzpyzd0w9Vrjd0S1ITqxWAG0YaP3589kHXbGwn2mESDzUkkzghoQPQ9gnsomi2aLuPpWljGl+8c40ZM6ZkLovHlPl6e3uzlD0ZJTb74v608TS50aNHZ6GrOCVP5M8HQVhT1NMc7+npKZsWb8M7i8PnzJlTF8M20n2/WVhIoeVRCf1YZy0L6wwVVFujSAybkDCMMGAMO1RQtDsbwlBA0pft7e0tE51kohxre6yS3+eZK05693eeJ2Y3/28Q3hS++fPnlwlqefZ97xxtmQ/bDsSWRy0Ybj5sPWGd3GcoMWxCwnDH+55hR48e3Qvlhfb+7K/YOE66F5ZPGRbx9wsssECZjyxz2gY0nlcTM6ssOnv27EydNjVOmF73xhtvDBuGbQTtZNih4uMmHzYhoQNQkWETEhKGFhLDJiQMI1RMTXw/+j/9xSSLyqSqwbjs3Llzy3yjar5SLb5UUfplq5qwNYKBiOkOtkpsKqypsaKVa08+bEJCB6Dp5P9WoohV2qncVTsn1L5jxjHFfNyt1mvPs3Kl64PArHFz7MFAHIPuRFjMETOriNfeyiFYIjFsQsIwQorDvheHla0q7Yqxj1LrAOGenp4yBop3Y48dD+eSaStdV6U81Fa0AC0qJ7PiyEqjgUIrfdhqozptR+SIkXYjZTolJHQQ3vcMW22N/TXMqtenrtR0K2bOomNXUiDj37VbJd5oo42AMLpj+eWXzxqzVfPXarVKKmEgVOK4le68efOya48bt8ftbmr5fKRRHQkJ7wMMmkqcbx1qHWc8vDhGO1S3asduhTJd6Riezx3XZmYOWqpFfW1FjXElxL5s3Lr2ueeey9YY1x/HGOzMOu+V7XDUCG644QagvA1QPm4eK/iOprTLhs/IHG/rqEX+2I2q6U1/YWs1D/394YcfDoSpYHl4IywUtxPjfvvtB4SwiTM97YFrT98HHnggm3NTL+Lu9v1tCvX2iap0b+x1pehhF8WNN94YCP2U1lprLSBMr3vmmWey/sDxtdcC+wTnJ55Xg1/UeH6SxQZdXV2ZWVj0RR0I+IXy+eQ3snh2z8ILLwyE/sh28bcHlRvo5ptvnh3D6QXeB/tox1+++IsqmukUKZJJnJAwjNBy0cnufzZhk5Xsvytb5hGnehW1gJF9FDvcyey66PyZSigSLOIJZHlTNBYbtASKzJq4WB3Cju15nA4gnGLn9DTZYrPNNitZ2yWXXML3v/99IFga/ZjxdYlOTjK49957S16PxSVZyeelqfzoo49mHS1jSyVufVPNElt00UWzKRJFaER08rw2wZMdnXX0la98BSCbnyOzet8nT56clU0OBJLolJDQAWgZw1rUbSH2JptsAgS7P+4Er59x7733su6665Ycy13Zn7EYZXG3DFMPimbr1CLBy+jxlAARN0fT2njhhRcy62GvvfYC4O677wYCg2o9yFTu5vYadq7LXXfdlQlSRYUEjYZ1ihIlnELg3B/PI5vOnTs3S4+0p7S+oe+xUVwtVlA1NBPWie+Z0+S//OUvA+WJK/587rnnsgkP2267LQBXXXVVgyuo6ToTwyYkDHe03Id157JdioqicAKAU8z+/ve/Z0z6ve99Dwj+hEwa4+GHHwaCkloPihg2Dp/k/ed4V45n68TWxeKLLw4ExXS55ZZjn332AcLU9jXXXBMIyrfpfbLyHXfcAYRO9N6TzTbbLFPFneZXaY31tDnVb84lYACBdZw87jVts802QB8jx8q11obMpXrvfWgGzTCss41s8r7SSisBYcrEnXfeCQQV34jFuHHjqqYxthKJYRMSOgCD1kj81ltvBfpmvBapraeddhoQJoMbd5Wlqs2eLbiOkp1rtdVW64UQy+1Poa412G+rVCfN6WP/9a9/zYLs+j9HHnkkEHwoS+NUovUJnWqnQrvLLrtk7FvLGn2GlWLCcZqcs32dFaTvrgWk5WDy/9e//vWy2KfnUW3V4lKFbWYOUCMMG8fXi/z1eHSKEYgll1wyux8D0agtMWxCQgegZQx71FFHAXD00UeXvK4fesIJJwCw66671nxx7sL6eXHD7FozjvKoVSWO47L598TxYUuvnJrudDfjmnPmzGGnnXYCwrR2d2tHVcQ7/Q477FByLtudLrHEEhlL1bLGep5hrL7LNvHQrUMOOQQI+sS0adPKYtTxDFl9weWXX77WyylEK0d1aPk4RdDX45g/lE9L1OLzPrTSt00Mm5DQAWhZ8n/MrEIlc8qUKUBQIp988kkAll122ey9Th8328ddW8h2rUz+j8uo4nP09PQUFgTIPDKSymLMRBMnTsz8PZVk86Tdtc2OMsvHDJwNN9wQCMOjzjzzzOw6Wu1L6XsbK3V9+tFi2rRpQChU+PGPf8w3vvENIGQQqZybCG+yfayo14NWDlKLraWTTz655H0+Q3OQ99prryxbT2XZiEc8miW2zuKigWaQGDYhYRihbXFYs2KuuOIKIOzeu+22GwBPPfUU0OeTmbmjcqfvZJzvpJNOAuDggw8G4IADDgBChUU9qNX/qVRgbAXN0ksvDYSRkKqrKrzGWseNG5cxjb/bYostAPjZz34GhLI01WIHYevH6/OOHDky892Lnl29PqzZVk5Bj9euNWSmj1bBvvvuC8APfvCDLEZpDrlMqg+r5fCpT30KKK/4qUePaMSH9fwyvc9DyyduYHfJJZcAwcJZaKGFGh44vssuuwChZLIWJB82IaED0DTDGiM1N9Zc2bPOOqvkfXGhthk9Mm1/cPc99NBDgaComo3SCJpRGFVRn3jiCSAMXTYuqVJuHq0MvPXWW/Pf//3fQFCQl1xySQAOO+wwIFgXqsHm5BoPNA777LPPlvmu3lN/zp07t4xhVdgr1WR6XK/Fa5Z9VLp9xscff3x2HaeccgoQcqH143yG66yzDgA33XQTEHzdn/70p0Bg4v40gziG2ooWMV7niiuuCARLUKvC+9UKncBjaKE8//zzZWs0t8BmDkUM2/bECb/QpoT5wfMBjR07NksiOPbYY4HwBVDIUHDx980kkNf7sLu7u7OHqDBkYYNhHENVJnqYQGHq5EILLZSFYhSbDAFo+m633XZACOf4xVK4yn85qvV7qidxYuTIkYU9kN1M7RzoNZpueN5555XdLws5br/9diB8+eMeyn5hTRH0A/v8889nIaBaTP78GmtB3Dvac5ju6mfONExTaG+88cay0sEYEombk7A5gcLrk08+yVe/+lWguIAlmcQJCR2AtjPsNddcA/SZhRB2WFMBR44cyR577AEEk9Pd1hCGIY4HH3wQCCKPjFBP641qiRP9tYiRwXyPYpIChszqzmobFlMnp02bloktWhauX5NRYUKTUQbQutA87U8E8z4YPpg9e3ZDiROx6aZ59rWvfQ0IRfZCi+fII4/MRCZDPRdddFHJ9caFBd5nU1S1xGpBKxIn/Kn1oKtiyCpm3tVXXz17zTV4n3zO8XcpDvfYHun444+v2p8sMWxCQgegbQzrjuXPooD5EksskfmE+rlOJff/7r6GNvQd9bHyDFtvv9dqa+zp6cmOZaK+4tcGG2wAhPBGfM5KBQTuyu7g+vG2ZTn99NOBEGyvR/RoNDUxLh/TKtIfNcnFdWot3HPPPRxxxBFAYNZ8exwIvqNi2vTp04Fgrdx///1AX7KMay1KrmiGYWPxTfHJJAjRyCQ6j12UIJHvmhgLhvHzTQybkNABaLsPa3qhfqpJ1iZQQCjOdncW+oSWk5nm6O6twupOHKcy5pFLZK97Pqx+pudTFTaRI/ZV4gL33t7ejG1ViVW6VUn11/2912tIpWgmTx4TJ04E4LXXXmuq83/cPuXSSy8FQpG3FpA+/CuvvJKpwPqEMta5554LhCT6onXI7r29vYUteCyQGD16dNNhHdeo8u+9Mw1zyy23BMKaISQ++Fn2c+ixFltsMSBYSRYWqFdoZT777LNlBf8i931MDJuQMNzRNoZ1x5X1VAmdweLr7kr9QZvfnSxWGuOGWbWgEf9Hn9k4pMH+2267reT8seJnHPahhx7K1qvS+sc//hEIzKo6bADf8i7L8fpDUWy1VbN1VDJtCGcx9y233AIEX+2www7L0hpNTYytH60U2dokDKMGRgamT59e1qImRitn68iopiDG6rGlk6NGjcrWogUlTAJ65plngGAdxc8n31JIPcAkmxjJh01I6AA0XV6n6udsEnfKCy+8ECgvfnaKdR5Fipxpedr+ppGZfN+KsrL43PH/J0yYkLVGURWVLeOdNMZDDz0E9MXrZKkHHngACPFps3psw+Kazj777KrXXtTmtFWI27z4rPfcc08grOXmm2/OihRsBaOFoCVhcYPxZq950003BYLvPtDwM+X16HtrIRgJmDNnThZ3XmSRRYDQ7tZ4ddzaNc7I0yLMv7deJIZNSBhGaJkPazmdSf8qZOZR6s/lC7ArnBcI+aZm+6i2qayahymTffe7380yVYpQq/+T9739t2uTRfStzWyKC5jNC77uuusyFVR/+NRTTwXK26rWw5JFzJ6f3t2KCexaR3Gc0fMvuOCC3HfffUBoGO7vZJl4grkjW7wPeRizjUve+muU/t656l5jvBZz3GXNOItuxIgRZcXvPm8twLhE0Pf5HbBAore3N1u39yGODycfNiGhA9C0D6vapW8quxgzVVF0t5Fh3Z3efffdbHfRn7DdjD6Tu51ZKfldDxprKC5UKd3NcyVqQN+uacG2sTl9awvrHd1oaZz5sV73a6+9lt0PGceCagvYfW8t7W9iH6pdiBvDFV3bP/7xj0wxN79W1tF3jzWCc845p99jdXV1lWVJtQOx/uB1yZJaF/lYa1yl41pjVTv/3CEwq5/XRRddtGwoXK358IlhExKGEVrmw8a7vsd1h9Iv6Q+qr8bqhPml5pvKgpWOVQ2xb9DT09MLxRlEXV1dmc9qmxuLz/0bY3bx9bvTvv322/z5z38G+tqpAPz+978Hwo4eM1BRs7BahnbNmzevJT5sPZBRLeq3ZlZWcpjXjjvu2PA5cuNUWhaHrXauXAVUmWYQP5N6B35DaCsUN8VPPmxCQgegZQzrceysoEJqdow7SFG1PpT7piKOjTbT7rIZhVF/ff/99weCLzt58mQg5J3aTUD/uKurK8vw0t/WX4+rRHLX5fWWvJ7vgFHExq3KdKoV+SHMWj8q+qrD1Spw6kErM52qoRH1vhGmjTHgLWIMqju1zKDziSeeCISudV1dXdnCNKs0Q5pZcBHqNYnHjBlTFl4Q3/zmNwG44IILgCDfW35nCdnWW2/NQQcdBITexfUiH+ooemb9mYwDZRI72XwgEiAG8gtbD1qZwJJM4oSEDsCATa9rJRopLhZFu3M9u2PcZqSf5HsgmP8rr7xyZiLWeh7LD2VvkTeJ86/ljznQDNvV1VU4m6gVUxoqTZh/7/dD8nNqyZ6hrXqQGDYhoQMwLBlWNOLc1zq9rj/ESRZFbBm/PmLEiLoaxTWKwfRhLV6wmKGdGC4M2wwSwyYkdACGNcM2gmq7c8yOef/McFPsQ9aTwF/NKijyz/PHrnaedoV1Yt+9EQ2hFWhFm9Oie2fyi8k8g4XEsAkJHYCKDJuQkDC0kBg2IWEYIX1hExKGEdIXNiFhGCF9YRMShhHSFzYhYRghfWETEoYR/h90LOYcbdbPwQAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3240, D: 0.2273, G:0.1541\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3260, D: 0.2331, G:0.1637\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 6, Iter: 3280, D: 0.2269, G:0.1655\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3300, D: 0.2316, G:0.1475\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3320, D: 0.2358, G:0.1434\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3340, D: 0.2379, G:0.1609\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3360, D: 0.2499, G:0.1587\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3380, D: 0.2304, G:0.1535\n" + ] + }, + { + "data": { + "image/png": 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66KGA2eFV5iUsx1oUErZPnz6AqTFcWVnJWWedBcCUKVM0hoT32PPJpeRKNmuo+/r5KF566SUA9ttvv6Tvr6ioSOXkS7h2GD4Gd6zjcJQBWfWHjQ9OkGdMUkc759///nfA9AWdNWsWYNzlv/nNbwBo3ry59x4FD/Tt2xeAe++9F0j01BUSjdMvXE2SVUEhkqIzZ870AiJEt27dAFi+fDlgdnZJ2iAe+Xyn8ek+WvP4z6N58+YJr5F2JP/DFVdcAUDv3r3zMlYbW6Lb3QvsNVW39auvvrrBe1SoTf2E8rkOTsI6HCVEVjZs9+7dgfrDZO1QOiuVTasddujQoQA8/vjjgDlv1H07duzoFc8+6qijALPbSTr36tULgIMPPjjD6TXEz/6xQylT7Zqas52EcOmllwKmhKbm8/TTTzNnzpyE68ouf+yxxwA47LDDAFOkO5fdOpkNe/vttwPGi5vldZP+fv369V75Vs3r4osvBuDCCy9M+p5827BBiffPxLNixQpvLuqqqPVXydow23o4G9bhKANCO4e1bQLtOtp533vvPcDYeddccw0AY8aM8Yo762xWY9JOJptQ7TbUAS4Ztk0iiSIJ47c7+3kxq6qqfD23u+yyC2B6uq5cuRIwdro0g5NOOsnrKarx6Yz25JNPBkzIpjyNQbBfG9ceI7CXOBaL+Uo7zXPbbbcF4KOPPkr6umTvlz/CTv7Qa++++27AdLnLhCglrDrS6UQiHp10SCO0Ywp0DivtMhPuu+8+AAYMGKBrOwnrcJQ6kQX/S5LIO3zGGWcAsGbNGsDYOnvvvTePPvooYNoiqEu3djKd+73zzjtAePYdBJujnydRv+/fvz9gPL/q/aqdd9WqVd455b777gsYr+khhxwCwJ577gk0THjIpJFSXKvHjM5h//znPwMwduxYv+sl/b0ivpJ5tO2YcT/CSD2LsgduvCdchQhatWqV8FpF0WkNw8BJWIejDMjqHDbhAj9ELNkpR5IMp59+OmDsT9kGijlu2rSpd87aokULwOzYm222GdDQZsgH8efKfuevtq0t77BioPX6E044wUvb2nHHHQGTlK+zPP2cSyRQNt3bKyoqGkjWdNcbNWoUkHhGKeSriEKy5hOtg9qEjh492vubNAtpi9Ka8oGTsA5HCRF5mVM7l1ZRTLLzampqvJhb2QiStIox/uUvfxno2kHws3+CFN2yvcSDBg0CoGvXrgDcdtttgPGQy/P7wgsveHbftddem/C3wYMHAw0bOSezofyQR1be7LBiie1nQ2eUss2DvCduHIB/gniG44rchk02DzvWXbRp0wYIt9ienw0b6Atrq725JHnri6FQtm+//dZLW5M7XIsrVU1HQMJPDQ+C32LbD32yMetLo5913HTAAQcARoWfOXMmYJLTa2trvc1Hr9Xi67hJQRfJEvxzmWMYX1its8aUTJ0NsrHEvy4X1T/KL6wCaORIkinXs2dPJk6c6DcewDwXYeCcTg5HGZCVSpxJsLNc4DLQVRdn9uzZALRv355XXnkFMGqiwvQUbGCjXVC7uZwAqVKgRJi78yWXXALAiy++CJgULQULSAI/9NBDXsK6alspGEFHVfpc7ETqTOrjirAlrBIXFDggrSD+demeCc1zhx12yHY48feLXCXWM6UgnfHjx3ummx9aW5lIueAkrMNRBmRVlziIfWXbhAr6l9TRtVavXu05ZGTD3nDDDYAJLtDBfbt27QDj9FBCQZcuXYB6CSD7VkW1dNQigu7OySSGbW+pvMi4ceMS5qyA/htvvNF7rbQFJTDovQprtBOnbf9AfLBCuhI1YSewa12WLFmSMFZ9HsuWLfO0o3ROpzCIUsLa/plMit/ptfLP2CGK06ZNA8xRZyqchHU4yoCMAie0wwTxXKpUqWwyucK1+0jf79Chg3e0ocBr2YKSrLLjJFknTJiQcO327dsD9cH4srOefPLJTKbWQKLKcx3vNdbfdJwkyT5mzBjAhFRq3Ar+X7JkiXcdSUolCkyfPh0wNu0XX3wBNAxayCYoIldsj74f33zzTdpnotA9c4MyZMgQwBTHix+vtCJpcfL0X3/99Qmv1eel473DDz8cCCZZ0+EkrMNRQoQWOKHkXTt4XYfKKv2onUu2wvr1673zS6XZSRpph9LurR3/u+++A4zE1esOOuggrzTNiSeemHSc6eyfbCSBPJ9KPrftoOrqai8kUYETkphK0lewiN99M/EW57vyf5MmTby5S8MRCmNUcbYwyMWGDRpDoOdHySvJuPXWWwG8Era6trQn+VwULKRi+nV1dRl1b4jHSViHo4TIScK2a9fON0xN3lnZpUrYVs/XE044Aajf6WSDamey0bns/fffD5gCWSrBItuxd+/enn0ZdOfKV6FteZBV/lWJykroV1KECl3n0r+lEL11ZMcrIUDJ7rbEDYNM17B9+/YNni3ba52Nba1r6Jmzo+TsEkJi9OjRnt3rh5OwDkcZEHkC++TJkwHTukFRM7IRBgwY4JWNPOeccwDT+kD2qOy/ffbZBzA71r/+9S/AnA9effXVno3nZ+vlU8LKpqmrq/Ns/P/+97+A8WzLlpWtb5PK1vKzg/ItYbfcckvfVhdRpNHlcw2T+Q7k6ZfvRVqmXqP1UKKDztrtaLdUOAnrcJQBWSWwB2kKpd1G5U9lZ6qwtKTPd99950kdlVhRQaq5c+cCJo1N0vq0004DTJyy7IHa2lovkkqJ1EGJ4pxQ12rcuLEnKZUKqPupVaFsXHnA/SRTfLy0XzuMfCN/RDxa71JBxQLVt1iJ6/Ho3F0andZM2MX0hc5tg0jWdDgJ63CUEKFl68jb+f777wMm2sO2JRctWgSYyJ6FCxd6JTbeffddwOx2O++8M2CkseKRJVHlkcxEOtq2weabbx4DI9mioKKiwusqr7FrTmrTKC+rSsUEwbav4nJS82rDtm7d2ovQEmoNqlKwmWQbpSMXG9YueqDCASr0rjNVaUR6LhTllAp59tXKJGhb0mQ4G9bhKAMi8xL7RZRo1xk4cCBQnxd7+eWXAyZGWNUZVARckkTeOXlWFR2USfvJfHoYpWXsu+++Xj6omoCdd955QH10FuDlBGvnt8uOZGJb58tLHF9QT2tjU+xeYtna0hCV66sSRqmQtqjWNWr09dxzzwENK5Rkgp+Ezblqoh/2YJV+JYeVaNmypdeP5//+7/8AUxrGLklilxXJpEp+tgRJio9/LTTs4FZTU+MFf8hhpiAQdbizkYqZ6oEvVEC9jqQUFLPFFlv4dnQrdrSRqm60wkzt8R999NFe/Ww903pu/eYaZskY75qhX9HhcERGzipxGLu8jHQZ+Pa1Mgl81xGRynTo2toNbVWjUaNGsWT3zIQg41MJGKVkSRVTORn9K6dd2N3roiBe40lX5dEvqTvL+xYkvDQdmXwXFFarYgc2zunkcJQBkRdhy2gwPteVXWH31klWl9hP2ikpvnv37nnfnSsqKhrY4zoCsO3yIJ+pHVSuf88++2wAbrjhhrxL2HygEMC2bdsWpYQNAyUQVFVVOQnrcJQ6OdmwmXhQC4UtuaK0f3IpcB4mhUivyyfFasOGibNhHY4yIKWEdTgcxYWTsA5HCZEy0qmysjIG4QZuF5ofm/1T7vOD/MyxqqoqbTB/mKcnzoZ1OMqAlBK2nCSrw5ELQVLl8uEPchLW4SghIsvWCZNSafPg+HGSLo7afl1OcetZv9PhcOSdkpCwQvG4+jeXEhzFSjYNnKNm/PjxAFx55ZVZX6OctSTNqaamBoADDzwQaFjEIYy5OwnrcJQQkZWIKVbcOWzp82Nbw3iKWsI2atQoozIbpVKWJJ4777zTq5xYKsQnrKejf//+9O/f33tPmzZtknY5qK6u9q0LVSqk+1z0d3WyyIai/sI6HI5E8qYS26ln6jPy1ltvMX/+fMBUCOzfvz8QruMlWc3eH34fmToV72hJkuaX8BpJHbu+bzbkWyWurKxs0Lne7uG79dZbA6Ze8U477ZRwDfWpCVIfulAqsd8Y/RxqSkZXuSJ1eWjUqFHaZ7skVWKHw5FIzsc66vd62WWXAabD3Msvv5zwOunt2lkkVf/4xz96Ff1V4kRu8L322gswNXuz7ZsD+TlO8LtHskR//Xz00UcDMG/evGgHlwUqyaou5KrVq7q7qsf72WefeWuoYnKSOpKwKoan50DSx+4pFBV77rknYJ4l1R9W7Ws/VDyvU6dOXrdE9Tq+6aabgIa+E/UAvuuuuwAjWUUy6Rq0M7yTsA5HCRGZDWvr9U8++SRg7NNnnnkGgJEjR3qlSdXnpG3btgnX0i7crFkzANauXZvtsCKxfzRu9QtSZ4KHH34YgMGDB6caDwBTpkwBYNy4cUBm3QySXDMUG1ZeW/U4kmTt1KkTYDoRglkjFYOfOHEiAEOGDAGMtJEEad26NWC0KmloQcinDauyrFOnTuXQQw8FzDqrVK0+F1X+lxahQusq8SofTRCcDetwlAGBbFj1sAki2WTXaDeR11AFstesWQPAtGnTgHq79IknngDqO9mBseskpbVTaQfLRcLmgh02KEkjqaKO8fq95tq0aVPPZrORrS+7aNSoUVEM3ZcmTZp4ZWLV91S+A2kIBx98MGC6BdpzeeSRRzjxxBMB49OQTTZjxgwApk+fDsCAAQOAersXjPTJN+pGp+fU7gCoZ1H9nmSXgukppFDEBx98EDBld2Uvd+zYETD+Gj0XH3zwQdbjdhLW4SghcrJhW7Vq5UkRG+1cslXU6Uvewr/85S9AvVRS5zA1UtJZpDx0ceMBjL3kJ7WSvSfbMqe77rorS5cuTfo3eUbVJEkeR91TdpmkTjzqgWtrC2FEa2Viw8ZiMU9z0OeqpIpLL70UqLffwEilf//734HHos9OfWLV4fy3v/0tEKxLXJIxJ3xIVVVVMUgsKB9PbW2tFweQDn0GWlt9Nl9++aXXx1g9fLVWssvld2jZsiVg/DUPPPBAoHvH42xYh6MMyNlL7HeepZaEkp5qs6GdTnr+woULueOOOwA8L5wdUypb6qSTTgKM5y6Ts9UwI51Gjx4NmHM4SXpFwmiXju+mLptNHvDJkycDRtIoAuyKK64AjHdVHtpMCCJhk0XnSPvRueugQYMS5rN69Wrfe6pd480335zw+7fffhswvgzZf7q/4qiHDh2abloe2XiJ/c45ZXfq8z/kkEMAY5dq7mPHjvWeO62Rnm1pSYpo0u/nzp0beE42TsI6HGVA5LHE7dq1A0ysbJcuXQCzi/fp08fzTtpNjGWTKDpFTZGL5Rz2mGOOARqet86cOdP3PeouL8mi82lJaWkXuSQ9Z2LDLl261DsLlbdeUUca4znnnJPw+0zGpPVXm00hKZSNxzSKc1jNcfbs2YDxGuuMvXHjxt7zKM+23dBZElh/z8Uf4SSsw1EG5D2BXWe6P//5z4H6s6lly5YB5sxOaGxhtp4PY3dWhIvtIbfbQCazmxRrvXjx4oT32oW8ZDspNjfIeGQzZxrpJDta0VZbbbUVACtXrgSMzXbBBRekHYvN8uXLATjiiCMAk63z1FNPAXDkkUemvUaq+UE4z6ndcNq28Rs3buzZufIr6Lm1GTNmDADXXntt1uPxk7B5+8K2atUKMB96r169gPqAijfeeANo6GxS6JuMealVuXSJC2OxdVxjP8D6Ymp8CkBYsmRJYDVSxx96GLJRq7INTdRmah9baOw6qtPxTjz2OOVskuqrzeziiy8G4MILL0z4fSbksoZaEyWQxDsG48djB+jHJ6friFJHUgp+0RdZppI+Lzlcg2y+wqnEDkcZUBQ1nSZNmgQYw19IyshRFUXPklzmaI/n2WefBWC33XYDzFHGFltsEbhqoK0aR+10iuf4448HzDr06dMn4e8KJtDRlKTnxx9/7I1XQRfSMmzJq4CRiy66CIDzzz8/6PA8olCJdaQllThZCpyOIuUkFa+99hpgAkqGDx+e63CchHU4yoGiqEucLmm32GrZqk6vjZITkqE52A4UGwWZS8qNHTsWMEEaYNL3wkYH/QrUt9lmm20SflZSBhh/gkrB+AVZyFGngAW77Ernzp2zKlCQKzqi0bgkYeX827hxYwPJKhRskS59zlX+dzh+ZOTNhrV3F/3cqHcTpwIAAA6HSURBVFEjL0DcLv9oH5OEQRj2j6SJjm3SeXJjsRgDBw4ETMCIPN4KLFDa2X333Rfommnul5UNe8011wAm9FLzE1oP2eaSQhs2bPBSxxQgoyM6v9BK2bLZaAtRJrArTVD+EwXtTJw40UuG0OegVFL7iC4MnA3rcJQBBfcS19XVNZAmsmfkuQuTKHZnW2sIgoIVlASg8jfysuaiVSSTsNkUsLO7sfnNL1mROXmJFc4nrUQnAiqFkw1RrKGtLUmyzpkzB0j0W+i1UfY/chLW4SgD8i5htTvpvuvWrfM8h/KcduvWDUjvPc6GYunL8vXXXwMm2VmfhySRvJXZUAy9dWTn2Z0GJZVs+zgTDSDKNTzuuOMAU0zBuk9Yt0mLk7AORxlQMC+x4is7duzoxeQqyFzSJYrz10JLWNu2s5EH1u/vQSgGCbv99tsDJqZY7LrrroB/4HwQolhDxVFL8xF+GkHUOAnrcJQBeYt0krSUl3C77bbzfn/AAQcARrLKY6q4znLCr2hdNp7mYkTjVwaL2lQoSkrJ/YrLLRZUFEGSVL6Eyy+/HKjXfGSHFxInYR2OEiJvNqx2LuV7KuPjgQce8M64VE4zF/stHYVuVaiYVZswJWsqG1Y2ctjSQhL0o48+AkzJG2lLWn/bVvSzHZMRp3mFtoYq/ta3b1/AZCCppGnQ8qhh42xYh6MMyNv2od1R51uSOLFYzCtNosgmlVF58cUX8zW8yFH0liSpmoH5eUtVnUCfW1hEZYepCoWqMSiXViVs5W2VhqF/9RwEIZOKDUFRkW/lsr755psALFiwIPR7hUFkX1hb9VLFQ1VGVNmXIUOGeM4mLaIWvRyRCaKav+o5ahP2F9WPZGGFwk59U9c2qb3xKIlbNZqU5GCHl2qt9XvNXzWn84Xup42mZ8+eCX9Xh4Jiw6nEDkcJEbnTSf1GtDvLwXTeeecB9QnC6YIJwqTQgRM2+vxzSTdLck1vjun6zoTFHnvsAZhACbs7g9LuMumR6keYa6hyPio+oA6E0hCViJ+J6h4GzunkcJQBeTvWUa8S9UGVbfvll19mVa40HXa5ypdeegmA7t27F5WEjYJkxzp23d18E0Z5FJGLhFXanB24ofHJsSWn08iRIwHT3zgVYRyZxWmbTsI6HKVOwRPY802UNmyYUiQXogr+V/qjCsmlQoXCL7nkkrBu71FsfogocDasw1EGpJSwDoejuHAS1uEoIVJGOv0YbINyn2NlZWUMoi0Ylm9+bGsYj5OwDkcJUbBWHeps3bFjx0INIXKuvvpqILuGT2FRTpLV4SSsw1FSuHPYAs/Rr4FwLhRDEbYoKbY1jAJnwzocZUDBbFgV0A5SGkTSR9KoVAhSWE1z02vVEGzFihVp31tKyFch30WxMW/ePMDkbeeC5qpWob/73e9yvqYorW+Aw/Ejp+A2bCwW83IpX3/99ahvF6n9oywNZW3E3ZMOHToAJutDFSWCtiPJxNZ1Nmz4pKrMEQV+NmzkX9i//e1vAPzqV7/SQAK/V+rgmDFjAJMa9cgjjwDZdQjwW+xcAvdt1TebOYZJvr+wFRUVDVLL9Bmo56zWMAzy8YXNpttfmDink8NRBoQuYVV/Vqqeils9/fTTQGZ1XqUGqmCXys3Y6mEQKRU3z6x35/bt2wOwcuXKhN9LlVcBMrtI2bp166iurk56TXvsYUh64uYYpvQ5/vjjAXjqqacAsx7xc7CdaNKC7CCSbOYXV5U/NAmbS9K5igGoOID6Bi1dujTb4Xg4CetwlAE5H+vY9puKVcmp8sILLyS8Tr1lWrVqBdTXg91///0TrqHX2g4WVZcXmRzzhCG5JC1WrVoFmMJp0gBUMnPYsGEA3Hzzzd7rdA11Hr/qqqtS3isbwkqg1/hvv/32hN+rXrS6kotNmzZ50k8SS2V/VHRtv/32A+DGG28EYNSoURmPK4p+wRqnitT5aYCVlZUNnkc943qP+gg1bdoUMN0PwsRJWIejhMj7sc61114LmDKnsVjM1wbV2FSaRFL5+uuvB2D06NEZ379QYW2SwiqWrjlnYocHJSwvsTQYHUlJgthFzhctWuRJUPVMOueccwBT1lbzC0MLCGMNNU5pfLYNK2kuO71NmzbeWtkSVHNRwbZ77rkn4b3Z4GxYh6MMyHt/WP0rb2F82U3tWNrBtBvfeeedgLEh77///sjHq5BJhVAGQeP94IMPAHOWV1tb63VF064rm0/vsSVw1KSSdPqbbFb18rUlq9psDB482PvdZ599BsCBBx4IGNswkwJuUaCicH/6058Aoz3IHyHPvgreqyyu5hGvCeo5VcleccsttwBw6623RjMJnIR1OEqKyGxY7UayexTU3r17d8BIsHfeecfzEi9cuDDhGj169ACMp1mcfPLJANx9991A9mF7P4wzNBvW9phr/MOHD/eaK+lvmtPAgQMB+OSTTwCze9vhjRmOI7ANmyrkzvbo2957SU214YhH3ld1Ntc1NH9J6yAd6XR9NVLLdA3r6up8TxT85i7Pr+bRrFkzb6zp7HBpJLlESTkb1uEoAyK3YbfffnsAnnvuOcC0kmzXrh1Qv/vp3E8pTltttRXQUOIKSVZR6DIo2o2lPWjnPeOMMwA48cQTvdfau7KiZA499NCIR5mcVBqWJIWdAikbXY2jAK677joAevXqBRgbXZJ1+fLlgDm71mcWRMJKsmZLMumqedu+iptuugkwkjUb7/27776b8LO0pTAakjkJ63CUEKHbsLY9aV//nXfeAYxNu3btWi/O1t7N9Hu1MNRZWS6F26KwYa+88krANGmWd1jJ2p06dfJe+/nnnwPQokULoKGt2qVLFyC3eNSws3UkbSQ15a3XGvfr18/LoJLE1LprDfVcqDl0LlpRGGs4e/ZsAPbaay/ArJlOKJLFFutv9hzt51YxxdIu/DTFVDgb1uEoA0K3YbVzPvroo0n/Lpt2+PDhQP0uZbenP+igg4D6OGMwWTK6tqR0nz59gGCtAKNEcbGyz0R87LPd2NjelfV3SdZiaawF8PzzzwPGzpZXX+uzYMECjj322IT3aPzShpYtW5aPoQZGfhN5woWdvSNp2qxZM+/E49NPPwWMH0b07dsXMH4aedGlTaaKLR46dCgAd9xxR8pxR3asownb6pPUimQ9YaU2KSxMDgB9IeSo6NevH2DUGqmXOkJIRRQq8eOPPw6YQ/Y333wTMIHu06dPD5y+pTQ0OS6mTJmS8XjCVomlCivIRQ/1KaecAsDcuXO99Mlu3bolvFcPvAImtMaa39Zbb53xePzW8JBDDgFMKmcqlJihwgpCwQ/6vbrvXXjhhb6bp60a2zXIsnFcOZXY4SgDIpOwUgcUcidJK5TYXlNT43sNHa5rZ1ewgSSZjHupKkHUxygkrO6r8Uj90+87dOjAxx9/nPA7qZly9Uu9v/TSSwH4wx/+AGRXKTIsCSvJoLTB1atXA0YVPPfcc4H64x0FCfgd04wYMQKAqVOnJlw7y3THrNcwnVNUc9p7770BE/yy0047BQ72kLNJa7z77rsHHZ6Hk7AORxkQmYS1w8kyCUUTslnlwPKrGavXBUlwzmdoonWfhNcIBc7L7lL5GUkABSnIWZepFpHL/OxyP0I2rBLcN23a5DlUbAeifS07CSQbcllDrcNRRx0FmIJ+mdC/f38ATjvtNMCEl9ro70pe0Zxra2vThp46CetwlAGRSVilLckLGFJxsQTWrVsHmJSuHXbYIci1QpOwsocUGCFv8c4775zxtTRHSTNJJN1D7n7t2mmuFaoNq7HZ3Rrkn5g3b54nZezK/kpTU+1plRDKhTDWUJ+zPl8dDe6yyy5AwyOYVatWeVKxdevWCX+TNmRres888wxgvNeZ4CSsw1EGhCZhVUZEh8o6Z1XisM6zskEBCfY568SJEwG47LLLAl+r2Dqf6VBdIXvyJgt9jvFednmU58+fn/SaURcSt23bWbNmeX6GyZMnA+a8XdJH7wmDKD39fmemPXr08EIM7dfYZX7kRdd3QeSSBupdI+07HQ5H0ZCThP3www+90DMFtcs7rF1Edk4m3mE/7LGGEUGSze4sW0USX57wHJMSEn6WXW6HvwW8Vl5bdTRv3tyzbydMmACYs3K/Era5EIWEzWZ8tk0fxvMpnIR1OMqAnG1YRXUoiHrWrFkADBgwQNfIeZD2GO0SMRleK7TdWTacJK68hyqeLYn74osvegkMceNIes3XXnsNMIH22SRv50vC6mx92LBh3pmsiswpiUFRPiorGlfmJev7FtoPoXRPnY2LCy64AIDHHnsMMKmF8hYnw8+udRLW4SgDQvMS6zoqFymvsV1AWrGz2mnbtm3r/U27s7zCfp3Xc/E8RulhtJsjBUFx0JLGQ4YMAUzctOJ3MxxPpBJW6yG/xL333utlt6j0p1qRKEvp1FNPBUxp1FwotISVJiW/jZ2lo9S9gw8+GPCP/kqFk7AORxkQuoTNR+J1mN63TOaotpKyy2xtQR5d5XqqiFkyZP8q/1KtLcJooJRvL3F1dbU3ZztBX/Ze7969gYatOrOhUBJWxeD9ypdKO5oxY0bO93IS1uEoAyKLJQ5DwspGUgynnfFRqHNYoegelQYJUsZS9p/fmZ2dzZLrHKOQPooblr/i+eef99bGjvqRNFJEVxgtIwttwwqVtVXEWdeuXQEz5zCfUxHZF/bBBx8ETPKzEoE1eLuyer4olsWOknyrxDvuuCMrVqwATPVB1SG2CxeEQaHXUJUWjzjiCMCU9fELGVWZH7sXTyqcSuxwlAF57w9baAq9O+eDsCWsnyMx/vdBXhMWxbaGmptSCRWOmeM1nYR1OEqdvPWHdURPmF3c40lX3jPoa3LFTlcrFsL83NWPyQ8nYR2OEsLZsGU+x3KfH4Q7R5WqzaW3URg4G9bhKANSSliHw1FcOAnrcJQQ7gvrcJQQ7gvrcJQQ7gvrcJQQ7gvrcJQQ7gvrcJQQ/w9SD+auUFL0dgAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3400, D: 0.2395, G:0.1331\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3420, D: 0.2219, G:0.1806\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3440, D: 0.2302, G:0.1929\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3460, D: 0.2245, G:0.1289\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3480, D: 0.2217, G:0.1593\n" + ] + }, + { + "data": { + "image/png": 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QtWtXL3JN34OKvo8cOTLrOaQrb2QS1jDKgMitxP6dpX///l77BklYvUY72qOPPhrY9bMtwga1S3FqfPopfVSnDEUMKVJo9uzZnlRUgrTeI31QsbV6nX76841TkauEzTRCJ5uxhEkuOqz837J/yN+t+GhZs2W91r0oVqxY4RVOk60ivvh50JiENYwyoGB+2ETtBzNp2JQvmfph48eXrFWi37Ks1prSVxVj2qBBA086+z9Lul669hvx10xWdjWXVh2lSC4SVrqo9E3psrKTqEStyv7ou5RlunHjxp401lr+9NNPgIspDpJkErZgD2z//v2BWP0mf68auQZ0PFFAudwO+ZBusRM9KJn2SdXD5+9aNm7cOM/NceSRRwIugELHZtXtzWezytfoFAbJOuIl2wQzIYyaTnJhqRKkwjX1MHbu3NkLflHBhGxDYrMRSHYkNowyIHAJm0+KURTpSZkeiXMZi7+7XbwbSLuq3+imXdcvafL5/opJwoZBmFUTiwWTsIZRBhQ8+D9qctmd89G34q6j62d1jVyubUan0sckrGGUAYEVYSs34i16mUo3f4BFIqmqcqf+wl3JrpGoQEAUun4i/B0CEo3DfyJI5xKLeg6ljklYwyghUuqwhmEUFyZhDaOESKnDrgnWt3KfY7nPD9aMOQqTsIZRQtgDaxglhD2whlFC2ANrGHHUrVu3RvvMYsMeWMMoISzSyTDiCLI8axiYhDWMEsIe2Iioqqry4pON8mTEiBGMGDEi1GtklF5X7IHaqjyornepKLTTXQ+tyuGoarz6xvpR2RklCygBPhXFFDjhTxgIgkKvYdw4ADfHXOpkp/hsC5wwjFInI6NT2JJVO5TS01SpTn1POnToADhp43/ftttuC8Dhhx/OoEGDQh1rNiTqQRTfUygRkqSq3qcqf5lI1rBJdtL6+uuvvZrM6uaujm7++are77Rp08IfcEgMGTIEcIX0tL4qqHfJJZeEdm2TsIZRQhS8RMz06dO9quvqv6muaPFd2hORSx3jKPQfGR600yYqhyl9WxXoVZdYktTfwU/lN+MT2JMRtA6rdVG/W3/t5MWLF3tr6GfgwIFArIM5uHmnm0MqCqXDqs60SqAef/zxgDtJ+bs25IPpsIZRBhRMwuq68+bNo1WrVglfo36k6t2prnbqbaLenf5yK2muG/rurLIomtewYcM44ogjANdVfvbs2TXe8+mnnwJud1Yfl1Sfn6ysTNASVl3J1f9U3dtEs2bNvJ5B/vtJpyB1i1NfnmQSOROSrWHYnSP23ntvwHVPVJe/0aNHB34tk7CGUQZELmFTXe/1118HYL/99kv52rvuuguAJk2aANCzZ08gJnnSFUyLQsJqDPrZoEGDnP2QO+ywAwAzZszI+D1BSVhZ5+XnllV04403BqBbt26AW49ESCeXZFVf3GxORX4KpcM2bdoUcL7zTPz+uWIS1jDKgMgkbLNmzQD4+eefa/0tWbsK/+/9HcXUcWzw4MEZjyOK3VkNr5544om0r5UEkt7lj57R76MoJO73swZRQH3ChAmA0//ysQ6LQkjYP//80+tSKOvwww8/HNr1TMIaRhkQmYT1S8d11lkHgLZt23r+vWQ0atQIcLrSrrvuCjjrZa7SB4KdYxASKQjy1WG1NptvvjkAc+bMAaBNmzYAfPjhh1mPaa+99gJg0qRJgPM/q6VoNkQpYXXSmTp1qmdP0PqGmcxhEtYwyoDQAlS1Sy9evLjG7xWHOWrUKCDml5SETRarKh+eWHfddYHCSzLtsLIWSsLK11oKxHd0V2SW1kgRPfKHZ6KT+9F3NHfuXAC22GILIHl2UrEh6/78+fO9+zM+NjxqTMIaRgkRug7bsGFDAJYtWwY4/adjx461Xis/7D777JPws4LQHYLUf3RKOPPMMwGXD5mPJVRzU2aSdP5sCMoPq3tD+qcs1sqiygW/HzaXqKRCWInr1q3rSdauXbsCzgIeBsl02NCOxHpQFc7WuXNnAMaNGwfAlClTgFh4YZ8+fWq8Jh3FYtxRULweVLmXlBqnG7558+Zemll8V/b41yj4Xwa2XB7UoNHDJPeFNtlsvn9169Nxevjw4QD07t0bcMftYmXhwoWAC9KBcB/UdNiR2DBKiMjcOnI6J0pB0hEy3Y5dLE73Y445BoCxY8fW+L2MY2PGjAHg7LPPBmJz1VHXP1f/nIKeYy7zU6inpOChhx4KwPfffw+4k8VNN90EwEUXXVTrM/zz1E+F9+nklQvJ1lD3VJDuFh2D42sVB7FG6TC3jmGUAZHVHUllCk9XgkbB52GnT2WKX7IqvFDhd0rWFnPmzGHLLbcE4OSTTwZq79IbbrhhGEPNieeffx6ALl26ALXDJcWBBx4IwPbbb8/+++8PuHnJyCik9+YjWdMRpGSV4VOFAxYuXOgZ34RSBP2uyzAxCWsYJUTk6XWyoI4fPx6IhRnKQqoibP5CZEESpUtAKWVvvvlm2pTB+++/H4BTTjkl7+sG5dbZeeedAfjoo4+A2qck/X/FihVeMEuy+fmTHPIhyjXUfKqrq2tZ+MPEdFjDKAMir52pUC/5aR977DFPsv7yyy9AcelzmSDf6dKlS2v8vl69et6/pdu1bNkScKcIWU9V9kYlW1UyppCoFKkS11XmRsjyn+h3n332GRBL7gCnu8ovW6xonVTyRkQpXVNRHKMwDCMjItNhDz/8cACeeeYZwOk/VVVVXlRPFLtvocqLaOe+9tprAVdoW4kD8uHGS+VcKWSrDqU8KgFCurnCGQ855JC8rxHlGuoEVFlZ6bVXSVS2NmhMhzWMMiAyHVYlYhJZCV944YWohhEo2TQJk+4ua+luu+0GuDhkfxuSUkWxt7Isy3erwmXFjsb7zjvvADVPPFFI1nSYhDWMEqLgZU5XrVoVaaOnfPSfbFsnyje5ZMmSWoXNFFObrAB3PhRCh9VpQ9FG/u9IdgpFB+WTaRWGDqs46c0220zXAPAi1GT1jgrTYQ2jDAhdtP3jH/8AXOaK+PXXXwGX0FwK+PXQZA18VQalb9++QCz7ReVdFWfco0cPoHibZGeLJObBBx8MuFYkW221FVD8/lf5x7Ue8qnL9hJfSqeQmIQ1jBIidB1WmfqyHsZ9dr4fnRNh+vDkW/7hhx8AVyb0tddeY4899gBcLm0uBc0ypRA6rMqVPvXUU4DLCZZXQN9JEIS5hrJT6ESg+7Z+/fqRWomT6bCRGZ1US1g3bL9+/QBXXfF/1wPCPSaGudhvv/024LrqxfdRve666wA49dRTAdef5owzzgDg7rvvDmoYWT2w+Rz1NtlkEwC+++67WpUttVlJFVAS/HPPPZfTteIpVPBLlJjRyTDKgNAkrNwXMvO//PLLAHz77beAk7hR1/Bd03bnqOan9ZZ7RInf6Qx0ubCmrWE8JmENo4QIXYeNDx4oBta03bnc5wdrxhyFSVjDKCEiD00sNGva7hzk/FTeNF23wbBZ09YwHpOwhlFCpJSwhmEUFyZhDaOESBn8P2zYsGpI3IqhVCkn/SdZZNiaZiWuqqqqhsI3RwsS02ENowwwK3GZz7Hc5wfBzjGMePZcPtMkrGGUAZEXEl8TqayszFi/UkxufIsIiCaTKUpU3EztTIqFML7fID/TJKxhlBAlJWGjlDLZXCtZG0w1k/rggw+836lFiRqA+d+r66nsqUqVFEKyZlt0LhuykaxRFvAOkjAylUzCGkYJYVbiiOaokiPKWuratSsQKx8DroTK6aefDrimyplIVv9pICorsQroLVq0yGtm/fjjjwOujKuKzmmenTp1AuC4447L+bprmqU/Hntgs5ijuopnW6W/oqLCKwmjh0vHpUGDBgGuL6yOoTr+5RIMkOiBzWTsmR499913XwAefPBBIFYZURuRv8vb8uXLAdf7V4kDSiTo06cPAP/+97+BzOZb6AdW35O6tL/44ospXy8D26abbgrAl19+mfYa5tYxjDKg4BI2/vqtWrUCYN68eUCscznA7rvvDjgDTZ7XK8jurF1W0svfdV5GGCX8S1L5u56DO16rtrOffI/EfsOYxq7ramzz588Hahrb/G4oP5qPpNTll18OwPXXX5/x+KJcQ5W6mT17ttcHWGjeOhXodKRaxurqoO9CPysrK9MaokzCGkYZEJlbR93aVIwtfrfq0qULECuXCTBr1iygtmRVZ3Z/jeNiZ+utt/bGrPlfcMEFgNPxdKrQzqsK+nq9OpsvXbq0lmQN2t0lySokMXQKOOusswC49NJLvdfI9XPHHXcA0L59ewD222+/GmPUWu6www6AK/sq3Xr06NH8+OOPgNO7o0S2BXXbk9FMJ6F4NBfdtwcddBAAP/30E+AkriSw1qdx48beSUvrnykmYQ2jhAhdwmpX0U4yc+ZMwJn3Ad544w0gud5z3333AU63ysclkAy5JSZOnBj4Z3/++eeeNVgV8T/88EPA7b7a2TVH/V86XyJdVoQdVNG5c2cAxo8fDzgJO3bsWAB69+5Nx44dAWcFvuKKKwDnvpJ0njx5MuC+ZxUjf+aZZwAYOHAgHTp0ANx3lCm5nDT875Hl94EHHgDcqW7p0qVJ+wOpF65OisOHDweSW7wXL17s/Vv2gIxDVzN6lWEYRUFoVmL10/QXCh86dCgAF154IQADBgzw2lTIV6mdVb1E/b1V8yFKC6P09qlTp3p6jVp2KLBgwIABgNOZ3nrrLQB22mmnpJ+bTpIEFTgh6ae2G5KKkgYaR+vWrT0dPFk4nuYrX6Qk7rRp0wC49dZbAbj66quztqAGsYbSN3V6u//++2v8/cILL/TakYwePRqAv//974DzKcsuc+KJJwLuVCT9PBud3KzEhlEGhKbDXnbZZYDbQSU5/FEf8V3N5s6dm/Czwux0F0aAtvj000+B2E6rXfa3336r8VPSSrrTLbfckvIzq6qqaqXe5YsakvkbWsn6OWfOHMB9Rzo9qbGZ/LHxr/Gv2S+//AI4/U3SRlJI/tio8fv2tQ5aF1nkjz76aK/JmXoe6x7X9yb7hO6pTz75BHBd3IPAJKxhlBCBS1j5ULfZZhsAHnvsMcBFMcn/GB8do4gQ7bbyOYowJWygqU//07E1XkmTyspKT6LIL/nuu+8CcNNNNwHO1ynrpB9JgtWrVwduFfZLVs1jxx13BJzEkK9VPtbPP/8cqJ1WCMmlv1IO1YayZ8+eNa7Rr18/7rrrrhxnkpxker/GLimotpiKOLv55psBGDJkCG3btq3xWo1dOq0agSlCT3PNhnRx6iZhDaOEyEnCprJStmvXrsbftAtJh3nkkUcAOPbYY4HYDqd4TVno5MeKb/ZczGj82mkPOOAAwEVkNW3a1GuvqabP8ltutdVWAJx33nlA7dOEvsd4KZat7y5T5F9VtJK/JYcsqVpLjSORhPWjxtU33HAD4JpeS8+TFf2ll17KefypSHcq0dx1ynv11VcBt5bjx4/3rOXyXnTr1g2Ao446CoBtt90WgD333BPILfE/nSXZJKxhlBCh+WGTfe4uu+wCxHyTELO+jRs3DnASQzu34k0VSRIEmfrwcomakX661157AbGoHYjpn4odVZaK/NHSob/44gugtv8vF3L1wyaTmP4MHK1dfLRaOvyxxNKPpcv7i8+lIgw/rMYn+0mTJk0Ad0/OnTvXW8P4mOD4/8sDojXNx9YQeQJ7EIYRhQtOmjQJcMdp3Sg6omQ5rsAWWzfZtddeC7jxbrHFFgA0b94ciAXT6xip4/7IkSMBdwSSQUPGDrlOciHfwAndeDqmrrfeeoAzHMoVl8lRWAEhUg9ksNJNLWOTvislO6QijAdW6pcCOhRKq3HLKBiP1LozzzwTcEUIvvrqK/94sx6PBU4YRhkQmoTV7qsjkK6jULwRI0YAsZ0sUepSPNqF/WlKuZBsd1YIpVwVqfAflxVSKQOKjA1y0QwcONALLtDnywj19NNPAy6w/p577qnx2bmQrYT1z+c///kP4CTFOeeck/NYJGEV1qhrXH311YAz1GltO3XqxOzZs1N+ZrI1lJTWySAbdBQeNmwY4IIiFOAydOhQ73dCLktJ2iCNgCZhDaMMCFzCyvQv6aJE7H79+gFOwsTX6k2HDFUKc8yHIPUfBehLL5OOrfFKuvTv39/TVTA2F/QAAA2SSURBVKXnSi9UWpkCKGbMmAHkV4M3WwkrQ5ikvlwrSvVLV5ImExYtWgS4dVfanXTWI488Eojp9OlOF1EkcPTq1QtwbihwyRD+wB79P8j6zSZhDaMMiLwImySwwriUdpUK6VhBVKLPZ3fW9ffYYw/ABcUrSP6FF14AnIRSEnerVq08y6tC8qSXK3xNKYWZWF7TEZSVWBbTXL53vUdIek+YMAGA119/HXABFAo+kC6fimzXsEmTJp6VPh2HHHII4NZSCSl9+/b1ypn60zzDCJ01CWsYZUDBy5zG89RTTwFw2GGH+ccR2DVykbDSN+X3laSRFbVly5aAk5oK6ZMPr1WrVl6Cgz84REnQU6ZMAZxkeuWVV7KcmSNbCSvpIwu+CoTLOnzjjTcCJC2RkgrNR5ZbeQ2kyytJ5NFHHwVi35WS4ZMRpg4rfdR/zy1dutRb37hxAMEUVvBjEtYwyoCi6l7Xo0cPwEkw7c7pilOHjfxs0l0VsK+oJFkP1W7i+OOPB1xpzAULFtSSrLKaijCKv2VCRUWFZ82+9957AbySPfIRS+Jmg+b55JNPAtCmTRvARXZJ0soqrtfPmzcv67DQfIoQaBwK6FdShsIvVfJ11apVtSTs/vvvD0TbVdEkrGGUEEUlYYXfwhhGQnOmNGvWzNtJpetJWkrXksTt27dv2s9TSdfu3bsD0RfLljSVtRqchFCUj/RnFROTDu+XJKkki9LU1PhJEUN67ddffw24uOv4z8hWUuVThEBWeZ3qlHyuz5RuXa9evVrzll0iyt69JmENo4QoKiuxIof8EU2FthIrzU9ROvLRqbylopIUyyqrscY9ePBgr2/q9OnTNY48ZlGTXPrDJpKO+vedd94JuOwTRUJJ6qgrvCK6Onbs6MXVym+52WabJRyrWnPIL3vaaadlNsk4wrQS63tRwr0Krv3xxx9e5JfK1yo1MAzMSmwYZUBR6LDauZO1kwwiwikfpG9pHNdccw3gonMkTRQnreRnFTdbtWpV4OVc4skx39L7t/RISRf5RpXnKWQllU6rpPt4/JJV0VI6fchHrc+ICv8pTdlZyjvebrvtANeaQ+OUjlu/fn0vgk3lawuBSVjDKCGKQodVPO1DDz0EuB0+wXjyvlYu+o90UsU/+/U/SR5J4GxbCAZNvrHEyj6Svq35yVfqL3czefJkz0f98ccf1/gsxQoLf0nVXMhlDdMVrtPf+/TpA7gGbDr1VVRUeH5plT7Vie/999/PcgbpSabDFsWRWDdIsgc1vjtAIdCDKvybnI70YXQPCJuKiopa81EivpIAdDMrbE8PrG7+Ll26eEdK1WrKJdgiTNKpJPq7QkTVo1YdKsDV7NL3oH6wQZJOKNmR2DBKiKKQsKeffnqN/2sHU1hYIZX8cicTg5Wkjz+pPj7oXUdh/5G4VFDiiRLpVfZH6YD77ruvF4KqWsXJyCdUMd17TMIaRglRFEanKImyP2yhCKo/bLGSzxomk37ppGIiXT8Ikl3XAicMowwoCh3WMMIgVfilnwwKvwU3sDw+1ySsYZQQKXVYwzCKC5OwhlFCpNRhzcJYmsTPsbKysvp/vyvcgAJmTVvDeEzCGkYJYVbiMqecJKthEtYwSoqyeGArKioKVgI1FXXr1q1VUK6UKdbvOUiqqqqSFlIoBsrigTWMNYWy0GGLVU/Lp6RNlMWpM6WYxhIWQTQjCxOTsIZRQpSFhC1WpL+uWrXKa0nZoUMHwBXYVrsPVdtQUbA1QZqVOgMHDgRcM24Vs9PahkFJp9c999xzAFx88cWAu9lTEabTXbWPlPB9+OGHA7GH8frrr6/x2qFDhwJ49XyD6C4vLL0uHGSMWnvttQHXkX727NlA8hJHuWCBE4ZRBhSFhPW7Cvw9TFQMTJJLfVvUHa53795A7Ehy6aWXprxWFFXjNf6HH34YgOOOO67Waw8++GDAdREIkqglbFVVlVdJUH151KNGZX6CpFAStlGjRkD6yo9hVPcUJmENo4QouISN341atGgBJC9r6pdg6hqnCvS33347Z511VsrrRbE7qzhZq1atgJiO4w+gCDMAIWwJK11OLpDKykqvw1+vXr0A18lPfWFliMmnl6solIRVH6BRo0YBrsevustvv/32Gh+QvrRqKkzCGkYZkJGETVc1PRe006611lqetU26qqxwsrp9+OGHNa4vyaqeoyr6nAlR7s4q1/rHH394u24YFkU/YUnYddZZB3A6nNbpkEMO8dauS5cugOs7pPfo70EUW0+2hipNGlbheZ0s1NlBpybdl0GGNJqENYwyIKPAiSAlq6yHnTp1AmK+UxVvlhRS/9EZM2YAeH05tbNLd1BHdBFWKcpc0U5cXV3t6azqfFfMJLPay+K7ePFiAK/XTN++fWncuDHgggfUnU6BItLn1QkwDMJu6aL5SneXhI0yWcAkrGGUEJFbieVDXbJkCQDLli2r1VNUO7ysv127dgVg3rx5AGywwQaAC66X5K1fv36tdhJ+otRhly1bBsROCOodG6buKoLSYZPZLrQ+8ZJYNon1118fgPnz5wNunbfcckvAWVTzoRBW4p49e3pRaXGd7gFnqwiyGZrpsIZRBkQW/K9d+oknngBgl112AaBx48b07NkToNYO9t577wFOgkpnkA4rJAnU7TsR8Y2bwkZ6nCKxwPmYSwmtmV/S+iVMZWUlhx12GOB0VDWO2nvvvQFYsGBBNIMOmPPOOw+AIUOGMHbs2Bp/0+khyjajJmENo4QITYfVzqp2ff7fb7TRRgDsvvvuDBgwAIA999wTgGOPPRaAb775BoCRI0cCLhXt559/znVYoeg/shJq/B988AEA1157LRCLkDnhhBMAGDduXL6XS0vQfth77rmnxk9/ZlFFRQVNmzYF3NrIviD/a5DW+0L40n///XfGjx8PQPfu3QHnh44iXlqYhDWMEiI0CdujRw8AOnfuDODpOPop6tSp4zVsVqZH+/btgZpSGFyzYOkOuRDl7nzggQcCMf1Hlm75joP0bfuJOlunsrLS8znrp3znOhWls95nQ5RrKOv3nDlzvEgqWcfDtIskk7CBG50uv/xywCWVz5kzB4C7774bcN2rN910UwCmTJnCq6++CjjnujqyDx48GICOHTsC8Pbbbwc93FCZOHEiEAsWl5tD6KiVT92nQqMb99Zbb/Xmo5/6m/5famj8cs0VyzzsSGwYJURgwf8yvGgnUmhas2bNALjtttsAOPTQQwEXFL58+XIvFFHGjLZt2wJw5ZVXeq8Bl9aUD1Eep5o3bw7EAj0++eSTGn9TwoMCDYIk0ZHYH7gfNLo35L6RAU4BI0GSbA2V2vfoo48Gdi25bBKFH0aVIhmPSVjDKCFCMzqde+65QCypHFxyr4xRKkq2/vrre8Hk9957LwAnnXQSEAs1BBfOGARRStiZM2cCsN122yV9jYIrwjLKRGF0evDBB9lwww0BaNmyJQCXXXYZAC+++GLg14tiDRUGK1tLPFor3Z9hSFqTsIZRBgRuJZa+Kye7dBslbiuYQFbj/v37e+ZyJT0/8MADQLCSNQqUfiXX1dZbb+39TSlZOk3svPPOgNutZ82aBUC7du2iGWyAzJo1ixtuuAFwaybpU2rIpdi6dWugZicArZVCT7///vtoB4dJWMMoKUJPr/M7lyVxVTJy2bJlnp4rC3O/fv3yvWxSoihzKt/dmDFjgJjl9KqrrgJqh12q+LnK3SgNTamFuYT0Ra3DDh8+3LM7yBqtgIMwCHMNlViSyHqv0ETFEkgKay2DxHRYwygDQk+vk4T1pyApwmfdddflxhtvBPIL6i8GJA0VlqeyKbI4gvMRSmdV2KWQrl9KbLDBBp7fPYoE/TBRcQTFBOy0004APPvsszXWEcKRrOkwCWsYJUToElbWwm7dugFw5513AngB/927d/cSz6NMBI6Ciy66CIDJkyd7kV6yQipuWsHx/qR8JeuXQqzxmDFjPAu59PdSRRZ+FViQlbhdu3ZeDEEhMQlrGCVE6FZif3uN888/H4Dp06cDMGnSpEDaN2RKmBZGRS0p9lmniyVLlnglU6Sj6uc+++wDBCuZorYSN2nSxDs5vPHGG2FfLpQ1VKK99FTFBuieXLBggZfuGUUpXbMSG0YZELoOK4upSsNox1JB8fjflTqSrEKRMZ06deK1114DYMcdd/R+B/D4448DLtm9mPGflvT/RYsWcfTRRwNuXYupoHsmyI4iP7JsBwsXLgRiFv8LLrgAgGHDhtV4r/97CROTsIZRQkRWSFzWUEU4qQJDfKxmIXSDKHS8tm3b8t133wG1rb5hzDlsHVYS5cEHHwSgadOmnk/yjDPO8H4H4ZQ3DXMNpctKssZn5kj6RkFkJWKSoaPgpEmTALx0rPgFVbK7v5xKqbNy5UovKF5GN1WCjN+wih1/IQOV/znttNOYO3cuAFOnTgVg9OjRBRhh/viPxv7fFxo7EhtGCVGwDuy77bYbAO+880789YBwj8aF6t4dJWEfiRMldEjVkSQKM+BjTVvDeEzCGkYJEZiETSYdVbyqWHS1NW13Lvf5wZoxR2ES1jBKiMCsxMkkdbFIVsMoB0zCGkYJkVKHNQyjuDAJaxglhD2whlFC2ANrGCWEPbCGUULYA2sYJYQ9sIZRQvw/IdTJlrqskDcAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3500, D: 0.2246, G:0.154\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3520, D: 0.2259, G:0.1799\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3540, D: 0.2351, G:0.1414\n" + ] + }, + { + "data": { + "image/png": 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NHaecckrSDbyS3qn/vEZmc5QRrEEsFixYwE477QTACy+8UPIcjvv8888Hglh6wQUXVDyOehnWDoNPPfVUwbFZ+fHrX/86ANdddx333XcfEBjVc1jfWTXHDuxKFCaU2/WhElG9kWs4fPhwAJ5++mkgiKjLly9PntO0pKHRyefR4773ve8BYS1Vh+bPn5/bpSHvXIW6N+QiMmxERAuhx0ITVdjnz5/fLWRrwoQJAMycOROAffbZByDRIeytevPNNwNhp7/ssssSvfZrX/taw+cgrJcss6YrvK+++uq89NJLQOgD69/qVOo7spjF2Cx3KtZbbz3++te/Zjr+tF4nsxaTUjQkaVg6+uij2WOPPYBQktZieK+88goQmES7hDriAQccAMAll1yS1XQSVBIKKKNdeumlAPz0pz8FgiSgBNDR0VE0UUDdVPvEHXfcAQQ9XWOqLq5C1093/yuGyLARES2EinTYLEKv0lAPynXNaB1Mm+Nly1dffRUIXcu1ws6bNw/o0hGcT7FialnoP45d/VL9yx1dpn355ZeBoBdVAse/9tprA6GDn32HKjxHQ/rD5pwzb6znnnsuv/3tbwGYPn163rFeT/3ebg6ucbp3TSWBE1msYa67BkgkBD0SrrF66AUXXMDvfvc7IOjpd999NxDsEldddRUQJKuRI0cC4RnPDY4o13EghiZGRPQBVKTDZsms6pizZs0CutpyuCO562phVFe77bbbgKBPaHlMy/3qxY2GrGAhNduOCHdnLaPV4IorrgCCr09fZ1bIothbmgEnTZqUjNe19Doeq+5qJ0Lbs5Q7d6OQZrb//u//BsK4Hn/8cSCsx1ZbbcU222wDBK+Ev1HiMz7AZ9vnxOfWz9vb22tOI4wMGxHRQmi6H9ad98c//jHQFdGjRVGmVJf1WK2XxZIBqtHLKtV/Sp3T66bH4c5rYLtWw1IwSstIoKlTpwKBkbxPWsQrQdbpdel5FrJs+tluu+0GhH7AxTqXV8P06circh0Ia2FprfFKRUav3XDDDUCXhffMM88EguSnd0Lo1XCtvvOd7wCBrUsl6acTLaIOGxHRB9A0P6z9Uf/lX/4FCLrZihUrEr1Ga68+SS2lWl/9TXp3bkQLw1L+MMflTi7z2C1dtiwFY23dWT2XUTObbrppLcNuCJQ2DHK3HejZZ58NdNklZB+behndo11BO0Qtne0rbbVZj/57/PHHA6F521ZbbQXAjBkzgPzexDvuuCMAp59+OhAs+WeddRYAt99+OwDTpk0DgpSRy6xptq00hTEybEREC6FpOqyWVNOwcneb5557Dgi7mvHAso16USUop8fU48NzV9SXqC7jNdVdxo8fD8CwYcOArnTAdJRM2vKurnrMMcfknXPKlClASE+rBFnrsDKsuqRskNuaJG1PkElNxauljWaxtWxkLLFW4k022QQIccPt7e1JdNq2224LhMgl0z2N3ho9ejQQIqxK2UPUg03dE1GHjYjoA2iaDqvPUuun/y5ZsiTZsbSQ6rcykqma8pKN9OO5Q5577rlAYJh9990XCE2a3VHVdadPn856660HdE+M9xzf/OY3gbArX3nllUB1zFoNcnf9clZ2M6u0gj7xxBNA0OUgRHt5/4339p4cfPDBANx6660Vj7HetawmQk82V2oy88bYgNVWW42jjz4aCNKDurWxxEbi+Xkl3os0s5YdZ7PdOprJzzvvPKArCF5Ry0Xef//9gSAmZoksw9rcSBR9TYHToJQLF163gWKlm5CGNh8Une4GYVSDakViN5NiSQWGWppCZwUQE7f32WefxLikCH/ssccCoQKFa1lsA1IELVYfKReVrmE1tY51JWpI0oDky/73v/89eT7T8AVVRHYuBv//+c9/Lnv99Jhj5f+IiD6AihjWcMJ333236gvcf//9QNhxZSOr6o0aNSoRRzQ2KYI1Ao0wWOjGUZx1x5WBC7mI0kYokxUMunjyySdrHk8lDKtKUkndI9fdlDMliLfffhuAiRMnsvXWWwNhXh5juN7zzz9f8hqyeCVun0oDJ6o5p/dB8VVjk+y8YMGCxDUplI6OO+44AB566CEgpB3KzulyM5UgGp0iIvoAGqbDpotZWdYlvUt1dnYm6UcGHlhRUL0iS2TJsJrvLWHj7uzOmltfWb1cN4fMY+B4GmlDW63hl1l2dtO4Mnv27KK/UTpSp5X1NCSaiqaeZ/G2agxMWa6hSQuWhFGacDw+x+effz6nnXYa0L2yoUXXTNHzX+9TLdUbI8NGRPQBNNxK7A574oknAsGSmptALJs4FvU3y41kiUbosEoCv/rVrwDYfffdgSAp7LnnnolbS3vA2LFjgcBIWaYwVsuwrlGxwmBpVKIbqtcbVppmMlPNagnYr3QNPfe4ceOS0Mg0DJXVtmJopXNUH3377bfZfvvtgcDCaSnIOanPVxpSWQiRYSMi+gAyD5xwV3OXUX6/6KKLgGBJdQeeNWtWElitr85yI60Cy5/op3OO+jWHDx+e6GqjRo0CQvqZbJYbxljo70ZC37CJCFrrTXvU/rDrrrsC8MgjjwCl9Wr9zN4DJSr/df21cTQi4MVz5rJrun+TDOrcZVZ/a1mYtdZaK0k+0fOhT1kfuuG3Jkl4H7U0ZzHHyLARES2EzBnWXcTkXVOP3MmMqjFQ/pBDDknYxPKa6ZIrrYItt9wSKNz1zftw2GGHAfCjH/0o77dGCJlILbOecMIJQCjDmTXa29vZYostgFBUTKu9KX6WnU1HnhXyL6cL6amj67M1FE8/sOxTjw6fW+wbSjOZxzh2S+dq1dYfrp7qWk6YMCFJzNBarHTg/bO0kWGa3oMsCt+JyLARES2EzK3E+ttyE9Qh+PRMCDb5+bXXXkuiYowYUUc68sgjq718WZSzMFYTMP6FL3wBCDG2//mf/wmEZGfPMXDgwCSCyWO1LHqM7GxEUJ1d3IpaidNW2ZtvvpkjjjgCCHO/8cYbgZASZ1yzv7X0pwkKixYt4pBDDgGCJKGUZEqaPmvZWz1fdpRxs4x0EgMGDOhmsTUxQ0+EbTQsJO6zd/XVVwNd90bmND7aOci03kcTHooVCa9kbaOVOCKiD6Bhflh3LvVS5Xctjl/84heBruR19Rh3WVmnEaVfGpn87Ph/8IMfAEFiOOqoo5JoKCO9tCQ+/PDDQJirO7tsXQuq8cPefvvtiZ7pOuibNFG/GLRDDB48OClMYDytUFqy9EpaZ9USrY5b6nkslslSbo7Dhg1LrNVpZtc/bJaQkqEZNuqnb731VlJGxrnKvt/61reArqZgEDJ+csZXdm4ip5VJZNiIiFZH5gw7Z84cAA488EAgRDapHx1++OFAfubHnXfe6fWAxiahZ8GwJtarb6UT6x2/Ft9XXnklmb/Wc5Ohbf6sLzeLiKdaI52EVlALqTkmS/lo6TfSZ+edd070XRPxDzroICD4LIuVhq0F1a5hv379krGqj2tnMKFeK7E5rUoXrsvgwYOT+G//VVdVh03n3VYaOVYIUYeNiOgDqJthC/kcc+GO6k6l1dDyIitWrEj0tyz9VcXQSB1WuOOqj3R0dCQ6nPHFWo1lIGE7CNtA1ILcOQ4ePLgT8puOFcM555wDBCuxa6vUpN6X1gMXLVrULb+2kWuZXsP+/ft35l67EKxqUkwaEko86rSW9Hn00UeTyCbLmP7Hf/wHEFi5XIOralCMYTMTiV1Eg72LIS0SDRo0qOKarNWgmOhVzwtbaUK0NZ7uuuuu5DNFMl9mDRS6Q9JdvetBrel1PsyOtbeinjUs1ofVl82QSY1hqm5Tp05Ngv/dhCzfo5hdiypXaWVIEUXiiIhWQmdnZ9H/gM6+9l96ju3t7Z3t7e0NveZll13Wucoqq3SussoqFf+mo6Ojs6Ojo+459vT9bsYaZnHOtra2zra2tuS+F3ouPCaL65V77oq9k5FhIyJaCE0vc9rTaIbRqaeRdeX/3oZWW8NC3f/K6btRh42I6ANoWuX/iIh6oSW3WlTCaI1EAU9FzeeKDBsR0UIoqcNGRET0LkSGjYhoIZTUYXu79a0WNMLCWE3SQrMTHOIatiailTgiog+g6VbialoA9gZU6DOr+HwfdptBFul1H2ZEho2IaCE0/YVdtmxZy7ArdDHimDFjGDNmDP37968qq2bAgAEMGDCgpqbMtaCtra1g6dFGw/S7SrBixYo+y67NuP+RYSMiWggxlrjIHG3RsNdee5U9p0XLbMeRRr9+/RKpopomw7UiayuxOaImc6ex4YYbJm0phEX4pk+fXu/luyFaiSMiIloCkWH/OcdKrJfqJxYhmzt3bt7nwpaFFqK76667GDduHBCY9dVXX02+g2zbk9TKsBbXtuRJI/HAAw8AoflxNfgwM2zTXlgfal+MRtZtKoV6XlhhCRWryZ9xxhkAXHjhhUBwWVm39qyzzkp6tggrRY4ZMybvc7vQ+yKPHDkSCB0B/jlm51J2jtWsoXWP6ulrmsbnPvc5AG644QYglBCyBEst+DC/sFEkjohoITScYa00Zw1Xe+pcfvnlQFfhL8XEdOW9RqDc7lxNtT9dPDJq+jf+3a9fv6TivxXuc67vuAB45plngNCjxp4v6Wp/pdCToYlpSSX993nnnQeEioPWeBarr756UuHR36bRSIY966yzgNA/KLef7BNPPAGQFGNLqxBKJnYNUDoqhHLPWWTYiIg+gJoYNnfXTDNEWg+aPXs2EEpCyjTuuP369Uu6grm76VLxt17DTtfler6UQi27szup7OjYNST9/Oc/zzs+XS70s5/9LNOmTSt4bu+X/XPTHeROOumkcsPrhp5gWNfEetO6c1zbNNMaomoZUfsQTZ48uayEVeka1pJo4W+mTp0KwE477QTAtGnTkj485X7r9ZTAapEYI8NGRPQB1K3DZhHMbhFyK82ndRe7kdv5Tmvsb37zm6qvVW53zu2HU+IcBT+3SLg7q/pn2kJcCurDniNtXa9kt24Ww5566qkAPP7444n0of6mdLTZZpsB5QNFvKcdHR2JRdweNgWOrVpK0jYgY9rNzi6KSgSuYT34+Mc/DsCkSZOAoK9Xg8iwERF9AHWn12nRc1epxYenrlfMKjhs2DAgWJEbmTxQiFlfeOEFIPR9LRZemGbFSjB48GAgsPC2224LhHviNXrKb10Ihip+//vfB+Dee+9lzz33BIJuane4YtKI90grca7V1HNkCe+r0IIr05YLQe3s7EysxEcddRQQ+kSl11s7jZ38yvWfqgaRYSMiWgh1b2XqadU2UNJa2L9/f5588kkg6DvqQeldUetrreUuq8HAgQOBrq57W265JRDYw3Gko5C09FaDxx9/HIDhw4cDIZxxo402AuAb3/gGUFqqaHRSuJKN59fmINZee+2kv6rMevfddwMl/YxAkF422GADoMsS3wxpwmSFWbNmAbDpppuWPH6zzTZL1n2dddYBwn03vfDll18G4I9//CMAL774IgAjRozIbNyRYSMiWgh1WYlXWmmlbt2n0/qH36e7lpdCOcuzllJZsBqdtlIL4xZbbAHAzJkzk8/cSddff/2C53aupaQN2dr+r2m93RQ2JZcpU6YA8O677wKhs3kpZG0llllKJe8rEbzxxhtVnbvQWpezAdRiJd56660BmDFjBgA333wzAOPHjwfCesuOjiFX0krjE5/4BBBYOu0V8HnQpz5hwoRyw0wQrcQREX0Ademwhx9+eLfd0L/VR00jK6Zftbe3J99pYVaHMQrGdDZx/fXX5x3XCOQyqzrcxRdfnPev+OQnPwmE3VnkWgdlyGINr42t1rfpPbFTu9doBmT9I488EujOrOmmyO+++26irylJuTbFoo3SkoXzHT16NPPmzQNg3XXXzWA2XZBZhcwqZFifuXSK5MyZM5NjjAdYY401gNDAO+1vl9XTz0U9iAwbEdFC6BUJ7O7GxXbdtBW0HitxPZkexXQ5xy8LqmdOnDix4nHp27zqqquAsHubU2vCd0dHR3K9XEs7BJ2pEh126NChQIhI+uexQMiokl1kFm0F6nO5MdMyk9ZW/33ttdfyrpuWyDyna/znP/858YWnc4VFI7N10jYYx7fqqqsm1vFi8QJCO0QxaaoSFNNhe0X3Ol/AcmKT7hODNAwszz7PDBsAABTaSURBVALFRLe2trZEXDVQ30V89tlngSA63n///UBwURRCehPyun5uksQHH3wAkIiHxxxzDAC33XYbkydPzjtnLeF0uYkVV155JQDf/va3gfDCCoPeH3roIQC++93vAqFu09y5cznxxBOBcP/OOeecvHOl61E7z/Qa33nnnZx22mlVzycrWAFDkdjxaXyqBK6/CREmrWSBKBJHRLQQ6mbYLJz2aVbTqb3hhhsC8OCDDwK11f+pdQyivb2dY489FggMo0SQDlFUFFK81QiRWzUxzazC+lAmOshIug40YGy99db84he/qG2SBdDZ2ZmE0OmeUIzVcGi641ZbbQWQMOBXvvIVAMaOHZsY2DTAHHrooUBgWOe/ww47AHDHHXfkjcO0vCOOOCKzueWi3HP62GOPASE5wPE5DyjvbjQIRANjIxAZNiKihdArjE633XYbEBLC0zDMzXIz9aAeg4W6nOGEG2+8MRCCBXbddVcgSARi4MCBCcN4v4ulyRl0oYFDd4mpZquttlqi/xVL26s2cCIdAOD5NS4pDWgIU6IopDuffvrpAFx00UVA97TAdOCI90PXx4gRIyrpZdQjRdjKjcs1MimgzmvFwImIiFZH3aGJWRRMKzYGg/+1mKrfFcPw4cPLOqlr2Z0rLTWiTmtwuJbYzs7OpKynQfKeK33OtMXcAA6TA/r161e2IFu1DGtpHsMLlXTGjh0LdKXPFZpnqTBTx2/gh1bXdFG5n/zkJ0BIGqgE1a7hc889lzB7PSi2/vWUgilxrciwERGtjrqsxFnsKKWcy+o5laKeELBSZSfdWdX1cpkzF1pT1fVMjt57772TgtpaH9OsrVXYcZiq5/eWMNloo40SlsoKSgQ777wzEHyOuUEVudAiLNZaa62kv5ABHj4b99xzDxB0WcP6nP+ZZ56ZzSRKIAt2Pf/884t+18iyvGlEho2IaCH0eKSTBZsLwUgmI5sqQa3RJaUSCWSDcqljMpRs8qMf/Qjo8p3efvvtQHEmN/FBhvJ72c9Sq7n/n+6BI7NXC1lP5vbaSiz6KJ2fPmF12+effz7x2Tr3dEsWWcjkAOfgtetBlgn8JhwYYSa0fufCgn3NRGTYiIgWQo/7YQtdP4vg6RLXK9kMS6aTCXITl9N6p8cY2SLLGDnkv2PGjOH3v/890N2CLNTXZckhQ4YAgZm8FyuvvHKmVuKhQ4cmYzHCTIuu8cHlsHjx4iTOOmcMQPlA+UqQZtBG+mH1g+tj3mWXXQB4+OGHux3rWjai21+0EkdE9AHUxbCdnZ1VlfQsdg5hAygjRYw71UKaxW5dS5sH9c50EbY0jH2WmY4//ngA7rvvvsRSaZmQ/fffH4BtttkGgGuvvRaAK664AgglRNWd9H0uWbKkrK5Wa4kYY6DNPjLyyc7ylrTVWq5uO2PGjCTOWCay3206cTwLVMuwRx99NNdcc02t1+r2WSP8rgWuGxk2IqLV0XAdVp0gzUq2dvjIRz6S7FSWYtluu+2A0F4hXf60HjQjDtV7qtX1nXfeSYpIG7FkVspll10GBN3ZjCTbkJRan2LW0WoZVlY3K6kY0jmtuSVwKol+ygqNWEPvoR4JdfJcW4M6qx6ILNrUFENk2IiIPoCSDLt48eJOCPpTFrDYsu038gbzT71RHcGyI5ZeMXKonp0tvXOtuuqqnVC4jGWtUPe2tUNbW1tiUXb+6rdaj/VXWh6lngJkPdnQuRlIr+HKK6/cCdk0skr7j3OhhNGMQuc1lYip5kX9wx/+AMBnPvOZkseZqKzrZtmyZdxyyy3J/wNJmJsPuS9uI0SQLF9U8frrrwMhSGLq1KnstttuQHgRdd+YIudmpRvBSpFuUs2A1SArdef0FlTzolpb2jIuuR3WC/3rs7fuuusma1RMzWsGokgcEdFCaJjRKd3X1ITmE044Ie/7Qtc35EsjU9qMnqVI3NHR0QnVhbWlO86Vw8KFC5OA+krD6Ap1HqgUfU0kTj8rWRqdZFjdbAaNCKWl8ePHJ1LPfffdBwQpMY2MyiZFo1NERKujx0ITNTq1t7cnRb/SHcz9u9IE8kqQxe6c5XhqQdq9kkZfY9g0GuHW0Y2jey39d1tbW0PWuxLXXN7xmY8gIiKiYejx4P9mo6cKeOVcz3E07BqRYfOR27+pVRAZNiKiD6Akw0ZERPQuRIaNiGghlIx0ivpPa6I36LCN0NVzGofVnCJZLKxQv7eldNM678CBA5M4AP8tZq33uulGZ7koZh22HO78+fOjDhsR0eqIVuI+PsdmzK9fv37dmKtaZq0mOqjYGlbD6sUY13MYmWYR+yFDhiRx3ekEgUqvaxncvfbaq9t35crgJMeVvEJERESvQmTYPj7H9PyaHaVViT5XLRq5hukUTzNy1lhjjW5tJGVpUYytC93rUg3EobueLiLDRkS0ECLD9vE5pvW7AsfWfJ1CVlJ1Mb8rlqtaT0ZLsTW0xFC6abbxwIWQbiFaCcwT9/zptpze02J6cv/+/YvGyefox5FhIyJaHT3OsG1tbUnhLhsJjxo1CghVLNyheku2Tm9HIYbNIkezGBsUOmc5v2ct8JzLli2r20qcRjp/22tZAPALX/hC4qs96qijgNAi82c/+xkQmFb9V8imhcalVKCU4nWXLl1afYmYRsKB3XrrrcmLatkUX1Sd2H5vWY9iaWW9Fc8880zS6zYN07h8ULIuWZOlsceEfTvxvfDCC0BXkvell14KhLrE9uWxnMomm2yS93ktKPbyFzMUiULB/4rs1mK25vJzzz0HdNUyBnjooYeS4+2MsP766wPw9NNP553j2WefBULP2wsvvDBvXLnqgeue7hNc7tmOInFERAuhaSKxu/Kjjz4KBEPASiutlOwq7kzHHHMMkCcCAXDJJZcA8K1vfavmcWQpEqdFQv91x5cRcs3/MqjM43dKEVkwbFaBE5bq+fnPfw6ErnUid352B7CAnnDtzjnnHAB++MMfAoVZELruYTnxPcsiBHZrsDSMc0y7bHLhnOxQIeNa0si6xb/97W8BuP/++4HQ4T63T09ajI9unYiIPoSm6bDTpk0DQo1hlW3oUugBnnzySaC7UcNq7KeeempzBlshZAA7z6VdJ7m79F133QXATTfdBMCkSZOAsKPLUBZ403XQqAAHmXzgwIFJGJ7GP/W7r33tawCMHDkybyzO0/mddtppSS+gNDyXVfNfffVVIBhuDNfLZVP/P224KtfHKc3M9tM57rjjElb0GO+/c7bbRCk4f+/dxhtvDIRnOV3i94tf/CIQ1vKjH/1o8p2flXI5FUJk2IiIFkLTGNZdSP3zkUceAbrKnz744IMFf+OuaCf23gLLXaqjyEDFsN9++yXHuvvfeeedeccccsghAEmn9kaHDsqq0N3xbx8graLp8MJ017YVK1Yk/Wyff/55IKSJCQMULA4vs5ZCtQkF3ltZVEtvoXNqFT733HPzzp1m8V//+tdA1xpr6b/qqquAoP+qw3oPPJdraY8lO/kNHz48KTyYnlO5OUaGjYhoITTcSqweonXYAOo999wTyNdlm4EsLIzu4O6SWv+EFl99j/orITDbySefDATr6YsvvgjAiBEjqh1ON9RrJXb8rpm9bZ23FtWrr74a6LKWyrr+du7cuXnn9Pu1114bCM9BLZJEuTUspPt6HZ+3tdZaC4Arr7wSCIXE7aY+evRoIFh+t9tuu0QqlMmV/JQm9EXbN8ki5IcffjgQ4gtK+VqLBYeIyLARES2Epvlhv/GNbwBhx9Ii3GxkwbDugquvvjoQLLybbropEBou5eqJQqup1kGZx13ahOlqsPPOOwOBEbPyw6qb6x+fN28eAF/60pcA+NSnPgUEfySEnr52llcflmX0FuirrKSL+b777gvAlClTgO5r2N7e3vnPz/N+Vyg80qgju8irpy9YsAAIoYhGPGm1zz2/Fu6JEycCgbWN1NMSfsMNNwDB76r+/thjj3Ub649//GMAJkyYUHCOyVwLfRgREdE70XCG1TK21VZbAd2DnStBb2nVIaPqc/z3f/93AK677rq8v0vNUX1WdhJGwxQqH1ItqmHY9vb2bq0v1VW930oDTz31FABHHnkkAF/96lcB+N73vpccq94mgwl1VyHDVoNiqWflirC1t7cnLCvLOSc73Tte/eRasWU85/zP6+eNR6j/7rTTTkCwcRSK2Cr3TEeGjYjoA2gYw8oyRnvcdtttQPfsFCgeMzpmzBgg+MKyQJaxxP/4xz+AkK0xefJkIPj/ZJVZs2YlEV7qUEZHpaN6skC1OqyW3WL6s2t29913A8HC7edvvPEGgwYNAuDyyy8HYO+99waCj/ewww4D4I477gAqS7sbO3YsEKLElFgqXcNCZU593o3l1s7w9ttvAzB9+nQg2FzMLrrpppuSz0466SQALrvsMiCs4dlnnw10SRy5cO1zdX3xzW9+EwjW6hhLHBHRh9AwhpVJ9F25g6gzaCUePnx4suv5m9133x2Ae++913HknTvLhs5ZzNFILaO4ZJ6hQ4cCXdZCdVQlDqH+qJUyC2Rd5tT7Lyup1zmX999/n4MPPjj5/1ycf/75QPd1rwfVMiwE67xW6XT0llFKjtfjtfieeuqp7LfffnlzkbVlaaUM9fNKigYUe7aL6bANNzoZuO9D/c477wAhsPzNN99M0rfszv7WW28BQUwx8deQwOHDhwO1JUNn+cKmqxTozlEM1JDR1taWJONbtUD8/ve/B+Dzn/983uc63xW7qkElL2w9FSF+9atfAaHiwqJFi5KQxHR6na6gHXfcEegeUFFLMYJibp2c74HyfXRz4VoalO98VO2GDBmSJKinEwW23357IIjT6Q2umgD/KBJHRPQhNJxhdXrrOB8/fjwAu+yyCwD33HNPsssrrihKujvLxhqu6kEjajp9/etfB+Dmm28Gwu4si22++ebdjEoy7uabb17v5buhEoaVOSoJXHB9ZIqZM2cCwYgyffr0JEwxDd15ipa1BIakUekaXnDBBQCcccYZZdUoXXOqYwb2K+IPGjSoWzVGXUCqCDJ5uY4AlfSrjW6diIg+gKaFJprOpON63LhxQJducPHFFwN0SzI2LEyl3t4m9QRSNLJqomFuunV0gxSChgn1odmzZ2c1jMyMTun7bIidupp69p/+9KeiKYYPPPAAEJI91OvSJWKqQaVrWInr0GAYmVR2dNx77LEH0OW60h6jdKLxSYOi0oOuK6/pXLXBzJ07t+yzGxk2IqIPoGkJ7DKrpu+DDjoIyA9rUzfSCqx+JbOK3to1Xmu31mElhn79+nVLkDa9S6u59yXtFmk0jjjiCK6//vqC36XLh5rAbUE9GaRUAr+J+1r6dXmVkpKUqKotSJd2o/j3tttum0gFzkV93NTIW265Je9zddlZs2YBXexokMNuu+0GwIEHHggEd54pkiaBWObUORto09nZ2S2hoeI5VnV0REREj6Lplf+1oObuylpQ075Bd0p3KAtm1YNG6rD6/dRl1VkGDBiQsIZz8j7ol83CeiqyDpxwXWRJmcvwym222Sb5TlZUN1Tf8znznpjGlk4KrwS1rKFjtnCavn7tJ9pLvvzlL+f97pVXXgG6wkwtoqav1vQ5LeRa/mVgi8DJpvbvqaUHbjKPsr+MiIjoNWh6q45C+k46AF49Q0ZVFzj22GObMcSaoc4qi4jFixd3S1EzscHAe/Ubd++sUI9FXb3a4HWLijl2g90nTpzItddeCwR9Tivruuuum3d9feuVhOtVOua0f1QoqQ0ePDhJHXzzzTeBoI87B/2u2lp8TvUjL1q0KLmO9gf/NmXSuV1xxRVAiNyzAIBJ/QsXLkzmlr4PZUu5lvw2IiKiV6HHutcNGzYM6JL73VX0c1lGxuZXFq/SemxR6lrKnzaze53lLbUEQ5ijO7nx0LKz5VdswlRvrG0W85NJ3njjDSD4VA34HzRoUCIFGU+t7qrl1O9vvfVWIBTVVrIo1siqEIqtoePU5qE+vcoqqyQ6tqyrDzldutR/P/3pTwNBWpozZ07iQzbtzzIy6vLaMPRqqBebWJBrCyhns4g6bEREH0CP94fNhaUmjRRRv/Bf9btK4l+LoVqGzS2RWQ6VdDlXL1SnSpff1Lrq5+7E1eig1TBsZ2dnWb1JWGxOVrTNyMKFCxOJwZS7//qv/wKCxdzGWrKg/+qn1TpbinmKZbIUK8Lm8bNnz04yi+677z4gsJ0sqNSgBCDTK+lMmDAh0X8do+d37TynpWGEBRh++tOfAl06b6EWlJDnEYkMGxHR6mg6w9qyQZ9U7u5ugrD5lsbbWrBM31k9aIYOmy6OnisR+P/qO0L98JRTTgGCRbYWVMOwlUgQFlZTn3N+udZcS8JoIZVhjWizzaS/teyLrFUqxjhtNa5nDb2+ZX3M07aguwXUjGp67733gK4ovAMOOAAIxdbSzbHSTZqnTp0KhFxnLdFvvvlmwsbOOy1JRR02IqIPoOkMK7PYCkEdBoI11d3PHUk9KAs0g2EtbWOmx3XXXZdEu6R9tGnss88+eeeoBblzXHPNNTshtMaoB+kG2+L9999PdFilIHNFZUxzRi2YnmWZnwEDBnRC0AcLnVud2WdL3VVPg8+aumy6feYHH3zA008/DQQ9PZ33qrTg9dOlXnNRrpVmsYoTTQ+ccLENep40aVLSQUyDS2619VaE4qGLsnTp0qTDmc719EL5QtfzohZCJS+q47R8i64n4cNuQrh/K14uWbIkMaIYapkubZPbGxUqC46oNICiEleQAQq+VL64zkVXTLoyol0FH3zwwaT/q6Kv57J0TNpwlq5b7N/Lli1L1r/adMMoEkdEtBB63K3T0dGR7H7NSJtrZuBELgz2t2BXtZ23q0HWCeyjRo0CQl+cHPdKcqxMpOuiHliYTvE0jXqC/9Opd86lkmJ0is9pCaRatLW1JaWBTClNIxqdIiL6AHqcYZuNnmLYZqJRdYkbIQGpDxaSONKGmXK9dRoxzkLnrPY69gnODagwgKZYYExk2IiIPoDIsH18jpV0r6smeTwrVFPkO41a1rAcK5YrrF4qwCR97mqKtKd169iBPSKiD6Ekw0ZERPQuRIaNiGghxBc2IqKFEF/YiIgWQnxhIyJaCPGFjYhoIcQXNiKihfD/AWjK6pLyZ3VCAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3560, D: 0.2326, G:0.1582\n" + ] + }, + { + "data": { + "image/png": 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MDh9HpJN2Z0kNipuWf67cyIdhKxFrHRXcNSykWLrWSvdhJp1Z1ycsTVRKSkoaV7lFYhfdu3c3aVuZoPCxQw45BLBlPIpBJfrDlhvFisTV8iMudA3btGmT0i29mC525eiiKHiR2MOjBlBxhi0EUTCAZ9jqRyXWcIUVVsg79DAKo5xnWA+PGkBsCexxoBRmdQMoPDwKQSGB/XG6uzzDenhUEcrGsJW2QLZ2y6dHbih8cXmGZ1gPjypCViuxh4dH64JnWA+PKkJWHdb78KoT3g9b/fB+WA+PGkDZf7Arr7xySp9Xj8oikUgU5KceOHAgAwcOjHFEHpngGdbDo4pQVbHEUSBO/cctVlYpZNNh82k72doR5xqqMbkalVcqfsDrsB4eNYCsDFtIYnC1YHmzMNb6/GD5mKPgGdbDo4qQ9QcbBEHVsWuhFs840alTJzp16sRjjz1G79696d27N507dzatR8qJuro6k6fZ2tEa13DEiBH06dOHPn360NDQYGwB5UZVpNcVYihpTRuMKuOPHTuW9u3bA7Z7m+reqrK8eszEhUpVOCzGyNWa1lC1r2fPnm1qjk2ZMgXAuLYK6e1UKqpjy/Xw8GiBxN50f0AQ1d/bb78dvP322+Z5IpEIfjIWZPybOXNmMHPmTPPZb7/9Nvj222+DddZZJ1hnnXXSfmfIkCHBkCFDMh4zzjm6f4sXLw4WL14c7Lrrrinvde7cOejcuXMs5y3X/Orq6oK6urpg5MiRQb9+/YJ+/foFzc3NQXNzcyDMmTMnmDNnjlnDUaNGBaNGjYpsfnHPccmSJcGSJUuCESNGBH379g369u1r5tLQ0BA0NDTEvobhP8+wHh5VhLIFTij5WDWO1dWurq6OK664AoDDDjsMsGVMpd+p0r7G2qVLF8D23ikEcboEpNOoj6jw3XffmTGra9rPfvYzwPaniRJxuXWkj6rLoNarb9++3HzzzQBceeWVABxwwAFJ31VQibq7qwOhak0Xgkq4daZNm8Zqq60GYOwR6mIhPTdKeLeOh0cNoGQrsVhPFjJ1NtMurOr3KuSsHTUds+u9VVddFYCFCxcCNtRP3bv1ObF2a3FXuMwqXHfddfzlL38B7LzjYNZi0alTJyA3U6gQ2YYbbgjAW2+9BbRYgPXd888/H0hl2Llz5wIwbNgwoDhmrSSefPJJc/+pP08cvW9zoXXc6R4eHnmhZB3WDXjX8T799FMA1l57bcAybD5sKJ+hnOeuE/3FF19MOuf2228PtOhS6jaWCZXQf5YuXWrmLX9knAXF0umwpRS3vuWWWwDLmpKm1J9mwoQJjB49GoBtt90WgKuuugpoacUShtuZsBiUcw3VUfGDDz4w85W+PmLEiLhO63VYD49aQMkMK2vfhx9+CFiGvfrqqwE4/PDDk153sXDhQtZbbz2AFHaUNU7NsmQ1Do0v1/BSUAmGXbZsWUZpIQ5EZSWWrqq1lU1BUL/Url27Ggbv2LFj0ntiJTH7nDlzAGvpLwblXENJkAsWLDBzLPcahuEZ1sOjilCylVi7r3DdddcBcMcddwB2N1J3csn/u+66KwAPPPBAxmNrR58+fToA/fr1S3rf7QjemmJQw1iwYIGRDjbZZBMA3njjjUoOKSvEKn/7298A+NWvfpX0vvziYs3wde/fvz9gmVWQhVl+aFnNL7rookjHni/yjXGWnWTixIkMHz4cqGxRfM+wHh5VhJJ02HCTWzHHueeeC8BvfvObqMaYcSeTnrTKKqsUcqyy67BffvmliWzSWDX2OFCIDltfX28suW7GkDrdf/zxxwAm0kespKglsIz11VdfAdY6LDbS69988w1grcSKFiqErSqxhm+99RYbbLABYMcui3cc8Dqsh0cNoCQdNpz/J50sSmaVhdmFdnxXp22tUNw0tPjz3NcqiaVLl2bMxT366KOTnksPTScdSNdzdVfhwAMPBODVV18FbJRQa7U7uNhjjz1MbMGWW24JxMuwmVCyW+f5558HYMiQIUA0lQN1QSZNmpT2/R9++AGwLoRCUE5xSsaZ2bNnpwRKxOkaKNSts+aaawLw2WefAdaIJ6Ofwgq7du0KwKmnngrYBP25c+dyzTXX6NxA6g933LhxgA1/PO200wArEheCSojEX331lVEJ3n//fQDWX3/92M7nRWIPjxpAyW6dwYMHAzZgv5Sgbu3Oa621Vtr3VQtp1KhRRZ+jnJAkEGbXYlIC44aYVdA6KMjBLX3yj3/8A7CBLfPnz08JOVWKpIxsMkrusssuAJx11lmRjV+I090SZtgJEyZEfvx84RnWw6OK0Koq/2snz5QgoJ1dDv1iUAn9J3yNqyE0UXaI008/HYCRI0cCsOKKKwI2WGbAgAEZj6GgCq2lkj/atm1b6HBSsLytYRieYT08qgitgmFPPvlkAM4+++ysn4tiZ6v07iyrqFwkMZ0vhWFd/U5Mly0JW24zlfeUVVilWvfaay8A3n33XcOornVYr0vfFdOWgkqv4eabbw5YF1VM5/MM6+FR7WgVDLvFFlsA8PLLLye9Lge9rK19+vQp+VyV2J3DRdj2228/AG6//fbYzleJ3joqIvD4448nvS4GV+H0KAqmV2INR48ezaWXXgqUvwhBGJ5hPTyqCBVn2IaGBhNRI4uiHlXIrW/fvpGdr5y7s/TGjh07mgifjTbaCLAFzOJAJRhWunkmnVhRUvLPloJKMOxKK63E7NmzdX7A2gHisEd4hvXwqAFUnGF//PHHFN/cwQcfDMBNN90U+fkqZWGUFDF58mTAlmyN6XxlZdguXboYS7LrQ9frik6bOHFiyedz17AcfYznzZtnihDoPNJh42iC5RnWw6MWUK5mWO6fGimF0aVLl6BLly5BY2Nj0NjYaJoOzZs3L5g3b14sTYbimFunTp2CTp06medHHXVUrI2Tss2xHOdr06ZN8MMPPwQ//PCDWUs919pFOf9y3qf669+/f9DU1BQ0NTUFHTp0CDp06FC2NfTNsDw8qhSxN3T+/e9/D8DDDz+c9LpyCsHmzmYqm6JWEoqiKUfj3FLgtrw49NBDTdFpFadTfqhblLsaEQSBsZQqokmP//3vf4HCGjq3RgwePDilNM7KK68M2MKDsr3EmTXkGdbDo4qQ1Uo8ZcqUAGDjjTeO7ITy0911111AS/MsZd+cc845kZ0nEyphJf7ss8+YP38+AJttthlQWkWOXAjPsbGxMQAbBxwlVKTtnXfeMRk+bpaOisTLD6v421Jiit017N69ewDWIh0lJPl89dVXprCc5qDKKHEgk5U4dreOXDY77rgjAGPHjgVs5bkgCMxiqhSJxCuZzaN0TFfiB1tuFOLWqaurK6rfjnM+UxVSIZhnnHEGYOsPu0nyJZ6v7GvYrVu3WDaETPBuHQ+PGkBsDOsq3jJCuB3YlyxZkpJypo53MlgIOpaqx4uRC4Fn2OjRo0cPbrjhBgCuvfZaAP7zn/8AtpCbpCWpAkqGl6pQCFr7Gnbr1g0oTUT3DOvhUQOIza3jMrd22ttuuw2wCntDQ4Pp3aK+r9J3xbDl7BrmUTgWLVrETjvtlPTa7rvvDsC9994LFNYfuLUin6R/iMf4JVTv1fPwWA5RtuB/9SVReZFskH4b7t0SFVq7/hMFKpFe50JBBUpJixLL2xqG4RnWw6OKkJVhPTw8Whc8w3p4VBGyWomXB90gijlWsiN3OqTTYWXhlP9TLUPClneNXwkJem/YsGEAPPTQQ4D1pcq3Pm/ePPO/vAE6nwLm9ag+sm6BcYUABkFgzqvPalwae1NTU+Rr+Oc//xmA8ePHl3qoSOB1WA+PGkDZS8TID6tyn+VGOSyMjz32GGD9yYUgivjp8BzbtGkTgGU4BemnS1HUa7onFGPsMp7afCrSrK6uLiWSyWVHtw2Le+zwoyLfxNZibx176dKlsa+h+tnecsstUR86L3iG9fCoAVS8CFu5UQzDuqzQWqHWjrNmzTJzXGGFFQKwOqLmki69Tzqiq8PqHlFBd/ee6dGjhzn+zJkzAXut3IJlghhfbKpzNjc3mzHqNenD0nfLwbCVgq7T4sWLPcN6eFQ7PMOWYY7jx483Vkgx0aBBgwB4/vnnIz9feI5t27YNwLKlWEt66IIFCwzbuZKEyvs88cQTgGXFdJKGjq9C6R988EHS62J0l7V1rLD1OJMOLSxZsiSyNTzkkEMA+Ne//pX3d8rhFfA6rIdHDcAzbAFzFEvI0ho6ho4NwMUXXwzAMcccAxRX1SHTLl5fX5+zCF06K7GrBwpha7QYVud0H8NjCL/esWNHOnTokDRu6bsqTeO2o1x11VUBm9micSxbtszocfqOxq7xzZs3r+xSUtu2bXNm6USJipWIKQXqP7PJJpsA0VRLjFMkzpZ+5bom3OsepZgVnmP79u0DsCJnOsNROiNSGErC0I9NhqJw/SbN79RTTwXgzDPPBGDdddcFYOuttwagd+/eABx99NEAdO7cOemY06dPNxX23eR2bQpR/GD33HNPAO6+++5Cv5oTbuJDMUZLLxJ7eNQAKsawP//5z4GW7t0qK/KnP/0JIKXXjnZf7VxRlt4oZo5KEVTivVjLhQqSff/994bF8mVSVST85JNPUt5TiZw5c+ak/W54jvX19QHYa+iGAi5btixlLGJLMYKkArGhDGgynDU3N5vXfvvb3wKw/vrrA/baqG/sb37zGwCOPPJIAA466CDASlO9e/c2VTQVEiljl67dokWLIpOSMrGfxq/rv2jRIuP20pzccEs91/2r6ybx/8477wRgm222yXkfeIb18KgBxF7534V2oalTpwItu/lFF10EwB/+8AcglWG148sprx2t3B0AZAxRMr7Gqd1SOpYMLXpMJBLstttuANx///0AjBw5EsjsTkjHrEImZk0HN1Ai7M7RHMQuetQa6fqK0TU/MYwknYEDB5riep9++ilg2fiNN94AoH///gCceOKJgGV6ram6+U2aNIk//vGPgLUFuEX6okRY0gDbtUEMmy7ARPPX9fj73/+e9Pj6668DsO222wJw2GGHAS3MKhRrq/AM6+FRRSibDqvdsWfPnkCyHrrrrrsCLbsrZO7S/fHHHwPQr1+/oseRS4e94oorAKtjgWUl7fiyhJ522mm5zpXyv9jxyiuvBOCvf/1rXuPu1asX0GJFHThwINBScf+nOSSdI13lf31GzCGdsVu3boYdxcaSYMSC+u4JJ5wAwJdffpn0/vnnn88999wDWL1d1+zf//43AH379gWsZKVrqSQQpbU1NDSwxRZbAHD55ZcDlv3UY2nu3LlZ17CYBBOxpa6h7sF9990XaCkml6kIoK6p5uZ2m19nnXUA+OKLL4AWCSZXryGvw3p41ABiZ9jLLrsMsIylgtPSU15++WVT8lQ7mPyAM2bMSDqWq28Ug2KsxOoPo7KrCpzIFAwv5tG8HnroIdOXxQ0RvP322wFrIZ82bRqA+bw71549e6Zcl2xzVHdyXTvXV9yuXTvj73R1RX1HY5GV/h//+AcAa6yxBgCjR49m1qxZAOywww4APPfccwBsvvnmgF1TN0TxpZdeSpr3vvvuy3nnnQekdjjXd9zA+Fxr2KlTp5SOgkKfPn2Alt454WugcW266aYAZn4ADzzwAIDpSChJ6/TTTweslCFfs3Rb6fHvvfdetuECnmE9PGoCZdNh3TC3bK0a1Cf2m2++AWzrDvkmtRsXOY6ifXjS8dw+tdp9lXCt59JtVlxxRWOV1fzd8D6xhyKD1D9XOn8uVg0jPEel17l6rqKJ5s6dm9EKq/muueaaQIuuClZHUz/U+vp6s2YK/pe9QWsoy7rrJVATrW+//dZ8XwkR7ljFksWsYabewtLXdR8+8sgjgPUBh6+JrOXS+XUPu1KTjiWdW5Bev3DhwpxSomdYD48aQGx+2I8++giwTZEEl1nS4ZVXXgFsiZXVV18dsHqG60ssF8ScShQXLr30UsDuuOo2Lx/r/PnzjcVSOqsLsdmHH34IWH3n3XffTfrcaqutZlgrH2jXl94tKSAc0+zqlWId6e6yUIthTjnlFMCy/2uvvWYaQE2ePBmwayYdUYwr/V7XSveBjjV16lSju4qNxViltGrJ5LMXg+p9xU2nO5ckuzfffDNp7GJL6e0bbgNwsnAAABM6SURBVLhh0vd0z8tuUIoNxjOsh0cVoWQdNpNuIP/VrbfemvS5QiI8FIPplh2RLuPqCPmgFB1WLHLhhRcCtlCX/MKSKsQekgCWLl1qdFCxlTB8+HDA+isFMaHYTpbFAQMGsNVWWwG2eVi2OTY0NKRNYA9DlmOxjXRvMcree+8NWKYTw+y///5Ai0SkaDRFaGm+Ym/phLK+ai11rilTpgAtvktJAxqXxqF7qFArcTq4lmexuSzfGn+YDV944QXA3geSFpTgf+yxxwKpaYqCmlsrsi8bvA7r4VEDiM1KfNNNNwE2Plis41ra0kE7mHxgTz31FJCafF0M8mXYsPVW7CF2VLxsFNAOf8kllwAwatSotO+Lqerq6gwD5ZPpIT+sm5Wy2WabAS1xr25JUp1TurrYceeddwasf1Q+9Xbt2hkrrwqUSyrS62Jvrb+e69oqEuqVV14xVmHZDMSs+m64yNxP4017IcI+Z7d8rKKPxIqHHnpoukMYzJs3z0h0ys9+7bXXAFKKxikTTTYXt9RNPlJm2RPYFb6mSSoYXIsRNhhpQdZaay3A1gPSRGWI0YWSuFiMESLTDzZbkrGukcRXhdEpYF1hfqVAwQhjx44FbAijbtLwWHL1WA3PsV27dgGk1hzWY11dnXGXyTCktZLLRddEvV71qPW444472GuvvQCYMGECYKtuKARTSQ9yV0ksvOaaawAr8ofvC/3IFIgg11ExIrHUKAV/6D7UMd1rqjRBpQU2Nzdn3NjcjutKcBg3bhxgNw59L1dY4k+f9SKxh0e1IzaGPfnkkwE4++yz074fFiNcppThQqKFRFGJV6WgGKOT2EJpU4KYRuZ6t8p9IdDurSTnPfbYI+n9oUOHAi0hnjfeeCOQ2UWULvhfY5JIqAD7qVOnmjSwp59+OukzCog/44wzkl4/4IADAGugefjhh7nrrrsAOOeccwDr4th+++2BFhaGljBGsIEUCsIQk6mbQBi6Pwo1OmksY8aMSfd2EtzfgcarfkITJkww8xfzu+7F6dOnAzbQR+L2WWedBVjDoru2GcbjGdbDo9oROcPus88+gDVIaJcWXPkfkot5ZR1sCY7z0PmLdgmIieSa+OyzzwBbKkaugN133x2AiRMnmu/KuKLQPblGVlttNQCOOuoowCZJ6FpI39Fuvt9++xmGzWeOKsLmGuzCOrvWxK2sKD1O7CwGka6mkMX333/flH5RqRgxp4L+lch+/PHHA9bI9MwzzySdo6GhIaX+sZv2F2WJGOmfSoVzEx/yQSbJSkULZIPJVpQgzTE9w3p4VDsiD00Uq0jOVxibmEXuCe3WvXr1MhZRWSddiJ0qBVlRb775ZsAG6IsBZNWW60ppeELbtm2NZVtM437W7ZeqgHyxiljt8MMPL2jsLmNozHINLViwwIxFr8kl45Z3kRVZ0oBCL9dbbz0TxKHgCoWVal6yzipQQscWA+t74fI3GrMe87GuZoLGIcv+k08+CdjrOmTIkKRzFQK3FrPu1wcffBDAFBmU9FRKqRvPsB4eVYTYgv8VkuiWE8mmp8qi+OijjwJ2t5PlUKxTbkj3kF9SaWViB/mapZfJpxgOenD9u5mug3y60g8VnqkyOrJM5ws3TM5lg8bGxqQK/mAlCLGPfJY6looSSOf98MMPjRSi44qtZTEVg0rSku4qySNcMtT1Geu9UiQtXX/ZIXR+XQ/XA1AIZIdQcXKl6OnY0mWjKCLnGdbDo4rQqlp1iFkzdS6vtJVYUHSMIN1bUT6ymCplLB+4Pj5BETrSM/NBeI49evQIwOqlri7b2Nhoji22U4FwpTnKkqrUM7GoGHnx4sWGlcWgCv2Tj1JMq8JqSmDXtQyXWpUk5eqsoc7sBa+hWxz9pJNOAqyPuRTovnSlKPmpVXytEHgrsYdHDaDshcSzQfpavn7ZSkHF1WQdFhSZJcuusHTp0ozWR6WOKeHbhZhKGDNmjIngETI1jwKrh7qsJSbv3bu3uc7SxTUv6V6KYlLEmRu4P2vWLGM5lu4qPU5ju++++wB49dVXAcv0btJ8XV1dxi5xpXSPkw1F99Zjjz0G2CJxkiaKgWKdBc0pConQRev8RXh4eKRFq9JhtXO7DOV26S6lJWOU7SYVoyrGc9OnxGaHHHKI8f8pq0X+V+l8USCkS6U0w8qE7t27m5Q4sePGG28M2PQwWenFcDvttBNgI3u+/vprk4onfVixyscddxxgrd6ff/45kFqkXEjXS1frLimh0HaTa665polKc9dIc1Qpm1IgSUVrq+yzYo6xdOlSr8N6eFQ7WgXDugXC0owjsnPF2dA5G9TIuJACalBco+fwHFdZZZUAbGK5LLvhIujSnxVlJL1TVk4l7CtbSpk1Kn6+8cYbm8ilAQMGANYeoXxXWZplOZevWvOSPrxgwYKUbu7SzUPlToteQ51H95rG6WaCSW+XRAS2gZnyXQVZwHVM+WUztZzJB95K7OFRA6i4lbhr164ZWy5Kl5GfU0xcSFHtXCiGwYpBocwqlDouxfCKpaSHykq7ZMkSo8eppYQaVkvPVPaRfKfKm1VceK9evQwryld7/fXXA/b6ylotFnf1VI2vrq7OsL8s2W55l1Igv7CKvim2Wbmqyrxy9c+tttrKjEOM6kLsrSgqRVVFiYqLxHfffbcJ6RJkzJCYInEyCkRR+b+UurLlQHiOHTp0CMDe7OlKxAgqRyMjk57L2CNjk9LFVHMpCAJzg8td4oqeWlMZpyQuahw6RyKRMBtFpvrTYaPaT98p+T7VuFTryg0vzAfbbbcdYH/8mZJZ8oEXiT08agAVZ1hI7a8puAYBdQtTcnwxqJTRyYXcH25JlEx1ngtBtqqJrlM/LC3onG7lfbdEiz6nY5555pmmiJzEZFcK0THl3lENZ9dV1759e3NN3PNK0vriiy9axRrGCc+wHh41gFbBsGnOC8RjCKoUwyrsz+1TuvXWWwO2aFkUCM+xbdu2SfMTK8r4lEgkUurqSp/UZ1zdXawYfq7X3BK0bjd3t9yLoGPV19enhCC6XSPcoALPsB4eHq0SrZJh47TGthYdNk6kY9hwgvhPnwFarrGb5C6mdQP0lbAut4ukhvnz55vPKMlfhQvc+8tN73O7rDc3N5vX3BBPnb+Y9Lpqg2dYD48aQFaG9fDwaF3wDOvhUUXIGpq4POgGUcxRES0KsK80wnP0a1id8Dqsh0cNoFVaiePE8rY7xzG/KIPxi0Gl11B9i0spjZoLnmE9PGoAnmFrfI61Pj9YPuYoeIb18Kgi+B9sCaivry+4edLaa6+dMQHawyMX/A/Ww6OKULESMYo9/eCDD4wfU4W6nnrqKcA2x2qtyDdndYUVVjBtIi+44ALANr1Sto7ySFWiRE2jPVoHunTpYtqKqPjc1VdfnfU76ZqXl4qK/WBffvlloCWR202qPvPMMwE49NBDAdtfM05EkTjuQu6PtdZayySs6zXNVel2eq5AfN0M6miufj3qa1otGD58OADjx48HSiubUkn873//MwkLWqurrroKgA033BCwm7DgphBGAS8Se3hUESrm1gkXA3NTvqLouJ3lvLG7BGbPng3YImazZ882qWh6vPDCCwHLoG7qmpjWrSOcD8rl1tHYRowYwRVXXAHA/vvvD8DFF18MWEbVmiqRXa+7BdbyQRxreNBBBwFw00036RyA7W20cOFCsybqF+ymH4bGl/TcfT+RSBg23mCDDdKOx7t1PDxqAGVjWLfsi1g1HN4mw8vf/vY3AM4777yoTm8QJ8Nqjuuttx5gq9uHIR1ZpVTCieRgO6HpuZi3kM5tcTOsW2Dg008/NZ373nzzTcD2rFHneJXAUe/ZcG/ZQhHnGqo8jjuuefPmmTHLviBDYia4hQEKgWdYD48aQMV0WO0+S5YsMf+rsn8pfUBzIdfufMABBwC2m3oxGDJkCGA7b59wwgmMHTsWsJ3h1OFs+vTpAPziF78AbB8bt9C32Kxbt26men4mdoqLYVUcXOfVNRo+fHiKnqYOAOqHI3uE9PpSkGsNi3Gn9O3bF8B0uZMEmM5rsM022wDw7LPPAraLu8qzysuh+1kId67PVWjQM6yHRw2gYgx79tlnA3DyySeb1+SYVtuEOBCn/tOjRw/AFj4Xew4YMIADDzwQgEsvvVTjSPquLI6ynsrS7FrK6+vrc/qK42JYFf9+4oknAGvhXmmllVLKt4bGkvSoJP9S1jiKNdT1lu4tCUjrJMjG0qZNGyM1SMKQtVg9dNw1dZk+XE423CIlHTzDenjUACoW6RRmCelncTJrOeD2GFXf1ebmZi655BLA7rby76mxlK6HGkxlQpSRWIVCbTZ22203wFpU0/nL5W+WnqtGWsV0JY8D8khoDi6zqqnXO++8Y17r3r07YO9Xt2u71lbXw00MUd/YXOyaDZ5hPTyqCGVnWPWCHThwoHlNO061tHN0kckOIMbt2rWriSEWXAtiufrUFoJzzz0XgJtvvhmwjKG1y8aw0vPEQmpsJp1QbR1fffXVWMaeCccffzyQ2m7ExY477ghYyzhY/Vu+8kzQdXGloWnTppn3iy2v4xnWw6OKUHYrsVjUbWXo/h8X4rASS+/MpIMHQZASJ+1iq622AmDSpEnmO8Uiaivxu+++C7RYu9Ohrq4uham6du0K2A7wihJSd3cxq5i2EJSyhm7GjR4VtfTAAw8AMGPGDJ2r4PHdcccdAAwbNizjZ3Ld695K7OFRAyi7Djt06FDAZmk0Njby9NNPtwzmJ70ijiydOKAGw5mY9bLLLgNaMkFkIdx9990BG0us2NsXX3wx6butQZ/XGPr37w/Y9VE0kKKDli1bRq9evQDre5ZfVrqs66cthllLgXRG11agR/mYleNaCnKVAHrvvfcyNvTOhYoFTugHe/bZZ5tQLkGujnXXXTfy88YhErtmfN2cCv5///33GTlyJIAJK1SIntwLMkLJFSTR2HUd5IOoRGKFFepmV2jddtttB8ALL7wAtBhR3GQGPSogQe4ddbVTCKYCFwpBMWuY6T6XYSjKZHO3v246eJHYw2M5QOwMK1FJzOImYqcT+UpJS8qFfHfnQjqji2GfeeYZwKbE7bHHHgA0NTXxyiuvAPDLX/4SsBKGAgx0DLkRdtllFwDuvffePGaVjKiNTmIDNzVQhqSTTjrJBMDrMwoyUIil5qnvloJSGNZVu6ZMmQLYRPJCq2CmwxtvvAHYRA9BkkpDQ4NRJ+TqSTNez7AeHtWO2I1OMkK4yr6Kis2ZM8foc2If6TnSb7Rrl9MAkw+zShJQ13FJD2IV13UAcOSRRwJ2TldeeSVgd34Z5e68886k1ysJrYuqWn733XeAnUNTU5ORKsRQkjZ0jXRtyuG6SwfdO+49tNFGGwE23bEU6BpssskmQKqEKNtGly5dMjJrLniG9fCoIlS8t86MGTPYYostAFtWRLugLKfarVWztxREaSXO5HrJxyWj3VhW4tD4AGtFzpS2lg1x6bBuUr0wa9YsE66n8bqJ67KcRmGXKGUNXSu25iapqJgyrHLr6RjCxIkTAWvLEAYPHpzixkszTq/DenhUO8rOsAqC184bBIHxV4l1BIWHzZ8/H7BV80txbkfJsLnKW+Y5nryOXeAxK9a9rkOHDoAN15TVO9/A+XwQpR/WRTHXXcfWXCU16VgqFaRgESXC5zimZ1gPj2pH2U2Q6dKKZKkTlNrUs2dPoPIdvzOhFH1MAfUqGXPUUUcBtu1DtUIJAmIbXaMjjjiiYmMCGD16NACXX345APfddx9grfKCq9uG0yJ1/+kzsrm4EWFu2qEYNR9mzQXPsB4eVYSy6bDy4TU1NSW93rNnTyPzz5w5E7C+R+mwipaKAlHqsLlSxJQy9+KLLxrLqoL/77rrrqTPKglAPrxSUEkd1k1fE6KM2S1lDQttAyNPRYcOHUyRQBeuFV3x4CqafttttyV9Ll06oguvw3p41ADKxrBqNiTdQTvdvffea+J2X3/9dSC1BEmUKGZ3ztSKUjum4oSl06jgmErEbLDBBsbS7R5DFnK3ZEwpqCTDZoruUQyxSsGWeI6SpSQllyvZPAqoGPzXX38N2P7GDz30UMHH8gzr4VEDKLsfVhkeylZZtmyZYZ/PP/8csPqurG7SCaJAFLuzmFVzUFSSSpmqwLawaNEiIy2kaz0YNSrBsMrbVRy4W/LTLUJXCuLwpZcSp65juA3eovKlh+EZ1sOjilB2P6wbOzts2DBGjBgB2KZCkydPLvew8oIirGTpliQgK7HLrIr6OfXUU00pkAcffBCAqVOnxj/gmKG429NPP934OQXNU+0m44RYvBgLtCRMlbJRtJ1sLjfeeCPQEm134oknAjB+/Pis44gzI6niwf/lRhwlYjLVCapUKlm5ReK//vWvpmeqQhJVs0jqTpSpkXH2R2ot8CKxh0cNoPLZ0TUAV0qpFLNWCk1NTQwePBiwbrtM1QCVStla1Z7WDs+wHh5VBK/D1vgca31+sHzMUfAM6+FRRfA/WA+PKoL/wXp4VBGy6rAeHh6tC55hPTyqCP4H6+FRRfA/WA+PKoL/wXp4VBH8D9bDo4rgf7AeHlWE/wd3VCRPLnU50QAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3580, D: 0.2382, G:0.1893\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3600, D: 0.2347, G:0.1704\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3620, D: 0.2283, G:0.1438\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3640, D: 0.2314, G:0.1611\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3660, D: 0.2324, G:0.1835\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3680, D: 0.2066, G:0.1826\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3700, D: 0.2341, G:0.1746\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3720, D: 0.2277, G:0.1422\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 7, Iter: 3740, D: 0.2371, G:0.153\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3760, D: 0.2482, G:0.1738\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3780, D: 0.2311, G:0.1715\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3800, D: 0.2536, G:0.1779\n" + ] + }, + { + "data": { + "image/png": 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idnPmzMmYXv0nLiReJCp1r4vT7JQOxo0bV1Z6dN111wWC1dMi4EpL2hq8vsUWWywrByoLbb/99kCwNOsB8D7IuBbf1m97ySWXZNcRd/TLJQwMSzMsJYv/+I//AILeXU+DskaRGDYhoQdQ1zJfiVlFLPu7SselPtWLLKMh5s2bl/nw2sGs9Uab1GJWIbMaN2387Oqrr15yLjM9rrnmGmBwZb700kuBwBadQlzmReStxMKxafFVV9dq63Vp6T333HOBQYa56KKLgHAfzUbys2yppVnWNgLMTKBZs2aVxfd6vkoZPZ2EEoXP8FFHHQUEe0A9enCrSAybkNBFaFmRMrshtpzZasKoJa2hso4642OPPdZunbXlY9je8swzzwRCnqv6mRZwWUUm8N5MmDCBnXfeubDxNIJ8wXAIjKpOqS8Vgi7mtpb3sfi7EU9ai40PnjhxYnatzrM+Ssubyqz6MoVRQ1qvF1988TKG1bZRr8+6XUyn3u244lYc7WRWkRg2IaGLUJeVuB49MC6mZesGs3bMxrAYm7Gkyy+/fFb8qxNoxsKoJVOfoX9lAiOEjCk1q+Wggw4CBq3sFiFTEmnnapy/xgkTJgxAsCXYKkOpYNSoUdl11Dsm2dn2IlOnTs0yaoyS8rNQP47jluOSpv39/WXF9ipEZ7XdSqxXw/FtttlmZfnaVsv40Ic+VNcxiygkXnjgRByCZ4C4L65OekPBLrjggrKO3u1EES4BHfvVyuBogLG64OTJk7OUsU6ITZXcOvHDnzcSWuFQkThOZ/MZietCOceTJk3K9q1W7dEX1u1c4GNjWH5sHt99NVwtXLiwsBc2Jhqv2XOZNnjwwQdnCepWR7R0jsUI2pUimUcSiRMSught761jnxZDEk0505BjqZJOoR1Od9O4DKmUNZQcJkyYkN2HOPiiHaiHYWWS1157ray4WaUk9+j4JZ/zXeplQ4MM7DcUp+55TMeRFxc9hkE2MdMvWLCgY4ETzuWhhx6aGc4MQbQIQVzdMTeuknE3gsSwCQk9gLYzrMEF9913X6uHagjVmKwdDGtAgW6O4Ual9DpXe++LnxcuXJgxQFwfOmZYP3uM/Oe4QFrMyrHhyHPpGsl36YsZK+4SMH/+/GEJTYxRdEnaPBLDJiT0ANrOsO1EzBr1lMEcrsDxTiJ/jf39/SWV/yvpn3H3gWoMGyd45Jkvvvd+jtlYRnVf9XyLGCxYsKBsPLG01EkddriQGDYhoQdQk2ETEhJGFhLDJiR0EWoG/3dCN3jHO96RJT93Am82Hbae64u7oo90NDOHpjcaYTcUjA8w9TOPTvvS80gMm5DQRehqK3EzSAzbOOL42naglcD4Xp/DPBLDJiR0EQp7Yfv7+5tquHvAAQeUfbfMMstk6XgJw48FCxa0lV1hkFmTx2JoJIZNSOgiFFZrs1GLmTqLTYgg6C8mv1tqxYJdcQvAhGIxlB5ZROxsnH860mDpWp/LfDbRSLCiJ4ZNSOgitM1KbAvJVVdd1WMBoWSqWfsDAwNZFQJbc2y88cZAYO1YN46rFjSCN5uFscjrkxUtoOdc5vNhO4F2zKE5rocffjgQfNM+t1OnTs2K0uXGUfFYRUgiHSsRUw0Gd5vQbNpdJZjsbRV5YVVCe3o20/ktvbDliO+rC6Hd4wzYP/jgg4HQF8i0QghBF1ZPrFT6pSg0M4dW67RPkLD+lsnyEoy1qSztc+ONN2bBFOussw7QXjdXcuskJPQAhi1wwqp0ixYtKqugF/d/GcpdZL1Yq8fXwnAw7KJFi7KUMWvsxn14ikSzIrH33V5BdiC04r1jVrJRRH7LW96SfafYuMYaawCVS8A0i1xt5abn0BJFdoSXWa2NvfbaawOhn7GGzwkTJmRJ9iuuuCIQui2eeOKJJedwO4/dDBLDJiT0AFpi2IGBgbZ0EM8fvxZcwawHrDFkiGO2nWGtQXzGGWcAg0na6nQazOwpc/zxxzuuws7fCMPm5y8ew/Tp04FQ+sZ+sO6TZxJ1P5nJbdT77OZeBBqdw2nTpmUliuIStUpv6uOOU71dfX6ttdbKeirZnd3uBldeeSUQeh4XgcSwCQk9gBEd/P/kk08CwZkdY6RZiXUF2K9GvXXxxRfnlFNOAUKPmdNOOw0g+94eNEUwbbM6rFKA/XqvvfZaIFT4v/rqq0u2t/P4rFmzePTRRysesx0SWCtzqLSgB0Jmtar/+uuv7zGB4Fp8/fXXs3I2c+fOLTmG81wkEsMmJPQAOsawzSRJ27Nk1qxZQNA3XCXtpqauWA/aybBaGC+77DJgMDkfBldpwyqXW245zwsEq7EWcbv+GXjSDNoVOBHDa9htt9348Y9/XPJdvE2RaGYOff5mzpwJBAu4YbBCv+zPf/5zIAT4LFiwIOsSb4CP1/yZz3wGCPaJuJhdJQyVspgYNiGhB1BY8P9QaIRZjZZRZ4h1hPPPPx9ojFmLQFwaROlEX52rptbTfFlQ/19rrbWA0L1bqCe2wqytQp3MSDN1VsuqyCBnnXUWAHvvvTdQmUW9Xn24SkXDlULn8/fJT34SCM+U0VuGJjp3M2bMAMKc//GPf8wszM6d1/+jH/0ICFFUcYReJTQbJZUYNiGhi9Axhq0FVz/1DIuyybQxZLROwRXTiKp77rkHCEzkOGWa008/HQjRM/mIl0033RQIPWXFscceC8B6660HBD2pE5BF7B0rK6rfXXfddUBoHaql+/777wcqx4Wrs3uPhhvOjXOx1157AfDTn/4UgHPOOQcI43V7f99xxx2z74xssjP9uuuuC4Qoqna2bkkMm5DQRRgRflhXeJnT6JK4NYQrmtEztZKgq8WutsNK7DliS3DeN2nkjIjHvssuuwAhE6aoVoVFXF+su1drQ1npu3Y0jCpiDtVhZVRbhcqSSlXqpb/4xS8yHd7IJmOJlRC91re//e1AyPRpBslKnJDQAxh2hp0wYQIvvfRSXdu6KhaZGNzK6mxsrZZfoQSgLj5mzBg22mgjAG655RYgWFxjvb2IBtft8sPqu6w0X1pXtSjrOzf+9v/+7/+AYvJkW5lDLflmixk77Phr4YYbbgBgyy239LwVt1Pv/fSnPw1UtrnoAal2P6oxbMeMTk62YV2iEbFhpFTVUyQ0yEFoqjcJQUPGHXfckTnXTQyIu7a1M92uVRgob6CAIv/dd98NDAaIzJ49Gyh3afgiOO8uUM1U2BwKt912G0AW4FAJJgFcdNFFQDC0mQ7oS+iipJozZ84c7r33XgC23nprIDwH3/jGN4Dwgu68885AMCyed955QAj0AVh55ZUBqoZ0VkMSiRMSughtF4k1ebuS7LrrrkBgo/PPP7+sfk6MuMdoKyhCJL799tsBOOaYY4AQJK94ZUiiq/YTTzyRSQeGL5rovdNOOwFwxRVXOL5Gh1OGekTiyZMnAyFRoREoTirqzZgxI0t632GHHYBgmBG1XECNopU5tN51XOxA6eHII48Egutu6aWX9hyZ9KCRaauttgICaxvI8/GPfxwI7jAZ33TFu+66a8iqkcnolJDQAyiMYWU/2dCVypAvVxf1jEp1jE0MloUt2GZ3gCJq2RbBsOqbBu67CquPx26oSZMmZcYFDReuzu0Oju+Ua063lbqqOrow2MJUybjLeiNoZg4NbrEMUbXia3ar83mWTRuR7uK0UHXXQw45BBi0fayyyipAsAPESAybkNADKMxK7Iq5+uqrA5SlW+2zzz5AKPBVCd/61rdKPltqcjiqxGu9dSXOQ2YVsorSilKGOs+4ceOy6z/77LNL9s0XMusk1OGa6WEkG+UTFXRTKUn4POhGueOOO4BgHRVbbLEFEEq3FAmt2c8991yZayUukGbYqZJALUaN9eA4OERm1bVlKONXv/pVYJDtZdZGwxgTwyYkdBEK02FNeTN96eGHHwZCaJehX1pUt9lmG2DQV2maUgxZx9U7Ny6g9bC9N47VER1PVnr88cdLvpeFi0xoGA4dNoaBIhYj1yvgZ9PXtJr7vEBg52osV+QcmqBuiZgii4MrPekF8e/48eMz9rdoXYykwyYk9ADqYthqvisIEUxaRNXf/GuxsT322AMIqVs1B/UGg7qt1lfD2zbYYIMhj1ENw8WwRszEukorfYKqYSQw7LRp04BQgjaedy2psU5bD+qdw1rPbQxTCU3aj6W7fElf35lJkyYB4dnPjQ+Aj370o0C41v322w8Y9IbEUmOMxLAJCT2AuqzExonGPrMVVlghs6qZeG3UR7V+sVrnKgVbx6Vg4jjjVph1uPHnP/+55LP6WSUrdDfD4P+llloKKGdWr1vWMUXtkksuKXws9TDrhhtuCARmtRi4yRjHHXccMNij2PYe7nPhhRcCg5FLAJdeemnJsb0mt1PKhNAqxILy9SIxbEJCF6FlK7H712LOSjBWc/311x/S2mvEkz4+2/0NlaJUZbzDosNqRbc0Se78hZ+raB1Wyce0MX3txon39fUNmdxuZpMSmVkyJns30kCqlTk89dRTAfj85z9f8v1XvvIVIJQstcyPkXpXXXVVJg1o2VWqNF7A59AiBB/5yEeAkN1jto/RVrWQdNiEhB5ASwz7yCOPsP322wMhgkn/UjXYCqGTRcbyGC6GrVZc2jjkZrJmqqEohlVXc668hji+dmBggMsvvxwIOmwM94ljjJtBJ+fQzKuNNtoouw/GJVuaVn3cxmaycm68VY9fzeecGDYhoQdQl5U4LoYmpk6dmv0WZ6qo09gwSd2hmvW4lzFq1KiyFVSrsfdtqNYN7ShmNhTuvPPOit/LCv79wx/+wHbbbQeUV5JwvPW0Ah1JsDLIT37yE2Awp1XfrNUqvDb1b0vYGh9sPLoRT5XmsNEc77pe2FqGkfg3S2tYDT2uuPdmguL/7rvvXpZ+qDm/3vsyUsrjQPlDZthlLSgydwviXq9HHHFE1ZpicQeIamWPipjDJBInJHQROlY10SSAa665pqhDNoVOGiyUNubOnZsZl0z0djU2tHO4OrC3G4qRRaZIDpfhsJNIRqeEhB7AsNcl7jSGY3Xu7+/vqA4/khi2HWh0DvPdA0cKGk0hzPZr35ASEhKKxojoXtfreDNayEcSRhq7QvMlexPDJiR0EWrqsAkJCSMLiWETEroINXXYZGEcxCmnnALA4Ycf3qZRFYtGrcTDEfbYCpIfNiEhoSuQ/LAFXGMrjaXagUoMq1UyLsPTCobyJbYLiWETEhK6AskPWwBGCrNWgq0wimRWIbMuueSSWdz0UGxru0nbTyrhDRdbdxsSwyYkdBEa0mFdHS1l2o0YLv3n+uuvB0JLzS984QtAaG1iNJSNry3P8vLLLzd8rpEUS2yhsiJLAiUdNiEhoSvQkA7baWa1BKYFoWWhdpQGbQXV/JgWHJs/fz7PPfccAA888AAQSmPKoObHfve73y35XtjU+owzzih8/NVg7u5QbSUg6KBvfetbgcGyoACbb745UN7ewr9PPPFEgSPufYxot459WN/5zneWfO+LbEeCRtCMOGVfHA0rMXwo7f1TqVeq/++yyy5AeKB90K0qWAQqicTtLtXjomV9I2vvWmrlPe95DxB6p1p50P2q9amphCQSJyQkdAVGNMNWK3YlZKdKHcFrHLPp1dn+ntXYMGYLJYEHH3wwYxa711Vj6yLQbqNTpQqPcc1i52Yo9cXtLSUzfvz4sl658X0fDoYdM2ZMdv6zzjoLgH333bdt50sMm5DQA2iJYefPn9/W7mvVxtaK0amdq7N6ouNTxx4/fjzLLrssEHrHFNnpO0YjDPvss8+y/PLLN3T8WB9edtllM2OZhskjjjiiZNv4OZk7dy4QitD99a9/BUKl/VroBMNOmTIFCDr1Qw89lH0nnENLuNp7pwgkhk1I6AG0FJrY6d6mt99+e0fPF8NSpbpkxBprrAGELnu6c2SP0aNHZzqr29oBrdOI2THPrieffDIQ2LEalHy0BM+YMSN7Fuwv8+1vfxsIlv4TTzwRCN3r1lxzTSB0IjzttNOAQd13JIQn2l1PTJkyJbtn3kN1+T333BPoTJpiYtiEhC7CiLISy0xDOepHqg4rKvVI9T4feeSRQEiKbwZDreTtshLbTd1WFAbwr7322tk1xzrsHXfcAQSrsQEhcRCMbPXG+D2U7UsAABWkSURBVGuOYzisxOuvvz633HILEHzNeT87wN133w0MdrprFUmHTUjoAYwIhtUHZ7evGFpWDXtrBa2sziuuuCIATz/9NBCiddTltBLaqbwS9Cl22krcin5lz9+LL74YCD1g7Uh35plnZnqdcyT7CBlUX6Zz7XbuN2bMmEzSio9R6fry19gMnKvDDjsMCF0FY0yZMiWbd6GEYSd6vQIrrLAC0Fr0WmLYhIQewIhIYK/GrEKdoR1ohHmeffbZks9G5BxyyCFAWKUrQcuy1tEHH3yw5rmKjv1thFm9Ti3IJi5oJb/uuuuAwLznnnsua6+9NlCdFeMYYRMLPPaxxx6b/VbtGK0gvp/6fY1GGwrPPPNMdg1Gsq2++uol26y00kpAsXHhMRLDJiR0EUaEDlttDOq29aR3NXCuwvQf2dmoHXVaoZ7a39+f+WinTp3a1DkaYchKOuzWW28NhET6WpCNtPyuvPLKQLCCXnvttUBIsl933XUz62+18fo59mXmxlnPpXmswq3ESyyxBBCs2HFhvT333JMf/OAHFffVt7zKKqu0OowMSYdNSOgBDDvDDnH+dpyvsNVZ/5s6n7qNFlGTs8eNG1cWh+q2rspHH300ADvvvDPQmhW5VT+s932ttdYCQoSZ7KhV3DzlcePGZfcizrTxe/VSmdV5f+ihhwDYZpttgKDT1kIn/bD6nvM6uDYX9fCzzz4bgP3337+w81Zj2GEzOllBYaRg0003BeDWW2+te5/zzz8fCCK77ojp06cDoev8QQcdlImXJrlvsMEGAOy6665AMH5osHA8d955JxDE006E7fky+UL62UR+xUWNUs8//3xVQ5ELT7XF15pPeUPNcHQiqHbOSq7Ec845BwhBMLqCinxhqyGJxAkJXYSOi8Qq9bGB5o3zFX26MrTD6BSH11166aUAbLvttsBggIHfKVbKxgcffDAwGHyQ/72SKFYvGhWJ82mAEAwwr7zyCgBbbbUVAI888ggQrtNaW5Wwxx57ADBz5sya544NXPWgkyKxpW3uvvvuiiGn7UIyOiUk9AA6rsO6eueZ/corr+z0MAqB13DUUUcB8LWvfQ0Ieqe/f+ELX8hYSSPLaqutBsAll1wCBKOMfzVoWCXx0EMPBdrjlNdtYwjo9ttvD8BvfvObkjFb3qYemF4Xw/HfdtttwMiv9H/XXXeVfafdwblrZ5hpjMSwCQldhI7psE8++SQQylxWOV9Rp6uKevWfE044ASgNmauGG264AQiseeqppwJw+umnA/Dwww9nieuG8xkaZ6C4n9UbhS4ErZX1WE6bdevY2UFW+fjHPw7AzTffDJQn7uehBKGEUG0uvUcmiDcThjhcZU7bUbKoxrmSDpuQ0O3oGMN2cnUaYhyFrc7qYxZYM2hAlrSg2OzZs6v6+dSHLrroIiD49Ay015qu9bgdDGtwg/7ka665BhiUDACOO+64kt8r7a8Epe/WY3ovtJLvtddeQLhnlpBpBMPBsH/605/KCtrHnfeKRGLYhIQeQNsZVpbROmwQvFh++eXrCkcrCp1YnbW6GhQOMG3aNADuvfdeIBQls/j5eeedV3IMrcSt6nheX63+q/p8jWSSLeOgdqUEO/Cde+65wKD1OJaUPI+Sg37ZRlAjkaDjDFvpPfHa1PULPl9i2ISEbkfbGNbVuprvbrg60LVzdZYVtaZqLT7ttNMyxjnppJOA6nGn6rDGFltSphFUYljjm/Wt5tlL3VQd3MQEx2z5U8euvza2aOd/i2Nwi4wP7iTDVopu0p6QLxxXNBLDJiT0ANrGsEOtpL3EsJb0/NKXvgQEy6+J3ocddhhPPfUUALNmzSrZ1ygZmXTixIlAKE4dRwyNGjWqoTKg9VzfN7/5TQAOPPBAIKTPaXdwrmpZQx9//HGgvGxKftxvjG2o4VSFcccLFizouA778ssvZxb7ThfSyyMxbEJCF6Ewhn3ve98LhMLRQ62k6j+dbvfRjgT2avGwWleffPLJjHksRh3rieq/jTBQtfM3yrBaopdZZhkA/v73v5ccX/Y0SsnoK+duYGCgLb7IauikDmvy/sorr5xFZ5nBY0G9Rx99tPDzJoZNSOgBtMywRvu46tiqIV+ALDpmcyMtCMMVh9pJNMKwX/nKV7KMIHOVbeLlHJov+7vf/Q4IlSaMC19zzTWzotqtolEd/Y192j6Hr7zySiYFDWc+bMt26djt4M0e7hczoT6cdNJJmYjrXOreMc1O94VzGy/CDzzwQFbhspKrpxF0siwMMGSXAdWN8ePHs99++3VsXNWQROKEhC7CsFdNLBpDGYKSSFwd1Vwvisj2uHW7973vfQDcdNNNZceSlYt0fXjeRYsWtX0OVQ8MABk/fnwWYGIpHd1f7UAyOiUk9AB6jmGHQmLY1rHqqqsCIXGh0xiOOex0Z/jEsAkJPYDEsD1+jd18fdU6+L3Z5jCPxLAJCV2EmgybkJAwspAYNiGhi1Az0mko3aCvry8rcG2KVv43KPfpGd6Vj4jRZ6eukvO3lewbt3WI/Yb5sLY44sq/CxcubFj/Mch9qE7xzW5fNHpFh62GTuiwW2yxBQA33nhj0YeuC0mHTUjoAdRlJa7GbJX8UnGES7WYYr+fOHFi1vDJYmAvvvhiQxeRP2e1ZOtcmdCKq7PMX2S39/y1d9JW8GZl2F6KY08Mm5DQA2jJD5uP/pDRcrpi2bZAltVhsa5K6VSynal7MZN7DNmw1jVsttlmQEgc7xYfXiOxuGbZeL/erAzbS0gMm5DQA6iLYWP2lPHy+8ZWYT/Hll+zH/z8z3/+s6o12H032mgjIJTrqJaRs8wyy2R5jWaWVBjPm2p1jq9PSUPJoxvRSYatlMEU59Bqg/ntb38LDLYXBXjwwQebPm9i2ISEHkBNhu3r6yuxvlU8QGSRi7e1jMgLL7xQ8vm///u/AZg5cybHHHMMAJtssgkQ2h7aGNkmROpzFsOyVIkr3QsvvJCdPy5F6ar4yiuvtH11Vsd2LK+++mqWQ6nu7nhyeidQPX62EYwkHTaWhoqw5BbJsI888ggAU6dOrbld3tby9NNPA6Et58c+9jEgzNnaa68NhFYnRbTUzMZRzwtrpzF7mOZRrQeMVRQvu+wyAC6++GIAJk+eDMAuu+wCDJYj8aVaaqmlAHj22WeBUKLEHqrXXXcdECY/NngNDAxkL7dd0TrRl8Uq/ZtuuikQqg5eccUVwODiYcV9RVFr/toZ7kc/+hEQJtl70kxK10h4YWu5/gCmTJkCdK573VCFDYSVLp944gmgdAF997vfDZSLuvZF+vCHPwyUz6EqXSNIInFCQg+gZmii7FSJWYWuFVewX/3qVwAcfvjhQCgvYudxVyG3nzhxYtk29iXdcccdgSB6/PKXvwRgq622KhlDvteJ4rIrvPV/FT3bAY0O9qCxQny+1rAShYxq8veMGTMA+OAHPwiE3jrbbLMNEFi6FpRMHMdIQCzZOB+nnHIKUM6sShyWZikaMbP6PMRFBGVWkVdNZFZDTlVr4iCd9ddfv+RzbDStBN+BuXPn1riKxLAJCV2Futw6OvFdSfKroSuVira9Rffdd18A9t57byCUFZGR7Rvb19eXrViugrp+7CXrKmhn8E984hNDX1hBvUX7+/urGoDUb7bccksg9NLx3BrP8jqP93K33XYDgv5zzjnnAHD88ccD8Je//KXWsErOU+sah7q+Aw44IKtLXARi41q156uTRqeBgYG2hCvKxj7b1m/WdXbVVVcBZEZV9eN6kHTYhIQewFDpdUCwdilnqyvlTd12odb6p0VNi6/MYfe2uGsbBOY+6KCDgNDTZdtttwVC7xf30TrbjKWxGmJdIs+uXreuKcejDmkvXLus6zJYbLHFsnRCj7fXXnsBgX09pvrP9ddfDwRJxO53eRSRUFAku0Jwj2h3cIxKFp0oZLb11lsD4R5WSsKol3HVUxctWpQ9s9of1lhjDQDOOussIDwPQknx+9//PgArrrgiENxCzSAxbEJCF6Gh4P9YZ+rv78/+189ql279oQY/aGlWD64U1G56nd3CzzzzTCCwtCuaPt7HHnsMKF21TR6X0Ryfq928efOa9sM69vXWWw8IrKcef/PNNwPBii0jDwwMlCU0fPGLXwTgv/7rv0rGKQMrsfzsZz+rd3gZhtMP24m+wMMR/D9q1KjsOXOOfvjDHwJBsrJPsHOst8COjuuss07d50s6bEJCD6ChZljx6rlw4cKMub7zne8AQeb/5Cc/CQS/mmwj06iHvvrqq3zgAx8AYP/99wdg8803HxzcG3qP++ib1BLtipdPL4ubMblvK20VPL4rq/qZlnGjmHbeeeeSa87fL8f6gx/8AIAPfehDJeeQedTbjz76aCAw7FBNm4Yb1ZjV644baHULfJ7Gjh1bFlmnd8P5NgbBTo7nnnsuAF//+teBUgnV+IBGfeeJYRMSuggNBf/H8Zj9/f3Zd9/+9rcBOPjgg4GwythT9O677wbg17/+NRB8lKuuumqmi2pFU/81Jve5554DYIMNNgCC5bQZa1u9+k8+hlSGff/73w/ADTfcAJTH+7qCxs2Spk+fniU7aEX3b25cQLCmH3DAAUCwhDd7jZ3SYTuhu+bO1bQOW6+V2OdTi38laC/ZeOONAfjxj39c8tc0O5+P888/HwjS5xDjTDpsQkK3o7BWHdOmTQOCX1EZXb1U1pQljTWeOXNmFilido5tDE8//XQgMK+RREKWquSjlPnjRPpWWhVWiyyqtlq/7W1vAwajvtTxjd7S3+u+WhTVWXfddVcg+GcrXaOI9duRxLDtiDCqxrD1NJUeKmvH359//nkg+Nij85dsqzRmrPDqq68OhLgCJS0/14PEsAkJPYC6rMRxUnV+lTJX9r777ivZx/jaT3/600BgAWNMtagODAxk/lVZRD3O3FkjmVwVjcmMC3WPGjWqzA87VLnVRlCNRbwHxpIa8aI/+eyzz8729VrN2jGqSz/1pz71KSBYyOvR04fbclzpvpxwwgkdH0ctZhUxs8ZS0z333AOEZ8/n6amnnspsJ7Fe6zF32GEHIMyl3o5bb70VqE8CGAo1ReLRo0cPQHjZ4rSi1157LbtgH1orS7iNL2EsEq600koAPPTQQ1x55ZVACG7wAZw4cWLJ9xdeeCEQxEVT6Tz3888/n92USi8ztKd798orrwwEd1N8Tyv1FjUp32RnDVp77rknEJzwzaTMdUokXmuttYDBOYzRztrA7Qyc8H7vvvvuQEg4qYVLL70UgJ122qnk+7j6iZ/rCc9MInFCQg+gKaNTvoqi7Pa3v/0NCGFZrrD2JlE8dN/PfOYzAFxwwQWZWdzQvqWXXhoILO33Bpab2P7zn/+85JgrrrhiGcsVUSKmXlFG1SFOmqgEV10lEiURwy69j83Udmo3wxr0YfmfPDpRdb+ZObQ4gmGujrOVGlrVqojGARZxgjsEI201t1Fi2ISEHkBDoYlx7eFFixZlzOqqkk9MhxAoYVihq5JGlcUWWywLnBAmuXtsg+l145hooG5raOOTTz5Z5hgvIgWtXiNBI6u0IZNKDSb6y+ambKnTjiRcfvnlwz2EhiGzrrnmmhV/r6f2dgxDU4X7ajQ1saMSagVk1EJi2ISELkJDDCtWW201YDC9LdcVDggm7QMPPBAg6x9rEIE624knnggMWoINPTSdzkJdWnrtZmdpGJnVoAID5j0OlPebaWfidCPmeoNE1MsNmNAFMG/ePCDo+EWh3jKftWCYqRXuuwmyn8+FNa21CvusVZPM8pZ+n2WTQGKYuKFEuM8++1QdV/ycDoXEsAkJXYSmrMSVLGvqpP61rIspRloULS6mHnT77beXrf5xIS+txoZ26e+65JJLgKDr5q9FtovH2g4fXqVO8PF4tHgrHThmw9b0MRvKWc16WanbX4x2WYlrFQfvZE/WRudw7NixmV0kLickC/rcVmO6MWPGZM+p5X2+973vVdzW+AElw0pW4qGQrMQJCT2AmjpsHOZXrfcrBJ+jK5UFsA1NVLd99NFHgZAcAIGJ9JWp16mbqvcaUWOCgaU3jJp64IEHsjIzlqCMk9zbgQq+3pLPY8eOzazk999/PxAY1DIjn/vc54DQc8gSMkOdq5MYznO3gldffTWztpuEIuLUyJtuugkILVUsVfqud70rKyxYDZaAsYRQNWYdN25cWSRevUgMm5DQRaipw/b39w9AsPhqxc1HM8Xpa7KyjaFMXNfPqC4hs9jHtRKMgDKo3phdi5TLVgaajxkzJlspqwXEV9N/TO1zRRVTpkzJdJHYChzrO7EeZ5rg7Nmzs4QF9R/jT003tNh0Ea0qitZhjQM3UUFoY1Av7xSqzaESWt5bAINzaBJFLAlWw1CSDsCXv/xlIDR2M+m9GUv8ULYWkRg2IaGLUFeJmLilZD6B1xXBFUILr8yrJU0GMxnd38eOHZtFoVgA+qKLLgKClU0d1SZZlk0xRe3qq68GBlPRqllqh8rWqRUZFVuxzbSJpQMT7k2Rc5U+5phjMjZWWlCHN1FaC3gjSc7VUDTDek+22GILIMSHd9IyHI2nYUt/q/r3/PnzM9tKO336pmbOnj07MWxCQrejrkinWHfLW4tj35w6mDmrcWdrGcX9Vlhhhcz6a9Kw7BOzjcXPZGBLcuj3qqRTx2OuhlorsOVcZFSjZD772c8CQbe2laaZSUoGM2bMyPzP6uXmWeqv1i87khDfE5k1/n24mLYZ6EVQehOWH9KarxS3ySabACEzrN3w/aiGxLAJCV2Emjrs+PHjByDorjFr5X2b6mgynKykJdFjaCW22VS+rIsxubY+MGbT1VBWUkeM/cKLFi0q0zfdJrdtCR3EpVxjTJ8+PdPLba6khdGYUS3g+vD22GMPIMSpTpo0KRuj5V/1w+lrLjLLqF06rOVtrJJRK9+3naimw1aLDqun3aRSms/ecKOalbil3jqVoIHKF8a/PuTua3mR6dOnZ+lIisYzZ84EBpPb8/v48guNAIb3TZo0KQuyqDF5LYcmmlblsS3nouPcB1uX1mqrrZaJ0b7k1q5ttPNerX61otUXVjdNK90S2olW5rDItMt2Irl1EhJ6AA0xbKUUrbifaoVjVPycF2eruY2qfV8NfX19GbMqrhVRImYoxIn9GjY0YFx44YVlNWz967UV6SoYzu51ncBwdK+rhEZT4xpBYtiEhB5AUzpsPhlYJlOvqifVrBp0GlczbWuE0r1SqaObhqtqRczauTrH0kRej/b649TBdiAxbPcjMWxCQg+gqf6w/n399dfLdK+hUs0qMbDf5V09lfaNWUlmzTOZjBozayf6k8bjreT2sKyNYZYJCY0gMWxCQhehpg6bkJAwspAYNiGhi5Be2ISELkJ6YRMSugjphU1I6CKkFzYhoYuQXtiEhC7C/weWs7o52TS4LwAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3820, D: 0.2275, G:0.1658\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3840, D: 0.2353, G:0.1524\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3860, D: 0.2922, G:0.325\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3880, D: 0.2309, G:0.1536\n" + ] + }, + { + "data": { + "image/png": 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XBvZLVTE6dRPQ+1RJX6l0/vPStDmKGU91dXXoa6X9qE/qo48+CtTNLF566SUA+vbtC8Dll18OwGOPPZZ0/HI2xdk/NhfWWWcdAL744gvALJHdcccdQFLKojd9f+qppwDYf//9AbNkp84Q6qOkXr+ZkKLff//9oY7XKazDUUaUfFmnpqbGUxHZd1IOlUrdZZddAGMHqdyk+rb66wNrpNLIZRPnkkCfPn0A0+M1DHJYyIHTr18/AG677TbAzCJUdnPttdf2+tfIvrKJe1ln4MCBAFx66aUAvP/++56yqgSQ7Ft1AlBvXbt7+eqrrw4YWzcMud7DJk2apCT7C9mlmr2NHTsWMDZ2jx49gLqaxJolDB06FDBKOmnSJKAuuAKMXa5ysPqc1DsMzoZ1OCqAoius1FNhXk2aNGGzzTYDYM6cOYCpNH/++edn3JbsIXn4amtrvZFUP+2O4LmOzj/99FOgksnOfOihhwDjGVXJmkyoCLlGct/xJR2/FFbXa9iwYVk71cetsLqHWpJIJBIcf/zxALz99tsAvPHGG4C5/rJZNVOQ+uRDrvfwkksu4cILL0x6TXbnJ598AqSGdWbyD8hjrxmh3nPrrbcCxlusmWD79u0DtxWEU1iHowKITWGnTp0KwB577AGkek6ljmpz4Uc9TF555RUguK2FOuDJNmjWrFnGYmoQrQ0rr7Cuob8frJ8lS5Z49rdQQIHW+zTLkE2lc5aaSiEWLVoUqPgiLoXVMcnOUyuSfv36eQHx2TzOK620EmDuT9heTH5yvYcdOnTw1ofVM0czFymrfAmarf3www/2PkMf41FHHQXAuHHjAGOn5xIk4RTW4agAYrdhZcM899xzAGy99dYp79Ex6L1aA7v22msB0y3MDs/T6Oi3U7NF0IQdnXfddVfArCOmQ2oiu8xWUT9SlnfeeQcwfWFl98p+l5IqWkbd67ROW1VVlXWEj0thDzzwQMB4RRW2N2DAgKT1ykzYs6V0ipwtXDHsPdQa68KFC73ZkGxXzeLuueeejMervrxS5kzo+ZRa6zzs+AE/ioDTMyScwjocFUDkChs0IkkN7RE2kUh4dqw9x5d3zfbkyWaVaqsTeJh42yht2GxqIsV95plnUkb0f/zjH0nvlT2obcrWv++++/I5rlgUtn///oDpj6uZ0CabbJI1tUwdzW+++WbA3EvdQ63jhqG+pdcJ+VRkn8seVhSV1unlz8mEU1iHowKIPL1OdoMUdsKECUCqsmo+v95663kjkt5z3HHHATB69Oikz9iRIooKKhWyi4K8w9OnTwfq7CWtzeocZHe/+eabgIk7DVJt2Y/5KC4YxVY2UC5cc801ALz77ruA8RJfcMEFQJ19LnWx0bWRx1SzKNn0uShrNjIlkisqTmvn8nXccsstBe9Xz61mVLYv49BDDwUyK2vYJHinsA5HGRGbl1g5k7JPZYcKRfL88ssv3lw/6FikZPIw263r41zDy4RyQBVb+9FHHwEmikfe7Ztuuok777wTMNk38hJrPVDnJM+ovIfKtcw3SiYK+04KotFfkWjKyPGveete7LbbbgBMmTIFSJ0dTZ48GcDL8rnyyitDH08+91C+Fc0EdD1z8QIHIdWWraptazaj66NY41w9/X4K+sKGSc2S40gPpL7Amir5P6//28s3UaZkxeGwsCsiKrE5XcC5HvqgaXQQ+Q5KUZyfQvA02MybNw+A7t27p7xX91sJ+fpS6/jlbNIgrMFaA1MY8rmHdmVJOzUzF+xlJ/ue6Fw0Nda551JVwzmdHI4KIKPTKVsi7ooVK7zRQwv+F198cdJ7VDUvKCDeX9M2qG5R7969Mx1myZGyKhlegf2aGktxwTgX5Iw77LDDQu0j3yR1BTco2ToXpJJSQYX3aervR+elkje2ospBdfXVVwPw5JNPAuaayaGVDzqejh07Br5HoYZS/BNOOAEwgSlyCCnNUdvUde/Vqxc9e/YEYMyYMRmPZ+edd07aRpcuXYD81NzGKazDUUYULb1u2223BeD555+39+EZ/JrzK61O1emU/BwFudo/q666as6V7W644QYAL+Wsuro6xa6RLT9x4kTAJEnIgVFIdfl0Nmw+5WRmzJgBwF133QXAqFGjko5NiQpt27b1yqPIVtSsSfbv3LlzAaPaSsK3/RaFOA7DLH3ZyQb2fVHoqIJwVD3xrbfe4uuvvwZg3XXXzXh8UlJtM596xc6GdTgqgIICJxo3bpySiBxU6tNWVpFIJLyRSJ9RwL3sjVKybNkyz64JE1IGxi6VXX/ZZZd5Sz/HHHMMYIK99R6N/HYxs0z9cnIhSLnWWGMNL4hF3k/ZdfL46p7qWBSwomT1Hj16eAEz6gCg9+qzsuOkuPL4a3YlGzhdidiwBCnrEUcc4YVRar9KgdPv48ePB4xKa0Ygdbzrrru8cjd6PnW9bN+LyhTZ9y6KJSSnsA5HGVGyImwanZYvX84+++wDGPvmkEMOAeKpBl/IOmyQnaUgB5X9vPfeewFje7/66qteoMBFF10EmGJksnd1PfwpYfmS7zqsfX5SGSmtzlM2mv9Y7cAIqYtC/3Te+qxUJh/vdz79kTSzUVG10047Lek4FeQiW1xBOgrpfPDBB72CCvL6K/jFnk3IB6OQznRk6zjobFiHowKITWE1oo0YMQKALbbYAjDqI5o1a+at0WYLUcyFoGDqKCKd5C2Up1cjrjyLUkf9XlVV5ZV1UfnPbGGYsq3yWbsrNNJJivr6668DJsJJP21WrFiRktyha/D4448DJr1O518Iud7D6urqlPYim266KWDCS9dee23AFNS7/vrrATPL+O677zwb9u9//ztg1rZVDmmvvfYCTAqh4hcUDqmEhzBRXU5hHY4KoOg2rJKf/YXE9t57b8AEisdJIQqrCBaNyrbNZKfb9erVCzDF5PzvtZPx/cXQCyWqWGJF9KgT3ZAhQwBSSob6sZMXFPgeZSO1KGZJ22yzDWDS7qSK9vEtWLAAqFsfVxMsBfUfeeSRgIlaU8E8pVXOnDkTyM8X4xTW4agAIldYKYYd8RLkDdtuu+0C12jjIIrRWeuPaiGiliIq8/LII48AZhSfP3++N+qq+ZX6qKpAl2Jd5ZUspJt3VAorW1ZKIdtLa6lSp2XLlnHEEUcA5hroPEUUXcp9KwuRZVy1bdsWMH4J+zlV46/333+fs88+GzDx0GmOCzBtR+Sb0epH0PpsOpzCOhwVQGw2rGwWu/SjIqPU1uDEE0/MdxceWg8Lk/GRq8I2atQoMPrm1VdfBYwHXCgSRqVS/UWobTQ62xkxhRB3IXFRqq7pUcyS8un8ns0OlwJrLVeF9k455RTArLmH2adTWIejAohcYTWKyLOo2NLtttsOMCqomE0VISsWUYzOilKSfSPPt912xM4MSYdyZ1VQPArVirsZVqkJuofZ2rRERTalzUe9bWIpEZMOTX3/97//AWa5Qss4Wnzed999dWC57qIgovjC6sH44IMPAJP0rLpVRx99NGAcGhMmTGD33XcHzHTZrgMUJQ31C1sIhXzJ9EwrSEid+wp5tt2U2OGoAGJzOtnbVdCBSoPETdC0JY7R2d5XutE6immS9pFpiv3Xe2JRWIVgFtLbNQryuYfqIGd3pSsmhSTpC6ewDkcZEXtoYrY0omJTX/uyRElDtWGjDH8sNU5hHY4KIPYv7PLlyyNX1+rq6qRF/HHjxnm9WxwNl0QiEZu6qlien2bNmnlJDpmwn9dCcArrcJQRGW1Yh8NRv3AK63CUERnLnDYkD6Pvd73Pe03lWlTqQ/aIPTuRR1zlLLXmV1VVlZJuKNtH5U5zIV0Xe+tng/QS2/jvpZIrlPqWjaByvfZ2/RRyT+1tOi+xw1EBlKzMaanINjpLvRTVs3Tp0pQopVwjfvwNv/RZ/W6rY6Zt+M4BMPHZUvKGrrBKvlAyhu/vKS0f80kUyDemIN3zki3yzSmsw1EBNHiF7dixYwJMm0QhRVu+fLk3OtsjrEZHu/yJ/XujRo28Al3nnnsuYApaz5kzJ+PxphvVbXtH+9Oo/fvvvzdIhc0U6aQSoypvExa/Etr3Io7Yat9MwCmsw1HuNHiF1Tn62wqCKWWZ6frYDZzjRCNvdXW1N8LbiqKmYosXL26QCivysU/tUj1qECY/wdVXX+3NZFTmVeVN+/fvD5iCBl27dgVMM23NxPz3LY1XOOM5escZ+owcDkfJKajdZCWg0fipp54CMpdzsZGy2uuiWsPzt9mwR/0gL6G9/qem0DfddBOQ2UMZVbxqWOprdky69iZ2UUD7uksVVTBNZVqlsN9//73XbkTrrWpHo4bXUl79fs899wCmZYeamo8ZM8a7ZrZdnO0eNvgp8XfffZcAU1Mp06J30JdZzgfVq9pyyy0BGDZsGFA3VVL5GHVNU80rdbVTDxf7Czl8+HAAzjjjDO81LV8E9elpaMs6M2fOTAD06dMn6X3qp/Prr796DkANnLbzScExck7pmmpq/PDDD3vLZ/qSnXnmmYAZuIN6D6XDLes4HA2ABq+wtbW1CTAjXbrrETT1k7JecMEFAHTv3h0wDiyNuLW1tZ7rXyO9psbqGiBn18iRIwHo3LkzAJ9++ikAgwYNAupG/NGjRwOm9+qxxx4beI5x3sN0S0651IjOl6B7GGQu1NTUePdQ01n1p9U91DRa90wzH22zf//+KVUwFy1aBBhH1cEHHwyYKXEhOIV1OCqAkivsHXfc4Y1M6vKmnjRpjifpdwXjy/7IVKVf2CNXdXV14q/XAaMa/oJqsjfU21Z2pXrrqKubyl2qzGkmNHKrO9pZZ50FwIcffgjAv//9b8Ao7qxZs4A6Z5R6maofjG13R62wCirZc889ATMb0LWuqakJdJboOu6www4APPvss4UeTs5lfvzPjd2jVcXZ5A+Q009/93ff02dVwlfn1rt377zPxT5GF/zvcFQQJVNYKUyjRo1S7Ed17ZaC2Sicr2fPnoHbD6q6n01h7ZHu999/p0mTJnovYGyWyy67DDA9dKT0spM0WicSCf773/8Cpmu3FOfQQw8FzCK7eg0pgF2zDx1Dy5Yts5bqzEVhZ82a5fUGuu666wC8boI65pNOOgkwIXhjx45N2sbuu+/Oa6+9BhjVD0Jd6T/77DPAdD7Ppfxrtnto07JlS09BZbPqp2Yn++23H2AKvasc7+abbw7UzSJ0jOr8oC7uds9fm3yWv5zCOhwVQNEUVmFdsns04rVs2dJTpCVLlgBGTbIFVecS5CDySWAXWjhXwTfZm6NGjQLMWqkS2DXijh49muOOOw4wXsh1110XMD1DFRjx4osvAsZOlOf5nXfeAerWYLOF3OVqw3711VeAaR8i5NGWXR8Fdpf6OO6h73W9P91MK+l3PYNTp04FTC8o/3qsZgfy0p9//vlJ79F1She4YaOADH0PbJzCOhwVQNFCE9WVXGiEuffee71OdrIRjzrqKMCMXIpK0QgnlYojNE7bOueccwC44oorvNmBVOHkk08GzExg0qRJgPFaqy+ojnOVVVbxop+kMLJDZctqnVU24HnnnZd0PDr3448/3rMhdVwa2XNNHRPrr78+YDymsu80Q8imsIlEwvuMZheyUW2bUdcwDFHe36BtKBpKz6N6u9ozgZqaGh588EHA3BvNSL788sucj9OeJYVNjncK63CUEbF3YJe3UyNZ0Pv8XHHFFQAMHjw47WeCiqCl227YZlj2+/3Hpdc02sq2U29beRLlXf3Pf/4D1KVkQd36pWxYeUUVDaWRVsHmsllff/11oG6dGoxqz5kzh9mzZ4c+x3zuobaraCDZd3rdn5gflqB7FYUNa3uJ82k8ptnSwoULATMj0IpFly5dPNtUjd3kc9F+tXZrpz8GJRz4/2Y/dy6B3eGoACK3Ye0RI4yyyjOnpsdBKPpHI1Wm0TlXu6d169aAyc7wf1776dixI2A8e7vtthtgMn2kntdeey0Aa665JlBn88ru7devH2BU6qOPPgLM+qtsfSXQy1OuJsHz5s1LsqvAXI9MXvVu3bp5n8+GbG+tK9vXJgplFfKwywOfjkwlRyG1VIv9fCQSiazbsBtr6/1qSN68eXMv0kkKrgwsxXK/8MILQKqXWD4CfxkiKbqOJ2wxPqewDkcZUYyK03MAABH0SURBVLSGzpm4+eabAWPH2Wj0a9euXb6H4z+unNtNyoPYtm1bAD744APAKI+KU19++eWAKR2ivNVWrVp52TdSViVKK1ZY0TPPPPNM0vukFrKHhw4dyrfffpt0fHaRuGXLlkUaS2zHLueC/Rwos+n0008HzDUsJApI52h7Wv2F9IK2L6XTT81sZMueffbZQN1112qGfBR6HnX9tV/ZuPJpaAagexlmnTZoHTa2L6ymYErMtlEoW69evbxgfz2sNppq9O3bN9/D8bAvRKNGjZJuti6m/yLry2OjqZGCIOTe13RKD8FFF13EfffdB8CMGTMAMz3Sl1yhcPoy2gnXWnJRkEM6fN0FUr6wdiXHXFBw+8svv5zzZ+3na4011gDMFyIfwn5hc0FBMLZJ99JLLwF1QRIaqG2T4O677wZMeOO0adOA1Km5ps4//vhj4LHqmVq+fLlzOjkc5U7kCmsvul955ZWASdSWssolPnToUNZZZx3ABEykOY5cDyMQe3Ru2rRpAlKnKf59ymmgYActd4igUEpdi88//9yraCjHhRKk5cAaP348YEITpYj6nMIex48f7ym5HBYalfWZXOsSK4hdy1Q2uQQwKIFdoZRB2yokKMK+h23atEmASVUU2RxNYO6Rjmf77bcHzHOqgJaBAwd6QSE2WvZS4IrqQyl4RN8F3Us57/y49DqHowIpWvC/bbv492sHmbdv3x4wI1SUBJUXkQNBJVm0pNGqVSvvtU6dOgHG3rHRCCy1lg3zzjvveLMIzSzWWmstwIzwUgF1CNDPnXbaCYCnn34aqFv+URqcja/cScmKsGmWdOuttya9rgAazRQUEBKFwuocg8rTVFVVBe5HKiy7U9dbKLCladOm3gxK11n+Bu1X/ob3338fMM+D7rkSCmbPnp2SDJPtHIVTWIejjCh5iRiINmwtxL5yWta5/PLLvTQq9cUZOnRo0nsUQPHEE08AZulCtuGkSZM82/W2225Lux8FVEycOBGA+fPnA6Yu8QknnADUBY9I/aX0dh3kqEIT81S/tK9r2UpqVAi5ptc1adIkMCVRyfnytcgfodUNLTv98ssv7LPPPoBJfdQ9VaEF+QD0WSmslsP0ur8wQ5Cd7RTW4agASq6wiUTCs+/k5VPgweGHHx7H/kKNzv5u2vIcyjurkDSNilOmTAHMiKoeK/4kfXmatV3ZdFIBjdYKVVQQiWwqlTR97733UrrnZfIwFuMeLl682LsWChYR+SQKZCOfBHZhr0crVVHFwFX2R8hLfNVVV3kqrfVY3SOpsHrsKNFDXmHNiPzeYfs4/L2T/nrdKazDUe7ErrAK55M62UyaNIkBAwbY+y10t4HYo3Pnzp0TYOxBjXz6uXTpUk/9evXqBZi0P9ksm222GWDsTYXdHXbYYUBdlJKioRSp1KFDB8AkCChaRvuSasvDqPDMa665xvNo2wHrGskXLVoUicIqcUMFs4OoqanJGkEV51q6XUg8l7S2jTbaCDDpjEHtNvx9gqWCuv4PPfQQYOxhbUttWRTdpu9abW1t1uvlbFiHowIomQ2rZF8pyl/7i2t3HkGxxLYt4U8o1rqbXlPyuTy3hxxyCICXWC4bRsW6ttxyS2/7ignWmu5zzz0HmMiamTNnAqR0StO+//jjj6yJ2cWyYcN4keuDpz8TsjM1kwmTupnhuAAzO1MpGdnF6YrCBZXjdQnsDkcFUPT+sFrnUgHnCRMmBKbVFYMgtfIXDdNsQLaLMm1UdE3tJ5SGpjaT6tA+cuRILypG3tJHH30UMJ5mFf/q0aMHYHqJ2lFTkOzB9lOMGYofRa/99NNPKeuIUhn793yyhbJRyLqxPL9Ko5MdrJ96XtMhG1ZZOHYEnNZ2RSYb1pdplfF4ncI6HGVEyWxYxcZ27drVUy55TOMkbKsO8fzzz3t5uFpn1fEqsknqaHspleN67LHHeh5XOz9Y71Gkk8rkqCO8UBbPKaec4nkhhT1LKJYNq9mCP0tJ/9dsQ+vNURLWhs2nGJu8xPKtaAXgxBNPZN999wXM2riat0kVg+KCM80AgvJinZfY4agAiq6wdkPd6urqotpeQV5ijXDprodtf2nEVuSK7CCttSpqRhEwzZs39+xgFQTXfrQ+rTXdgw46KGnfdilNMKOyXRrGV4SsIIXdY489AFMcT+jYFcGleOcOHTp410ClbBR/HdSG0sYupJaJsKVqw+TDZjse3fN1113Xu692m5GgYw5SVn9jraDvX9FLxASh3pqqcdS6deu8SnrkS9ibbb0n6XeVSlGnbQX521UTFeh+4IEHel96TavVa1apWKoTZO9TxyPnx59//hlYVsT3EMQ6JZazSfeyffv2XkdBdTjINAAWSq7d68AElchRmCtt27YNDP4JS6ZnLNM9THpfQUfgcDiKSmwKayuKyqvYDqZ8KvEVQpDCZpqSSd20xKJp/VZbbQWYcij2ufgdCqq4p2JsQdijsK6XXvf3cw2aVkWlsEr1U2CIjVRryZIlKcegqaS9vBMF+Tid7MqPcfRlCiLMvuwZgFNYh6MCKHl6XbHJNjprNPQrbrZR2A4Kt23Mqqoqz2ETFOyQLYlff6+pqclq8xdrWWfIkCEATJ8+3XOaSVntWUmU5BOaKEeR3Xk9F8IuEwXdU78jK2gb6UrVJh1DDsfrcDhKTINX2JqamgQEJ4M3btzYUw2/yoFRk2wjb1VVVUrnO73X/qxt86Xzttp2rW3LFjuB3c+sWbMAE64n73GURBn8H5ZMhdzy3R6kdmJ0XmKHo4LIqLAOh6N+4RTW4SgjMi6SNQQbttLPMY7zU6F3FVgvNg3tHvpxCutwlBGhvMRKqo7D41dsGtroXOnnB5Vxjq4ZlsNRgTT4ddhKP8dKPz+Ar7/+OgF4BekrAaewDkcFUC8UNs38HTBZMWpfoTzMbbbZBjB5qbngFDZ/9t9/fwAeeOCBqDaZF3HeQ8V8q+BAqXAK63BUAEUvc5qOXXfdFahrOASm2bFyA+0yI7ayqmSo2j46okWZN2rSrN9vvvlmANZff32vUXe5E6WyBpWjLYR6MSUWOsGzzjoLqOuFCibYXoHR6pCminxBlefSUd+mxLr+Rx55JGA6kxe4Te8chw8fngA488wzA99vXz+7JI4672kAVcDEmDFjgLoBtWfPnoDpihAn9eUeqtfv448/DpgKmJpWq8eSeuzkgpsSOxwVQL1QWDtNTMqpolfdunUDTF+aQmrdlmp0DnKsqTPc5ptvDpju3kFd1MKQr9PJTvlS3xkVilNt5WnTpgF4HckXLFhA165dkz578cUXA3V1lKOm1Aqrzg8bbLBBTp+T0o4YMcKrsBlU2M0prMNRAdQLhQ06Btt2jWhfJRmdX3rpJQCvgJpdWziu/qlhzk9V7mWrqtq9uq6p055sWB27AhWWLl3q1SqWGsuW/f777ws6l3SU4h5WV1en9L3J1oEuiNmzZ3slYk888cS073EK63BUACVT2IcffhiAvfbay3vtvffeA0wBbrv6ehRKW2r7x0ZlVbVUEkVBgUwKa1fD9/cq1fXXsej6jx07FjB2qd7fsmVLoM6nINWVZ1/3Vd7jKAslxHkPVZZXnSl03DfccAOnnnoqYJ5DrWZcc801ALzxxhuAKSyfSXGzqbFTWIejAii6wgaVAoW6UQzi8SyKUits0PW2i7QVuI/QNmxtba3XM2bkyJGAUdTffvsNgN69ewPwwgsvAObe+QvI6bX7778fgB122AEwnmatTUZBHPcwbH9WP5qlyKPfrl07AD777DMdZ9rPtWjRwuuZFIRTWIejAii6wqbrKarROYq2Dhrl1FnNplQKK0+sHfomz6tspyjI1UsshVA7FSlrq1at0r5/v/32A6BNmzZAXfdydZuX6kixtK684YYbAsY+DtNZTtvUDCDd+UHwOWqf8lxnIozC6rqod+/gwYMBePvtt9O+32/jQ7LfQP6BIFvWKazDUQEUPfhfAeN+ZLtGQZCylpqgAPAolTVfNNtRJJl6vQbx/PPPA8YuHTFihKdMzzzzDGC6lGsGZzcZC9Oz1VbWXMmkrFK222+/HQjnxdYsyb+ykQmtTQvNKvz7zxWnsA5HGVE0hdWcXVkpfpTVIIrZCrBYFLPLfK5oXTGbsgp1npdqzp07l8ceewwwMdGie/fuAFx//fUA7LLLLoDJcAlD2GysXLq4q6CgiiGoOXcQXbp08TzeYTn++OOB1Fj5DTfc0PseSOHD4hTW4SgjiuYlPuqoowCTBC123nlnL4/QRnaQvHJRRwFBcbzEWa5xHPsrahE2f6MoNcOSKsqOk8dWXvxCotYKuYfy6CqaTtdfbTHtNpny9K5YsYItt9wSMOocNjE93f3PN9Ip9i/sRhttBMC7776b9LrqMy1cuDAlAF5LHUpoVwB569atgcK+uKVa1rGP2e5aFvG+Ur6wxTIz7E7nupeaTkaR0JHPPZSDTNN+hRF26dIFgHPOOQeAK6+8EjBLMUqh03nkgi1SmtKvueaa3hJaEG5Zx+GoADI6nRTIUEi4nALKNaJ+8MEHgHFc9OzZ05sS2wv1c+fOTft6uZFO1YrtUCvW/rRco2dHsySpi56DMIETUfLJJ58AdeoGqSlxkyZNAmD8+PFAapGE3XbbzasdFhbNJoRmVQsWLOCLL74AoGPHjjlt0ymsw1FGxGbDauQaNWoUACeccEK+m4o0dLGYNmy6a3vjjTcCcPLJJ8e127Q2rBb9FV4XF1JOBYTIeSO1mTJlCgAXXXQRYAItROPGjZNC+NKRzz20l1aUfL9gwYKkv+tZs9UxEzqnY445BsCrIOnrpp70e5htOxvW4agAYlNYeeX0UzarRhnN51dbbTVvVP72228BU+RL7+3fvz9gFFbpXlFW/lcxrLZt2+a8zQz7SnmtGAEUUS/r2B5m27fhX9axgxxUY1rLOSqN8uCDDwImuCCXusb5KGzQc67lHflackHXZc899wRg8uTJgLHLFcjRt29fwKQnpsOfqvjX8TqFdTjKndhCE7WobBdR1kgir1xtba1nV2l0UfiaRnIFgWvtSkHqURKFsmqtMd2aXX0OTcyGrbD2qoFfvaSsUhfNmqSguv+2ovgpZj/ifJRVz6c+q3JHtrJ+8803QLiElLDr0k5hHY4yIjYb9tJLLwXg/PPPz3wAPvtHP6WgUiyt3crTWQhxeImzrXEuW7bMG3WLQdw2bBgU0qfQREVAafahNh+HH344YJIDTjnllKwd5HK9hzU1NVljCWbMmAHAtttuG/ge+VJGjBgBmAR+PacqsaPIpjfffDNwW9mS650N63BUALEprN1Ww56jq9DXgAEDAm1SRZZoZIuCOBTWXuOzGThwIOPGjSt0N6GJSmFziXTbbrvtALMaIOVo0aIFYMqGHnDAAQA8/fTTgLHzciGfe6hEkieeeCLt3/v16wfAc889l/S67OjNNtvMi0NWSR2VcNVn+vTpA8C8efNCnEVmnMI6HBVA7Nk69vbTtTWQjdq5c2fARONEWRrTt//YIp1kr6lplG8fUe0iFHGl19lrrLI/77nnHu89EyZMAExElx3JJE+qlNeOBgpDPvdQ3eNVhrUQFNuu+62UvCiiyHyzNaewDke5E3uJGI0YinixbZbp06d761jyHJYbAwcOBFKVtdyRcsiGVYF3eT+ltD/99JMXrSa7V3aecplvueUWwDSstlcG4kZZOGFR0+r27dsDdcepfGw7lzWssupcMxUSz3Y9nMI6HGVE0YqwBXkDo/QAlwqpiZQoiqyiYhK0zqpMG6EYbxX4lkd45ZVX9vwPslknTpwIwKBBgwDTblNrlldddRVgsmXixp796Fy1Pi4PsK6FWmy++OKLQN36bJjibplI58vIFPGVdhv1oT9sMSl1b51iELXTSUEQ6havZ+bCCy8EYPjw4d6DZ4dl6r0KSFBN40LI9R5WV1enfCFUqfO6664Ltc8ZM2Z4QfzFwC3rOBwVgFPYCj/HUp2fpn+ZuhXmS0O7h36cwjocZURk3pFcjWdH5eC3Ee1wxiiV1eEU1uEoKyJTWKesDRf/coWUNVuKXByo8JsSDcJQzJlhFGWDncI6HGVERi+xw+GoXziFdTjKCPeFdTjKCPeFdTjKCPeFdTjKCPeFdTjKCPeFdTjKiP8HMw2CFhDbeIMAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3900, D: 0.2397, G:0.1561\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3920, D: 0.2279, G:0.1664\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3940, D: 0.2408, G:0.1666\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3960, D: 0.2266, G:0.1439\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 3980, D: 0.2525, G:0.1469\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4000, D: 0.2124, G:0.1748\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4020, D: 0.2604, G:0.1735\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4040, D: 0.252, G:0.1622\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4060, D: 0.2457, G:0.1416\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4080, D: 0.2648, G:0.1458\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4100, D: 0.2331, G:0.1613\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4120, D: 0.2227, G:0.1495\n" + ] + }, + { + "data": { + "image/png": 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8mylTpgBhyvnUqVMB2HjjjQH4y1/+AtR2rrWmdZxr6/Q9odXkfBqDTwpXALbddlsgzJBNS0P9Tppha0EjfFjllkorbXOq/z5//vzMY/a51Id1QuMuu+xS8/FEHzYiog1QrrwOyHfymD6sKZl11lknmXK91lprAYGF3H8jJ73lAaerC6PDP/nJTwCS2aSF+N3vfgcEFtY/8ruyma+b0WhAZp05cyYAY8aMAWDFFVcEgs9ayKzQ7Yc6o+bWW28FQmd9GVVL4bjjjgNCg24lie6j2dBXTUe5J06cCITMROF09XTWIs289957L9CY4oXIsBERLYSafNhamHerrbYCguih0C912ttjjz1WdLsmpvUV6okK5+H/uGJ6HdIrbC3+2UUXXQTAdtttB8Cqq64K1GZd1OrDDhs2DAgCFufMONlNaeiMGTPKbkt2SRcWeG+Nejsd7he/+EXF267nHuqb+gx5fN/+9reBIIOthBUdv3L//fcX/Xt6bk81iD5sREQboKI8bBEBfdU7MkfltlzBZsyYkazo1ZbXNbuB3GWXXQbkK3N0W5ttthkQ5pXefffdAMkApmbgmGOOAYJPLrNq4cj2xa7/3nvvDcDll18OwCmnnAKE6KuxC1n7zjvvBIJfLFvdfPPNuV7fNLKUWOZbq4FxCKF+4NprrwVCPl5/PQ9Eho2IaCE0PA+r7tTBUKpEXGGXXXZZtt56ayB7Urn5LVeqepqA1eL/6I8ddthhAJx55pk9/m4EtBYh+3rrrQcEP2innXYCQtRYpVOp40r7W9X6sOlnQFWSQv705PU01lprrcQntTWM3zUKrC5cdj7nnHOAcE1ffPFFoDsaq2KsxPH2i/I6LQ4jyOrFtUjqQfRhIyLaAA0vr3P1vu2224Dgs6mdXWmllXoxq6vw6aefDsAtt9wCwNlnnw2EFUxmduJ5o3xaGeyss84CejOsOeXNN98c6O2ffe1rX0s+a2XHiSee2GMbRmj1gx555BEgVASJwnPMK7+X9km9vpVGu3fbbbdefqff1RoSWksyq/vca6+9ALjwwgurPv5m45577gF65maBspZBHogMGxHRQqiIYWtpVap/ZdTQipujjz4agG9+85tAtx+Q9sVcuS6++GIgrGj6SVtssQUARx11FADHH398xcdVD9TDppGuaknXrsoeEDTDMotF+mpw3cYVV1wBhGixGuNGYLnllgNg5ZVXBoK6yvuhBZG2YDxW60IL4WdVOqkt1pLwfe+h5/vKK68k1+Rvf/tbXeeVJwqtDeMKG220EdCYJgmZx9GooNOsWbMA2HLLLYEgdp8wYQLQ8+E3jXDEEUcAIUXwjW98AwjJbOV6iu1HjBgBhMXhnXfeKRtCrzZgUdhVMOtaZXVVqAQGLhSSpJEuiq4E1QadNOFNLQlNPINQeSDdy8vXhQHEcqZ4XwSdllxyyeTZSgt3vDfeqzwQg04REW2AhgWd7MNkqNuO/CaulYi99957ScLeAnVFFjfddBMQpHIGnTbZZJMe29Jkq4aFKsX777/fS4Lo/1//+teBYLpXAoNJFjpkMatWg+kRRQx546233koE+/WkpyqFzGpx9x577NHrMxYX+Oz0B7z44ouZ6Zo8mbUcIsNGRLQQcvdhLQDWL3JFVXpmOH/SpEkATJs2LRFXKFtbdtllgVDu9ac//QkITr3F3QY7Zs+eDVSW1qnF/8kKumkBHHrooUB3kTOE0jLZ8+STT+b73/8+EPxwz8HyLq0Gz91roczt05/+NNDdiqdcG55ahRNaOueeey7Q2LLGrPYzWe8Vopk+rOWAW265ZdLe54UXXgDCs+79zhPRh42IaAPk7sPKIDYgM5zvKn7AAQcAIdJ24IEHJqvtqFGjgMBcpoJMAdg6RgbQr2uEYKKwvWiaWfW/d9xxxx7vywyFUWvoTl3YfsVifVNXtnb1HGzsZtG7rVdEOXatBemGaPq0HmueTGtaJ116ZgO+zTbbjE9+8pNA4/z2SuAxLL/88kC3SMfeyx6z1s+Xv/zlph1XZNiIiBZC7j6sq7Ws5P9GcGVJ21o+8cQTyd8UvisMl0nTHfaNIvq/DFDJFLYs/yfd+K0euA19nEKGSk/cU4poLjotdxNGIt9///2yApZ6Z+sceOCBQCgqz7MJ+P777w/AT3/6U6B3e53PfvazSaFIFhrpwzqhQQtR0c7YsWMT6aliFqPGRrPznEwffdiIiDZA7gyrb6Ki6YwzzgCCT6jCaddddwW6VylX8D/84Q9A8GGNnFpcbNsU26jUgkauztUU1vuZ1VZbDQhR4zz2UyvDGomW4dJWUrlm4YVI+6iykRFVG8/bIkbMnz8/8XOzmL2R91DrSFWTfv3GG2+cxCZ8hi3Snzx5cl67TxAZNiKiDVBXlLijoyNzWpysaJTYcjoHCvn+M888kyibVAEZLXaFHTduHBAidiussAJAWV+n2TCarfa2kAn1y1VzObpDJZMYPXo0EJrVaZkoxK+niXo5eE/cp21abVCWPoZC7XTaN9di0CpKs6UWmLClqg0O+gppvbrFKttuu20yRmWNNdYAQnGCSrtmTLKPDBsR0UJoWLWOK6r+iKuQjKKPs8wyyyT5PkcuOsbDiOnhhx8OhPK6LJa5+OKLkwqfLDRDJeO5Gm0dP358EnW0AkZ/SD/9hBNOAHrnXWtBvVFi/U3vg75tGs899xzQHdn1nmSVmOn3Oaolndutphi/kfcwraf29ZtvvpkMKPN/y+t8bvNsDhh92IiINkDDqnVcZdKFy443UDN72GGHJQ26LGa21ac5ukrbq5Zj10bD6iGbgqsxfv/995PicP1x/Vw1xq7WhUOgoblF3F5v/VALybUUzJ0K2RLKF297/4XRcdvneB2mT5/OzjvvXNPx1wOzFrLkQw891OPv99xzT6JwM3eezrf6fDZiRIeIDBsR0UJo2qiONApbZ7odt+tKZqQuTzTDh5VtXGEvv/zyhEGMbKtcks308RwGfOWVV9a8/1p92HRVkv62jKLqx84UhVHRdGvUgv33eK2FoRUlS+sPV4I876EZB60jz9VqMiuYttlmm2TIldfHyjN92jyR5cPWZBLn4VQXKzZPz1vtLzDUbyF9OZgOKdYHSFG5N92HPiuw00ykg3k+vCLdV6oWyaI/VFHND7URcAF19pPnZIrRvtGPP/54klZUvqibUy2KpUMrRTSJIyJaCV1dXZn/gK6++NfR0dH1oZmT+7++OMc999wz89wmTZrUNWnSpIadY1/dwzz+dXZ2dnV2dvaLe9jsf1m/yciwEREthLqFE7X0LO5LZAUsWu08SqGaoNOXvvSlXvNN87wWjZg02F9m6zQSUTgREdEGaPj0uv6Gj9rq3O7nBx+NcxSRYSMiWgglGTYiIqJ/ITJsREQLoaTS6aPgGzTiHPNs6FYLmuXDNrNwuxB53EOj15Zwpsv9lMcq8G+2JRp92IiINkCMEn94jo3IF9aCSo4jLcpPo1kM21e563IMm2d5W+G2mvmMRIaNiGgDtATDNrL1RrlzrKeyoq/QCnnYdMF7NSzdannYYgO/y/n/kWEjItoADWsRkyf6kuGK7TuL8X3f4nRX1IULFyaNqdNIN/tKF78X23fWfvO+To2MAvcXzXYz/NJCZhXlWupkITJsREQLoSUYtr/AVdERDT/4wQ+A3qu0+dc99tgDgF//+tfJNtKftdOBq7BjF21/6mBs0UxroxyzFhv+lM5v+jf9OIdzb7nllkBozeLw61pGW9qW1XY7opIodrnraUO4oUOHJmNn7LwxZcoUoLviCUK7mRdffLHstrMsrnSzujRaIuiUJ+qZwO7/6QfZh7ScOQvBzDz44IOBMCHOXkjrrLMOALfcckvZbXgc6UWgWUGnLbbYAoDZs2cn72U9T05zsM2K/ZDsw2wbnmuvvTaZFiDSP7yse+g0BVu4VIP0PlwE7GI5bdq05G8HHXQQENJq3/rWtwCSyQD+GOtJK8WgU0REGyAybJlzHDp0aDL/0wCRc1/nz59f0T4HDBjQ67uagk4zuOyyywA49thjgSCNKzaNPM2w6dfNTussWrSobEM2TV1N35dffhmA5ZZbDoCf/exnQGCrQlTKsJUgK8jk/VFWatdELYNRo0Yl3/Xeed017/2uc5GctSTTVuPORIaNiGgD9BnD2tZzrbXW4tZbbwV6BwecgOaMl9122w3omS7JQpYAvxGrc9a+/dySSy6Z9B12GwZhDFjIMH7H92tBKYbNI1XjMRamopyjesghhwBh1q/sIoMJU1/pdrcDBgyoasI85Puc6lMbQ/B+vfrqq8lM269+9atA8JVfeuklIFxT5ySdfvrpNR9HZNiIiDZAw9I6rqhpez89xbqzs5Phw4cDsGDBAqA3g6244ooAXHjhhUCI3J155plFPw/1l7YVSwm4n6x0gauxzO/M1EcffZR///d/B4IPq88qM22++eZAuAZpJnTuTbEm69Uk/91eMYF8ue2YxtJ3L0xBrLnmmkBgHxu7ORc4vW19WGcsrbvuukDw6ZuNVVZZBQizjJxj/POf/xzo9kenTp0KBPb1sz5rXlunIDibaO7cuUA+xQiRYSMiWgi5+bCuoOPHjwdg0003BeCFF14AgthAvP7660DPVVp/xhXL0RjmJoUrmVG4bbbZBujO4ZVDnv5PmgX1y3z97LPPAjBy5MiEWRz94IiIs846Cwh++dlnnw2EOTaiUFhQThDQ6CixTFEor3MCodFgc7TTp08HwrXR0jCH6fPh/FynG5ZCI3zYX/7yl0B3TAVg/fXXd19A97XWYhJOvNNXLZzmB8Fa8t5VYwlFHzYiog2QG8PKJhtssAEATz/9NBCiwTLq0UcfDZDIvIYOHZqofiZPngwE/+rhhx8Ggk+gnydkpc985jM9jqEUyq3OpVbBtBRP+Z0radoHN5c3efLkHtP6Cv8fM2YMEHy/sWPHAoG93ZZ52Tlz5iT+nvGBUueYJ8NmPSuDBg1KZshecMEFRT9TzipwTuzzzz/fS45Z5DiK3sN0zrrYPtP3Vz/TOcZpeaOW0KRJk/jd734HBP9beK9+9KMfAWHinfDeaXVUosSKDBsR0QbIfVSHzKr6wxzVrFmzANhpp52AwEYjRoxI8q1pXHrppQDsu+++Hg8QVkdZy/xY4bRyWdson6jF/1HppB+mLzN06FAgRG69FvosHt+mm27KqaeeCgQrYPfddweCz+f/bsN5sn7vP/7jP5JrIStnoVqG3XvvvQG44oorgHAvZQ5jC0ZHzRF7XRYsWJBEWcspnnwurrvuOiBEzUWj8rBZbWO0dNyn98xz1ooaOHBg8rcsy+bvf/87EFr4yNZamcZmog8bEfERQd0Mm2YZtzdz5kwgKF+MehoVrmSVSUcjVc/oCwhzefPmzSu7zXqqdYyE6lNrJfh3z1UG9vV7772X5PBOPvlkIEQhzznnnB6vzWe6Gl999dVAiEiOGzeO888/H8gefJ2XD6vO97HHHgN6T1cXs2bNSo7XihnZ+YknngBIKnD8nNaKOeu0z14KWfewluZr3juPw234TOnbVoI11lgDgCuvvBIIke/zzjsPCFZTFkMXIjJsREQbIPc8rNtzpUrrTivZxhFHHAGE/Jarr/nW++67D8hmmFKohWGNDsuo5hLTK7nn6nnIUHPmzEk+q2+kpfHGG28A4fqoAJNZjTx6DGeffXbCto1iWCP96TGU+nuew4knngh05zC1blStqVjy3lkHK6655hogFLSbp6+HYauBDG8k3/pcr7N/9/5UAq/PvffeCwQtQsFxZn63XEVSso28xf+1/FBF1rEo+p82bVrV2yyyj6pudmdnJxtttBHQHSCDkOx/7rnngBBIUs5mAMmOBMUCKVkBNM3p1VdfHYDRo0cDsN122wHdIpLVVlsNCOZmqXOs5h7+z//8DxBkgulj1SUwreG9XrhwYVLw4EN7xx13AL3dF+HnXXQ0lYcPH16260TWPayka0W62UC6CMIFp9LSyUK4f9N+HoeL0WmnnVbxtqJJHBHRBsiNYWvtAr/44otzySWXAEE04Mp91FFHAUGul0envWoZdtddd2XGjBkA3HDDDUBgfFuFKPhIm8SF3fKySgGzZrjIqM888wzQU3Du/ixLLHWO1SB1VFIAABQLSURBVNzDtDAkXShQLmWTcSwl36+le2Ce0sR0EYLmrH2bSiFtVaRTRFoPBmajNDEi4iOGPm8Rc9RRRyUBCMX+rkz6cwcccAAQBNr1oJbV2YCRwTBTNHbHc+WUiUoVh5fraSxk8auuugoIqYC5c+ey9tpr99hfkeBXVQz7wAMPACEQpiXxne98p9xXK4YBqh/+8IdF/+75Dxw4sGxxfT2pOdnPIhWvr+/rh5ZqjiC0oAysGWMx2OS20nGdyLARER8RNJ1hXW1c8e69996kaZUFArZLacR81XpW50MPPRQI0T4Zt5zgHcIKrtWg/6PMUPnlF7/4RSD4tl4D2U854Ifn0mN/stSiRYvqSut43EaqKyl5y4Llg4cffniPY/R/2d1UUjGUauP64d+rTj9aVG5JnNtWWmvb1WKxGVM+tkJVWqskUYsnXY5XTc/lyLAREW2Apnf+N4cp/vznPyclTOkCdmEJVKG4v9HQEpg5cyZbb701ELryK9Ez/6qFoE+rhaAPuHDhwmRlV3zhOSsQt0Tw8ccfB4JgXOGEfv2gQYMy/atapgJ0dXX16tbvuafFD7Xg61//OpAdYa4kGpvHtAPPycIFhRxuW/bzGbSgpFTJpmJ/2/3oeytscZsWxcvq6RK+ahAZNiKihdA0H1afbZdddgFCBG399ddPfMGs0RN5Isv/KbVPj9UWmJdffjnQs/QKggXg6i1LDh8+PPFvHGtRqBKCMJfF4mt9Jo/LRnSXXXZZUqKWJXKvJkq8++67c9FFFwFBvK6CSwmmyipL4ipB1r3TJ6zn3qbv4eDBg7ugd8vUUsUAlhQed9xxQChOUKGlwsxI8LvvvptYR+lWte5HK8jnWEusILZQ8zmKyLARES2EpkeJXW1koXnz5vXKu6kMqUZ4XSmqjTCusMIKiRrGqKm+i+xoQzHhSmu+9PXXX09893RRuNfjt7/9LUDSaiXtp8rmH3zwQS8m0WeSAath2GL3P61CymMKXMHxVPS5Uqj2HnZ0dJRlOeMNThq08EFr5uWXX07YVitIqzGti05Hh21CMHHixNInVoDIsBERbYCmRYnTbSyN/BpZhbD6ppm1kZPAy+H5559Pmns98sgjQKhAkXnV9MqCVrXY1qWrqyuxGmRaNasWN99+++3JZ4tB/2mppZbqxbAyq/nBarD77rsnap+0FtrXHrN+tL66Wm9LAYvBbTjkqxyWX375XpmEetHV1ZUwpT5q4RgVCBaO11A/Xm3APvvsw4MPPggEltYKuummm4CQQ08jzaxTpkxJZgdXi8iwEREthIb7sNr36lNV9KiNffPNNxMfyXrTRuZba1HJ6LsYbczyg6z40McVw4YNS+orze+ZkyuH9HTx6dOnJ1PBs1CtlljGTrdpqcffnDNnDhCqjmyrUw6dnZ1lW7zUo3TyHNON0mTH2267DQgWoYPYBg0alFwnc+NmAVSrWUfs/a+lVU25AvbIsBERLYTc62H1Cfzfti5W2uy4444ATJgwAeiuxNFPSI8mTK9QOY1KrGh1Tre7rAW15JPTrGZ+MKsVbDFUy7BG7G2Ut/322wOhRU+a0dPDmP/5z38mrVZkn0bGHSq9h8WeI2MJMqufcbCaEXcZ2Oe48L10NwpjF1nPSi3XomEtYuz2pwkkTMLbf8n2I/avNYAxZMgQVl55ZSB0RLdNR7UoNFuyUO5m19J5r5no6OgoW8ZXb08nJ+rZwVE4y8gFthHClkpQy/SGShdPP2dqRnnhG2+8kcgs7UOlhLZS90bU0ns5+W5Ve4qIiOhTNCzotP/++wNw0kknAb0ne4kBAwYkgalqyo9qRSMmn/U1SpWftcP5pVHtPayE0WpBWmaZp8URGTYiog3Q8LROVr/ivvIRa5G1uZJ6zHkWJ9hOxKkGeSAybG/YEKCSrvvlkE7BNOIZjwwbEdEG6PMmbLUgvaLV0z7Sc3SKmrLIUtvKc7XOQnr2TCXnmFeLmP6O/hKHqNbSKvX5cm1wRGTYiIgWQkmGjYiI6F+IDBsR0UIoWV5Xi29Qzq4vFkFLf6fasR+FMsJyubE8pYlZ52peuZ42rfVEomOUuPURfdiIiDZAVVHiSpivWs1mLXrPSpC1rVp0qHkcS97bg5KDpiLDtjgiw0ZEtAFK+rBpRq3EpyzHJKVYOj3cOMt/tJzJ3KnlXlB/WVeDWqvWrH7xHK2Qgfry0BHNR573JzJsREQLoaQPO2DAgC6obWXoq1U/a78F7UUb7v+UsiLSBeq1XB/PRbYu5ad/FPy7j8I5isiwEREthJI+bNY4wwo1uxUdwJAhQ3Ktg83ab5ZPm8WG9VgI5mHd9kUXXZSMvTj//POB0FzN5l8/+clPgDCAKh03KDyerHPJo0l3Mdhp4bvf/S4AZ599NhCGQC299NK89tprRb+bFVMo5pv3RxhPefLJJ5Oma8888wwQmgXa9sg2SP/5n//ZsONpmPg/64G3zYjm3FtvvcVXvvIVABZffHEAbrnlFiAI37OOMT1t7e23307+VknK48Pj7ILQEqRw/mo5+IP0ofR4ncVzzjnnAN3d9OyoaGscW+Y4a8i2OPYa8qEodh7l0mv1msQuLuX6SL300ksAjBw5Mll0r7/+eiDMH7Lfb55ohkmcfqYWLlyYXHefU9sfOYHP3lj2Vc5zfpCIJnFERAuhYUEnYQd1e7YecsghQJjTOXr06F7pCacBFHasg9Atf6eddgJCX9h//OMfQDfjaI5mlb6lV65azjHNcOeeey4Ahx12WI/XTi8bPXp0clyakd/4xjeAwOyaxr/5zW+AMM2uFtTKsE44cHaMc3Ht1asloRmbvj/FoCnsdz2verr7N4JhtYq0FJzxu9deewGh2yLAX//6VyDM9PUzNiFIdwGtBZFhIyLaALlJE7X5/d8C7OnTpwPw1FNPAcEhdxtuE8LcHbuuK56XefXrnGitsEK/uKurK9lvuvu+yFOa6Lm6OuuHHnrooUCwJt5+++3kGNP7sY+vfXGdgFZJP+QsMUZeaR2vwcMPPwwEK8B757W+9957ufTSS4EwKygLfqeefs+N9GG9hz5jzk165513ksYFWbBPc6GQp1ZEho2IaANUNb2ulDTRVd7/DXnrZ2622WZAYBAjbS+88AJ333030D1JDYLf41zNY445BgjNyWVlWUp/tZ5yNlEJs6bTJ0YJ99xzTyBED4888kig2xf0mE0TuJL7/llnnQWQTKOvJd1RaK3kAe/dvHnzSu5ngw02SK7bhhtuCIQJ52nUw6yNhPfFrv52+Teesu+++2ZaX2Yrsqy6PBEZNiKihVCVD5teYQYPHpzkHi+44AIAvvWtbwHBD/3yl78MhOZm+l2uRqeeemqyKhullF0cA3LhhRcCYd6qq56fq2Ziez3+j8eupaH4wUivLOksUqd5P/jgg71mqLqi77bbbgCcccYZAIwfPx4I4yCcCyurLbbYYsl7/bm8Li34EI4B0UesBY30YY1mP//880AQsuy8886ZghWfde+LEXBf14Low0ZEtAFqUjoVixYbBZUNVfAYFXTOptPLHXw1atSospFZo7D+70Q8VTSuiksssUSmRE5krc5ZJX2Fxefp81bZYu7O/KWfV6o2YcKE5D1Xac9Z1nZCnMwj06699tpAzybm1QxS6iuGNbK8zjrrAOG61jIlPo1GMqz3yWvsM6clWQpaiD6Dnmst0tvIsBERbYCqosSicIVfb731gJBfPeiggwAYN24cELSYzg9V9K3SCUjGTT755JNF9+fqLCvNnDmzx3Go09W3rQVZ0ctC1k8z2+qrrw6Ec/XvrrDOWz3ggAP4xS9+UXT7+sEOC7vooosAmDx5co/PuVrnEQlvBNJWkswq7rjjjmYfUlUYO3YsEFRzxkO0iL7yla/w+9//HsjOlpgB0RpqxHC3yLARES2E3Kp1tPXNr1rpYVTQPKyaWnNXCxYsyFyx9E3TpWbpae8qUObPn1+2NUwj/J/Zs2cDsPnmmwOBJa+88kqgm13S1/nzn/88EK6LGmOZysj3q6++6nEn//eXJmzmn807A0ycOBGAU045pcdnX3nlFSD4efWgEfdQ3bd5WC1Ayx633HLL5PlLK8u22morAGbNmgWErIH/W9VUDaIPGxHRBqibYV3tzZFa53nXXXcBgTnMMwpZ8s0338wslJe1rc+0cFqtrtpjme2uu+4qWyGRtTpXoyH2szK8K6vn6nHpFx177LFJna3b18Iw+qgPa3WTEeiTTz4ZCEXkpY6n2CClZkSJBw8enMQhHnzwQSDcO4/J8/S860GeDOu18/iMseh/qkh7+eWXe13ndIWPtc5mC3wWfT7VJVeCLIatKeiU2jAQfpBXXXUVENIVih+cg+rD7QW68cYbGT16NACPPPIIAP/93/8NBPGAKZcZM2YA8PjjjwPhYruPO++8s+7zqASem/I1k+wGhB577DEglFuNGDGCJ554AggleL/61a+AkFzXbBoxYgQQzPwDDjgA6ClAKNLDqeJjzwN2zbjuuuuA0g+ix23hQH/DUUcdBYTgqK6IctdCIb/XeaWVVgLg2Wef7bEtX5tuNDVXzQ+1HKJJHBHRQqjJJC40DY477jggrLaag37G1Wju3LlACBy53xNPPDEJVBx//PEA/PjHPwaCxE8W0txWnKHpoXxs6tSpmVOxC6aoV2ROVWIiG0CZNm0aEIIMih0Mmp1wwgmJdFOT8MADDwSCpFMLxb5JtorR3Kpm3lDeJrHXwvTVlClTerxfiH322QcI7suwYcMAes08qgd5msQWoSv0sLWNRRgF+0iO/XOf+xwQmhBst912QBC9bLTRRkCwGCudEVWIGHSKiGgD1C1NVLQwdepUIDjYMokMYdjcNIXYYostElZ+6KGHgFCi5cpk+uD+++8Hgj+kGGGPPfZItu22slDp6lyMYfWlDSYobrB1jekdixJ8PXr06IR9N9lkEyAICbwervQKOBRl6P8UXvNyLFUtw6YDXiI9BT4tr1T0PmfOnF5N7HydPlaDU5Zf7rrrrgBce+215Q4zQZ4Me8kllwChrY+BROMhY8aM6XV8xi68Lj7jMqy+7Omnnw4Ea6oaRIaNiGgD5JbWkSEsLVp11VWBwCBGUPWDbIfZ0dGRyMGyWp6Y1vHvijJkH4sBCsv9KmkBWuk5puGKaTsUS+csYNcSsPHctttum0SMLdJXSPDAAw8AJEX8Sjx9X8vEz1eCen1Yr7OxA0Ue+++/P9Dtk0Ng0ayGdx8eS8l9TZgwAQi+fCXI4x5qLfm8eL191kzvFEyMSL6rD+v9Pfzww4EggtF6UlBRCyLDRkS0AerOw7oaK7z/1Kc+BYSmayaXXYUVVOiTFTJHlujBbSkoV/5o7uzGG28EYIcddqj3dErCY1awr5WwwQYb9Pi7uVT9+ddff72HUAQCK2uZXHHFFUAQHshM6VKtSsq8qoU+rOxiDEF2kWnTBRKlmNV7lJZtapFpnVTDrHnCc7HBgrll/XJz6cYSCmF24pprrgFCtNh7VK4RXT2IDBsR0UKoqUVM4et03lPGNZKY9kvT4v9SbV1kMlfDNddcEwgFBTKZ7P7GG2+UnQ+bRyNxz9VzePrpp4HA9LKiftKxxx6bsJTHp4rLkizF5koQbRuaHg/h50sd16JFiyr2YTs6OpISSfPcXgvjDumic5uy2aStGNK+X7pZfPpZ8jznzZuXZB6y0Ajxvw0EbLSQVq8Vwmfav/na4x45ciTQsyiiWkQfNiKiDVCVD5tmIVcWCFHZNLOl/VJXpVK+mKut6p/vfe97QPClZFhHJjgqoaOjo+rJ6+WmmBcqXITnbf5t1KhRAInqy/I687ELFixIvmOuzkizUWMVQTKrx6NPpWqmknOpBl1dXcyZM6fHezKtFoTso6KrGLOqfXZb5sP1j/XNjTTL2t6vwhY4fYEbbrgBCNfQ2IIa766ursRqMO+fbvf6/e9/HwhR40YgMmxERAuhrmFYAwYMyMx36r+lc3rm7lydhgwZkvh86o6txkm3RbGRuK1ksiaQl0IjSrOMhKvi8XjVT3d1dSXv2VzcczMaqW9v5Dk9pqSw9Yra6SzkpSU2N6k6Kx2HKGy3WqDVBoLiSeWQvqzfrQeNbMKWbsZXzPKyla/32xiAw8PKRfKXWmqpsmNNow8bEdEGyK1FTNr3cKWyosXom3WhFgavscYaSeGvjdpuuukmIGgxZeWf/vSnQKjmsZ2HLD58+PCkfrHagc71wHNVgbXKKqsAQYG12mqrJcovNan77bcfEPxBV2mHh+mX14JGFbDrT3uesunxxx+fVFDZ7jZrGLTRbj938MEHJ9uA7gqtcg3bGsmw6tTVtXu8Y8aMSXx7GdQshr6+keVCv7dWRIaNiGgD5MawsqCRUxUirsJGCbXdZdj58+cnuVg1mvrFMpeaYf09/cBSyKofrXR1LqZrzsrxpkd4qHyxAmTkyJGJwkmttSu31+Okk04CQvNxr2M1rWuKnWOe7JOl9W42GsmwaWjxGCmHUGlkVNha8DxVaFkMm9sPVhhMsdWG5o39jezVqyRs6aWXTpx4nXZnptoexR9/+uEtVqReTVf8D7eR+832h+1xTZ48OZFgenympAzCOHk9D/SHzv+NRDPuYXrBLywsyXMObBaiSRwR0QaoiGGraU8ismbVFEOWsF1xtRPeqkGWKdlMc0ostthiyfUoJS3MC5FhWx+RYSMi2gBVMWz6swMGDMglAJHFhrUweznUsjobvjdw1EjUcs593Ze42YgMGxER0RLIPUqcJ2pJaZTznVt1dS5k3nLplciwrY/IsBERbYCSDBsREdG/EBk2IqKFEH+wEREthPiDjYhoIcQfbERECyH+YCMiWgjxBxsR0UL4f5fkMoCOHDYjAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4140, D: 0.2416, G:0.1823\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4160, D: 0.2413, G:0.1609\n" + ] + }, + { + "data": { + "image/png": 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htzyODiOP8/zzzwOhS58PoV3rCkM4Xr+33nqry9/sItAIPM/Cpbs1ivsynTctiROJJqLuTidn2p133hmAr3zlKwDstNNOQNfeohI7KJxpVQI/M06R6+jo6FZqxH4oKkA9Kv+vvfbaAPztb38DuvePfemll7Kgv5UVDav83//9HxAq5N97771dxlYNhWMcNGhQlw9yuVsYflFFrGyo08fX+vcvfOELAJx11lkAPPnkkwBMnjw5K5Tn59oX9vrrrweC8rp0tsK+Di7v8YIFC7L76fH9LMM6CxcurLvTyTEXdpu3i8UTTzyR9+G6kZxOiUQLUJHClup0rrL5005np512GgBHHnkkUFmSQzGsdaziOksXdgYvZ3M3dB9jT6l7xfjIRz4CBDvt8ccfB7rXZH777bez1zpzn3feeUBYHeywww5AuG52e+tpY0M1G9h9z4gRI4DgHyj8XEM/rnBcFagsv/jFL4DQl1e13GWXXbIaza4gVMejjjoKgH322QcI19fkf8dQmMLomIuxYMGChoV1/L6+++67mU1vF/l6khQ2kWgBKvISl6q4rw1jMW3Vz0rqqtAxxxwDwKRJkwA49dRTsz6vdrvWVrKHjt27nY3tcvfcc88BQSW0dWqhlLKqPBY8//znPw8ENXGMFhhXVY877rjs3L0uBxxwABBUS8XRTvNYPRWvrsS+1Z42ZGNhuML0Qu+rqyDtam1yVwoWgbcqvp+xyy67ZJ5874l2nrarnl+vkd3qvd4HH3xwdj7FCnbX0vGhN+JVyxZbbAGEMQ4dOjTz9Ptdv+uuu+p2PsVICptINBF18xIbm9x9992BMHNdcMEFQOi7OXz48CxG+OijjwIhRuksbNsP+59oa6ms2o4GtktRi5c47gxuH1j/r4r5Ov9fmByi0my++eYAXHnllUDo2n744Ydn76mWUjas90GP8HvvvZed79ChQ7u8xp8rrrgiEOxq7+mPf/xjAL773e92U8z99tsPCLa5eO9MpNh+++09T6DznroqMIHC6+v9f+edd+rW43f8+PEATJ8+HQjfvRdeeCFb9dRT6SXZsIlEC5C7wjr7/P3vfwdCvC1WDDNu1lxzzV63yWkTamPFcTpjrS+//HKvrSzqkfwfK4DjUE0KvYpmEam42kObbLIJEGKbKmA5rTliT3JP2+vikiz+f+7cuZmiOQ697trqerTNcPrOd74DhFXBlltumamwfgdXUI7H8amaf/7zn4Fgu+qR7ujoyDzJnk+8Cb4ecVhXPq4QClM3obPdyujRo4GwEskj4lGMpLCJRAuQWy6xM/duu+0GBGWVF198EQieXLNl2tra+M1vftPlvTEqq6uB2Is4Y8YMoHu7ynpzxBFHAMGLah6wqmlc+C9/+UvmLTWB3rGa8RUn1pejrFJq84Sfp7Kp3IUlXPx3bM996UtfAoJ3+KSTTgLCysE2m+3t7Vlc99prrwVg7NixQFBY7/thhx0GwAMPPNDlWKrqvHnzunnq87QZ49WImXe///3vu7xOZZXvf//7md1dT2XtjaSwiUQTkZsN6yxojFS7SDtnwoQJQJidnel/9KMfZXZMtfmzxn4ffvjhzMtajDxtWGOqNqt2l5FK+73vfQ+A/fffnz/+8Y9AsI2Mt+oRzzM/tZSXOFarQnXWl+B90HtvLH3PPfcEQiz11FNPBeCxxx7jscceA0L2k1vjLr30UiDEpO+77z4gbN3zZ6Gd7/HjaEA9bNhf/vKXQLCli21HXHHFFbNVkB5+86TrQbJhE4kWIDeFNVvJuKK7HEQ7SRvGWXPatGlZ5oz7YlUh7Ttn6djGcjbcd999gc7Z0t8Vox5eYs/LXS7uK73zzjuBTvvd3Siqhl5avavuF82Dnsqcxh5rr/HChQu72WSufrT3jI+7l9dr/M1vfhPo9CrrHTb2rApvttlmQLAVfY/31vPQj1G4AihWZjXPMqcewxWBvoOe7FS/dxaMu+6664AQcy6WJddb7ndPFFPYmp1OPoh+IWI03t24fvHFFwNhw/ZJJ53E6aefDoQlmA4KH9SVV14Z6P6lPuSQQwDYcccdgcocNXniF8qH0Enp/PPPz17jQ+CXXSdLng9qT/hAxBiCmjt3brfr6z3bZpttgPBldtlryM6ElcUXXzwzhUw00Al14403AnDHHXcAcNFFFwEhvGWoqPChjKsySj02jvuZTqjxvfQ7Vfgwej2sSlEslTI+Rh6kJXEi0UTUvCQ2UdwwzpgxYwB48MEHAbjqqquAMCsfe+yxQNdZx2VhXJ3PGdZaPg8//DAQZjS37qlokyZN4gc/+EHJ8y13SVxJvx5XFyYcmI5n2t0OO+yQbc2Kl+yO0ZBUHorbU+KE44i3/rmxovBcfI0q42v+/e9/A2EFNGrUKCCEaCA4G/fYYw8gpJca4jIU9rWvfQ2AcePGAUHVBw8enKla7CjzO1PPDezF7vuMGTPYeOONu/xOh5ljLmdLZrkkp1Mi0QLUbMNaHiU2yCVO/u6JWFnFz1RZRZvLgL6ouHlQSSVHHSw6m9zg4Iz72muvZarhBm9ncsfuRoK8KTYOnU8dHR1dHFCFGPKw3I5b87RTCwuoOR59GtqEOiNNpFhvvfWAsJJw3NrH7777brduAX4PitnjxXA8lWykMA1T/4j39vrrr++msCaF6KCqxrlUKUlhE4kmomaF1QvobBYrbD0w+VqcQS1P01fcfffdQPB26yWePn16Vt9W9XWrmkkXKpJ2o+GQW2+9taZzKrYRXvUqPKZKqh9Cv4QeZe+x6qjne86cOdlqwm2Vf/jDH4DgZVW5LDJnidQ4BXDppZfOPM7eV8fQW8guppotioYIY84++2yOP/54IKyc1llnHSCsIg1p1pOksIlEE1GTl3jcuHFcfvnlQNjUbPJ3PRKkzz33XCCkyDnzF0u764k8N7DHxP1MRTsIgprdfPPNQIhH62l2q6CbJQrO0/Pv9TxLpSZ6bh5v+PDhWQlYEz9UQdXSxAC9xd7zM888E+gsC6N96crhf//3fwGYOnUqEPwNRgQszlZYggU6Fdnz0GaOr0EjypzGLLPMMtkYtLGNfLjtLk+SlziRaAFqUti5c+dmnsJtt90WCLNinEheC8YoTZg300S7r7Brd29lSitV2Gp6/+jFNvZYmIGlSmijumFg5syZQFBc47bV9B0qHOOyyy7bAcF7G5d/GThwYLfr63ZG46x6cN3sMG3aNAC22morAKZMmZKtHL7//e8DwY63d472ruPfYIMNgOD5jYvDFZ6j97eg9UmfdK+LW4foaTf2P3nyZCDEqS1ZWw1JYROJFqAmL7EzLoRGSc60xurK8Ro7Yw0bNgwIGUPGvf7nf/4H6NyKByHj5qabbgLo0p4jz2wTqEzhVI/Yi1qIKuEWPNVDlatFWXtCVVhhhRWAUAS8UGm9nsZIzfM1Ud9zNJZqdpL54Ysvvni3hlp6gVVa7XhtWNXJPPApU6YAnf6BHjKbgMYUPyvFF7/4RSBk74nF/xxjpfHiSkgKm0g0ETUp7Ny5czNlcHuVu3H8vzOoRbjcyO3vhw8fnq31Y/vODcLGt/Q4unVPJTaWt+SSS1a8Y6c37+ugQYO6eHl7QgXyvPSeGnPVToVQwMxi6XpetXvzpqAwW5dz9ZoNGTIkUz09tWbweC8vu+wyICiHKwdXQNttt11mexq73WijjYBgF2ujGsu03IzlRP3s6dOnZ43BPFdXAIW+ir4gbjPiasWVSOyBj2PMeZAUNpFoImrereOso6oYs7PkiR4zZ0dnUpXlox/9aBbXVO2cmeI9ttrF7jTRe6k6zZ49u9f4bx4b2OO4rwqlmuy9995AsMvWWmutzE5XveISKeZLe/2M8VVDT7t1PNf4Wnd0dGTxbNEe9bvx1a9+FQhF16SwPKr31/K1NtAynmwMXVv6uOOOA4IH3ffPnz+/W5zb8yvILe4TL3FcBMAVlI2+HEMe/ofkJU4kWoCaFdbW8l/+8peBoCraLM7WKqvH0y5pa2vrlhuswlq1wEwS1bO3Hf6lqMfsfOKJJwLBe6r9pk07e/bszO555ZVXgFCULN5/mUf8uieFFY9jts78+fO7tRGNd7m4gnBnjYX1zGb69re/zc9//nMglH5x1eN+WNVRG1a/gFlNRhMKPcGFqhudX58orMT3xu+y+QFxofsqj9GjwuZe+d+aPn4hCuvNQnj4TPN67733sgfWFDmTqWfNmtXlvXmQ5wPrQ1fYlxZC4vvPfvYzAC655JIsocCloQ+mGwasW5wHPdV0Eq+1iRTt7e3dvoDeO5d+PsDeS/8/cuRIoPMhtJDBn/70JyCE4Eys+e53v+u5dfkZp5cuXLgwm7Cd7GNHYp41naohLoMTmxR5kJbEiUQLULfudf2VvnBYlBMaypOekv9j1SxMTYyXxPESNK5SGW8kGDBgQBZiE//mz2LOwDjtsK2trdvx4g3ijezAXop6blhPCptItAC59dZJFKeR6hqjSnkOsZq2tbVlyQ4WU4sdXzpPYpXsSWGK2b9xYTdTJmMHV0dHR7eN6rHd21/o7XzqocBJYROJJiLZsC0+RsdXbNvh4osvnv0uVrs42ULiUNTAgQO7KalqHIc44uSCeNP/vHnzMhXWLjZhwdBQX4R1Cm3rRpBs2ESiBSipsIlEon+RFDaRaCJKeolb3b6D1h9jq48PFo0xSlLYRKKJSA9sItFEpAc2kWgi+sUD29bW1ucFthKJZqBfPLCJRKI8+kUucYoFJxLlkRQ2kWgi+oXCtjpvvfVWVj7EPNm4isKGG24IwD333NPl91Y38PWVlnGNOfDAAwE455xzavqcRlK4k6deTJw4EYDTTz8dCHnMPRWD70v6PPl/qaWWyhLBt956ayB0R4s3MOdBNUH32CHmNbPcijV94r9b1+i5557L/mYC+6WXXgqETny9Od180M4999xev7jVJk5Uuh3M8T355JPZ5gKT+62aaIeDCRMmdDmGbL755gDccsst5Z5mQxInfvzjHwOhtE2jSYkTiUQL0PAlcbw16/XXX88KgrnN6/e//z0ABx10EBA6mfcVseI4BpW12AbrQmUVN3jbed3rYF1nFUklsrq8S+PCv3k8K1LWulG+XGV1DFb+f/XVV7PKilZNtBCdHQ7s2mA9aitKOhZrTKvMjeLXv/41EArm2cnC1VO85bCv6R9nkUgkyqLPbFgr4O+3335Z75lis5g2rf14aqE3+ycuoVLkM4DeO7KXgxX+re9s97pSxz/88MMBOOOMM4qdX12T/y3vUti9MEalspNfjKVMtX39rHfeeSfrRqjjJ1455GHD6h/xGDrz7Cohfif/+c9/Zvcm7khRj6SfZMMmEi1AwxX2M5/5DAAHH3wwAF/5yleyouLOchYSf/TRR4Fg2zqz5VUVHyobY9yv1D4xuv5Vy7iTwfPPP58pjlh8WqUZMWIEEPrz1ELeCqsaleq9a8c5u7OrOnaU19s6fvz4Lu+zCLtF2aH3+5vHPSx4b4+vs0OD4bipU6dmnnpxheV3+ayzzgLyiWokhU0kWoCGeYnXWWcdILRy0GZ7//332XbbbQFYddVVAbj11lu7vNdO69oQek4bTWy7xoXH4q5vstJKK2Xx12KBeGf0uFhZI4k9+KJixF0FDzjgAACOOOKILI4aK9YVV1wBhLYrYsc3ldcetPZoypO2trbMVn711VcBWH/99YFQjtU4sR3p4nEsu+yyfOxjHwNg++23B4Ky6uE/9dRTgdDVz8/Ok6SwiUQTUXeF1f6xm7cd3kzRO+iggzL1OfLIIwG4+eabgZA+d9999wF9p6yiAtn4yfNRkeyFG7Pddttx3nnnlfxsr1M9GiuVS7G+piqpf1dh7dqnnQrdy6lapDz+bJUstv/qQUdHR3Z8V2vGgb3ecXOumNdff71blOKvf/0r0L3j+pw5c4Bg/+ZZSD4pbCLRRNTNS2z2krONXlJjeJ/73OcAmDlzZqZcq622GgDXXXcdEGZw/2+WjDNZNdTiYTTDp6ATOFBZNkxvMbv4GNWQl5d47NixANx1111Az7nRxfCaFBtv3AZk1113BUKOdSmquYd+D/3+2ZfW6x3fu55i7N6TWDnXW289AO6//34g2Ou1tBBNXuJEogWomw2rzbbzzmFoE3cAABHBSURBVDsDYaZy9pk5cybQaTPoTbOhs924tRFefPHFLp+dR4ZRNRRTvXKUVUUZNmwYAIcccggQvKReg760YWNUVvn0pz8NlFbY3pRVlZ49ezbQuHto9pQ89NBDQIheSLFMt0GDBmVqHNvjKqu4A6keJIVNJJqIutmw2j933nknEGau66+/HujMcAIYOXIko0ePBkJ+8YwZM4DgaTQLyFm5FvLcS1lJxpVj0YtaTFG1rd54442Kj1FwXg0pJK4f4rrrrssU6aqrrgJCtlpMHnm3tdxDj3/33XcDQWGNoXufVNPCggHlruy8Z7Xs8Clmw9btgTUks99++wEhBGI6n1vCLrroInbbbTcAfvKTnwAwZswYIDgiTF/Lo+JALTfbh22rrbYC4M9//nPN5xOjybDFFlsA4YtvWKwc6v3AxmGLUg9hnFySB5XewwEDBnDUUUcBYRud5o3nvu666wJw2mmnAWEy8sGFkEDihgaTfw477DAgJFD4mU7GOqeKhc3KGaOkJXEi0UTkrrBrrrkmEGbfTTbZBAgz20YbbQQEo3/YsGFsttlmQAimu5QYOXIkELZA5ZE4UYvCFksbjK+hzhk7mnv+5bD//vsDwWnn7Bxv6SpFvRTWVZH3p5zQUz3qMdVyD3U+uUoQ1dPUxdVXXx0IYaDx48dzwgknADBq1CggKKbqfO+99wIhCcbvi6uka665psv7SpEUNpFoAXIP6zzyyCNAWMdbjc6kd1XSrWjnn38+U6ZMAUJpGFPA3HZXaltXIyncAlaIY73ooouAsHlBVRw0aFA3p5OJ5IasLLGi400niAkohVx99dVASEKvN6ZcPvvss0B5oZj+1snBlYqrhBjvi+qnshaOw3IyMZbKceWlI8ttlib8+AwcffTRbLfddgBsuummFY0jKWwi0UTkbsOa3H/bbbd1+b0K4yykjTBo0CDefPNNAJ5++mmPCwR7wm11edAsvUVVMcMPhsna2trqVua0GCrFhRdeCAT1sUQrhDCUSfX1pJ738PjjjwfI7NVCiqWgxhsevD/xpgC/15dffnmv2wiTDZtItAB1LxHj7CM92aN6lPXcORP5WuNZlcSxilHp7PyhD30oU5RGcPTRRwNw8sknA+WXNCmkUoWNy6aqILvvvjsQquFrw3sfLPO69NJLZ++NOxh4/41n5nEtK72HgwcP7tWjbYTCjfW1pL/6fXassYe/nM0rSWETiRagz1t1QJiBTGN0u1J/yJJpNHHyuVlVloIt8zNqsmG93pb8fOKJJ7r8XpvWc11hhRW6xcgtJG4WmwUL9FfUQj3uoRvpf/GLX1T9Gaq4K8JaPOVJYROJFqBfKKwztoXIVFxt2th2qoVGKqxZX4888kg3O/Hss88GQmZTb4nilczWtSps3HpEdTRjyw0KN954IxAKmhWep/ab91Z7uJKc6BLn16erJBXUbYaf+MQngPD9NXZuSVcL4VdCUthEogXoF/1htVnNQtHOMe84D2WtlkmTJnHMMceUfI1KpIqY8VSqdIw51m4zjFtEmIfstZG//vWvFWfHlGLw4MHZ+bvlz7xac2Itbh6XeS1UVr3BcVaPSluPkp99hfFos9Vir6/5A8WUdbHFFqu62HhS2ESiiegXNuw222wDdN9fetJJJwFw3HHH5Xasetg/xhgr2ZVTDPdaWlQ9jlsPGTKk1+NUYsOOHz+e6dOnd/mds78rhDiWrvL6946OjkyF/SxLwVgiyNhkX+9prgfFysrYUtRsNVlttdUyz3uJz0w2bCLR7PSZwjprb7jhhlnecTyTO9PrleyLLJlycKeN6hjvAS3Hw2vWkPm5V155ZdXnU62X+PbbbweCKloET7WXuMznKaeckjWi/sY3vgGEyg3u1nL3ke0saqG/KayF7o2Za8PWIw7bZw+sDqb33nsvezDjLU4uvfrL5ucY6yi7Rc6lavzADhw4MOt8NnnyZCDUtrKbX7GeO9VQa1jHh8sJ0lpajtcN+oZ1esJyP9/5zncqPXyv9LcHViqZoMv4rLQkTiSanT53Os2fP5+vf/3rQHCX67iwp0ueG9j72+wcJ1TkQaOqJhai2WJYpxZ6uyb97R5aaMEwTx4khU0kWoA+V9hG099m53rQk8LWQ8n7ikXtHhaSFDaRaCLSA7uI0NHR0RLq2lfkucWzFtIDm0g0ESVt2EQi0b9ICptINBElt9ctCt63Vh9jfx+fmVBmRpXDonYPC0kKm0g0ESkO2+JjzGN8fdXxvhitcA9tT2M71piksIlEC9DUCusmaYthlUMrzM69UW8btq+zpvrqHn7+858HOjvO15uksIlEC9DUCtsTzbbTox40k5e4GhpxDy378/bbbwOdpV7dsO/+bUsaWeLozDPPBGDChAlAKBFja9VKSAqbSLQAfV4i5pFHHskqNXzta18DQkNkZ7SHHnoIgC233LLm47aiwv7mN78BwvXLW2HjVYv3xYLazz//fFYmRyyuPXLkSABuuukmoLxGUL3RyHvo2MsptRtXnPCnbVQttVOO7V9MYRtel9jyKWuvvTbQOQgvhrVxYuy506y0t7dny6Jtt90W6F7rtxZHjg9qtRQ79uOPPw6EexVXUSwV5rn00ku7vLcvKeypO2rUKACeeeYZoPgYll9+eQBefvnl7Hfnn38+AEcccQQQeuJax8puDvF1dNLKg7QkTiSaiIYviWfOnAmEPqKjRo3KfmcPnUcffRQIvWniSvQqgoXcXKL1hO/xMxqxnFphhRUAeP311wG4//77syWi/OlPfwJg9OjRQBirFfLjGT4eRymqXRKrnMceeywQOt///Oc/z8YBofeR3QlGjx6dKZXVHq3+mMcSOKYR99Dv1g477ADAH//4R26++WYAtthiiy6v/fCHPwyE0kY77rgjUFvHiuR0SiRagIoUNo8UtU996lMA3HHHHUBnH52ll166x9c6o22++ebxeVV9/EbMzq4Q1lhjjW5/c9ZVKZ3JRYW1oNeYMWMAuPfee4HO0qO92bl5OZ0sa/rxj38cgGuvvRYI999zO+KII7LxWLZ12rRpAPzoRz+q9vBFaaTTyRXOkCFDGDZsGBC609WTpLCJRAvQMBtWG8yfbqeaNm0axx9/PBC8nVbB16b62c9+BsAhhxwCBC/c6aefDoQtWuWUQ61nbx1n3p7KidhLRdXafvvtAbj66quBYO8uu+yyQFDiu+66C4DNNtsM6FTmeimsZWVV+YcffhgItmp8fV0dDB8+PPMoi+PQk5ontdxDz8vrXQyvf+Fq7rHHHgNCV3lXPfUgKWwi0QI0TGGNv2rrFLbnUHWefPLJLu+xD4tbkLR1/Wkf2UrIU2FVUotoP/jgg0CYxffee2+gMz5nuwu9jsZhLULtqmK55ZYDOhNKILTDUN2OPfbYXn0I1SrsFVdcAcCXvvSlct/i8br922tTjw0C9bRh9913XwDOO+88Pzv7m2NqRL/ipLCJRAtQd4XVa6hSaPfYaGmVVVbp1c4ZMWIEELqCvfvuu0B1M109ZmfVb86cOUCIX15yySVA5wpB293GWdq9dlhfZZVVgGCnf/aznwWCt1j72LGXolHJ/8aO//GPfxQeG+i563xe1OMeqqTG9AsbmUFnAr8x82I45jwUOClsItEC1C2XeK211gJC/mXcOtIeo+utt14Wby2G9l4xlllmGaA+HslSnHLKKUDIFbWf6tZbbw0Eb2JbW1s2g6usrjBsHjV8+HAgzNK+Tnv9lltuAWDs2LF1Gk3lzJo1q9vvzFZrNvxeWhTBXr9i79dKKJajPXDgwKobvCWFTSSaiLoprAqyxx57AGE2Nv5lvm01M008czUi86QntDdVRceinV5oy3jOvsYthOPGjQOKNwN+6aWXANh4443zH0CFuEoy82zo0KHdXvPKK68A/ae1RW+4EvzWt74FBM++99Tv8corr5xlPfk3fSpeB69PjK//whe+AISMsWpICptINBF18xKvs846QNcdHRCyZ/LgnHPOAeDAAw8s+z31iMM6s+ph1D537+XMmTOz17rC0O5WUW+//XYANtlkk2pPJ6PeXuI4xloYFy6Mr9eLcu9hJXuM//WvfwFhtWZRhY022gjojH/vtddeAKy00kolP+uoo44CQh7BTjvtBIQ9wuVQzEtctwdWZ4rbrfbZZ59qPyqj2OaDStzp9XhgxfNy2esXptTy0DDNkCFDqj2NbjS6plNP3yHrGd1zzz31OF5u99CNDSaqXHzxxUBXh6E4mf7tb38r+Zm33XYbAJtuuikQvhemfC655JJccMEFQKpLnEi0NLk5nWI1MV3PJYZLYpcYlaCCmkxgSY5ddtkF6Lv6uLHSmzARb6yfP39+tyWiq4E8lbUS8qzmv2DBgm4Ol9VXXx2oj8LmyQ9/+EMgLOX9rvl91rx59tlns43qMV5D7/HUqVOBEJLbYIMNADj00EOBsGmlGpLCJhJNRG42rEntzqjaBFbWs7JfHrasSuZPU+OqqUZXj6qCuvlV0ZdffrlbCMTwhw6MUmVuKqUcG7ZYGKkSvP4vvfRSt1RE0zTdzFBu+K69vb1XX0Sxe1hNamBvVREtrHbQQQdlvzM5RD9NjLaqqZtWTXQV8sEHH1RcP1uSwiYSTUTNNqwFua666qrOD/zPLHLGGWcAIU3PbUvVENsV2hsm0vd1F/n4+G6V83wnT57MiSeeCAQv5KRJk4DqEkfyoBZlFVcJc+fO7WbfuUG/2PiK2dC1JM5X8l7rJLsJPV5xGJI58sgjs/O88MILgeIpsD4L/lRZ9WkUKmy139mksIlEE5F7HPayyy4DQhEuN3VbuLmik/vPbGf6mF62OFFiqaWWAspLUazn5udiNlRbW1v2N+0er7sxPX0AeawWGhWHNVF+0qRJ2fkvueSSQPCYxuPR62piQjllW2MqvYfrrrtu9j3s4bPKOuZTTz2VpYnqlzFqoU3rJhX9EXGBvUpINmwi0QLUZMO2t7dnKqgtMmPGDCDYtLWgjWrJFWesWGH7KvlfYmVVZSZOnAjAMcccwyc/+UkgbBgwluyKJC4c3td2eTnYl/dXv/pV5qtwPMXO325wjSizIj2pq4UD4iL1xVh11VWz3jiir+Loo48Gglc4/h445jxICptINBG52bCTJ08Gwmyjd9BtdPGG4FLYrc4NBE899RQQsmfybIFQTxvPRP8DDjggs11dgWjDuTpwM0AeNMqGdcVzzz33ZAqlt9jSNn6/jMua/aP962fEBfhKkcc91JY28+6GG26o9CMy/K4bs9XXYtmfavw3yYZNJFqA3HKJnVW0aU8++WQgxLFOOOEEAL73ve91Hvg/M/LFF1+cFWiztKTYdCj+fTHGjBmTbefLi8JWhWIvVO3NuA2jdpFZX5dffnm2QdoC6rYsqWcx6ryxNceUKVOAsKPFlVAhFizwb3pWXVFo76mslsQpp8hcHrgxXTvT2KorhHKKyJkfry2rkvoMFFPW1157Lcu1r5SksIlEE5F7iRizPNyQLccddxzQPe924cKF7LrrrkDInDGuZXG2n/70p2UdO291hc7zdHXgTwuh6Ql3JrUc6zbbbAOQeYYXLlyY2XCWelGV6+0N9lpazqQWzA+/++67gdC6oifcoeL9diXxy1/+EggRAGmUssZ4D41ElMKV3v7771/ydb3d02rVFZLCJhJNRd0LiWvD6Pk1C2T33XcHOsu8OOOYZ6viaveZl5wHtXgY3XGjh9f86HPPPTc+BhAUaI011sgUVdvdnS7+vtjOj2podMWJ+fPn9xrH1N7zdTaHroZy72Gehb0bTcNLxMTEyd46aAYPHpw5IuLQQJzGlwd5hARMiDjttNPiz+7y//XXXx/o7Mp31llnAWHZbGfy2HTIg0Y9sD09EHEPJbfX5bEkl0aG5vqKFNZJJFqAhilsf6ERs7PhHAuRPfDAA5mjSieMy+p60OglcaNJCptIJJqCpLAtPsb+Or5KagbH1HIP65GQXw+SwiYSLUBS2BYfYyPGN2DAgFzKpZbLonYPC0kKm0g0ESUVNpFI9C+SwiYSTUR6YBOJJiI9sIlEE5Ee2ESiiUgPbCLRRKQHNpFoIv4f5ypEJSFIoCAAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4180, D: 0.2644, G:0.1373\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4200, D: 0.2297, G:0.1897\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 8, Iter: 4220, D: 0.2467, G:0.1753\n" + ] + }, + { + "data": { + "image/png": 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bb78NOCmjz4O/HiNGjADg8ssvz/l941xDSdRXX30VIJSiOj2pr+9aa60V2iS++uorwPXYUT9YdSnU6VJrO2TIEAD++te/AtmFu5qENYwqIDYdVhXeDzroIMBJCumd6QKh1YPzjDPOSPqbrMTqViddVoXG586dC7g+Pdn0WC2mhNWO6pv911lnHT7//PO013jqqacA2HDDDQHX/UBW9VyIW8JGrZ1vW8iFQsqexrGG8lrIW6GTgYrZf/rpp4ArYt+lS5dQCkfhfx4KSa80CWsYVUBsftj99tsv6XfpZMLffR599FGgocu6JOnGG28MwJw5c5KeK6LaPpSre7nfXV6S1df1o6QruF1YluZzzjkHcD14ZD1Wd7tSkjHVKw/JKh1e137mmWeA4hTjTkSfHVlqtWZ6X3UNlO1l0qRJgFvTTNIVUgY/AIT2C1mXC8EkrGFUEEXzw0rKKBrE12UT9R/terK6FZM49B/NRacG+ds0/ly6/nXr1g2AN998M+navu8uF/LVYWXdVrSPGD58OEBoaxA6FbRr1y5cV506tL6rr7560nOFTkmSfLmQ6xrW1tY20iM1LtlHdtxxR8BJXp32cmnipY6NXbp0AWDPPfcEXDd3dcLzxg5YIXHDqEqKFks8bdq0pN+1w8mvpf6w4CKC/DhbIcl17bXXAi5KqNRIF1FRL0mHVClYPtpJE4psAXDqqacCTrIqemvNNdcEnCW8FCieWT81Jj8wPpWFN8rqK3+mj04QpWDFihXh/dQaaQ1lX9AJ4NBDDw1fky26T2eeeSbg1lQWf1mgU5Udslhiw6hiYtdhtXOmieXVtYGGqJE11lgj6TEfRQUNGzYMcB3ZRaK1MtPOmI8O+/XXXwNOWqhw1u677w7AwIEDAbj99tuB1GU1fV0lKvNI0ky7sU9dXV1GvSoXHfa8884LI7JkwfVPAf4c8sH/nOlamWK/I66V8xr269cPcN4JZUE9++yzgIu80ikql4IC+vwp8suPR+7VqxfgIqC++eabjNc3HdYwqoDYJKz0TumhUdeVBJ45cybg8g4TUYaPIprkC1Mkkf6uvERJPknCdOSzO0vaaQdVIrOikNZdd10AOnbsCDSWVEEQhLuw/Kxjx45NmpMsi9J3fKu67ls2Vsu4Ip3ysXqnGEvKx3XP0vmo01wzpzWsr68P5zJ48GDAff5kS+nTp0/SeBULoDhhlfJJRGukeHnlvyrqr3379oDzqct+M2HChMS5ZDVHEdsXVoP997//nfZ5CgrfdNNNw8eyDXHza+moKsBVV10FNCQZZyKfxdYXUAuggAgdfXUPFSwiB7mOV4nP0VwVQK5rau76YKUzZOWSQpjPF9Y/tudD1OfKd/vkee2811Bz0oap2lpKjVt77bWTfibWz/arU/jBHgrgkctIm4JcRHLdbbTRRmH/XPXTzTRHYUdiw6ggCnbrSLE+/PDDAbeDHXXUUQDceuutgDv6fvbZZ0nPq62tDY95euyDDz4AXDU64RtzxowZA7hA7WKwePFi9t57bwCmTJkCuCOijqn+ySBRsur5kpzHHXcc4IwccjckPhfcMVySKFFi5WKgyYdCJGsmd42kTzbqS1wsXrw4VNV++OEHwN1nGaPEZZddBrhQxG233RZo+OzJQKWACKkvcgkpvU6/y32jU6XcZTU1NaFkzbX/j0lYw6ggYtNhfb0nzrrE2smkZ6iMykUXXQQ0JBCAk+YZxpl31fhrrrkm6X1kdMqkgwdBEJ4iJIGku44ePRqAQYMGAS5RPZtSMFGUs0SMpEpUX5tCpLeIWsN0hjmNRz+loyroXzqlXIyyE8iQOHXq1PDzJ0OhEgQOOeSQpGsuWLAAgHHjxgFOsibaPPSZiTLsmQ5rGFVAwTqs0qN84izHqQR1WVYlYeXeKXaYm3ZbhUSqDKt2cqXAqUTp1ltvDcD06dOBBqki94B0U+30KqN57rnnAm4umQItmhJdunQJ09KixhmHZM1EOpeX1lCSVGGEeo3WTBLPL+Oy1VZbhfr39ddfn/QarbvcjPp8ytYg/VmBNvPmzePll18GcneZmYQ1jAqiYAm78847A5n9r4XQt2/fpN9lmZa0UhFoFQKLG+kekpI77LAD4Pyu+imn/KhRowDo3r07AJ988kn4Wuku+imf7owZMwC3S/s0RckqqTl79uxwfv7ffCt3KSRtKuQb9z8jd999N+BKlSroQZ8tjbtt27ah1JUtQ3PWzz322ANwRQFPO+00wNkrTjjhBMDpuvlgEtYwKojYg/8l/dTWoBDkz1Jkia4pSaZuYSrGnQ2FJLArBNIvTSo9XsWo/dKdLVq0CHUVvzCX/HCag6K2/GTnXMIEm1IhcfkmVZxPen0hFLKGkpz9+/fXawEnNbUeu+22G+Ci6err68M1UYiq4gRU0E36saKjpLtus802gAtN9LuuZzNHYRLWMCqI2BLYJQGylaypJIYCwqdOnQo4f6dKUUqyyjqseNBSoegcP3C9R48eQLQfNnGOkqzSh3Qf9DNVGZHE5zd1FOWliCIlccvfWUp0Alq6dGmj+z158mTASUNFyymxQ2uqtZs1axYPPfQQAPPnzwdcWRk9/uGHHwLOi7DOOusADTp+XJiENYwKoiAJ+8MPP4Tnd798pHRj6QzSFWRRk48qUYfW/3VNvyTmfffdB8CAAQMKGXZONGvWrFGyseKhFcGi4mVRJVUS/yYJq/hSWZLV0iKKpmglFsOHDw8LtJ188slJf5NkjbPUZ7bo85N48tFjfvaOpKJQ5o30zsTnai3kDZAfVgX1VIBQKXnKCNJ75lLYzcckrGFUELFZiRVzqYidU045JenvshYqWkTvu2zZskblTfONlkm0xkaRq4VxzTXXDH2l/niUoSQfXqrxQMPpQ9lK0st1SlDZEOVO5lMyxadUVmKt5axZs0Ip5hddK4bfNZ8yp6KQPNwolMCuSDw131ZjtnwwK7FhVAGxSVjtMpJGshLmgtp9yL+aTyOoTMTZDEtIh7njjjuA1E2Q/BhVv82HkGVTenA++k6pJGyibUHNjf/+978DLm5WvsiY3zf2NcwHP2dZPly/nKnWMBdLf9FLxCS8EQAnnXQS4AKlFY515ZVXAnD00UcDyceVUgS4N5XFFsceeyzgKi6qu7w2vlyq94lcv7D6kh144IE5v9fK92u0djrqK9UsTpraGqrq5FlnnQU4t5/cj/lgR2LDqAKK1lunqVLu3bkUaXOlDk1s1apVIzdJMSmkCEE53WNSG+SehOgesiZhDaMKMAm7co6FdMtuapQz+H/XXXcFXNB8MchHwvrusqYgcdNhEtYwqgCTsFU+x1LPL5cuDPlSzDWU5PWTU+KWxJlOdCZhDaMKSCthDcNoWpiENYwKIm16XbXrd1D9cyzm/BR2qM6FpaIUa5hOxyxHRF44rqK9o2EYsWNW4iqfY9T8KtnvXMo1zKUvb5yYhDWMKiC2ImxGZVGJkrUclFqyZsIkrGFUEE1SwsZRJsWIj0wle1LZQVQ+RkXX1cZx3rx5RRtnNjT1GOJMmIQ1jAriZ28lrq+vDyD7lvWVQFNq1VEI2eaKFmOOfoG9JUuWsP766wOujI9fHD2T9FY5XJXHTUfJSsQUgmrhXHLJJYDrsK4AbB2RdWRWTd9caj+V263jL2ox3AZN8Qurnqm/+93vgMaJ7rkcVfNZQ93nqGB+VfpXv16hI/y0adPCL5oqXGpD0dj1U50cN95446THE99Tj6233nqA6zwQNUdhR2LDqCDKLmG7du0aVoVXn9fTTz8dcNUI1dNTldXPOOMMADbbbDMAXn31VQCGDRuWsWhZOcLavvrqq3C3VYX/F154Qe8f99s3CQmr05LCF1WX2k9b0/xVpE/9itIRxxrqlKbq/Brv6NGjARg0aBDgjGWJPPnkk4Cr8K/n6JSko7P/OYjqvQSuL7AkrUlYw6gCSi5hVY9XCvjgwYM57rjjAJg0aVLSc5944gkAJk6cCMDbb78NwLPPPpv0PNXAHTt2bNizM4piSljVpZ02bRoAW221FdCws2qX1c6eMJ7wOXHRFCSsj3S1e+65B3BdDkeOHAm43rpffPFFxmtlWsNUdgFfj9TPKOmn094WW2wRvl7PVQ/kbbfdFnA1uROkY9J7pkKflShjp0lYw6gCii5h/Y5u6sStnpq1tbWRO5H0ivbt2wNw2223AW53kl7Ur18/oEF3yFS8uRgSVlJTUkLSY8011wQa5qFudZtuuqneN+kaKoEZ1acnF4olYaMsue3btw97pmZ6jfQ7FUxX13Ldq2wCKwpZw6jPu/rgHHLIIYD73OozmKlnE7jToz6v2ZwW0ozTJKxhVDpFl7Dyb8myO3DgwKS/19bWZvS9RfUqkcVRPTwnTpwY6rNRFEPCyvIr/5zYZpttAHjttdfCMWuu+l0WcrXLOPjggwsdTtF1WElNSZ3mzZszbNgwAK655hrASajVVlsNcCGKWjP9XSciSaWamprw+tkWKMtmjpJ2kuzPPPMM4PRQBUMUkhShOQ0fPhxwFudcSLCWm4Q1jEqnaMH/2iUVhXTUUUcl/V26XDrpqk7s0nvee+89wFkB/S7vmaRr3GiOe++9d9Lj6jL+2muvhY/df//9ABxwwAGA809q55duv/322wPw4osvFmvYeaP+pyplqvu+aNGicK1OO+00wJ0ctt56awAmT54MuCZouh9qHCX9b8mSJey2225AY29AISjUUGPWuPT5lI3F7zKYaM1Vg6/vvvsu6dp+J0L5dnfYYQfA6cXZoM6NUZiENYwKomgSVmfxyy67DHA6m3ytvr6XCvWYlWQVkmzqJyv9adSoUUUJ4o8KQo/Sd9RiMxFZgX1/n3QqFTLr1asXUFoJu/nmmwPw7rvvpvz7TjvtBDhbge6x/OLt2rUL47xHjRoFwI033gg4f6aswGo/2bZtW8DdD3Utl4SLG41VUUnStXUiUFyA9HLptDoZ9O7dO/RKSBpLWuuUdPnllwMwYsQIAA477LDY52ES1jAqiNitxH7yufRN7VR77LEHEG8CsSTxK6+8Qu/evdM+N1cLY6IVWz8lBebOnQu4mFKdIk488UQAxo0bF15Hutpnn32W9Nx33nkHcPq3XzK0FA2dn3/+eQB22WUXwEXuyLf6zTffAI1PB9LZZsyYEereujfS6/wiBLIaS5Lp8yEr8sKFCzN2bY/D0j906FAAbrjhhlxfGvrZFfHkW7X1u3RYnZYKyUgSJmENo4KIXcJKN9F1lXHTuXNnwGXipCPXEpzazYMgyPiaOP2wkjB6fzU17tixI+AisYIgCNsvKvNIFkehcfsSKpsIG59cJWxUSR5F/+yzzz4ArLrqqoCznOpk06FDh1Cv1QlB4/eRvicbh6zJffr0ARp0zdmzZ2seGeeX7Rwl3XRq0OdRa+QjS6/mnA06DSVavMHN0Y/6S0eUhI3d6KQbry7T1113HeAW8sMPPwTgyiuvBJKD4f0AiWxRmOOBBx6Y77Bz4swzzwQahxfKUKHObTK0LFy4sNEXVF9EfYBVA0lfXN2LYlNTU9Poi6ovpAyGxx57LODWUB9EfaHmz58ffkj1Wj+NrmvXroAzsuloLBVJxQimTZvGjBkzABd4Egcaq9xn+qJqk9XaiXRfVP+LKfS71lD34vHHHweS3X/51i2zI7FhVBCxH4nlkFaAgMz8StiWpJErIQiCrI99Slj33Tw6hi9cuDCjQh/Hkdg3Mnz55ZeAC7/U7qxTxvrrrx+6N1Q+RGmAviGiS5cuAHzwwQe5DisklyNx8+bNwxONpKCkjxK15Yo755xzksYqSXj88ceH9Y0khX1DnYxqMi7tv//+APzf//0f4NawW7duoQsmSvrks4Y6vsugpbXTWmncfrqd1qeuri6UmJpDwnjSvnc+KZRmdDKMKiA2CavdQ9f76KOPAJf6NmDAAMA5qBWidtNNN4WFr5TwnSs9e/YE3I6fjjiNTgpBlM4qd4hC684991yg4R4oYECSRScOSWe5N/xdOB/3V65Gp8GDBwPOICaJOn78eMC5opSgMHbsWMBJGkkvcOmBCoSQ9JaO7ocE+h3PsylGV8gaKslAxiclHyjBvhD8pBTZa3RqUuLLvffe2ygJxMckrGFUAQWbImXNvLaYTPcAABBUSURBVPvuuwFXolShaRMmTEh6XDvaxRdfDDRYUlVcLRPapSWtpG9kI1njRBJUYW4alyyd0tOVtNCqVSsee+wxwKV1yfmu3Vim/3JUpL/qqqsAF3rou97kpvJLrzz44INAQ2CALMrSBbVGChiRNVYJEbISy31Sql4/Cq7X51bFz+JA90/34KyzzgLcaUqpfC1atGhkt8m23K1JWMOoJIIgiPwHBNn+a9asWdCsWbNg0aJFwaJFixr9vaamJlipazT6N3z48GD48OFBbW1tUFtbGz6+ySabBJtssknQpk2boE2bNuHf6+vrg/r6+rTXjPpXyByj/tXV1QV1dXWNxtOxY8egY8eOwYQJE4LWrVsHrVu3Dp8zZsyYYMyYMYFYvHhxsHjx4oLH4s8x03NbtWoVNG/ePGjevHk4lvHjxwfjx48P59WuXbugXbt24Wveeuut4K233gpWrFgRrFixIhgxYkT4Wq3l8uXLg+XLlwc+CxYsCBYsWBAMGTIkGDJkSMHzi2sN4/ynuS9btixYtmxZOPeo70Y2c9Q/k7CGUUHEZiXec889Adh1110BF8n03HPPAa5cZCoUIeKX8RB+KFchHciKUSImF/xoLs1BFmdZK5UknY9ul6uVWPdTutZ2222X9vkaU2JUlizLinSTzioddsqUKYALiJc1PJ8WJXGsoe7vmDFjgOz63eSKdFlFUSl9UbaOVMjXu3jxYrMSG0alE1vA6q233go4X56iZlIVXQO3O/fo0SOMepJlUf49lR3xKYclNS6UkiXLq+ai8itKZSsV9fX1oTSUBVvRQCou58fMag0TfcaygquogOYlSSareD5kKrqdD7Lk+3PxTzT5NCvzo7wUseWXEkpFpjmahDWMCqIgHTaxBYX8iPKrCT0u3eXpp58G3O6zdOnSUKoo+umpp54CiuObK7cOq51dc5Yuq508DnLVYYXGoCyjU045BWhc3kY+VOlmffv2DduqCD9bJ07iWEPNRWmPKoOjqLs777wTgGuvvRaAU089NXytYtrVOjOq2Lh+VzE5xVNfeeWVnH322WnHZ5FOhlEFxGYlVraGdFfpRQ8//DDgykyqUJXiLLt3785NN90EOCulH1cbp6Qtl4SNSl7W75pzHHPNVcJKZ/XL0/hxt02FONdQn3/ZSxS19sknnwDJ+bK+Lq+mZ4p8Ezo96Vr5NDozCWsYVUDRWnXIpyp9yC80/cc//hFokCyKyVXmSjGtwKWUsKkkV2J7C3CxrbKiKjOmEPLVYSuFONdQOb7KT/7tb38LNJaKn3/+Oeuss07KayjHWU27E8aV83h00vrxxx9Tvrhk/WH92rca2Jw5c2JJbcqWchyJW7ZsGX5RZbaXUcavNB8H9oUtnFTuHN8FpISX/v37A84YpzI06m6Qz9rakdgwqoCSd2AvN+UyOul4pEJxSjtUaJxSseKgEiWsXHp33XVXxueW2zXnI5VOhQziUOlMwhpGFWAStsrnWO75NVXXnFwxcs2UGrmIomoUm4Q1jCqgaiVsVAqeSdjMFEMq+kQFayRia9gYk7CGUUGklbCGYTQtTMIaRgWRNoH956AbFHOOicXVS3GSSRXWZmtYmZgOaxhVQGl6GlYg6qquhIR8iNPKqg7n6uadinwKmhmVhUlYw6ggqtYPG0VT0X+K6etsSpFOxaCprGExMR3WMKoA02FXko/EU/FptdDMBiWuqymUysIalYfsG7J3lAKTsIZRQZgOG8Mc77vvPsAVmku4NtAQN6vSOFdccQXQuFWFJLuyOHxJn2qdVDJVxboTnms6bI7o5NO5c2cA3n77bSC5XKueo7Xp27cvQFjiVY+3bdsWaLwuqYg62UXpsE3qC6v+mkp5Up0jpSCp1Iaq+emmrr322lm/RxyLnVhJL/Gnet+2a9cu4zVUMkYfCFWHb9mypcYFuJIyqn6fSFSKVrG+sBpTQv+XRs/x6xH7Y/Q/oPn0SSrGF3b06NEADB8+PGlciZUR9QXURpkJXSNV9clM8zajk2FUAWWXsDU1NWEvHUkmpVwdddRRgOtHI2mjquz77LNP0rWy2aX9nat58+YBRCcSJ6IiWxpvirSvcE5RKPBBgRCq56xu7fvuuy/guv2paN1DDz0EOGmejrgkbKdOnQDXzU5Hfx0N1QupVatW4Zx1HyVhx40bB7g+PepyWEioZpwSdujQoQDccMMNgKsprNNeIup/JImpbgF6rd+h8bjjjgPcPcgFk7CGUQWUXcK++uqr9OzZE3C7rt/PRDubdr1+/foBrueLJHFNTU3GnTvb3TlRF1N/IPVwnTlzJuCkRhyoJOahhx4KuJ3/+uuvB+DSSy8FsutjWqiEVUlaVb9Xv17V35X+qXvtV8RPhXorPf/880B+1fBFHBJWc1E94qhTkeZaW1sb+RzdB9lYdAJ57733AHeqUA3qbDAJaxhVQNkkbGKvGaWFydr76aefAk6X9ctHbrjhhnm/r79z1dbWBonXTjdW9XSV5BHSOydPngw4Pe7GG28E4KSTTgrnqM586psriSNrpHZ8lfu8/fbbASfdc51jLmuoU4V0Vo1V66J10IlDevfs2bNDSfSXv/wFcFbXWbNmAa7jm6rj+93uciEOCZvJ/pBP1f6oz1Ce1zIJaxiVTsklrKye0pPSIYudLKvq7i0dQWPPxsIr8tmdpatKWkyfPh1wuvRWW20FwFtvvQU4nTvx3qpbnySoipB9++23gNOVdLrQ8/Lps5qrhFXPmM8//zzp8a233hpwYZSSFOqh2qNHj4xj0elEvVGnTp0KFJb0UIiE1WdH3eald+66665AdJ/jdEgfVssZ35eeDyZhDaMKKHnwfzaSddCgQYDrLTtgwAAgOhSvGCRGokiyCvnXNA7pn+mkhqSUpO7LL7+c9D7a+a+++uqk16WTrKkaNuWDL1n9BAVZO7V2qSRrVIidfJY333wzAH369AGcVCp1EUBJ1JdeeglwkU0aRy6SNWrs+fhds8UkrGFUECXTYdPFTkqf0y7dvXt3ACZNmgS4nX7nnXcueByZ9B9Jl8QWgdnGu6aTeHrtkCFDAJcEoEBxoX66hZSmiSvSadSoUYCT+loPxXgnMmfOHMCdHI488kgA5s2bBzhdXPpyqjjkbIkz0kk+ZvUz9tdQvv9vvvkmfI6fTufrqr6lOZ9ThOmwhlEFlEyHTbfLSJpIN5JOmE0ETdyksjhn2iGlWyu2tlu3bgA8+eSTgJOaABtssAHQWLKKQiRrvvgZNa1btwacRffCCy9Mev6qq64KuFPCqaeeGuqEkqxjx44FnDSSf7YQyVoMJDWFJOx5550HwL333gs4H3Q6/JNHMfRzk7CGUUGU3EosXUa7z0EHHRTmVypGt5z48bHpfLxq5Cu/q3bjk08+GUiWrLL2jhw5MuW1nn76aSDaJ1pMNEdFXykbSlZhtWZU1pSkpOa/ZMmSMPrLlypK6t9mm20Al+mkU0kxG27lgx/N9cEHH2R8jdZ2xowZxRvYSkpmdFJyt1CYXyL6Muu4mM0xJFfyCU3MxJQpUwDYa6+98r7GO++8AzRO0cqHfI1OMpI8++yzgOsWr4AQbSapSAyST/e43uOcc84B3LE6F4qRwK60xRxDQAF3jI7zc2tGJ8OoAkoemignvILB58yZEwbzF5JylS3Z7s4HH3ww0JA4LiPMDz/8kPQcSQtJkeuuuw6AU045Jedxvfnmm4CTsDpO67idC7lK2Ey9WhX8cMwxxwBOKirl74EHHgj/rzpHKjbgu0lUM0nH6nykUTEkrNIYpRbonvipleDUJbn+ZCjs378/AC+88ALg1tBPq6utrc2oCpiENYwqoOQStlevXoDTk6ZPnx7u3H7aWjGI2p01rueee67Raw477DAAJkyYADhji3ZOBXunM1BJGg8bNgyAW265BYC9994bgEceeQRw0lqSSTu9wiA7d+4cJkFMnDgRgM8++wyAddddF4C5c+fmpcMq4UBBEJlqNSeeiPScqNOIjE0qs1MIcUpYP61OgRS+VFy6dGm4JlH4+nqU4dIkrGH8TChbArt2nZqaGj766CPABU5E6VJxkM/urFSs2bNnA3DxxRcDznoqPUdhjWeeeSYAZ599NtCQ6K1yNkpM991HStGT60QoNE5peB9//HEYfJHNHHNZQ7lxlAIZJwq2yCW4Pop81lBro5/SsRXcsu222wLuROMzevRozjrrrLTvoTVWMUHpxQnjBOD888/P2C3CJKxhVAEll7Dyd6m8yJIlS0Ln9ODBgwHnuC8G2e7OifqHJL6khI/00AceeABweposoRtvvHGoZ0a9j5zvUUnPif1qMyVGN8XK/74fNvGEBcUvJO6/j05JCkGUfhplh8gmGV2v9U9P8h7IRtC/f/+MIZomYQ2jCii6hFUZFSWj+5azBQsWsPrqqwMNJU/B6XPFoBAL40UXXQS4YPioXVdlXpRaJv0IXCkSzVXW02zLidTW1kZKo4RonSYjYWV9VxEA+ZXzKX0jCllD3WetjYrBKfxSBedkiRdKLQTo3bs34MJJNRdZfmULUKii/u6PIRUJPl6TsIZR6RRdwvoSVbqrymj06NEjjHRSelqqJPK4KGR31tilc2ucmRg9enRY9kY+3GxRmpp29S5duuRULL3cEtZHuqLWNp/g/zj8sG+88QbgiiX4xdH1WdR6Jd5zScgDDjgAcNZgPVf6uu+3lQU6G0u56bCGUQ0EQRD5Dwhy/desWbOgWbNmQYcOHYIOHTqEj7dp0yZo06ZNo9+PP/74nN+jkH+5zvHHH38M/9+yZcugZcuWwdKlS4OlS5cGUSxfvjxYvnx5sGLFimDFihWRz8uGmpqaYKUECYCgefPm4b9s5hjHPautrQ1WZjUVct+DIAiC+vr6oL6+vqRr2KlTp0b3b9myZcGyZcsi7/sqq6wSrLLKKuHrDj/88PCzPW/evGDevHlZr6Gu4a9lLnPUP5OwhlFBxJ7AvnLHC/M75T8MPL1LZS9VUqSpUldX1yjDSNZYWTgVNSO9XM25comblZVS+k1UMvTaa6/dKLe4WChGWs2mRboYY41t0003BRpHrRVakjUf5s6dG/5fdgff7+o36fbtKXV1deHYs80qu+qqq5J+978D+WAS1jAqiNisxL5fTVJHu7SiPdRoSTtZjx49cmq1UShBDBZGjV0/VYxavubx48cDLjb6jjvuCE8Suj+KM77mmmuSrq37KCtqLrtyKh9eIVZirZ3yX9UKU9JJZV7222+/UCLJr6nf1bBapxOVz1Fz6HyIYw2FrMW6d2rOrPEphrt58+ahD9kvoKfKHFp/WZHjbKkpShaa6JfgSKyIWMq6PnEudlMlKLJbR53HVSlx6dKl4YdUX0y/2uAee+wBuKNyIQkG5VpDfQG1qc6fPx+IJ2XQJ+oLa0diw6ggyt6BvdRUmoTVsVThbuqCkI5iS9hc2HLLLQGXihgHlbaG+WAS1jCqAJOwK+eYrg5xIT1SikGm0M2mJGGLgUlYwzAqgpJX/m+qpHMtNRXJKoqRFGFUBiZhDaOCSKvDGobRtDAJaxgVhH1hDaOCsC+sYVQQ9oU1jArCvrCGUUHYF9YwKoj/B5x11KqsD18AAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4240, D: 0.2423, G:0.1493\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4260, D: 0.2556, G:0.1344\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4280, D: 0.2356, G:0.1586\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4300, D: 0.2414, G:0.1516\n" + ] + }, + { + "data": { + "image/png": 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PcP1ZevbsCbhO67JpFfesqakJZll1DvvLX/6S9t5y48++6uKmDuW6dyFVVJHqbKib35Zbbgm4Xrjq9uf36fnTn/6U17UXgvrMqJu9YsOKpWvVpPYXqfhxRdnqXbt2BVzUQH9XF3f1ZR0xYkRsUQJ9rpYsWRKcb8cddwRcD6hizqWV1NChQwF48MEHCz5WVExhDaOCKMqGTW3MpBls5MiRgFNUdWKXEt99990AHHLIIcFx/GNoNl60aBEQj42gY69bt65oL7G61ske00yuVcM555wDwE033RR6DHlLpWaygydPnpx2DGVVbbHFFoDzPGejWBtWStG7d2/AxSo1Trp/3QPA+++/D0CHDh2A+uZRABtvvDEAN954IwC/+c1vAHffaii2atUqIFpmWr42bF1dXTBGWh20bNkScCoZ1hlR/584cSL9+vXLel1xdmo0G9YwqoDYMp00u8iTuHTpUsCV75B3ePPNNwecl3DFihWBnaYSH+o/2li8xD6tW7cGnOrJHpLXUPem9pOp9OrVC4Dp06fregAXtzzvvPMAGD16dNoxM8U6tWqRp17ko7D77rsvU6dO9d+v9wLhHeZFTU1N6Cpo1qxZgIt/yu5VfHn77bcHnL2sgungVizyUqdcX15juNFGG/Hzn/8cgDvvvBNwKxrdo+Lhffr0CT2Onov8M+qn27lzZyDa6icqprCGUQUklkvsr+elrNOmTQMI8oenTZsW2BN++4IkiENh/XuTkmoHzh/+8AcA7rjjDqB+5aAYsxRG79G9KhOoW7dugJvFC9n4H1ccVn4Iea513/l0mg/7fB155JEA/P3vf087dhSvbTFj+PDDDwMwePDgqG9pQMeOHQF47733Cj5GLkxhDaMKSGw/rG8HKZYnz6MUZ+HChUFsTAqVawaT6si+y2cvaxxIOUeNGpX2e9ky8g4rt9jfjZSK1ErxyJ/+9KcAPPnkk4DzRMdpH0Vl4sSJAHz11VdAfZYVuPvMhLzBw4cPz3rs+++/P+3/pSqNo11ShaBr1CpJ2XpaFfmF9G644QYAzj777LT3F0PJqib6FR1OPfVUoD5JQjcop5O/RI6TJJL/H3nkEQAGDhyoYwKZl/b6girZQl9EbUfUJoBCkkYyha7iuD+lS2oLWrb6v7nGTN3L5bgrhELGsG/fvoDbVFHgeQEXqtSmFYW5lFCz7777As6xWOC5bElsGJVO2eoSS2E+/fTTQH39a1HtJIWGFPTOB99pE6fCaiWgsIqcMIsXLwZcuiE4J4u23r3xxhsA7LPPPoBLlPjrX/+adgx/G1gU4nI6FZIYn6t3ahwbwctRl/i5554LQj7+0lfk44zLhSmsYVQBBSmsnC0XXHBB5BNp1lGCgJLeW7RoEdgAYs6cOQAMGzYMcCGQGTNmAG4m86+9pqYmZy+cOGdn3dP8+fPTfmoTtILz/fv3p0ePHgDceuutAGy33XaAqx4pu1CrCtn6hdjx5SgR41+nxkGpiUr6j+lcZan8H3UsFLrUpnjfORnxXKawhlHpFKSwvlrmdcIMdpFvE+i4sj8VTlCpT20KKAR/5mrbtm0duMQFn1atWgWeTR/Z4VJHPZdMYaa33noLgK233hpwitOuXTsAhgwZAsBTTz0FuO11UfCfaakVtl27dkE6ptD1a0tenGVaS6GwGqe333479bwA/POf/wTgJz/5Scb3TpgwAcgezsuFKaxhVAFl717XoUOHBgkS2lKmGWr58uUAvP7664DzNEYt6J1KKe2fVOWTh1u2e8r1AOHbuwqhVAobVngPnLf76KOPjv285bJh9XnzNyP4pVDj2GZnCmsYVUDZFRYa2rC77rorAK+99lra7+OgHLNzpmese9asrZhuTOcricKmZFalnhto/Bs4CkFbArV11M/esw3shmGkkTUlI9vm6Tg5/fTTAdfZTl44nd8vJ1mKRPFs964tYdoUnYvu3bsHm/Gff/55wJWBVZwyH8rda1de+tRMLnmFfRu9mlDusO9nkNIWEz2JiimsYVQQZbdha2trgzin4pmKb+63336AU1zfRig2C+i7Y+Z9EG0H1HVLcaTKsnW01WzAgAENvMD6fyxbrkoUh9X9+btT6urqgjErxHOfL+WyYYXywc8880wAXn75ZSDehllmwxpGFVB2hf3oo48alEsRujbFMFWgS8W088kGSjlmwbOz9oX6m8k33XRTwBXDVkEvbXQHt3qQOilOqTacIt/u7plISmFVtsa3u4899tigeVQpKJfCquia8gJ8lBUlX0xc+eCpmMIaRgWRVWHXX3/9OihsH2pU2rdvzxNPPAG4qgu6JpVJUYkNFdlWeVEhlUotkRlGnLOzn3Gl/8u2TY2tnnzyyQDMnj0bgIsuughwBdXlcb7nnnsKvZyAuBV2q622AlzJnhdeeAGAMWPGAPXF4UvpsS63Det/Z+QVli8hLOMpn6hLmMKWfUkMcO211wJuSakq8qpVq7q/mQL1+ZJkf1gtZ/WF1bMdMmRIsARW3yB1o0siZBbXF1alTtT9XX2R1FMoTseZaNasWU5zoFxfWDlBZfLoc6ia26r19Le//Q2AX/ziFwWfy5bEhlEFlFxhMyW3KwwSFpjO9t588Weu2traOshP6cKWNtpKdssttwAuPLVu3bogqK76zOr/mgRxKayec1i3dClLlH44cVLuDeyqO60+Qfpc/vrXvwZctcQiz2UKaxiVTtlt2JYtWwZ1b6Mqp0pk5uMMS9kKVlaHRSlIOvlf5U7V1a7UlNvpFMdKLxemsIZRBZTdhm3RokWgsKWgXLNzHAkRPrKR/edXjiJsxeJvVYPwTQ7lGMP27duHJkwkgSmsYVQBWRXWMIzGhSmsYVQQWTewV4r9kw/l9jCWgig2bCk8nUlRzWOoGP+aNWvMhjWMSierwio3UhktQtuMtN0KGs+MXWxZm8ZyH3ExaNAgAB577LG038d9f43xualVSlyZWEnco3/MXJ9bU1jDqCAKisOWuwhYFMJmw2q2f0QUGzbTGKqAmnYblUJRCiHqGGpXkZqUAfTu3RtwHe+Lwd+tlM89+c8hW5mftHMWeK2GYZSBxDOd2rRpA7imxyr3qc3qTZo0KandUwqFlc0kG6pUqDTJnDlzInuJa2trMzbvKhZ/M3ecpT+reZWk51RTU2MKaxiVTuwKe9dddwGu2Laa2vpkyh3NRRyFzUs5O+se16xZE3jWVXxbpTGbN28OFFZQLoxy5BIroqAdVH5ZV7+FqCjED5JrDPP5bOUq/n355ZcDMGLEiCB3W/teVVlCNr8fTRHaA71kyZIGf/Nt11xx2JIl/2dbJupv+jAryVo1mk466SQgvy+30BdCVQvDBrsYR1rHjh0BV/NIX059eCdPnkzPnj0BN7jqC3vuuecC0epRRSXpL6wf7qutraV///4ATJo0CXAbEvRc/Ylpt912A+DVV18FXNhp6NChOSevYibdsC+ovigffPABALfffjvQcJM6uM+hxlL1mYXvjGrbti0Aq1atanA9G264IVA/IYArHWROJ8OoAmJXWC1HNIPOmDEDcDOYlhXjx48H6tXpwAMPBOCKK64AYOrUqYCr+P/ggw8C0KNHj7RzZXOrlzKso+sYMGAAAKNHjwbcBu8VK1YECvPSSy8BcNZZZwGusqJm348++si/3ryvJymF1X2qjrS6yK9bt65Br1jVLpaz8Ve/+hXgitB16NAh7ffnnXdecKxc+GPYpUuXOnAdGPJBndZVzVKfG42XH3ZJvUbds4oHaoWoonVaidx5550A/PKXv0w7ZjZMYQ2jCohNYTUj6acUVDWD33zzTcDZMDrv448/zp577gk4W1bHGDhwIEBQtzhMNfNxMhSjsDq/ZlLN6Kol3KtXL8Cpi+5n2bJlQanWUaNGAW6WVslM2cFS2rDN6amEOeFK5XRK7Sqoe9YK4YgjjgBg7ty5ACxYsABw93PTTTcBcMkllwAuaWPZsmU5VTbqGKoH0pw5cxr8TderMONmm20GOLvU78JXV1cXdG3wO1WoKIGKCeozrjFv37494MY01fEmP4fKHoXdozCFNYwKoujUxAMOOABwCrr99tsDMGXKlOA14PqOaNaT13bdunVst912gOuZqt456mEjj93QoUMB6NOnD+CUVefIFO7x1bcQhZUnTzOs7/mUt9S3bfT/P//5z4Gdc/fddwMuRe60004LXgMu0UTvHTt2LAAnnHCCrjenDZS0wmqlsfPOOwP1m0C0GpKayXeh+5D3VX2IVJR87733Bpy3vGnTpsFzDSvOV8gYygt/8cUXA+7zcP311wMwfPjwjO/TZ2rWrFlBedPp06cDbpUwbNgwwKU7arXUtWvXtGPpuY0ZM4Yzzjgj6/WawhpGFVCUDduuXbug3428wgsXLgRcTHLp0qUA3HzzzQDMnDkTcDNXqr2injlvvfUW4GykxYsXA7DXXnsBzv649957ATfDSeWzEYcNq3uVWso+U58cxZp32mknoP5ZaEbXMWTPXHbZZYBbRehedcz9998fcEF3rUyi3mOUMZS3V2iFoNWJf07dw+mnnw7Ue8PVbiVX6Vkd+6GHHgKcd1j+gKZNm8Zmw2a6Zo2Z2mj4iRxCn0+N9euvvx56bD2nQw89FIAHHngAgDfeeANwmxDk+9B3IhumsIZRBRSlsDNmzAg8vMrckapoja5ZR3ZJhnMENsoFF1wAODtDHmZlpWh2HDx4MABPPfUU4FS9VatWQSZLGPnOzqnXJ8WXza0GV/LwKeNFKwVlPC1ZsiQ4xuGHHw403FDuK7COpS7uUrOw9Lewe8x1f61bt26gimHZQLpPxZuVpXT55ZcH3fdybZ+TDatjqWWJVLWQGGXYPSrGnZo59ZOf/ARwsX6f2267Le06VQAg9dqUlSS/hNDz0opFqyjdo1aI69atyzmOprCGUQUUHYeVl09eTuVNKndW9qgUOBuffPIJ4OwKKZRmLima7OHHH38ccDNYjx49chYljyPTSTE62TlSCyX0SxV1vV9++WWD6/J7h2ocpM56FtqG+Oyzz0a+vmK9xL5KXn311YBTG12j7O4zzzwz8rGldnqG6pur1VPqc9J5/MZhxYyhnwctX4v8IlLFvn37AjBx4sTgvfpcduvWDXC2qv7vtx3V6vPhhx8G4LDDDgOcBz0bprCGUQVkN/gioNzYXXfdFXDxLjVMuvLKK7O+P9VGlDrLg6e4lzp9y06SbXvUUUcBMGHCBAA6d+7MsmXLCrqPKLt1/IbNspGkFppJFZ8TmVYx+p1idqeccgrg7CLZlcqnzlTuJMo1F7ILyc8L7tevH+B8BYoha1dVttiwVlbKgJIfQDFLrZIyrYySaMkplRs3bhzgVFwK6zdn3nbbbYF6m1cZS7pveX19/4yem2x9+ScyKati2cqKyoUprGFUEAXZsKntHlXqVFlA8q5pZtJsFDbTDxs2jPvvvx+AW2+9FXB2jY9mOKmPbMXjjz8eqM+WUTPdsPtKYreO7B7tztG9T5s2Dah/XitXrkx7j/KM5TnUvckukq3n7+eNQrE2rB+HlborZ1rXrqw1rTi+O1/asaQ2fs6s1HOrrbbK9/Iij6F24ijLLhXdg65LnnHF8rV60ucZXN6zdmP58Wt95uQ9P+644wBnJ+v1xezWKcrp1LJly2CwtI1KaXwa5Hnz5gFuKaQB04eiZcuWQYBZoYxcH1J9KLTUOPvss4Nz6UsS1iUuiS+slsoaZDmMdJ1NmzYNBknXqmXmqaeeCrjl5pNPPgm4Jdt9992X9/XElZqosdSk/O677wLuS5Y6+ereNXmpvpTGw0ev0xjms2T3x7Bp06Z13/0+8jFE6hiBS+zRBNq5c+dM5097ryZXPRcluehzXIhZYk4nw6gCilLYXr16BduotBFZx9OSQ+qjmUp/l5Hdtm3bwP3thy60TNFPOSY0a2v5ePDBBwP1SQlyOmn50aVLF8AtzZJcEmsm1aojNe1Ns6uWzTIVtOFBS3klhWgThUJFms2j1LMqVGF1DiXiK21S59R9aZmo5PZVq1YFzpqRI0cCLuXQR2mbvqMx0+cwLIGjmDHU5gpdh1Z+umfVINNK6Jhjjkk9r86X8di+M0orLTlg5WBMRSsqOai0dW/FihWmsIZR6RSdOFejOAYAAA1pSURBVKEN11JUzT5KetDmACmG7FWFgXbbbbfA3unevTvgkuj3228/wG2rk7L66vTII48E15NE4ngYWj2kqHfGn+BsUiUfKIVTDjMlg+y7776As4cU5snHPivWhlVaqdTnuuuu07EAt3KQj+HGG28MipX56NnIRpfKDBkyBICnn34638sLHcOwtMhWrVoF9yJllUNKqzOFIzWmwt8ymQk5l1S0oVOnToD73Kr0ka6rWbNmoT6WsHsUprCGUUEUnTgh1VN5FK3BpYKyQ/xSoJptFi1axKxZswBXCEvqrPId8iQq6K3Nz0obSy0dmroxPml0XZq9hTymsrnXrl0bbEDfZ599AHj//fcBZ//qGcjGUkBfx9Yxk+wmoLFSArzKu2jrmzzbUn2p5KpVqwLFUChI16kiBEp2kaLG2QnAL5Xj25jffPNN8DcppT6f+r1WivIx6BiKfrRt2za4R6mw7vHEE08E3JjqHjW2Sl1U0ksudc2GKaxhVBBFKezXX38deANVqvTYY48FXDK3CiOr7Iu8cEqOGDt2bKC28q4p1U9xLM2gSnOTnbfHHnsA8OijjwL1KpRPgkGx+NvuNKNKZVSU66uvvgquS4F6rRa0mtAsrbRMeV2lCNrSJ69xEkj19FMJIEruUDxWCRS6dnCKpPRMjZ2eiVIQ/dfnQ9h7MhShS/v/mjVrAl+BSo7Kp6KkCimrb6tqxQNuS6S2SMpvo5KtyjHQZ13PS55wbZAZPnx4wf2kTGENo4Io2kusImJK0FfMSbas4qDywqmlg2anurq6wBb0PXR+3Et2nPDbQUBpvcQ+UlbN+FplDB8+PFDho48+GnBxV2UySWH1LHJtE8xGvl7iqOmPssW0ZVIFsydOnBiMu1ZBuQoJRCFqMfgomU767GhspH66Xr1XHnHFmrXh5Ntvvw02aNxxxx1p75Ftr+2U2kjg29b+6jMT2tb3zDPPmJfYMCqdohS2efPmwaysMpe33HIL0DAvWJ4xed203h83bhxHHnkk4GY1eX31XtnJyqy56qqrAGd/nH/++UD9Vj7NysW26shWnNw/tp8rqu5m2uD9xRdfNLCzpEBaaSgzS+fVTz23fLyqURQ2m6r628h8tGqSOk2aNCnYLqfYY66SL8V0Yo86hirOt8ceewRdFZXb7je00r1oZaOyrLLTa2trg052flxVEQBtB5UPRp8L2ctamXz22WcNCofnukdhCmsYFURsrTrCOm772TGyU7PFS3O1qQhTvylTpgTZUWHEacNqVpbnVzaetmjpPlq0aBFsr1OOqvKnlYstj6xin8VQaKZTpqJlqWjVpL/LDuzWrVsQx9R4hrVzLKa3r4g6hpm8ysqKk+dWRdn0Wl2/fq/C6HvvvXcQjdC9+L4XrR5ffPFFwLVJ9YsqbLDBBkGecdjzMIU1jCqgaIWVcmpNnmQmjvZnqjmWSpVIiWtra4McZWVJ+RSjsFJBFfnWTKu4m0qR6nrkTezUqVMQd9Xz0kpAm52V26qYnsreFNuhPA4vuL96euWVVwCnooccckiQ0ZTLJlU8VLnVhShuvmPYqlWrwFb1s+T0fH1bXmOoVVLqfcnfoEhIGCqHqiLrqcg21irFt+lNYQ2jCoi9oXMpUKaTbMcwNc1EHNUKVBpFe1eVa6tmxfL8yv5ZtWpVkAmm5mGKySlrRvE+ZYIprlkISTfDUjaWWqVkolAvcL7Nvr57T8FlcBRD1WpNEQl5dGW3nnvuuaE7dlSJQ5lt2omVqc1lVBIpEVMq5NxRul6UPrBhxOl00odSgyvngupbqTPa7Nmzg8356pKmKoL5dg1v3759aLhFJPWF1XJdSe5R8J2OWlYXkwAf5xjquhRm0xI900YCLZ+V+KAECW2ZzLW8z2cSsyWxYVQBsStsIQWnNLspcaLQ2sJRiHN2lhNMYQ4tiYRqCrdv376g6oeFkpTC6n4VwlH4aptttinJdkaRZHqpjyp5nnLKKYGTTQUGM1VjjEIhy35hCmsYFUSjtmGVZCBbMA5KMTuXUk0zkbTTKZ8kiEJWXLkoZAyTuI4kMYU1jCqgUStsGFG8bWHpi6W0f1IJK9mZBEkrbNJofLV5XGl8olxjGCdahSkdVH4BYQprGFVAVoU1DKNxYQprGBVE1joelWgb5KIa7J9cVLoNm4vv2ximYgprGBWEfWENo4KwL6xhVBD2hTVKynrrrRfEyI38sS+sYVQQxVd7Now8KGYvs2EKaxgVhSlsiVFDJZU3VXGwakF5wPfffz/gSuKoaoj+rlzaYlqSfB9pVMn/xVSDj0pjCbqPHDkScF0L/L5CxVCOxAmNnbq3jx49GnCbHeRo0uvUHUGdItZff32gPgk+17K5sYxhkljihGFUAY1iSaxZV9uoVONWlQaXLl1angtLANXFvfjii9N+X4rVRZLoulWXWvfjd7FTTWeVWdGG8rBuA0Y6prCGUUGUzYZVn5aVK1eG1nv1u8PFQTnsnyZNmrBkyRLAFTJThzOVblUt4zgohw0rG1T1lOVUEiqNOmrUKMB1D9Dr8ylo1lhtWL80kD6/qvKv0rZRMBvWMKqAsilsSg+RjF3GUlHRrzgKaJW7RIzs89mzZwOue8EhhxwS27nKobBhnyP93u/bW+S5Ch7DLH2DAaeOUkvZ1jNmzODQQw8FGpasUU8ldX6Qoi5cuBBwqyeFuhTSy4YprGFUAYkrrGY0dbdTjE0z2eTJk0N7usYxG/uU2/5RiwoV3ercuXPa7+Og1ArbunXrYOUgf4OKqisxRPHYOLzgxXSvy3Csoq+nR48egFs9vfPOO0DD2PrVV18NwO677x4klIRhCmsYVUCjyHTyu7ZnuI7YzlVuhfWfd5z2eco5SqKwUtP58+cHbUlOOOEEAE4++WQArrjiCsA10opyn7kKsccxhroOfbbU+1ftRzKhFjJaFSmmfMwxxwD1du531we4VZOvtFE+z6awhlEFNAqFDbuGarJhNdv6m7eTvsck70/dyVOzlKRcGlM/0ykO8h3Db775poHK+d7rXLbsjjvuyNy5c6NeX8ZzpFxv8G9lhvlxeFNYw6gCGkUu8fcBxVvbtm0L5Nc1vrGSKf9Xdu1tt91W6ssJJVVdpW5du3YF4Pnnn8/4HrXQyGf7n5/hpHMpF/6GG25o8J58M9xMYQ2jgjAbtgT3OGjQICZMmJD2OzWvXr58eeznS9qGzfaZUQNkZQW1b98+7tMXNYbyIWh1oBWBdk9J8RRb7dOnT+Tr8j3PinpI4fOJ+YbZsI3yC+tXJYizz2o5vrBLlixhs8020/mSPl1iX1h9mLVJ/aqrrmrwmq233hpwaXlJEOcY+k6nQrY56rXql6tQXffu3QF4/fXXCzmmOZ0Mo9Ipu8LmOH8S5yu5wqbeo5ZHSVYPjFth/eSODTbYAHDL+bq6uiB8I1UZPHgw0HCjfhzEMYZ77703AC+88ELe59fmeyVZaOvk5ptvrusBwnsUR8EU1jCqgEalsH7BroTOl7jCKiSQKeFcTo5SFZqL8/585ZBvYe3atQ1Kwug1cW5qEOVKftE9hqVXDhs2DIDx48cDzqYtBFNYw6gCGpXCppw3yfOVbHaeMmUKUB8akNL4pVOSIG6F7dWrFwAvvvgi4MrbrFy5EqhXVZWIWbRoEQAdO3Ys9rShxDmGWhGEbTzxzpv17xpb2azFrKJMYQ2jCihbamKllvOMwj333AOkB93jLBReaqSs+jl16lTAKUpdXR3z588H4Prrrwdg8eLFADzxxBMAnHnmmaW74DzIpay6RxXLy4RWFaXoG2QKaxgVRNkUNmxbUTWgJO+jjjoKqC9vqdhdJdKmTRsAPvjgAwCuueYaAN577z0Apk+fHpQpvffeewHYcsstS32ZiSBPeKZtgqXIWvMxhTWMCqJsXmLNXJm2PiVJKbzE2titeGxdXV1QdK0UxOUllqoogV+FsHv37g0473GfPn2C5l6TJ08u9HSRKcUYjhs3DoDDDjsMcJ7x784X9+kaYF5iw6gCyqawW2yxBeC8id+dL6nTBZRidi53Y6u4FNbPbFIsWSVrP/300+B11113HQAXXnghkGzf1yTHUPesLKXUz2S/fv0AePrpp+M6XSimsIZRBZgNG+M9+rbrxx9/nPcx4lDnuDOdlP/84YcfAg3zhVu1ahWobik6yic5hnfffTfgSpd654nrNDkxhTWMKqDszbC88yV1utTzJm7DKuNFCjRt2jT22WefSO/VXlPl6RZCoQqrFqBq9hSm9rJdlSt94oknctlllwGuGkWSJDGGWhWpdIz2+mo8unbtGrTgKAWNtkTMjTfeyFlnnZX0aQJKmfxfLudTObrXlfJeCxnDadOmAeScOOUs06Sr6pa9e/emb9++AEyaNCnfS84bWxIbRhVQMoX1HTFy0ABBgTLVb02ScvfWKQWlUtjGsIL47joKvoB58+YBrgP8wIED0/4+YsQIAMaOHdugL2ySmMIaRhVQdhu21JjCVj6lGEOVgVFIq9SYwhpGFWC9dQwjA6VSVm1ZVCeIXJjCGkYFkdWGNQyjcWEKaxgVhH1hDaOCsC+sYVQQ9oU1jArCvrCGUUHYF9YwKoj/B8u/Pgz6IGTyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4320, D: 0.24, G:0.1539\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4340, D: 0.2359, G:0.1498\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4360, D: 0.2352, G:0.1732\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4380, D: 0.2757, G:0.1349\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4400, D: 0.2153, G:0.1859\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4420, D: 0.2408, G:0.1462\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4440, D: 0.2336, G:0.1265\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4460, D: 0.2331, G:0.1548\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4480, D: 0.2404, G:0.1535\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4500, D: 0.2615, G:0.1515\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4520, D: 0.2354, G:0.15\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4540, D: 0.2278, G:0.1534\n" + ] + }, + { + "data": { + "image/png": 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kq6zA0g1jT2cAe++9N+A6wyW6XxhIcmqt9Hx1atP3pWPLIjxjxgx23313oF7axiJbhbr95ROTsIZRRuRkJY5EIjz99NOA01kfeughwHVR9/H10EgkEhe5s88++zS4hvQj6cfanddbb73gGpC/sDZJEnWI1zgkaXUC8MeRKCJJpwLNX6cKdT2XhJUE1vPKhFx1WH/cqZ7rsmXLAikTc98GX+sEoS6GfmSR1nLVqlUpxxeGDivJqv/60Uv6vtZ45cqVgYRVxJPWTpbl7bffHnA6bi6YDmsYFUDOfljpBJI+H3/8MVAfewnO36jdKVHM6aGHHgq43XbIkCGA87/J77XLLrsATpfUDjd+/HigYROnZPPKZneW3iU/sP4r3bl79+6AiwPWz2PHMGzYMMBZkqdOndrgHkpDHDlyJAAXXHAB4CSPnrPu2RhhWYklJdUOVGNIpKNLF9fpaNy4cYDzy/s2Dp3I9HmIxdd3fcKQsDq56HO4bNmyhL+nuR5//PF88cUXgPPDy0+sOeh0kM4pIRUmYQ2jAshZwursv9FGGwFON9OOK5Sm9NVXXwFuFzryyCODyCbpgu3atWtwLT9tTdfYaqutGvw8HcLYnSUdZs2aBdTHmQJMnDgRgAkTJgAuc2jfffcNLMnSd5Kly22yySbB3wA89dRTDX5eXV2dMsUuX35YnYB0GhBVVVVpp57pGelUtWjRIgA6dOiQ9jgKEUus9Tn22GOB+tjus846C4AbbrgBiLd46yQSBiZhDaMCyKnMqaQqOH+bdNkePXoA8NZbbwHOtyoJsvnmmwP1upwyV6Sb+tJZFkVJJ91X95IlD8Ipo+rTunVrwEkH6TvSyzVe3XvOnDkAzJs3D6gvh+NLpeOPPx5w+rf03q233rrBPceOHQs4SRxWYbrvv/8ecDpmOmgNfWm69tprp+3/7tevX4OvM5GsYZLKvy299Nlnnw1+X/YYfe60FmHorOmS05F4woQJwYdWL6KOfjrWylCh44L/Msbih/z5x0a9hHpAcrfo4dfU1ATGjqOOOirZPbI+ToVZCfD5558H4PDDDwecMUphjjJKqUKE0hWnTJkS5/T3yXdootZWm3Rj6IXQhqQ0w1woxJHYd9ENGTIkCPbX508d61988UXA0usMw/DIScJ26dIlMI7cddddgNuRdHzS8VGha4mSA3Q8njFjBuCOSdrJ/OOk7pFuOKT3txnvzkoF1H1lZMpHuKACEHQ0fv/99wFnhJo6dWrKxIp8S1gZitIJ6kgnTDNTilX5Up/hnXbaCXCfPxlNTcIahtGAnN06kj4qJqbrySGtXem6664DYOHChQD88Y9/BOrTr7QLH3jggYDbweUe2WKLLTSeBvf23SprrbVWUJYlGdnszgrVkz7uJ+OHie6lk4jCMvVM9t9//2Deyci3hNV6KJCgMSpJwmr9ZTu59957ARgwYIA/vpzvZRLWMCqAnCXsdtttB8DkyZMBl7Cs8302O6wfmJ3MZaDUp0MOOQRIT4fIZXdWCGLv3r0BVyLk22+/TfcSKZGE1brIvTN//nyg3lKtrgnCt2jmS8IqQCDWjZaMM888E4D7778/rNsHFEvCam3krlPY6F577QXEp93lgklYw6gAcgqcGDt2bFDqRBZDhQ3KyukTW5gsGZKUKmrm+2GV7KxEAz+UMV/IGqhkhNgE7rDQaUJzUzKACtR9/fXXcX+Tr0r0fjK61kVlU2QtjU1kkJ4n3buS0OeuU6dOADz66KOAK85XCEzCGkYZkZOEHTRoULC7K1Ln6KOPBpz/ULuSwrl8yVpVVZVU9zz55JOB+lKoQFBKU7qCQhWlW3Xs2JFvvvkm4bWUKJAN0qU1x44dOwIuYVmhkrKAZ5JQn2ycCtPUbq7SKYXsPq95qLyn/OXyi0+ZMiX4XUWWSWfV2hVyvPlGqYNKUtGJTt4NRa/lE5OwhlFG5CRhYyWjksv9cpdCQd+y7Or32rVrl1T3VFEr31KqBkuK6ZUOm0y6grPsZYOkhMq3SMJIokrCHnPMMYALGE8H+al1TTXxkh/bL72ST5QIID+zTi4am56z/MD6fl1dXSBddBqpJMkK9Rbit99+G4Dzzz8fcM8htnBCvjEJaxhlRMH6w7Zt2xZwlt7GrMQ+yfqvauxKVRszZkxKX2wuPjxJekVtqfi3eq0qo+aggw4CXCG12LEmQ89FqYRKjlY2j3TadMjUD6tkcq3Rk08+CcSn8inWW9FkGuO0adMCPfe5554DnE4bVjpgLMXww1ZVVQVpoLJlaN3zEVNufljDqABy0mEhfYtobW1t1vfQDqb8WOVhSufyy6iEzfDhwwFnAZU0UQMr6XLSN2VF1Phqa2sDHU9xuIqSGjRoEODicjVXRdFI2uUT5XMKFb+TT1gRPt26dWvw30RUqg7bvHnzQIeVT9yP4stH8QQfk7CGUUYUTIfNBu1gyq1VU6pbb70VSF4GszGy0X8UtaWIFj2zK6+8EnANrOR7lE59wAEHAPW5v8ph9aOjVDJTVmxZ1+W3zoZsY4n9Sh8qI7PlllsCrpqIzxtvvBH4j+WrzWdrkULqsIoT7tOnT+D/V3bY0KFDAQLJG6ZkTabDlvQL66OavL169QKy6+5ejPIiEF9NX6GceinytdjZzM+v9zR48GDAHd919Fd39UwMiGGQzzX0j7kKtRw2bFiw6arHrdbS7wEVBmZ0MowKIGcJm0sYXrpop9fOnwvFSs0SyXrchvkcw06vk5FF4aYiUe+gQlDIU5LUrlWrVgUuq759+2ocCf82jI6DJmENowIIXYfNh8TNl/T55doZX1Thg6rsn4pEPXDzSb5LxBSbYgVOJOt0lw9MwhpGBVBWVuIwKLYOWwhMwpY/JmENowJoVMIahlFamIQ1jDKi0eD/pqAbVPocK31+0DTmKEzCGkYZYS+sYZQR9sIaRhlhL6xhlBH2whpGGWEvrGGUEfbCGkYZkXMRtjBJll+pLIljjz0WqG/CZZQXKquiRt6KsNPaGulREsH/L730EgBHHHEE4FLQVJVQXdJUikOVBEeNGgW4rnLp0NSc7qUyP7+ioOojaW0zoamtYSy2vRlGGVG0I7F6ykycODHo/uUfhefMmQO4yoIqgqWyHerTqor8Q4cO5aGHHgJcf5hSQSeZ9957D4AePXoUczgFQ/2DH3jgAcBVg9QaDhgwAICnn366CKPLjIsvvhiAm266qWhjMAlrGGVE0XXY2traoMia9BsZn2SQUCc3fV9Isvbs2ROor2CfKl2wEPpPOimLfpe/E088EQin0n8xdVi/AJn/LLRmuZyACrGGOr116dIFgD333JN3331X9wNcP1j1P1I95zBqMpsOaxgVQNEkrHajWbNmBX02paOqwr/68UiH9cuctmjRAnASOB3C3J1V5V59a7WzTp48GXAduxOhOUlPjxlPtsMJKKaEVf9fdfDzCXt+v1wz6zlOmjQJcF3U9T5oLWPdTv6pSJ9hv7BePuYoTMIaRhlRMAnbqVMnABYsWADAl19+CdRbC9XZuxCEuTv7/Wr9bm/agb/99ls6dOjQ6LXUS3bYsGHZDiegmBI2lf5eDAlbU1MT54FQd3l5KzSu5cuXA9n1bYoZT9Z/K0zCGkYFUDAJK4kqvU/6qvTQQpEPC6MisFatWpX0d9S1e4cddgCc9PUpVx1WkUzJ2naEMS8R5hom+/xrvNtuuy0An332WXCiSuVLNwlrGAZQQAnr3+fHH38EXOvCQhHG7vzEE08AzneaSQB7oXW8YuuwYUrWmHvkvIa5jFNeCfUt9pHVWKeObDAJaxgVQNEkrKiqqsprq8oE4yiJTI98SqJiSNhUumDI98poDddff/3Ad6qm4D4bbrghAEuWLMlkHA2+VvSWorlywSSsYVQABcvWkZ4nS+rGG28MNNylkllbw2iQW2wGDhwIwJgxY4o8kvBR3nIs+ZCs2bJs2TKWLVvW4HuyoWhdXnzxxYyv60dDyQOSTwp2JNYCPvroowDsscceAHz88cesu+66APTr1w9wzmsdU8JMlSvGkXj06NGcfPLJuj8Ad999NwDnnHOOxhHa/Qp1JG7MlZPPF7ZU1BqhNQ3D2BRzTTsSG0bZE41Gk/4DomH9i0Qi0UgkEq2rq4vW1dUF37/iiiuC/+/WrVu0W7du0VatWkVbtWoVXbx4cXTx4sXBz5s1axZt1qxZTuPI5xz9f3379o327ds3CkRramqiNTU1wXMQ+bhvoeaXiHzeL8w1rK6ujlZXV4c6/3zOUf9MwhpGGVEwHVZOZoUkNjqoX/QflRORSX7p0qUAQcJ7NhRL/5k7dy5AkOiggJF8uz3yMT+FVargQCyFMDYVW4fVHGV00lrKkBUGpsMaRgVQMLeOJOvrr78OwL777pv0d7WDSbJ++umngEuJKkc6d+6c8Pt++c9yINFYN9pooyKMpLBoDWXZF3IZFeJ0YRLWMMqIohdhS3JfAL777jsAhg8fDsC4ceNyvnYx9J8VK1YEidI+5aTD+gn7sRQyUKLYOqz/zihwIswQW9NhDaMCyLsOm6xQVSK0S0tXUBB1GJK1mLRs2TJu/tqN/bIypUw+JEk5oeKAPo2dPMLGJKxhlBF5l7CyBs+YMQOIL1UKbuf2Y1JVqLncWblyZdz3yrFrWzLJGqb/sZQZPXp0g69lY1HMeyEov0+NYTRh8iZhp0yZAsDWW28NwJFHHgm4AlaitrY2kMLSdzPRe8uB+fPnB89Bu3IlUegyP8VCJwzpqrlE3GWLSVjDKCPy5odVMq/KmyrJeZdddgEaJv/6JT/zmaheDB9eXV1dsCsXwtIath82le5ajoX0MmXp0qVssMEGgPt8duvWDUicwJ8r5oc1jAogbzqsInv0X99SKklT6CJshaRly5ZAvc6j+ZfTXDt27Njoz9dbb70CjaT4xFaRePbZZwFXGaWQFCw0UZXv1ZF72rRpYV06Iwp5nEpUQkXHKr/GUJjkKzTRN7oUi2IciRO9J356ndJBQ7qfHYkNo+wpVIkY/1/Lli2jLVu2zGspkUT/CjHHFi1aRFu0aBGdOXNmdObMmdGBAwfGlRHp3r17tHv37nmfY6Gfb6Ws4bx586Lz5s2LLly4MLpw4cLopZdeGqxh586do507d05aGmbAgAHRAQMGhDpHKxFjGGVISabX5ZNip2YVgnyXiCk2xVjDddddN66AQj51etNhDaMCMAlb4XOs9PlB05ijMAlrGGVEoxLWMIzSwiSsYZQRjYYmNgXdoNLnWKrzk2U1mxNeU1vDWEzCGkYZUbBC4pWEgr+POeaYotw/3/1y1f4zWeH2TNquJKPYtpNSiYvOFJOwhlFGmB+2wudYrPnV1tYC0LZt29Cvne4a5qInFxvTYQ2jAjAJm8c5FquYXCIJu/POOwMwa9asjK9XapKqEk9Jq1atAmCttdYCTMIaRkVgErZAc0xX2u6xxx4AvPPOO1nfqxR02GSW7G233RaAzz77LOtrl5qEldW8R48eALzyyiuAs6JnU0onmYQtiRdWRy7Vf1qxYkXe7pWPxb700ksBuPHGGwHXseDwww/XPYLjpF/jSnWf5EpZtGgRAHvvvTcAb775ZnCNX8afcjy5vrDpuo1ix6Tf7dSpEwD77LMPAP379wdc/aOTTz450+HEUewX9plnngHguOOOA+L75eoF1vNJ5P5KtZ52JDaMCqAkJOzixYsBJ3UOPvhgwB2bwgwQ8HeumpqaKGTWAV3H24MOOgiAV199FYArrrgCgDvvvBNwBdfmzp0bdw11q1OBtgULFgCw6aabAs4dosJe2qXzKWGTHdt/+OGHBt/Xf9u1a6d7xF1LNYtViE5rrCOx37VPa5yOga4YErZNmzZcffXVAJx99tkJf+frr78GXDd6vwif5tqhQwc+/fRTwHVo9DEJaxgVQGihiSr16JfvlKSQI3369OkA3HXXXUC9Qq7uX5Ie6kOjiuq//e1vARg/fnxYww3IRLKed955gNspb775ZsDpfJKwQnMGp9tpF/Y79UmySoe95pprALcDX3fddWmPM1teeOEFwJ0crrzySgBatGgBwE477QTADTfcADgdLhGSlHo2kjb+CcF/Do1RyDBC2RT0efa7UyRCa+yfnvzevyNGjEgqWVNhEoLfKW0AAAqVSURBVNYwyojQdVjtpNpt1Fune/fugJOwm222GVDfL1ZO4yVLlgCw//77A/D+++8DriP7vHnzMh1OHOnqP41ZSiVxNt54Y8DN0d9ZTz/9dAAee+yxwBqsn/mB9dJ3TzzxRAAmTpwIwEcffdTgnumQqQ4rySU986yzzgLgsssuA9zavfXWWwAcddRRAEydOhWoPyX16tULcLqrTkeal6zEmtfxxx8PZBdUkg8dds6cOYA73fmBDOmgHsjST9Wp8JxzzgHc6SKd3sCmwxpGBZA3K7F2Ze02snZuueWWgJOWzZo1Y5111gGchJUfK8F4sh1OQKa787vvvhsEM3zzzTcAfPXVV4A7CchaOn/+fADeeOMNwO3a/fv357HHHgPgpptuAmDhwoUN7iMJql65AwcObPC1TiSZzjGdNZT01xpJyogvvvgCcNJQ6yV/+ahRo3jttdcAeP311wEnoWK7FIKTWKtXrwbqLabgdPdM5wfpzTGZjUUBKrvvvruulfQamqPmv+eeewLOiq5+sXqndMrYfvvtgYaf61QtW0zCGkYFUBJ+WO1qvr4oC66srfJv5UKmu3NVVVUg/eQL7dmzJ+D8xEpkly9V1lRJ01gLYyqrqL8e/fr1A+Cll14CnGRKcY2sdFjdW7u/JEc6aI6TJk0C4De/+Q3gpLKenfTi5557DnD6fyaEqcPefffdgPOt+ontet4rVqwIJKi8GoceeigAo0ePbvQeWsO//e1vwfdefvllwMUc+JiENYwKIHQJK11BVtB0/Jzanf3fVRytJNegQYMyHU4cuezOmpOidaTDSm+75557GoxTetyaNWsC63my5+GfMrQuf//73wECK2yrVq1SjjPXSCfpWn5P33SQNFEZnaVLlwLOgq7TimKqE1nhU8Uy57KGOgEoVjuZFVgnAn2etZbZkOgdS2WPMQlrGBVAzpFOvoVRlsNMIoik55x55pkA3HfffYBLNZN0KQaRSCTY6Q877DDANaeWDis95Mgjj4z7W5Hqedx///0J/1ZRR7EdwPOF1uHxxx8HXIRZKoYPH86DDz4IOL1X45dkkv738MMPA7DjjjsC8OGHH8ZdL1/F5QAOPPBAwEm9ZMXYdCLIRbKKzTffHHDehdhxSOKni0lYwygjQtNhfUujH/XTGNtttx3gfI6SNvLZZmNJTEam+k/btm0Dq+Ds2bMBJ2H32msvIJz8XcUSy5cr5JuWvzLsbJ127doFETnSHX3pkwyt+c8//xwXvSMpKZ+lLPzTpk0DXL6s5ptJydRsdFjdRxF4vkSVHUKReBdddFHa40lFIi9Itjps3t06Ev364F1yySWAc1ifffbZgWKvRAFNpmPHjoALWAiDTBd7q622CoxfcnfIzaQj5BZbbJH1eM4//3wAbrnlFiA+0EDIIFRXVxcEZsh14pPJC1tdXR3MI9mmq+O8PsQjR45MOS9dQ0d5P6FDRqdsyOaF1Rz8IH6pcjLmKeBH42xM4KRKQleA//fffx/3s1RhimZ0MowKIHRLhowLp5xySv0Nftlhr7rqKsCF3ClVbuHChcHuK3eCdsEwJWu2zJs3L0hIP/roowFXuyeVwzwWzU2BAzpeS7IK36WRKC0tmWTNhjVr1gTSTgYWpc3JNeMfJxMZaiSpFEygU4nmLVeY3ChdunQBEhudwuaDDz6IS5QXMpoKHYnTIZXKIAOcQjul4kG8O8k33ibDJKxhlBGh6bDJyosorUo7iHQJJbD//ve/D3a922+/HXBSSGFhYdbFzUb/0f1HjRoFuNOCdkONa5tttgFc8rlOE5FIhK5duwLOYJUKPUffnVNVVZXS7ZGJDrtgwYLA4KWQS+nImofm2xg6GfhBMDpxxSbzA1x++eVAvY0AYMiQISnvIbJZw2SfnXwmxWud/M9vJBKxwAnDaAqEpsMmS0T2Q+lkaZX1rU+fPoFbR64L1XGVZbnYFed1/zPOOAOAP/zhD4BLCbvgggsAp+v9+c9/BpzO+9NPPwWSVWmFkiy+VVjJ+kpLVGrWJ598Ev7EqHcnKUnh4osvbjA2udOkZyvkUpZtnTBeeOGFuLQ5uXPkJRD6uUre+KVvYiVPIdb9iSeeAOCkk07K+hoas+asNU4mRSORSMoOgckwCWsYZUTR0+vWrFkT7Lrt27cHUlvKciGb9Drh646+biJLqEqnSOp07do1COJXOONtt90GuJIp0vF0ylAPHCVJ61rpPJtMdNh11lmHBx54AHAhoPIrK5hBFu0DDjgAgBNOOAFwdohYpMPqmegavv9TlvdDDjmkwT179OgR+EDTLbKdao6rV69OWhRBJWhlf/CJXWN9Frp16wa48j2yAuvzm4xM9GXTYQ2jAihaB/brr78eqNdTZUH1pYdSsWRxLHQXOKiXqpIOstjK4u0neCuiRaGMko6rV69m8uTJAOyyyy6AKxGjsEZFdUninHbaaYCbezqSVVbeTJg+fTp9+vQBXBGxwYMHAy4BX3qWJKv8z7FS0y8yLnzJKr3/wgsvBJxnQIHxP/30U+glglq0aJFUWisGwNcpZYfQswDnp/bLlgolww8YMABw4ZhhWqJNwhpGGVF0HbZ169bBjqUym/kkjO7dfkOrZH+jr2tqaoLvyTr+yCOPADB06FCAoA2E0tTUAW3XXXcF0isNI7ItESNrvZIv9ttvP8BZUJUK9uijjwLQt29foF4P9lMQpbPL3yoLs6Kl5AHw7zlo0KCM/MzpzDESiQQlWpWyGXMtwJVwVUJHGOQiWU2HNYwKoGg6rFi6dGkgYdNtc1gIYqWkL2WTlU7xf0/zSZQ6Jp+nfkeZStKL1RZEkvXaa68F4tuBhIHGrYLXiiUeNmwY4KSfxtq7d+8Gf7dy5cpgfKeeeirg2nlonorRffrpp4F4nVFRYdXV1aGvf3V1dVBy1pewIhPJqkIL9957L+CK8GXTSiZZAn0yTMIaRhlRdB22Xbt2gVXw22+/zfftCtqqUMnavXv3jtNh/QR/RclIUilfeNy4cRnfN1MdVlZt6ZUTJkwAXEuOdJBFVFbgZIX1xNtvvw04S7po06ZNwvzRWHKJJU6Wb5wOI0aMAJw3YOzYsUD6Db2SxdsnwnRYw6gAii5hmzdvnnZ5EEniTMqJ+BSjGXCiDBvprIpLlj9SEU/nnnsukF51B59MJay/8/v6pfQrxXjr97KJSFNpWMUpf/755xlfI4w11JxkpZeuLX105syZADz55JNAvVVfvvRUJWuzGYf/HiaTsESj0aT/gGi+/82ZMycaiUSivzz0KBCtqqqKVlVVJf2bVD9v7F8+51hdXR2trq5O63c155EjR0ZHjhwZfH/atGnRadOm5TSOsObXvn37aPv27ePGnMs1tXaFXMNsxjx79uzo7Nmzg6+nT58e2uckmznqnx2JDaOMKPqRGOLdOfl07xTySBw7Dx19dJySkcnvXuZ3as+GbCv/54Lf0c4nE4NLKsI8Ehc7dVPoeB1TEsiMToZR7pSEhC0kxTA6Qbyhwi+6lW1CcyKKIWELSTHWsFmzZqEYmdLFJKxhVAAmYSt8jpWg3/k0tTWMxSSsYZQRjUpYwzBKC5OwhlFG2AtrGGWEvbCGUUbYC2sYZYS9sIZRRtgLaxhlxP8D79/mS7A2OkkAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4560, D: 0.2263, G:0.1634\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4580, D: 0.2205, G:0.1791\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4600, D: 0.2402, G:0.1616\n" + ] + }, + { + "data": { + "image/png": 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ld+Xpeo5hHRxKHVkxbFhDoT+OAcyKpIZJKkbXSit7aeTIkZEFweVhFPNKsmTUqFEAXg5qKhQjhheEVM8wVwQxrG1vKTNp6dKl7LDDDoDJC7eF3OXhVT2sGnvJLq6srPSyujbffHMA/vKXvwBw7bXXAqYYXvA3vwKzI6qoqEgqXNcOQP6JYjR0fuuttzx73HfdvF3PMayDQxkgNplTO+9Tq5FsG/vzU6dOBUxlSBAU51STLLF1LmgoDJtPBImw2RCrzpo1K4kpwsTk7N9rznv06OFlLknGVllQttde5/CrhPjP6f8+2pVGYuFiiLAtX77c8zdI8UM5BXZlUhwIY9jYwzrZQMFzJVN8+OGHebvWqvrC6ktv6xL7EwLCku2z+SKmO9buxO7/v8+5FHjOQm6JJXEjZxqYYhPpE2vxizPBw22JHRzKAA2CYfPpgLERB8MWcrzZIIhhxaxyJPnZVFs9MYQS9BXOiQPpGNe/A9A4lOao3rGzZ8/WOQrGsLvvvjtQvx1XiDKdsykOIUDHsA4OZYAGwbCFxKpmw6q3jm2Xqrzvp59+8tjWTt6wCwVsJhaqqqqSpF/DygXF9LJT7X6xK1euTGImOZ90jWXLluVtDiWLo75OYfecbziGdXAoA8TOsLat1NCwqjGs7k+hCLv4vrKyMslTG+alTRXuCQvv2UUBOsc666wDBEvK6Bw6VuNQWecvv/yySs2hH45hHRxKCCkZ1sHBoWHBMayDQwkhpUTMqmAblPs9xnF/6eKKTZs2LagXtVTnUEJ0UdquOBvWwaEM4OKwZX6P5X5/kN09yiOeTfsPxbBtIb044RjWwaEMUPYMa9tfjmELAzGX3ZwsG0iET1JAUefQL5reUPO+w+AY1sGhDFDSDJtNVUQ5MqwtPJcJwwY1+2roKPU59GeXCel2gt6xeR6bg4NDjMi5oXMxUSpZWmuuuaanprHffvsB8PzzzwOmWkUtT9Q0WWwZ1sLDj0zaHKpJVTaSrGH48MMPPfE1ibtJIFyie8oPVjOwt956CzDStrZ4fBB0jUxRUVGR8XfFlrDxtzTRvWlOw56/GpBLDkkIsqejjq9BbYlVMK2iYUlvSEVeD0BfYmneqk9n48aNC6L8HxVKuJ86dSq9evUCTHGENICVcKDJVwdzafKeeuqpGV83306ndJ32UkEvrhL5FSKxtadTIZc5tIsUOnbsCJgeQOpAoN63F154IVDfA0oyMVLnHDNmDGB6PnXu3DnyPej+tWC6LbGDQxmiQTHs2WefDZieodLJFZMqUG2XcKnb2siRI9PKmmSzOmdbMii2b9Sokade3759+8DPiq20e5D2r7oi6N6jbJ3yxbBiEHVmyIRhtbXUVlPQ/Wy44YaA6ZeUCtnMoTrv9e7dG0juorjPPvsAsOuuuwJw3HHHAYYJKyoqPIZVSaBSDTVXc+fOBYzEjv6fTXKGY1gHhzJAg2BYiW3ZLGLr0doOGGnfdujQAajvdaL+tGEd5Qphw8qBIgeMdgR+iJ3UH0gOFdk0YnPZWOr2B+GF5L6/54Vh9Wyj9OE96qijANOlzqcpnPC5xx9/HDCdAiTJ4j+HuukJueyS9Nw1noMOOgiABx54ADD9g8TEmodGjRp5vYS06/GNBzBsLEeiHW7z26lh9xZ2j4JjWAeHEkKDYFi7g5mglUk/Zf/YItTqFH7RRRd5x8pWlO0o5INhu3btCpguB/Z9rL/++l7HN0ll6rlLulOrto6VF1l9g3ScwiGpEDfDSjLm0UcfBYxPwXe9JKFyW5zcloixoXDT6quvnuSjsBF1Dm071Q+x4WuvvQYYr7Agm3vgwIFAfYeEoUOHAjBp0iQAxo4dC8DJJ58MGGbdeuutE86hudT//c8r7Hk4hnVwKAMUnWEnTJjACSecEOmz55xzDgBXXHEFELwCF7I/rJhA11JM1faE/nGdSOeUfThy5EjAeMyVUNGxY0eP8cKQLcOmKzkTSwaJsMn/EGbfhu2iBEUE1NU+FbKZwylTpgAwevRowBSR289SOwP1yFXf4z333NO7f/koZIfKa/7yyy8Dyfd4/vnnA6bdx/Dhw71dWdR7FBzDOjiUEIrGsF26dAFMrCoVlOH04IMPAsZOCmLRdDHTOBlWtom668njKWgVHzx4sLf6hqFFixaAsbu0AivbS3Z6PuOwtr0pJlXvV8UbtYMQY6y22moMGjQIgM8++wwwjOkbU8pryz4eMmSIN69hjB82h1GKQbQDszPidM/qkKjMNGWr/fTTT955dawy7I455hjAzOH3338feG3/zkTecNm1NhzDOjiUAYrGsGKSH3/8Me1nX3rpJcDkGKdCJjHKPz6XtbyI3VhK0GqtXqmLFi0KXf3DGmvp3LKx7rjjDqDeM5lOxCtuL7EYVex54403AoZFH3nkEc+OUzZUUJvGIIilRowY4R+/xh54TDZzKGZVJ3jt8GyEiarX1NSw7777AvDkk08mHOP/TBDkPZaHf9iwYZ5PIuwYx7AODmWAgjOsVrp33nkHqGfaP//5z4GfledROZtxIE4bVrsDsYnNtKk8w4rLKntIK61ihjpWmU5i1ebNm3srdhiyZdioggD6nMa+1lpreeV6UUv97Oy1qF70P8aX9Rz62n0AJv6reOzee+8NmPj3TTfd5L+Orh95rGDYeunSpUD9vSu7Lywf2zGsg0MZoOAF7FpZt9hiCyD1yqrczYYC2Sqnn346AJMnTwbgpJNOAoynUx7TVNDKatswyo+WLbhgwQLArNLffPNNpFzedAiyE6Myhz6n3UCbNm0yKqIH8yzD4rJx44cffki4nthOXtq+ffsCRkhAtrWee5MmTUI9ujZ0DfleHnvsMcA8txYtWnj576rDjYq8v7C2a14TlepFVRqeSp4aCjQRSoE8/vjjATMxKs2KAjtlUlDyebdu3QDjsLC7weWKTLagNrRgbLzxxpGPUYG6tpxCodQMDz/8cMAsqk888QRgwoqffvopUF9AEoTffvstqVesPqtjBd2TCgj69esHmBDR9OnTefPNN4FohRR+uC2xg0MJoeBOp7D0Nuu6cV/WQy4OC7Gb3TVcieN2skA2UOKHtl8qlp4+fTpgUhZTId8SMbr/oBRMG5nOZVVVVdqC70znsKKiwguT6bkqjHTttdcChDo+7bFB9IJ0hS5fffVVwKRd1tXVeefS8xFrawfqnE4ODmWAgjud5syZAySXM0HDV0FUSGD+/PmAkaYRK+YCnUNSKQozSFWwT58+QL0dlC4ZIRN069bNKwu0y8E0H+nK3eJEHNcIEDTzugfIP6I0Sz3vTM4bFZon2a7+82hsYtR0BR2CY1gHhxJC3hlWnkR50myPmh+XXnppvoeTFWbOnAkYz61CMQq633LLLYApZM4GsrHktRSrKcC//fbbA+lT/TLFqaeeyqGHHgqE26SFYNY4EZT+qbCN/vbuu+8CJuVz4sSJCb/3Hwv1vpdMQ1eS591ggw2SxtepUyfAlOZFFfpzDOvgUELIG8NqBfnwww8BOPbYYwGTVCDZyVGjRnkCY+eeey6QW+/OfEBjVcmV7G+toLkwqyAvoVZ0MYGegcSq48ahhx7qxZMlfaLEkKuuuirlsb5ePt7vbFssXWJ8PuEXPJdnW3Om7+F1110HJDOrkE2cWD4OpZVqHBIenzdvXlKng6jPxzGsg0MJIe9x2LDzDxs2DKgvAli8eDGQnPiejyyYTGN4zZo18zKZunfvnvLcmXgRlQKnUrX9998/4e+SFbn44osBkylk20NByCQOW1NT4xVXaK6UthcGW7YzqAesbDHZxXZRum98CdeOgmxi6WElkcosk3CaWqTIUy7Z2Shd/nQv2hWFfR+qqqq8nYd2VjZcHNbBoQwQG8MG9ChN+LvybBUHE2ttueWW3gr/9NNPA0ZUOh/IdHWuqqrySqFUdKyVU7Ivu+22W8LvBWW6/Pzzz563XDsLCcrZNp9ioSrZy7XNQ9j9aX4++ugjb2zpYoHK1FERuF+GVvFiec5lM4qpBNtGzwfDhokCgNnRyJ7Uc5Ykz+uvv55wrMTY7rvvPk90XFJFtpSrYrqyYXWO22+/HTAFLz179kx7/45hHRzKALF5iSXUpVZ89goiNlLcS9UTjRs39j4T1iiqmKitrfXyeVVErgoLMatYRTFU9Q/V/XTo0IEXX3wRMIXrNlQArp1KFGbNpLeqjWzK6sSssre1W1q2bBkPP/xwwmdtZpVQmdqXpJIGSicRkw5itiCmVZWOyucU15Z9qs9qd6SyvM6dO3sSMf/+978B0+tXZZbKGBNkL8s+3mWXXRLuD0y2nKIp6fKzHcM6OJQQYrNhw+KHdh2nbBtVR3z33XdeQ6BrrrkGqLer8oWoNqx2DF999ZVnm8ijGLUO1F7pg6AGwqkywDJFJl7iFStWeG1C0rUBsb8r8u7ffPPNXo2wzY5iEHtOU9mZ6RB1DuVRX7RoUVJs384hlliCPqedgPIJfv/999Bic/u5qNZ1xx13BIykqUQJlE+fCs6GdXAoA8TGsNqfS4hZNoDsK6242qM/9dRTABx55JFeE9981sEKUVdnjdMvC2I3fAqTDJE38f777wfqdxc280j5wI6/xoFs62ElhC1PqWw21eCqbYXuQRUwM2bMSDqX2C1Kg+ZMEXUOo7D4GWecAZh8cNnWrVu3Bky8XPZ7EP72t78BcMMNNwCEiuQFeYTDxhjGsLG9sBpMpqVYa6+9dlZOk2xRiP6wxUa2L6xSRLXY6mfYd0Qv+NKlSz3pF4Xm8ol8zGFYeEULtz91UGWW2uqm04nOBm5L7OBQBih697pCoxwZ9rjjjgPMlixXiZhskhlsgbJssd5663nOrKiavdncYy5OrzixzjrrAEaDWztTx7AODmWAvDOsHBNKHCg2ypFhbcQtwqaeOioXKzaizqF2Ctttt53nNLKT/6MybNOmTb1j0+08lASjhJps4BjWwaEM4GzYMr/HQtmwhUScc9hQ79UxrINDGSAlwzo4ODQsOIZ1cCghpCyvK3f7DqCqqqoOUnsL00lQ2iVxQbsW2yupJHP9305dVIaN0tz8tpZ9rgDxgLy26ig2spnDdMinLZuNqKCzYR0cygDOSxxyj/5VMerqGxbba9mypZdUHsYCYdewfx9UIJFKXmRVmMPGjRvXQbTu71HZzpZ+jRvpvlOOYR0cygANkmElLxJWppQL0jGshMj8Up7pWFE2pC3K5T/OLtezV1jbTrarn/znCmNhYeXKlUVn2O222w6A2bNnx37uXOKwela24JydAWVLlvphizWk2xUFjSHdbs0xrINDGaBBMmw+Ya9clZWVdZDMkv6Gu7Zwdi4SnTrGZsWw1VoIsr3CWLq6urroDJtPhDFsHJ5ee350rjXWWMP7bgR57oPOkcs4HMM6OJQBCt7QWZAU6N133+3ZZ7Zcir1CqdI/XcuETCDmCmNRP6NlumLqXK1bt/ZUCQI8upHO5R+HHXcVitFwCupFzvr16wfgKU+oyVQ6SKjsjTfeyHkcmcyP5r1Hjx6AaTYmOVZJ1kqg7vDDD/eabA8cODDwemHMmqoyKFM2LviW2E4caNSokaebo07V0mgNwymnnAJE/1L4kYtqfNhn7CQIlVdJN7h58+aehpXuUZrFUoqU6qDkcuxr+CfW3ral0gOKcw5T6A95/z7iiCMAuPPOOwPHKJ2rww47LOtxZOp0qqioSDJ5tPirQ9/gwYMB44xSWOeBBx4A4JBDDvG0tcPUEwXNcapevrZz0yn/OziUIfLOsJtssglgepdo5dVK17RpU2+1E9TL5vvvvwdMf5N77rkHSFZnV9e7KMjGYSEGtQXmNA6FMNR7R86fsWPHAjB06FBPOkWsq+vpGDGt1Ptsx4Zv/BndYz52SR06dAASFREfeughwDCVoHnWM9lyyy0BeP/99wPP3bZtW6/TQhjCHIepno29O9A9SNlfv1fRuSRv9L2dM2dOXsKMNnw7EsewDg6ljoLZsOpHstNOOwHwyy+/APUJBWLIq6++GqjXKgbDzlqdtcJNmTIFqO8eninCEsftsIo/jKLVWZ3O1LVAavbSsNUxX375JZCYSGHbnTZzPvbYY4ARUpP9I41gwd9FwD6H/rZixYq8MKxkULXDEEvV1R6c+jEAABOnSURBVNV5967np7FoVzR8+PCEv6dCut46YbukMBu7oqLC26XJyaTdmWxp7WxGjBiR8PsXXngBqBdJCyv+yAYuNdHBYRVAbGEdO7Av20DhGyVRq0OAVsGOHTty/fXXA8ZdLs+cViHZspKEVP9Y9ShRd7BsEJaw4PfO6t9iPclwdunSBTDMP2/ePMD0A1WHvptuusnrBqDr6LM9e/YETN8erehhXupU3utseslGgfoMqTu55kU9f1999dWkY2wPejbJJZl+3n42fh+Hnq+81FLrHzp0KGBSR+U/0c5AaNq0qddvRzu+XATEs02qcAzr4FBCiL1Vx1lnnQXApZdemvLz8gyvueaaHmOFQbajYleKWR577LGA6TQmuzgV0sXwdB8aX4sWLTzbTWyhcWilFUvKW3zuuecC8O233wL1nuBevXolnHfq1KkAXHbZZQAcfPDBAFxxxRUAfPLJJwnj8c+T/Ts7GT3u5H/Z4oplCvI96Pn7oZ4148aNy/XySchU5hTM3L333ntAfcIOmHtTixF1ERRbKyFk//335+KLLwbMjk/P3f7e2YktsvW//vprINpOyNmwDg5lgNgYVt5feTWzkcVIB61YdmbRoEGDANNdOxXslatRo0Z1/nHaXty2bdt6sVIxvVZUraSy5aZPnw7g9btVadnkyZO9zyoeK5aWV1L2oVLkZBfK4yz4S7PsVE1fnDgWhrWZ3P6uLF26FKj3U+iz6jHbrVs3AA488EAAr0N7HC0yMs10WrRokedvkJCAYsjyyo8fPx6Af/zjHwC89NJLAB6rbrzxxp7nWP2Bla0mD7h2Ueo1qx2Ivq+yj6uqqrxYs/1+uDisg0MZoSTK65TXOXHiRMAwrWxfeYmXLl3KhAkTADjxxBMDz5VNHqrNNMqXffDBBwG8pHBlyaj3q7zHkyZNSmKW2267DYADDjgAMCut7GXtWFTwbtup9r/9n4mLYcOuIygCIKYFMzf33nsvAPvttx9gMrziQNQ5lBDCeuutR58+fQDzXenbty8Aw4YNAwzjKpdbrTSVwdWuXTsv/i4oCvDss88ChlFtMQJld6ldSF1dneetVi6ByyV2cChDNGiGFWPYpW+CVvFMKj+irs6Ky1VWVnp5pbasiDyh559/fsJ45XnUuGtra70VVLasbCnZud27dwfMCq8mYl988QUQXFIY5ifIVy5xNrFD5eKqAiuVjE4G48go02nNNdekbdu2AAwYMACoj42D8Sn4M8gA9txzT6De/6BzqApLc3LrrbcCpuJH55IvRX4IZfDJB6K5D0LQLinh76FHOjg4NDg0SIZVPWFYFY68saNGjQKM3RcFmVZ6tGzZ0rO/FixYAJiVUp5QtWGUHaTWmmLTFStWJMVwZffJ3nnuuecAuPzyywG82kvbHgoaZ4A3N68MK4bRbmCbbbYJPUYxamW6bbvttnGMI2MRNmU9icG0Y7GL/jW3KqhXZt6kSZO8f2+11VaA8S+I0WXTnnnmmYCpi5Y3WTu1VCJs6Rg27y9smDpCEJQCZ4cybOghK5VRQe8oCHthU8HeCsuZofQ1uetV4KBkCH2h/VA6m46xt/l6XpttthlgQkgLFy5MOleYxlTciRNaTOSgUbfwTLbImjM9y0zTD/0Ie2H17Gy1EP8Lkq6LQyrohdOxQ4YMAUwSjBZbLWD9+/cHzCKgRaKiosJb5JWoke4eBbcldnAoIeRd0ymMWbUatm7d2tvS2oa/Da2SCmpnwqzpzhmGiooKz2Gh1DI5iPRT49FWXltlbZXmz5/vlc+JMe0wh4oldtttN8Cwtc1EfrbIR3JKEJQC+uabbwJmJxQkW3P00Ucn/O20004DTLKBIMkcFUXEodMV9l3r3LmzVwqZbYlcRUWFd+zJJ58M4BWtaD5UHHLHHXcA0KZNGwAvSUIMXV1d7XW1FxRG8ofIguAY1sGhhJB3Gzad+/6VV17xCtUlxmZDY5SS3Q477ACYRIVMkGniuL84Ww4TjWfGjBmAsVVVyC5nhO7ryy+/9Gw3rbZKCNeqrR3H2WefDZiAvW3jVFZWpg2FZOJ0GjdunBeeshGlK186KNVPxQ2+cWV8LiFMhMB+Lv7vXjZSOzZU5CCmVCKE5llhHX0PtCMTVB66fPlyj41lU+s74xufs2EdHEodOTFsVVVVkv0kD6rsuTBBLa0sH3/8MRtuuCFgVnIxmm8cCf9Xony6srwgZCPgZUNJFTfffDNgmFd2qexPyZH07duXu+66CzA6vAqe63kpnVE2nZ2KqGdSU1MTWrAddI/FTn5JIYECpPdbhBybcVgnzN4P20XYO8N1113XmzN9d/U90HwoFVWfE6MK+vvvv//uHRNWEuoY1sGhDFD0xInVV189VHBZheCXXHJJbNdLF8OTpzEo2d53DiCZaTVOpRsqsf/nn3/2vIIS+9p+++0BE49V+Zfir4p9BhWp23Krqe6x2Awr2932zuYjDmuf2xYPTzW+dCmTrVq18qIZOq/mW4n88jfYbN67d2/AyP/U1dWlTQ5yDOvgUAbIKQ57/PHHJ3WotlcsQYJpSr4WJEbmh1auqMyaSzzStmEErbCNGzf2VkPFSu3PjBw5EoC99toLwGNTFbL379/fy3BSQfeYMWMA42FWjFMpic8//3zCtfzexKD2HRprpojSqzQXxCkNGhW6nyBmVWxUc7nFFlsAJi4sNpREj571pZde6nmHlZo5bdo0wMy3Pqudl3Zr8iIrU+yUU04Jjbems+kdwzo4lBCysmH9GRsBxwCGdZ555hnAeHS14tqeYDBeNCVNp4Pd1TwKMo3hVVRUJLGvbecouVviXMqMCipKUBK6npPyTJVNpFI9Zb7Iiyi2iNKhLlcb1paeEVPYImypsnNUUqZMJyFsR5MJos6hf+dl5wHbcj+6F70PdnHGmDFjOO6444Dk9jNqO2Jf385bVnz2+++/T7urcTasg0MZIHYvsVY1rWj6v2wGrdb6O5isHlU/5ALFQoMqZSC7TKew3+n/Ylx75xEUL1XTK9kzygTafffdAVM4rfHbFSGNGzdOYtl8ldfZ9ylW9O8+BJWeKZdYMjo2cvEO+8aVcRzWhu5B/gnt7mw21DzMmjWL++67DzDzqeiAvdOUL0HfB2Xk6Xm2b98+bUmoY1gHhzJAzgybrr5QjPr2228DJv4olpAdmglS2a6ZNhmyM51s9vCzYzq7Q7a3VljZR5WVlWy66aaA8VLKVpX4uPJOVQStDDF/S0f/uPzjUbaUz86NlWHjgLzksu9zQRwMmw3ExrJZtRuaP38+AOuvvz5g6rltyVzZvIrTQ7I/RKy8fPlyx7AODqWOnOthw5hVObFiG1XfK89SAlVRqk9spPIKZ8sKNjNnEtvVsbJlbJumpqbGs+E32GADwNhMqvhRC03J0Iit7XH4a08FPY+sGyylaNMY5bw1NTVJMWCN1/YsFxJxiJYLFRUVXm64YuZ2W5cwZtX1/cwq2DH1oMhLwjjicjopgX/RokWAKUWSezzq5OcbmSb/R1lQ7DRGe3vdr18/T9FfPV3knFFqmroL6AXVT7/yIiR207PhO6bBpCbmA1G3xEEvbLYvcUVFhaflpCQgaRtLl1phMG2d7ZTbTN4B53RycCgDFDz5Xyuckt3nzp1bUNaN2lvHryNsS4+kW6W1DZQDoWXLlsycOROAzTffHDCK+EqJ0xZYesQKddnwpxIWSoStoSEOp1OmO76qqiqvx6zmyD6XbapEYfEwkULHsA4OZYCCMaxWH6WA2Yn0hULY6qzQSIBUR+hqHCY7EiScZjsglEBhi5SpRMsuosgkkWNVZdhM7NMwZrvyyisBoy08ePBgAN59913vu6sOfTbsxH27vC8Km7vudQ4OZYSiF7DnC1Ftg3T36PcSp2PUVM/STnrXMX7pF//vfQH0pHPZieypOp+V8hyGIRcbNh0LKylH4RW/WIH9vPPpe3E2rINDGSAlwzo4ODQsOIZ1cCghpMwbc/ZPacLZsKUPZ8M6OJQB3Avr4FBCcC+sg0MJwb2wDnnBiBEjGDFiRLGHUXZwL6yDQwmhbDOdwrCqeRjL/f5g1bhHwTGsg0MJoXj6HTnAFrpuKJDSgNoMzpo1C6ivdVX+78MPPwzAWWedBZiWEDYkzhYkK1Jo2Pm3kl7VvVRXV3u50lJZUE6ufg4dOjThmIEDBwJGvrWhQtI9Bx98sHcvkveJQ7I1UzSoLXHYWOJ8MHEWP0s9fsmSJYBRhpSUiErmFi9enFR6pS+/FPJVzhUH4t4Sqx/QnXfeqfMDcMwxxwCmexuY4gapQ0rXV8c8+OCDQHJH9kyQzy3xeeedB8BFF12U8PsLL7zQ+5sQVpIZB9yW2MGhDOAYNsI9piufE8N26NABMIrwWnl/+OEHbwuscjptHbWV1M84xOriZthx48YBppetuhb4NaXD5mi99dYD4MQTTwQMK4vBJJHz0UcfRR5PsZxO6667LmA0pPO5JXYM6+BQBmgQTqd02r9+QbQon48DfqYLK1Q/55xzAGOPSnNYxc+jR49OOq90mcWw0rgNk8YsBiZOnAjASy+9BJgxqTufOhpsueWWgOmXGoSrrroKMN355Hy65557ANPhoBQgZi0mHMM6OJQQGoQNm6IPDgAbbbQRAAsXLozjWjnbPwrRqNerbLrrr78+7bE77bQTYMSntWvQ/9UFLhdkasNK9F3sLw+v+uEMHz4cgCeffDLhc1HsbHnHtSuSHSibVcJmmaBYNmwhfCy+azkb1sGh1FF0hm3VqpXnZU0xjtiul8vqbPf9lC2XCU444QQAJkyYkHCusB5F2SBXL/GOO+4IwBtvvKFz6LyRz6H7kricZG3VvzfODuyF+J4G3bvdkiXm6zmGdXAodRSdYT///HMvfpliHJHPl67rXByrsy3ynQnkNR00aBAA48ePB+CGG27I+FxhyJZh44gBy9ut/rfTpk0DjL2v5l+y2bNBQ2FYF4d1cHBIiaIzbJrr5+N6RfEwKmZ5wQUXACYrSn1zFeOMA/kqrwsTZweTBaWdjZhUn+3duzcAL774YtrrzJ07F4DNNtss8O+OYR0cHEoCRc908rdPLGeoJEuMqsbXcTBrLjZ1JghiVqi3w3V/Y8eOTfibPM6Z5AqHMauDY1gHh5JC0RhWbf3OOOOMYg2hoHj99dcBE3/NpR7URr6ZVZDdrdxh2a0nnXRS6DGqh+3atSsAL7/8cj6HGDuuu+66Yg8hAUVzOvXt2xeAZ599NulvCkTnY6tcLKeTtoavvPIKYLax+XasxXF/TzzxBGB6pSrJQymj6mkL9X1UwbygKqsbNWoUYJQmFixYkPV4nNPJwcGhJFC0LXEQs6qMq9ycUKeccgrXXnttwu+USFAK6NSpE2DSDZXA72cYpWlOnjwZgF69egEmvHPXXXcBZo51jm+++QZouHNeDN2mVHAM6+BQQmhQiROfffYZYGyjPF2vYPZP8+bNgXpWscvMJBkj+ZU4EZcNK7tTjqLFixcDRulRaYgrVqzwSvLCHGBh3euzgbNhHRwcSgJFY1hJhPolUQphLxRidRab6t6aN2/uycjsvPPOgNEszgfiYthnnnkGMAXuksDZf//9AXj//feB7PShdS4lkGjuq6qqQhM0hGIzbDG+p4JjWAeHEkLBvcRandIVrZcyxKZjxowB4O677/ZYVxIx+WTYuDB9+nTAeH6XLVsGmB1EOib0Qyx99tlnA0bQTIJvsu0zOWexMHPmTAB23333rM+RbSmjY1gHhxJCwWxYCXlJ3jLkenFdLhT5tH/UU0csqsygqqoqBgwYABjRsagSKcoYeueddzTetMdkYsNuscUWfPDBBwm/mz9/PmBipRrrhRdeCJh0vSj3YDOJ7HqlZj766KOAYW8xcSoU24bdcMMNAWN/5+l6zoZ1cCh1FIxhtdLKvvNDv7v11lsBY/vlA/lcndu2bQuYrB/Z6c2aNfMKuGfMmKHrRjqnisY1T3V1dYHP0I8oDLvNNtsA8N577yX9TV5fsd8OO+wA1LMxGDtTbPjrr7+Gxl/VSOuBBx4AjMi6sqY057KXJaUa9f7AxWEdHBwaKPLOsGKIOXPmAMHFyZIE2XrrrYH8egrzsTqLLT788EMgdaZW+/btAZM1pFVaubdqj6F5UWMtNZGKgmzjsGpuJXlTez7kHdZPCd516NDBk2/VsWorqcJ2tWYUdM4mTZpozN65/buJdPeX6T1GhbLUUsWYtVvS7ilOOIZ1cCgD5J1h5RWWlzgIWmULUYidj9VZ7KHxy6OrAu/1118/yaOqPFyt4Kpe6tevn8YFGPs+k7hdrplOe+yxBwD33XcfYLzFkrPR/apL/Mcff5x2XPq7WpGIibPJKW4oNqxqetW4O+brOYZ1cCh1FMxLLAbZe++9geAYXqnHYcNQU1Pj2X3ykiqXunv37oCRkIkDceUSyzZv3bo1ANtuuy1gPLmKKS9ZssSrtNpkk00SzqGMpi5dugBmR5HO050KxWZYfU81p7ncS4rrBb4MRS+vq6yszMsNh6FYEjGFRL50iTOBzJw4ewYJq9oc+uG2xA4OJYSi6xIXkl0dCod8MKuDY1gHh5JCwV7YJUuWlHVJHUCPHj3o0aNHsYfhkAMqKyvz0u81LjTckTk4OCSh6F7iQmNV8zCW+/3BqnGPgmNYB4cSQkqGdXBwaFhwDOvgUEJwL6yDQwnBvbAODiUE98I6OJQQ3Avr4FBCcC+sg0MJ4f8BxuVXavP6SecAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4620, D: 0.2598, G:0.14\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4640, D: 0.236, G:0.1782\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4660, D: 0.2559, G:0.1637\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9, Iter: 4680, D: 0.2377, G:0.1485\n" + ] + }, + { + "data": { + "image/png": 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5GjFiBOCCHrRay9J25513AnDIIYcEx1B1fI1JQfQq7ixrXMuWLQGCruZnnHEGkN5q6etZUfUfdWpr1apVoH9LGvr3I7UPbCqpOwONLbXTGzS8P7IBqHtAPhSqw/rPhDrvyRqusWq+li9fzvnnnw+4XYWksPR6uURkj5BLLh9KHZqo+6CdSBLWYdNhDaMCyCtwQjraDz/8ELzWokULwEld9YaRZJFUlkS9+eabgbqVN1tQv3RGHVuf33777YHMaWy+rlhTUxNpdd5rr72AOr1a+rZ0VbH77rsDzscsXfyBBx4AXACIL1VzQdIsl8LsuUjYWbNmBQUBNFf698YbbwTcDsbX3XVdtbW1wdzoedL8K+jF992q76pvA4lCqSWs/5sxCWsYRloyStjq6upaSJ90DnXSS5bTu+66C4Brr70WcCunn8wtv62aP7Vq1SqrhJVkW7BgQd2g/7eiKTRMq3iU9Luoq7POUVNTE4RESq/18XV6jVdtOVq2bBlY3NUZThbHsILVisxSx3b1bk0NBw3rC5uLhK2trQ2uVcfRbkT39frrrwec39uP7KmtrQ2+o+LpkydPBlyvWY1Vlv0LLrgg07AyUioJqyg+P2rOJKxhGGnJS8Km0qFDB8C1VvRXn7Zt2wLw0ksvAU7CbLjhhgB06tQp6yBleZQPV76+2bNnA04KVVVVZZWyUVfnTNesVhyy6Pp+YunN0vGbNWsWREPJdzl+/PjgvdTv+uh+aWcShSgSds6cOUCdD/HRRx8FXFtJpTdKar777rsAbLLJJvWOkamwgOwcH3/8MeAsq9qdpSRjRL4uUapIJ98LodgCeUbixCSsYVQAGcN/osSIagWV1JNvSiundJr+/fsDTipKwmRCUkcRRNL7hNo5pvO/FppRkilOVlE9sgZLWgjp9dLbamtrA4kjFC8tvdHXZefPnw8420DcKBVswIABgc9cBQI07my6WabdjHYX77//PkAQh+u35iwX0j0HSUjWbJiENYwyIi8/rFbnBQsWBFFAS5Ys8b8LuCgZfUfRMZJ8mXQY6VBa8fWv9EFFz0g/kr80E7nqP4sWLQoiq5544gkA+vXrl/U8UXnqqacA2Geffeq9Lj1YydJnnXUWAKNHj856zMaUwP78888D0KVLF8A9B7qufHZCpdBhu3Xr1qAYfpL5r2E6bE5GpyjVC7VtHTVqFOCMUNr6HXHEEUD6QAD9+OV0V9qd3CpC39WWWAEM3tiBhg9CrpPdrl07Hn74YcC5qPxE7kIIu//aZutep1aQzMWwlsvDvOuuuwIwderUqF/Jiq5PY9YPNixFLeIxi/6DTZ0nJbYocCeh85nRyTDKnYLrEmubKpO/jE4bb7wxAI8//jjgAvYVDJ4q+fT/bJLru+++A9xWuWfPngC899572YaZGgiR8+rsu21kMFLgRj4ceuihAAwZMgRwW0adSwEGgwcPzvnYuUrYJBLR/VrTw4YNq/dvIfeu1BK2GKVgTMIaRgWQrXsdEL7ypgYq9OrVC4D7778fgLfeegtwuouOJSOTpGqqSyRs5VIonIw9MmQpWCMKhUgPBRYoNK0Q6SA0dklWoXtw++23F3yOqCRZ4kVuv0svvRQov8JtsluA2z36tpZiYhLWMMqInAInfN1t6dKlwWuy/iroX1JQuqzIxbIqC/P06dMBF3yvoHrfErzRRhsF1se4pMZmm23GQQcdVO+YOq+s2L/73e8AF4YpKal70axZswYhmwrmnzt3bjD2VKTjDho0CHCBB6uvvjpbbbUV4FwmjQldu9x3SvpQzWmViM0nJLGYSIqqTBG4UjClkKzCJKxhlBEFV/5XqQ9JNpVLkYR96KGHADjuuOOC72TDL6ciS7TC2fzABVmNs/X5gcIsjNn80JIq0uMlJTOhFDaF8imsUSu8UMkYJRxkopSBE9JZkwzbK4aVODW9Uuj5ki0jScxKbBgVQF61PxU2V1NTE0gbSR+lHN16660AXHbZZYALmFe7jZNOOgmoC2mU7qeVS+l0KlEiKX3KKacATodQWt20adOyjtkP0M+HbBFG0suiSFYF3F988cX1Xte91f3UDiiKZL388suzfiZpVKpWlnWlRJYbf/rTnxq85offlgKTsIZRRuSkw/p+2ZVXXjmQiipApoZQKhEiy9q4ceMA6Nu3LwBdu3YF6qyf8m/pWL4O6KfVKanbL5UShVKVFwnzaevatDMRvr6s76dLpPYphQ6rsqZCunguxeOiUow5VFqormPu3LmBN6AYmA5rGBVATjqsLx3WXXfdwDqslUjlK6+++moAzj77bAA++ugjwEWO9OnTB4CxY8dywgknAC46Sk2mJDlfeeUVwOmI0vNU2EzW2V9++YUPPvgAICjd2VjQvVNBM6XLqXRO7969AXcfdb/87zdWFOc9fPhwAM4991zA2Q7ibPdZDJTCKU477bQSjaQ+JmENo4zIyw+b6ksN+75fZkRSUMXXFCW0dOnSQCfzfZAqWHbHHXcADfUh+WlTI56yJUSXQodt3rw5xx9/PABDhw4FnIVbJVMkmfxxH3nkkQAMHDgQgO22267B8dXaU42tly1bVnQdVjst+eVVEkdzFmdkU5Jz6BdD0PNbXV1d1F2O6bCGUQFEkrCpLRnASc9cLICKhVXTLOlyLVq0CHRRrW6bbbYZ4PJdlTMq6aQYXVWCSM1XDWujkGszrCj4RdIzIR3Vb6mpv2UlVl6x8odT26FEpRRWYv++qwSMdkt+edNCdNpi7JJ0PTfddBNQt/NRy5JsyMJcSCvRvErExHEj0nVcB1cXd+ONNw4WALkw/HA9oSryqp8bJfFax9IDlERoorbsMqQpEESGmOrqanbbbTcAZsyYUe+7t9xyC+CS8NWRTwkWYdXmMxH3D1Zj8Bdobd979+7NM888AzRM7tC9KqQ/r0+SP1glZaj8S1h3haSxLbFhVAAFVf7v3Llzg/Is+ZQbiauGcHV1dWyV/zOhLbsCOLKxbNkyZs2aBTjjW/fu3YG6usDgitblUgEyjGJviRcvXhwYmfwev+k6HRZKqbvXFQOTsIZRAeRkdIrS4TyvQUQoRVPoOUQcq3OUcq//O1eD13baaSfA9auRK0v3OB9jjD+epCRs586dAadvp7u+UhQoi/MafVtLqTAJaxgVQOxW4mzSsFB9NRtyH6mXrD+OJFfnsGvv0KFDg1DDqPeh0Mr4K4J+tyJcozAJaxhlREYJaxhG48IkrGGUEdkKiVec+F3R9J84rq9U0T5hrGhzmIpJWMMoI+IL8DQqlsYiWQ2TsIZRVpSFhFXK2ezZs0s8EmNFI4r+7mcoJdnwyySsYZQRiefDNjYai4VRSc5qpemX1BFvvvkmAN26dYt87FysxOmS/gvBL3agBHaViPFzalVwT/chCqWIVktlzJgxABx88MH1Xldy/mGHHQbA+PHjARcvruKBUe65WYkNowIoCx223KmqqmpQhMxvqKQyOT65SNZ8iDvSrU2bNoCTrCrn88477wAu40fSRsXonnrqqVjHEUY2Ceq3FH399deBuqL5HTt2BBpWz9CuQbnA+lfnGjt2bKRzp34m9H3bEid3janJ23oAZLzwJybOdK6kg//9hITPPvssqIY5ZcoUAFq3bg243rnqALFo0SKABn2FVcdaVRczEXUOM/1A9ONTn6Z58+YBLr1R/XqVRFJTUxOkMYaRbQ5131ZaaaWs9Z5sS2wYFUBZbIkbazX/bInsKvy277778sILLwCub64qLqojX6nGmAuSis899xwAO++8c/C6egQtXrwYcKVulLCve3HnnXcCsP/++wO59dLNlXSSVXWdlYapzhSqYqln7euvv653jEmTJrHPPvsA7p7+61//AuCggw6KNB7tSAqppmgS1jDKiMR02AsvvBCAYcOGAW5VkrFFEmeHHXYIPcYxxxwDuNKfwlfyczGcxKHD5lNGRPdBVfolUR588EGgsFU3zfhi0WFTexalQ3M5bty4oN60dPT58+cDdWVswc1ZWLlTdW6/+OKLg35Dn3/+edrPlmIOq6qqgmd6q622Alz/IPVAVpeGdN9NPWfE8ZkOaxjlTuwSVuZ8FWTu0qUL4NwWa621Vs6DDOPoo48G4IEHHmjwXtiqVurAibB+sHGStJVYElD6aSrbbrst0LAbggJF5L6Rzujr1lEKjpdiDrt16xZ0ZdezrWdLlvFciOBeMglrGOVO7BJWBbPVJ1aEnWfx4sWsvfba9V7zw9uydciT9S2KjlBqCRtnydYwkpKwCnKQTUE+1uHDhwc6utqThKG50n3QDkyvy0qbiu/3LeYc6hmbNGlSEMQiq/D06dM1ntjPaxLWMCqA2CSsdNQWLVrUe12WPumuatmgfqnPPPNMVgkZ9vqIESMAZ62LQjFXZ12Xn36VNFEkbC6SXq1JZPmVT1XMnz8/a4SSzqcIKEUQyaqsuRw8eHDW8RRjDuWnVQRUbW1tEBWlZzzJxH6TsIZRASQeS6wY0SuvvBJwaVRRInCkDykFS/gB2rmQ5Orct29fwFlAr7vuOgDmzp0b1ykiEZcOK6mopl/t27ev974ifbbYYot8xljvb3kX2rRpk9WCnuQcKthfOwHtHGpqagK/c5IJ6sIkrGFUALHFEmuF1D5fUTKKnfUTlKPEtvqSVfTq1SvvcSaJmjNPnjwZgKlTpwIu2qcxkakFiHY/mkNfsipKzY9Ai4IszUrqHjlyJOAaWH/yySd5JbUXSqtWrQB3TZpLcfzxxwex1Bp7KTAJaxhlRGwSVpJVKP505syZOR8rm+Uyn8iSYiCfoiyeqZJVOxCh+6W8TCV6F4tMeph2P4rKEop3njBhAuBKpeTCyy+/DLgE9osvvhiob48opmQV8l7I2yEfa48ePYC6uVVmUZiFPelGb9DIEtiz/VB1o7R9UQpUjudIzGChmj5Kgk6dQD3kChDXFlDujbZt28Y1jJyNTn4AvAwvCjN8/vnnAWdMmzFjBuC6yGdC90DphPrB3njjjQCcfvrpgPsB+zWf0pHEHCpAQgupAn8WLlwIwOGHH06/fv0A6NmzZ73vPP300wDsvffeAGy55ZYAvP3223mPx4xOhlEBNIoEdiU9h9G8efN6f+cjWZNEW6Vbb70VcMHha665JlC3zXrooYcAJ1mFai4PGjQIgFGjRiU/YA/tbKS++G4aSVaRLtlCW8lJkyYB8O233wLQqVMnAE444QTAJQE8/PDDgFMVokjWJFFgvwxuSlbXzuDoo48OUiN32WUXgKDnr1QH3Ue5f7Rz0T1Q5/pCMAlrGGVEyXXYoUOHctFFF6V9T0XM/FC4QihGWJuS0iU99ttvv+C9YnSCyzdwQoEQMpZpZ/Ppp58Czs2mMFO9/8wzzwQBMs8++yzgru/LL78EXFEzPW+PPvoo4ErF5EK2Ocwl7FJzIyPTV199BTQMylm4cCFDhgwBXJkbfUbnk+1C96WQXYPpsIZRARQkYaurq/M2YatIl1bgVLSCSe+JkyQkrPROJSEopHK33XYDoF+/fpGC2lORa+icc87JeTy5SNglS5YEYXhKSD/zzDMBJzH22GMPwFmN33rrrXrHkIU3HQpE0JxqtyRpng9JzOH1118P1BXMA7cjkJRs1qwZffr0AVw3hq5duwKukJ4q/ivFVEkCsmVIt83HEi5MwhpGGVF0HVarjAIrUn2AktbZCjYXQhKr8x133AHAgAED6r2usMxWrVoF+ngY2Qqe5UKuOqx2Ai+++CLgdj9vvPEG4KycCi5QAIUsvsuXL28wZ0p4kD6nY8pHqWPJ0poLScyhCp2r0Nqpp54KuGeypqYm8P/7QULyDhx77LGAC6CRXq/XdR+nT5/O9ttvDziPgo9JWMOoAIrmh1UB56FDhwL1Jau6fB111FHFGk6sSIfxJaysrVFKaUqvkf44evRoILlSMhpTkyZNgiSFDh06AHDGGWcAzkcsi68kiSzB8i1369Yt0NuEAuWVnta/f3/A9ZmR3lwqfEuybCmKCZAdQlbjvn37Bnqt+gTJP60Ef+2OVIBOftclS5YAzhIN4ZI1GyZhDaOMKLoO659v7ty5DVaoJElC/1EMseKC11lnnXrvb7755kFwvyJ8DjnkEMCt6H4hukLIRYdt0qRJEOzvB6+/8oMnLmgAAA3ySURBVMorgIuR1Vh1vZJK999/f6g1W/G30glVwEwWVPl4M6X5+amYcc6hXwzuiy++AFxpI+18xo4dG3gt9J6SH8477zyNC3DxxzqmdP5c4glMhzWMCqBoEjbsPG+88Uag38bBY489BoRH0CQhYeV/vfbaa9O+P3bs2MCHqYZeWtklPWRNlS4Ype1iGLlI2NatWwdSRWPyJa2so9JLP/vss3rvp3YU1zFuu+02wFlItetQ8nchKWhJzKGstgMHDgRcG8xUJCml0+uape9ql6T7pEg3za38sbNnz846HpOwhlEBJC5htbrI75bmHIWeIiO+NTDJWGJleDz55JOAW3HDuqsnRaFF2FQ87vzzzwfcrkANq1QkTb7L1Ig36eZqySG9Tf53Wb9lic6HqHOYT0K5ypuqDYzo06cPN9xwAwBz5swBXHTUjjvuWO+zik9+/PHHI5/XxySsYVQAsUtYSVK1U1QUjRrnitQYzWJSjGydYrTjyETchcQ1R9Jd/ailN998M5CgkrDy0er4+o78moVQqnYrak2iKC75qf2yrHHMf5iETWxLHHZcGYNkHEp6S+xT6t46xSCp3jqaKxldtOVs1qxZg4D2JBetxjqHclXFoQLZltgwKoDYJWzY8eS+0EqswPFi01hX5zjJRcKmumREWBqYX2QuaRprj99iYBLWMCqAogdO+L1fi02Sq7NCLNWLplQk3YE9CXJxwZiENQyjLCh5EbZis6KtzuV4fdkszJUwh7leozAJaxhlREYJaxhG48IkrGGUERlLxDRW3aCQLmGVoP9kI+4O7I1tF7aizWEqJmENo4xoFM2wciXJ/puGI5tkTd3pKFnbLwFqxItJWMMoI0ruh00Xy+qjNC61d2hs5UUaG43JD9uyZUvAZbDkow/7NosVbQ5TMQlrGGVEySRsJktvapFrcC0jDj74YMAVsVKhrxNPPDHyeVe01VnX17FjR4Cg3Go5ouJmP//88wo1h6mYhDWMMiI2K3G+vtHmzZvz2muvAa5gtXRWFadWQevvv/++3nePO+44AN5++23AtZJINx41Ul7R0G4lSck6atQoBg0alPY9tV9R25JrrrkGgJdffhmA7t27A9Gem6VLlxY81qj4OcGffPIJ7dq1A9yzpQbX/nPpo2PomIWQ01OcyZGuG965c2fA9RVR0rMGrXq7qu3TtGlTtt56a8Bd+E033QS4fiz6rqoR7rrrrvWOPXz4cKB+9T7/AUiy43ljxk9rzMdg53fW23PPPQGnqmyyySbB8VWjd9iwYQBcddVVAOy1116Aq0usv/UjzFTbK5e+qoWi6v733nsvkP5ZD1MjdX+0dRf+34VgW2LDKCNiNzppNZQ0Disnkq7ciL6jerg9evQAoGfPnkD9ol/eOIFoLoNiGJ3SXdstt9wCuE53SZKvW0f9YVXp0pfKus9SL55++mmgbn70GX1H+K/rGJLS6uz2wQcfAE4CR72+/x0z7znUTk89gNRVzu/nM2vWrKBmcbZev/puIWqYGZ0MowKI3RKjMinSYYUvBdNJVvVfadOmDQBXXHEFAJtuuingDBfSbdXhXHVhFy1aRO/evQGYPHlybNcUhurS+r1S/F1FkyZNuPvuu+u9Jr3QN6TMnz8fcLq+epKqFm6SSLJKMvh6v+ZOUlFdHWpqahpIVuG/rmMcdthhgOs1myqNdK269iS4+OKLAVc/+9VXXwWcHcXfTaTeC70XZg+QO1LvP/jgg4Azki5btizvxAqTsIZRRpQ8NBHcKqbAca0+6s+iTtcqjSpL5LrrrgvA1VdfDcAFF1yQ9Vxx6D9h+poqw6vnqVhllVWCzmeqgC+dKdUVlYp6kLZt2zbX4dW7xqZNm9ZCQ50sH3zJol477du3z/rdk046CXDBLnru1K9GXcvVoT0TpQ5+0bxnu6fqraO5zgXTYQ2jAmgUEtbvryqn+qOPPgq4rt3dunUDXEC5pFYUUqRDwauz9GRZNKdOnQrkVsJVne0WLVqUcbz5kKuVOJuPVmPR/Zb+7feEBae/9+rVC4AXXngBCA8eUGDFc889B7iu7plIUsJKj5fFPB0K9FFPWSE/rO7PNttsA8DMmTNzHodJWMOoADJaiQuJjsmG9L1Zs2bx7rvvAjBx4kTA9eYcNWoUAPfccw/gGmlppffH16RJE6ZPnw44H66Is8yJpEFYAyjp5Jkic8Ika1LIcim9S2F1CxYsyOpXVNSPJIdIlazyZ2ru1G1c9z0sLE8SOY6udoWgOZN3Y9asWQBsvvnmQJ20nDFjRsZj6Bo7deoEON9ynJiENYwyomAd1l+5s6GVbPHixQCsuuqqgSRSR2/5wuTnlL/vnXfe0biAhjpU0pFOGqfiTSX5U44V9VChY5U+LF0qH3LRYXfaaafAZuCjpAu/w7jPmDFjghIxsvZeeeWVaT+ruZWE92Odo5CkDutHdeXSAKx169ZAND08G6bDGkYFkJiVWJJXkSQbbrghAJdffnm993/66afA/6roHkkyZf5In5AUV/ZDunSrJNs86JjS+dRt3j93JnRN0tt9hgwZArgsl3wotESMGhMroimMOXPmAHU7If++a1f07bffAg1jiYXmcNVVVw2+L9/zggUL0p43TgmrrvJ69tJZvrORzy4hwjFNwhpGuZO4H1a+KEUrSf+Tvjpnzhy6dOkCuMglrcqKYHr//fcBF1s6YsQIIHvicDriWJ3D7plW5cMPPxyAhQsXAnVWZelC8tWFUYj/NWV8eUlYScUff/xRx0n7OelzvtU4E9qNyIbh54juvvvuAEyfPj2rPaQYkU5R7CGKbT/kkEMA51s/77zz4jh/2gchsTIMevAUXHDmmWcCBFn7enBramqCidekyjwu14y2Gkp9GjNmDAD77bcfULzK9EcccUTG92Wk2XbbbQGXnNChQ4eidS0PI1MSuNQT/VDl6FfAu58mNnTo0JzPLzVCcxW29YwjhDIfwsJjjznmGKCuUoYEilCixs033wy4+5dkxwTbEhtGGZH4lljSUytnplItCj2cNm0a4IL9JQH0r7bZklpy99TW1mYN9ohjO6XzajVea621cj1EA+TmUophIRRqdMomGfLZtp988smAk/SjR4+u977KAp122mlRxle04P9U9452hSqoMGXKlLTfySVENQwzOhlGBZBY4IQc6VqVJFm1YqWmailMUcYlGSS0UinNbt999633eqm71ykhWeOWjq1rlSP966+/Tj1/2mPFYWxKOUdeElZzGOaekHTMp6CddiMyzCi8VOdUON+HH36Y9VilTq/LJjlbtWoFFBZ+ahLWMCqAgqzELVu2DJLJjzzySADGjRsHuJAuSUG9/8gjjwAugOCbb75h4MCBQMMCblrJZCYPs74lmaSQDo3DL/si8im+Jcf9vHnz8h9YHowYMYKzzjoLcPYGST/tnkQ+kvWAAw4A3Nzq2C+99BLggu1TJWsu4YBRaNKkSazWZ83/nXfeCcDxxx9f733tqOLcNQmTsIZRRhQkYZcsWRJYci+88ELAJf5K8qr4t/ywSlgeOXIkULdqy2GvPb/0HOm/Sob2q9fLL6YABWhY9DoJ8rH+hRXKVmhesSWrOOecc3jssceAcImWzXeaWsDdt5AqrNRPr9t5550B2GWXXeods7a2Nigbo7IyhRK3b1djVfCPz/jx42M9XyomYQ2jjIhkJc4UuaGgfkkKheVJKkp6+ilaqSuyeocq6FwRIwpZVBB4WMK4xhVFVymVhTGsNKZ2InH4ckUUK3E6X6HmQVFJvrVY5Vz1vgrKbbDBBoFtQmVTNIcK6pc+rPMpKkyRRH6p2HTEWeYn6rnSPfPyYsjy7d8n7RCzJU9kwqzEhlEBFOSHra6uDhKRtaJKh9TfYSVBxfLly4P+rn5CuAgbo3y9WumiUCoJq1Q0BYynnD/2c+Xih23atGkDH7mknT9W7V6kj6pY+4ABA4I0tZQx1DumUPc6JXKoKHwuxDmHSjSRDUEtRBR5pp1juufXfy7zSYoIwySsYVQABVmJq6qqghVKK+nZZ58NOF+USpKGrbhTpkwJikdHtb5qtfMzK9ZYY42gJKp8pPkkJMdNjx49gqgnIaux2pKocHixSfWtKiZbeqd2SSr7orI1siXI35yujYh/v5WJpeclStF3ZXZpdxInukbp44oLCJuH2trarM+S+hQniUlYwygjIumwuRZaAxf3qxhRFQu/5JJLALjvvvuAhmVW4saPmimFDrtkyZLAAu4zePBgwCXrx0E6Hda3ek6YMAFwRdpTkdRVUrmyqG6//XYgWktIH18q+TuLTN3M/bzcOOdQ96FPnz6AyxDLBX2nkA7x/vyYDmsYFUBeVuJ0Pqqo8bxJxv1WVVVl1YNLIWEnTJjAgQcemPEziuJS4epCqhbkm63jZ1hpZxVFcqgBmDKvVC1EbRzD8IuzRdnFJTGHGrd2D5kKm2t3KB9y0pb+VGJLYNe2IJd+N6WgmD9YPegTJ04Mqkf6hos4kp19Mv1goywEYZ9RLa1BgwYB4ZUQk6bU6XXFwLbEhlEBxF4iRulU6jzX2Cjm6qyggHHjxgX/zxZoHweFlohJsohYPqirodIsC5nDct0JCpOwhlFGxCZhs63KcaS9hRmscjFkFVPCphpx8kn+zpdC+8PmKmGbN28euO0U4nfXXXel/WwcyemmwxqGURY0ig7s+eJbJ5PuXlcuFKrDNnZWtDlMxSSsYZQRGSWsYRiNC5OwhlFG2A/WMMoI+8EaRhlhP1jDKCPsB2sYZYT9YA2jjPh/8Efd8fxS+U4AAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make the discriminator\n", + "D = discriminator()\n", + "\n", + "# Make the generator\n", + "G = generator()\n", + "\n", + "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n", + "D_solver, G_solver = get_solvers()\n", + "\n", + "# Run it!\n", + "run_a_gan(D, G, D_solver, G_solver, ls_discriminator_loss, ls_generator_loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Convolutional GANs\n", + "In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like \"sharp edges\" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks as our discriminators and generators.\n", + "\n", + "#### Discriminator\n", + "We will use a discriminator inspired by the TensorFlow MNIST classification [tutorial](https://www.tensorflow.org/get_started/mnist/pros), which is able to get above 99% accuracy on the MNIST dataset fairly quickly. *Be sure to check the dimensions of x and reshape when needed*, fully connected blocks expect [N,D] Tensors while conv2d blocks expect [N,H,W,C] Tensors. Please use `tf.keras.layers` to define the following architecture:\n", + "\n", + "Architecture:\n", + "* Conv2D: 32 Filters, 5x5, Stride 1, padding 0\n", + "* Leaky ReLU(alpha=0.01)\n", + "* Max Pool 2x2, Stride 2\n", + "* Conv2D: 64 Filters, 5x5, Stride 1, padding 0\n", + "* Leaky ReLU(alpha=0.01)\n", + "* Max Pool 2x2, Stride 2\n", + "* Flatten\n", + "* Fully Connected with output size 4 x 4 x 64\n", + "* Leaky ReLU(alpha=0.01)\n", + "* Fully Connected with output size 1\n", + "\n", + "Once again, please use biases for all convolutional and fully connected layers, and use the default parameter initializers. Note that a padding of 0 can be accomplished with the 'VALID' padding option." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_70\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "reshape_26 (Reshape) (None, 28, 28, 1) 0 \n", + "_________________________________________________________________\n", + "conv2d_101 (Conv2D) (None, 24, 24, 32) 832 \n", + "_________________________________________________________________\n", + "leaky_re_lu_157 (LeakyReLU) (None, 24, 24, 32) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_75 (MaxPooling (None, 12, 12, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_102 (Conv2D) (None, 8, 8, 64) 51264 \n", + "_________________________________________________________________\n", + "leaky_re_lu_158 (LeakyReLU) (None, 8, 8, 64) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_76 (MaxPooling (None, 4, 4, 64) 0 \n", + "_________________________________________________________________\n", + "flatten_50 (Flatten) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense_147 (Dense) (None, 1024) 1049600 \n", + "_________________________________________________________________\n", + "leaky_re_lu_159 (LeakyReLU) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense_148 (Dense) (None, 1) 1025 \n", + "=================================================================\n", + "Total params: 1,102,721\n", + "Trainable params: 1,102,721\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Correct number of parameters in discriminator.\n" + ] + } + ], + "source": [ + "from tensorflow.keras.layers import Dense, LeakyReLU, Flatten, Conv2D,MaxPool2D, Reshape\n", + "\n", + "def discriminator():\n", + " \"\"\"Compute discriminator score for a batch of input images.\n", + " \n", + " Inputs:\n", + " - x: TensorFlow Tensor of flattened input images, shape [batch_size, 784]\n", + " \n", + " Returns:\n", + " TensorFlow Tensor with shape [batch_size, 1], containing the score \n", + " for an image being real for each input image.\n", + " \"\"\"\n", + " model = tf.keras.models.Sequential([\n", + " # TODO: implement architecture\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " Reshape(( 28, 28, 1), input_shape=(28*28,)),\n", + " \n", + " Conv2D(32, 5, strides=(1,1),padding='valid'),\n", + " LeakyReLU(0.01),\n", + " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", + " \n", + " Conv2D(64, 5, strides=(1,1), padding='valid'),\n", + " LeakyReLU(0.01),\n", + " MaxPool2D(pool_size=(2,2), strides=(2,2)),\n", + " \n", + " Flatten(),\n", + " \n", + " Dense(4*4*64),\n", + " LeakyReLU(0.01),\n", + " Dense(1)\n", + " \n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " ])\n", + " return model\n", + "\n", + "model = discriminator()\n", + "model.summary()\n", + "test_discriminator(1102721)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generator\n", + "For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. Please use `tf.keras.layers` for your implementation. You might find the documentation for [tf.keras.layers.Conv2DTranspose](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Conv2DTranspose) useful. The architecture is as follows.\n", + "\n", + "Architecture:\n", + "* Fully connected with output size 1024 \n", + "* `ReLU`\n", + "* BatchNorm\n", + "* Fully connected with output size 7 x 7 x 128 \n", + "* `ReLU`\n", + "* BatchNorm\n", + "* Resize into Image Tensor of size 7, 7, 128\n", + "* Conv2D^T (transpose): 64 filters of 4x4, stride 2\n", + "* `ReLU`\n", + "* BatchNorm\n", + "* Conv2d^T (transpose): 1 filter of 4x4, stride 2\n", + "* `TanH`\n", + "\n", + "Once again, use biases for the fully connected and transpose convolutional layers. Please use the default initializers for your parameters. For padding, choose the 'same' option for transpose convolutions. For Batch Normalization, assume we are always in 'training' mode." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_64\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_135 (Dense) (None, 1024) 103424 \n", + "_________________________________________________________________\n", + "re_lu_40 (ReLU) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_33 (Batc (None, 1024) 4096 \n", + "_________________________________________________________________\n", + "dense_136 (Dense) (None, 6272) 6428800 \n", + "_________________________________________________________________\n", + "re_lu_41 (ReLU) (None, 6272) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_34 (Batc (None, 6272) 25088 \n", + "_________________________________________________________________\n", + "reshape_20 (Reshape) (None, 7, 7, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_transpose_22 (Conv2DT (None, 16, 16, 64) 131136 \n", + "_________________________________________________________________\n", + "re_lu_42 (ReLU) (None, 16, 16, 64) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_35 (Batc (None, 16, 16, 64) 256 \n", + "_________________________________________________________________\n", + "conv2d_transpose_23 (Conv2DT (None, 34, 34, 1) 1025 \n", + "_________________________________________________________________\n", + "activation_14 (Activation) (None, 34, 34, 1) 0 \n", + "=================================================================\n", + "Total params: 6,693,825\n", + "Trainable params: 6,679,105\n", + "Non-trainable params: 14,720\n", + "_________________________________________________________________\n", + "Correct number of parameters in generator.\n" + ] + } + ], + "source": [ + "from tensorflow.keras.layers import BatchNormalization, LeakyReLU, Flatten, Conv2D,MaxPool2D, Conv2DTranspose, Reshape, Activation\n", + "def generator(noise_dim=NOISE_DIM):\n", + " \"\"\"Generate images from a random noise vector.\n", + " \n", + " Inputs:\n", + " - z: TensorFlow Tensor of random noise with shape [batch_size, noise_dim]\n", + " \n", + " Returns:\n", + " TensorFlow Tensor of generated images, with shape [batch_size, 784].\n", + " \"\"\"\n", + " model = tf.keras.models.Sequential([\n", + " # TODO: implement architecture\n", + " # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + "\n", + " Dense(1024, input_shape=(noise_dim,)),\n", + " ReLU(),\n", + " BatchNormalization(),\n", + " Dense(7*7*128),\n", + " ReLU(),\n", + " BatchNormalization(),\n", + " Reshape((7,7,128)),\n", + " Conv2DTranspose(64, 4, strides=(2,2)),\n", + " ReLU(),\n", + " BatchNormalization(),\n", + " Conv2DTranspose(1, 4, strides=(2,2)),\n", + " Activation('tanh')\n", + " \n", + " \n", + " ]) \n", + "\n", + " # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n", + " return model\n", + "\n", + "model = generator(100)\n", + "model.summary()\n", + "test_generator(6595521)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to recreate our network since we've changed our functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and evaluate a DCGAN\n", + "This is the one part of A3 that significantly benefits from using a GPU. It takes 3 minutes on a GPU for the requested five epochs. Or about 50 minutes on a dual core laptop on CPU (feel free to use 3 epochs if you do it on CPU)." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_72\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "reshape_28 (Reshape) (None, 28, 28, 1) 0 \n", + "_________________________________________________________________\n", + "conv2d_105 (Conv2D) (None, 24, 24, 32) 832 \n", + "_________________________________________________________________\n", + "leaky_re_lu_163 (LeakyReLU) (None, 24, 24, 32) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_79 (MaxPooling (None, 12, 12, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_106 (Conv2D) (None, 8, 8, 64) 51264 \n", + "_________________________________________________________________\n", + "leaky_re_lu_164 (LeakyReLU) (None, 8, 8, 64) 0 \n", + "_________________________________________________________________\n", + "max_pooling2d_80 (MaxPooling (None, 4, 4, 64) 0 \n", + "_________________________________________________________________\n", + "flatten_52 (Flatten) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense_151 (Dense) (None, 1024) 1049600 \n", + "_________________________________________________________________\n", + "leaky_re_lu_165 (LeakyReLU) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense_152 (Dense) (None, 1) 1025 \n", + "=================================================================\n", + "Total params: 1,102,721\n", + "Trainable params: 1,102,721\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n", + "Model: \"sequential_73\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_153 (Dense) (None, 1024) 99328 \n", + "_________________________________________________________________\n", + "re_lu_49 (ReLU) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_42 (Batc (None, 1024) 4096 \n", + "_________________________________________________________________\n", + "dense_154 (Dense) (None, 6272) 6428800 \n", + "_________________________________________________________________\n", + "re_lu_50 (ReLU) (None, 6272) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_43 (Batc (None, 6272) 25088 \n", + "_________________________________________________________________\n", + "reshape_29 (Reshape) (None, 7, 7, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_transpose_28 (Conv2DT (None, 16, 16, 64) 131136 \n", + "_________________________________________________________________\n", + "re_lu_51 (ReLU) (None, 16, 16, 64) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_44 (Batc (None, 16, 16, 64) 256 \n", + "_________________________________________________________________\n", + "conv2d_transpose_29 (Conv2DT (None, 34, 34, 1) 1025 \n", + "_________________________________________________________________\n", + "activation_17 (Activation) (None, 34, 34, 1) 0 \n", + "=================================================================\n", + "Total params: 6,689,729\n", + "Trainable params: 6,675,009\n", + "Non-trainable params: 14,720\n", + "_________________________________________________________________\n", + "None\n", + "(128, 784)\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": "Input to reshape is a tensor with 147968 values, but the requested shape has 100352 [Op:Reshape]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Run it!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mrun_a_gan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiscriminator_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_a_gan\u001b[0;34m(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every, print_every, batch_size, num_epochs, noise_size)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mg_fake_seed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_noise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mfake_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg_fake_seed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mlogits_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfake_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m784\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0md_total_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits_fake\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(tensor, shape, name)\u001b[0m\n\u001b[1;32m 7695\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7696\u001b[0m return reshape_eager_fallback(\n\u001b[0;32m-> 7697\u001b[0;31m tensor, shape, name=name, ctx=_ctx)\n\u001b[0m\u001b[1;32m 7698\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7699\u001b[0m \u001b[0;32mpass\u001b[0m \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mreshape_eager_fallback\u001b[0;34m(tensor, shape, name, ctx)\u001b[0m\n\u001b[1;32m 7745\u001b[0m \u001b[0m_attrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"T\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_attr_T\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Tshape\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_attr_Tshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7746\u001b[0m _result = _execute.execute(b\"Reshape\", 1, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0;32m-> 7747\u001b[0;31m ctx=_ctx, name=name)\n\u001b[0m\u001b[1;32m 7748\u001b[0m _execute.record_gradient(\n\u001b[1;32m 7749\u001b[0m \"Reshape\", _inputs_flat, _attrs, _result, name)\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_keras_symbolic_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: Input to reshape is a tensor with 147968 values, but the requested shape has 100352 [Op:Reshape]" + ] + } + ], + "source": [ + "# Make the discriminator\n", + "D = discriminator()\n", + "\n", + "# Make the generator\n", + "G = generator()\n", + "print(D.summary())\n", + "print(G.summary())\n", + "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n", + "D_solver, G_solver = get_solvers()\n", + "\n", + "# Run it!\n", + "run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, num_epochs=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## INLINE QUESTION 1\n", + "\n", + "We will look at an example to see why alternating minimization of the same objective (like in a GAN) can be tricky business.\n", + "\n", + "Consider $f(x,y)=xy$. What does $\\min_x\\max_y f(x,y)$ evaluate to? (Hint: minmax tries to minimize the maximum value achievable.)\n", + "\n", + "Now try to evaluate this function numerically for 6 steps, starting at the point $(1,1)$, \n", + "by using alternating gradient (first updating y, then updating x using that updated y) with step size $1$. **Here step size is the learning_rate, and steps will be learning_rate * gradient.**\n", + "You'll find that writing out the update step in terms of $x_t,y_t,x_{t+1},y_{t+1}$ will be useful.\n", + "\n", + "Breifly explain what $\\min_x\\max_y f(x,y)$ evaluates to and record the six pairs of explicit values for $(x_t,y_t)$ in the table below.\n", + "\n", + "### Your answer:\n", + " \n", + " $y_0$ | $y_1$ | $y_2$ | $y_3$ | $y_4$ | $y_5$ | $y_6$ \n", + " ----- | ----- | ----- | ----- | ----- | ----- | ----- \n", + " 1 | | | | | | \n", + " $x_0$ | $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | $x_6$ \n", + " 1 | | | | | | \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## INLINE QUESTION 2\n", + "Using this method, will we ever reach the optimal value? Why or why not?\n", + "\n", + "### Your answer: \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-inline" + ] + }, + "source": [ + "## INLINE QUESTION 3\n", + "If the generator loss decreases during training while the discriminator loss stays at a constant high value from the start, is this a good sign? Why or why not? A qualitative answer is sufficient.\n", + "\n", + "### Your answer: \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/assignment3/.ipynb_checkpoints/LSTM_Captioning-checkpoint.ipynb b/assignment3/.ipynb_checkpoints/LSTM_Captioning-checkpoint.ipynb new file mode 100755 index 0000000..9c23c15 --- /dev/null +++ b/assignment3/.ipynb_checkpoints/LSTM_Captioning-checkpoint.ipynb @@ -0,0 +1,554 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "pdf-title" + ] + }, + "source": [ + "# Image Captioning with LSTMs\n", + "In the previous exercise you implemented a vanilla RNN and applied it to image captioning. In this notebook you will implement the LSTM update rule and use it for image captioning." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "pdf-ignore" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# As usual, a bit of setup\n", + "import time, os, json\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", + "from cs231n.rnn_layers import *\n", + "from cs231n.captioning_solver import CaptioningSolver\n", + "from cs231n.classifiers.rnn import CaptioningRNN\n", + "from cs231n.coco_utils import load_coco_data, sample_coco_minibatch, decode_captions\n", + "from cs231n.image_utils import image_from_url\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# for auto-reloading external modules\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "def rel_error(x, y):\n", + " \"\"\" returns relative error \"\"\"\n", + " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load MS-COCO data\n", + "As in the previous notebook, we will use the Microsoft COCO dataset for captioning." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_captions (400135, 17) int32\n", + "train_image_idxs (400135,) int32\n", + "val_captions (195954, 17) int32\n", + "val_image_idxs (195954,) int32\n", + "train_features (82783, 512) float32\n", + "val_features (40504, 512) float32\n", + "idx_to_word 1004\n", + "word_to_idx 1004\n", + "train_urls (82783,) (40504,)