diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Application - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Application - v1.ipynb new file mode 100644 index 0000000..325a22d --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Application - v1.ipynb @@ -0,0 +1,895 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Networks: Application\n", + "\n", + "Welcome to Course 4's second assignment! In this notebook, you will:\n", + "\n", + "- Implement helper functions that you will use when implementing a TensorFlow model\n", + "- Implement a fully functioning ConvNet using TensorFlow \n", + "\n", + "**After this assignment you will be able to:**\n", + "\n", + "- Build and train a ConvNet in TensorFlow for a classification problem \n", + "\n", + "We assume here that you are already familiar with TensorFlow. If you are not, please refer the *TensorFlow Tutorial* of the third week of Course 2 (\"*Improving deep neural networks*\")." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.0 - TensorFlow model\n", + "\n", + "In the previous assignment, you built helper functions using numpy to understand the mechanics behind convolutional neural networks. Most practical applications of deep learning today are built using programming frameworks, which have many built-in functions you can simply call. \n", + "\n", + "As usual, we will start by loading in the packages. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "from PIL import Image\n", + "from scipy import ndimage\n", + "import tensorflow as tf\n", + "from tensorflow.python.framework import ops\n", + "from cnn_utils import *\n", + "\n", + "%matplotlib inline\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the next cell to load the \"SIGNS\" dataset you are going to use." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Loading the data (signs)\n", + "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a reminder, the SIGNS dataset is a collection of 6 signs representing numbers from 0 to 5.\n", + "\n", + "\n", + "\n", + "The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 2\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example of a picture\n", + "index = 6\n", + "plt.imshow(X_train_orig[index])\n", + "print (\"y = \" + str(np.squeeze(Y_train_orig[:, index])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Course 2, you had built a fully-connected network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.\n", + "\n", + "To get started, let's examine the shapes of your data. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of training examples = 1080\n", + "number of test examples = 120\n", + "X_train shape: (1080, 64, 64, 3)\n", + "Y_train shape: (1080, 6)\n", + "X_test shape: (120, 64, 64, 3)\n", + "Y_test shape: (120, 6)\n" + ] + } + ], + "source": [ + "X_train = X_train_orig/255.\n", + "X_test = X_test_orig/255.\n", + "Y_train = convert_to_one_hot(Y_train_orig, 6).T\n", + "Y_test = convert_to_one_hot(Y_test_orig, 6).T\n", + "print (\"number of training examples = \" + str(X_train.shape[0]))\n", + "print (\"number of test examples = \" + str(X_test.shape[0]))\n", + "print (\"X_train shape: \" + str(X_train.shape))\n", + "print (\"Y_train shape: \" + str(Y_train.shape))\n", + "print (\"X_test shape: \" + str(X_test.shape))\n", + "print (\"Y_test shape: \" + str(Y_test.shape))\n", + "conv_layers = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1.1 - Create placeholders\n", + "\n", + "TensorFlow requires that you create placeholders for the input data that will be fed into the model when running the session.\n", + "\n", + "**Exercise**: Implement the function below to create placeholders for the input image X and the output Y. You should not define the number of training examples for the moment. To do so, you could use \"None\" as the batch size, it will give you the flexibility to choose it later. Hence X should be of dimension **[None, n_H0, n_W0, n_C0]** and Y should be of dimension **[None, n_y]**. [Hint](https://www.tensorflow.org/api_docs/python/tf/placeholder)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: create_placeholders\n", + "\n", + "def create_placeholders(n_H0, n_W0, n_C0, n_y):\n", + " \"\"\"\n", + " Creates the placeholders for the tensorflow session.\n", + " \n", + " Arguments:\n", + " n_H0 -- scalar, height of an input image\n", + " n_W0 -- scalar, width of an input image\n", + " n_C0 -- scalar, number of channels of the input\n", + " n_y -- scalar, number of classes\n", + " \n", + " Returns:\n", + " X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype \"float\"\n", + " Y -- placeholder for the input labels, of shape [None, n_y] and dtype \"float\"\n", + " \"\"\"\n", + "\n", + " ### START CODE HERE ### (≈2 lines)\n", + " X = tf.placeholder(tf.float32, shape=[None, n_H0, n_W0, n_C0])\n", + " Y = tf.placeholder(tf.float32, shape=[None, n_y])\n", + " ### END CODE HERE ###\n", + " \n", + " return X, Y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X = Tensor(\"Placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n", + "Y = Tensor(\"Placeholder_1:0\", shape=(?, 6), dtype=float32)\n" + ] + } + ], + "source": [ + "X, Y = create_placeholders(64, 64, 3, 6)\n", + "print (\"X = \" + str(X))\n", + "print (\"Y = \" + str(Y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + " X = Tensor(\"Placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n", + "\n", + "
\n", + " Y = Tensor(\"Placeholder_1:0\", shape=(?, 6), dtype=float32)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Initialize parameters\n", + "\n", + "You will initialize weights/filters $W1$ and $W2$ using `tf.contrib.layers.xavier_initializer(seed = 0)`. You don't need to worry about bias variables as you will soon see that TensorFlow functions take care of the bias. Note also that you will only initialize the weights/filters for the conv2d functions. TensorFlow initializes the layers for the fully connected part automatically. We will talk more about that later in this assignment.\n", + "\n", + "**Exercise:** Implement initialize_parameters(). The dimensions for each group of filters are provided below. Reminder - to initialize a parameter $W$ of shape [1,2,3,4] in Tensorflow, use:\n", + "```python\n", + "W = tf.get_variable(\"W\", [1,2,3,4], initializer = ...)\n", + "```\n", + "[More Info](https://www.tensorflow.org/api_docs/python/tf/get_variable)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters():\n", + " \"\"\"\n", + " Initializes weight parameters to build a neural network with tensorflow. The shapes are:\n", + " W1 : [4, 4, 3, 8]\n", + " W2 : [2, 2, 8, 16]\n", + " Returns:\n", + " parameters -- a dictionary of tensors containing W1, W2\n", + " \"\"\"\n", + " \n", + " tf.set_random_seed(1) # so that your \"random\" numbers match ours\n", + " \n", + " ### START CODE HERE ### (approx. 2 lines of code)\n", + " W1 = tf.get_variable('W1',[4, 4, 3, 8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n", + " W2 = tf.get_variable('W2',[2, 2, 8, 16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n", + " ### END CODE HERE ###\n", + "\n", + " parameters = {\"W1\": W1,\n", + " \"W2\": W2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394\n", + " -0.06847463 0.05245192]\n", + "W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058\n", + " -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228\n", + " -0.22779644 -0.1601823 -0.16117483 -0.10286498]\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "with tf.Session() as sess_test:\n", + " parameters = initialize_parameters()\n", + " init = tf.global_variables_initializer()\n", + " sess_test.run(init)\n", + " print(\"W1 = \" + str(parameters[\"W1\"].eval()[1,1,1]))\n", + " print(\"W2 = \" + str(parameters[\"W2\"].eval()[1,1,1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected Output:**\n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " W1 = \n", + " \n", + "[ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394
\n", + " -0.06847463 0.05245192]\n", + "
\n", + " W2 = \n", + " \n", + "[-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058
\n", + " -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228
\n", + " -0.22779644 -0.1601823 -0.16117483 -0.10286498]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Forward propagation\n", + "\n", + "In TensorFlow, there are built-in functions that carry out the convolution steps for you.\n", + "\n", + "- **tf.nn.conv2d(X,W1, strides = [1,s,s,1], padding = 'SAME'):** given an input $X$ and a group of filters $W1$, this function convolves $W1$'s filters on X. The third input ([1,f,f,1]) represents the strides for each dimension of the input (m, n_H_prev, n_W_prev, n_C_prev). You can read the full documentation [here](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)\n", + "\n", + "- **tf.nn.max_pool(A, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME'):** given an input A, this function uses a window of size (f, f) and strides of size (s, s) to carry out max pooling over each window. You can read the full documentation [here](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool)\n", + "\n", + "- **tf.nn.relu(Z1):** computes the elementwise ReLU of Z1 (which can be any shape). You can read the full documentation [here.](https://www.tensorflow.org/api_docs/python/tf/nn/relu)\n", + "\n", + "- **tf.contrib.layers.flatten(P)**: given an input P, this function flattens each example into a 1D vector it while maintaining the batch-size. It returns a flattened tensor with shape [batch_size, k]. You can read the full documentation [here.](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/flatten)\n", + "\n", + "- **tf.contrib.layers.fully_connected(F, num_outputs):** given a the flattened input F, it returns the output computed using a fully connected layer. You can read the full documentation [here.](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/fully_connected)\n", + "\n", + "In the last function above (`tf.contrib.layers.fully_connected`), the fully connected layer automatically initializes weights in the graph and keeps on training them as you train the model. Hence, you did not need to initialize those weights when initializing the parameters. \n", + "\n", + "\n", + "**Exercise**: \n", + "\n", + "Implement the `forward_propagation` function below to build the following model: `CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED`. You should use the functions above. \n", + "\n", + "In detail, we will use the following parameters for all the steps:\n", + " - Conv2D: stride 1, padding is \"SAME\"\n", + " - ReLU\n", + " - Max pool: Use an 8 by 8 filter size and an 8 by 8 stride, padding is \"SAME\"\n", + " - Conv2D: stride 1, padding is \"SAME\"\n", + " - ReLU\n", + " - Max pool: Use a 4 by 4 filter size and a 4 by 4 stride, padding is \"SAME\"\n", + " - Flatten the previous output.\n", + " - FULLYCONNECTED (FC) layer: Apply a fully connected layer without an non-linear activation function. Do not call the softmax here. This will result in 6 neurons in the output layer, which then get passed later to a softmax. In TensorFlow, the softmax and cost function are lumped together into a single function, which you'll call in a different function when computing the cost. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(X, parameters):\n", + " \"\"\"\n", + " Implements the forward propagation for the model:\n", + " CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n", + " \n", + " Arguments:\n", + " X -- input dataset placeholder, of shape (input size, number of examples)\n", + " parameters -- python dictionary containing your parameters \"W1\", \"W2\"\n", + " the shapes are given in initialize_parameters\n", + "\n", + " Returns:\n", + " Z3 -- the output of the last LINEAR unit\n", + " \"\"\"\n", + " \n", + " # Retrieve the parameters from the dictionary \"parameters\" \n", + " W1 = parameters['W1']\n", + " W2 = parameters['W2']\n", + " \n", + " ### START CODE HERE ###\n", + " # CONV2D: stride of 1, padding 'SAME'\n", + " Z1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = 'SAME')\n", + " # RELU\n", + " A1 = tf.nn.relu(Z1)\n", + " # MAXPOOL: window 8x8, sride 8, padding 'SAME'\n", + " P1 = tf.nn.max_pool(A1, ksize = [1,8,8,1], strides = [1,8,8,1], padding = 'SAME')\n", + " # CONV2D: filters W2, stride 1, padding 'SAME'\n", + " Z2 = tf.nn.conv2d(P1,W2, strides = [1,1,1,1], padding = 'SAME')\n", + " # RELU\n", + " A2 = tf.nn.relu(Z2)\n", + " # MAXPOOL: window 4x4, stride 4, padding 'SAME'\n", + " P2 = tf.nn.max_pool(A2, ksize = [1,4,4,1], strides = [1,4,4,1], padding = 'SAME')\n", + " # FLATTEN\n", + " P2 = tf.contrib.layers.flatten(P2)\n", + " # FULLY-CONNECTED without non-linear activation function (not not call softmax).\n", + " # 6 neurons in output layer. Hint: one of the arguments should be \"activation_fn=None\" \n", + " Z3 = tf.contrib.layers.fully_connected(P2, 6, activation_fn=None)\n", + " ### END CODE HERE ###\n", + "\n", + " return Z3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z3 = [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]\n", + " [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as sess:\n", + " np.random.seed(1)\n", + " X, Y = create_placeholders(64, 64, 3, 6)\n", + " parameters = initialize_parameters()\n", + " Z3 = forward_propagation(X, parameters)\n", + " init = tf.global_variables_initializer()\n", + " sess.run(init)\n", + " a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)})\n", + " print(\"Z3 = \" + str(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " Z3 =\n", + " \n", + " [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]
\n", + " [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 - Compute cost\n", + "\n", + "Implement the compute cost function below. You might find these two functions helpful: \n", + "\n", + "- **tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y):** computes the softmax entropy loss. This function both computes the softmax activation function as well as the resulting loss. You can check the full documentation [here.](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits)\n", + "- **tf.reduce_mean:** computes the mean of elements across dimensions of a tensor. Use this to sum the losses over all the examples to get the overall cost. You can check the full documentation [here.](https://www.tensorflow.org/api_docs/python/tf/reduce_mean)\n", + "\n", + "** Exercise**: Compute the cost below using the function above." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost \n", + "\n", + "def compute_cost(Z3, Y):\n", + " \"\"\"\n", + " Computes the cost\n", + " \n", + " Arguments:\n", + " Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)\n", + " Y -- \"true\" labels vector placeholder, same shape as Z3\n", + " \n", + " Returns:\n", + " cost - Tensor of the cost function\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (1 line of code)\n", + " cost = tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y)\n", + " cost = tf.reduce_mean(cost)\n", + " ### END CODE HERE ###\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 2.91034\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as sess:\n", + " np.random.seed(1)\n", + " X, Y = create_placeholders(64, 64, 3, 6)\n", + " parameters = initialize_parameters()\n", + " Z3 = forward_propagation(X, parameters)\n", + " cost = compute_cost(Z3, Y)\n", + " init = tf.global_variables_initializer()\n", + " sess.run(init)\n", + " a = sess.run(cost, {X: np.random.randn(4,64,64,3), Y: np.random.randn(4,6)})\n", + " print(\"cost = \" + str(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + "
\n", + " cost =\n", + " \n", + " 2.91034\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 Model \n", + "\n", + "Finally you will merge the helper functions you implemented above to build a model. You will train it on the SIGNS dataset. \n", + "\n", + "You have implemented `random_mini_batches()` in the Optimization programming assignment of course 2. Remember that this function returns a list of mini-batches. \n", + "\n", + "**Exercise**: Complete the function below. \n", + "\n", + "The model below should:\n", + "\n", + "- create placeholders\n", + "- initialize parameters\n", + "- forward propagate\n", + "- compute the cost\n", + "- create an optimizer\n", + "\n", + "Finally you will create a session and run a for loop for num_epochs, get the mini-batches, and then for each mini-batch you will optimize the function. [Hint for initializing the variables](https://www.tensorflow.org/api_docs/python/tf/global_variables_initializer)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,\n", + " num_epochs = 100, minibatch_size = 64, print_cost = True):\n", + " \"\"\"\n", + " Implements a three-layer ConvNet in Tensorflow:\n", + " CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n", + " \n", + " Arguments:\n", + " X_train -- training set, of shape (None, 64, 64, 3)\n", + " Y_train -- test set, of shape (None, n_y = 6)\n", + " X_test -- training set, of shape (None, 64, 64, 3)\n", + " Y_test -- test set, of shape (None, n_y = 6)\n", + " learning_rate -- learning rate of the optimization\n", + " num_epochs -- number of epochs of the optimization loop\n", + " minibatch_size -- size of a minibatch\n", + " print_cost -- True to print the cost every 100 epochs\n", + " \n", + " Returns:\n", + " train_accuracy -- real number, accuracy on the train set (X_train)\n", + " test_accuracy -- real number, testing accuracy on the test set (X_test)\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables\n", + " tf.set_random_seed(1) # to keep results consistent (tensorflow seed)\n", + " seed = 3 # to keep results consistent (numpy seed)\n", + " (m, n_H0, n_W0, n_C0) = X_train.shape \n", + " n_y = Y_train.shape[1] \n", + " costs = [] # To keep track of the cost\n", + " \n", + " # Create Placeholders of the correct shape\n", + " ### START CODE HERE ### (1 line)\n", + " X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)\n", + " ### END CODE HERE ###\n", + "\n", + " # Initialize parameters\n", + " ### START CODE HERE ### (1 line)\n", + " parameters = initialize_parameters()\n", + " ### END CODE HERE ###\n", + " \n", + " # Forward propagation: Build the forward propagation in the tensorflow graph\n", + " ### START CODE HERE ### (1 line)\n", + " Z3 = forward_propagation(X, parameters)\n", + " ### END CODE HERE ###\n", + " \n", + " # Cost function: Add cost function to tensorflow graph\n", + " ### START CODE HERE ### (1 line)\n", + " cost = compute_cost(Z3, Y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.\n", + " ### START CODE HERE ### (1 line)\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + " ### END CODE HERE ###\n", + " \n", + " # Initialize all the variables globally\n", + " init = tf.global_variables_initializer()\n", + " \n", + " # Start the session to compute the tensorflow graph\n", + " with tf.Session() as sess:\n", + " \n", + " # Run the initialization\n", + " sess.run(init)\n", + " \n", + " # Do the training loop\n", + " for epoch in range(num_epochs):\n", + "\n", + " minibatch_cost = 0.\n", + " num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\n", + " seed = seed + 1\n", + " minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n", + "\n", + " for minibatch in minibatches:\n", + "\n", + " # Select a minibatch\n", + " (minibatch_X, minibatch_Y) = minibatch\n", + " # IMPORTANT: The line that runs the graph on a minibatch.\n", + " # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).\n", + " ### START CODE HERE ### (1 line)\n", + " _ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n", + " ### END CODE HERE ###\n", + " \n", + " minibatch_cost += temp_cost / num_minibatches\n", + " \n", + "\n", + " # Print the cost every epoch\n", + " if print_cost == True and epoch % 5 == 0:\n", + " print (\"Cost after epoch %i: %f\" % (epoch, minibatch_cost))\n", + " if print_cost == True and epoch % 1 == 0:\n", + " costs.append(minibatch_cost)\n", + " \n", + " \n", + " # plot the cost\n", + " plt.plot(np.squeeze(costs))\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (per tens)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + "\n", + " # Calculate the correct predictions\n", + " predict_op = tf.argmax(Z3, 1)\n", + " correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))\n", + " \n", + " # Calculate accuracy on the test set\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print(accuracy)\n", + " train_accuracy = accuracy.eval({X: X_train, Y: Y_train})\n", + " test_accuracy = accuracy.eval({X: X_test, Y: Y_test})\n", + " print(\"Train Accuracy:\", train_accuracy)\n", + " print(\"Test Accuracy:\", test_accuracy)\n", + " \n", + " return train_accuracy, test_accuracy, parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to train your model for 100 epochs. Check if your cost after epoch 0 and 5 matches our output. If not, stop the cell and go back to your code!" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 1.917929\n", + "Cost after epoch 5: 1.506757\n", + "Cost after epoch 10: 0.955359\n", + "Cost after epoch 15: 0.845802\n", + "Cost after epoch 20: 0.701174\n", + "Cost after epoch 25: 0.571977\n", + "Cost after epoch 30: 0.518435\n", + "Cost after epoch 35: 0.495806\n", + "Cost after epoch 40: 0.429827\n", + "Cost after epoch 45: 0.407291\n", + "Cost after epoch 50: 0.366394\n", + "Cost after epoch 55: 0.376922\n", + "Cost after epoch 60: 0.299491\n", + "Cost after epoch 65: 0.338870\n", + "Cost after epoch 70: 0.316400\n", + "Cost after epoch 75: 0.310413\n", + "Cost after epoch 80: 0.249549\n", + "Cost after epoch 85: 0.243457\n", + "Cost after epoch 90: 0.200031\n", + "Cost after epoch 95: 0.175452\n" + ] + }, + { + "data": { + "image/png": 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3ljDqUZ/0eMorq1m2ea+/QzHGmHpnCaMeZbePByBn/YksZW6MMY2bJYx6lNIykoyEaHLW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\n3TirSzKx0eEkt4z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n", + "Train Accuracy: 0.940741\n", + "Test Accuracy: 0.783333\n" + ] + } + ], + "source": [ + "_, _, parameters = model(X_train, Y_train, X_test, Y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**: although it may not match perfectly, your expected output should be close to ours and your cost value should decrease.\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + " \n", + "
\n", + " **Cost after epoch 0 =**\n", + " \n", + " 1.917929\n", + "
\n", + " **Cost after epoch 5 =**\n", + " \n", + " 1.506757\n", + "
\n", + " **Train Accuracy =**\n", + " \n", + " 0.940741\n", + "
\n", + " **Test Accuracy =**\n", + " \n", + " 0.783333\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You have finised the assignment and built a model that recognizes SIGN language with almost 80% accuracy on the test set. If you wish, feel free to play around with this dataset further. You can actually improve its accuracy by spending more time tuning the hyperparameters, or using regularization (as this model clearly has a high variance). \n", + "\n", + "Once again, here's a thumbs up for your work! " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AkwC+eO3TMRgMo0LMbv/3kf3j9PDGTsdgMIwKYyDwzHLxi/Osk+KZJs7MPEtNIdvFL55M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\n5BS2+A2GnGIMpr4MDz+G7Fg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname = \"images/thumbs_up.jpg\"\n", + "image = np.array(ndimage.imread(fname, flatten=False))\n", + "my_image = scipy.misc.imresize(image, size=(64,64))\n", + "plt.imshow(my_image)" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "convolutional-neural-networks", + "graded_item_id": "bwbJV", + "launcher_item_id": "0TkXB" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Step by Step - v2.ipynb b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Step by Step - v2.ipynb new file mode 100644 index 0000000..1d4c40f --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/Convolution model - Step by Step - v2.ipynb @@ -0,0 +1,1388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Networks: Step by Step\n", + "\n", + "Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. \n", + "\n", + "**Notation**:\n", + "- Superscript $[l]$ denotes an object of the $l^{th}$ layer. \n", + " - Example: $a^{[4]}$ is the $4^{th}$ layer activation. $W^{[5]}$ and $b^{[5]}$ are the $5^{th}$ layer parameters.\n", + "\n", + "\n", + "- Superscript $(i)$ denotes an object from the $i^{th}$ example. \n", + " - Example: $x^{(i)}$ is the $i^{th}$ training example input.\n", + " \n", + " \n", + "- Lowerscript $i$ denotes the $i^{th}$ entry of a vector.\n", + " - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the activations in layer $l$, assuming this is a fully connected (FC) layer.\n", + " \n", + " \n", + "- $n_H$, $n_W$ and $n_C$ denote respectively the height, width and number of channels of a given layer. If you want to reference a specific layer $l$, you can also write $n_H^{[l]}$, $n_W^{[l]}$, $n_C^{[l]}$. \n", + "- $n_{H_{prev}}$, $n_{W_{prev}}$ and $n_{C_{prev}}$ denote respectively the height, width and number of channels of the previous layer. If referencing a specific layer $l$, this could also be denoted $n_H^{[l-1]}$, $n_W^{[l-1]}$, $n_C^{[l-1]}$. \n", + "\n", + "We assume that you are already familiar with `numpy` and/or have completed the previous courses of the specialization. Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages\n", + "\n", + "Let's first import all the packages that you will need during this assignment. \n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n", + "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Outline of the Assignment\n", + "\n", + "You will be implementing the building blocks of a convolutional neural network! Each function you will implement will have detailed instructions that will walk you through the steps needed:\n", + "\n", + "- Convolution functions, including:\n", + " - Zero Padding\n", + " - Convolve window \n", + " - Convolution forward\n", + " - Convolution backward (optional)\n", + "- Pooling functions, including:\n", + " - Pooling forward\n", + " - Create mask \n", + " - Distribute value\n", + " - Pooling backward (optional)\n", + " \n", + "This notebook will ask you to implement these functions from scratch in `numpy`. In the next notebook, you will use the TensorFlow equivalents of these functions to build the following model:\n", + "\n", + "\n", + "\n", + "**Note** that for every forward function, there is its corresponding backward equivalent. Hence, at every step of your forward module you will store some parameters in a cache. These parameters are used to compute gradients during backpropagation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Convolutional Neural Networks\n", + "\n", + "Although programming frameworks make convolutions easy to use, they remain one of the hardest concepts to understand in Deep Learning. A convolution layer transforms an input volume into an output volume of different size, as shown below. \n", + "\n", + "\n", + "\n", + "In this part, you will build every step of the convolution layer. You will first implement two helper functions: one for zero padding and the other for computing the convolution function itself. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Zero-Padding\n", + "\n", + "Zero-padding adds zeros around the border of an image:\n", + "\n", + "\n", + "
**Figure 1** : **Zero-Padding**
Image (3 channels, RGB) with a padding of 2.
\n", + "\n", + "The main benefits of padding are the following:\n", + "\n", + "- It allows you to use a CONV layer without necessarily shrinking the height and width of the volumes. This is important for building deeper networks, since otherwise the height/width would shrink as you go to deeper layers. An important special case is the \"same\" convolution, in which the height/width is exactly preserved after one layer. \n", + "\n", + "- It helps us keep more of the information at the border of an image. Without padding, very few values at the next layer would be affected by pixels as the edges of an image.\n", + "\n", + "**Exercise**: Implement the following function, which pads all the images of a batch of examples X with zeros. [Use np.pad](https://docs.scipy.org/doc/numpy/reference/generated/numpy.pad.html). Note if you want to pad the array \"a\" of shape $(5,5,5,5,5)$ with `pad = 1` for the 2nd dimension, `pad = 3` for the 4th dimension and `pad = 0` for the rest, you would do:\n", + "```python\n", + "a = np.pad(a, ((0,0), (1,1), (0,0), (3,3), (0,0)), 'constant', constant_values = (..,..))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: zero_pad\n", + "\n", + "def zero_pad(X, pad):\n", + " \"\"\"\n", + " Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image, \n", + " as illustrated in Figure 1.\n", + " \n", + " Argument:\n", + " X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m images\n", + " pad -- integer, amount of padding around each image on vertical and horizontal dimensions\n", + " \n", + " Returns:\n", + " X_pad -- padded image of shape (m, n_H + 2*pad, n_W + 2*pad, n_C)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈ 1 line)\n", + " X_pad = np.pad(X, ((0, 0), (pad, pad), (pad, pad), (0, 0)), 'constant', constant_values=0)\n", + " ### END CODE HERE ###\n", + " \n", + " return X_pad" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x.shape = (4, 3, 3, 2)\n", + "x_pad.shape = (4, 7, 7, 2)\n", + "x[1,1] = [[ 0.90085595 -0.68372786]\n", + " [-0.12289023 -0.93576943]\n", + " [-0.26788808 0.53035547]]\n", + "x_pad[1,1] = [[ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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zybThx4BrJG0usZ6mehKdrW4JyJeBhZIuTE7erga2llxTW2tmimkbq9z+UrX3OyK+HhFz\nI2IBtffnhxFxY4n1tKQnUVcEZEQMArcBT1M7uf0vEfFauVWNTtK3gX8DLpF0SNIfl11TiqEpptfU\n3Sl+RdlF5aGi+0vHvt85GupJ9AqwCLgn6wa74s98zMzGoys+QZqZjYcD0swshQPSzCyFA9LMLIUD\n0swshQPSzCyFA9LMLIUD0swsxf8DtS5DRn4HHEIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(4, 3, 3, 2)\n", + "x_pad = zero_pad(x, 2)\n", + "print (\"x.shape =\", x.shape)\n", + "print (\"x_pad.shape =\", x_pad.shape)\n", + "print (\"x[1,1] =\", x[1,1])\n", + "print (\"x_pad[1,1] =\", x_pad[1,1])\n", + "\n", + "fig, axarr = plt.subplots(1, 2)\n", + "axarr[0].set_title('x')\n", + "axarr[0].imshow(x[0,:,:,0])\n", + "axarr[1].set_title('x_pad')\n", + "axarr[1].imshow(x_pad[0,:,:,0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **x.shape**:\n", + " \n", + " (4, 3, 3, 2)\n", + "
\n", + " **x_pad.shape**:\n", + " \n", + " (4, 7, 7, 2)\n", + "
\n", + " **x[1,1]**:\n", + " \n", + " [[ 0.90085595 -0.68372786]\n", + " [-0.12289023 -0.93576943]\n", + " [-0.26788808 0.53035547]]\n", + "
\n", + " **x_pad[1,1]**:\n", + " \n", + " [[ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]\n", + " [ 0. 0.]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Single step of convolution \n", + "\n", + "In this part, implement a single step of convolution, in which you apply the filter to a single position of the input. This will be used to build a convolutional unit, which: \n", + "\n", + "- Takes an input volume \n", + "- Applies a filter at every position of the input\n", + "- Outputs another volume (usually of different size)\n", + "\n", + "\n", + "
**Figure 2** : **Convolution operation**
with a filter of 2x2 and a stride of 1 (stride = amount you move the window each time you slide)
\n", + "\n", + "In a computer vision application, each value in the matrix on the left corresponds to a single pixel value, and we convolve a 3x3 filter with the image by multiplying its values element-wise with the original matrix, then summing them up and adding a bias. In this first step of the exercise, you will implement a single step of convolution, corresponding to applying a filter to just one of the positions to get a single real-valued output. \n", + "\n", + "Later in this notebook, you'll apply this function to multiple positions of the input to implement the full convolutional operation. \n", + "\n", + "**Exercise**: Implement conv_single_step(). [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.sum.html).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: conv_single_step\n", + "\n", + "def conv_single_step(a_slice_prev, W, b):\n", + " \"\"\"\n", + " Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation \n", + " of the previous layer.\n", + " \n", + " Arguments:\n", + " a_slice_prev -- slice of input data of shape (f, f, n_C_prev)\n", + " W -- Weight parameters contained in a window - matrix of shape (f, f, n_C_prev)\n", + " b -- Bias parameters contained in a window - matrix of shape (1, 1, 1)\n", + " \n", + " Returns:\n", + " Z -- a scalar value, result of convolving the sliding window (W, b) on a slice x of the input data\n", + " \"\"\"\n", + "\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " # Element-wise product between a_slice and W. Do not add the bias yet.\n", + " s = np.multiply(a_slice_prev, W)\n", + " # Sum over all entries of the volume s.\n", + " Z = np.sum(s)\n", + " # Add bias b to Z. Cast b to a float() so that Z results in a scalar value.\n", + " Z = Z + float(b)\n", + " ### END CODE HERE ###\n", + "\n", + " return Z" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z = -6.99908945068\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "a_slice_prev = np.random.randn(4, 4, 3)\n", + "W = np.random.randn(4, 4, 3)\n", + "b = np.random.randn(1, 1, 1)\n", + "\n", + "Z = conv_single_step(a_slice_prev, W, b)\n", + "print(\"Z =\", Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Z**\n", + " \n", + " -6.99908945068\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 3.3 - Convolutional Neural Networks - Forward pass\n", + "\n", + "In the forward pass, you will take many filters and convolve them on the input. Each 'convolution' gives you a 2D matrix output. You will then stack these outputs to get a 3D volume: \n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "**Exercise**: Implement the function below to convolve the filters W on an input activation A_prev. This function takes as input A_prev, the activations output by the previous layer (for a batch of m inputs), F filters/weights denoted by W, and a bias vector denoted by b, where each filter has its own (single) bias. Finally you also have access to the hyperparameters dictionary which contains the stride and the padding. \n", + "\n", + "**Hint**: \n", + "1. To select a 2x2 slice at the upper left corner of a matrix \"a_prev\" (shape (5,5,3)), you would do:\n", + "```python\n", + "a_slice_prev = a_prev[0:2,0:2,:]\n", + "```\n", + "This will be useful when you will define `a_slice_prev` below, using the `start/end` indexes you will define.\n", + "2. To define a_slice you will need to first define its corners `vert_start`, `vert_end`, `horiz_start` and `horiz_end`. This figure may be helpful for you to find how each of the corner can be defined using h, w, f and s in the code below.\n", + "\n", + "\n", + "
**Figure 3** : **Definition of a slice using vertical and horizontal start/end (with a 2x2 filter)**
This figure shows only a single channel.
\n", + "\n", + "\n", + "**Reminder**:\n", + "The formulas relating the output shape of the convolution to the input shape is:\n", + "$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n", + "$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n", + "$$ n_C = \\text{number of filters used in the convolution}$$\n", + "\n", + "For this exercise, we won't worry about vectorization, and will just implement everything with for-loops." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: conv_forward\n", + "\n", + "def conv_forward(A_prev, W, b, hparameters):\n", + " \"\"\"\n", + " Implements the forward propagation for a convolution function\n", + " \n", + " Arguments:\n", + " A_prev -- output activations of the previous layer, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n", + " W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)\n", + " b -- Biases, numpy array of shape (1, 1, 1, n_C)\n", + " hparameters -- python dictionary containing \"stride\" and \"pad\"\n", + " \n", + " Returns:\n", + " Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)\n", + " cache -- cache of values needed for the conv_backward() function\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from A_prev's shape (≈1 line) \n", + " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", + " \n", + " # Retrieve dimensions from W's shape (≈1 line)\n", + " (f, f, n_C_prev, n_C) = W.shape\n", + " \n", + " # Retrieve information from \"hparameters\" (≈2 lines)\n", + " stride = hparameters['stride']\n", + " pad = hparameters['pad']\n", + " \n", + " # Compute the dimensions of the CONV output volume using the formula given above. Hint: use int() to floor. (≈2 lines)\n", + " n_H = int((n_H_prev - f + 2 * pad) / stride) + 1\n", + " n_W = int((n_W_prev - f + 2 * pad) / stride) + 1\n", + " \n", + " # Initialize the output volume Z with zeros. (≈1 line)\n", + " Z = np.zeros((m, n_H, n_W, n_C))\n", + " \n", + " # Create A_prev_pad by padding A_prev\n", + " A_prev_pad = zero_pad(A_prev, pad)\n", + " \n", + " for i in range(m): # loop over the batch of training examples\n", + " a_prev_pad = A_prev_pad[i] # Select ith training example's padded activation\n", + " for h in range(n_H): # loop over vertical axis of the output volume\n", + " for w in range(n_W): # loop over horizontal axis of the output volume\n", + " for c in range(n_C): # loop over channels (= #filters) of the output volume\n", + " \n", + " # Find the corners of the current \"slice\" (≈4 lines)\n", + " vert_start = h * stride\n", + " vert_end = vert_start + f\n", + " horiz_start = w * stride\n", + " horiz_end = horiz_start + f\n", + " \n", + " # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)\n", + " a_slice_prev = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n", + " \n", + " # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈1 line)\n", + " Z[i, h, w, c] = conv_single_step(a_slice_prev, W[...,c], b[...,c])\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Making sure your output shape is correct\n", + " assert(Z.shape == (m, n_H, n_W, n_C))\n", + " \n", + " # Save information in \"cache\" for the backprop\n", + " cache = (A_prev, W, b, hparameters)\n", + " \n", + " return Z, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z's mean = 0.0489952035289\n", + "Z[3,2,1] = [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437\n", + " 5.18531798 8.75898442]\n", + "cache_conv[0][1][2][3] = [-0.20075807 0.18656139 0.41005165]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "A_prev = np.random.randn(10,4,4,3)\n", + "W = np.random.randn(2,2,3,8)\n", + "b = np.random.randn(1,1,1,8)\n", + "hparameters = {\"pad\" : 2,\n", + " \"stride\": 2}\n", + "\n", + "Z, cache_conv = conv_forward(A_prev, W, b, hparameters)\n", + "print(\"Z's mean =\", np.mean(Z))\n", + "print(\"Z[3,2,1] =\", Z[3,2,1])\n", + "print(\"cache_conv[0][1][2][3] =\", cache_conv[0][1][2][3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Z's mean**\n", + " \n", + " 0.0489952035289\n", + "
\n", + " **Z[3,2,1]**\n", + " \n", + " [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437\n", + " 5.18531798 8.75898442]\n", + "
\n", + " **cache_conv[0][1][2][3]**\n", + " \n", + " [-0.20075807 0.18656139 0.41005165]\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, CONV layer should also contain an activation, in which case we would add the following line of code:\n", + "\n", + "```python\n", + "# Convolve the window to get back one output neuron\n", + "Z[i, h, w, c] = ...\n", + "# Apply activation\n", + "A[i, h, w, c] = activation(Z[i, h, w, c])\n", + "```\n", + "\n", + "You don't need to do it here. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Pooling layer \n", + "\n", + "The pooling (POOL) layer reduces the height and width of the input. It helps reduce computation, as well as helps make feature detectors more invariant to its position in the input. The two types of pooling layers are: \n", + "\n", + "- Max-pooling layer: slides an ($f, f$) window over the input and stores the max value of the window in the output.\n", + "\n", + "- Average-pooling layer: slides an ($f, f$) window over the input and stores the average value of the window in the output.\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "These pooling layers have no parameters for backpropagation to train. However, they have hyperparameters such as the window size $f$. This specifies the height and width of the fxf window you would compute a max or average over. \n", + "\n", + "### 4.1 - Forward Pooling\n", + "Now, you are going to implement MAX-POOL and AVG-POOL, in the same function. \n", + "\n", + "**Exercise**: Implement the forward pass of the pooling layer. Follow the hints in the comments below.\n", + "\n", + "**Reminder**:\n", + "As there's no padding, the formulas binding the output shape of the pooling to the input shape is:\n", + "$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f}{stride} \\rfloor +1 $$\n", + "$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f}{stride} \\rfloor +1 $$\n", + "$$ n_C = n_{C_{prev}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: pool_forward\n", + "\n", + "def pool_forward(A_prev, hparameters, mode = \"max\"):\n", + " \"\"\"\n", + " Implements the forward pass of the pooling layer\n", + " \n", + " Arguments:\n", + " A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n", + " hparameters -- python dictionary containing \"f\" and \"stride\"\n", + " mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n", + " \n", + " Returns:\n", + " A -- output of the pool layer, a numpy array of shape (m, n_H, n_W, n_C)\n", + " cache -- cache used in the backward pass of the pooling layer, contains the input and hparameters \n", + " \"\"\"\n", + " \n", + " # Retrieve dimensions from the input shape\n", + " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", + " \n", + " # Retrieve hyperparameters from \"hparameters\"\n", + " f = hparameters[\"f\"]\n", + " stride = hparameters[\"stride\"]\n", + " \n", + " # Define the dimensions of the output\n", + " n_H = int(1 + (n_H_prev - f) / stride)\n", + " n_W = int(1 + (n_W_prev - f) / stride)\n", + " n_C = n_C_prev\n", + " \n", + " # Initialize output matrix A\n", + " A = np.zeros((m, n_H, n_W, n_C)) \n", + " \n", + " ### START CODE HERE ###\n", + " for i in range(m): # loop over the training examples\n", + " for h in range(n_H): # loop on the vertical axis of the output volume\n", + " for w in range(n_W): # loop on the horizontal axis of the output volume\n", + " for c in range (n_C): # loop over the channels of the output volume\n", + " \n", + " # Find the corners of the current \"slice\" (≈4 lines)\n", + " vert_start = h * stride\n", + " vert_end = vert_start + f\n", + " horiz_start = w * stride\n", + " horiz_end = horiz_start + f\n", + " \n", + " # Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)\n", + " a_prev_slice = A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]\n", + " \n", + " # Compute the pooling operation on the slice. Use an if statment to differentiate the modes. Use np.max/np.mean.\n", + " if mode == \"max\":\n", + " A[i, h, w, c] = np.max(a_prev_slice)\n", + " elif mode == \"average\":\n", + " A[i, h, w, c] = np.mean(a_prev_slice)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Store the input and hparameters in \"cache\" for pool_backward()\n", + " cache = (A_prev, hparameters)\n", + " \n", + " # Making sure your output shape is correct\n", + " assert(A.shape == (m, n_H, n_W, n_C))\n", + " \n", + " return A, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode = max\n", + "A = [[[[ 1.74481176 0.86540763 1.13376944]]]\n", + "\n", + "\n", + " [[[ 1.13162939 1.51981682 2.18557541]]]]\n", + "\n", + "mode = average\n", + "A = [[[[ 0.02105773 -0.20328806 -0.40389855]]]\n", + "\n", + "\n", + " [[[-0.22154621 0.51716526 0.48155844]]]]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "A_prev = np.random.randn(2, 4, 4, 3)\n", + "hparameters = {\"stride\" : 2, \"f\": 3}\n", + "\n", + "A, cache = pool_forward(A_prev, hparameters)\n", + "print(\"mode = max\")\n", + "print(\"A =\", A)\n", + "print()\n", + "A, cache = pool_forward(A_prev, hparameters, mode = \"average\")\n", + "print(\"mode = average\")\n", + "print(\"A =\", A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output:**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " A =\n", + " \n", + " [[[[ 1.74481176 0.86540763 1.13376944]]]\n", + "\n", + "\n", + " [[[ 1.13162939 1.51981682 2.18557541]]]]\n", + "\n", + "
\n", + " A =\n", + " \n", + " [[[[ 0.02105773 -0.20328806 -0.40389855]]]\n", + "\n", + "\n", + " [[[-0.22154621 0.51716526 0.48155844]]]]\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You have now implemented the forward passes of all the layers of a convolutional network. \n", + "\n", + "The remainer of this notebook is optional, and will not be graded.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - Backpropagation in convolutional neural networks (OPTIONAL / UNGRADED)\n", + "\n", + "In modern deep learning frameworks, you only have to implement the forward pass, and the framework takes care of the backward pass, so most deep learning engineers don't need to bother with the details of the backward pass. The backward pass for convolutional networks is complicated. If you wish however, you can work through this optional portion of the notebook to get a sense of what backprop in a convolutional network looks like. \n", + "\n", + "When in an earlier course you implemented a simple (fully connected) neural network, you used backpropagation to compute the derivatives with respect to the cost to update the parameters. Similarly, in convolutional neural networks you can to calculate the derivatives with respect to the cost in order to update the parameters. The backprop equations are not trivial and we did not derive them in lecture, but we briefly presented them below.\n", + "\n", + "### 5.1 - Convolutional layer backward pass \n", + "\n", + "Let's start by implementing the backward pass for a CONV layer. \n", + "\n", + "#### 5.1.1 - Computing dA:\n", + "This is the formula for computing $dA$ with respect to the cost for a certain filter $W_c$ and a given training example:\n", + "\n", + "$$ dA += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^{n_W} W_c \\times dZ_{hw} \\tag{1}$$\n", + "\n", + "Where $W_c$ is a filter and $dZ_{hw}$ is a scalar corresponding to the gradient of the cost with respect to the output of the conv layer Z at the hth row and wth column (corresponding to the dot product taken at the ith stride left and jth stride down). Note that at each time, we multiply the the same filter $W_c$ by a different dZ when updating dA. We do so mainly because when computing the forward propagation, each filter is dotted and summed by a different a_slice. Therefore when computing the backprop for dA, we are just adding the gradients of all the a_slices. \n", + "\n", + "In code, inside the appropriate for-loops, this formula translates into:\n", + "```python\n", + "da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n", + "```\n", + "\n", + "#### 5.1.2 - Computing dW:\n", + "This is the formula for computing $dW_c$ ($dW_c$ is the derivative of one filter) with respect to the loss:\n", + "\n", + "$$ dW_c += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^ {n_W} a_{slice} \\times dZ_{hw} \\tag{2}$$\n", + "\n", + "Where $a_{slice}$ corresponds to the slice which was used to generate the acitivation $Z_{ij}$. Hence, this ends up giving us the gradient for $W$ with respect to that slice. Since it is the same $W$, we will just add up all such gradients to get $dW$. \n", + "\n", + "In code, inside the appropriate for-loops, this formula translates into:\n", + "```python\n", + "dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n", + "```\n", + "\n", + "#### 5.1.3 - Computing db:\n", + "\n", + "This is the formula for computing $db$ with respect to the cost for a certain filter $W_c$:\n", + "\n", + "$$ db = \\sum_h \\sum_w dZ_{hw} \\tag{3}$$\n", + "\n", + "As you have previously seen in basic neural networks, db is computed by summing $dZ$. In this case, you are just summing over all the gradients of the conv output (Z) with respect to the cost. \n", + "\n", + "In code, inside the appropriate for-loops, this formula translates into:\n", + "```python\n", + "db[:,:,:,c] += dZ[i, h, w, c]\n", + "```\n", + "\n", + "**Exercise**: Implement the `conv_backward` function below. You should sum over all the training examples, filters, heights, and widths. You should then compute the derivatives using formulas 1, 2 and 3 above. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def conv_backward(dZ, cache):\n", + " \"\"\"\n", + " Implement the backward propagation for a convolution function\n", + " \n", + " Arguments:\n", + " dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)\n", + " cache -- cache of values needed for the conv_backward(), output of conv_forward()\n", + " \n", + " Returns:\n", + " dA_prev -- gradient of the cost with respect to the input of the conv layer (A_prev),\n", + " numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n", + " dW -- gradient of the cost with respect to the weights of the conv layer (W)\n", + " numpy array of shape (f, f, n_C_prev, n_C)\n", + " db -- gradient of the cost with respect to the biases of the conv layer (b)\n", + " numpy array of shape (1, 1, 1, n_C)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve information from \"cache\"\n", + " (A_prev, W, b, hparameters) = cache\n", + " \n", + " # Retrieve dimensions from A_prev's shape\n", + " (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n", + " \n", + " # Retrieve dimensions from W's shape\n", + " (f, f, n_C_prev, n_C) = W.shape\n", + " \n", + " # Retrieve information from \"hparameters\"\n", + " stride = hparameters[\"stride\"]\n", + " pad = hparameters[\"pad\"]\n", + " \n", + " # Retrieve dimensions from dZ's shape\n", + " (m, n_H, n_W, n_C) = dZ.shape\n", + " \n", + " # Initialize dA_prev, dW, db with the correct shapes\n", + " dA_prev = np.zeros((m, n_H_prev, n_W_prev, n_C_prev)) \n", + " dW = np.zeros((f, f, n_C_prev, n_C))\n", + " db = np.zeros((1, 1, 1, n_C))\n", + "\n", + " # Pad A_prev and dA_prev\n", + " A_prev_pad = zero_pad(A_prev, pad)\n", + " dA_prev_pad = zero_pad(dA_prev, pad)\n", + " \n", + " for i in range(m): # loop over the training examples\n", + " \n", + " # select ith training example from A_prev_pad and dA_prev_pad\n", + " a_prev_pad = A_prev_pad[i]\n", + " da_prev_pad = dA_prev_pad[i]\n", + " \n", + " for h in range(n_H): # loop over vertical axis of the output volume\n", + " for w in range(n_W): # loop over horizontal axis of the output volume\n", + " for c in range(n_C): # loop over the channels of the output volume\n", + " \n", + " # Find the corners of the current \"slice\"\n", + " vert_start = h\n", + " vert_end = vert_start + f\n", + " horiz_start = w\n", + " horiz_end = horiz_start + f\n", + " \n", + " # Use the corners to define the slice from a_prev_pad\n", + " a_slice = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n", + "\n", + " # Update gradients for the window and the filter's parameters using the code formulas given above\n", + " da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n", + " dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n", + " db[:,:,:,c] += dZ[i, h, w, c]\n", + " \n", + " # Set the ith training example's dA_prev to the unpaded da_prev_pad (Hint: use X[pad:-pad, pad:-pad, :])\n", + " dA_prev[i, :, :, :] = da_prev_pad[pad:-pad, pad:-pad, :]\n", + " ### END CODE HERE ###\n", + " \n", + " # Making sure your output shape is correct\n", + " assert(dA_prev.shape == (m, n_H_prev, n_W_prev, n_C_prev))\n", + " \n", + " return dA_prev, dW, db" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dA_mean = 0.634770447265\n", + "dW_mean = 1.55726574285\n", + "db_mean = 7.83923256462\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "dA, dW, db = conv_backward(Z, cache_conv)\n", + "print(\"dA_mean =\", np.mean(dA))\n", + "print(\"dW_mean =\", np.mean(dW))\n", + "print(\"db_mean =\", np.mean(db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected Output: **\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **dA_mean**\n", + " \n", + " 1.45243777754\n", + "
\n", + " **dW_mean**\n", + " \n", + " 1.72699145831\n", + "
\n", + " **db_mean**\n", + " \n", + " 7.83923256462\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.2 Pooling layer - backward pass\n", + "\n", + "Next, let's implement the backward pass for the pooling layer, starting with the MAX-POOL layer. Even though a pooling layer has no parameters for backprop to update, you still need to backpropagation the gradient through the pooling layer in order to compute gradients for layers that came before the pooling layer. \n", + "\n", + "### 5.2.1 Max pooling - backward pass \n", + "\n", + "Before jumping into the backpropagation of the pooling layer, you are going to build a helper function called `create_mask_from_window()` which does the following: \n", + "\n", + "$$ X = \\begin{bmatrix}\n", + "1 && 3 \\\\\n", + "4 && 2\n", + "\\end{bmatrix} \\quad \\rightarrow \\quad M =\\begin{bmatrix}\n", + "0 && 0 \\\\\n", + "1 && 0\n", + "\\end{bmatrix}\\tag{4}$$\n", + "\n", + "As you can see, this function creates a \"mask\" matrix which keeps track of where the maximum of the matrix is. True (1) indicates the position of the maximum in X, the other entries are False (0). You'll see later that the backward pass for average pooling will be similar to this but using a different mask. \n", + "\n", + "**Exercise**: Implement `create_mask_from_window()`. This function will be helpful for pooling backward. \n", + "Hints:\n", + "- [np.max()]() may be helpful. It computes the maximum of an array.\n", + "- If you have a matrix X and a scalar x: `A = (X == x)` will return a matrix A of the same size as X such that:\n", + "```\n", + "A[i,j] = True if X[i,j] = x\n", + "A[i,j] = False if X[i,j] != x\n", + "```\n", + "- Here, you don't need to consider cases where there are several maxima in a matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_mask_from_window(x):\n", + " \"\"\"\n", + " Creates a mask from an input matrix x, to identify the max entry of x.\n", + " \n", + " Arguments:\n", + " x -- Array of shape (f, f)\n", + " \n", + " Returns:\n", + " mask -- Array of the same shape as window, contains a True at the position corresponding to the max entry of x.\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈1 line)\n", + " mask = x == np.max(x)\n", + " ### END CODE HERE ###\n", + " \n", + " return mask" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [[ 1.62434536 -0.61175641 -0.52817175]\n", + " [-1.07296862 0.86540763 -2.3015387 ]]\n", + "mask = [[ True False False]\n", + " [False False False]]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(2,3)\n", + "mask = create_mask_from_window(x)\n", + "print('x = ', x)\n", + "print(\"mask = \", mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Expected Output:** \n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "**x =**\n", + "\n", + "\n", + "[[ 1.62434536 -0.61175641 -0.52817175]
\n", + " [-1.07296862 0.86540763 -2.3015387 ]]\n", + "\n", + "
\n", + "**mask =**\n", + "\n", + "[[ True False False]
\n", + " [False False False]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why do we keep track of the position of the max? It's because this is the input value that ultimately influenced the output, and therefore the cost. Backprop is computing gradients with respect to the cost, so anything that influences the ultimate cost should have a non-zero gradient. So, backprop will \"propagate\" the gradient back to this particular input value that had influenced the cost. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2.2 - Average pooling - backward pass \n", + "\n", + "In max pooling, for each input window, all the \"influence\" on the output came from a single input value--the max. In average pooling, every element of the input window has equal influence on the output. So to implement backprop, you will now implement a helper function that reflects this.\n", + "\n", + "For example if we did average pooling in the forward pass using a 2x2 filter, then the mask you'll use for the backward pass will look like: \n", + "$$ dZ = 1 \\quad \\rightarrow \\quad dZ =\\begin{bmatrix}\n", + "1/4 && 1/4 \\\\\n", + "1/4 && 1/4\n", + "\\end{bmatrix}\\tag{5}$$\n", + "\n", + "This implies that each position in the $dZ$ matrix contributes equally to output because in the forward pass, we took an average. \n", + "\n", + "**Exercise**: Implement the function below to equally distribute a value dz through a matrix of dimension shape. [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ones.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def distribute_value(dz, shape):\n", + " \"\"\"\n", + " Distributes the input value in the matrix of dimension shape\n", + " \n", + " Arguments:\n", + " dz -- input scalar\n", + " shape -- the shape (n_H, n_W) of the output matrix for which we want to distribute the value of dz\n", + " \n", + " Returns:\n", + " a -- Array of size (n_H, n_W) for which we distributed the value of dz\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from shape (≈1 line)\n", + " (n_H, n_W) = shape\n", + " \n", + " # Compute the value to distribute on the matrix (≈1 line)\n", + " average = dz / (n_H * n_W)\n", + " \n", + " # Create a matrix where every entry is the \"average\" value (≈1 line)\n", + " a = np.ones(shape) * average\n", + " ### END CODE HERE ###\n", + " \n", + " return a" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "distributed value = [[ 0.5 0.5]\n", + " [ 0.5 0.5]]\n" + ] + } + ], + "source": [ + "a = distribute_value(2, (2,2))\n", + "print('distributed value =', a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "
\n", + "distributed_value =\n", + "\n", + "[[ 0.5 0.5]\n", + " \n", + "[ 0.5 0.5]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2.3 Putting it together: Pooling backward \n", + "\n", + "You now have everything you need to compute backward propagation on a pooling layer.\n", + "\n", + "**Exercise**: Implement the `pool_backward` function in both modes (`\"max\"` and `\"average\"`). You will once again use 4 for-loops (iterating over training examples, height, width, and channels). You should use an `if/elif` statement to see if the mode is equal to `'max'` or `'average'`. If it is equal to 'average' you should use the `distribute_value()` function you implemented above to create a matrix of the same shape as `a_slice`. Otherwise, the mode is equal to '`max`', and you will create a mask with `create_mask_from_window()` and multiply it by the corresponding value of dZ." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def pool_backward(dA, cache, mode = \"max\"):\n", + " \"\"\"\n", + " Implements the backward pass of the pooling layer\n", + " \n", + " Arguments:\n", + " dA -- gradient of cost with respect to the output of the pooling layer, same shape as A\n", + " cache -- cache output from the forward pass of the pooling layer, contains the layer's input and hparameters \n", + " mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n", + " \n", + " Returns:\n", + " dA_prev -- gradient of cost with respect to the input of the pooling layer, same shape as A_prev\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Retrieve information from cache (≈1 line)\n", + " (A_prev, hparameters) = cache\n", + " \n", + " # Retrieve hyperparameters from \"hparameters\" (≈2 lines)\n", + " stride = hparameters[\"stride\"]\n", + " f = hparameters[\"f\"]\n", + " \n", + " # Retrieve dimensions from A_prev's shape and dA's shape (≈2 lines)\n", + " m, n_H_prev, n_W_prev, n_C_prev = A_prev.shape\n", + " m, n_H, n_W, n_C = dA.shape\n", + " \n", + " # Initialize dA_prev with zeros (≈1 line)\n", + " dA_prev = np.zeros(A_prev.shape)\n", + " \n", + " for i in range(m): # loop over the training examples\n", + " # select training example from A_prev (≈1 line)\n", + " a_prev = A_prev[i]\n", + " for h in range(n_H): # loop on the vertical axis\n", + " for w in range(n_W): # loop on the horizontal axis\n", + " for c in range(n_C): # loop over the channels (depth)\n", + " # Find the corners of the current \"slice\" (≈4 lines)\n", + " vert_start = h\n", + " vert_end = vert_start + f\n", + " horiz_start = w\n", + " horiz_end = horiz_start + f\n", + " \n", + " # Compute the backward propagation in both modes.\n", + " if mode == \"max\":\n", + " # Use the corners and \"c\" to define the current slice from a_prev (≈1 line)\n", + " a_prev_slice = a_prev[vert_start:vert_end, horiz_start:horiz_end, c]\n", + " # Create the mask from a_prev_slice (≈1 line)\n", + " mask = create_mask_from_window(a_prev_slice)\n", + " # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)\n", + " dA_prev[i, vert_start:vert_end, horiz_start:horiz_end, c] += np.multiply(mask, dA[i, h, w, c])\n", + " \n", + " elif mode == \"average\":\n", + " # Get the value a from dA (≈1 line)\n", + " da = dA[i, h, w, c]\n", + " # Define the shape of the filter as fxf (≈1 line)\n", + " shape = (f, f)\n", + " # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)\n", + " dA_prev[i, vert_start:vert_end, horiz_start:horiz_end, c] += distribute_value(da, shape)\n", + " \n", + " ### END CODE ###\n", + " \n", + " # Making sure your output shape is correct\n", + " assert(dA_prev.shape == A_prev.shape)\n", + " \n", + " return dA_prev" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mode = max\n", + "mean of dA = 0.145713902729\n", + "dA_prev[1,1] = [[ 0. 0. ]\n", + " [ 5.05844394 -1.68282702]\n", + " [ 0. 0. ]]\n", + "\n", + "mode = average\n", + "mean of dA = 0.145713902729\n", + "dA_prev[1,1] = [[ 0.08485462 0.2787552 ]\n", + " [ 1.26461098 -0.25749373]\n", + " [ 1.17975636 -0.53624893]]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "A_prev = np.random.randn(5, 5, 3, 2)\n", + "hparameters = {\"stride\" : 1, \"f\": 2}\n", + "A, cache = pool_forward(A_prev, hparameters)\n", + "dA = np.random.randn(5, 4, 2, 2)\n", + "\n", + "dA_prev = pool_backward(dA, cache, mode = \"max\")\n", + "print(\"mode = max\")\n", + "print('mean of dA = ', np.mean(dA))\n", + "print('dA_prev[1,1] = ', dA_prev[1,1]) \n", + "print()\n", + "dA_prev = pool_backward(dA, cache, mode = \"average\")\n", + "print(\"mode = average\")\n", + "print('mean of dA = ', np.mean(dA))\n", + "print('dA_prev[1,1] = ', dA_prev[1,1]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "mode = max:\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "**mean of dA =**\n", + "\n", + "\n", + "0.145713902729\n", + "\n", + "
\n", + "**dA_prev[1,1] =** \n", + "\n", + "[[ 0. 0. ]
\n", + " [ 5.05844394 -1.68282702]
\n", + " [ 0. 0. ]]\n", + "
\n", + "\n", + "mode = average\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "**mean of dA =**\n", + "\n", + "\n", + "0.145713902729\n", + "\n", + "
\n", + "**dA_prev[1,1] =** \n", + "\n", + "[[ 0.08485462 0.2787552 ]
\n", + " [ 1.26461098 -0.25749373]
\n", + " [ 1.17975636 -0.53624893]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations !\n", + "\n", + "Congratulation on completing this assignment. You now understand how convolutional neural networks work. You have implemented all the building blocks of a neural network. In the next assignment you will implement a ConvNet using TensorFlow." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "convolutional-neural-networks", + "graded_item_id": "qO8ng", + "launcher_item_id": "7XDi8" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/images/Convolution_schematic.gif b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week1/images/Convolution_schematic.gif new file mode 100644 index 0000000..d8c73dc Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural 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b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/Residual Networks - v1.ipynb @@ -0,0 +1,3184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Residual Networks\n", + "\n", + "Welcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by [He et al.](https://arxiv.org/pdf/1512.03385.pdf), allow you to train much deeper networks than were previously practically feasible.\n", + "\n", + "**In this assignment, you will:**\n", + "- Implement the basic building blocks of ResNets. \n", + "- Put together these building blocks to implement and train a state-of-the-art neural network for image classification. \n", + "\n", + "This assignment will be done in Keras. \n", + "\n", + "Before jumping into the problem, let's run the cell below to load the required packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras import layers\n", + "from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n", + "from keras.models import Model, load_model\n", + "from keras.preprocessing import image\n", + "from keras.utils import layer_utils\n", + "from keras.utils.data_utils import get_file\n", + "from keras.applications.imagenet_utils import preprocess_input\n", + "import pydot\n", + "from IPython.display import SVG\n", + "from keras.utils.vis_utils import model_to_dot\n", + "from keras.utils import plot_model\n", + "from resnets_utils import *\n", + "from keras.initializers import glorot_uniform\n", + "import scipy.misc\n", + "from matplotlib.pyplot import imshow\n", + "%matplotlib inline\n", + "\n", + "import keras.backend as K\n", + "K.set_image_data_format('channels_last')\n", + "K.set_learning_phase(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - The problem of very deep neural networks\n", + "\n", + "Last week, you built your first convolutional neural network. In recent years, neural networks have become deeper, with state-of-the-art networks going from just a few layers (e.g., AlexNet) to over a hundred layers.\n", + "\n", + "The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the lower layers) to very complex features (at the deeper layers). However, using a deeper network doesn't always help. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent unbearably slow. More specifically, during gradient descent, as you backprop from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero (or, in rare cases, grow exponentially quickly and \"explode\" to take very large values). \n", + "\n", + "During training, you might therefore see the magnitude (or norm) of the gradient for the earlier layers descrease to zero very rapidly as training proceeds: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
**Figure 1** : **Vanishing gradient**
The speed of learning decreases very rapidly for the early layers as the network trains
\n", + "\n", + "You are now going to solve this problem by building a Residual Network!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Building a Residual Network\n", + "\n", + "In ResNets, a \"shortcut\" or a \"skip connection\" allows the gradient to be directly backpropagated to earlier layers: \n", + "\n", + "\n", + "
**Figure 2** : A ResNet block showing a **skip-connection**
\n", + "\n", + "The image on the left shows the \"main path\" through the network. The image on the right adds a shortcut to the main path. By stacking these ResNet blocks on top of each other, you can form a very deep network. \n", + "\n", + "We also saw in lecture that having ResNet blocks with the shortcut also makes it very easy for one of the blocks to learn an identity function. This means that you can stack on additional ResNet blocks with little risk of harming training set performance. (There is also some evidence that the ease of learning an identity function--even more than skip connections helping with vanishing gradients--accounts for ResNets' remarkable performance.)\n", + "\n", + "Two main types of blocks are used in a ResNet, depending mainly on whether the input/output dimensions are same or different. You are going to implement both of them. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - The identity block\n", + "\n", + "The identity block is the standard block used in ResNets, and corresponds to the case where the input activation (say $a^{[l]}$) has the same dimension as the output activation (say $a^{[l+2]}$). To flesh out the different steps of what happens in a ResNet's identity block, here is an alternative diagram showing the individual steps:\n", + "\n", + "\n", + "
**Figure 3** : **Identity block.** Skip connection \"skips over\" 2 layers.
\n", + "\n", + "The upper path is the \"shortcut path.\" The lower path is the \"main path.\" In this diagram, we have also made explicit the CONV2D and ReLU steps in each layer. To speed up training we have also added a BatchNorm step. Don't worry about this being complicated to implement--you'll see that BatchNorm is just one line of code in Keras! \n", + "\n", + "In this exercise, you'll actually implement a slightly more powerful version of this identity block, in which the skip connection \"skips over\" 3 hidden layers rather than 2 layers. It looks like this: \n", + "\n", + "\n", + "
**Figure 4** : **Identity block.** Skip connection \"skips over\" 3 layers.
\n", + "\n", + "Here're the individual steps.\n", + "\n", + "First component of main path: \n", + "- The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (1,1). Its padding is \"valid\" and its name should be `conv_name_base + '2a'`. Use 0 as the seed for the random initialization. \n", + "- The first BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2a'`.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + "\n", + "Second component of main path:\n", + "- The second CONV2D has $F_2$ filters of shape $(f,f)$ and a stride of (1,1). Its padding is \"same\" and its name should be `conv_name_base + '2b'`. Use 0 as the seed for the random initialization. \n", + "- The second BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2b'`.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + "\n", + "Third component of main path:\n", + "- The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is \"valid\" and its name should be `conv_name_base + '2c'`. Use 0 as the seed for the random initialization. \n", + "- The third BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2c'`. Note that there is no ReLU activation function in this component. \n", + "\n", + "Final step: \n", + "- The shortcut and the input are added together.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + "\n", + "**Exercise**: Implement the ResNet identity block. We have implemented the first component of the main path. Please read over this carefully to make sure you understand what it is doing. You should implement the rest. \n", + "- To implement the Conv2D step: [See reference](https://keras.io/layers/convolutional/#conv2d)\n", + "- To implement BatchNorm: [See reference](https://faroit.github.io/keras-docs/1.2.2/layers/normalization/) (axis: Integer, the axis that should be normalized (typically the channels axis))\n", + "- For the activation, use: `Activation('relu')(X)`\n", + "- To add the value passed forward by the shortcut: [See reference](https://keras.io/layers/merge/#add)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: identity_block\n", + "\n", + "def identity_block(X, f, filters, stage, block):\n", + " \"\"\"\n", + " Implementation of the identity block as defined in Figure 3\n", + " \n", + " Arguments:\n", + " X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n", + " f -- integer, specifying the shape of the middle CONV's window for the main path\n", + " filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n", + " stage -- integer, used to name the layers, depending on their position in the network\n", + " block -- string/character, used to name the layers, depending on their position in the network\n", + " \n", + " Returns:\n", + " X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n", + " \"\"\"\n", + " \n", + " # defining name basis\n", + " conv_name_base = 'res' + str(stage) + block + '_branch'\n", + " bn_name_base = 'bn' + str(stage) + block + '_branch'\n", + " \n", + " # Retrieve Filters\n", + " F1, F2, F3 = filters\n", + " \n", + " # Save the input value. You'll need this later to add back to the main path. \n", + " X_shortcut = X\n", + " \n", + " # First component of main path\n", + " X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n", + " X = Activation('relu')(X)\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Second component of main path (≈3 lines)\n", + " X = Conv2D(filters = F2, kernel_size = (f,f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n", + " X = Activation('relu')(X)\n", + "\n", + " # Third component of main path (≈2 lines)\n", + " X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n", + "\n", + " # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n", + " X = layers.Add()([X, X_shortcut])\n", + " X = Activation('relu')(X)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return X" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "out = [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003]\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " np.random.seed(1)\n", + " A_prev = tf.placeholder(\"float\", [3, 4, 4, 6])\n", + " X = np.random.randn(3, 4, 4, 6)\n", + " A = identity_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')\n", + " test.run(tf.global_variables_initializer())\n", + " out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})\n", + " print(\"out = \" + str(out[0][1][1][0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **out**\n", + " \n", + " [ 0.94822985 0. 1.16101444 2.747859 0. 1.36677003]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 - The convolutional block\n", + "\n", + "You've implemented the ResNet identity block. Next, the ResNet \"convolutional block\" is the other type of block. You can use this type of block when the input and output dimensions don't match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path: \n", + "\n", + "\n", + "
**Figure 4** : **Convolutional block**
\n", + "\n", + "The CONV2D layer in the shortcut path is used to resize the input $x$ to a different dimension, so that the dimensions match up in the final addition needed to add the shortcut value back to the main path. (This plays a similar role as the matrix $W_s$ discussed in lecture.) For example, to reduce the activation dimensions's height and width by a factor of 2, you can use a 1x1 convolution with a stride of 2. The CONV2D layer on the shortcut path does not use any non-linear activation function. Its main role is to just apply a (learned) linear function that reduces the dimension of the input, so that the dimensions match up for the later addition step. \n", + "\n", + "The details of the convolutional block are as follows. \n", + "\n", + "First component of main path:\n", + "- The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (s,s). Its padding is \"valid\" and its name should be `conv_name_base + '2a'`. \n", + "- The first BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2a'`.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + "\n", + "Second component of main path:\n", + "- The second CONV2D has $F_2$ filters of (f,f) and a stride of (1,1). Its padding is \"same\" and it's name should be `conv_name_base + '2b'`.\n", + "- The second BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2b'`.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + "\n", + "Third component of main path:\n", + "- The third CONV2D has $F_3$ filters of (1,1) and a stride of (1,1). Its padding is \"valid\" and it's name should be `conv_name_base + '2c'`.\n", + "- The third BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '2c'`. Note that there is no ReLU activation function in this component. \n", + "\n", + "Shortcut path:\n", + "- The CONV2D has $F_3$ filters of shape (1,1) and a stride of (s,s). Its padding is \"valid\" and its name should be `conv_name_base + '1'`.\n", + "- The BatchNorm is normalizing the channels axis. Its name should be `bn_name_base + '1'`. \n", + "\n", + "Final step: \n", + "- The shortcut and the main path values are added together.\n", + "- Then apply the ReLU activation function. This has no name and no hyperparameters. \n", + " \n", + "**Exercise**: Implement the convolutional block. We have implemented the first component of the main path; you should implement the rest. As before, always use 0 as the seed for the random initialization, to ensure consistency with our grader.\n", + "- [Conv Hint](https://keras.io/layers/convolutional/#conv2d)\n", + "- [BatchNorm Hint](https://keras.io/layers/normalization/#batchnormalization) (axis: Integer, the axis that should be normalized (typically the features axis))\n", + "- For the activation, use: `Activation('relu')(X)`\n", + "- [Addition Hint](https://keras.io/layers/merge/#add)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: convolutional_block\n", + "\n", + "def convolutional_block(X, f, filters, stage, block, s = 2):\n", + " \"\"\"\n", + " Implementation of the convolutional block as defined in Figure 4\n", + " \n", + " Arguments:\n", + " X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n", + " f -- integer, specifying the shape of the middle CONV's window for the main path\n", + " filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n", + " stage -- integer, used to name the layers, depending on their position in the network\n", + " block -- string/character, used to name the layers, depending on their position in the network\n", + " s -- Integer, specifying the stride to be used\n", + " \n", + " Returns:\n", + " X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)\n", + " \"\"\"\n", + " \n", + " # defining name basis\n", + " conv_name_base = 'res' + str(stage) + block + '_branch'\n", + " bn_name_base = 'bn' + str(stage) + block + '_branch'\n", + " \n", + " # Retrieve Filters\n", + " F1, F2, F3 = filters\n", + " \n", + " # Save the input value\n", + " X_shortcut = X\n", + "\n", + "\n", + " ##### MAIN PATH #####\n", + " # First component of main path \n", + " X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n", + " X = Activation('relu')(X)\n", + " \n", + " ### START CODE HERE ###\n", + "\n", + " # Second component of main path (≈3 lines)\n", + " X = Conv2D(F2, (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n", + " X = Activation('relu')(X)\n", + "\n", + " # Third component of main path (≈2 lines)\n", + " X = Conv2D(F3, (1, 1), strides = (1,1), name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n", + "\n", + " ##### SHORTCUT PATH #### (≈2 lines)\n", + " X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '1', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)\n", + " X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)\n", + "\n", + " # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)\n", + " X = layers.Add()([X, X_shortcut])\n", + " X = Activation('relu')(X)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return X" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "out = [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603]\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " np.random.seed(1)\n", + " A_prev = tf.placeholder(\"float\", [3, 4, 4, 6])\n", + " X = np.random.randn(3, 4, 4, 6)\n", + " A = convolutional_block(A_prev, f = 2, filters = [2, 4, 6], stage = 1, block = 'a')\n", + " test.run(tf.global_variables_initializer())\n", + " out = test.run([A], feed_dict={A_prev: X, K.learning_phase(): 0})\n", + " print(\"out = \" + str(out[0][1][1][0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **out**\n", + " \n", + " [ 0.09018463 1.23489773 0.46822017 0.0367176 0. 0.65516603]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Building your first ResNet model (50 layers)\n", + "\n", + "You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. \"ID BLOCK\" in the diagram stands for \"Identity block,\" and \"ID BLOCK x3\" means you should stack 3 identity blocks together.\n", + "\n", + "\n", + "
**Figure 5** : **ResNet-50 model**
\n", + "\n", + "The details of this ResNet-50 model are:\n", + "- Zero-padding pads the input with a pad of (3,3)\n", + "- Stage 1:\n", + " - The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). Its name is \"conv1\".\n", + " - BatchNorm is applied to the channels axis of the input.\n", + " - MaxPooling uses a (3,3) window and a (2,2) stride.\n", + "- Stage 2:\n", + " - The convolutional block uses three set of filters of size [64,64,256], \"f\" is 3, \"s\" is 1 and the block is \"a\".\n", + " - The 2 identity blocks use three set of filters of size [64,64,256], \"f\" is 3 and the blocks are \"b\" and \"c\".\n", + "- Stage 3:\n", + " - The convolutional block uses three set of filters of size [128,128,512], \"f\" is 3, \"s\" is 2 and the block is \"a\".\n", + " - The 3 identity blocks use three set of filters of size [128,128,512], \"f\" is 3 and the blocks are \"b\", \"c\" and \"d\".\n", + "- Stage 4:\n", + " - The convolutional block uses three set of filters of size [256, 256, 1024], \"f\" is 3, \"s\" is 2 and the block is \"a\".\n", + " - The 5 identity blocks use three set of filters of size [256, 256, 1024], \"f\" is 3 and the blocks are \"b\", \"c\", \"d\", \"e\" and \"f\".\n", + "- Stage 5:\n", + " - The convolutional block uses three set of filters of size [512, 512, 2048], \"f\" is 3, \"s\" is 2 and the block is \"a\".\n", + " - The 2 identity blocks use three set of filters of size [256, 256, 2048], \"f\" is 3 and the blocks are \"b\" and \"c\".\n", + "- The 2D Average Pooling uses a window of shape (2,2) and its name is \"avg_pool\".\n", + "- The flatten doesn't have any hyperparameters or name.\n", + "- The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation. Its name should be `'fc' + str(classes)`.\n", + "\n", + "**Exercise**: Implement the ResNet with 50 layers described in the figure above. We have implemented Stages 1 and 2. Please implement the rest. (The syntax for implementing Stages 3-5 should be quite similar to that of Stage 2.) Make sure you follow the naming convention in the text above. \n", + "\n", + "You'll need to use this function: \n", + "- Average pooling [see reference](https://keras.io/layers/pooling/#averagepooling2d)\n", + "\n", + "Here're some other functions we used in the code below:\n", + "- Conv2D: [See reference](https://keras.io/layers/convolutional/#conv2d)\n", + "- BatchNorm: [See reference](https://keras.io/layers/normalization/#batchnormalization) (axis: Integer, the axis that should be normalized (typically the features axis))\n", + "- Zero padding: [See reference](https://keras.io/layers/convolutional/#zeropadding2d)\n", + "- Max pooling: [See reference](https://keras.io/layers/pooling/#maxpooling2d)\n", + "- Fully conected layer: [See reference](https://keras.io/layers/core/#dense)\n", + "- Addition: [See reference](https://keras.io/layers/merge/#add)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: ResNet50\n", + "\n", + "def ResNet50(input_shape = (64, 64, 3), classes = 6):\n", + " \"\"\"\n", + " Implementation of the popular ResNet50 the following architecture:\n", + " CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n", + " -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n", + "\n", + " Arguments:\n", + " input_shape -- shape of the images of the dataset\n", + " classes -- integer, number of classes\n", + "\n", + " Returns:\n", + " model -- a Model() instance in Keras\n", + " \"\"\"\n", + " \n", + " # Define the input as a tensor with shape input_shape\n", + " X_input = Input(input_shape)\n", + "\n", + " \n", + " # Zero-Padding\n", + " X = ZeroPadding2D((3, 3))(X_input)\n", + " \n", + " # Stage 1\n", + " X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)\n", + " X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)\n", + " X = Activation('relu')(X)\n", + " X = MaxPooling2D((3, 3), strides=(2, 2))(X)\n", + "\n", + " # Stage 2\n", + " X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)\n", + " X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')\n", + " X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')\n", + "\n", + " ### START CODE HERE ###\n", + "\n", + " # Stage 3 (≈4 lines)\n", + " X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)\n", + " X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')\n", + " X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')\n", + " X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')\n", + "\n", + " # Stage 4 (≈6 lines)\n", + " X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)\n", + " X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')\n", + " X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')\n", + " X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')\n", + " X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')\n", + " X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')\n", + "\n", + " # Stage 5 (≈3 lines)\n", + " X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)\n", + " X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')\n", + " X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')\n", + "\n", + " # AVGPOOL (≈1 line). Use \"X = AveragePooling2D(...)(X)\"\n", + " X = AveragePooling2D((2,2), name='avg_pool')(X)\n", + " \n", + " ### END CODE HERE ###\n", + "\n", + " # output layer\n", + " X = Flatten()(X)\n", + " X = Dense(classes, activation='softmax', name='fc' + str(classes), kernel_initializer = glorot_uniform(seed=0))(X)\n", + " \n", + " \n", + " # Create model\n", + " model = Model(inputs = X_input, outputs = X, name='ResNet50')\n", + "\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following code to build the model's graph. If your implementation is not correct you will know it by checking your accuracy when running `model.fit(...)` below." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "model = ResNet50(input_shape = (64, 64, 3), classes = 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As seen in the Keras Tutorial Notebook, prior training a model, you need to configure the learning process by compiling the model." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is now ready to be trained. The only thing you need is a dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load the SIGNS Dataset.\n", + "\n", + "\n", + "
**Figure 6** : **SIGNS dataset**
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of training examples = 1080\n", + "number of test examples = 120\n", + "X_train shape: (1080, 64, 64, 3)\n", + "Y_train shape: (1080, 6)\n", + "X_test shape: (120, 64, 64, 3)\n", + "Y_test shape: (120, 6)\n" + ] + } + ], + "source": [ + "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()\n", + "\n", + "# Normalize image vectors\n", + "X_train = X_train_orig/255.\n", + "X_test = X_test_orig/255.\n", + "\n", + "# Convert training and test labels to one hot matrices\n", + "Y_train = convert_to_one_hot(Y_train_orig, 6).T\n", + "Y_test = convert_to_one_hot(Y_test_orig, 6).T\n", + "\n", + "print (\"number of training examples = \" + str(X_train.shape[0]))\n", + "print (\"number of test examples = \" + str(X_test.shape[0]))\n", + "print (\"X_train shape: \" + str(X_train.shape))\n", + "print (\"Y_train shape: \" + str(Y_train.shape))\n", + "print (\"X_test shape: \" + str(X_test.shape))\n", + "print (\"Y_test shape: \" + str(Y_test.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to train your model on 2 epochs with a batch size of 32. On a CPU it should take you around 5min per epoch. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2\n", + "1080/1080 [==============================] - 238s - loss: 3.1064 - acc: 0.2370 \n", + "Epoch 2/2\n", + "1080/1080 [==============================] - 256s - loss: 2.6301 - acc: 0.2954 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, Y_train, epochs = 2, batch_size = 32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " ** Epoch 1/2**\n", + " \n", + " loss: between 1 and 5, acc: between 0.2 and 0.5, although your results can be different from ours.\n", + "
\n", + " ** Epoch 2/2**\n", + " \n", + " loss: between 1 and 5, acc: between 0.2 and 0.5, you should see your loss decreasing and the accuracy increasing.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how this model (trained on only two epochs) performs on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "120/120 [==============================] - 8s \n", + "Loss = 0.530178320408\n", + "Test Accuracy = 0.866666662693\n" + ] + } + ], + "source": [ + "preds = model.evaluate(X_test, Y_test)\n", + "print (\"Loss = \" + str(preds[0]))\n", + "print (\"Test Accuracy = \" + str(preds[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Test Accuracy**\n", + " \n", + " between 0.16 and 0.25\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purpose of this assignment, we've asked you to train the model only for two epochs. You can see that it achieves poor performances. Please go ahead and submit your assignment; to check correctness, the online grader will run your code only for a small number of epochs as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have finished this official (graded) part of this assignment, you can also optionally train the ResNet for more iterations, if you want. We get a lot better performance when we train for ~20 epochs, but this will take more than an hour when training on a CPU. \n", + "\n", + "Using a GPU, we've trained our own ResNet50 model's weights on the SIGNS dataset. You can load and run our trained model on the test set in the cells below. It may take ≈1min to load the model." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = load_model('ResNet50.h5') " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "120/120 [==============================] - 11s \n", + "Loss = 0.530178320408\n", + "Test Accuracy = 0.866666662693\n" + ] + } + ], + "source": [ + "preds = model.evaluate(X_test, Y_test)\n", + "print (\"Loss = \" + str(preds[0]))\n", + "print (\"Test Accuracy = \" + str(preds[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ResNet50 is a powerful model for image classification when it is trained for an adequate number of iterations. We hope you can use what you've learnt and apply it to your own classification problem to perform state-of-the-art accuracy.\n", + "\n", + "Congratulations on finishing this assignment! You've now implemented a state-of-the-art image classification system! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Test on your own image (Optional/Ungraded)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you wish, you can also take a picture of your own hand and see the output of the model. To do this:\n", + " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", + " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", + " 3. Write your image's name in the following code\n", + " 4. Run the code and check if the algorithm is right! " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input image shape: (1, 64, 64, 3)\n", + "class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = \n", + "[[ 1. 0. 0. 0. 0. 0.]]\n" + ] + }, + { + "data": { + "image/png": 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E7k1rTQDqQ7kRbm+rWjfQXoFiedlwip0acwPVmhoFyhZetZzaHseBZ8kqKhMS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yCrJuTN+o96NXYR71yZlYvLHYhiESdTwNVVnu5ExTKdEKIKPAR6cuZKOF79eOMACBVBRJsHZA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_path = 'images/my_image.jpg'\n", + "img = image.load_img(img_path, target_size=(64, 64))\n", + "x = image.img_to_array(img)\n", + "x = np.expand_dims(x, axis=0)\n", + "x = preprocess_input(x)\n", + "print('Input image shape:', x.shape)\n", + "my_image = scipy.misc.imread(img_path)\n", + "imshow(my_image)\n", + "print(\"class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = \")\n", + "print(model.predict(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also print a summary of your model by running the following code." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "____________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "====================================================================================================\n", + "input_1 (InputLayer) (None, 64, 64, 3) 0 \n", + "____________________________________________________________________________________________________\n", + "zero_padding2d_1 (ZeroPadding2D) (None, 70, 70, 3) 0 input_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv1 (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn_conv1 (BatchNormalization) (None, 32, 32, 64) 256 conv1[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_4 (Activation) (None, 32, 32, 64) 0 bn_conv1[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 15, 15, 64) 0 activation_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2a_branch2a (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2a_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_5 (Activation) (None, 15, 15, 64) 0 bn2a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2a_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2a_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_6 (Activation) (None, 15, 15, 64) 0 bn2a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2a_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2a_branch1 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2a_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2a_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2a_branch1 (BatchNormalization (None, 15, 15, 256) 1024 res2a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_2 (Add) (None, 15, 15, 256) 0 bn2a_branch2c[0][0] \n", + " bn2a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_7 (Activation) (None, 15, 15, 256) 0 add_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2b_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2b_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_8 (Activation) (None, 15, 15, 64) 0 bn2b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2b_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2b_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_9 (Activation) (None, 15, 15, 64) 0 bn2b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2b_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_9[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2b_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2b_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_3 (Add) (None, 15, 15, 256) 0 bn2b_branch2c[0][0] \n", + " activation_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_10 (Activation) (None, 15, 15, 256) 0 add_3[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2c_branch2a (Conv2D) (None, 15, 15, 64) 16448 activation_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2c_branch2a (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_11 (Activation) (None, 15, 15, 64) 0 bn2c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2c_branch2b (Conv2D) (None, 15, 15, 64) 36928 activation_11[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2c_branch2b (BatchNormalizatio (None, 15, 15, 64) 256 res2c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_12 (Activation) (None, 15, 15, 64) 0 bn2c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res2c_branch2c (Conv2D) (None, 15, 15, 256) 16640 activation_12[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn2c_branch2c (BatchNormalizatio (None, 15, 15, 256) 1024 res2c_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_4 (Add) (None, 15, 15, 256) 0 bn2c_branch2c[0][0] \n", + " activation_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_13 (Activation) (None, 15, 15, 256) 0 add_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3a_branch2a (Conv2D) (None, 8, 8, 128) 32896 activation_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3a_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_14 (Activation) (None, 8, 8, 128) 0 bn3a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3a_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_14[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3a_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_15 (Activation) (None, 8, 8, 128) 0 bn3a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3a_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_15[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3a_branch1 (Conv2D) (None, 8, 8, 512) 131584 activation_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3a_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3a_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3a_branch1 (BatchNormalization (None, 8, 8, 512) 2048 res3a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_5 (Add) (None, 8, 8, 512) 0 bn3a_branch2c[0][0] \n", + " bn3a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_16 (Activation) (None, 8, 8, 512) 0 add_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3b_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3b_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_17 (Activation) (None, 8, 8, 128) 0 bn3b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3b_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_17[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3b_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_18 (Activation) (None, 8, 8, 128) 0 bn3b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3b_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_18[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3b_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3b_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_6 (Add) (None, 8, 8, 512) 0 bn3b_branch2c[0][0] \n", + " activation_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_19 (Activation) (None, 8, 8, 512) 0 add_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3c_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_19[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3c_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_20 (Activation) (None, 8, 8, 128) 0 bn3c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3c_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_20[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3c_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_21 (Activation) (None, 8, 8, 128) 0 bn3c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3c_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_21[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3c_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3c_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_7 (Add) (None, 8, 8, 512) 0 bn3c_branch2c[0][0] \n", + " activation_19[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_22 (Activation) (None, 8, 8, 512) 0 add_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3d_branch2a (Conv2D) (None, 8, 8, 128) 65664 activation_22[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3d_branch2a (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_23 (Activation) (None, 8, 8, 128) 0 bn3d_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3d_branch2b (Conv2D) (None, 8, 8, 128) 147584 activation_23[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3d_branch2b (BatchNormalizatio (None, 8, 8, 128) 512 res3d_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_24 (Activation) (None, 8, 8, 128) 0 bn3d_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res3d_branch2c (Conv2D) (None, 8, 8, 512) 66048 activation_24[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn3d_branch2c (BatchNormalizatio (None, 8, 8, 512) 2048 res3d_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_8 (Add) (None, 8, 8, 512) 0 bn3d_branch2c[0][0] \n", + " activation_22[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_25 (Activation) (None, 8, 8, 512) 0 add_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4a_branch2a (Conv2D) (None, 4, 4, 256) 131328 activation_25[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4a_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_26 (Activation) (None, 4, 4, 256) 0 bn4a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4a_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_26[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4a_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_27 (Activation) (None, 4, 4, 256) 0 bn4a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4a_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_27[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4a_branch1 (Conv2D) (None, 4, 4, 1024) 525312 activation_25[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4a_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4a_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4a_branch1 (BatchNormalization (None, 4, 4, 1024) 4096 res4a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_9 (Add) (None, 4, 4, 1024) 0 bn4a_branch2c[0][0] \n", + " bn4a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_28 (Activation) (None, 4, 4, 1024) 0 add_9[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4b_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_28[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4b_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_29 (Activation) (None, 4, 4, 256) 0 bn4b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4b_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_29[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4b_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_30 (Activation) (None, 4, 4, 256) 0 bn4b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4b_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_30[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4b_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4b_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_10 (Add) (None, 4, 4, 1024) 0 bn4b_branch2c[0][0] \n", + " activation_28[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_31 (Activation) (None, 4, 4, 1024) 0 add_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4c_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_31[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4c_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_32 (Activation) (None, 4, 4, 256) 0 bn4c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4c_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_32[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4c_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_33 (Activation) (None, 4, 4, 256) 0 bn4c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4c_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_33[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4c_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4c_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_11 (Add) (None, 4, 4, 1024) 0 bn4c_branch2c[0][0] \n", + " activation_31[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_34 (Activation) (None, 4, 4, 1024) 0 add_11[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4d_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_34[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4d_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_35 (Activation) (None, 4, 4, 256) 0 bn4d_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4d_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_35[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4d_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4d_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_36 (Activation) (None, 4, 4, 256) 0 bn4d_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4d_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_36[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4d_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4d_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_12 (Add) (None, 4, 4, 1024) 0 bn4d_branch2c[0][0] \n", + " activation_34[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_37 (Activation) (None, 4, 4, 1024) 0 add_12[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4e_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_37[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4e_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_38 (Activation) (None, 4, 4, 256) 0 bn4e_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4e_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_38[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4e_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4e_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_39 (Activation) (None, 4, 4, 256) 0 bn4e_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4e_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_39[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4e_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4e_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_13 (Add) (None, 4, 4, 1024) 0 bn4e_branch2c[0][0] \n", + " activation_37[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_40 (Activation) (None, 4, 4, 1024) 0 add_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4f_branch2a (Conv2D) (None, 4, 4, 256) 262400 activation_40[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4f_branch2a (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_41 (Activation) (None, 4, 4, 256) 0 bn4f_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4f_branch2b (Conv2D) (None, 4, 4, 256) 590080 activation_41[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4f_branch2b (BatchNormalizatio (None, 4, 4, 256) 1024 res4f_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_42 (Activation) (None, 4, 4, 256) 0 bn4f_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res4f_branch2c (Conv2D) (None, 4, 4, 1024) 263168 activation_42[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn4f_branch2c (BatchNormalizatio (None, 4, 4, 1024) 4096 res4f_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_14 (Add) (None, 4, 4, 1024) 0 bn4f_branch2c[0][0] \n", + " activation_40[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_43 (Activation) (None, 4, 4, 1024) 0 add_14[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5a_branch2a (Conv2D) (None, 2, 2, 512) 524800 activation_43[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5a_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_44 (Activation) (None, 2, 2, 512) 0 bn5a_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5a_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_44[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5a_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_45 (Activation) (None, 2, 2, 512) 0 bn5a_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5a_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_45[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5a_branch1 (Conv2D) (None, 2, 2, 2048) 2099200 activation_43[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5a_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5a_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5a_branch1 (BatchNormalization (None, 2, 2, 2048) 8192 res5a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_15 (Add) (None, 2, 2, 2048) 0 bn5a_branch2c[0][0] \n", + " bn5a_branch1[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_46 (Activation) (None, 2, 2, 2048) 0 add_15[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5b_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_46[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5b_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_47 (Activation) (None, 2, 2, 512) 0 bn5b_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5b_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_47[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5b_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_48 (Activation) (None, 2, 2, 512) 0 bn5b_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5b_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_48[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5b_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5b_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_16 (Add) (None, 2, 2, 2048) 0 bn5b_branch2c[0][0] \n", + " activation_46[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_49 (Activation) (None, 2, 2, 2048) 0 add_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5c_branch2a (Conv2D) (None, 2, 2, 512) 1049088 activation_49[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5c_branch2a (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_50 (Activation) (None, 2, 2, 512) 0 bn5c_branch2a[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5c_branch2b (Conv2D) (None, 2, 2, 512) 2359808 activation_50[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5c_branch2b (BatchNormalizatio (None, 2, 2, 512) 2048 res5c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_51 (Activation) (None, 2, 2, 512) 0 bn5c_branch2b[0][0] \n", + "____________________________________________________________________________________________________\n", + "res5c_branch2c (Conv2D) (None, 2, 2, 2048) 1050624 activation_51[0][0] \n", + "____________________________________________________________________________________________________\n", + "bn5c_branch2c (BatchNormalizatio (None, 2, 2, 2048) 8192 res5c_branch2c[0][0] \n", + "____________________________________________________________________________________________________\n", + "add_17 (Add) (None, 2, 2, 2048) 0 bn5c_branch2c[0][0] \n", + " activation_49[0][0] \n", + "____________________________________________________________________________________________________\n", + "activation_52 (Activation) (None, 2, 2, 2048) 0 add_17[0][0] \n", + "____________________________________________________________________________________________________\n", + "avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 activation_52[0][0] \n", + "____________________________________________________________________________________________________\n", + "flatten_1 (Flatten) (None, 2048) 0 avg_pool[0][0] \n", + "____________________________________________________________________________________________________\n", + "fc6 (Dense) (None, 6) 12294 flatten_1[0][0] \n", + "====================================================================================================\n", + "Total params: 23,600,006\n", + "Trainable params: 23,546,886\n", + "Non-trainable params: 53,120\n", + "____________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, run the code below to visualize your ResNet50. You can also download a .png picture of your model by going to \"File -> Open...-> model.png\"." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "139828333973120\n", + "\n", + "input_1: InputLayer\n", + "\n", + "\n", + "139828333973232\n", + "\n", + "zero_padding2d_1: ZeroPadding2D\n", + "\n", + "\n", + "139828333973120->139828333973232\n", + "\n", + "\n", + "\n", + "\n", + "139828333973288\n", + "\n", + "conv1: Conv2D\n", + "\n", + "\n", + "139828333973232->139828333973288\n", + "\n", + "\n", + "\n", + "\n", + "139828333973456\n", + "\n", + "bn_conv1: BatchNormalization\n", + "\n", + "\n", + "139828333973288->139828333973456\n", + "\n", + "\n", + "\n", + "\n", + "139828334015544\n", + "\n", + "activation_4: Activation\n", + "\n", + "\n", + "139828333973456->139828334015544\n", + "\n", + "\n", + "\n", + "\n", + "139828334015600\n", + "\n", + "max_pooling2d_1: 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"\n", + "activation_52: Activation\n", + "\n", + "\n", + "139828333771968->139828333772024\n", + "\n", + "\n", + "\n", + "\n", + "139828333772080\n", + "\n", + "avg_pool: AveragePooling2D\n", + "\n", + "\n", + "139828333772024->139828333772080\n", + "\n", + "\n", + "\n", + "\n", + "139828333772248\n", + "\n", + "flatten_1: Flatten\n", + "\n", + "\n", + "139828333772080->139828333772248\n", + "\n", + "\n", + "\n", + "\n", + "139828333772360\n", + "\n", + "fc6: Dense\n", + "\n", + "\n", + "139828333772248->139828333772360\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_model(model, to_file='model.png')\n", + "SVG(model_to_dot(model).create(prog='dot', format='svg'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember:**\n", + "- Very deep \"plain\" networks don't work in practice because they are hard to train due to vanishing gradients. \n", + "- The skip-connections help to address the Vanishing Gradient problem. They also make it easy for a ResNet block to learn an identity function. \n", + "- There are two main type of blocks: The identity block and the convolutional block. \n", + "- Very deep Residual Networks are built by stacking these blocks together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References \n", + "\n", + "This notebook presents the ResNet algorithm due to He et al. (2015). The implementation here also took significant inspiration and follows the structure given in the github repository of Francois Chollet: \n", + "\n", + "- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - [Deep Residual Learning for Image Recognition (2015)](https://arxiv.org/abs/1512.03385)\n", + "- Francois Chollet's github repository: https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "convolutional-neural-networks", + "graded_item_id": "OEpi5", + "launcher_item_id": "jK9EQ" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/convblock_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/convblock_kiank.png new file mode 100644 index 0000000..f6919d8 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/convblock_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock2_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock2_kiank.png new file mode 100644 index 0000000..a6f3f13 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock2_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock3_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock3_kiank.png new file mode 100644 index 0000000..4232ec7 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/idblock3_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/my_image.jpg b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/my_image.jpg new file mode 100644 index 0000000..06b6e7d Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/my_image.jpg differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/resnet_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/resnet_kiank.png new file mode 100644 index 0000000..a5da084 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/resnet_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/signs_data_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/signs_data_kiank.png new file mode 100644 index 0000000..6b144b2 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/signs_data_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/skip_connection_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/skip_connection_kiank.png new file mode 100644 index 0000000..07fd583 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/skip_connection_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/vanishing_grad_kiank.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/vanishing_grad_kiank.png new file mode 100644 index 0000000..7cb826c Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/images/vanishing_grad_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/model.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/model.png new file mode 100644 index 0000000..d978761 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week2/ResNets/model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week3/Car detection for Autonomous Driving/Autonomous driving application - Car detection - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week3/Car detection for Autonomous Driving/Autonomous driving application - Car detection - v1.ipynb new file mode 100644 index 0000000..2716243 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week3/Car detection for Autonomous Driving/Autonomous driving application - Car detection - v1.ipynb @@ -0,0 +1,1395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Autonomous driving - Car detection\n", + "\n", + "Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). \n", + "\n", + "**You will learn to**:\n", + "- Use object detection on a car detection dataset\n", + "- Deal with bounding boxes\n", + "\n", + "Run the following cell to load the packages and dependencies that are going to be useful for your journey!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import argparse\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import imshow\n", + "import scipy.io\n", + "import scipy.misc\n", + "import numpy as np\n", + "import pandas as pd\n", + "import PIL\n", + "import tensorflow as tf\n", + "from keras import backend as K\n", + "from keras.layers import Input, Lambda, Conv2D\n", + "from keras.models import load_model, Model\n", + "from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes\n", + "from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Problem Statement\n", + "\n", + "You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. \n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "
Pictures taken from a car-mounted camera while driving around Silicon Valley.
We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles.\n", + "
\n", + "\n", + "\n", + "\n", + "You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.\n", + "\n", + "\n", + "
**Figure 1** : **Definition of a box**
\n", + "\n", + "If you have 80 classes that you want YOLO to recognize, you can represent the class label $c$ either as an integer from 1 to 80, or as an 80-dimensional vector (with 80 numbers) one component of which is 1 and the rest of which are 0. The video lectures had used the latter representation; in this notebook, we will use both representations, depending on which is more convenient for a particular step. \n", + "\n", + "In this exercise, you will learn how YOLO works, then apply it to car detection. Because the YOLO model is very computationally expensive to train, we will load pre-trained weights for you to use. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - YOLO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "YOLO (\"you only look once\") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm \"only looks once\" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.\n", + "\n", + "### 2.1 - Model details\n", + "\n", + "First things to know:\n", + "- The **input** is a batch of images of shape (m, 608, 608, 3)\n", + "- The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers $(p_c, b_x, b_y, b_h, b_w, c)$ as explained above. If you expand $c$ into an 80-dimensional vector, each bounding box is then represented by 85 numbers. \n", + "\n", + "We will use 5 anchor boxes. So you can think of the YOLO architecture as the following: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).\n", + "\n", + "Lets look in greater detail at what this encoding represents. \n", + "\n", + "\n", + "
**Figure 2** : **Encoding architecture for YOLO**
\n", + "\n", + "If the center/midpoint of an object falls into a grid cell, that grid cell is responsible for detecting that object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height.\n", + "\n", + "For simplicity, we will flatten the last two last dimensions of the shape (19, 19, 5, 85) encoding. So the output of the Deep CNN is (19, 19, 425).\n", + "\n", + "\n", + "
**Figure 3** : **Flattening the last two last dimensions**
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for each box (of each cell) we will compute the following elementwise product and extract a probability that the box contains a certain class.\n", + "\n", + "\n", + "
**Figure 4** : **Find the class detected by each box**
\n", + "\n", + "Here's one way to visualize what YOLO is predicting on an image:\n", + "- For each of the 19x19 grid cells, find the maximum of the probability scores (taking a max across both the 5 anchor boxes and across different classes). \n", + "- Color that grid cell according to what object that grid cell considers the most likely.\n", + "\n", + "Doing this results in this picture: \n", + "\n", + "\n", + "
**Figure 5** : Each of the 19x19 grid cells colored according to which class has the largest predicted probability in that cell.
\n", + "\n", + "Note that this visualization isn't a core part of the YOLO algorithm itself for making predictions; it's just a nice way of visualizing an intermediate result of the algorithm. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to visualize YOLO's output is to plot the bounding boxes that it outputs. Doing that results in a visualization like this: \n", + "\n", + "\n", + "
**Figure 6** : Each cell gives you 5 boxes. In total, the model predicts: 19x19x5 = 1805 boxes just by looking once at the image (one forward pass through the network)! Different colors denote different classes.
\n", + "\n", + "In the figure above, we plotted only boxes that the model had assigned a high probability to, but this is still too many boxes. You'd like to filter the algorithm's output down to a much smaller number of detected objects. To do so, you'll use non-max suppression. Specifically, you'll carry out these steps: \n", + "- Get rid of boxes with a low score (meaning, the box is not very confident about detecting a class)\n", + "- Select only one box when several boxes overlap with each other and detect the same object.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 - Filtering with a threshold on class scores\n", + "\n", + "You are going to apply a first filter by thresholding. You would like to get rid of any box for which the class \"score\" is less than a chosen threshold. \n", + "\n", + "The model gives you a total of 19x19x5x85 numbers, with each box described by 85 numbers. It'll be convenient to rearrange the (19,19,5,85) (or (19,19,425)) dimensional tensor into the following variables: \n", + "- `box_confidence`: tensor of shape $(19 \\times 19, 5, 1)$ containing $p_c$ (confidence probability that there's some object) for each of the 5 boxes predicted in each of the 19x19 cells.\n", + "- `boxes`: tensor of shape $(19 \\times 19, 5, 4)$ containing $(b_x, b_y, b_h, b_w)$ for each of the 5 boxes per cell.\n", + "- `box_class_probs`: tensor of shape $(19 \\times 19, 5, 80)$ containing the detection probabilities $(c_1, c_2, ... c_{80})$ for each of the 80 classes for each of the 5 boxes per cell.\n", + "\n", + "**Exercise**: Implement `yolo_filter_boxes()`.\n", + "1. Compute box scores by doing the elementwise product as described in Figure 4. The following code may help you choose the right operator: \n", + "```python\n", + "a = np.random.randn(19*19, 5, 1)\n", + "b = np.random.randn(19*19, 5, 80)\n", + "c = a * b # shape of c will be (19*19, 5, 80)\n", + "```\n", + "2. For each box, find:\n", + " - the index of the class with the maximum box score ([Hint](https://keras.io/backend/#argmax)) (Be careful with what axis you choose; consider using axis=-1)\n", + " - the corresponding box score ([Hint](https://keras.io/backend/#max)) (Be careful with what axis you choose; consider using axis=-1)\n", + "3. Create a mask by using a threshold. As a reminder: `([0.9, 0.3, 0.4, 0.5, 0.1] < 0.4)` returns: `[False, True, False, False, True]`. The mask should be True for the boxes you want to keep. \n", + "4. Use TensorFlow to apply the mask to box_class_scores, boxes and box_classes to filter out the boxes we don't want. You should be left with just the subset of boxes you want to keep. ([Hint](https://www.tensorflow.org/api_docs/python/tf/boolean_mask))\n", + "\n", + "Reminder: to call a Keras function, you should use `K.function(...)`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: yolo_filter_boxes\n", + "\n", + "def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):\n", + " \"\"\"Filters YOLO boxes by thresholding on object and class confidence.\n", + " \n", + " Arguments:\n", + " box_confidence -- tensor of shape (19, 19, 5, 1)\n", + " boxes -- tensor of shape (19, 19, 5, 4)\n", + " box_class_probs -- tensor of shape (19, 19, 5, 80)\n", + " threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n", + " \n", + " Returns:\n", + " scores -- tensor of shape (None,), containing the class probability score for selected boxes\n", + " boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes\n", + " classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes\n", + " \n", + " Note: \"None\" is here because you don't know the exact number of selected boxes, as it depends on the threshold. \n", + " For example, the actual output size of scores would be (10,) if there are 10 boxes.\n", + " \"\"\"\n", + " \n", + " # Step 1: Compute box scores\n", + " ### START CODE HERE ### (≈ 1 line)\n", + " box_scores = np.multiply(box_confidence, box_class_probs)\n", + " ### END CODE HERE ###\n", + " \n", + " # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score\n", + " ### START CODE HERE ### (≈ 2 lines)\n", + " box_classes = K.argmax(box_scores, axis=-1)\n", + " box_class_scores = K.max(box_scores, axis=-1)\n", + " ### END CODE HERE ###\n", + " \n", + " # Step 3: Create a filtering mask based on \"box_class_scores\" by using \"threshold\". The mask should have the\n", + " # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)\n", + " ### START CODE HERE ### (≈ 1 line)\n", + " filtering_mask = K.greater_equal(box_class_scores, threshold)\n", + " ### END CODE HERE ###\n", + " \n", + " # Step 4: Apply the mask to scores, boxes and classes\n", + " ### START CODE HERE ### (≈ 3 lines)\n", + " scores = tf.boolean_mask(box_class_scores, filtering_mask)\n", + " boxes = tf.boolean_mask(boxes, filtering_mask)\n", + " classes = tf.boolean_mask(box_classes, filtering_mask)\n", + " ### END CODE HERE ###\n", + " \n", + " return scores, boxes, classes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores[2] = 10.7506\n", + "boxes[2] = [ 8.42653275 3.27136683 -0.5313437 -4.94137383]\n", + "classes[2] = 7\n", + "scores.shape = (?,)\n", + "boxes.shape = (?, 4)\n", + "classes.shape = (?,)\n" + ] + } + ], + "source": [ + "with tf.Session() as test_a:\n", + " box_confidence = tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1)\n", + " boxes = tf.random_normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)\n", + " box_class_probs = tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)\n", + " scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)\n", + " print(\"scores[2] = \" + str(scores[2].eval()))\n", + " print(\"boxes[2] = \" + str(boxes[2].eval()))\n", + " print(\"classes[2] = \" + str(classes[2].eval()))\n", + " print(\"scores.shape = \" + str(scores.shape))\n", + " print(\"boxes.shape = \" + str(boxes.shape))\n", + " print(\"classes.shape = \" + str(classes.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **scores[2]**\n", + " \n", + " 10.7506\n", + "
\n", + " **boxes[2]**\n", + " \n", + " [ 8.42653275 3.27136683 -0.5313437 -4.94137383]\n", + "
\n", + " **classes[2]**\n", + " \n", + " 7\n", + "
\n", + " **scores.shape**\n", + " \n", + " (?,)\n", + "
\n", + " **boxes.shape**\n", + " \n", + " (?, 4)\n", + "
\n", + " **classes.shape**\n", + " \n", + " (?,)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 - Non-max suppression ###\n", + "\n", + "Even after filtering by thresholding over the classes scores, you still end up a lot of overlapping boxes. A second filter for selecting the right boxes is called non-maximum suppression (NMS). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "
**Figure 7** : In this example, the model has predicted 3 cars, but it's actually 3 predictions of the same car. Running non-max suppression (NMS) will select only the most accurate (highest probabiliy) one of the 3 boxes.
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Non-max suppression uses the very important function called **\"Intersection over Union\"**, or IoU.\n", + "\n", + "
**Figure 8** : Definition of \"Intersection over Union\".
\n", + "\n", + "**Exercise**: Implement iou(). Some hints:\n", + "- In this exercise only, we define a box using its two corners (upper left and lower right): (x1, y1, x2, y2) rather than the midpoint and height/width.\n", + "- To calculate the area of a rectangle you need to multiply its height (y2 - y1) by its width (x2 - x1)\n", + "- You'll also need to find the coordinates (xi1, yi1, xi2, yi2) of the intersection of two boxes. Remember that:\n", + " - xi1 = maximum of the x1 coordinates of the two boxes\n", + " - yi1 = maximum of the y1 coordinates of the two boxes\n", + " - xi2 = minimum of the x2 coordinates of the two boxes\n", + " - yi2 = minimum of the y2 coordinates of the two boxes\n", + " \n", + "In this code, we use the convention that (0,0) is the top-left corner of an image, (1,0) is the upper-right corner, and (1,1) the lower-right corner. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: iou\n", + "\n", + "def iou(box1, box2):\n", + " \"\"\"Implement the intersection over union (IoU) between box1 and box2\n", + " \n", + " Arguments:\n", + " box1 -- first box, list object with coordinates (x1, y1, x2, y2)\n", + " box2 -- second box, list object with coordinates (x1, y1, x2, y2)\n", + " \"\"\"\n", + "\n", + " # Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.\n", + " ### START CODE HERE ### (≈ 5 lines)\n", + " xi1 = max(box1[0], box2[0])\n", + " yi1 = max(box1[1], box2[1])\n", + " xi2 = min(box1[2], box2[2])\n", + " yi2 = min(box1[3], box2[3])\n", + " inter_area = (xi2 - xi1)*(yi2 - yi1)\n", + " ### END CODE HERE ### \n", + "\n", + " # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\n", + " ### START CODE HERE ### (≈ 3 lines)\n", + " box1_area = (box1[3] - box1[1])*(box1[2]- box1[0])\n", + " box2_area = (box2[3] - box2[1])*(box2[2]- box2[0])\n", + " union_area = (box1_area + box2_area) - inter_area\n", + " ### END CODE HERE ###\n", + " \n", + " # compute the IoU\n", + " ### START CODE HERE ### (≈ 1 line)\n", + " iou = inter_area / union_area\n", + " ### END CODE HERE ###\n", + "\n", + " return iou" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iou = 0.14285714285714285\n" + ] + } + ], + "source": [ + "box1 = (2, 1, 4, 3)\n", + "box2 = (1, 2, 3, 4) \n", + "print(\"iou = \" + str(iou(box1, box2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **iou = **\n", + " \n", + " 0.14285714285714285\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You are now ready to implement non-max suppression. The key steps are: \n", + "1. Select the box that has the highest score.\n", + "2. Compute its overlap with all other boxes, and remove boxes that overlap it more than `iou_threshold`.\n", + "3. Go back to step 1 and iterate until there's no more boxes with a lower score than the current selected box.\n", + "\n", + "This will remove all boxes that have a large overlap with the selected boxes. Only the \"best\" boxes remain.\n", + "\n", + "**Exercise**: Implement yolo_non_max_suppression() using TensorFlow. TensorFlow has two built-in functions that are used to implement non-max suppression (so you don't actually need to use your `iou()` implementation):\n", + "- [tf.image.non_max_suppression()](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression)\n", + "- [K.gather()](https://www.tensorflow.org/api_docs/python/tf/gather)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: yolo_non_max_suppression\n", + "\n", + "def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):\n", + " \"\"\"\n", + " Applies Non-max suppression (NMS) to set of boxes\n", + " \n", + " Arguments:\n", + " scores -- tensor of shape (None,), output of yolo_filter_boxes()\n", + " boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)\n", + " classes -- tensor of shape (None,), output of yolo_filter_boxes()\n", + " max_boxes -- integer, maximum number of predicted boxes you'd like\n", + " iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n", + " \n", + " Returns:\n", + " scores -- tensor of shape (, None), predicted score for each box\n", + " boxes -- tensor of shape (4, None), predicted box coordinates\n", + " classes -- tensor of shape (, None), predicted class for each box\n", + " \n", + " Note: The \"None\" dimension of the output tensors has obviously to be less than max_boxes. Note also that this\n", + " function will transpose the shapes of scores, boxes, classes. This is made for convenience.\n", + " \"\"\"\n", + " \n", + " max_boxes_tensor = K.variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression()\n", + " K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor\n", + " \n", + " # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep\n", + " ### START CODE HERE ### (≈ 1 line)\n", + " nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold)\n", + "\n", + " ### END CODE HERE ###\n", + " \n", + " # Use K.gather() to select only nms_indices from scores, boxes and classes\n", + " ### START CODE HERE ### (≈ 3 lines)\n", + " scores = K.gather(scores, nms_indices)\n", + " boxes = K.gather(boxes, nms_indices)\n", + " classes = K.gather(classes, nms_indices)\n", + " ### END CODE HERE ###\n", + " \n", + " return scores, boxes, classes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores[2] = 6.9384\n", + "boxes[2] = [-5.299932 3.13798141 4.45036697 0.95942086]\n", + "classes[2] = -2.24527\n", + "scores.shape = (10,)\n", + "boxes.shape = (10, 4)\n", + "classes.shape = (10,)\n" + ] + } + ], + "source": [ + "with tf.Session() as test_b:\n", + " scores = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n", + " boxes = tf.random_normal([54, 4], mean=1, stddev=4, seed = 1)\n", + " classes = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n", + " scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes)\n", + " print(\"scores[2] = \" + str(scores[2].eval()))\n", + " print(\"boxes[2] = \" + str(boxes[2].eval()))\n", + " print(\"classes[2] = \" + str(classes[2].eval()))\n", + " print(\"scores.shape = \" + str(scores.eval().shape))\n", + " print(\"boxes.shape = \" + str(boxes.eval().shape))\n", + " print(\"classes.shape = \" + str(classes.eval().shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **scores[2]**\n", + " \n", + " 6.9384\n", + "
\n", + " **boxes[2]**\n", + " \n", + " [-5.299932 3.13798141 4.45036697 0.95942086]\n", + "
\n", + " **classes[2]**\n", + " \n", + " -2.24527\n", + "
\n", + " **scores.shape**\n", + " \n", + " (10,)\n", + "
\n", + " **boxes.shape**\n", + " \n", + " (10, 4)\n", + "
\n", + " **classes.shape**\n", + " \n", + " (10,)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Wrapping up the filtering\n", + "\n", + "It's time to implement a function taking the output of the deep CNN (the 19x19x5x85 dimensional encoding) and filtering through all the boxes using the functions you've just implemented. \n", + "\n", + "**Exercise**: Implement `yolo_eval()` which takes the output of the YOLO encoding and filters the boxes using score threshold and NMS. There's just one last implementational detail you have to know. There're a few ways of representing boxes, such as via their corners or via their midpoint and height/width. YOLO converts between a few such formats at different times, using the following functions (which we have provided): \n", + "\n", + "```python\n", + "boxes = yolo_boxes_to_corners(box_xy, box_wh) \n", + "```\n", + "which converts the yolo box coordinates (x,y,w,h) to box corners' coordinates (x1, y1, x2, y2) to fit the input of `yolo_filter_boxes`\n", + "```python\n", + "boxes = scale_boxes(boxes, image_shape)\n", + "```\n", + "YOLO's network was trained to run on 608x608 images. If you are testing this data on a different size image--for example, the car detection dataset had 720x1280 images--this step rescales the boxes so that they can be plotted on top of the original 720x1280 image. \n", + "\n", + "Don't worry about these two functions; we'll show you where they need to be called. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: yolo_eval\n", + "\n", + "def yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):\n", + " \"\"\"\n", + " Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.\n", + " \n", + " Arguments:\n", + " yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:\n", + " box_confidence: tensor of shape (None, 19, 19, 5, 1)\n", + " box_xy: tensor of shape (None, 19, 19, 5, 2)\n", + " box_wh: tensor of shape (None, 19, 19, 5, 2)\n", + " box_class_probs: tensor of shape (None, 19, 19, 5, 80)\n", + " image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)\n", + " max_boxes -- integer, maximum number of predicted boxes you'd like\n", + " score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n", + " iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n", + " \n", + " Returns:\n", + " scores -- tensor of shape (None, ), predicted score for each box\n", + " boxes -- tensor of shape (None, 4), predicted box coordinates\n", + " classes -- tensor of shape (None,), predicted class for each box\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### \n", + " \n", + " # Retrieve outputs of the YOLO model (≈1 line)\n", + " box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs\n", + "\n", + " # Convert boxes to be ready for filtering functions \n", + " boxes = yolo_boxes_to_corners(box_xy, box_wh)\n", + "\n", + " # Use one of the functions you've implemented to perform Score-filtering with a threshold of score_threshold (≈1 line)\n", + " scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = score_threshold)\n", + " \n", + " # Scale boxes back to original image shape.\n", + " boxes = scale_boxes(boxes, image_shape)\n", + "\n", + " # Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)\n", + " scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes = max_boxes, iou_threshold = iou_threshold)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return scores, boxes, classes" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scores[2] = 138.791\n", + "boxes[2] = [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141]\n", + "classes[2] = 54\n", + "scores.shape = (10,)\n", + "boxes.shape = (10, 4)\n", + "classes.shape = (10,)\n" + ] + } + ], + "source": [ + "with tf.Session() as test_b:\n", + " yolo_outputs = (tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1),\n", + " tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n", + " tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n", + " tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1))\n", + " scores, boxes, classes = yolo_eval(yolo_outputs)\n", + " print(\"scores[2] = \" + str(scores[2].eval()))\n", + " print(\"boxes[2] = \" + str(boxes[2].eval()))\n", + " print(\"classes[2] = \" + str(classes[2].eval()))\n", + " print(\"scores.shape = \" + str(scores.eval().shape))\n", + " print(\"boxes.shape = \" + str(boxes.eval().shape))\n", + " print(\"classes.shape = \" + str(classes.eval().shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **scores[2]**\n", + " \n", + " 138.791\n", + "
\n", + " **boxes[2]**\n", + " \n", + " [ 1292.32971191 -278.52166748 3876.98925781 -835.56494141]\n", + "
\n", + " **classes[2]**\n", + " \n", + " 54\n", + "
\n", + " **scores.shape**\n", + " \n", + " (10,)\n", + "
\n", + " **boxes.shape**\n", + " \n", + " (10, 4)\n", + "
\n", + " **classes.shape**\n", + " \n", + " (10,)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**Summary for YOLO**:\n", + "- Input image (608, 608, 3)\n", + "- The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output. \n", + "- After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):\n", + " - Each cell in a 19x19 grid over the input image gives 425 numbers. \n", + " - 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture. \n", + " - 85 = 5 + 80 where 5 is because $(p_c, b_x, b_y, b_h, b_w)$ has 5 numbers, and and 80 is the number of classes we'd like to detect\n", + "- You then select only few boxes based on:\n", + " - Score-thresholding: throw away boxes that have detected a class with a score less than the threshold\n", + " - Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes\n", + "- This gives you YOLO's final output. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Test YOLO pretrained model on images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this part, you are going to use a pretrained model and test it on the car detection dataset. As usual, you start by **creating a session to start your graph**. Run the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sess = K.get_session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Defining classes, anchors and image shape." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that we are trying to detect 80 classes, and are using 5 anchor boxes. We have gathered the information about the 80 classes and 5 boxes in two files \"coco_classes.txt\" and \"yolo_anchors.txt\". Let's load these quantities into the model by running the next cell. \n", + "\n", + "The car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class_names = read_classes(\"model_data/coco_classes.txt\")\n", + "anchors = read_anchors(\"model_data/yolo_anchors.txt\")\n", + "image_shape = (720., 1280.) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Loading a pretrained model\n", + "\n", + "Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. You are going to load an existing pretrained Keras YOLO model stored in \"yolo.h5\". (These weights come from the official YOLO website, and were converted using a function written by Allan Zelener. References are at the end of this notebook. Technically, these are the parameters from the \"YOLOv2\" model, but we will more simply refer to it as \"YOLO\" in this notebook.) Run the cell below to load the model from this file." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/keras/models.py:251: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n", + " warnings.warn('No training configuration found in save file: '\n" + ] + } + ], + "source": [ + "yolo_model = load_model(\"model_data/yolo.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This loads the weights of a trained YOLO model. Here's a summary of the layers your model contains." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "____________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "====================================================================================================\n", + "input_1 (InputLayer) (None, 608, 608, 3) 0 \n", + "____________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 608, 608, 32) 864 input_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_1 (BatchNorm (None, 608, 608, 32) 128 conv2d_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_1 (LeakyReLU) (None, 608, 608, 32) 0 batch_normalization_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2D) (None, 304, 304, 32) 0 leaky_re_lu_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 304, 304, 64) 18432 max_pooling2d_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_2 (BatchNorm (None, 304, 304, 64) 256 conv2d_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_2 (LeakyReLU) (None, 304, 304, 64) 0 batch_normalization_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2D) (None, 152, 152, 64) 0 leaky_re_lu_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 152, 152, 128) 73728 max_pooling2d_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_3 (BatchNorm (None, 152, 152, 128) 512 conv2d_3[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_3 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_3[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 152, 152, 64) 8192 leaky_re_lu_3[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_4 (BatchNorm (None, 152, 152, 64) 256 conv2d_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_4 (LeakyReLU) (None, 152, 152, 64) 0 batch_normalization_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 152, 152, 128) 73728 leaky_re_lu_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_5 (BatchNorm (None, 152, 152, 128) 512 conv2d_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_5 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2D) (None, 76, 76, 128) 0 leaky_re_lu_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_6 (Conv2D) (None, 76, 76, 256) 294912 max_pooling2d_3[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_6 (BatchNorm (None, 76, 76, 256) 1024 conv2d_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_6 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_7 (Conv2D) (None, 76, 76, 128) 32768 leaky_re_lu_6[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_7 (BatchNorm (None, 76, 76, 128) 512 conv2d_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_7 (LeakyReLU) (None, 76, 76, 128) 0 batch_normalization_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_8 (Conv2D) (None, 76, 76, 256) 294912 leaky_re_lu_7[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_8 (BatchNorm (None, 76, 76, 256) 1024 conv2d_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_8 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_4 (MaxPooling2D) (None, 38, 38, 256) 0 leaky_re_lu_8[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 38, 38, 512) 1179648 max_pooling2d_4[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_9 (BatchNorm (None, 38, 38, 512) 2048 conv2d_9[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_9 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_9[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_10 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_9[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_10 (BatchNor (None, 38, 38, 256) 1024 conv2d_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_10 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_11 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_10[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_11 (BatchNor (None, 38, 38, 512) 2048 conv2d_11[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_11 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_11[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_12 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_11[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_12 (BatchNor (None, 38, 38, 256) 1024 conv2d_12[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_12 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_12[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_13 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_12[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_13 (BatchNor (None, 38, 38, 512) 2048 conv2d_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_13 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "max_pooling2d_5 (MaxPooling2D) (None, 19, 19, 512) 0 leaky_re_lu_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_14 (Conv2D) (None, 19, 19, 1024) 4718592 max_pooling2d_5[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_14 (BatchNor (None, 19, 19, 1024) 4096 conv2d_14[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_14 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_14[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_15 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_14[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_15 (BatchNor (None, 19, 19, 512) 2048 conv2d_15[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_15 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_15[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_16 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_15[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_16 (BatchNor (None, 19, 19, 1024) 4096 conv2d_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_16 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_17 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_16[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_17 (BatchNor (None, 19, 19, 512) 2048 conv2d_17[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_17 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_17[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_18 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_17[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_18 (BatchNor (None, 19, 19, 1024) 4096 conv2d_18[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_18 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_18[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_19 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_18[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_19 (BatchNor (None, 19, 19, 1024) 4096 conv2d_19[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_21 (Conv2D) (None, 38, 38, 64) 32768 leaky_re_lu_13[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_19 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_19[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_21 (BatchNor (None, 38, 38, 64) 256 conv2d_21[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_20 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_19[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_21 (LeakyReLU) (None, 38, 38, 64) 0 batch_normalization_21[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_20 (BatchNor (None, 19, 19, 1024) 4096 conv2d_20[0][0] \n", + "____________________________________________________________________________________________________\n", + "space_to_depth_x2 (Lambda) (None, 19, 19, 256) 0 leaky_re_lu_21[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_20 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_20[0][0] \n", + "____________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 19, 19, 1280) 0 space_to_depth_x2[0][0] \n", + " leaky_re_lu_20[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_22 (Conv2D) (None, 19, 19, 1024) 11796480 concatenate_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "batch_normalization_22 (BatchNor (None, 19, 19, 1024) 4096 conv2d_22[0][0] \n", + "____________________________________________________________________________________________________\n", + "leaky_re_lu_22 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_22[0][0] \n", + "____________________________________________________________________________________________________\n", + "conv2d_23 (Conv2D) (None, 19, 19, 425) 435625 leaky_re_lu_22[0][0] \n", + "====================================================================================================\n", + "Total params: 50,983,561\n", + "Trainable params: 50,962,889\n", + "Non-trainable params: 20,672\n", + "____________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "yolo_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: On some computers, you may see a warning message from Keras. Don't worry about it if you do--it is fine.\n", + "\n", + "**Reminder**: this model converts a preprocessed batch of input images (shape: (m, 608, 608, 3)) into a tensor of shape (m, 19, 19, 5, 85) as explained in Figure (2)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 - Convert output of the model to usable bounding box tensors\n", + "\n", + "The output of `yolo_model` is a (m, 19, 19, 5, 85) tensor that needs to pass through non-trivial processing and conversion. The following cell does that for you." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You added `yolo_outputs` to your graph. This set of 4 tensors is ready to be used as input by your `yolo_eval` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 - Filtering boxes\n", + "\n", + "`yolo_outputs` gave you all the predicted boxes of `yolo_model` in the correct format. You're now ready to perform filtering and select only the best boxes. Lets now call `yolo_eval`, which you had previously implemented, to do this. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 - Run the graph on an image\n", + "\n", + "Let the fun begin. You have created a (`sess`) graph that can be summarized as follows:\n", + "\n", + "1. yolo_model.input is given to `yolo_model`. The model is used to compute the output yolo_model.output \n", + "2. yolo_model.output is processed by `yolo_head`. It gives you yolo_outputs \n", + "3. yolo_outputs goes through a filtering function, `yolo_eval`. It outputs your predictions: scores, boxes, classes \n", + "\n", + "**Exercise**: Implement predict() which runs the graph to test YOLO on an image.\n", + "You will need to run a TensorFlow session, to have it compute `scores, boxes, classes`.\n", + "\n", + "The code below also uses the following function:\n", + "```python\n", + "image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n", + "```\n", + "which outputs:\n", + "- image: a python (PIL) representation of your image used for drawing boxes. You won't need to use it.\n", + "- image_data: a numpy-array representing the image. This will be the input to the CNN.\n", + "\n", + "**Important note**: when a model uses BatchNorm (as is the case in YOLO), you will need to pass an additional placeholder in the feed_dict {K.learning_phase(): 0}." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def predict(sess, image_file):\n", + " \"\"\"\n", + " Runs the graph stored in \"sess\" to predict boxes for \"image_file\". Prints and plots the preditions.\n", + " \n", + " Arguments:\n", + " sess -- your tensorflow/Keras session containing the YOLO graph\n", + " image_file -- name of an image stored in the \"images\" folder.\n", + " \n", + " Returns:\n", + " out_scores -- tensor of shape (None, ), scores of the predicted boxes\n", + " out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes\n", + " out_classes -- tensor of shape (None, ), class index of the predicted boxes\n", + " \n", + " Note: \"None\" actually represents the number of predicted boxes, it varies between 0 and max_boxes. \n", + " \"\"\"\n", + "\n", + " # Preprocess your image\n", + " image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n", + "\n", + " # Run the session with the correct tensors and choose the correct placeholders in the feed_dict.\n", + " # You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})\n", + " ### START CODE HERE ### (≈ 1 line)\n", + " out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict={yolo_model.input: image_data, K.learning_phase(): 0})\n", + " ### END CODE HERE ###\n", + "\n", + " # Print predictions info\n", + " print('Found {} boxes for {}'.format(len(out_boxes), image_file))\n", + " # Generate colors for drawing bounding boxes.\n", + " colors = generate_colors(class_names)\n", + " # Draw bounding boxes on the image file\n", + " draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)\n", + " # Save the predicted bounding box on the image\n", + " image.save(os.path.join(\"out\", image_file), quality=90)\n", + " # Display the results in the notebook\n", + " output_image = scipy.misc.imread(os.path.join(\"out\", image_file))\n", + " imshow(output_image)\n", + " \n", + " return out_scores, out_boxes, out_classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell on the \"test.jpg\" image to verify that your function is correct." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 boxes for test.jpg\n", + "car 0.60 (925, 285) (1045, 374)\n", + "car 0.66 (706, 279) (786, 350)\n", + "bus 0.67 (5, 266) (220, 407)\n", + "car 0.70 (947, 324) (1280, 705)\n", + "car 0.74 (159, 303) (346, 440)\n", + "car 0.80 (761, 282) (942, 412)\n", + "car 0.89 (367, 300) (745, 648)\n" + ] + }, + { + "data": { + "image/png": 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z1VZ6T04QX5jhY88/RSJ+ndhImIzUGBNJKhZSNLVu5MJb19i+62E6airJqICh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8tFQv4M9U6FTHKEymGDTslE90WoVI0HM69h4TZdzp0YZbG4S5IEEW2f+3/859ysl7m7uMSx/nEi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FhD0SRVFnV00BOxGG2MUC733vf+JDH/0Y41t28vgTD3HuDRcxfXiKsRhyqSRxO5geYHJ+iZx2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JPAHcA/f6PvqjyLGqecoUvC1JuXUqIzMDZhkk9oN9rMFxFpZ5t0DHt0wk+/fI3U79NWhr/8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\ndap1RpNBAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out_scores, out_boxes, out_classes = predict(sess, \"test.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Found 7 boxes for test.jpg**\n", + "
\n", + " **car**\n", + " \n", + " 0.60 (925, 285) (1045, 374)\n", + "
\n", + " **car**\n", + " \n", + " 0.66 (706, 279) (786, 350)\n", + "
\n", + " **bus**\n", + " \n", + " 0.67 (5, 266) (220, 407)\n", + "
\n", + " **car**\n", + " \n", + " 0.70 (947, 324) (1280, 705)\n", + "
\n", + " **car**\n", + " \n", + " 0.74 (159, 303) (346, 440)\n", + "
\n", + " **car**\n", + " \n", + " 0.80 (761, 282) (942, 412)\n", + "
\n", + " **car**\n", + " \n", + " 0.89 (367, 300) (745, 648)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model you've just run is actually able to detect 80 different classes listed in \"coco_classes.txt\". To test the model on your own images:\n", + " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", + " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", + " 3. Write your image's name in the cell above code\n", + " 4. Run the code and see the output of the algorithm!\n", + "\n", + "If you were to run your session in a for loop over all your images. Here's what you would get:\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "
Predictions of the YOLO model on pictures taken from a camera while driving around the Silicon Valley
Thanks [drive.ai](https://www.drive.ai/) for providing this dataset!
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "**What you should remember**:\n", + "- YOLO is a state-of-the-art object detection model that is fast and accurate\n", + "- It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume. \n", + "- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.\n", + "- You filter through all the boxes using non-max suppression. Specifically: \n", + " - Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes\n", + " - Intersection over Union (IoU) thresholding to eliminate overlapping boxes\n", + "- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**References**: The ideas presented in this notebook came primarily from the two YOLO papers. The implementation here also took significant inspiration and used many components from Allan Zelener's github repository. The pretrained weights used in this exercise came from the official YOLO website. \n", + "- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi - [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640) (2015)\n", + "- Joseph Redmon, Ali Farhadi - [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) (2016)\n", + "- Allan Zelener - [YAD2K: Yet Another Darknet 2 Keras](https://github.com/allanzelener/YAD2K)\n", + "- The official YOLO website (https://pjreddie.com/darknet/yolo/) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Car detection dataset**:\n", + "\"Creative
The Drive.ai Sample Dataset (provided by drive.ai) is licensed under a Creative Commons Attribution 4.0 International License. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. 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the Happy House - v2.ipynb new file mode 100644 index 0000000..32c8437 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Face Recognition/Face Recognition for the Happy House - v2.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Face Recognition for the Happy House\n", + "\n", + "Welcome to the first assignment of week 4! Here you will build a face recognition system. Many of the ideas presented here are from [FaceNet](https://arxiv.org/pdf/1503.03832.pdf). In lecture, we also talked about [DeepFace](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf). \n", + "\n", + "Face recognition problems commonly fall into two categories: \n", + "\n", + "- **Face Verification** - \"is this the claimed person?\". For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem. \n", + "- **Face Recognition** - \"who is this person?\". For example, the video lecture showed a face recognition video (https://www.youtube.com/watch?v=wr4rx0Spihs) of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem. \n", + "\n", + "FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.\n", + " \n", + "**In this assignment, you will:**\n", + "- Implement the triplet loss function\n", + "- Use a pretrained model to map face images into 128-dimensional encodings\n", + "- Use these encodings to perform face verification and face recognition\n", + "\n", + "In this exercise, we will be using a pre-trained model which represents ConvNet activations using a \"channels first\" convention, as opposed to the \"channels last\" convention used in lecture and previous programming assignments. In other words, a batch of images will be of shape $(m, n_C, n_H, n_W)$ instead of $(m, n_H, n_W, n_C)$. Both of these conventions have a reasonable amount of traction among open-source implementations; there isn't a uniform standard yet within the deep learning community. \n", + "\n", + "Let's load the required packages. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\n", + "from keras.models import Model\n", + "from keras.layers.normalization import BatchNormalization\n", + "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n", + "from keras.layers.merge import Concatenate\n", + "from keras.layers.core import Lambda, Flatten, Dense\n", + "from keras.initializers import glorot_uniform\n", + "from keras.engine.topology import Layer\n", + "from keras import backend as K\n", + "K.set_image_data_format('channels_first')\n", + "import cv2\n", + "import os\n", + "import numpy as np\n", + "from numpy import genfromtxt\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "from fr_utils import *\n", + "from inception_blocks_v2 import *\n", + "\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "np.set_printoptions(threshold=np.nan)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0 - Naive Face Verification\n", + "\n", + "In Face Verification, you're given two images and you have to tell if they are of the same person. The simplest way to do this is to compare the two images pixel-by-pixel. If the distance between the raw images are less than a chosen threshold, it may be the same person! \n", + "\n", + "\n", + "
**Figure 1**
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Of course, this algorithm performs really poorly, since the pixel values change dramatically due to variations in lighting, orientation of the person's face, even minor changes in head position, and so on. \n", + "\n", + "You'll see that rather than using the raw image, you can learn an encoding $f(img)$ so that element-wise comparisons of this encoding gives more accurate judgements as to whether two pictures are of the same person." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Encoding face images into a 128-dimensional vector \n", + "\n", + "### 1.1 - Using an ConvNet to compute encodings\n", + "\n", + "The FaceNet model takes a lot of data and a long time to train. So following common practice in applied deep learning settings, let's just load weights that someone else has already trained. The network architecture follows the Inception model from [Szegedy *et al.*](https://arxiv.org/abs/1409.4842). We have provided an inception network implementation. You can look in the file `inception_blocks.py` to see how it is implemented (do so by going to \"File->Open...\" at the top of the Jupyter notebook). \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key things you need to know are:\n", + "\n", + "- This network uses 96x96 dimensional RGB images as its input. Specifically, inputs a face image (or batch of $m$ face images) as a tensor of shape $(m, n_C, n_H, n_W) = (m, 3, 96, 96)$ \n", + "- It outputs a matrix of shape $(m, 128)$ that encodes each input face image into a 128-dimensional vector\n", + "\n", + "Run the cell below to create the model for face images." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "FRmodel = faceRecoModel(input_shape=(3, 96, 96))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Params: 3743280\n" + ] + } + ], + "source": [ + "print(\"Total Params:\", FRmodel.count_params())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected Output **\n", + "\n", + "
\n", + "Total Params: 3743280\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using a 128-neuron fully connected layer as its last layer, the model ensures that the output is an encoding vector of size 128. You then use the encodings the compare two face images as follows:\n", + "\n", + "\n", + "
**Figure 2**:
By computing a distance between two encodings and thresholding, you can determine if the two pictures represent the same person
\n", + "\n", + "So, an encoding is a good one if: \n", + "- The encodings of two images of the same person are quite similar to each other \n", + "- The encodings of two images of different persons are very different\n", + "\n", + "The triplet loss function formalizes this, and tries to \"push\" the encodings of two images of the same person (Anchor and Positive) closer together, while \"pulling\" the encodings of two images of different persons (Anchor, Negative) further apart. \n", + "\n", + "\n", + "
\n", + "
**Figure 3**:
In the next part, we will call the pictures from left to right: Anchor (A), Positive (P), Negative (N)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### 1.2 - The Triplet Loss\n", + "\n", + "For an image $x$, we denote its encoding $f(x)$, where $f$ is the function computed by the neural network.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Training will use triplets of images $(A, P, N)$: \n", + "\n", + "- A is an \"Anchor\" image--a picture of a person. \n", + "- P is a \"Positive\" image--a picture of the same person as the Anchor image.\n", + "- N is a \"Negative\" image--a picture of a different person than the Anchor image.\n", + "\n", + "These triplets are picked from our training dataset. We will write $(A^{(i)}, P^{(i)}, N^{(i)})$ to denote the $i$-th training example. \n", + "\n", + "You'd like to make sure that an image $A^{(i)}$ of an individual is closer to the Positive $P^{(i)}$ than to the Negative image $N^{(i)}$) by at least a margin $\\alpha$:\n", + "\n", + "$$\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2 + \\alpha < \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$$\n", + "\n", + "You would thus like to minimize the following \"triplet cost\":\n", + "\n", + "$$\\mathcal{J} = \\sum^{N}_{i=1} \\large[ \\small \\underbrace{\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2}_\\text{(1)} - \\underbrace{\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2}_\\text{(2)} + \\alpha \\large ] \\small_+ \\tag{3}$$\n", + "\n", + "Here, we are using the notation \"$[z]_+$\" to denote $max(z,0)$. \n", + "\n", + "Notes:\n", + "- The term (1) is the squared distance between the anchor \"A\" and the positive \"P\" for a given triplet; you want this to be small. \n", + "- The term (2) is the squared distance between the anchor \"A\" and the negative \"N\" for a given triplet, you want this to be relatively large, so it thus makes sense to have a minus sign preceding it. \n", + "- $\\alpha$ is called the margin. It is a hyperparameter that you should pick manually. We will use $\\alpha = 0.2$. \n", + "\n", + "Most implementations also normalize the encoding vectors to have norm equal one (i.e., $\\mid \\mid f(img)\\mid \\mid_2$=1); you won't have to worry about that here.\n", + "\n", + "**Exercise**: Implement the triplet loss as defined by formula (3). Here are the 4 steps:\n", + "1. Compute the distance between the encodings of \"anchor\" and \"positive\": $\\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2$\n", + "2. Compute the distance between the encodings of \"anchor\" and \"negative\": $\\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2$\n", + "3. Compute the formula per training example: $ \\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid - \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2 + \\alpha$\n", + "3. Compute the full formula by taking the max with zero and summing over the training examples:\n", + "$$\\mathcal{J} = \\sum^{N}_{i=1} \\large[ \\small \\mid \\mid f(A^{(i)}) - f(P^{(i)}) \\mid \\mid_2^2 - \\mid \\mid f(A^{(i)}) - f(N^{(i)}) \\mid \\mid_2^2+ \\alpha \\large ] \\small_+ \\tag{3}$$\n", + "\n", + "Useful functions: `tf.reduce_sum()`, `tf.square()`, `tf.subtract()`, `tf.add()`, `tf.reduce_mean`, `tf.maximum()`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: triplet_loss\n", + "\n", + "def triplet_loss(y_true, y_pred, alpha = 0.2):\n", + " \"\"\"\n", + " Implementation of the triplet loss as defined by formula (3)\n", + " \n", + " Arguments:\n", + " y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.\n", + " y_pred -- python list containing three objects:\n", + " anchor -- the encodings for the anchor images, of shape (None, 128)\n", + " positive -- the encodings for the positive images, of shape (None, 128)\n", + " negative -- the encodings for the negative images, of shape (None, 128)\n", + " \n", + " Returns:\n", + " loss -- real number, value of the loss\n", + " \"\"\"\n", + " \n", + " anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines)\n", + " # Step 1: Compute the (encoding) distance between the anchor and the positive\n", + " pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)))\n", + " # Step 2: Compute the (encoding) distance between the anchor and the negative\n", + " neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)))\n", + " # Step 3: subtract the two previous distances and add alpha.\n", + " basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)\n", + " # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.\n", + " loss = tf.maximum(tf.reduce_mean(basic_loss), 0.0)\n", + " ### END CODE HERE ###\n", + " \n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss = 350.026\n" + ] + } + ], + "source": [ + "with tf.Session() as test:\n", + " tf.set_random_seed(1)\n", + " y_true = (None, None, None)\n", + " y_pred = (tf.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),\n", + " tf.random_normal([3, 128], mean=1, stddev=1, seed = 1),\n", + " tf.random_normal([3, 128], mean=3, stddev=4, seed = 1))\n", + " loss = triplet_loss(y_true, y_pred)\n", + " \n", + " print(\"loss = \" + str(loss.eval()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **loss**\n", + " \n", + " 350.026\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Loading the trained model\n", + "\n", + "FaceNet is trained by minimizing the triplet loss. But since training requires a lot of data and a lot of computation, we won't train it from scratch here. Instead, we load a previously trained model. Load a model using the following cell; this might take a couple of minutes to run. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "FRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])\n", + "load_weights_from_FaceNet(FRmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here're some examples of distances between the encodings between three individuals:\n", + "\n", + "\n", + "
\n", + "
**Figure 4**:
Example of distance outputs between three individuals' encodings
\n", + "\n", + "Let's now use this model to perform face verification and face recognition! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Applying the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Back to the Happy House! Residents are living blissfully since you implemented happiness recognition for the house in an earlier assignment. \n", + "\n", + "However, several issues keep coming up: The Happy House became so happy that every happy person in the neighborhood is coming to hang out in your living room. It is getting really crowded, which is having a negative impact on the residents of the house. All these random happy people are also eating all your food. \n", + "\n", + "So, you decide to change the door entry policy, and not just let random happy people enter anymore, even if they are happy! Instead, you'd like to build a **Face verification** system so as to only let people from a specified list come in. To get admitted, each person has to swipe an ID card (identification card) to identify themselves at the door. The face recognition system then checks that they are who they claim to be. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Face Verification\n", + "\n", + "Let's build a database containing one encoding vector for each person allowed to enter the happy house. To generate the encoding we use `img_to_encoding(image_path, model)` which basically runs the forward propagation of the model on the specified image. \n", + "\n", + "Run the following code to build the database (represented as a python dictionary). This database maps each person's name to a 128-dimensional encoding of their face." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "database = {}\n", + "database[\"danielle\"] = img_to_encoding(\"images/danielle.png\", FRmodel)\n", + "database[\"younes\"] = img_to_encoding(\"images/younes.jpg\", FRmodel)\n", + "database[\"tian\"] = img_to_encoding(\"images/tian.jpg\", FRmodel)\n", + "database[\"andrew\"] = img_to_encoding(\"images/andrew.jpg\", FRmodel)\n", + "database[\"kian\"] = img_to_encoding(\"images/kian.jpg\", FRmodel)\n", + "database[\"dan\"] = img_to_encoding(\"images/dan.jpg\", FRmodel)\n", + "database[\"sebastiano\"] = img_to_encoding(\"images/sebastiano.jpg\", FRmodel)\n", + "database[\"bertrand\"] = img_to_encoding(\"images/bertrand.jpg\", FRmodel)\n", + "database[\"kevin\"] = img_to_encoding(\"images/kevin.jpg\", FRmodel)\n", + "database[\"felix\"] = img_to_encoding(\"images/felix.jpg\", FRmodel)\n", + "database[\"benoit\"] = img_to_encoding(\"images/benoit.jpg\", FRmodel)\n", + "database[\"arnaud\"] = img_to_encoding(\"images/arnaud.jpg\", FRmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, when someone shows up at your front door and swipes their ID card (thus giving you their name), you can look up their encoding in the database, and use it to check if the person standing at the front door matches the name on the ID.\n", + "\n", + "**Exercise**: Implement the verify() function which checks if the front-door camera picture (`image_path`) is actually the person called \"identity\". You will have to go through the following steps:\n", + "1. Compute the encoding of the image from image_path\n", + "2. Compute the distance about this encoding and the encoding of the identity image stored in the database\n", + "3. Open the door if the distance is less than 0.7, else do not open.\n", + "\n", + "As presented above, you should use the L2 distance (np.linalg.norm). (Note: In this implementation, compare the L2 distance, not the square of the L2 distance, to the threshold 0.7.) " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: verify\n", + "\n", + "def verify(image_path, identity, database, model):\n", + " \"\"\"\n", + " Function that verifies if the person on the \"image_path\" image is \"identity\".\n", + " \n", + " Arguments:\n", + " image_path -- path to an image\n", + " identity -- string, name of the person you'd like to verify the identity. Has to be a resident of the Happy house.\n", + " database -- python dictionary mapping names of allowed people's names (strings) to their encodings (vectors).\n", + " model -- your Inception model instance in Keras\n", + " \n", + " Returns:\n", + " dist -- distance between the image_path and the image of \"identity\" in the database.\n", + " door_open -- True, if the door should open. False otherwise.\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Step 1: Compute the encoding for the image. Use img_to_encoding() see example above. (≈ 1 line)\n", + " encoding = img_to_encoding(image_path, model)\n", + " \n", + " # Step 2: Compute distance with identity's image (≈ 1 line)\n", + " dist = np.linalg.norm(encoding-database[identity])\n", + " \n", + " # Step 3: Open the door if dist < 0.7, else don't open (≈ 3 lines)\n", + " if dist < 0.7:\n", + " print(\"It's \" + str(identity) + \", welcome home!\")\n", + " door_open = True\n", + " else:\n", + " print(\"It's not \" + str(identity) + \", please go away\")\n", + " door_open = False\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return dist, door_open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Younes is trying to enter the Happy House and the camera takes a picture of him (\"images/camera_0.jpg\"). Let's run your verification algorithm on this picture:\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It's younes, welcome home!\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.65939283, True)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "verify(\"images/camera_0.jpg\", \"younes\", database, FRmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **It's younes, welcome home!**\n", + " \n", + " (0.65939283, True)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Benoit, who broke the aquarium last weekend, has been banned from the house and removed from the database. He stole Kian's ID card and came back to the house to try to present himself as Kian. The front-door camera took a picture of Benoit (\"images/camera_2.jpg). Let's run the verification algorithm to check if benoit can enter.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It's not kian, please go away\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.86224014, False)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "verify(\"images/camera_2.jpg\", \"kian\", database, FRmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **It's not kian, please go away**\n", + " \n", + " (0.86224014, False)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Face Recognition\n", + "\n", + "Your face verification system is mostly working well. But since Kian got his ID card stolen, when he came back to the house that evening he couldn't get in! \n", + "\n", + "To reduce such shenanigans, you'd like to change your face verification system to a face recognition system. This way, no one has to carry an ID card anymore. An authorized person can just walk up to the house, and the front door will unlock for them! \n", + "\n", + "You'll implement a face recognition system that takes as input an image, and figures out if it is one of the authorized persons (and if so, who). Unlike the previous face verification system, we will no longer get a person's name as another input. \n", + "\n", + "**Exercise**: Implement `who_is_it()`. You will have to go through the following steps:\n", + "1. Compute the target encoding of the image from image_path\n", + "2. Find the encoding from the database that has smallest distance with the target encoding. \n", + " - Initialize the `min_dist` variable to a large enough number (100). It will help you keep track of what is the closest encoding to the input's encoding.\n", + " - Loop over the database dictionary's names and encodings. To loop use `for (name, db_enc) in database.items()`.\n", + " - Compute L2 distance between the target \"encoding\" and the current \"encoding\" from the database.\n", + " - If this distance is less than the min_dist, then set min_dist to dist, and identity to name." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: who_is_it\n", + "\n", + "def who_is_it(image_path, database, model):\n", + " \"\"\"\n", + " Implements face recognition for the happy house by finding who is the person on the image_path image.\n", + " \n", + " Arguments:\n", + " image_path -- path to an image\n", + " database -- database containing image encodings along with the name of the person on the image\n", + " model -- your Inception model instance in Keras\n", + " \n", + " Returns:\n", + " min_dist -- the minimum distance between image_path encoding and the encodings from the database\n", + " identity -- string, the name prediction for the person on image_path\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### \n", + " \n", + " ## Step 1: Compute the target \"encoding\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\n", + " encoding = img_to_encoding(image_path, model)\n", + " \n", + " ## Step 2: Find the closest encoding ##\n", + " \n", + " # Initialize \"min_dist\" to a large value, say 100 (≈1 line)\n", + " min_dist = 100\n", + " \n", + " # Loop over the database dictionary's names and encodings.\n", + " for (name, db_enc) in database.items():\n", + " \n", + " # Compute L2 distance between the target \"encoding\" and the current \"emb\" from the database. (≈ 1 line)\n", + " dist = np.linalg.norm(encoding-db_enc)\n", + "\n", + " # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\n", + " if dist < min_dist:\n", + " min_dist = dist\n", + " identity = name\n", + "\n", + " ### END CODE HERE ###\n", + " \n", + " if min_dist > 0.7:\n", + " print(\"Not in the database.\")\n", + " else:\n", + " print (\"it's \" + str(identity) + \", the distance is \" + str(min_dist))\n", + " \n", + " return min_dist, identity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Younes is at the front-door and the camera takes a picture of him (\"images/camera_0.jpg\"). Let's see if your who_it_is() algorithm identifies Younes. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "it's younes, the distance is 0.659393\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.65939283, 'younes')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "who_is_it(\"images/camera_0.jpg\", database, FRmodel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **it's younes, the distance is 0.659393**\n", + " \n", + " (0.65939283, 'younes')\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change \"`camera_0.jpg`\" (picture of younes) to \"`camera_1.jpg`\" (picture of bertrand) and see the result." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your Happy House is running well. It only lets in authorized persons, and people don't need to carry an ID card around anymore! \n", + "\n", + "You've now seen how a state-of-the-art face recognition system works.\n", + "\n", + "Although we won't implement it here, here're some ways to further improve the algorithm:\n", + "- Put more images of each person (under different lighting conditions, taken on different days, etc.) into the database. Then given a new image, compare the new face to multiple pictures of the person. This would increae accuracy.\n", + "- Crop the images to just contain the face, and less of the \"border\" region around the face. This preprocessing removes some of the irrelevant pixels around the face, and also makes the algorithm more robust.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- Face verification solves an easier 1:1 matching problem; face recognition addresses a harder 1:K matching problem. \n", + "- The triplet loss is an effective loss function for training a neural network to learn an encoding of a face image.\n", + "- The same encoding can be used for verification and recognition. Measuring distances between two images' encodings allows you to determine whether they are pictures of the same person. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats on finishing this assignment! \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References:\n", + "\n", + "- Florian Schroff, Dmitry Kalenichenko, James Philbin (2015). [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/pdf/1503.03832.pdf)\n", + "- Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf (2014). [DeepFace: Closing the gap to human-level performance in face verification](https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf) \n", + "- The pretrained model we use is inspired by Victor Sy Wang's implementation and was loaded using his code: https://github.com/iwantooxxoox/Keras-OpenFace.\n", + "- Our implementation also took a lot of inspiration from the official FaceNet github repository: https://github.com/davidsandberg/facenet \n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "convolutional-neural-networks", + "graded_item_id": "IaknP", + "launcher_item_id": "5UMr4" + }, + "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": 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Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Face Recognition/images/younes.jpg b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Face Recognition/images/younes.jpg new file mode 100644 index 0000000..0df0cee Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Face Recognition/images/younes.jpg differ diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Neural Style Transfer/Art Generation with Neural Style Transfer - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Neural Style Transfer/Art Generation with Neural Style Transfer - v1.ipynb new file mode 100644 index 0000000..2130bab --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Neural Style Transfer/Art Generation with Neural Style Transfer - v1.ipynb @@ -0,0 +1,1266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Learning & Art: Neural Style Transfer\n", + "\n", + "Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). \n", + "\n", + "**In this assignment, you will:**\n", + "- Implement the neural style transfer algorithm \n", + "- Generate novel artistic images using your algorithm \n", + "\n", + "Most of the algorithms you've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you'll optimize a cost function to get pixel values!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import scipy.io\n", + "import scipy.misc\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import imshow\n", + "from PIL import Image\n", + "from nst_utils import *\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Problem Statement\n", + "\n", + "Neural Style Transfer (NST) is one of the most fun techniques in deep learning. As seen below, it merges two images, namely, a \"content\" image (C) and a \"style\" image (S), to create a \"generated\" image (G). The generated image G combines the \"content\" of the image C with the \"style\" of image S. \n", + "\n", + "In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style image S).\n", + "\n", + "\n", + "Let's see how you can do this. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Transfer Learning\n", + "\n", + "Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of that. The idea of using a network trained on a different task and applying it to a new task is called transfer learning. \n", + "\n", + "Following the original NST paper (https://arxiv.org/abs/1508.06576), we will use the VGG network. Specifically, we'll use VGG-19, a 19-layer version of the VGG network. This model has already been trained on the very large ImageNet database, and thus has learned to recognize a variety of low level features (at the earlier layers) and high level features (at the deeper layers). \n", + "\n", + "Run the following code to load parameters from the VGG model. This may take a few seconds. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'input': , 'conv1_1': , 'conv1_2': , 'avgpool1': , 'conv2_1': , 'conv2_2': , 'avgpool2': , 'conv3_1': , 'conv3_2': , 'conv3_3': , 'conv3_4': , 'avgpool3': , 'conv4_1': , 'conv4_2': , 'conv4_3': , 'conv4_4': , 'avgpool4': , 'conv5_1': , 'conv5_2': , 'conv5_3': , 'conv5_4': , 'avgpool5': }\n" + ] + } + ], + "source": [ + "model = load_vgg_model(\"pretrained-model/imagenet-vgg-verydeep-19.mat\")\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is stored in a python dictionary where each variable name is the key and the corresponding value is a tensor containing that variable's value. To run an image through this network, you just have to feed the image to the model. In TensorFlow, you can do so using the [tf.assign](https://www.tensorflow.org/api_docs/python/tf/assign) function. In particular, you will use the assign function like this: \n", + "```python\n", + "model[\"input\"].assign(image)\n", + "```\n", + "This assigns the image as an input to the model. After this, if you want to access the activations of a particular layer, say layer `4_2` when the network is run on this image, you would run a TensorFlow session on the correct tensor `conv4_2`, as follows: \n", + "```python\n", + "sess.run(model[\"conv4_2\"])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Neural Style Transfer \n", + "\n", + "We will build the NST algorithm in three steps:\n", + "\n", + "- Build the content cost function $J_{content}(C,G)$\n", + "- Build the style cost function $J_{style}(S,G)$\n", + "- Put it together to get $J(G) = \\alpha J_{content}(C,G) + \\beta J_{style}(S,G)$. \n", + "\n", + "### 3.1 - Computing the content cost\n", + "\n", + "In our running example, the content image C will be the picture of the Louvre Museum in Paris. Run the code below to see a picture of the Louvre." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tlvz8Bc2UwPflPnMelTB92h/8zh3uNN3re2CayIgpLKc1sXjfZUZtRPK8EYFmPFZtmvrF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IGgh0K+RKVSweNy4HTWoFgC+VyKcsnE5vDi9IRx+O3YvDacfifN7XXoQglL0vGEIrh8fkLh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IlYr7Yk\ngz5aax4+fMyHPvQhPvGJjzMcjk1BKLDcxigS/prniBxchxmenp7yxhtvsJwvWC7nvP766zz3/C0L\neA8GA6bjCcvlksFgwOnxY5E6y1PberlarRiNpMuqaRqybcqNGzeIk4izszOuXLlKN9313r179AZD\nmwKPRgPWizWDpE9ZVxaTUlo6hW7evElWFm/iVLquPABJr28nT2qlTcqeG/L1lpvXb7JNNybK0Mzn\nc7nx83PCuOd57EymbMuU9XprmiNOLC90MBgQ+RGO7/D48WNc1xUKzypld2+HwPV48OQxicF2d3b2\nyNLcCoYILUjz5Mkjoaf5HlHUo21r05EU0jQVq9WGXpKQG8hhdrbAcTGiJZKar9dr2rrh5s2b3H1w\nn73dXVv0KYqCdJtz5coVOyJnNBqZiFbGe5R5xvWbNyzx3HEcXAfqqmW93eA5gsWXZclgJEP0XN+n\nMRoEjVYEnsdoNOJ0JvqkgefStjVlLXheHIS2GNmYCE4hqXmH02ZlYZ1Xr9cjK3J2hmOOz06JY+Hg\ndiySLm1frVaMxmNbbK2qinS7lXEoRydWK6AuZZTIarGklwjGHIYhTdvarEQpoRHqqqaoZJPpHK/j\nOLQKm1UErmfYKgl1q6mbAhrJLrr7vesO00rJIMkgwHGkLdYzlfzZbIbCIY4i6qo1ylotINGzF7g0\n1fk01A7nL+vK4rHdde6oW67rynjqvLKtwF/4ylf+8EegKKVGwPcB/z6A1roESqXUDwHfb972k8Cv\nAH8F+CHgZ7TWBfC6UuoO4lz/n9/vGEmS8PLLL1OWJUdHR7St4B1NW7FczHjllVct1tbNyjkXpNZG\nsFl24Bs3blgsZptluEoGv/n+uXDDo0ePuHHzNroRruBzt27y4N59fup/f0X0RF2X4+NT47REEJ/y\nF+UAAAelSURBVGI8HhIFIXkpKZjjYtM0B8XR8SN2d3fQbSVtsFVK0vOhLaAtSDe17VhSSjMaiaMt\niorj41PCUHQJ2qbh0eMHTHd2iGMh6z94/Ihh0mc8HnN8fMwwuc2on/CN119j1OuLyIQLMnWyIOn1\ncF3FcjlnOt1lZvA+oWHlFGkGWrC1a9eusVgtDcEa7t69y/7+nr0pkyRhvV5anmxb1VLsiUUScD6f\n40YSoZ2dnQnNpmyEOpVLBJBnucXQjo+PmYx38TyPhw8eMhiPWS0XzGYLVmtRTXr05DGj0YjBYMB8\neYrjOEwmE45Pz8y4DDmX0529N7U8djO9RC1JsVzNLUXq5s2bHD1+IsplhsJU5BVxP7Yb9SuvvCKR\naS+2c5DKsmRnZ0KeSutyd82rqiL0AxsNLjYbvDRjZ2fHFqdmiwVtXaI8l93RlNPTUx48esTh4VV7\n/0qc4Rpam6bKM7abjGHSN11PMjkBsFShDkuX1smYspG0f7mUSaFRFFAUwtXtvkfTViS9gaTKAxlu\neXZ2hnmmbdbRIpH7yenR+VTSp4btgbQS66qmaoQzWzU1TVPJyJG2ITYTEpowZDQaGbEVicqLvMBx\nzt3Oei3TRj3TbCDwRoXjuMRxRJoK7umorqnBNU5XHGoQBKT5FlSL78uMLwAfCNrQbkRd+2lXmGzb\nlrRIoWnpNGSfhX0r8n3PASfA31VKfRD4IvAXgQOt9WPznifAgfn5GvD/PvX/H5jX3mRKqT8P/Hnz\n6+Zv/y9/7ww4PX/H3W/5S/z/s9/Xv3e2y5vWc+F2uZ5vbm+39cDbb02X6/nm9tK3+wHfikP1gA8D\nP6q1/jWl1P8E/NjTb9BaayWClN+yaa1/AviJ7nel1Be+3XD7Wdrler65Xa7nD7a325ou1/PNTSn1\nhW/3M5w/+C08AB5orX/N/P6PEAd7pJQ6NAs5BI7Nvz8Ebjz1/6+b1y7t0i7t0t7R9gc6VK31E+C+\nUqoLh/8Y8DvALwB/zrz254BPm59/AfhhpVSolHoOeAH49We66ku7tEu7tLehfasjUH4U+Pumwv8a\n8B8gzvjnlFI/ggCefwZAa/3bSqmfQ5xuDfyFb1bhf8p+4g9+y1tql+v55na5nj/Y3m5rulzPN7dv\nez1vC2L/pV3apV3aO8G+FQz10i7t0i7t0r4Fu3CHqpT6E0qpryul7pgGgbfimP+rUupYKfXVp16b\nKqU+q5R61fw9eerfftys7+tKqX/rD2E9N5RS/0wp9TtKqd9WSv3Fi1yTUipSSv26UurLZj1//SLX\n89QxXKXUbyqlfvFtsp43lFK/pZT6UlchvuD7aKyU+kdKqd9VSn1NKfU9F3gPvWTOS/dnpZT6Sxd8\nfv5Tcz9/VSn10+Y+f7br6aTJLuIP4ALfAJ4HAuDLwHveguN+H8JU+OpTr/0PwI+Zn38M+O/Nz+8x\n6woRTu43APcZr+cQ+LD5eQC8Yo57IWtCtKf75mcf+DXg4xd5jsxx/jPgHwC/eNHXzBznDWD3X3nt\nIu+jnwT+I/NzAIwv+hyZY7kIV/3WBd7T14DXgdj8/nNIs9IzXc8zP3n/ml/ye4DPPPX7jwM//hYd\n+zZvdqhfBw7Nz4fA13+vNQGfAb7nD3ltnwb+zbfDmpA2438JfOwi14PQ7z4P/ADnDvVCzw+/t0O9\nkDUBI+Mw1NthPf/KGv448M8v+PxcA+4DU6QY/4tmXc90PRed8ndfsrPfs6vqLbJv1vn1lq1RKXUb\n+BASFV7Ymkx6/SWEX/xZLTzkizxHfxP4L4D2qdcu+ppp4HNKqS8q6fy7yDU93dH4m0qpTyqlkgtc\nz9P2w4i+Bxe1Hq31Q+BvAPeAx8BSa/1Lz3o9F+1Q35amZUt6y+kPSqk+8I+Bv6S1Xl3kmrTWjdb6\nO5HI8LuVUu+7qPUopf5t4Fhr/cXf7z0XdM0+Yc7RDwJ/QSn1fRe4pq6j8X/WWn8I2PJ7dDS+hesB\nwFAt/xTwD//Vf3uL76EJojPyHCLalCil/uyzXs9FO9S3U1fVhXZ+KaV8xJn+fa31z78d1gSgtV4A\n/wz4Exe4nj8C/Cml1BvAzwA/oJT6exe4HsBGPWitj4H/AxEBuqg1vV07Gn8Q+Jda6yPz+0Wt598A\nXtdan2itK+Dnge991uu5aIf6G8ALSqnnzE72w0in1UXYhXV+KaUU8HeAr2mt/8eLXpNSak+JBi5K\nqRjBc3/3otajtf5xrfV1rfVt5B75Za31n72o9QAopRKl1KD7GcHjvnpRa9Jv347Gf5fzdL877kWs\n5x7wcaVUzzxvfwz42jNfzx8GCP2vCRb/SaSq/Q3gr71Fx/xpBEepkJ39R4AdpOjxKqLhOn3q/X/N\nrO/rwA/+IaznE0iq8RXgS+bPn7yoNQEfAH7TrOerwH9pXr+wc/TUcb6f86LURV6z55Eq8JeB3+7u\n3Qte03cCXzDX7VPA5ILXkwBnwOip1y5yPX8dCQy+CvwUUsF/puu57JS6tEu7tEt7RnbRKf+lXdql\nXdo7xi4d6qVd2qVd2jOyS4d6aZd2aZf2jOzSoV7apV3apT0ju3Sol3Zpl3Zpz8guHeqlXdqlXdoz\nskuHemmXdmmX9ozs0qFe2qVd2qU9I/v/AOeT38MGcZAdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "content_image = scipy.misc.imread(\"images/louvre.jpg\")\n", + "imshow(content_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The content image (C) shows the Louvre museum's pyramid surrounded by old Paris buildings, against a sunny sky with a few clouds.\n", + "\n", + "** 3.1.1 - How do you ensure the generated image G matches the content of the image C?**\n", + "\n", + "As we saw in lecture, the earlier (shallower) layers of a ConvNet tend to detect lower-level features such as edges and simple textures, and the later (deeper) layers tend to detect higher-level features such as more complex textures as well as object classes. \n", + "\n", + "We would like the \"generated\" image G to have similar content as the input image C. Suppose you have chosen some layer's activations to represent the content of an image. In practice, you'll get the most visually pleasing results if you choose a layer in the middle of the network--neither too shallow nor too deep. (After you have finished this exercise, feel free to come back and experiment with using different layers, to see how the results vary.)\n", + "\n", + "So, suppose you have picked one particular hidden layer to use. Now, set the image C as the input to the pretrained VGG network, and run forward propagation. Let $a^{(C)}$ be the hidden layer activations in the layer you had chosen. (In lecture, we had written this as $a^{[l](C)}$, but here we'll drop the superscript $[l]$ to simplify the notation.) This will be a $n_H \\times n_W \\times n_C$ tensor. Repeat this process with the image G: Set G as the input, and run forward progation. Let $$a^{(G)}$$ be the corresponding hidden layer activation. We will define as the content cost function as:\n", + "\n", + "$$J_{content}(C,G) = \\frac{1}{4 \\times n_H \\times n_W \\times n_C}\\sum _{ \\text{all entries}} (a^{(C)} - a^{(G)})^2\\tag{1} $$\n", + "\n", + "Here, $n_H, n_W$ and $n_C$ are the height, width and number of channels of the hidden layer you have chosen, and appear in a normalization term in the cost. For clarity, note that $a^{(C)}$ and $a^{(G)}$ are the volumes corresponding to a hidden layer's activations. In order to compute the cost $J_{content}(C,G)$, it might also be convenient to unroll these 3D volumes into a 2D matrix, as shown below. (Technically this unrolling step isn't needed to compute $J_{content}$, but it will be good practice for when you do need to carry out a similar operation later for computing the style const $J_{style}$.)\n", + "\n", + "\n", + "\n", + "**Exercise:** Compute the \"content cost\" using TensorFlow. \n", + "\n", + "**Instructions**: The 3 steps to implement this function are:\n", + "1. Retrieve dimensions from a_G: \n", + " - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`\n", + "2. Unroll a_C and a_G as explained in the picture above\n", + " - If you are stuck, take a look at [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape).\n", + "3. Compute the content cost:\n", + " - If you are stuck, take a look at [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_content_cost\n", + "\n", + "def compute_content_cost(a_C, a_G):\n", + " \"\"\"\n", + " Computes the content cost\n", + " \n", + " Arguments:\n", + " a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C \n", + " a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G\n", + " \n", + " Returns: \n", + " J_content -- scalar that you compute using equation 1 above.\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from a_G (≈1 line)\n", + " m, n_H, n_W, n_C = a_G.get_shape().as_list()\n", + " \n", + " # Reshape a_C and a_G (≈2 lines)\n", + " a_C_unrolled = tf.transpose(a_C)\n", + " a_G_unrolled = tf.transpose(a_G)\n", + " \n", + " # compute the cost with tensorflow (≈1 line)\n", + " J_content = (1/ (4* n_H * n_W * n_C)) * tf.reduce_sum(tf.pow((a_G_unrolled - a_C_unrolled), 2))\n", + " ### END CODE HERE ###\n", + " \n", + " return J_content" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J_content = 6.76559\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " tf.set_random_seed(1)\n", + " a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n", + " a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n", + " J_content = compute_content_cost(a_C, a_G)\n", + " print(\"J_content = \" + str(J_content.eval()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **J_content**\n", + " \n", + " 6.76559\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- The content cost takes a hidden layer activation of the neural network, and measures how different $a^{(C)}$ and $a^{(G)}$ are. \n", + "- When we minimize the content cost later, this will help make sure $G$ has similar content as $C$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Computing the style cost\n", + "\n", + "For our running example, we will use the following style image: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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F8zFwu7/nL54fmfzCMUwgjWCe7gb8M08HTDPOhJ4LXXSvPkY52luj1srdMmOtY+aI3lGt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\nU/b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bpzaCi6rgdHnJsirYW1zj8sWWWGiSEZycXXI4m3Hnzm0QHZeFY1m1XG223Lp9DWEb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jTba1YHa0KAy9tnHJ4uWa3O6doNdmwIGNrBMpsfI20EIvPzEn03rUoLJLebDaNK6IxDWIcu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XM7IM4UnqFNONCKHZPz7gYg6TYbkskD9acLIX0lWBFJp2PCGyjGFyrD66oPr396zrG87n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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "style_image = scipy.misc.imread(\"images/monet_800600.jpg\")\n", + "imshow(style_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This painting was painted in the style of *[impressionism](https://en.wikipedia.org/wiki/Impressionism)*.\n", + "\n", + "Lets see how you can now define a \"style\" const function $J_{style}(S,G)$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.1 - Style matrix\n", + "\n", + "The style matrix is also called a \"Gram matrix.\" In linear algebra, the Gram matrix G of a set of vectors $(v_{1},\\dots ,v_{n})$ is the matrix of dot products, whose entries are ${\\displaystyle G_{ij} = v_{i}^T v_{j} = np.dot(v_{i}, v_{j}) }$. In other words, $G_{ij}$ compares how similar $v_i$ is to $v_j$: If they are highly similar, you would expect them to have a large dot product, and thus for $G_{ij}$ to be large. \n", + "\n", + "Note that there is an unfortunate collision in the variable names used here. We are following common terminology used in the literature, but $G$ is used to denote the Style matrix (or Gram matrix) as well as to denote the generated image $G$. We will try to make sure which $G$ we are referring to is always clear from the context. \n", + "\n", + "In NST, you can compute the Style matrix by multiplying the \"unrolled\" filter matrix with their transpose:\n", + "\n", + "\n", + "\n", + "The result is a matrix of dimension $(n_C,n_C)$ where $n_C$ is the number of filters. The value $G_{ij}$ measures how similar the activations of filter $i$ are to the activations of filter $j$. \n", + "\n", + "One important part of the gram matrix is that the diagonal elements such as $G_{ii}$ also measures how active filter $i$ is. For example, suppose filter $i$ is detecting vertical textures in the image. Then $G_{ii}$ measures how common vertical textures are in the image as a whole: If $G_{ii}$ is large, this means that the image has a lot of vertical texture. \n", + "\n", + "By capturing the prevalence of different types of features ($G_{ii}$), as well as how much different features occur together ($G_{ij}$), the Style matrix $G$ measures the style of an image. \n", + "\n", + "**Exercise**:\n", + "Using TensorFlow, implement a function that computes the Gram matrix of a matrix A. The formula is: The gram matrix of A is $G_A = AA^T$. If you are stuck, take a look at [Hint 1](https://www.tensorflow.org/api_docs/python/tf/matmul) and [Hint 2](https://www.tensorflow.org/api_docs/python/tf/transpose)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: gram_matrix\n", + "\n", + "def gram_matrix(A):\n", + " \"\"\"\n", + " Argument:\n", + " A -- matrix of shape (n_C, n_H*n_W)\n", + " \n", + " Returns:\n", + " GA -- Gram matrix of A, of shape (n_C, n_C)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈1 line)\n", + " GA = tf.matmul(A, tf.transpose(A))\n", + " ### END CODE HERE ###\n", + " \n", + " return GA" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GA = [[ 6.42230511 -4.42912197 -2.09668207]\n", + " [ -4.42912197 19.46583748 19.56387138]\n", + " [ -2.09668207 19.56387138 20.6864624 ]]\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " tf.set_random_seed(1)\n", + " A = tf.random_normal([3, 2*1], mean=1, stddev=4)\n", + " GA = gram_matrix(A)\n", + " \n", + " print(\"GA = \" + str(GA.eval()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **GA**\n", + " \n", + " [[ 6.42230511 -4.42912197 -2.09668207]
\n", + " [ -4.42912197 19.46583748 19.56387138]
\n", + " [ -2.09668207 19.56387138 20.6864624 ]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.2 - Style cost" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After generating the Style matrix (Gram matrix), your goal will be to minimize the distance between the Gram matrix of the \"style\" image S and that of the \"generated\" image G. For now, we are using only a single hidden layer $a^{[l]}$, and the corresponding style cost for this layer is defined as: \n", + "\n", + "$$J_{style}^{[l]}(S,G) = \\frac{1}{4 \\times {n_C}^2 \\times (n_H \\times n_W)^2} \\sum _{i=1}^{n_C}\\sum_{j=1}^{n_C}(G^{(S)}_{ij} - G^{(G)}_{ij})^2\\tag{2} $$\n", + "\n", + "where $G^{(S)}$ and $G^{(G)}$ are respectively the Gram matrices of the \"style\" image and the \"generated\" image, computed using the hidden layer activations for a particular hidden layer in the network. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Compute the style cost for a single layer. \n", + "\n", + "**Instructions**: The 3 steps to implement this function are:\n", + "1. Retrieve dimensions from the hidden layer activations a_G: \n", + " - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`\n", + "2. Unroll the hidden layer activations a_S and a_G into 2D matrices, as explained in the picture above.\n", + " - You may find [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape) useful.\n", + "3. Compute the Style matrix of the images S and G. (Use the function you had previously written.) \n", + "4. Compute the Style cost:\n", + " - You may find [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract) useful." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_layer_style_cost\n", + "\n", + "def compute_layer_style_cost(a_S, a_G):\n", + " \"\"\"\n", + " Arguments:\n", + " a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S \n", + " a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G\n", + " \n", + " Returns: \n", + " J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from a_G (≈1 line)\n", + " m, n_H, n_W, n_C = a_G.get_shape().as_list()\n", + " \n", + " # Reshape the images to have them of shape (n_H*n_W, n_C) (≈2 lines)\n", + " a_S = tf.transpose(tf.reshape(a_S, [n_H*n_W, n_C]))\n", + " a_G = tf.transpose(tf.reshape(a_G, [n_H*n_W, n_C]))\n", + "\n", + " # Computing gram_matrices for both images S and G (≈2 lines)\n", + " GS = gram_matrix(a_S)\n", + " GG = gram_matrix(a_G)\n", + "\n", + " # Computing the loss (≈1 line)\n", + " J_style_layer = (1./(4 * n_C**2 * (n_H*n_W)**2)) * tf.reduce_sum(tf.pow((GS - GG), 2))\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return J_style_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J_style_layer = 9.19028\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " tf.set_random_seed(1)\n", + " a_S = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n", + " a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n", + " J_style_layer = compute_layer_style_cost(a_S, a_G)\n", + " \n", + " print(\"J_style_layer = \" + str(J_style_layer.eval()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **J_style_layer**\n", + " \n", + " 9.19028\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.3 Style Weights\n", + "\n", + "So far you have captured the style from only one layer. We'll get better results if we \"merge\" style costs from several different layers. After completing this exercise, feel free to come back and experiment with different weights to see how it changes the generated image $G$. But for now, this is a pretty reasonable default: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "STYLE_LAYERS = [\n", + " ('conv1_1', 0.2),\n", + " ('conv2_1', 0.2),\n", + " ('conv3_1', 0.2),\n", + " ('conv4_1', 0.2),\n", + " ('conv5_1', 0.2)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can combine the style costs for different layers as follows:\n", + "\n", + "$$J_{style}(S,G) = \\sum_{l} \\lambda^{[l]} J^{[l]}_{style}(S,G)$$\n", + "\n", + "where the values for $\\lambda^{[l]}$ are given in `STYLE_LAYERS`. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've implemented a compute_style_cost(...) function. It simply calls your `compute_layer_style_cost(...)` several times, and weights their results using the values in `STYLE_LAYERS`. Read over it to make sure you understand what it's doing. \n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def compute_style_cost(model, STYLE_LAYERS):\n", + " \"\"\"\n", + " Computes the overall style cost from several chosen layers\n", + " \n", + " Arguments:\n", + " model -- our tensorflow model\n", + " STYLE_LAYERS -- A python list containing:\n", + " - the names of the layers we would like to extract style from\n", + " - a coefficient for each of them\n", + " \n", + " Returns: \n", + " J_style -- tensor representing a scalar value, style cost defined above by equation (2)\n", + " \"\"\"\n", + " \n", + " # initialize the overall style cost\n", + " J_style = 0\n", + "\n", + " for layer_name, coeff in STYLE_LAYERS:\n", + "\n", + " # Select the output tensor of the currently selected layer\n", + " out = model[layer_name]\n", + "\n", + " # Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out\n", + " a_S = sess.run(out)\n", + "\n", + " # Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name] \n", + " # and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that\n", + " # when we run the session, this will be the activations drawn from the appropriate layer, with G as input.\n", + " a_G = out\n", + " \n", + " # Compute style_cost for the current layer\n", + " J_style_layer = compute_layer_style_cost(a_S, a_G)\n", + "\n", + " # Add coeff * J_style_layer of this layer to overall style cost\n", + " J_style += coeff * J_style_layer\n", + "\n", + " return J_style" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: In the inner-loop of the for-loop above, `a_G` is a tensor and hasn't been evaluated yet. It will be evaluated and updated at each iteration when we run the TensorFlow graph in model_nn() below.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "**What you should remember**:\n", + "- The style of an image can be represented using the Gram matrix of a hidden layer's activations. However, we get even better results combining this representation from multiple different layers. This is in contrast to the content representation, where usually using just a single hidden layer is sufficient.\n", + "- Minimizing the style cost will cause the image $G$ to follow the style of the image $S$. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 - Defining the total cost to optimize" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's create a cost function that minimizes both the style and the content cost. The formula is: \n", + "\n", + "$$J(G) = \\alpha J_{content}(C,G) + \\beta J_{style}(S,G)$$\n", + "\n", + "**Exercise**: Implement the total cost function which includes both the content cost and the style cost. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: total_cost\n", + "\n", + "def total_cost(J_content, J_style, alpha = 10, beta = 40):\n", + " \"\"\"\n", + " Computes the total cost function\n", + " \n", + " Arguments:\n", + " J_content -- content cost coded above\n", + " J_style -- style cost coded above\n", + " alpha -- hyperparameter weighting the importance of the content cost\n", + " beta -- hyperparameter weighting the importance of the style cost\n", + " \n", + " Returns:\n", + " J -- total cost as defined by the formula above.\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈1 line)\n", + " J = alpha * J_content + beta * J_style\n", + " ### END CODE HERE ###\n", + " \n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J = 35.34667875478276\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as test:\n", + " np.random.seed(3)\n", + " J_content = np.random.randn() \n", + " J_style = np.random.randn()\n", + " J = total_cost(J_content, J_style)\n", + " print(\"J = \" + str(J))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **J**\n", + " \n", + " 35.34667875478276\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- The total cost is a linear combination of the content cost $J_{content}(C,G)$ and the style cost $J_{style}(S,G)$\n", + "- $\\alpha$ and $\\beta$ are hyperparameters that control the relative weighting between content and style" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Solving the optimization problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's put everything together to implement Neural Style Transfer!\n", + "\n", + "\n", + "Here's what the program will have to do:\n", + "\n", + "\n", + "1. Create an Interactive Session\n", + "2. Load the content image \n", + "3. Load the style image\n", + "4. Randomly initialize the image to be generated \n", + "5. Load the VGG16 model\n", + "7. Build the TensorFlow graph:\n", + " - Run the content image through the VGG16 model and compute the content cost\n", + " - Run the style image through the VGG16 model and compute the style cost\n", + " - Compute the total cost\n", + " - Define the optimizer and the learning rate\n", + "8. Initialize the TensorFlow graph and run it for a large number of iterations, updating the generated image at every step.\n", + "\n", + "\n", + "Lets go through the individual steps in detail. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've previously implemented the overall cost $J(G)$. We'll now set up TensorFlow to optimize this with respect to $G$. To do so, your program has to reset the graph and use an \"[Interactive Session](https://www.tensorflow.org/api_docs/python/tf/InteractiveSession)\". Unlike a regular session, the \"Interactive Session\" installs itself as the default session to build a graph. This allows you to run variables without constantly needing to refer to the session object, which simplifies the code. \n", + "\n", + "Lets start the interactive session." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reset the graph\n", + "tf.reset_default_graph()\n", + "\n", + "# Start interactive session\n", + "sess = tf.InteractiveSession()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load, reshape, and normalize our \"content\" image (the Louvre museum picture):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "content_image = scipy.misc.imread(\"images/louvre_small.jpg\")\n", + "content_image = reshape_and_normalize_image(content_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load, reshape and normalize our \"style\" image (Claude Monet's painting):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "style_image = scipy.misc.imread(\"images/monet.jpg\")\n", + "style_image = reshape_and_normalize_image(style_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we initialize the \"generated\" image as a noisy image created from the content_image. By initializing the pixels of the generated image to be mostly noise but still slightly correlated with the content image, this will help the content of the \"generated\" image more rapidly match the content of the \"content\" image. (Feel free to look in `nst_utils.py` to see the details of `generate_noise_image(...)`; to do so, click \"File-->Open...\" at the upper-left corner of this Jupyter notebook.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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LT3mdNo7x+vmslfGjZ5IBGSnLud4NhrSPI/TsbKJ1TmL1TwcSR7lw6KOgeoYL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IAAAg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gYA5MACz+eWafgoKC0r9KoKzVgc9z41CNWcngxz2QunLUppSTU6WD6+JLvBiXyiHr1xg5pDMw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6CEddyEq7zEvNmCznasJXt5IbIxE6WGo/x0UfYn3Rhmp0V3FSlBYzkpRVyllXr8pL/HiUrl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\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UB3RT0PwC2Y9V9D6gydGIbhY/K0f/YwbhX1cRckYS9U81DK4ajmRAI/YpbjSvPkCN9kSk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\nLSRR452UWp4n03ZWFu2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "generated_image = generate_noise_image(content_image)\n", + "imshow(generated_image[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, as explained in part (2), let's load the VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = load_vgg_model(\"pretrained-model/imagenet-vgg-verydeep-19.mat\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the program to compute the content cost, we will now assign `a_C` and `a_G` to be the appropriate hidden layer activations. We will use layer `conv4_2` to compute the content cost. The code below does the following:\n", + "\n", + "1. Assign the content image to be the input to the VGG model.\n", + "2. Set a_C to be the tensor giving the hidden layer activation for layer \"conv4_2\".\n", + "3. Set a_G to be the tensor giving the hidden layer activation for the same layer. \n", + "4. Compute the content cost using a_C and a_G." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Assign the content image to be the input of the VGG model. \n", + "sess.run(model['input'].assign(content_image))\n", + "\n", + "# Select the output tensor of layer conv4_2\n", + "out = model['conv4_2']\n", + "\n", + "# Set a_C to be the hidden layer activation from the layer we have selected\n", + "a_C = sess.run(out)\n", + "\n", + "# Set a_G to be the hidden layer activation from same layer. Here, a_G references model['conv4_2'] \n", + "# and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that\n", + "# when we run the session, this will be the activations drawn from the appropriate layer, with G as input.\n", + "a_G = out\n", + "\n", + "# Compute the content cost\n", + "J_content = compute_content_cost(a_C, a_G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: At this point, a_G is a tensor and hasn't been evaluated. It will be evaluated and updated at each iteration when we run the Tensorflow graph in model_nn() below." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Assign the input of the model to be the \"style\" image \n", + "sess.run(model['input'].assign(style_image))\n", + "\n", + "# Compute the style cost\n", + "J_style = compute_style_cost(model, STYLE_LAYERS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Now that you have J_content and J_style, compute the total cost J by calling `total_cost()`. Use `alpha = 10` and `beta = 40`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### START CODE HERE ### (1 line)\n", + "J = total_cost(J_content, J_style, alpha = 10, beta = 40)\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'd previously learned how to set up the Adam optimizer in TensorFlow. Lets do that here, using a learning rate of 2.0. [See reference](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# define optimizer (1 line)\n", + "optimizer = tf.train.AdamOptimizer(2.0)\n", + "\n", + "# define train_step (1 line)\n", + "train_step = optimizer.minimize(J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement the model_nn() function which initializes the variables of the tensorflow graph, assigns the input image (initial generated image) as the input of the VGG16 model and runs the train_step for a large number of steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def model_nn(sess, input_image, num_iterations = 200):\n", + " \n", + " # Initialize global variables (you need to run the session on the initializer)\n", + " ### START CODE HERE ### (1 line)\n", + " sess.run(tf.global_variables_initializer())\n", + " ### END CODE HERE ###\n", + " \n", + " # Run the noisy input image (initial generated image) through the model. Use assign().\n", + " ### START CODE HERE ### (1 line)\n", + " sess.run(model['input'].assign(input_image))\n", + " ### END CODE HERE ###\n", + " \n", + " for i in range(num_iterations):\n", + " \n", + " # Run the session on the train_step to minimize the total cost\n", + " ### START CODE HERE ### (1 line)\n", + " sess.run(train_step)\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute the generated image by running the session on the current model['input']\n", + " ### START CODE HERE ### (1 line)\n", + " generated_image = sess.run(model['input'])\n", + " ### END CODE HERE ###\n", + "\n", + " # Print every 20 iteration.\n", + " if i%20 == 0:\n", + " Jt, Jc, Js = sess.run([J, J_content, J_style])\n", + " print(\"Iteration \" + str(i) + \" :\")\n", + " print(\"total cost = \" + str(Jt))\n", + " print(\"content cost = \" + str(Jc))\n", + " print(\"style cost = \" + str(Js))\n", + " \n", + " # save current generated image in the \"/output\" directory\n", + " save_image(\"output/\" + str(i) + \".png\", generated_image)\n", + " \n", + " # save last generated image\n", + " save_image('output/generated_image.jpg', generated_image)\n", + " \n", + " return generated_image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to generate an artistic image. It should take about 3min on CPU for every 20 iterations but you start observing attractive results after ≈140 iterations. Neural Style Transfer is generally trained using GPUs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 :\n", + "total cost = 5.05035e+09\n", + "content cost = 7877.67\n", + "style cost = 1.26257e+08\n", + "Iteration 20 :\n", + "total cost = 9.43272e+08\n", + "content cost = 15187.2\n", + "style cost = 2.3578e+07\n", + "Iteration 40 :\n", + "total cost = 4.84905e+08\n", + "content cost = 16785.1\n", + "style cost = 1.21184e+07\n", + "Iteration 60 :\n", + "total cost = 3.12573e+08\n", + "content cost = 17466.2\n", + "style cost = 7.80995e+06\n", + "Iteration 80 :\n", + "total cost = 2.28117e+08\n", + "content cost = 17715.2\n", + "style cost = 5.69849e+06\n" + ] + } + ], + "source": [ + "model_nn(sess, generated_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Iteration 0 : **\n", + " \n", + " total cost = 5.05035e+09
\n", + " content cost = 7877.67
\n", + " style cost = 1.26257e+08\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You're done! After running this, in the upper bar of the notebook click on \"File\" and then \"Open\". Go to the \"/output\" directory to see all the saved images. Open \"generated_image\" to see the generated image! :)\n", + "\n", + "You should see something the image presented below on the right:\n", + "\n", + "\n", + "\n", + "We didn't want you to wait too long to see an initial result, and so had set the hyperparameters accordingly. To get the best looking results, running the optimization algorithm longer (and perhaps with a smaller learning rate) might work better. After completing and submitting this assignment, we encourage you to come back and play more with this notebook, and see if you can generate even better looking images. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are few other examples:\n", + "\n", + "- The beautiful ruins of the ancient city of Persepolis (Iran) with the style of Van Gogh (The Starry Night)\n", + "\n", + "\n", + "- The tomb of Cyrus the great in Pasargadae with the style of a Ceramic Kashi from Ispahan.\n", + "\n", + "\n", + "- A scientific study of a turbulent fluid with the style of a abstract blue fluid painting.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - Test with your own image (Optional/Ungraded)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, you can also rerun the algorithm on your own images! \n", + "\n", + "To do so, go back to part 4 and change the content image and style image with your own pictures. In detail, here's what you should do:\n", + "\n", + "1. Click on \"File -> Open\" in the upper tab of the notebook\n", + "2. Go to \"/images\" and upload your images (requirement: (WIDTH = 300, HEIGHT = 225)), rename them \"my_content.png\" and \"my_style.png\" for example.\n", + "3. Change the code in part (3.4) from :\n", + "```python\n", + "content_image = scipy.misc.imread(\"images/louvre.jpg\")\n", + "style_image = scipy.misc.imread(\"images/claude-monet.jpg\")\n", + "```\n", + "to:\n", + "```python\n", + "content_image = scipy.misc.imread(\"images/my_content.jpg\")\n", + "style_image = scipy.misc.imread(\"images/my_style.jpg\")\n", + "```\n", + "4. Rerun the cells (you may need to restart the Kernel in the upper tab of the notebook).\n", + "\n", + "You can also tune your hyperparameters: \n", + "- Which layers are responsible for representing the style? STYLE_LAYERS\n", + "- How many iterations do you want to run the algorithm? num_iterations\n", + "- What is the relative weighting between content and style? alpha/beta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 - Conclusion\n", + "\n", + "Great job on completing this assignment! You are now able to use Neural Style Transfer to generate artistic images. This is also your first time building a model in which the optimization algorithm updates the pixel values rather than the neural network's parameters. Deep learning has many different types of models and this is only one of them! \n", + "\n", + "\n", + "What you should remember:\n", + "- Neural Style Transfer is an algorithm that given a content image C and a style image S can generate an artistic image\n", + "- It uses representations (hidden layer activations) based on a pretrained ConvNet. \n", + "- The content cost function is computed using one hidden layer's activations.\n", + "- The style cost function for one layer is computed using the Gram matrix of that layer's activations. The overall style cost function is obtained using several hidden layers.\n", + "- Optimizing the total cost function results in synthesizing new images. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was the final programming exercise of this course. Congratulations--you've finished all the programming exercises of this course on Convolutional Networks! We hope to also see you in Course 5, on Sequence models! \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### References:\n", + "\n", + "The Neural Style Transfer algorithm was due to Gatys et al. (2015). Harish Narayanan and Github user \"log0\" also have highly readable write-ups from which we drew inspiration. The pre-trained network used in this implementation is a VGG network, which is due to Simonyan and Zisserman (2015). Pre-trained weights were from the work of the MathConvNet team. \n", + "\n", + "- Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, (2015). A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576) \n", + "- Harish Narayanan, Convolutional neural networks for artistic style transfer. https://harishnarayanan.org/writing/artistic-style-transfer/\n", + "- Log0, TensorFlow Implementation of \"A Neural Algorithm of Artistic Style\". http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style\n", + "- Karen Simonyan and Andrew Zisserman (2015). Very deep convolutional networks for large-scale image recognition (https://arxiv.org/pdf/1409.1556.pdf)\n", + "- MatConvNet. http://www.vlfeat.org/matconvnet/pretrained/\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "convolutional-neural-networks", + "graded_item_id": "owWbQ", + "launcher_item_id": "lEthw" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Neural Style Transfer/images/NST_GM.png b/Deep Learning Notebooks by Andrew NG/Convolutional Neural Networks/Week4/Neural Style Transfer/images/NST_GM.png new file mode 100644 index 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tuning, Regularization and Optimization/Gradient Checking.ipynb b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Gradient Checking.ipynb new file mode 100644 index 0000000..589448b --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Gradient Checking.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Checking\n", + "\n", + "Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. \n", + "\n", + "You are part of a team working to make mobile payments available globally, and are asked to build a deep learning model to detect fraud--whenever someone makes a payment, you want to see if the payment might be fraudulent, such as if the user's account has been taken over by a hacker. \n", + "\n", + "But backpropagation is quite challenging to implement, and sometimes has bugs. Because this is a mission-critical application, your company's CEO wants to be really certain that your implementation of backpropagation is correct. Your CEO says, \"Give me a proof that your backpropagation is actually working!\" To give this reassurance, you are going to use \"gradient checking\".\n", + "\n", + "Let's do it!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Packages\n", + "import numpy as np\n", + "from testCases import *\n", + "from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) How does gradient checking work?\n", + "\n", + "Backpropagation computes the gradients $\\frac{\\partial J}{\\partial \\theta}$, where $\\theta$ denotes the parameters of the model. $J$ is computed using forward propagation and your loss function.\n", + "\n", + "Because forward propagation is relatively easy to implement, you're confident you got that right, and so you're almost 100% sure that you're computing the cost $J$ correctly. Thus, you can use your code for computing $J$ to verify the code for computing $\\frac{\\partial J}{\\partial \\theta}$. \n", + "\n", + "Let's look back at the definition of a derivative (or gradient):\n", + "$$ \\frac{\\partial J}{\\partial \\theta} = \\lim_{\\varepsilon \\to 0} \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon} \\tag{1}$$\n", + "\n", + "If you're not familiar with the \"$\\displaystyle \\lim_{\\varepsilon \\to 0}$\" notation, it's just a way of saying \"when $\\varepsilon$ is really really small.\"\n", + "\n", + "We know the following:\n", + "\n", + "- $\\frac{\\partial J}{\\partial \\theta}$ is what you want to make sure you're computing correctly. \n", + "- You can compute $J(\\theta + \\varepsilon)$ and $J(\\theta - \\varepsilon)$ (in the case that $\\theta$ is a real number), since you're confident your implementation for $J$ is correct. \n", + "\n", + "Lets use equation (1) and a small value for $\\varepsilon$ to convince your CEO that your code for computing $\\frac{\\partial J}{\\partial \\theta}$ is correct!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) 1-dimensional gradient checking\n", + "\n", + "Consider a 1D linear function $J(\\theta) = \\theta x$. The model contains only a single real-valued parameter $\\theta$, and takes $x$ as input.\n", + "\n", + "You will implement code to compute $J(.)$ and its derivative $\\frac{\\partial J}{\\partial \\theta}$. You will then use gradient checking to make sure your derivative computation for $J$ is correct. \n", + "\n", + "\n", + "
**Figure 1** : **1D linear model**
\n", + "\n", + "The diagram above shows the key computation steps: First start with $x$, then evaluate the function $J(x)$ (\"forward propagation\"). Then compute the derivative $\\frac{\\partial J}{\\partial \\theta}$ (\"backward propagation\"). \n", + "\n", + "**Exercise**: implement \"forward propagation\" and \"backward propagation\" for this simple function. I.e., compute both $J(.)$ (\"forward propagation\") and its derivative with respect to $\\theta$ (\"backward propagation\"), in two separate functions. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(x, theta):\n", + " \"\"\"\n", + " Implement the linear forward propagation (compute J) presented in Figure 1 (J(theta) = theta * x)\n", + " \n", + " Arguments:\n", + " x -- a real-valued input\n", + " theta -- our parameter, a real number as well\n", + " \n", + " Returns:\n", + " J -- the value of function J, computed using the formula J(theta) = theta * x\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (approx. 1 line)\n", + " J = np.dot(theta, x)\n", + " ### END CODE HERE ###\n", + " \n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J = 8\n" + ] + } + ], + "source": [ + "x, theta = 2, 4\n", + "J = forward_propagation(x, theta)\n", + "print (\"J = \" + str(J))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
** J ** 8
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Now, implement the backward propagation step (derivative computation) of Figure 1. That is, compute the derivative of $J(\\theta) = \\theta x$ with respect to $\\theta$. To save you from doing the calculus, you should get $dtheta = \\frac { \\partial J }{ \\partial \\theta} = x$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation\n", + "\n", + "def backward_propagation(x, theta):\n", + " \"\"\"\n", + " Computes the derivative of J with respect to theta (see Figure 1).\n", + " \n", + " Arguments:\n", + " x -- a real-valued input\n", + " theta -- our parameter, a real number as well\n", + " \n", + " Returns:\n", + " dtheta -- the gradient of the cost with respect to theta\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (approx. 1 line)\n", + " dtheta = x\n", + " ### END CODE HERE ###\n", + " \n", + " return dtheta" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dtheta = 2\n" + ] + } + ], + "source": [ + "x, theta = 2, 4\n", + "dtheta = backward_propagation(x, theta)\n", + "print (\"dtheta = \" + str(dtheta))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
** dtheta ** 2
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: To show that the `backward_propagation()` function is correctly computing the gradient $\\frac{\\partial J}{\\partial \\theta}$, let's implement gradient checking.\n", + "\n", + "**Instructions**:\n", + "- First compute \"gradapprox\" using the formula above (1) and a small value of $\\varepsilon$. Here are the Steps to follow:\n", + " 1. $\\theta^{+} = \\theta + \\varepsilon$\n", + " 2. $\\theta^{-} = \\theta - \\varepsilon$\n", + " 3. $J^{+} = J(\\theta^{+})$\n", + " 4. $J^{-} = J(\\theta^{-})$\n", + " 5. $gradapprox = \\frac{J^{+} - J^{-}}{2 \\varepsilon}$\n", + "- Then compute the gradient using backward propagation, and store the result in a variable \"grad\"\n", + "- Finally, compute the relative difference between \"gradapprox\" and the \"grad\" using the following formula:\n", + "$$ difference = \\frac {\\mid\\mid grad - gradapprox \\mid\\mid_2}{\\mid\\mid grad \\mid\\mid_2 + \\mid\\mid gradapprox \\mid\\mid_2} \\tag{2}$$\n", + "You will need 3 Steps to compute this formula:\n", + " - 1'. compute the numerator using np.linalg.norm(...)\n", + " - 2'. compute the denominator. You will need to call np.linalg.norm(...) twice.\n", + " - 3'. divide them.\n", + "- If this difference is small (say less than $10^{-7}$), you can be quite confident that you have computed your gradient correctly. Otherwise, there may be a mistake in the gradient computation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: gradient_check\n", + "\n", + "def gradient_check(x, theta, epsilon=1e-7):\n", + " \"\"\"\n", + " Implement the backward propagation presented in Figure 1.\n", + " \n", + " Arguments:\n", + " x -- a real-valued input\n", + " theta -- our parameter, a real number as well\n", + " epsilon -- tiny shift to the input to compute approximated gradient with formula(1)\n", + " \n", + " Returns:\n", + " difference -- difference (2) between the approximated gradient and the backward propagation gradient\n", + " \"\"\"\n", + " \n", + " # Compute gradapprox using left side of formula (1). epsilon is small enough, you don't need to worry about the limit.\n", + " ### START CODE HERE ### (approx. 5 lines)\n", + " thetaplus = theta + epsilon # Step 1\n", + " thetaminus = theta - epsilon # Step 2\n", + " J_plus = forward_propagation(x, thetaplus) # Step 3\n", + " J_minus = forward_propagation(x, thetaminus) # Step 4\n", + " gradapprox = (J_plus - J_minus) / (2 * epsilon) # Step 5\n", + " ### END CODE HERE ###\n", + " \n", + " # Check if gradapprox is close enough to the output of backward_propagation()\n", + " ### START CODE HERE ### (approx. 1 line)\n", + " grad = backward_propagation(x, theta)\n", + " ### END CODE HERE ###\n", + " \n", + " ### START CODE HERE ### (approx. 1 line)\n", + " numerator = np.linalg.norm(grad - gradapprox) # Step 1'\n", + " denominator = np.linalg.norm(grad) + np.linalg.norm(gradapprox) # Step 2'\n", + " difference = numerator / denominator # Step 3'\n", + " ### END CODE HERE ###\n", + " \n", + " if difference < 1e-7:\n", + " print(\"The gradient is correct!\")\n", + " else:\n", + " print(\"The gradient is wrong!\")\n", + " \n", + " return difference" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The gradient is correct!\n", + "difference = 2.91933588329e-10\n" + ] + } + ], + "source": [ + "x, theta = 2, 4\n", + "difference = gradient_check(x, theta)\n", + "print(\"difference = \" + str(difference))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "The gradient is correct!\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
** difference ** 2.9193358103083e-10
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats, the difference is smaller than the $10^{-7}$ threshold. So you can have high confidence that you've correctly computed the gradient in `backward_propagation()`. \n", + "\n", + "Now, in the more general case, your cost function $J$ has more than a single 1D input. When you are training a neural network, $\\theta$ actually consists of multiple matrices $W^{[l]}$ and biases $b^{[l]}$! It is important to know how to do a gradient check with higher-dimensional inputs. Let's do it!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) N-dimensional gradient checking" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The following figure describes the forward and backward propagation of your fraud detection model.\n", + "\n", + "\n", + "
**Figure 2** : **deep neural network**
*LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID*
\n", + "\n", + "Let's look at your implementations for forward propagation and backward propagation. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def forward_propagation_n(X, Y, parameters):\n", + " \"\"\"\n", + " Implements the forward propagation (and computes the cost) presented in Figure 3.\n", + " \n", + " Arguments:\n", + " X -- training set for m examples\n", + " Y -- labels for m examples \n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n", + " W1 -- weight matrix of shape (5, 4)\n", + " b1 -- bias vector of shape (5, 1)\n", + " W2 -- weight matrix of shape (3, 5)\n", + " b2 -- bias vector of shape (3, 1)\n", + " W3 -- weight matrix of shape (1, 3)\n", + " b3 -- bias vector of shape (1, 1)\n", + " \n", + " Returns:\n", + " cost -- the cost function (logistic cost for one example)\n", + " \"\"\"\n", + " \n", + " # retrieve parameters\n", + " m = X.shape[1]\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " W3 = parameters[\"W3\"]\n", + " b3 = parameters[\"b3\"]\n", + "\n", + " # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n", + " Z1 = np.dot(W1, X) + b1\n", + " A1 = relu(Z1)\n", + " Z2 = np.dot(W2, A1) + b2\n", + " A2 = relu(Z2)\n", + " Z3 = np.dot(W3, A2) + b3\n", + " A3 = sigmoid(Z3)\n", + "\n", + " # Cost\n", + " logprobs = np.multiply(-np.log(A3), Y) + np.multiply(-np.log(1 - A3), 1 - Y)\n", + " cost = 1. / m * np.sum(logprobs)\n", + " \n", + " cache = (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3)\n", + " \n", + " return cost, cache" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, run backward propagation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def backward_propagation_n(X, Y, cache):\n", + " \"\"\"\n", + " Implement the backward propagation presented in figure 2.\n", + " \n", + " Arguments:\n", + " X -- input datapoint, of shape (input size, 1)\n", + " Y -- true \"label\"\n", + " cache -- cache output from forward_propagation_n()\n", + " \n", + " Returns:\n", + " gradients -- A dictionary with the gradients of the cost with respect to each parameter, activation and pre-activation variables.\n", + " \"\"\"\n", + " \n", + " m = X.shape[1]\n", + " (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n", + " \n", + " dZ3 = A3 - Y\n", + " dW3 = 1. / m * np.dot(dZ3, A2.T)\n", + " db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n", + " \n", + " dA2 = np.dot(W3.T, dZ3)\n", + " dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n", + " dW2 = 1. / m * np.dot(dZ2, A1.T) * 2 # Should not multiply by 2\n", + " db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n", + " \n", + " dA1 = np.dot(W2.T, dZ2)\n", + " dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n", + " dW1 = 1. / m * np.dot(dZ1, X.T)\n", + " db1 = 4. / m * np.sum(dZ1, axis=1, keepdims=True) # Should not multiply by 4\n", + " \n", + " gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\n", + " \"dA2\": dA2, \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2,\n", + " \"dA1\": dA1, \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "You obtained some results on the fraud detection test set but you are not 100% sure of your model. Nobody's perfect! Let's implement gradient checking to verify if your gradients are correct." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**How does gradient checking work?**.\n", + "\n", + "As in 1) and 2), you want to compare \"gradapprox\" to the gradient computed by backpropagation. The formula is still:\n", + "\n", + "$$ \\frac{\\partial J}{\\partial \\theta} = \\lim_{\\varepsilon \\to 0} \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon} \\tag{1}$$\n", + "\n", + "However, $\\theta$ is not a scalar anymore. It is a dictionary called \"parameters\". We implemented a function \"`dictionary_to_vector()`\" for you. It converts the \"parameters\" dictionary into a vector called \"values\", obtained by reshaping all parameters (W1, b1, W2, b2, W3, b3) into vectors and concatenating them.\n", + "\n", + "The inverse function is \"`vector_to_dictionary`\" which outputs back the \"parameters\" dictionary.\n", + "\n", + "\n", + "
**Figure 2** : **dictionary_to_vector() and vector_to_dictionary()**
You will need these functions in gradient_check_n()
\n", + "\n", + "We have also converted the \"gradients\" dictionary into a vector \"grad\" using gradients_to_vector(). You don't need to worry about that.\n", + "\n", + "**Exercise**: Implement gradient_check_n().\n", + "\n", + "**Instructions**: Here is pseudo-code that will help you implement the gradient check.\n", + "\n", + "For each i in num_parameters:\n", + "- To compute `J_plus[i]`:\n", + " 1. Set $\\theta^{+}$ to `np.copy(parameters_values)`\n", + " 2. Set $\\theta^{+}_i$ to $\\theta^{+}_i + \\varepsilon$\n", + " 3. Calculate $J^{+}_i$ using to `forward_propagation_n(x, y, vector_to_dictionary(`$\\theta^{+}$ `))`. \n", + "- To compute `J_minus[i]`: do the same thing with $\\theta^{-}$\n", + "- Compute $gradapprox[i] = \\frac{J^{+}_i - J^{-}_i}{2 \\varepsilon}$\n", + "\n", + "Thus, you get a vector gradapprox, where gradapprox[i] is an approximation of the gradient with respect to `parameter_values[i]`. You can now compare this gradapprox vector to the gradients vector from backpropagation. Just like for the 1D case (Steps 1', 2', 3'), compute: \n", + "$$ difference = \\frac {\\| grad - gradapprox \\|_2}{\\| grad \\|_2 + \\| gradapprox \\|_2 } \\tag{3}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: gradient_check_n\n", + "\n", + "def gradient_check_n(parameters, gradients, X, Y, epsilon=1e-7):\n", + " \"\"\"\n", + " Checks if backward_propagation_n computes correctly the gradient of the cost output by forward_propagation_n\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n", + " grad -- output of backward_propagation_n, contains gradients of the cost with respect to the parameters. \n", + " x -- input datapoint, of shape (input size, 1)\n", + " y -- true \"label\"\n", + " epsilon -- tiny shift to the input to compute approximated gradient with formula(1)\n", + " \n", + " Returns:\n", + " difference -- difference (2) between the approximated gradient and the backward propagation gradient\n", + " \"\"\"\n", + " \n", + " # Set-up variables\n", + " parameters_values, _ = dictionary_to_vector(parameters)\n", + " grad = gradients_to_vector(gradients)\n", + " num_parameters = parameters_values.shape[0]\n", + " J_plus = np.zeros((num_parameters, 1))\n", + " J_minus = np.zeros((num_parameters, 1))\n", + " gradapprox = np.zeros((num_parameters, 1))\n", + " \n", + " # Compute gradapprox\n", + " for i in range(num_parameters):\n", + " \n", + " # Compute J_plus[i]. Inputs: \"parameters_values, epsilon\". Output = \"J_plus[i]\".\n", + " # \"_\" is used because the function you have to outputs two parameters but we only care about the first one\n", + " ### START CODE HERE ### (approx. 3 lines)\n", + " thetaplus = np.copy(parameters_values) # Step 1\n", + " thetaplus[i][0] = thetaplus[i][0] + epsilon # Step 2\n", + " J_plus[i], _ = forward_propagation_n(X, Y, vector_to_dictionary(thetaplus)) # Step 3\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute J_minus[i]. Inputs: \"parameters_values, epsilon\". Output = \"J_minus[i]\".\n", + " ### START CODE HERE ### (approx. 3 lines)\n", + " thetaminus = np.copy(parameters_values) # Step 1\n", + " thetaminus[i][0] = thetaminus[i][0] - epsilon # Step 2 \n", + " J_minus[i], _ = forward_propagation_n(X, Y, vector_to_dictionary(thetaminus)) # Step 3\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute gradapprox[i]\n", + " ### START CODE HERE ### (approx. 1 line)\n", + " gradapprox[i] = (J_plus[i] - J_minus[i]) / (2 * epsilon)\n", + " ### END CODE HERE ###\n", + " \n", + " # Compare gradapprox to backward propagation gradients by computing difference.\n", + " ### START CODE HERE ### (approx. 1 line)\n", + " numerator = np.linalg.norm(grad - gradapprox) # Step 1'\n", + " denominator = np.linalg.norm(grad) + np.linalg.norm(gradapprox) # Step 2'\n", + " difference = numerator / denominator # Step 3'\n", + " ### END CODE HERE ###\n", + "\n", + " if difference > 1e-7:\n", + " print(\"\\033[93m\" + \"There is a mistake in the backward propagation! difference = \" + str(difference) + \"\\033[0m\")\n", + " else:\n", + " print(\"\\033[92m\" + \"Your backward propagation works perfectly fine! difference = \" + str(difference) + \"\\033[0m\")\n", + " \n", + " return difference" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[93mThere is a mistake in the backward propagation! difference = 0.285093156781\u001b[0m\n" + ] + } + ], + "source": [ + "X, Y, parameters = gradient_check_n_test_case()\n", + "\n", + "cost, cache = forward_propagation_n(X, Y, parameters)\n", + "gradients = backward_propagation_n(X, Y, cache)\n", + "difference = gradient_check_n(parameters, gradients, X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
** There is a mistake in the backward propagation!** difference = 0.285093156781
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems that there were errors in the `backward_propagation_n` code we gave you! Good that you've implemented the gradient check. Go back to `backward_propagation` and try to find/correct the errors *(Hint: check dW2 and db1)*. Rerun the gradient check when you think you've fixed it. Remember you'll need to re-execute the cell defining `backward_propagation_n()` if you modify the code. \n", + "\n", + "Can you get gradient check to declare your derivative computation correct? Even though this part of the assignment isn't graded, we strongly urge you to try to find the bug and re-run gradient check until you're convinced backprop is now correctly implemented. \n", + "\n", + "**Note** \n", + "- Gradient Checking is slow! Approximating the gradient with $\\frac{\\partial J}{\\partial \\theta} \\approx \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon}$ is computationally costly. For this reason, we don't run gradient checking at every iteration during training. Just a few times to check if the gradient is correct. \n", + "- Gradient Checking, at least as we've presented it, doesn't work with dropout. You would usually run the gradient check algorithm without dropout to make sure your backprop is correct, then add dropout. \n", + "\n", + "Congrats, you can be confident that your deep learning model for fraud detection is working correctly! You can even use this to convince your CEO. :) \n", + "\n", + "\n", + "**What you should remember from this notebook**:\n", + "- Gradient checking verifies closeness between the gradients from backpropagation and the numerical approximation of the gradient (computed using forward propagation).\n", + "- Gradient checking is slow, so we don't run it in every iteration of training. You would usually run it only to make sure your code is correct, then turn it off and use backprop for the actual learning process. " + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "deep-neural-network", + "graded_item_id": "n6NBD", + "launcher_item_id": "yfOsE" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Initialization.ipynb b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Initialization.ipynb new file mode 100644 index 0000000..6994b0c --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Initialization.ipynb @@ -0,0 +1,1015 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialization\n", + "\n", + "Welcome to the first assignment of \"Improving Deep Neural Networks\". \n", + "\n", + "Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning. \n", + "\n", + "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead to different results. \n", + "\n", + "A well chosen initialization can:\n", + "- Speed up the convergence of gradient descent\n", + "- Increase the odds of gradient descent converging to a lower training (and generalization) error \n", + "\n", + "To get started, run the following cell to load the packages and the planar dataset you will try to classify." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IkLa7oFaGDTyLhO0bsykushEZdfaKeA+kFfHZu6twOY89F7nZZbz+3CL++/m1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TAAAg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\nY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sklearn\n", + "import sklearn.datasets\n", + "from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n", + "from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "# load image dataset: blue/red dots in circles\n", + "train_X, train_Y, test_X, test_Y = load_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You would like a classifier to separate the blue dots from the red dots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Neural Network model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with: \n", + "- *Zeros initialization* -- setting `initialization = \"zeros\"` in the input argument.\n", + "- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values. \n", + "- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n", + "\n", + "**Instructions**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def model(X, Y, learning_rate=0.01, num_iterations=15000, print_cost=True, initialization=\"he\"):\n", + " \"\"\"\n", + " Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n", + " \n", + " Arguments:\n", + " X -- input data, of shape (2, number of examples)\n", + " Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n", + " learning_rate -- learning rate for gradient descent \n", + " num_iterations -- number of iterations to run gradient descent\n", + " print_cost -- if True, print the cost every 1000 iterations\n", + " initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model\n", + " \"\"\"\n", + " \n", + " grads = {}\n", + " costs = [] # to keep track of the loss\n", + " m = X.shape[1] # number of examples\n", + " layers_dims = [X.shape[0], 10, 5, 1]\n", + " \n", + " # Initialize parameters dictionary.\n", + " if initialization == \"zeros\":\n", + " parameters = initialize_parameters_zeros(layers_dims)\n", + " elif initialization == \"random\":\n", + " parameters = initialize_parameters_random(layers_dims)\n", + " elif initialization == \"he\":\n", + " parameters = initialize_parameters_he(layers_dims)\n", + "\n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + "\n", + " # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n", + " a3, cache = forward_propagation(X, parameters)\n", + " \n", + " # Loss\n", + " cost = compute_loss(a3, Y)\n", + "\n", + " # Backward propagation.\n", + " grads = backward_propagation(X, Y, cache)\n", + " \n", + " # Update parameters.\n", + " parameters = update_parameters(parameters, grads, learning_rate)\n", + " \n", + " # Print the loss every 1000 iterations\n", + " if print_cost and i % 1000 == 0:\n", + " print(\"Cost after iteration {}: {}\".format(i, cost))\n", + " costs.append(cost)\n", + " \n", + " # plot the loss\n", + " plt.plot(costs)\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (per hundreds)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Zero initialization\n", + "\n", + "There are two types of parameters to initialize in a neural network:\n", + "- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n", + "- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n", + "\n", + "**Exercise**: Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry\", but lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters_zeros \n", + "\n", + "def initialize_parameters_zeros(layers_dims):\n", + " \"\"\"\n", + " Arguments:\n", + " layer_dims -- python array (list) containing the size of each layer.\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n", + " W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n", + " b1 -- bias vector of shape (layers_dims[1], 1)\n", + " ...\n", + " WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n", + " bL -- bias vector of shape (layers_dims[L], 1)\n", + " \"\"\"\n", + " \n", + " parameters = {}\n", + " L = len(layers_dims) # number of layers in the network\n", + " \n", + " for l in range(1, L):\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " parameters['W' + str(l)] = np.zeros((layers_dims[l], layers_dims[l - 1]))\n", + " parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n", + " ### END CODE HERE ###\n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "b1 = [[ 0.]\n", + " [ 0.]]\n", + "W2 = [[ 0. 0.]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_parameters_zeros([3,2,1])\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **W1**\n", + " \n", + " [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "
\n", + " **b1**\n", + " \n", + " [[ 0.]\n", + " [ 0.]]\n", + "
\n", + " **W2**\n", + " \n", + " [[ 0. 0.]]\n", + "
\n", + " **b2**\n", + " \n", + " [[ 0.]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following code to train your model on 15,000 iterations using zeros initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.6931471805599453\n", + "Cost after iteration 1000: 0.6931471805599453\n", + "Cost after iteration 2000: 0.6931471805599453\n", + "Cost after iteration 3000: 0.6931471805599453\n", + "Cost after iteration 4000: 0.6931471805599453\n", + "Cost after iteration 5000: 0.6931471805599453\n", + "Cost after iteration 6000: 0.6931471805599453\n", + "Cost after iteration 7000: 0.6931471805599453\n", + "Cost after iteration 8000: 0.6931471805599453\n", + "Cost after iteration 9000: 0.6931471805599453\n", + "Cost after iteration 10000: 0.6931471805599455\n", + "Cost after iteration 11000: 0.6931471805599453\n", + "Cost after iteration 12000: 0.6931471805599453\n", + "Cost after iteration 13000: 0.6931471805599453\n", + "Cost after iteration 14000: 0.6931471805599453\n" + ] + }, + { + "data": { + "image/png": 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Ksj/0rt/HD4A/Lz8T66CY6fR7+E7FfSd3tv1vwEcoDq/2tRr4kz5tb5W0laQX\nAy8C1mzCuIbqV8AryufHAK3GW/UzYFpZE8D8Afr2V+tSnnn/9gb+rFw+0Ps0E1g55FFF9JGZYTTF\nbcBT5eHOi4AzKQ7r3VKe+NEDvKXFetcAC8vP9dZQHCrttRi4TdIttt9eaf82cAhwK8Vs7RO27y/D\ntJUdgaskbUsxW/poiz5LgS9IUmUGdzdFyO4ELLT9O0nnDXFcQ/XVsrZbKd6LgWaXlDUsAP6fpCco\nfjHYsZ/u/dX6bYoZ3+3lGH9Y9h/ofToUOG1TBxfRK3etiNhCSDoT+K7t70m6CLja9uVtLqvtJB0I\nfNT2Se2uJbZcOUwaseX4LDCp3UWMQXsAn2p3EbFly8wwIiIaLzPDiIhovIRhREQ0XsIwIiIaL2EY\nERGNlzCMiIjG+/8qs5fH0fJiOQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the train set:\n", + "Accuracy: 0.5\n", + "On the test set:\n", + "Accuracy: 0.5\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y, initialization = \"zeros\")\n", + "print (\"On the train set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print (\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The performance is really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0]]\n", + "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n" + ] + } + ], + "source": [ + "print(\"predictions_train = \" + str(predictions_train))\n", + "print(\"predictions_test = \" + str(predictions_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LCAy4qX9jwe/z3pzC+xM/CnOpWS4L+4z+3P2HJJxOndPYpUsiygKaGbM+PyxZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model with Zeros initialization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 1.5])\n", + "axes.set_ylim([-1.5, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is predicting 0 for every example. \n", + "\n", + "In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n", + "- It is however okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Random initialization\n", + "\n", + "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n", + "\n", + "**Exercise**: Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters_random\n", + "\n", + "def initialize_parameters_random(layers_dims):\n", + " \"\"\"\n", + " Arguments:\n", + " layer_dims -- python array (list) containing the size of each layer.\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n", + " W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n", + " b1 -- bias vector of shape (layers_dims[1], 1)\n", + " ...\n", + " WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n", + " bL -- bias vector of shape (layers_dims[L], 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(3) # This seed makes sure your \"random\" numbers will be the as ours\n", + " parameters = {}\n", + " L = len(layers_dims) # integer representing the number of layers\n", + " \n", + " for l in range(1, L):\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l - 1]) * 10\n", + " parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n", + " ### END CODE HERE ###\n", + "\n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 17.88628473 4.36509851 0.96497468]\n", + " [-18.63492703 -2.77388203 -3.54758979]]\n", + "b1 = [[ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.82741481 -6.27000677]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_parameters_random([3, 2, 1])\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **W1**\n", + " \n", + " [[ 17.88628473 4.36509851 0.96497468]\n", + " [-18.63492703 -2.77388203 -3.54758979]]\n", + "
\n", + " **b1**\n", + " \n", + " [[ 0.]\n", + " [ 0.]]\n", + "
\n", + " **W2**\n", + " \n", + " [[-0.82741481 -6.27000677]]\n", + "
\n", + " **b2**\n", + " \n", + " [[ 0.]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following code to train your model on 15,000 iterations using random initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/work/week5/Initialization/init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n", + " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n", + "/home/jovyan/work/week5/Initialization/init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n", + " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 1000: 0.6237287551108738\n", + "Cost after iteration 2000: 0.5981106708339466\n", + "Cost after iteration 3000: 0.5638353726276827\n", + "Cost after iteration 4000: 0.550152614449184\n", + "Cost after iteration 5000: 0.5444235275228304\n", + "Cost after iteration 6000: 0.5374184054630083\n", + "Cost after iteration 7000: 0.47357131493578297\n", + "Cost after iteration 8000: 0.39775634899580387\n", + "Cost after iteration 9000: 0.3934632865981078\n", + "Cost after iteration 10000: 0.39202525076484457\n", + "Cost after iteration 11000: 0.38921493051297673\n", + "Cost after iteration 12000: 0.38614221789840486\n", + "Cost after iteration 13000: 0.38497849983013926\n", + "Cost after iteration 14000: 0.38278397192120406\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the train set:\n", + "Accuracy: 0.83\n", + "On the test set:\n", + "Accuracy: 0.86\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y, initialization = \"random\")\n", + "print(\"On the train set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print(\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff; a more numerically sophisticated implementation would fix this. But this isn't worth worrying about for our purposes. \n", + "\n", + "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1\n", + " 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 0\n", + " 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1\n", + " 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 0\n", + " 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1\n", + " 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 0 1 1\n", + " 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1\n", + " 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1\n", + " 1 1 1 0]]\n", + "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0\n", + " 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1\n", + " 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n" + ] + } + ], + "source": [ + "print(predictions_train)\n", + "print(predictions_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JCpGIoeMqN+D6l6/WPv/JrRONWuLn4cAWiistKDXnjKVFj+WFznUMPVfhOIpo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model with large random initialization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 1.5])\n", + "axes.set_ylim([-1.5, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Observations**:\n", + "- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n", + "- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n", + "- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n", + "\n", + "\n", + "**In summary**:\n", + "- Initializing weights to very large random values does not work well. \n", + "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - He initialization\n", + "\n", + "Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n", + "\n", + "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n", + "\n", + "**Hint**: This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters_he\n", + "\n", + "def initialize_parameters_he(layers_dims):\n", + " \"\"\"\n", + " Arguments:\n", + " layer_dims -- python array (list) containing the size of each layer.\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n", + " W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n", + " b1 -- bias vector of shape (layers_dims[1], 1)\n", + " ...\n", + " WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n", + " bL -- bias vector of shape (layers_dims[L], 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(3)\n", + " parameters = {}\n", + " L = len(layers_dims) - 1 # integer representing the number of layers\n", + " \n", + " for l in range(1, L + 1):\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l - 1]) * np.sqrt(2 / layers_dims[l - 1])\n", + " parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n", + " ### END CODE HERE ###\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 1.78862847 0.43650985]\n", + " [ 0.09649747 -1.8634927 ]\n", + " [-0.2773882 -0.35475898]\n", + " [-0.08274148 -0.62700068]]\n", + "b1 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_parameters_he([2, 4, 1])\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **W1**\n", + " \n", + " [[ 1.78862847 0.43650985]\n", + " [ 0.09649747 -1.8634927 ]\n", + " [-0.2773882 -0.35475898]\n", + " [-0.08274148 -0.62700068]]\n", + "
\n", + " **b1**\n", + " \n", + " [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "
\n", + " **W2**\n", + " \n", + " [[-0.03098412 -0.33744411 -0.92904268 0.62552248]]\n", + "
\n", + " **b2**\n", + " \n", + " [[ 0.]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following code to train your model on 15,000 iterations using He initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.8830537463419761\n", + "Cost after iteration 1000: 0.6879825919728063\n", + "Cost after iteration 2000: 0.6751286264523371\n", + "Cost after iteration 3000: 0.6526117768893807\n", + "Cost after iteration 4000: 0.6082958970572938\n", + "Cost after iteration 5000: 0.5304944491717495\n", + "Cost after iteration 6000: 0.4138645817071794\n", + "Cost after iteration 7000: 0.3117803464844441\n", + "Cost after iteration 8000: 0.23696215330322562\n", + "Cost after iteration 9000: 0.18597287209206836\n", + "Cost after iteration 10000: 0.1501555628037182\n", + "Cost after iteration 11000: 0.12325079292273548\n", + "Cost after iteration 12000: 0.09917746546525937\n", + "Cost after iteration 13000: 0.0845705595402428\n", + "Cost after iteration 14000: 0.07357895962677366\n" + ] + }, + { + "data": { + "image/png": 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wYLe2ZLVs0Zi7IiLSZCn04kRqShLdc1rRPafV59ZV1zhLN+5g7pptfLS6jLlrtvHW0tID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\nCUOhJyIiCUOhJyIiCeP/A/RfZM1G9hqrAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the train set:\n", + "Accuracy: 0.993333333333\n", + "On the test set:\n", + "Accuracy: 0.96\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y, initialization = \"he\")\n", + "print(\"On the train set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print(\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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PsnZuEi873LIqlJJbgYnC1EbtaEnIY/ATF78R90Sum0kiApYNSyf6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\nqMPwLVu7uNotFSult7Pts3Rqb1T1yLs/BsDGWodyB8JADxcswPJ9fvT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model with He initialization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 1.5])\n", + "axes.set_ylim([-1.5, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Observations**:\n", + "- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 5 - Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Model**\n", + " \n", + " **Train accuracy**\n", + " \n", + " **Problem/Comment**\n", + "
\n", + " 3-layer NN with zeros initialization\n", + " \n", + " 50%\n", + " \n", + " fails to break symmetry\n", + "
\n", + " 3-layer NN with large random initialization\n", + " \n", + " 83%\n", + " \n", + " too large weights \n", + "
\n", + " 3-layer NN with He initialization\n", + " \n", + " 99%\n", + " \n", + " recommended method\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember from this notebook**:\n", + "- Different initializations lead to different results\n", + "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n", + "- Don't intialize to values that are too large\n", + "- He initialization works well for networks with ReLU activations. " + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "deep-neural-network", + "graded_item_id": "XOESP", + "launcher_item_id": "8IhFN" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Optimization methods.ipynb b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Optimization methods.ipynb new file mode 100644 index 0000000..71041bb --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Optimization methods.ipynb @@ -0,0 +1,1650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization Methods\n", + "\n", + "Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorithm can be the difference between waiting days vs. just a few hours to get a good result. \n", + "\n", + "Gradient descent goes \"downhill\" on a cost function $J$. Think of it as trying to do this: \n", + "\n", + "
**Figure 1** : **Minimizing the cost is like finding the lowest point in a hilly landscape**
At each step of the training, you update your parameters following a certain direction to try to get to the lowest possible point.
\n", + "\n", + "**Notations**: As usual, $\\frac{\\partial J}{\\partial a } = $ `da` for any variable `a`.\n", + "\n", + "To get started, run the following code to import the libraries you will need." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.io\n", + "import math\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation\n", + "from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_dataset\n", + "from testCases import *\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Gradient Descent\n", + "\n", + "A simple optimization method in machine learning is gradient descent (GD). When you take gradient steps with respect to all $m$ examples on each step, it is also called Batch Gradient Descent. \n", + "\n", + "**Warm-up exercise**: Implement the gradient descent update rule. The gradient descent rule is, for $l = 1, ..., L$: \n", + "$$ W^{[l]} = W^{[l]} - \\alpha \\text{ } dW^{[l]} \\tag{1}$$\n", + "$$ b^{[l]} = b^{[l]} - \\alpha \\text{ } db^{[l]} \\tag{2}$$\n", + "\n", + "where L is the number of layers and $\\alpha$ is the learning rate. All parameters should be stored in the `parameters` dictionary. Note that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$. You need to shift `l` to `l+1` when coding." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters_with_gd\n", + "\n", + "def update_parameters_with_gd(parameters, grads, learning_rate):\n", + " \"\"\"\n", + " Update parameters using one step of gradient descent\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters to be updated:\n", + " parameters['W' + str(l)] = Wl\n", + " parameters['b' + str(l)] = bl\n", + " grads -- python dictionary containing your gradients to update each parameters:\n", + " grads['dW' + str(l)] = dWl\n", + " grads['db' + str(l)] = dbl\n", + " learning_rate -- the learning rate, scalar.\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " \"\"\"\n", + "\n", + " L = len(parameters) // 2 # number of layers in the neural networks\n", + "\n", + " # Update rule for each parameter\n", + " for l in range(L):\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n", + " parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n", + " ### END CODE HERE ###\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 1.63535156 -0.62320365 -0.53718766]\n", + " [-1.07799357 0.85639907 -2.29470142]]\n", + "b1 = [[ 1.74604067]\n", + " [-0.75184921]]\n", + "W2 = [[ 0.32171798 -0.25467393 1.46902454]\n", + " [-2.05617317 -0.31554548 -0.3756023 ]\n", + " [ 1.1404819 -1.09976462 -0.1612551 ]]\n", + "b2 = [[-0.88020257]\n", + " [ 0.02561572]\n", + " [ 0.57539477]]\n" + ] + } + ], + "source": [ + "parameters, grads, learning_rate = update_parameters_with_gd_test_case()\n", + "\n", + "parameters = update_parameters_with_gd(parameters, grads, learning_rate)\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[ 1.63535156 -0.62320365 -0.53718766]\n", + " [-1.07799357 0.85639907 -2.29470142]]
**b1** [[ 1.74604067]\n", + " [-0.75184921]]
**W2** [[ 0.32171798 -0.25467393 1.46902454]\n", + " [-2.05617317 -0.31554548 -0.3756023 ]\n", + " [ 1.1404819 -1.09976462 -0.1612551 ]]
**b2** [[-0.88020257]\n", + " [ 0.02561572]\n", + " [ 0.57539477]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variant of this is Stochastic Gradient Descent (SGD), which is equivalent to mini-batch gradient descent where each mini-batch has just 1 example. The update rule that you have just implemented does not change. What changes is that you would be computing gradients on just one training example at a time, rather than on the whole training set. The code examples below illustrate the difference between stochastic gradient descent and (batch) gradient descent. \n", + "\n", + "- **(Batch) Gradient Descent**:\n", + "\n", + "``` python\n", + "X = data_input\n", + "Y = labels\n", + "parameters = initialize_parameters(layers_dims)\n", + "for i in range(0, num_iterations):\n", + " # Forward propagation\n", + " a, caches = forward_propagation(X, parameters)\n", + " # Compute cost.\n", + " cost = compute_cost(a, Y)\n", + " # Backward propagation.\n", + " grads = backward_propagation(a, caches, parameters)\n", + " # Update parameters.\n", + " parameters = update_parameters(parameters, grads)\n", + " \n", + "```\n", + "\n", + "- **Stochastic Gradient Descent**:\n", + "\n", + "```python\n", + "X = data_input\n", + "Y = labels\n", + "parameters = initialize_parameters(layers_dims)\n", + "for i in range(0, num_iterations):\n", + " for j in range(0, m):\n", + " # Forward propagation\n", + " a, caches = forward_propagation(X[:,j], parameters)\n", + " # Compute cost\n", + " cost = compute_cost(a, Y[:,j])\n", + " # Backward propagation\n", + " grads = backward_propagation(a, caches, parameters)\n", + " # Update parameters.\n", + " parameters = update_parameters(parameters, grads)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Stochastic Gradient Descent, you use only 1 training example before updating the gradients. When the training set is large, SGD can be faster. But the parameters will \"oscillate\" toward the minimum rather than converge smoothly. Here is an illustration of this: \n", + "\n", + "\n", + "
**Figure 1** : **SGD vs GD**
\"+\" denotes a minimum of the cost. SGD leads to many oscillations to reach convergence. But each step is a lot faster to compute for SGD than for GD, as it uses only one training example (vs. the whole batch for GD).
\n", + "\n", + "**Note** also that implementing SGD requires 3 for-loops in total:\n", + "1. Over the number of iterations\n", + "2. Over the $m$ training examples\n", + "3. Over the layers (to update all parameters, from $(W^{[1]},b^{[1]})$ to $(W^{[L]},b^{[L]})$)\n", + "\n", + "In practice, you'll often get faster results if you do not use neither the whole training set, nor only one training example, to perform each update. Mini-batch gradient descent uses an intermediate number of examples for each step. With mini-batch gradient descent, you loop over the mini-batches instead of looping over individual training examples.\n", + "\n", + "\n", + "
**Figure 2** : **SGD vs Mini-Batch GD**
\"+\" denotes a minimum of the cost. Using mini-batches in your optimization algorithm often leads to faster optimization.
\n", + "\n", + "\n", + "**What you should remember**:\n", + "- The difference between gradient descent, mini-batch gradient descent and stochastic gradient descent is the number of examples you use to perform one update step.\n", + "- You have to tune a learning rate hyperparameter $\\alpha$.\n", + "- With a well-turned mini-batch size, usually it outperforms either gradient descent or stochastic gradient descent (particularly when the training set is large)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Mini-Batch Gradient descent\n", + "\n", + "Let's learn how to build mini-batches from the training set (X, Y).\n", + "\n", + "There are two steps:\n", + "- **Shuffle**: Create a shuffled version of the training set (X, Y) as shown below. Each column of X and Y represents a training example. Note that the random shuffling is done synchronously between X and Y. Such that after the shuffling the $i^{th}$ column of X is the example corresponding to the $i^{th}$ label in Y. The shuffling step ensures that examples will be split randomly into different mini-batches. \n", + "\n", + "\n", + "\n", + "- **Partition**: Partition the shuffled (X, Y) into mini-batches of size `mini_batch_size` (here 64). Note that the number of training examples is not always divisible by `mini_batch_size`. The last mini batch might be smaller, but you don't need to worry about this. When the final mini-batch is smaller than the full `mini_batch_size`, it will look like this: \n", + "\n", + "\n", + "\n", + "**Exercise**: Implement `random_mini_batches`. We coded the shuffling part for you. To help you with the partitioning step, we give you the following code that selects the indexes for the $1^{st}$ and $2^{nd}$ mini-batches:\n", + "```python\n", + "first_mini_batch_X = shuffled_X[:, 0 : mini_batch_size]\n", + "second_mini_batch_X = shuffled_X[:, mini_batch_size : 2 * mini_batch_size]\n", + "...\n", + "```\n", + "\n", + "Note that the last mini-batch might end up smaller than `mini_batch_size=64`. Let $\\lfloor s \\rfloor$ represents $s$ rounded down to the nearest integer (this is `math.floor(s)` in Python). If the total number of examples is not a multiple of `mini_batch_size=64` then there will be $\\lfloor \\frac{m}{mini\\_batch\\_size}\\rfloor$ mini-batches with a full 64 examples, and the number of examples in the final mini-batch will be ($m-mini_\\_batch_\\_size \\times \\lfloor \\frac{m}{mini\\_batch\\_size}\\rfloor$). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: random_mini_batches\n", + "\n", + "def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):\n", + " \"\"\"\n", + " Creates a list of random minibatches from (X, Y)\n", + " \n", + " Arguments:\n", + " X -- input data, of shape (input size, number of examples)\n", + " Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\n", + " mini_batch_size -- size of the mini-batches, integer\n", + " \n", + " Returns:\n", + " mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)\n", + " \"\"\"\n", + " \n", + " np.random.seed(seed) # To make your \"random\" minibatches the same as ours\n", + " m = X.shape[1] # number of training examples\n", + " mini_batches = []\n", + " \n", + " # Step 1: Shuffle (X, Y)\n", + " permutation = list(np.random.permutation(m))\n", + " shuffled_X = X[:, permutation]\n", + " shuffled_Y = Y[:, permutation].reshape((1,m))\n", + "\n", + " # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.\n", + " num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning\n", + " for k in range(0, num_complete_minibatches):\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " mini_batch_X = shuffled_X[:,k * mini_batch_size:(k + 1) * mini_batch_size]\n", + " mini_batch_Y = shuffled_Y[:,k * mini_batch_size:(k + 1) * mini_batch_size]\n", + " ### END CODE HERE ###\n", + " mini_batch = (mini_batch_X, mini_batch_Y)\n", + " mini_batches.append(mini_batch)\n", + " \n", + " # Handling the end case (last mini-batch < mini_batch_size)\n", + " if m % mini_batch_size != 0:\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " end = m - mini_batch_size * math.floor(m / mini_batch_size)\n", + " mini_batch_X = shuffled_X[:,num_complete_minibatches * mini_batch_size:]\n", + " mini_batch_Y = shuffled_Y[:,num_complete_minibatches * mini_batch_size:]\n", + " ### END CODE HERE ###\n", + " mini_batch = (mini_batch_X, mini_batch_Y)\n", + " mini_batches.append(mini_batch)\n", + " \n", + " return mini_batches" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of the 1st mini_batch_X: (12288, 64)\n", + "shape of the 2nd mini_batch_X: (12288, 64)\n", + "shape of the 3rd mini_batch_X: (12288, 20)\n", + "shape of the 1st mini_batch_Y: (1, 64)\n", + "shape of the 2nd mini_batch_Y: (1, 64)\n", + "shape of the 3rd mini_batch_Y: (1, 20)\n", + "mini batch sanity check: [ 0.90085595 -0.7612069 0.2344157 ]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess, mini_batch_size = random_mini_batches_test_case()\n", + "mini_batches = random_mini_batches(X_assess, Y_assess, mini_batch_size)\n", + "\n", + "print(\"shape of the 1st mini_batch_X: \" + str(mini_batches[0][0].shape))\n", + "print(\"shape of the 2nd mini_batch_X: \" + str(mini_batches[1][0].shape))\n", + "print(\"shape of the 3rd mini_batch_X: \" + str(mini_batches[2][0].shape))\n", + "print(\"shape of the 1st mini_batch_Y: \" + str(mini_batches[0][1].shape))\n", + "print(\"shape of the 2nd mini_batch_Y: \" + str(mini_batches[1][1].shape)) \n", + "print(\"shape of the 3rd mini_batch_Y: \" + str(mini_batches[2][1].shape))\n", + "print(\"mini batch sanity check: \" + str(mini_batches[0][0][0][0:3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**shape of the 1st mini_batch_X** (12288, 64)
**shape of the 2nd mini_batch_X** (12288, 64)
**shape of the 3rd mini_batch_X** (12288, 20)
**shape of the 1st mini_batch_Y** (1, 64)
**shape of the 2nd mini_batch_Y** (1, 64)
**shape of the 3rd mini_batch_Y** (1, 20)
**mini batch sanity check** [ 0.90085595 -0.7612069 0.2344157 ]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- Shuffling and Partitioning are the two steps required to build mini-batches\n", + "- Powers of two are often chosen to be the mini-batch size, e.g., 16, 32, 64, 128." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Momentum\n", + "\n", + "Because mini-batch gradient descent makes a parameter update after seeing just a subset of examples, the direction of the update has some variance, and so the path taken by mini-batch gradient descent will \"oscillate\" toward convergence. Using momentum can reduce these oscillations. \n", + "\n", + "Momentum takes into account the past gradients to smooth out the update. We will store the 'direction' of the previous gradients in the variable $v$. Formally, this will be the exponentially weighted average of the gradient on previous steps. You can also think of $v$ as the \"velocity\" of a ball rolling downhill, building up speed (and momentum) according to the direction of the gradient/slope of the hill. \n", + "\n", + "\n", + "
**Figure 3**: The red arrows shows the direction taken by one step of mini-batch gradient descent with momentum. The blue points show the direction of the gradient (with respect to the current mini-batch) on each step. Rather than just following the gradient, we let the gradient influence $v$ and then take a step in the direction of $v$.
\n", + "\n", + "\n", + "**Exercise**: Initialize the velocity. The velocity, $v$, is a python dictionary that needs to be initialized with arrays of zeros. Its keys are the same as those in the `grads` dictionary, that is:\n", + "for $l =1,...,L$:\n", + "```python\n", + "v[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n", + "v[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n", + "```\n", + "**Note** that the iterator l starts at 0 in the for loop while the first parameters are v[\"dW1\"] and v[\"db1\"] (that's a \"one\" on the superscript). This is why we are shifting l to l+1 in the `for` loop." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_velocity\n", + "\n", + "def initialize_velocity(parameters):\n", + " \"\"\"\n", + " Initializes the velocity as a python dictionary with:\n", + " - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n", + " - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.\n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters.\n", + " parameters['W' + str(l)] = Wl\n", + " parameters['b' + str(l)] = bl\n", + " \n", + " Returns:\n", + " v -- python dictionary containing the current velocity.\n", + " v['dW' + str(l)] = velocity of dWl\n", + " v['db' + str(l)] = velocity of dbl\n", + " \"\"\"\n", + " \n", + " L = len(parameters) // 2 # number of layers in the neural networks\n", + " v = {}\n", + " \n", + " # Initialize velocity\n", + " for l in range(L):\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " v[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l+1)])\n", + " v[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l+1)])\n", + " ### END CODE HERE ###\n", + " \n", + " return v" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "v[\"dW1\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "v[\"db1\"] = [[ 0.]\n", + " [ 0.]]\n", + "v[\"dW2\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "v[\"db2\"] = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_velocity_test_case()\n", + "\n", + "v = initialize_velocity(parameters)\n", + "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n", + "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n", + "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n", + "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**v[\"dW1\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**v[\"db1\"]** [[ 0.]\n", + " [ 0.]]
**v[\"dW2\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**v[\"db2\"]** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Now, implement the parameters update with momentum. The momentum update rule is, for $l = 1, ..., L$: \n", + "\n", + "$$ \\begin{cases}\n", + "v_{dW^{[l]}} = \\beta v_{dW^{[l]}} + (1 - \\beta) dW^{[l]} \\\\\n", + "W^{[l]} = W^{[l]} - \\alpha v_{dW^{[l]}}\n", + "\\end{cases}\\tag{3}$$\n", + "\n", + "$$\\begin{cases}\n", + "v_{db^{[l]}} = \\beta v_{db^{[l]}} + (1 - \\beta) db^{[l]} \\\\\n", + "b^{[l]} = b^{[l]} - \\alpha v_{db^{[l]}} \n", + "\\end{cases}\\tag{4}$$\n", + "\n", + "where L is the number of layers, $\\beta$ is the momentum and $\\alpha$ is the learning rate. All parameters should be stored in the `parameters` dictionary. Note that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$ (that's a \"one\" on the superscript). So you will need to shift `l` to `l+1` when coding." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters_with_momentum\n", + "\n", + "def update_parameters_with_momentum(parameters, grads, v, beta, learning_rate):\n", + " \"\"\"\n", + " Update parameters using Momentum\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters:\n", + " parameters['W' + str(l)] = Wl\n", + " parameters['b' + str(l)] = bl\n", + " grads -- python dictionary containing your gradients for each parameters:\n", + " grads['dW' + str(l)] = dWl\n", + " grads['db' + str(l)] = dbl\n", + " v -- python dictionary containing the current velocity:\n", + " v['dW' + str(l)] = ...\n", + " v['db' + str(l)] = ...\n", + " beta -- the momentum hyperparameter, scalar\n", + " learning_rate -- the learning rate, scalar\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " v -- python dictionary containing your updated velocities\n", + " \"\"\"\n", + "\n", + " L = len(parameters) // 2 # number of layers in the neural networks\n", + " \n", + " # Momentum update for each parameter\n", + " for l in range(L):\n", + " \n", + " ### START CODE HERE ### (approx. 4 lines)\n", + " # compute velocities\n", + " v[\"dW\" + str(l + 1)] = beta * v[\"dW\" + str(l + 1)] + (1 - beta) * grads['dW' + str(l + 1)]\n", + " v[\"db\" + str(l + 1)] = beta * v[\"db\" + str(l + 1)] + (1 - beta) * grads['db' + str(l + 1)]\n", + " # update parameters\n", + " parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * v[\"dW\" + str(l + 1)]\n", + " parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * v[\"db\" + str(l + 1)]\n", + " ### END CODE HERE ###\n", + " \n", + " return parameters, v" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 1.62544598 -0.61290114 -0.52907334]\n", + " [-1.07347112 0.86450677 -2.30085497]]\n", + "b1 = [[ 1.74493465]\n", + " [-0.76027113]]\n", + "W2 = [[ 0.31930698 -0.24990073 1.4627996 ]\n", + " [-2.05974396 -0.32173003 -0.38320915]\n", + " [ 1.13444069 -1.0998786 -0.1713109 ]]\n", + "b2 = [[-0.87809283]\n", + " [ 0.04055394]\n", + " [ 0.58207317]]\n", + "v[\"dW1\"] = [[-0.11006192 0.11447237 0.09015907]\n", + " [ 0.05024943 0.09008559 -0.06837279]]\n", + "v[\"db1\"] = [[-0.01228902]\n", + " [-0.09357694]]\n", + "v[\"dW2\"] = [[-0.02678881 0.05303555 -0.06916608]\n", + " [-0.03967535 -0.06871727 -0.08452056]\n", + " [-0.06712461 -0.00126646 -0.11173103]]\n", + "v[\"db2\"] = [[ 0.02344157]\n", + " [ 0.16598022]\n", + " [ 0.07420442]]\n" + ] + } + ], + "source": [ + "parameters, grads, v = update_parameters_with_momentum_test_case()\n", + "\n", + "parameters, v = update_parameters_with_momentum(parameters, grads, v, beta = 0.9, learning_rate = 0.01)\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))\n", + "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n", + "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n", + "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n", + "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[ 1.62544598 -0.61290114 -0.52907334]\n", + " [-1.07347112 0.86450677 -2.30085497]]
**b1** [[ 1.74493465]\n", + " [-0.76027113]]
**W2** [[ 0.31930698 -0.24990073 1.4627996 ]\n", + " [-2.05974396 -0.32173003 -0.38320915]\n", + " [ 1.13444069 -1.0998786 -0.1713109 ]]
**b2** [[-0.87809283]\n", + " [ 0.04055394]\n", + " [ 0.58207317]]
**v[\"dW1\"]** [[-0.11006192 0.11447237 0.09015907]\n", + " [ 0.05024943 0.09008559 -0.06837279]]
**v[\"db1\"]** [[-0.01228902]\n", + " [-0.09357694]]
**v[\"dW2\"]** [[-0.02678881 0.05303555 -0.06916608]\n", + " [-0.03967535 -0.06871727 -0.08452056]\n", + " [-0.06712461 -0.00126646 -0.11173103]]
**v[\"db2\"]** [[ 0.02344157]\n", + " [ 0.16598022]\n", + " [ 0.07420442]]
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Note** that:\n", + "- The velocity is initialized with zeros. So the algorithm will take a few iterations to \"build up\" velocity and start to take bigger steps.\n", + "- If $\\beta = 0$, then this just becomes standard gradient descent without momentum. \n", + "\n", + "**How do you choose $\\beta$?**\n", + "\n", + "- The larger the momentum $\\beta$ is, the smoother the update because the more we take the past gradients into account. But if $\\beta$ is too big, it could also smooth out the updates too much. \n", + "- Common values for $\\beta$ range from 0.8 to 0.999. If you don't feel inclined to tune this, $\\beta = 0.9$ is often a reasonable default. \n", + "- Tuning the optimal $\\beta$ for your model might need trying several values to see what works best in term of reducing the value of the cost function $J$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you should remember**:\n", + "- Momentum takes past gradients into account to smooth out the steps of gradient descent. It can be applied with batch gradient descent, mini-batch gradient descent or stochastic gradient descent.\n", + "- You have to tune a momentum hyperparameter $\\beta$ and a learning rate $\\alpha$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Adam\n", + "\n", + "Adam is one of the most effective optimization algorithms for training neural networks. It combines ideas from RMSProp (described in lecture) and Momentum. \n", + "\n", + "**How does Adam work?**\n", + "1. It calculates an exponentially weighted average of past gradients, and stores it in variables $v$ (before bias correction) and $v^{corrected}$ (with bias correction). \n", + "2. It calculates an exponentially weighted average of the squares of the past gradients, and stores it in variables $s$ (before bias correction) and $s^{corrected}$ (with bias correction). \n", + "3. It updates parameters in a direction based on combining information from \"1\" and \"2\".\n", + "\n", + "The update rule is, for $l = 1, ..., L$: \n", + "\n", + "$$\\begin{cases}\n", + "v_{dW^{[l]}} = \\beta_1 v_{dW^{[l]}} + (1 - \\beta_1) \\frac{\\partial \\mathcal{J} }{ \\partial W^{[l]} } \\\\\n", + "v^{corrected}_{dW^{[l]}} = \\frac{v_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n", + "s_{dW^{[l]}} = \\beta_2 s_{dW^{[l]}} + (1 - \\beta_2) (\\frac{\\partial \\mathcal{J} }{\\partial W^{[l]} })^2 \\\\\n", + "s^{corrected}_{dW^{[l]}} = \\frac{s_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n", + "W^{[l]} = W^{[l]} - \\alpha \\frac{v^{corrected}_{dW^{[l]}}}{\\sqrt{s^{corrected}_{dW^{[l]}}} + \\varepsilon}\n", + "\\end{cases}$$\n", + "where:\n", + "- t counts the number of steps taken of Adam \n", + "- L is the number of layers\n", + "- $\\beta_1$ and $\\beta_2$ are hyperparameters that control the two exponentially weighted averages. \n", + "- $\\alpha$ is the learning rate\n", + "- $\\varepsilon$ is a very small number to avoid dividing by zero\n", + "\n", + "As usual, we will store all parameters in the `parameters` dictionary " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Initialize the Adam variables $v, s$ which keep track of the past information.\n", + "\n", + "**Instruction**: The variables $v, s$ are python dictionaries that need to be initialized with arrays of zeros. Their keys are the same as for `grads`, that is:\n", + "for $l = 1, ..., L$:\n", + "```python\n", + "v[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n", + "v[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n", + "s[\"dW\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"W\" + str(l+1)])\n", + "s[\"db\" + str(l+1)] = ... #(numpy array of zeros with the same shape as parameters[\"b\" + str(l+1)])\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_adam\n", + "\n", + "def initialize_adam(parameters) :\n", + " \"\"\"\n", + " Initializes v and s as two python dictionaries with:\n", + " - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\" \n", + " - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters.\n", + " parameters[\"W\" + str(l)] = Wl\n", + " parameters[\"b\" + str(l)] = bl\n", + " \n", + " Returns: \n", + " v -- python dictionary that will contain the exponentially weighted average of the gradient.\n", + " v[\"dW\" + str(l)] = ...\n", + " v[\"db\" + str(l)] = ...\n", + " s -- python dictionary that will contain the exponentially weighted average of the squared gradient.\n", + " s[\"dW\" + str(l)] = ...\n", + " s[\"db\" + str(l)] = ...\n", + "\n", + " \"\"\"\n", + " \n", + " L = len(parameters) // 2 # number of layers in the neural networks\n", + " v = {}\n", + " s = {}\n", + " \n", + " # Initialize v, s. Input: \"parameters\". Outputs: \"v, s\".\n", + " for l in range(L):\n", + " ### START CODE HERE ### (approx. 4 lines)\n", + " v[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\n", + " v[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\n", + "\n", + " s[\"dW\" + str(l+1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\n", + " s[\"db\" + str(l+1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\n", + " ### END CODE HERE ###\n", + " \n", + " return v, s" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "v[\"dW1\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "v[\"db1\"] = [[ 0.]\n", + " [ 0.]]\n", + "v[\"dW2\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "v[\"db2\"] = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "s[\"dW1\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "s[\"db1\"] = [[ 0.]\n", + " [ 0.]]\n", + "s[\"dW2\"] = [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]\n", + "s[\"db2\"] = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_adam_test_case()\n", + "\n", + "v, s = initialize_adam(parameters)\n", + "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n", + "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n", + "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n", + "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))\n", + "print(\"s[\\\"dW1\\\"] = \" + str(s[\"dW1\"]))\n", + "print(\"s[\\\"db1\\\"] = \" + str(s[\"db1\"]))\n", + "print(\"s[\\\"dW2\\\"] = \" + str(s[\"dW2\"]))\n", + "print(\"s[\\\"db2\\\"] = \" + str(s[\"db2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
**v[\"dW1\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**v[\"db1\"]** [[ 0.]\n", + " [ 0.]]
**v[\"dW2\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**v[\"db2\"]** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]
**s[\"dW1\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**s[\"db1\"]** [[ 0.]\n", + " [ 0.]]
**s[\"dW2\"]** [[ 0. 0. 0.]\n", + " [ 0. 0. 0.]\n", + " [ 0. 0. 0.]]
**s[\"db2\"]** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Now, implement the parameters update with Adam. Recall the general update rule is, for $l = 1, ..., L$: \n", + "\n", + "$$\\begin{cases}\n", + "v_{W^{[l]}} = \\beta_1 v_{W^{[l]}} + (1 - \\beta_1) \\frac{\\partial J }{ \\partial W^{[l]} } \\\\\n", + "v^{corrected}_{W^{[l]}} = \\frac{v_{W^{[l]}}}{1 - (\\beta_1)^t} \\\\\n", + "s_{W^{[l]}} = \\beta_2 s_{W^{[l]}} + (1 - \\beta_2) (\\frac{\\partial J }{\\partial W^{[l]} })^2 \\\\\n", + "s^{corrected}_{W^{[l]}} = \\frac{s_{W^{[l]}}}{1 - (\\beta_2)^t} \\\\\n", + "W^{[l]} = W^{[l]} - \\alpha \\frac{v^{corrected}_{W^{[l]}}}{\\sqrt{s^{corrected}_{W^{[l]}}}+\\varepsilon}\n", + "\\end{cases}$$\n", + "\n", + "\n", + "**Note** that the iterator `l` starts at 0 in the `for` loop while the first parameters are $W^{[1]}$ and $b^{[1]}$. You need to shift `l` to `l+1` when coding." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters_with_adam\n", + "\n", + "def update_parameters_with_adam(parameters, grads, v, s, t, learning_rate=0.01,\n", + " beta1=0.9, beta2=0.999, epsilon=1e-8):\n", + " \"\"\"\n", + " Update parameters using Adam\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters:\n", + " parameters['W' + str(l)] = Wl\n", + " parameters['b' + str(l)] = bl\n", + " grads -- python dictionary containing your gradients for each parameters:\n", + " grads['dW' + str(l)] = dWl\n", + " grads['db' + str(l)] = dbl\n", + " v -- Adam variable, moving average of the first gradient, python dictionary\n", + " s -- Adam variable, moving average of the squared gradient, python dictionary\n", + " learning_rate -- the learning rate, scalar.\n", + " beta1 -- Exponential decay hyperparameter for the first moment estimates \n", + " beta2 -- Exponential decay hyperparameter for the second moment estimates \n", + " epsilon -- hyperparameter preventing division by zero in Adam updates\n", + "\n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " v -- Adam variable, moving average of the first gradient, python dictionary\n", + " s -- Adam variable, moving average of the squared gradient, python dictionary\n", + " \"\"\"\n", + " \n", + " L = len(parameters) // 2 # number of layers in the neural networks\n", + " v_corrected = {} # Initializing first moment estimate, python dictionary\n", + " s_corrected = {} # Initializing second moment estimate, python dictionary\n", + " \n", + " # Perform Adam update on all parameters\n", + " for l in range(L):\n", + " # Moving average of the gradients. Inputs: \"v, grads, beta1\". Output: \"v\".\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " v[\"dW\" + str(l + 1)] = beta1 * v[\"dW\" + str(l + 1)] + (1 - beta1) * grads['dW' + str(l + 1)]\n", + " v[\"db\" + str(l + 1)] = beta1 * v[\"db\" + str(l + 1)] + (1 - beta1) * grads['db' + str(l + 1)]\n", + " ### END CODE HERE ###\n", + "\n", + " # Compute bias-corrected first moment estimate. Inputs: \"v, beta1, t\". Output: \"v_corrected\".\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " v_corrected[\"dW\" + str(l + 1)] = v[\"dW\" + str(l + 1)] / (1 - np.power(beta1, t))\n", + " v_corrected[\"db\" + str(l + 1)] = v[\"db\" + str(l + 1)] / (1 - np.power(beta1, t))\n", + " ### END CODE HERE ###\n", + "\n", + " # Moving average of the squared gradients. Inputs: \"s, grads, beta2\". Output: \"s\".\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " s[\"dW\" + str(l + 1)] = beta2 * s[\"dW\" + str(l + 1)] + (1 - beta2) * np.power(grads['dW' + str(l + 1)], 2)\n", + " s[\"db\" + str(l + 1)] = beta2 * s[\"db\" + str(l + 1)] + (1 - beta2) * np.power(grads['db' + str(l + 1)], 2)\n", + " ### END CODE HERE ###\n", + "\n", + " # Compute bias-corrected second raw moment estimate. Inputs: \"s, beta2, t\". Output: \"s_corrected\".\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " s_corrected[\"dW\" + str(l + 1)] = s[\"dW\" + str(l + 1)] / (1 - np.power(beta2, t))\n", + " s_corrected[\"db\" + str(l + 1)] = s[\"db\" + str(l + 1)] / (1 - np.power(beta2, t))\n", + " ### END CODE HERE ###\n", + "\n", + " # Update parameters. Inputs: \"parameters, learning_rate, v_corrected, s_corrected, epsilon\". Output: \"parameters\".\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * v_corrected[\"dW\" + str(l + 1)] / np.sqrt(s[\"dW\" + str(l + 1)] + epsilon)\n", + " parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * v_corrected[\"db\" + str(l + 1)] / np.sqrt(s[\"db\" + str(l + 1)] + epsilon)\n", + " ### END CODE HERE ###\n", + "\n", + " return parameters, v, s" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 1.79078034 -0.77819144 -0.69460639]\n", + " [-1.23940099 0.69897299 -2.13510481]]\n", + "b1 = [[ 1.91119235]\n", + " [-0.59477218]]\n", + "W2 = [[ 0.48546317 -0.41580308 1.62854186]\n", + " [-1.89371033 -0.1559833 -0.21761985]\n", + " [ 1.30020326 -0.93841334 -0.00599321]]\n", + "b2 = [[-1.04427894]\n", + " [-0.12422162]\n", + " [ 0.41638106]]\n", + "v[\"dW1\"] = [[-0.11006192 0.11447237 0.09015907]\n", + " [ 0.05024943 0.09008559 -0.06837279]]\n", + "v[\"db1\"] = [[-0.01228902]\n", + " [-0.09357694]]\n", + "v[\"dW2\"] = [[-0.02678881 0.05303555 -0.06916608]\n", + " [-0.03967535 -0.06871727 -0.08452056]\n", + " [-0.06712461 -0.00126646 -0.11173103]]\n", + "v[\"db2\"] = [[ 0.02344157]\n", + " [ 0.16598022]\n", + " [ 0.07420442]]\n", + "s[\"dW1\"] = [[ 0.00121136 0.00131039 0.00081287]\n", + " [ 0.0002525 0.00081154 0.00046748]]\n", + "s[\"db1\"] = [[ 1.51020075e-05]\n", + " [ 8.75664434e-04]]\n", + "s[\"dW2\"] = [[ 7.17640232e-05 2.81276921e-04 4.78394595e-04]\n", + " [ 1.57413361e-04 4.72206320e-04 7.14372576e-04]\n", + " [ 4.50571368e-04 1.60392066e-07 1.24838242e-03]]\n", + "s[\"db2\"] = [[ 5.49507194e-05]\n", + " [ 2.75494327e-03]\n", + " [ 5.50629536e-04]]\n" + ] + } + ], + "source": [ + "parameters, grads, v, s = update_parameters_with_adam_test_case()\n", + "parameters, v, s = update_parameters_with_adam(parameters, grads, v, s, t = 2)\n", + "\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))\n", + "print(\"v[\\\"dW1\\\"] = \" + str(v[\"dW1\"]))\n", + "print(\"v[\\\"db1\\\"] = \" + str(v[\"db1\"]))\n", + "print(\"v[\\\"dW2\\\"] = \" + str(v[\"dW2\"]))\n", + "print(\"v[\\\"db2\\\"] = \" + str(v[\"db2\"]))\n", + "print(\"s[\\\"dW1\\\"] = \" + str(s[\"dW1\"]))\n", + "print(\"s[\\\"db1\\\"] = \" + str(s[\"db1\"]))\n", + "print(\"s[\\\"dW2\\\"] = \" + str(s[\"dW2\"]))\n", + "print(\"s[\\\"db2\\\"] = \" + str(s[\"db2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[ 1.63178673 -0.61919778 -0.53561312]\n", + " [-1.08040999 0.85796626 -2.29409733]]
**b1** [[ 1.75225313]\n", + " [-0.75376553]]
**W2** [[ 0.32648046 -0.25681174 1.46954931]\n", + " [-2.05269934 -0.31497584 -0.37661299]\n", + " [ 1.14121081 -1.09245036 -0.16498684]]
**b2** [[-0.88529978]\n", + " [ 0.03477238]\n", + " [ 0.57537385]]
**v[\"dW1\"]** [[-0.11006192 0.11447237 0.09015907]\n", + " [ 0.05024943 0.09008559 -0.06837279]]
**v[\"db1\"]** [[-0.01228902]\n", + " [-0.09357694]]
**v[\"dW2\"]** [[-0.02678881 0.05303555 -0.06916608]\n", + " [-0.03967535 -0.06871727 -0.08452056]\n", + " [-0.06712461 -0.00126646 -0.11173103]]
**v[\"db2\"]** [[ 0.02344157]\n", + " [ 0.16598022]\n", + " [ 0.07420442]]
**s[\"dW1\"]** [[ 0.00121136 0.00131039 0.00081287]\n", + " [ 0.0002525 0.00081154 0.00046748]]
**s[\"db1\"]** [[ 1.51020075e-05]\n", + " [ 8.75664434e-04]]
**s[\"dW2\"]** [[ 7.17640232e-05 2.81276921e-04 4.78394595e-04]\n", + " [ 1.57413361e-04 4.72206320e-04 7.14372576e-04]\n", + " [ 4.50571368e-04 1.60392066e-07 1.24838242e-03]]
**s[\"db2\"]** [[ 5.49507194e-05]\n", + " [ 2.75494327e-03]\n", + " [ 5.50629536e-04]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now have three working optimization algorithms (mini-batch gradient descent, Momentum, Adam). Let's implement a model with each of these optimizers and observe the difference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - Model with different optimization algorithms\n", + "\n", + "Lets use the following \"moons\" dataset to test the different optimization methods. (The dataset is named \"moons\" because the data from each of the two classes looks a bit like a crescent-shaped moon.) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pOWkUwU3qEtmtJb0/fpResx/xuC68Y1OfiT+B9aMuqz234tQsEn9ZR8qqHcgq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I8dQMdp/sebcSSfp+77GZdTx1HWW/bVJ1t48JmpxMSdCuKH9ufw9AO/4HR69TYL8ux8+OZ60lJy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\nv5qYYyZ0Z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_X, train_Y = load_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have already implemented a 3-layer neural network. You will train it with: \n", + "- Mini-batch **Gradient Descent**: it will call your function:\n", + " - `update_parameters_with_gd()`\n", + "- Mini-batch **Momentum**: it will call your functions:\n", + " - `initialize_velocity()` and `update_parameters_with_momentum()`\n", + "- Mini-batch **Adam**: it will call your functions:\n", + " - `initialize_adam()` and `update_parameters_with_adam()`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def model(X, Y, layers_dims, optimizer, learning_rate=0.0007, mini_batch_size=64, beta=0.9,\n", + " beta1=0.9, beta2=0.999, epsilon=1e-8, num_epochs=10000, print_cost=True):\n", + " \"\"\"\n", + " 3-layer neural network model which can be run in different optimizer modes.\n", + " \n", + " Arguments:\n", + " X -- input data, of shape (2, number of examples)\n", + " Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\n", + " layers_dims -- python list, containing the size of each layer\n", + " learning_rate -- the learning rate, scalar.\n", + " mini_batch_size -- the size of a mini batch\n", + " beta -- Momentum hyperparameter\n", + " beta1 -- Exponential decay hyperparameter for the past gradients estimates \n", + " beta2 -- Exponential decay hyperparameter for the past squared gradients estimates \n", + " epsilon -- hyperparameter preventing division by zero in Adam updates\n", + " num_epochs -- number of epochs\n", + " print_cost -- True to print the cost every 1000 epochs\n", + "\n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " \"\"\"\n", + "\n", + " L = len(layers_dims) # number of layers in the neural networks\n", + " costs = [] # to keep track of the cost\n", + " t = 0 # initializing the counter required for Adam update\n", + " seed = 10 # For grading purposes, so that your \"random\" minibatches are the same as ours\n", + " \n", + " # Initialize parameters\n", + " parameters = initialize_parameters(layers_dims)\n", + "\n", + " # Initialize the optimizer\n", + " if optimizer == \"gd\":\n", + " pass # no initialization required for gradient descent\n", + " elif optimizer == \"momentum\":\n", + " v = initialize_velocity(parameters)\n", + " elif optimizer == \"adam\":\n", + " v, s = initialize_adam(parameters)\n", + " \n", + " # Optimization loop\n", + " for i in range(num_epochs):\n", + " \n", + " # Define the random minibatches. We increment the seed to reshuffle differently the dataset after each epoch\n", + " seed = seed + 1\n", + " minibatches = random_mini_batches(X, Y, mini_batch_size, seed)\n", + "\n", + " for minibatch in minibatches:\n", + "\n", + " # Select a minibatch\n", + " (minibatch_X, minibatch_Y) = minibatch\n", + "\n", + " # Forward propagation\n", + " a3, caches = forward_propagation(minibatch_X, parameters)\n", + "\n", + " # Compute cost\n", + " cost = compute_cost(a3, minibatch_Y)\n", + "\n", + " # Backward propagation\n", + " grads = backward_propagation(minibatch_X, minibatch_Y, caches)\n", + "\n", + " # Update parameters\n", + " if optimizer == \"gd\":\n", + " parameters = update_parameters_with_gd(parameters, grads, learning_rate)\n", + " elif optimizer == \"momentum\":\n", + " parameters, v = update_parameters_with_momentum(parameters, grads, v, beta, learning_rate)\n", + " elif optimizer == \"adam\":\n", + " t = t + 1 # Adam counter\n", + " parameters, v, s = update_parameters_with_adam(parameters, grads, v, s,\n", + " t, learning_rate, beta1, beta2, epsilon)\n", + " \n", + " # Print the cost every 1000 epoch\n", + " if print_cost and i % 1000 == 0:\n", + " print(\"Cost after epoch %i: %f\" % (i, cost))\n", + " if print_cost and i % 100 == 0:\n", + " costs.append(cost)\n", + " \n", + " # plot the cost\n", + " plt.plot(costs)\n", + " plt.ylabel('cost')\n", + " plt.xlabel('epochs (per 100)')\n", + " plt.title(\"Learning rate = \" + str(learning_rate))\n", + " plt.show()\n", + "\n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will now run this 3 layer neural network with each of the 3 optimization methods.\n", + "\n", + "### 5.1 - Mini-batch Gradient descent\n", + "\n", + "Run the following code to see how the model does with mini-batch gradient descent." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 0.690736\n", + "Cost after epoch 1000: 0.685273\n", + "Cost after epoch 2000: 0.647072\n", + "Cost after epoch 3000: 0.619525\n", + "Cost after epoch 4000: 0.576584\n", + "Cost after epoch 5000: 0.607243\n", + "Cost after epoch 6000: 0.529403\n", + "Cost after epoch 7000: 0.460768\n", + "Cost after epoch 8000: 0.465586\n", + "Cost after epoch 9000: 0.464518\n" + ] + }, + { + "data": { + "image/png": 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wlqP9wbwuUpgT5yP9xTNK+yasgvvyLcOmajcjoVmSScVzpwwX6Qdu3ADAsQGj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\nzUrB63Lw1++8gruu7VzuraxorASY6dncMcPlGH1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9nVu/+S16F5eYHx/j+ZfeQTF58yb2Rewe0V/eMaG3zwo1lpYllTKHMtUZnmF0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train 3-layer model\n", + "layers_dims = [train_X.shape[0], 5, 2, 1]\n", + "parameters = model(train_X, train_Y, layers_dims, optimizer=\"gd\")\n", + "\n", + "# Predict\n", + "predictions = predict(train_X, train_Y, parameters)\n", + "\n", + "# Plot decision boundary\n", + "plt.title(\"Model with Gradient Descent optimization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 2.5])\n", + "axes.set_ylim([-1, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 5.2 - Mini-batch gradient descent with momentum\n", + "\n", + "Run the following code to see how the model does with momentum. Because this example is relatively simple, the gains from using momemtum are small; but for more complex problems you might see bigger gains." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 0.690741\n", + "Cost after epoch 1000: 0.685341\n", + "Cost after epoch 2000: 0.647145\n", + "Cost after epoch 3000: 0.619594\n", + "Cost after epoch 4000: 0.576665\n", + "Cost after epoch 5000: 0.607324\n", + "Cost after epoch 6000: 0.529476\n", + "Cost after epoch 7000: 0.460936\n", + "Cost after epoch 8000: 0.465780\n", + "Cost after epoch 9000: 0.464740\n" + ] + }, + { + "data": { + "image/png": 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0SxQthiuQHesa+bPbtnLrtvbiJ1eATW0BBoJRDvQH86ZIAS5qN4ptSkmV9k0Y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\nNEsIHRmWhrXnlzMyjCWK7rfWepx87A3rsNkqF31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vH0vDbK0EVB2vC/msS6lYoj++v3Ntx3NzXOg/gZL6fabsNImQRb5hbJ5h2gpz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train 3-layer model\n", + "layers_dims = [train_X.shape[0], 5, 2, 1]\n", + "parameters = model(train_X, train_Y, layers_dims, beta=0.9, optimizer=\"momentum\")\n", + "\n", + "# Predict\n", + "predictions = predict(train_X, train_Y, parameters)\n", + "\n", + "# Plot decision boundary\n", + "plt.title(\"Model with Momentum optimization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 2.5])\n", + "axes.set_ylim([-1, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 5.3 - Mini-batch with Adam mode\n", + "\n", + "Run the following code to see how the model does with Adam." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 0.687550\n", + "Cost after epoch 1000: 0.173593\n", + "Cost after epoch 2000: 0.150145\n", + "Cost after epoch 3000: 0.072939\n", + "Cost after epoch 4000: 0.125896\n", + "Cost after epoch 5000: 0.104185\n", + "Cost after epoch 6000: 0.116069\n", + "Cost after epoch 7000: 0.031774\n", + "Cost after epoch 8000: 0.112908\n", + "Cost after epoch 9000: 0.197732\n" + ] + }, + { + "data": { + "image/png": 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wgO1p9vskgMcA9Ou4low4eKYewzBMwaCn6NUB6ND83ClvUyGiOgDvA/BjHdeRlcnp6dH5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\nMbRbjudlsvQApRXZ9Cw9IQQ6hgNqnFC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dMAxh5nyCyWmF7yksW0L3MTJmsTjfmfiTzujOHv1OpRKwvuJSqShSKWF0wj6y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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train 3-layer model\n", + "layers_dims = [train_X.shape[0], 5, 2, 1]\n", + "parameters = model(train_X, train_Y, layers_dims, optimizer=\"adam\")\n", + "\n", + "# Predict\n", + "predictions = predict(train_X, train_Y, parameters)\n", + "\n", + "# Plot decision boundary\n", + "plt.title(\"Model with Adam optimization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-1.5, 2.5])\n", + "axes.set_ylim([-1, 1.5])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 5.4 - Summary\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **optimization method**\n", + " \n", + " **accuracy**\n", + " \n", + " **cost shape**\n", + "
\n", + " Gradient descent\n", + " \n", + " 79.7%\n", + " \n", + " oscillations\n", + "
\n", + " Momentum\n", + " \n", + " 79.7%\n", + " \n", + " oscillations\n", + "
\n", + " Adam\n", + " \n", + " 94%\n", + " \n", + " smoother\n", + "
\n", + "\n", + "Momentum usually helps, but given the small learning rate and the simplistic dataset, its impact is almost negligeable. Also, the huge oscillations you see in the cost come from the fact that some minibatches are more difficult thans others for the optimization algorithm.\n", + "\n", + "Adam on the other hand, clearly outperforms mini-batch gradient descent and Momentum. If you run the model for more epochs on this simple dataset, all three methods will lead to very good results. However, you've seen that Adam converges a lot faster.\n", + "\n", + "Some advantages of Adam include:\n", + "- Relatively low memory requirements (though higher than gradient descent and gradient descent with momentum) \n", + "- Usually works well even with little tuning of hyperparameters (except $\\alpha$)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**References**:\n", + "\n", + "- Adam paper: https://arxiv.org/pdf/1412.6980.pdf" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "deep-neural-network", + "graded_item_id": "Ckiv2", + "launcher_item_id": "eNLYh" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Regularization.ipynb b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Regularization.ipynb new file mode 100644 index 0000000..4636934 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Regularization.ipynb @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regularization\n", + "\n", + "Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!\n", + "\n", + "**You will learn to:** Use regularization in your deep learning models.\n", + "\n", + "Let's first import the packages you are going to use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec\n", + "from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import scipy.io\n", + "from testCases import *\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Problem Statement**: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head. \n", + "\n", + "\n", + "
**Figure 1** : **Football field**
The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head
\n", + "\n", + "\n", + "They give you the following 2D dataset from France's past 10 games." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ts3Pr3a9tva519c3D3RrlFYPMBTMbPpWhXYcwRo3rVk13UpLAHGDgiuuq927t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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_X, train_Y, test_X, test_Y = load_2D_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each dot corresponds to a position on the football field where a football player has hit the ball with his/her head after the French goal keeper has shot the ball from the left side of the football field.\n", + "- If the dot is blue, it means the French player managed to hit the ball with his/her head\n", + "- If the dot is red, it means the other team's player hit the ball with their head\n", + "\n", + "**Your goal**: Use a deep learning model to find the positions on the field where the goalkeeper should kick the ball." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis of the dataset**: This dataset is a little noisy, but it looks like a diagonal line separating the upper left half (blue) from the lower right half (red) would work well. \n", + "\n", + "You will first try a non-regularized model. Then you'll learn how to regularize it and decide which model you will choose to solve the French Football Corporation's problem. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Non-regularized model\n", + "\n", + "You will use the following neural network (already implemented for you below). This model can be used:\n", + "- in *regularization mode* -- by setting the `lambd` input to a non-zero value. We use \"`lambd`\" instead of \"`lambda`\" because \"`lambda`\" is a reserved keyword in Python. \n", + "- in *dropout mode* -- by setting the `keep_prob` to a value less than one\n", + "\n", + "You will first try the model without any regularization. Then, you will implement:\n", + "- *L2 regularization* -- functions: \"`compute_cost_with_regularization()`\" and \"`backward_propagation_with_regularization()`\"\n", + "- *Dropout* -- functions: \"`forward_propagation_with_dropout()`\" and \"`backward_propagation_with_dropout()`\"\n", + "\n", + "In each part, you will run this model with the correct inputs so that it calls the functions you've implemented. Take a look at the code below to familiarize yourself with the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1):\n", + " \"\"\"\n", + " Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n", + " \n", + " Arguments:\n", + " X -- input data, of shape (input size, number of examples)\n", + " Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (output size, number of examples)\n", + " learning_rate -- learning rate of the optimization\n", + " num_iterations -- number of iterations of the optimization loop\n", + " print_cost -- If True, print the cost every 10000 iterations\n", + " lambd -- regularization hyperparameter, scalar\n", + " keep_prob - probability of keeping a neuron active during drop-out, scalar.\n", + " \n", + " Returns:\n", + " parameters -- parameters learned by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " grads = {}\n", + " costs = [] # to keep track of the cost\n", + " m = X.shape[1] # number of examples\n", + " layers_dims = [X.shape[0], 20, 3, 1]\n", + " \n", + " # Initialize parameters dictionary.\n", + " parameters = initialize_parameters(layers_dims)\n", + "\n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + "\n", + " # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n", + " if keep_prob == 1:\n", + " a3, cache = forward_propagation(X, parameters)\n", + " elif keep_prob < 1:\n", + " a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob)\n", + " \n", + " # Cost function\n", + " if lambd == 0:\n", + " cost = compute_cost(a3, Y)\n", + " else:\n", + " cost = compute_cost_with_regularization(a3, Y, parameters, lambd)\n", + " \n", + " # Backward propagation.\n", + " assert(lambd == 0 or keep_prob == 1) # it is possible to use both L2 regularization and dropout, \n", + " # but this assignment will only explore one at a time\n", + " if lambd == 0 and keep_prob == 1:\n", + " grads = backward_propagation(X, Y, cache)\n", + " elif lambd != 0:\n", + " grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n", + " elif keep_prob < 1:\n", + " grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n", + " \n", + " # Update parameters.\n", + " parameters = update_parameters(parameters, grads, learning_rate)\n", + " \n", + " # Print the loss every 10000 iterations\n", + " if print_cost and i % 10000 == 0:\n", + " print(\"Cost after iteration {}: {}\".format(i, cost))\n", + " if print_cost and i % 1000 == 0:\n", + " costs.append(cost)\n", + " \n", + " # plot the cost\n", + " plt.plot(costs)\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (x1,000)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's train the model without any regularization, and observe the accuracy on the train/test sets." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.6557412523481002\n", + "Cost after iteration 10000: 0.16329987525724216\n", + "Cost after iteration 20000: 0.13851642423255986\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the training set:\n", + "Accuracy: 0.947867298578\n", + "On the test set:\n", + "Accuracy: 0.915\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y)\n", + "print(\"On the training set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print(\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train accuracy is 94.8% while the test accuracy is 91.5%. This is the **baseline model** (you will observe the impact of regularization on this model). Run the following code to plot the decision boundary of your model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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sImFIqLf/JSCWP6zgeGE3LbheSLI1lyZdD0CVZi4xUZH94+j8sR/2CqcWp2zm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model without regularization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-0.75, 0.40])\n", + "axes.set_ylim([-0.75, 0.65])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The non-regularized model is obviously overfitting the training set. It is fitting the noisy points! Lets now look at two techniques to reduce overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - L2 Regularization\n", + "\n", + "The standard way to avoid overfitting is called **L2 regularization**. It consists of appropriately modifying your cost function, from:\n", + "$$J = -\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} \\tag{1}$$\n", + "To:\n", + "$$J_{regularized} = \\small \\underbrace{-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} }_\\text{cross-entropy cost} + \\underbrace{\\frac{1}{m} \\frac{\\lambda}{2} \\sum\\limits_l\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2} }_\\text{L2 regularization cost} \\tag{2}$$\n", + "\n", + "Let's modify your cost and observe the consequences.\n", + "\n", + "**Exercise**: Implement `compute_cost_with_regularization()` which computes the cost given by formula (2). To calculate $\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2}$ , use :\n", + "```python\n", + "np.sum(np.square(Wl))\n", + "```\n", + "Note that you have to do this for $W^{[1]}$, $W^{[2]}$ and $W^{[3]}$, then sum the three terms and multiply by $ \\frac{1}{m} \\frac{\\lambda}{2} $." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost_with_regularization\n", + "\n", + "def compute_cost_with_regularization(A3, Y, parameters, lambd):\n", + " \"\"\"\n", + " Implement the cost function with L2 regularization. See formula (2) above.\n", + " \n", + " Arguments:\n", + " A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)\n", + " Y -- \"true\" labels vector, of shape (output size, number of examples)\n", + " parameters -- python dictionary containing parameters of the model\n", + " \n", + " Returns:\n", + " cost - value of the regularized loss function (formula (2))\n", + " \"\"\"\n", + " m = Y.shape[1]\n", + " W1 = parameters[\"W1\"]\n", + " W2 = parameters[\"W2\"]\n", + " W3 = parameters[\"W3\"]\n", + " \n", + " cross_entropy_cost = compute_cost(A3, Y) # This gives you the cross-entropy part of the cost\n", + " \n", + " ### START CODE HERE ### (approx. 1 line)\n", + " L2_regularization_cost = lambd * (np.sum(np.square(W1)) + np.sum(np.square(W2)) + np.sum(np.square(W3))) / (2 * m)\n", + " ### END CODER HERE ###\n", + " \n", + " cost = cross_entropy_cost + L2_regularization_cost\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 1.78648594516\n" + ] + } + ], + "source": [ + "A3, Y_assess, parameters = compute_cost_with_regularization_test_case()\n", + "\n", + "print(\"cost = \" + str(compute_cost_with_regularization(A3, Y_assess, parameters, lambd = 0.1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **cost**\n", + " \n", + " 1.78648594516\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost. \n", + "\n", + "**Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\\frac{d}{dW} ( \\frac{1}{2}\\frac{\\lambda}{m} W^2) = \\frac{\\lambda}{m} W$)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation_with_regularization\n", + "\n", + "def backward_propagation_with_regularization(X, Y, cache, lambd):\n", + " \"\"\"\n", + " Implements the backward propagation of our baseline model to which we added an L2 regularization.\n", + " \n", + " Arguments:\n", + " X -- input dataset, of shape (input size, number of examples)\n", + " Y -- \"true\" labels vector, of shape (output size, number of examples)\n", + " cache -- cache output from forward_propagation()\n", + " lambd -- regularization hyperparameter, scalar\n", + " \n", + " Returns:\n", + " gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n", + " \"\"\"\n", + " \n", + " m = X.shape[1]\n", + " (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n", + " \n", + " dZ3 = A3 - Y\n", + " \n", + " ### START CODE HERE ### (approx. 1 line)\n", + " dW3 = 1. / m * np.dot(dZ3, A2.T) + (lambd * W3) / m\n", + " ### END CODE HERE ###\n", + " db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n", + " \n", + " dA2 = np.dot(W3.T, dZ3)\n", + " dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n", + " ### START CODE HERE ### (approx. 1 line)\n", + " dW2 = 1. / m * np.dot(dZ2, A1.T) + (lambd * W2) / m\n", + " ### END CODE HERE ###\n", + " db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n", + " \n", + " dA1 = np.dot(W2.T, dZ2)\n", + " dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n", + " ### START CODE HERE ### (approx. 1 line)\n", + " dW1 = 1. / m * np.dot(dZ1, X.T) + (lambd * W1) / m\n", + " ### END CODE HERE ###\n", + " db1 = 1. / m * np.sum(dZ1, axis=1, keepdims=True)\n", + " \n", + " gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3, \"dA2\": dA2,\n", + " \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n", + " \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dW1 = [[-0.25604646 0.12298827 -0.28297129]\n", + " [-0.17706303 0.34536094 -0.4410571 ]]\n", + "dW2 = [[ 0.79276486 0.85133918]\n", + " [-0.0957219 -0.01720463]\n", + " [-0.13100772 -0.03750433]]\n", + "dW3 = [[-1.77691347 -0.11832879 -0.09397446]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess, cache = backward_propagation_with_regularization_test_case()\n", + "\n", + "grads = backward_propagation_with_regularization(X_assess, Y_assess, cache, lambd=0.7)\n", + "print (\"dW1 = \" + str(grads[\"dW1\"]))\n", + "print (\"dW2 = \" + str(grads[\"dW2\"]))\n", + "print (\"dW3 = \" + str(grads[\"dW3\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **dW1**\n", + " \n", + " [[-0.25604646 0.12298827 -0.28297129]\n", + " [-0.17706303 0.34536094 -0.4410571 ]]\n", + "
\n", + " **dW2**\n", + " \n", + " [[ 0.79276486 0.85133918]\n", + " [-0.0957219 -0.01720463]\n", + " [-0.13100772 -0.03750433]]\n", + "
\n", + " **dW3**\n", + " \n", + " [[-1.77691347 -0.11832879 -0.09397446]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now run the model with L2 regularization $(\\lambda = 0.7)$. The `model()` function will call: \n", + "- `compute_cost_with_regularization` instead of `compute_cost`\n", + "- `backward_propagation_with_regularization` instead of `backward_propagation`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.6974484493131264\n", + "Cost after iteration 10000: 0.2684918873282239\n", + "Cost after iteration 20000: 0.2680916337127301\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the train set:\n", + "Accuracy: 0.938388625592\n", + "On the test set:\n", + "Accuracy: 0.93\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y, lambd=0.7)\n", + "print(\"On the train set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print(\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats, the test set accuracy increased to 93%. You have saved the French football team!\n", + "\n", + "You are not overfitting the training data anymore. Let's plot the decision boundary." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7rHRzDVPDGErDI4HbCkjXvDgrtpCYqqSal3KGGwIhbj48Jhs3C8w/qJFqdATd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model with L2-regularization\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-0.75,0.40])\n", + "axes.set_ylim([-0.75,0.65])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Observations**:\n", + "- The value of $\\lambda$ is a hyperparameter that you can tune using a dev set.\n", + "- L2 regularization makes your decision boundary smoother. If $\\lambda$ is too large, it is also possible to \"oversmooth\", resulting in a model with high bias.\n", + "\n", + "**What is L2-regularization actually doing?**:\n", + "\n", + "L2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. It becomes too costly for the cost to have large weights! This leads to a smoother model in which the output changes more slowly as the input changes. \n", + "\n", + "\n", + "**What you should remember** -- the implications of L2-regularization on:\n", + "- The cost computation:\n", + " - A regularization term is added to the cost\n", + "- The backpropagation function:\n", + " - There are extra terms in the gradients with respect to weight matrices\n", + "- Weights end up smaller (\"weight decay\"): \n", + " - Weights are pushed to smaller values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Dropout\n", + "\n", + "Finally, **dropout** is a widely used regularization technique that is specific to deep learning. \n", + "**It randomly shuts down some neurons in each iteration.** Watch these two videos to see what this means!\n", + "\n", + " \n", + "\n", + "\n", + "
\n", + "\n", + "
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Figure 2 : Drop-out on the second hidden layer.
At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\\_prob$ or keep it with probability $keep\\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration.
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Figure 3 : Drop-out on the first and third hidden layers.
$1^{st}$ layer: we shut down on average 40% of the neurons. $3^{rd}$ layer: we shut down on average 20% of the neurons.
\n", + "\n", + "\n", + "When you shut some neurons down, you actually modify your model. The idea behind drop-out is that at each iteration, you train a different model that uses only a subset of your neurons. With dropout, your neurons thus become less sensitive to the activation of one other specific neuron, because that other neuron might be shut down at any time. \n", + "\n", + "### 3.1 - Forward propagation with dropout\n", + "\n", + "**Exercise**: Implement the forward propagation with dropout. You are using a 3 layer neural network, and will add dropout to the first and second hidden layers. We will not apply dropout to the input layer or output layer. \n", + "\n", + "**Instructions**:\n", + "You would like to shut down some neurons in the first and second layers. To do that, you are going to carry out 4 Steps:\n", + "1. In lecture, we dicussed creating a variable $d^{[1]}$ with the same shape as $a^{[1]}$ using `np.random.rand()` to randomly get numbers between 0 and 1. Here, you will use a vectorized implementation, so create a random matrix $D^{[1]} = [d^{[1](1)} d^{[1](2)} ... d^{[1](m)}] $ of the same dimension as $A^{[1]}$.\n", + "2. Set each entry of $D^{[1]}$ to be 0 with probability (`1-keep_prob`) or 1 with probability (`keep_prob`), by thresholding values in $D^{[1]}$ appropriately. Hint: to set all the entries of a matrix X to 0 (if entry is less than 0.5) or 1 (if entry is more than 0.5) you would do: `X = (X < 0.5)`. Note that 0 and 1 are respectively equivalent to False and True.\n", + "3. Set $A^{[1]}$ to $A^{[1]} * D^{[1]}$. (You are shutting down some neurons). You can think of $D^{[1]}$ as a mask, so that when it is multiplied with another matrix, it shuts down some of the values.\n", + "4. Divide $A^{[1]}$ by `keep_prob`. By doing this you are assuring that the result of the cost will still have the same expected value as without drop-out. (This technique is also called inverted dropout.)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation_with_dropout\n", + "\n", + "def forward_propagation_with_dropout(X, parameters, keep_prob=0.5):\n", + " \"\"\"\n", + " Implements the forward propagation: LINEAR -> RELU + DROPOUT -> LINEAR -> RELU + DROPOUT -> LINEAR -> SIGMOID.\n", + " \n", + " Arguments:\n", + " X -- input dataset, of shape (2, number of examples)\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n", + " W1 -- weight matrix of shape (20, 2)\n", + " b1 -- bias vector of shape (20, 1)\n", + " W2 -- weight matrix of shape (3, 20)\n", + " b2 -- bias vector of shape (3, 1)\n", + " W3 -- weight matrix of shape (1, 3)\n", + " b3 -- bias vector of shape (1, 1)\n", + " keep_prob - probability of keeping a neuron active during drop-out, scalar\n", + " \n", + " Returns:\n", + " A3 -- last activation value, output of the forward propagation, of shape (1,1)\n", + " cache -- tuple, information stored for computing the backward propagation\n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + " \n", + " # retrieve parameters\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " W3 = parameters[\"W3\"]\n", + " b3 = parameters[\"b3\"]\n", + " \n", + " # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n", + " Z1 = np.dot(W1, X) + b1\n", + " A1 = relu(Z1)\n", + " ### START CODE HERE ### (approx. 4 lines) # Steps 1-4 below correspond to the Steps 1-4 described above. \n", + " D1 = np.random.rand(A1.shape[0], A1.shape[1]) # Step 1: initialize matrix D1 = np.random.rand(..., ...)\n", + " D1 = D1 < keep_prob # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)\n", + " A1 = A1 * D1 # Step 3: shut down some neurons of A1\n", + " A1 = A1 / keep_prob # Step 4: scale the value of neurons that haven't been shut down\n", + " ### END CODE HERE ###\n", + " Z2 = np.dot(W2, A1) + b2\n", + " A2 = relu(Z2)\n", + " ### START CODE HERE ### (approx. 4 lines)\n", + " D2 = np.random.rand(A2.shape[0], A2.shape[1]) # Step 1: initialize matrix D2 = np.random.rand(..., ...)\n", + " D2 = D2 < keep_prob # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold) \n", + " A2 = A2 * D2 # Step 3: shut down some neurons of A2\n", + " A2 = A2 / keep_prob # Step 4: scale the value of neurons that haven't been shut down\n", + " ### END CODE HERE ###\n", + " Z3 = np.dot(W3, A2) + b3\n", + " A3 = sigmoid(Z3)\n", + " \n", + " cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n", + " \n", + " return A3, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A3 = [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n" + ] + } + ], + "source": [ + "X_assess, parameters = forward_propagation_with_dropout_test_case()\n", + "\n", + "A3, cache = forward_propagation_with_dropout(X_assess, parameters, keep_prob=0.7)\n", + "print (\"A3 = \" + str(A3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **A3**\n", + " \n", + " [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Backward propagation with dropout\n", + "\n", + "**Exercise**: Implement the backward propagation with dropout. As before, you are training a 3 layer network. Add dropout to the first and second hidden layers, using the masks $D^{[1]}$ and $D^{[2]}$ stored in the cache. \n", + "\n", + "**Instruction**:\n", + "Backpropagation with dropout is actually quite easy. You will have to carry out 2 Steps:\n", + "1. You had previously shut down some neurons during forward propagation, by applying a mask $D^{[1]}$ to `A1`. In backpropagation, you will have to shut down the same neurons, by reapplying the same mask $D^{[1]}$ to `dA1`. \n", + "2. During forward propagation, you had divided `A1` by `keep_prob`. In backpropagation, you'll therefore have to divide `dA1` by `keep_prob` again (the calculus interpretation is that if $A^{[1]}$ is scaled by `keep_prob`, then its derivative $dA^{[1]}$ is also scaled by the same `keep_prob`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation_with_dropout\n", + "\n", + "def backward_propagation_with_dropout(X, Y, cache, keep_prob):\n", + " \"\"\"\n", + " Implements the backward propagation of our baseline model to which we added dropout.\n", + " \n", + " Arguments:\n", + " X -- input dataset, of shape (2, number of examples)\n", + " Y -- \"true\" labels vector, of shape (output size, number of examples)\n", + " cache -- cache output from forward_propagation_with_dropout()\n", + " keep_prob - probability of keeping a neuron active during drop-out, scalar\n", + " \n", + " Returns:\n", + " gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n", + " \"\"\"\n", + " \n", + " m = X.shape[1]\n", + " (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n", + " \n", + " dZ3 = A3 - Y\n", + " dW3 = 1. / m * np.dot(dZ3, A2.T)\n", + " db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n", + " dA2 = np.dot(W3.T, dZ3)\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dA2 = dA2 * D2 # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation\n", + " dA2 = dA2 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n", + " ### END CODE HERE ###\n", + " dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n", + " dW2 = 1. / m * np.dot(dZ2, A1.T)\n", + " db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n", + " \n", + " dA1 = np.dot(W2.T, dZ2)\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dA1 = dA1 * D1 # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation\n", + " dA1 = dA1 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n", + " ### END CODE HERE ###\n", + " dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n", + " dW1 = 1. / m * np.dot(dZ1, X.T)\n", + " db1 = 1. / m * np.sum(dZ1, axis=1, keepdims=True)\n", + " \n", + " gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n", + " \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n", + " \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dA1 = [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n", + " [ 0.65515713 0. -0.00337459 0. -0. ]]\n", + "dA2 = [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n", + " [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n", + " [ 0. 0. -0.00292733 0. -0. ]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess, cache = backward_propagation_with_dropout_test_case()\n", + "\n", + "gradients = backward_propagation_with_dropout(X_assess, Y_assess, cache, keep_prob=0.8)\n", + "\n", + "print (\"dA1 = \" + str(gradients[\"dA1\"]))\n", + "print (\"dA2 = \" + str(gradients[\"dA2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **dA1**\n", + " \n", + " [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n", + " [ 0.65515713 0. -0.00337459 0. -0. ]]\n", + "
\n", + " **dA2**\n", + " \n", + " [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n", + " [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n", + " [ 0. 0. -0.00292733 0. -0. ]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now run the model with dropout (`keep_prob = 0.86`). It means at every iteration you shut down each neurons of layer 1 and 2 with 24% probability. The function `model()` will now call:\n", + "- `forward_propagation_with_dropout` instead of `forward_propagation`.\n", + "- `backward_propagation_with_dropout` instead of `backward_propagation`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.6543912405149825\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jovyan/work/week5/Regularization/reg_utils.py:236: RuntimeWarning: divide by zero encountered in log\n", + " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n", + "/home/jovyan/work/week5/Regularization/reg_utils.py:236: RuntimeWarning: invalid value encountered in multiply\n", + " logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 10000: 0.06101698657490559\n", + "Cost after iteration 20000: 0.060582435798513114\n" + ] + }, + { + "data": { + "image/png": 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MrDQcemZmVhoOPTNA0u3pz8mSzurndf9rT6+VF0nvkPT5XpZ5V9q1frukaTWW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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On the train set:\n", + "Accuracy: 0.928909952607\n", + "On the test set:\n", + "Accuracy: 0.95\n" + ] + } + ], + "source": [ + "parameters = model(train_X, train_Y, keep_prob=0.86, learning_rate=0.3)\n", + "\n", + "print(\"On the train set:\")\n", + "predictions_train = predict(train_X, train_Y, parameters)\n", + "print(\"On the test set:\")\n", + "predictions_test = predict(test_X, test_Y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dropout works great! The test accuracy has increased again (to 95%)! Your model is not overfitting the training set and does a great job on the test set. The French football team will be forever grateful to you! \n", + "\n", + "Run the code below to plot the decision boundary." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GhCvVCMZed+0ldG3WbxRGls9INNri7yCi8UzzYEJRshF3DxHdf6eNK7lDk4Uc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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Model with dropout\")\n", + "axes = plt.gca()\n", + "axes.set_xlim([-0.75, 0.40])\n", + "axes.set_ylim([-0.75, 0.65])\n", + "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Note**:\n", + "- A **common mistake** when using dropout is to use it both in training and testing. You should use dropout (randomly eliminate nodes) only in training. \n", + "- Deep learning frameworks like [tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/dropout), [PaddlePaddle](http://doc.paddlepaddle.org/release_doc/0.9.0/doc/ui/api/trainer_config_helpers/attrs.html), [keras](https://keras.io/layers/core/#dropout) or [caffe](http://caffe.berkeleyvision.org/tutorial/layers/dropout.html) come with a dropout layer implementation. Don't stress - you will soon learn some of these frameworks.\n", + "\n", + "\n", + "**What you should remember about dropout:**\n", + "- Dropout is a regularization technique.\n", + "- You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.\n", + "- Apply dropout both during forward and backward propagation.\n", + "- During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Here are the results of our three models**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **model**\n", + " \n", + " **train accuracy**\n", + " \n", + " **test accuracy**\n", + "
\n", + " 3-layer NN without regularization\n", + " \n", + " 95%\n", + " \n", + " 91.5%\n", + "
\n", + " 3-layer NN with L2-regularization\n", + " \n", + " 94%\n", + " \n", + " 93%\n", + "
\n", + " 3-layer NN with dropout\n", + " \n", + " 93%\n", + " \n", + " 95%\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that regularization hurts training set performance! This is because it limits the ability of the network to overfit to the training set. But since it ultimately gives better test accuracy, it is helping your system. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations for finishing this assignment! And also for revolutionizing French football. :-) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "**What we want you to remember from this notebook**:\n", + "- Regularization will help you reduce overfitting.\n", + "- Regularization will drive your weights to lower values.\n", + "- L2 regularization and Dropout are two very effective regularization techniques." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "deep-neural-network", + "graded_item_id": "SXQaI", + "launcher_item_id": "UAwhh" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Tensorflow Tutorial.ipynb b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Tensorflow Tutorial.ipynb new file mode 100644 index 0000000..de3f58d --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Tensorflow Tutorial.ipynb @@ -0,0 +1,1640 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial\n", + "\n", + "Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up your machine learning development significantly. All of these frameworks also have a lot of documentation, which you should feel free to read. In this assignment, you will learn to do the following in TensorFlow: \n", + "\n", + "- Initialize variables\n", + "- Start your own session\n", + "- Train algorithms \n", + "- Implement a Neural Network\n", + "\n", + "Programing frameworks can not only shorten your coding time, but sometimes also perform optimizations that speed up your code. \n", + "\n", + "## 1 - Exploring the Tensorflow Library\n", + "\n", + "To start, you will import the library:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import math\n", + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.python.framework import ops\n", + "from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict\n", + "\n", + "%matplotlib inline\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have imported the library, we will walk you through its different applications. You will start with an example, where we compute for you the loss of one training example. \n", + "$$loss = \\mathcal{L}(\\hat{y}, y) = (\\hat y^{(i)} - y^{(i)})^2 \\tag{1}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n" + ] + } + ], + "source": [ + "y_hat = tf.constant(36, name='y_hat') # Define y_hat constant. Set to 36.\n", + "y = tf.constant(39, name='y') # Define y. Set to 39\n", + "\n", + "loss = tf.Variable((y - y_hat)**2, name='loss') # Create a variable for the loss\n", + "\n", + "init = tf.global_variables_initializer() # When init is run later (session.run(init)),\n", + " # the loss variable will be initialized and ready to be computed\n", + "with tf.Session() as session: # Create a session and print the output\n", + " session.run(init) # Initializes the variables\n", + " print(session.run(loss)) # Prints the loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Writing and running programs in TensorFlow has the following steps:\n", + "\n", + "1. Create Tensors (variables) that are not yet executed/evaluated. \n", + "2. Write operations between those Tensors.\n", + "3. Initialize your Tensors. \n", + "4. Create a Session. \n", + "5. Run the Session. This will run the operations you'd written above. \n", + "\n", + "Therefore, when we created a variable for the loss, we simply defined the loss as a function of other quantities, but did not evaluate its value. To evaluate it, we had to run `init=tf.global_variables_initializer()`. That initialized the loss variable, and in the last line we were finally able to evaluate the value of `loss` and print its value.\n", + "\n", + "Now let us look at an easy example. Run the cell below:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"Mul:0\", shape=(), dtype=int32)\n" + ] + } + ], + "source": [ + "a = tf.constant(2)\n", + "b = tf.constant(10)\n", + "c = tf.multiply(a,b)\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, you will not see 20! You got a tensor saying that the result is a tensor that does not have the shape attribute, and is of type \"int32\". All you did was put in the 'computation graph', but you have not run this computation yet. In order to actually multiply the two numbers, you will have to create a session and run it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "sess = tf.Session()\n", + "print(sess.run(c))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! To summarize, **remember to initialize your variables, create a session and run the operations inside the session**. \n", + "\n", + "Next, you'll also have to know about placeholders. A placeholder is an object whose value you can specify only later. \n", + "To specify values for a placeholder, you can pass in values by using a \"feed dictionary\" (`feed_dict` variable). Below, we created a placeholder for x. This allows us to pass in a number later when we run the session. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "# Change the value of x in the feed_dict\n", + "\n", + "x = tf.placeholder(tf.int64, name = 'x')\n", + "print(sess.run(2 * x, feed_dict = {x: 3}))\n", + "sess.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you first defined `x` you did not have to specify a value for it. A placeholder is simply a variable that you will assign data to only later, when running the session. We say that you **feed data** to these placeholders when running the session. \n", + "\n", + "Here's what's happening: When you specify the operations needed for a computation, you are telling TensorFlow how to construct a computation graph. The computation graph can have some placeholders whose values you will specify only later. Finally, when you run the session, you are telling TensorFlow to execute the computation graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 - Linear function\n", + "\n", + "Lets start this programming exercise by computing the following equation: $Y = WX + b$, where $W$ and $X$ are random matrices and b is a random vector. \n", + "\n", + "**Exercise**: Compute $WX + b$ where $W, X$, and $b$ are drawn from a random normal distribution. W is of shape (4, 3), X is (3,1) and b is (4,1). As an example, here is how you would define a constant X that has shape (3,1):\n", + "```python\n", + "X = tf.constant(np.random.randn(3,1), name = \"X\")\n", + "\n", + "```\n", + "You might find the following functions helpful: \n", + "- tf.matmul(..., ...) to do a matrix multiplication\n", + "- tf.add(..., ...) to do an addition\n", + "- np.random.randn(...) to initialize randomly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: linear_function\n", + "\n", + "def linear_function():\n", + " \"\"\"\n", + " Implements a linear function: \n", + " Initializes W to be a random tensor of shape (4,3)\n", + " Initializes X to be a random tensor of shape (3,1)\n", + " Initializes b to be a random tensor of shape (4,1)\n", + " Returns: \n", + " result -- runs the session for Y = WX + b \n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + " \n", + " ### START CODE HERE ### (4 lines of code)\n", + " X = np.random.randn(3, 1)\n", + " W = np.random.randn(4, 3)\n", + " b = np.random.randn(4, 1)\n", + " Y = tf.add(tf.matmul(W, X), b)\n", + " ### END CODE HERE ### \n", + " \n", + " # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate\n", + " \n", + " ### START CODE HERE ###\n", + " sess = tf.Session()\n", + " result = sess.run(Y)\n", + " ### END CODE HERE ### \n", + " \n", + " # close the session \n", + " sess.close()\n", + "\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "result = [[-2.15657382]\n", + " [ 2.95891446]\n", + " [-1.08926781]\n", + " [-0.84538042]]\n" + ] + } + ], + "source": [ + "print( \"result = \" + str(linear_function()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*** Expected Output ***: \n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
\n", + "**result**\n", + "\n", + "[[-2.15657382]\n", + " [ 2.95891446]\n", + " [-1.08926781]\n", + " [-0.84538042]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Computing the sigmoid \n", + "Great! You just implemented a linear function. Tensorflow offers a variety of commonly used neural network functions like `tf.sigmoid` and `tf.softmax`. For this exercise lets compute the sigmoid function of an input. \n", + "\n", + "You will do this exercise using a placeholder variable `x`. When running the session, you should use the feed dictionary to pass in the input `z`. In this exercise, you will have to (i) create a placeholder `x`, (ii) define the operations needed to compute the sigmoid using `tf.sigmoid`, and then (iii) run the session. \n", + "\n", + "** Exercise **: Implement the sigmoid function below. You should use the following: \n", + "\n", + "- `tf.placeholder(tf.float32, name = \"...\")`\n", + "- `tf.sigmoid(...)`\n", + "- `sess.run(..., feed_dict = {x: z})`\n", + "\n", + "\n", + "Note that there are two typical ways to create and use sessions in tensorflow: \n", + "\n", + "**Method 1:**\n", + "```python\n", + "sess = tf.Session()\n", + "# Run the variables initialization (if needed), run the operations\n", + "result = sess.run(..., feed_dict = {...})\n", + "sess.close() # Close the session\n", + "```\n", + "**Method 2:**\n", + "```python\n", + "with tf.Session() as sess: \n", + " # run the variables initialization (if needed), run the operations\n", + " result = sess.run(..., feed_dict = {...})\n", + " # This takes care of closing the session for you :)\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: sigmoid\n", + "\n", + "def sigmoid(z):\n", + " \"\"\"\n", + " Computes the sigmoid of z\n", + " \n", + " Arguments:\n", + " z -- input value, scalar or vector\n", + " \n", + " Returns: \n", + " results -- the sigmoid of z\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### ( approx. 4 lines of code)\n", + " # Create a placeholder for x. Name it 'x'.\n", + " x = tf.placeholder(tf.float32, name=\"x\")\n", + "\n", + " # compute sigmoid(x)\n", + " sigmoid = tf.sigmoid(x)\n", + "\n", + " # Create a session, and run it. Please use the method 2 explained above. \n", + " # You should use a feed_dict to pass z's value to x. \n", + " with tf.Session() as sess: \n", + " # Run session and call the output \"result\"\n", + " result = result = sess.run(sigmoid, feed_dict = {x: z})\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sigmoid(0) = 0.5\n", + "sigmoid(12) = 0.999994\n" + ] + } + ], + "source": [ + "print (\"sigmoid(0) = \" + str(sigmoid(0)))\n", + "print (\"sigmoid(12) = \" + str(sigmoid(12)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*** Expected Output ***: \n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
\n", + "**sigmoid(0)**\n", + "\n", + "0.5\n", + "
\n", + "**sigmoid(12)**\n", + "\n", + "0.999994\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**To summarize, you how know how to**:\n", + "1. Create placeholders\n", + "2. Specify the computation graph corresponding to operations you want to compute\n", + "3. Create the session\n", + "4. Run the session, using a feed dictionary if necessary to specify placeholder variables' values. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 - Computing the Cost\n", + "\n", + "You can also use a built-in function to compute the cost of your neural network. So instead of needing to write code to compute this as a function of $a^{[2](i)}$ and $y^{(i)}$ for i=1...m: \n", + "$$ J = - \\frac{1}{m} \\sum_{i = 1}^m \\large ( \\small y^{(i)} \\log a^{ [2] (i)} + (1-y^{(i)})\\log (1-a^{ [2] (i)} )\\large )\\small\\tag{2}$$\n", + "\n", + "you can do it in one line of code in tensorflow!\n", + "\n", + "**Exercise**: Implement the cross entropy loss. The function you will use is: \n", + "\n", + "\n", + "- `tf.nn.sigmoid_cross_entropy_with_logits(logits = ..., labels = ...)`\n", + "\n", + "Your code should input `z`, compute the sigmoid (to get `a`) and then compute the cross entropy cost $J$. All this can be done using one call to `tf.nn.sigmoid_cross_entropy_with_logits`, which computes\n", + "\n", + "$$- \\frac{1}{m} \\sum_{i = 1}^m \\large ( \\small y^{(i)} \\log \\sigma(z^{[2](i)}) + (1-y^{(i)})\\log (1-\\sigma(z^{[2](i)})\\large )\\small\\tag{2}$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: cost\n", + "\n", + "def cost(logits, labels):\n", + " \"\"\"\n", + "    Computes the cost using the sigmoid cross entropy\n", + "    \n", + "    Arguments:\n", + "    logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)\n", + "    labels -- vector of labels y (1 or 0) \n", + " \n", + " Note: What we've been calling \"z\" and \"y\" in this class are respectively called \"logits\" and \"labels\" \n", + " in the TensorFlow documentation. So logits will feed into z, and labels into y. \n", + "    \n", + "    Returns:\n", + "    cost -- runs the session of the cost (formula (2))\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### \n", + " \n", + " # Create the placeholders for \"logits\" (z) and \"labels\" (y) (approx. 2 lines)\n", + " z = tf.placeholder(tf.float32, name=\"z\")\n", + " y = tf.placeholder(tf.float32, name=\"y\")\n", + " \n", + " # Use the loss function (approx. 1 line)\n", + " cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z, labels=y)\n", + " \n", + " # Create a session (approx. 1 line). See method 1 above.\n", + " sess = tf.Session()\n", + " \n", + " # Run the session (approx. 1 line).\n", + " cost = sess.run(cost, feed_dict={z: logits, y: labels})\n", + " \n", + " # Close the session (approx. 1 line). See method 1 above.\n", + " sess.close()\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = [ 1.00538719 1.03664088 0.41385433 0.39956614]\n" + ] + } + ], + "source": [ + "logits = sigmoid(np.array([0.2, 0.4, 0.7, 0.9]))\n", + "cost = cost(logits, np.array([0, 0, 1, 1]))\n", + "print (\"cost = \" + str(cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected Output** : \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **cost**\n", + " \n", + " [ 1.00538719 1.03664088 0.41385433 0.39956614]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 - Using One Hot encodings\n", + "\n", + "Many times in deep learning you will have a y vector with numbers ranging from 0 to C-1, where C is the number of classes. If C is for example 4, then you might have the following y vector which you will need to convert as follows:\n", + "\n", + "\n", + "\n", + "\n", + "This is called a \"one hot\" encoding, because in the converted representation exactly one element of each column is \"hot\" (meaning set to 1). To do this conversion in numpy, you might have to write a few lines of code. In tensorflow, you can use one line of code: \n", + "\n", + "- tf.one_hot(labels, depth, axis) \n", + "\n", + "**Exercise:** Implement the function below to take one vector of labels and the total number of classes $C$, and return the one hot encoding. Use `tf.one_hot()` to do this. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: one_hot_matrix\n", + "\n", + "def one_hot_matrix(labels, C):\n", + " \"\"\"\n", + " Creates a matrix where the i-th row corresponds to the ith class number and the jth column\n", + " corresponds to the jth training example. So if example j had a label i. Then entry (i,j) \n", + " will be 1. \n", + " \n", + " Arguments:\n", + " labels -- vector containing the labels \n", + " C -- number of classes, the depth of the one hot dimension\n", + " \n", + " Returns: \n", + " one_hot -- one hot matrix\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)\n", + " C = tf.constant(C, name='C')\n", + " \n", + " # Use tf.one_hot, be careful with the axis (approx. 1 line)\n", + " one_hot_matrix = tf.one_hot(indices=labels, depth=C, axis=0)\n", + " \n", + " # Create the session (approx. 1 line)\n", + " sess = tf.Session()\n", + " \n", + " # Run the session (approx. 1 line)\n", + " one_hot = sess.run(one_hot_matrix)\n", + " \n", + " # Close the session (approx. 1 line). See method 1 above.\n", + " sess.close()\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return one_hot" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "one_hot = [[ 0. 0. 0. 1. 0. 0.]\n", + " [ 1. 0. 0. 0. 0. 1.]\n", + " [ 0. 1. 0. 0. 1. 0.]\n", + " [ 0. 0. 1. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "labels = np.array([1,2,3,0,2,1])\n", + "one_hot = one_hot_matrix(labels, C=4)\n", + "print (\"one_hot = \" + str(one_hot))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **one_hot**\n", + " \n", + " [[ 0. 0. 0. 1. 0. 0.]\n", + " [ 1. 0. 0. 0. 0. 1.]\n", + " [ 0. 1. 0. 0. 1. 0.]\n", + " [ 0. 0. 1. 0. 0. 0.]]\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.5 - Initialize with zeros and ones\n", + "\n", + "Now you will learn how to initialize a vector of zeros and ones. The function you will be calling is `tf.ones()`. To initialize with zeros you could use tf.zeros() instead. These functions take in a shape and return an array of dimension shape full of zeros and ones respectively. \n", + "\n", + "**Exercise:** Implement the function below to take in a shape and to return an array (of the shape's dimension of ones). \n", + "\n", + " - tf.ones(shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: ones\n", + "\n", + "def ones(shape):\n", + " \"\"\"\n", + " Creates an array of ones of dimension shape\n", + " \n", + " Arguments:\n", + " shape -- shape of the array you want to create\n", + " \n", + " Returns: \n", + " ones -- array containing only ones\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Create \"ones\" tensor using tf.ones(...). (approx. 1 line)\n", + " ones = tf.ones(shape)\n", + " \n", + " # Create the session (approx. 1 line)\n", + " sess = tf.Session()\n", + " \n", + " # Run the session to compute 'ones' (approx. 1 line)\n", + " ones = sess.run(ones)\n", + " \n", + " # Close the session (approx. 1 line). See method 1 above.\n", + " sess.close()\n", + " \n", + " ### END CODE HERE ###\n", + " return ones" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ones = [ 1. 1. 1.]\n" + ] + } + ], + "source": [ + "print (\"ones = \" + str(ones([3])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output:**\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **ones**\n", + " \n", + " [ 1. 1. 1.]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Building your first neural network in tensorflow\n", + "\n", + "In this part of the assignment you will build a neural network using tensorflow. Remember that there are two parts to implement a tensorflow model:\n", + "\n", + "- Create the computation graph\n", + "- Run the graph\n", + "\n", + "Let's delve into the problem you'd like to solve!\n", + "\n", + "### 2.0 - Problem statement: SIGNS Dataset\n", + "\n", + "One afternoon, with some friends we decided to teach our computers to decipher sign language. We spent a few hours taking pictures in front of a white wall and came up with the following dataset. It's now your job to build an algorithm that would facilitate communications from a speech-impaired person to someone who doesn't understand sign language.\n", + "\n", + "- **Training set**: 1080 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (180 pictures per number).\n", + "- **Test set**: 120 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (20 pictures per number).\n", + "\n", + "Note that this is a subset of the SIGNS dataset. The complete dataset contains many more signs.\n", + "\n", + "Here are examples for each number, and how an explanation of how we represent the labels. These are the original pictures, before we lowered the image resolutoion to 64 by 64 pixels.\n", + "
**Figure 1**: SIGNS dataset
\n", + "\n", + "\n", + "Run the following code to load the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Loading the dataset\n", + "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change the index below and run the cell to visualize some examples in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 5\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example of a picture\n", + "index = 0\n", + "plt.imshow(X_train_orig[index])\n", + "print (\"y = \" + str(np.squeeze(Y_train_orig[:, index])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual you flatten the image dataset, then normalize it by dividing by 255. On top of that, you will convert each label to a one-hot vector as shown in Figure 1. Run the cell below to do so." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of training examples = 1080\n", + "number of test examples = 120\n", + "X_train shape: (12288, 1080)\n", + "Y_train shape: (6, 1080)\n", + "X_test shape: (12288, 120)\n", + "Y_test shape: (6, 120)\n" + ] + } + ], + "source": [ + "# Flatten the training and test images\n", + "X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T\n", + "X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T\n", + "# Normalize image vectors\n", + "X_train = X_train_flatten / 255.\n", + "X_test = X_test_flatten / 255.\n", + "# Convert training and test labels to one hot matrices\n", + "Y_train = convert_to_one_hot(Y_train_orig, 6)\n", + "Y_test = convert_to_one_hot(Y_test_orig, 6)\n", + "\n", + "print(\"number of training examples = \" + str(X_train.shape[1]))\n", + "print(\"number of test examples = \" + str(X_test.shape[1]))\n", + "print(\"X_train shape: \" + str(X_train.shape))\n", + "print(\"Y_train shape: \" + str(Y_train.shape))\n", + "print(\"X_test shape: \" + str(X_test.shape))\n", + "print(\"Y_test shape: \" + str(Y_test.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note** that 12288 comes from $64 \\times 64 \\times 3$. Each image is square, 64 by 64 pixels, and 3 is for the RGB colors. Please make sure all these shapes make sense to you before continuing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Your goal** is to build an algorithm capable of recognizing a sign with high accuracy. To do so, you are going to build a tensorflow model that is almost the same as one you have previously built in numpy for cat recognition (but now using a softmax output). It is a great occasion to compare your numpy implementation to the tensorflow one. \n", + "\n", + "**The model** is *LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX*. The SIGMOID output layer has been converted to a SOFTMAX. A SOFTMAX layer generalizes SIGMOID to when there are more than two classes. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - Create placeholders\n", + "\n", + "Your first task is to create placeholders for `X` and `Y`. This will allow you to later pass your training data in when you run your session. \n", + "\n", + "**Exercise:** Implement the function below to create the placeholders in tensorflow." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: create_placeholders\n", + "\n", + "def create_placeholders(n_x, n_y):\n", + " \"\"\"\n", + " Creates the placeholders for the tensorflow session.\n", + " \n", + " Arguments:\n", + " n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)\n", + " n_y -- scalar, number of classes (from 0 to 5, so -> 6)\n", + " \n", + " Returns:\n", + " X -- placeholder for the data input, of shape [n_x, None] and dtype \"float\"\n", + " Y -- placeholder for the input labels, of shape [n_y, None] and dtype \"float\"\n", + " \n", + " Tips:\n", + " - You will use None because it let's us be flexible on the number of examples you will for the placeholders.\n", + " In fact, the number of examples during test/train is different.\n", + " \"\"\"\n", + "\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " X = tf.placeholder(tf.float32, [n_x, None], name=\"X\")\n", + " Y = tf.placeholder(tf.float32, [n_y, None], name=\"Y\")\n", + " ### END CODE HERE ###\n", + " \n", + " return X, Y" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X = Tensor(\"X:0\", shape=(12288, ?), dtype=float32)\n", + "Y = Tensor(\"Y:0\", shape=(6, ?), dtype=float32)\n" + ] + } + ], + "source": [ + "X, Y = create_placeholders(12288, 6)\n", + "print(\"X = \" + str(X))\n", + "print(\"Y = \" + str(Y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **X**\n", + " \n", + " Tensor(\"Placeholder_1:0\", shape=(12288, ?), dtype=float32) (not necessarily Placeholder_1)\n", + "
\n", + " **Y**\n", + " \n", + " Tensor(\"Placeholder_2:0\", shape=(6, ?), dtype=float32) (not necessarily Placeholder_2)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 - Initializing the parameters\n", + "\n", + "Your second task is to initialize the parameters in tensorflow.\n", + "\n", + "**Exercise:** Implement the function below to initialize the parameters in tensorflow. You are going use Xavier Initialization for weights and Zero Initialization for biases. The shapes are given below. As an example, to help you, for W1 and b1 you could use: \n", + "\n", + "```python\n", + "W1 = tf.get_variable(\"W1\", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))\n", + "b1 = tf.get_variable(\"b1\", [25,1], initializer = tf.zeros_initializer())\n", + "```\n", + "Please use `seed = 1` to make sure your results match ours." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters():\n", + " \"\"\"\n", + " Initializes parameters to build a neural network with tensorflow. The shapes are:\n", + " W1 : [25, 12288]\n", + " b1 : [25, 1]\n", + " W2 : [12, 25]\n", + " b2 : [12, 1]\n", + " W3 : [6, 12]\n", + " b3 : [6, 1]\n", + " \n", + " Returns:\n", + " parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3\n", + " \"\"\"\n", + " \n", + " tf.set_random_seed(1) # so that your \"random\" numbers match ours\n", + " \n", + " ### START CODE HERE ### (approx. 6 lines of code)\n", + " W1 = tf.get_variable(\"W1\", [25, 12288], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", + " b1 = tf.get_variable(\"b1\", [25, 1], initializer = tf.zeros_initializer())\n", + " W2 = tf.get_variable(\"W2\", [12, 25], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", + " b2 = tf.get_variable(\"b2\", [12, 1], initializer = tf.zeros_initializer())\n", + " W3 = tf.get_variable(\"W3\", [6, 12], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", + " b3 = tf.get_variable(\"b3\", [6, 1], initializer = tf.zeros_initializer())\n", + " ### END CODE HERE ###\n", + "\n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2,\n", + " \"W3\": W3,\n", + " \"b3\": b3}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = \n", + "b1 = \n", + "W2 = \n", + "b2 = \n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "with tf.Session() as sess:\n", + " parameters = initialize_parameters()\n", + " print(\"W1 = \" + str(parameters[\"W1\"]))\n", + " print(\"b1 = \" + str(parameters[\"b1\"]))\n", + " print(\"W2 = \" + str(parameters[\"W2\"]))\n", + " print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **W1**\n", + " \n", + " < tf.Variable 'W1:0' shape=(25, 12288) dtype=float32_ref >\n", + "
\n", + " **b1**\n", + " \n", + " < tf.Variable 'b1:0' shape=(25, 1) dtype=float32_ref >\n", + "
\n", + " **W2**\n", + " \n", + " < tf.Variable 'W2:0' shape=(12, 25) dtype=float32_ref >\n", + "
\n", + " **b2**\n", + " \n", + " < tf.Variable 'b2:0' shape=(12, 1) dtype=float32_ref >\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the parameters haven't been evaluated yet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 - Forward propagation in tensorflow \n", + "\n", + "You will now implement the forward propagation module in tensorflow. The function will take in a dictionary of parameters and it will complete the forward pass. The functions you will be using are: \n", + "\n", + "- `tf.add(...,...)` to do an addition\n", + "- `tf.matmul(...,...)` to do a matrix multiplication\n", + "- `tf.nn.relu(...)` to apply the ReLU activation\n", + "\n", + "**Question:** Implement the forward pass of the neural network. We commented for you the numpy equivalents so that you can compare the tensorflow implementation to numpy. It is important to note that the forward propagation stops at `z3`. The reason is that in tensorflow the last linear layer output is given as input to the function computing the loss. Therefore, you don't need `a3`!\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(X, parameters):\n", + " \"\"\"\n", + " Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX\n", + " \n", + " Arguments:\n", + " X -- input dataset placeholder, of shape (input size, number of examples)\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\"\n", + " the shapes are given in initialize_parameters\n", + "\n", + " Returns:\n", + " Z3 -- the output of the last LINEAR unit\n", + " \"\"\"\n", + " \n", + " # Retrieve the parameters from the dictionary \"parameters\" \n", + " W1 = parameters['W1']\n", + " b1 = parameters['b1']\n", + " W2 = parameters['W2']\n", + " b2 = parameters['b2']\n", + " W3 = parameters['W3']\n", + " b3 = parameters['b3']\n", + " \n", + " ### START CODE HERE ### (approx. 5 lines) # Numpy Equivalents:\n", + " Z1 = tf.add(tf.matmul(W1, X), b1) # Z1 = np.dot(W1, X) + b1\n", + " A1 = tf.nn.relu(Z1) # A1 = relu(Z1)\n", + " Z2 = tf.add(tf.matmul(W2, A1), b2) # Z2 = np.dot(W2, a1) + b2\n", + " A2 = tf.nn.relu(Z2) # A2 = relu(Z2)\n", + " Z3 = tf.add(tf.matmul(W3, A2), b3) # Z3 = np.dot(W3,Z2) + b3\n", + " ### END CODE HERE ###\n", + " \n", + " return Z3" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z3 = Tensor(\"Add_2:0\", shape=(6, ?), dtype=float32)\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as sess:\n", + " X, Y = create_placeholders(12288, 6)\n", + " parameters = initialize_parameters()\n", + " Z3 = forward_propagation(X, parameters)\n", + " print(\"Z3 = \" + str(Z3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Z3**\n", + " \n", + " Tensor(\"Add_2:0\", shape=(6, ?), dtype=float32)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may have noticed that the forward propagation doesn't output any cache. You will understand why below, when we get to brackpropagation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Compute cost\n", + "\n", + "As seen before, it is very easy to compute the cost using:\n", + "```python\n", + "tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = ..., labels = ...))\n", + "```\n", + "**Question**: Implement the cost function below. \n", + "- It is important to know that the \"`logits`\" and \"`labels`\" inputs of `tf.nn.softmax_cross_entropy_with_logits` are expected to be of shape (number of examples, num_classes). We have thus transposed Z3 and Y for you.\n", + "- Besides, `tf.reduce_mean` basically does the summation over the examples." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost \n", + "\n", + "def compute_cost(Z3, Y):\n", + " \"\"\"\n", + " Computes the cost\n", + " \n", + " Arguments:\n", + " Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)\n", + " Y -- \"true\" labels vector placeholder, same shape as Z3\n", + " \n", + " Returns:\n", + " cost - Tensor of the cost function\n", + " \"\"\"\n", + " \n", + " # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)\n", + " logits = tf.transpose(Z3)\n", + " labels = tf.transpose(Y)\n", + " \n", + " ### START CODE HERE ### (1 line of code)\n", + " cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n", + " ### END CODE HERE ###\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = Tensor(\"Mean:0\", shape=(), dtype=float32)\n" + ] + } + ], + "source": [ + "tf.reset_default_graph()\n", + "\n", + "with tf.Session() as sess:\n", + " X, Y = create_placeholders(12288, 6)\n", + " parameters = initialize_parameters()\n", + " Z3 = forward_propagation(X, parameters)\n", + " cost = compute_cost(Z3, Y)\n", + " print(\"cost = \" + str(cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **cost**\n", + " \n", + " Tensor(\"Mean:0\", shape=(), dtype=float32)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 - Backward propagation & parameter updates\n", + "\n", + "This is where you become grateful to programming frameworks. All the backpropagation and the parameters update is taken care of in 1 line of code. It is very easy to incorporate this line in the model.\n", + "\n", + "After you compute the cost function. You will create an \"`optimizer`\" object. You have to call this object along with the cost when running the tf.session. When called, it will perform an optimization on the given cost with the chosen method and learning rate.\n", + "\n", + "For instance, for gradient descent the optimizer would be:\n", + "```python\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost)\n", + "```\n", + "\n", + "To make the optimization you would do:\n", + "```python\n", + "_ , c = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n", + "```\n", + "\n", + "This computes the backpropagation by passing through the tensorflow graph in the reverse order. From cost to inputs.\n", + "\n", + "**Note** When coding, we often use `_` as a \"throwaway\" variable to store values that we won't need to use later. Here, `_` takes on the evaluated value of `optimizer`, which we don't need (and `c` takes the value of the `cost` variable). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6 - Building the model\n", + "\n", + "Now, you will bring it all together! \n", + "\n", + "**Exercise:** Implement the model. You will be calling the functions you had previously implemented." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,\n", + " num_epochs = 1500, minibatch_size = 32, print_cost = True):\n", + " \"\"\"\n", + " Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.\n", + " \n", + " Arguments:\n", + " X_train -- training set, of shape (input size = 12288, number of training examples = 1080)\n", + " Y_train -- test set, of shape (output size = 6, number of training examples = 1080)\n", + " X_test -- training set, of shape (input size = 12288, number of training examples = 120)\n", + " Y_test -- test set, of shape (output size = 6, number of test examples = 120)\n", + " learning_rate -- learning rate of the optimization\n", + " num_epochs -- number of epochs of the optimization loop\n", + " minibatch_size -- size of a minibatch\n", + " print_cost -- True to print the cost every 100 epochs\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables\n", + " tf.set_random_seed(1) # to keep consistent results\n", + " seed = 3 # to keep consistent results\n", + " (n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)\n", + " n_y = Y_train.shape[0] # n_y : output size\n", + " costs = [] # To keep track of the cost\n", + " \n", + " # Create Placeholders of shape (n_x, n_y)\n", + " ### START CODE HERE ### (1 line)\n", + " X, Y = create_placeholders(n_x, n_y)\n", + " ### END CODE HERE ###\n", + "\n", + " # Initialize parameters\n", + " ### START CODE HERE ### (1 line)\n", + " parameters = initialize_parameters()\n", + " ### END CODE HERE ###\n", + " \n", + " # Forward propagation: Build the forward propagation in the tensorflow graph\n", + " ### START CODE HERE ### (1 line)\n", + " Z3 = forward_propagation(X, parameters)\n", + " ### END CODE HERE ###\n", + " \n", + " # Cost function: Add cost function to tensorflow graph\n", + " ### START CODE HERE ### (1 line)\n", + " cost = compute_cost(Z3, Y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.\n", + " ### START CODE HERE ### (1 line)\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + " ### END CODE HERE ###\n", + " \n", + " # Initialize all the variables\n", + " init = tf.global_variables_initializer()\n", + "\n", + " # Start the session to compute the tensorflow graph\n", + " with tf.Session() as sess:\n", + " \n", + " # Run the initialization\n", + " sess.run(init)\n", + " \n", + " # Do the training loop\n", + " for epoch in range(num_epochs):\n", + "\n", + " epoch_cost = 0. # Defines a cost related to an epoch\n", + " num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\n", + " seed = seed + 1\n", + " minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n", + "\n", + " for minibatch in minibatches:\n", + "\n", + " # Select a minibatch\n", + " (minibatch_X, minibatch_Y) = minibatch\n", + " \n", + " # IMPORTANT: The line that runs the graph on a minibatch.\n", + " # Run the session to execute the \"optimizer\" and the \"cost\", the feedict should contain a minibatch for (X,Y).\n", + " ### START CODE HERE ### (1 line)\n", + " _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n", + " ### END CODE HERE ###\n", + " \n", + " epoch_cost += minibatch_cost / num_minibatches\n", + "\n", + " # Print the cost every epoch\n", + " if print_cost == True and epoch % 100 == 0:\n", + " print (\"Cost after epoch %i: %f\" % (epoch, epoch_cost))\n", + " if print_cost == True and epoch % 5 == 0:\n", + " costs.append(epoch_cost)\n", + " \n", + " # plot the cost\n", + " plt.plot(np.squeeze(costs))\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (per tens)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + "\n", + " # lets save the parameters in a variable\n", + " parameters = sess.run(parameters)\n", + " print(\"Parameters have been trained!\")\n", + "\n", + " # Calculate the correct predictions\n", + " correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))\n", + "\n", + " # Calculate accuracy on the test set\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + "\n", + " print(\"Train Accuracy:\", accuracy.eval({X: X_train, Y: Y_train}))\n", + " print(\"Test Accuracy:\", accuracy.eval({X: X_test, Y: Y_test}))\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Run the following cell to train your model! On our machine it takes about 5 minutes. Your \"Cost after epoch 100\" should be 1.016458. If it's not, don't waste time; interrupt the training by clicking on the square (⬛) in the upper bar of the notebook, and try to correct your code. If it is the correct cost, take a break and come back in 5 minutes!" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after epoch 0: 1.855702\n", + "Cost after epoch 100: 1.016458\n", + "Cost after epoch 200: 0.733102\n", + "Cost after epoch 300: 0.572940\n", + "Cost after epoch 400: 0.468774\n", + "Cost after epoch 500: 0.381021\n", + "Cost after epoch 600: 0.313822\n", + "Cost after epoch 700: 0.254158\n", + "Cost after epoch 800: 0.203829\n", + "Cost after epoch 900: 0.166421\n", + "Cost after epoch 1000: 0.141486\n", + "Cost after epoch 1100: 0.107580\n", + "Cost after epoch 1200: 0.086270\n", + "Cost after epoch 1300: 0.059371\n", + "Cost after epoch 1400: 0.052228\n" + ] + }, + { + "data": { + "image/png": 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5edUOZq+pYMuuGmat3MG/Fm9lSJ8sPn/OSP6xcDMfOGEw758wMOpwRbo9JQzp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\ni4hIx+hWZxhmVg6sP8LN+wI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters have been trained!\n", + "Train Accuracy: 0.999074\n", + "Test Accuracy: 0.716667\n" + ] + } + ], + "source": [ + "parameters = model(X_train, Y_train, X_test, Y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **Train Accuracy**\n", + " \n", + " 0.999074\n", + "
\n", + " **Test Accuracy**\n", + " \n", + " 0.716667\n", + "
\n", + "\n", + "Amazing, your algorithm can recognize a sign representing a figure between 0 and 5 with 71.7% accuracy.\n", + "\n", + "**Insights**:\n", + "- Your model seems big enough to fit the training set well. However, given the difference between train and test accuracy, you could try to add L2 or dropout regularization to reduce overfitting. \n", + "- Think about the session as a block of code to train the model. Each time you run the session on a minibatch, it trains the parameters. In total you have run the session a large number of times (1500 epochs) until you obtained well trained parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.7 - Test with your own image (optional / ungraded exercise)\n", + "\n", + "Congratulations on finishing this assignment. You can now take a picture of your hand and see the output of your model. To do that:\n", + " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", + " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", + " 3. Write your image's name in the following code\n", + " 4. Run the code and check if the algorithm is right!" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your algorithm predicts: y = 3\n" + ] + }, + { + "data": { + "image/png": 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Dq9LbYYSnj7UGmkQBXyn3DTc6jcM7amsNFv3eJagLcgqTjgyhRvbGwlgsDeAM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\n1jtJbaDkJc9QjelRc8E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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy\n", + "from PIL import Image\n", + "from scipy import ndimage\n", + "\n", + "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n", + "my_image = \"thumbs_up.jpg\"\n", + "## END CODE HERE ##\n", + "\n", + "# We preprocess your image to fit your algorithm.\n", + "fname = \"images/\" + my_image\n", + "image = np.array(ndimage.imread(fname, flatten=False))\n", + "my_image = scipy.misc.imresize(image, size=(64, 64)).reshape((1, 64 * 64 * 3)).T\n", + "my_image_prediction = predict(my_image, parameters)\n", + "\n", + "plt.imshow(image)\n", + "print(\"Your algorithm predicts: y = \" + str(np.squeeze(my_image_prediction)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You indeed deserved a \"thumbs-up\" although as you can see the algorithm seems to classify it incorrectly. The reason is that the training set doesn't contain any \"thumbs-up\", so the model doesn't know how to deal with it! We call that a \"mismatched data distribution\" and it is one of the various of the next course on \"Structuring Machine Learning Projects\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "**What you should remember**:\n", + "- Tensorflow is a programming framework used in deep learning\n", + "- The two main object classes in tensorflow are Tensors and Operators. \n", + "- When you code in tensorflow you have to take the following steps:\n", + " - Create a graph containing Tensors (Variables, Placeholders ...) and Operations (tf.matmul, tf.add, ...)\n", + " - Create a session\n", + " - Initialize the session\n", + " - Run the session to execute the graph\n", + "- You can execute the graph multiple times as you've seen in model()\n", + "- The backpropagation and optimization is automatically done when running the session on the \"optimizer\" object." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "deep-neural-network", + "graded_item_id": "BFd89", + "launcher_item_id": "AH2rK" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/images/1Dgrad_kiank.png b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/images/1Dgrad_kiank.png new file mode 100644 index 0000000..dc49c27 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Improving Deep Neural Networks Hyperparameter tuning, Regularization and 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Network - Step by Step.ipynb @@ -0,0 +1,1510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building your Deep Neural Network: Step by Step\n", + "\n", + "Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!\n", + "\n", + "- In this notebook, you will implement all the functions required to build a deep neural network.\n", + "- In the next assignment, you will use these functions to build a deep neural network for image classification.\n", + "\n", + "**After this assignment you will be able to:**\n", + "- Use non-linear units like ReLU to improve your model\n", + "- Build a deeper neural network (with more than 1 hidden layer)\n", + "- Implement an easy-to-use neural network class\n", + "\n", + "**Notation**:\n", + "- Superscript $[l]$ denotes a quantity associated with the $l^{th}$ layer. \n", + " - Example: $a^{[L]}$ is the $L^{th}$ layer activation. $W^{[L]}$ and $b^{[L]}$ are the $L^{th}$ layer parameters.\n", + "- Superscript $(i)$ denotes a quantity associated with the $i^{th}$ example. \n", + " - Example: $x^{(i)}$ is the $i^{th}$ training example.\n", + "- Lowerscript $i$ denotes the $i^{th}$ entry of a vector.\n", + " - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the $l^{th}$ layer's activations).\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages\n", + "\n", + "Let's first import all the packages that you will need during this assignment. \n", + "- [numpy](www.numpy.org) is the main package for scientific computing with Python.\n", + "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n", + "- dnn_utils provides some necessary functions for this notebook.\n", + "- testCases provides some test cases to assess the correctness of your functions\n", + "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work. Please don't change the seed. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "from testCases import *\n", + "from dnn_utils import sigmoid, sigmoid_backward, relu, relu_backward\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 2 - Outline of the Assignment\n", + "\n", + "To build your neural network, you will be implementing several \"helper functions\". These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps. Here is an outline of this assignment, you will:\n", + "\n", + "- Initialize the parameters for a two-layer network and for an $L$-layer neural network.\n", + "- Implement the forward propagation module (shown in purple in the figure below).\n", + " - Complete the LINEAR part of a layer's forward propagation step (resulting in $Z^{[l]}$).\n", + " - We give you the ACTIVATION function (relu/sigmoid).\n", + " - Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function.\n", + " - Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer $L$). This gives you a new L_model_forward function.\n", + "- Compute the loss.\n", + "- Implement the backward propagation module (denoted in red in the figure below).\n", + " - Complete the LINEAR part of a layer's backward propagation step.\n", + " - We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward) \n", + " - Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function.\n", + " - Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function\n", + "- Finally update the parameters.\n", + "\n", + "\n", + "
**Figure 1**

\n", + "\n", + "\n", + "**Note** that for every forward function, there is a corresponding backward function. That is why at every step of your forward module you will be storing some values in a cache. The cached values are useful for computing gradients. In the backpropagation module you will then use the cache to calculate the gradients. This assignment will show you exactly how to carry out each of these steps. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 3 - Initialization\n", + "\n", + "You will write two helper functions that will initialize the parameters for your model. The first function will be used to initialize parameters for a two layer model. The second one will generalize this initialization process to $L$ layers.\n", + "\n", + "### 3.1 - 2-layer Neural Network\n", + "\n", + "**Exercise**: Create and initialize the parameters of the 2-layer neural network.\n", + "\n", + "**Instructions**:\n", + "- The model's structure is: *LINEAR -> RELU -> LINEAR -> SIGMOID*. \n", + "- Use random initialization for the weight matrices. Use `np.random.randn(shape)*0.01` with the correct shape.\n", + "- Use zero initialization for the biases. Use `np.zeros(shape)`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters(n_x, n_h, n_y):\n", + " \"\"\"\n", + " Argument:\n", + " n_x -- size of the input layer\n", + " n_h -- size of the hidden layer\n", + " n_y -- size of the output layer\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your parameters:\n", + " W1 -- weight matrix of shape (n_h, n_x)\n", + " b1 -- bias vector of shape (n_h, 1)\n", + " W2 -- weight matrix of shape (n_y, n_h)\n", + " b2 -- bias vector of shape (n_y, 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = np.random.randn(n_h, n_x) * 0.01\n", + " b1 = np.zeros(shape=(n_h, 1))\n", + " W2 = np.random.randn(n_y, n_h) * 0.01\n", + " b2 = np.zeros(shape=(n_y, 1))\n", + " ### END CODE HERE ###\n", + " \n", + " assert(W1.shape == (n_h, n_x))\n", + " assert(b1.shape == (n_h, 1))\n", + " assert(W2.shape == (n_y, n_h))\n", + " assert(b2.shape == (n_y, 1))\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 0.01624345 -0.00611756]\n", + " [-0.00528172 -0.01072969]]\n", + "b1 = [[ 0.]\n", + " [ 0.]]\n", + "W2 = [[ 0.00865408 -0.02301539]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_parameters(2,2,1)\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[ 0.01624345 -0.00611756]\n", + " [-0.00528172 -0.01072969]]
**b1**[[ 0.]\n", + " [ 0.]]
**W2** [[ 0.00865408 -0.02301539]]
**b2** [[ 0.]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 3.2 - L-layer Neural Network\n", + "\n", + "The initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and bias vectors. When completing the `initialize_parameters_deep`, you should make sure that your dimensions match between each layer. Recall that $n^{[l]}$ is the number of units in layer $l$. Thus for example if the size of our input $X$ is $(12288, 209)$ (with $m=209$ examples) then:\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
**Shape of W** **Shape of b** **Activation** **Shape of Activation**
**Layer 1** $(n^{[1]},12288)$ $(n^{[1]},1)$ $Z^{[1]} = W^{[1]} X + b^{[1]} $ $(n^{[1]},209)$
**Layer 2** $(n^{[2]}, n^{[1]})$ $(n^{[2]},1)$ $Z^{[2]} = W^{[2]} A^{[1]} + b^{[2]}$ $(n^{[2]}, 209)$
$\\vdots$ $\\vdots$ $\\vdots$ $\\vdots$ $\\vdots$
**Layer L-1** $(n^{[L-1]}, n^{[L-2]})$ $(n^{[L-1]}, 1)$ $Z^{[L-1]} = W^{[L-1]} A^{[L-2]} + b^{[L-1]}$ $(n^{[L-1]}, 209)$
**Layer L** $(n^{[L]}, n^{[L-1]})$ $(n^{[L]}, 1)$ $Z^{[L]} = W^{[L]} A^{[L-1]} + b^{[L]}$ $(n^{[L]}, 209)$
\n", + "\n", + "Remember that when we compute $W X + b$ in python, it carries out broadcasting. For example, if: \n", + "\n", + "$$ W = \\begin{bmatrix}\n", + " j & k & l\\\\\n", + " m & n & o \\\\\n", + " p & q & r \n", + "\\end{bmatrix}\\;\\;\\; X = \\begin{bmatrix}\n", + " a & b & c\\\\\n", + " d & e & f \\\\\n", + " g & h & i \n", + "\\end{bmatrix} \\;\\;\\; b =\\begin{bmatrix}\n", + " s \\\\\n", + " t \\\\\n", + " u\n", + "\\end{bmatrix}\\tag{2}$$\n", + "\n", + "Then $WX + b$ will be:\n", + "\n", + "$$ WX + b = \\begin{bmatrix}\n", + " (ja + kd + lg) + s & (jb + ke + lh) + s & (jc + kf + li)+ s\\\\\n", + " (ma + nd + og) + t & (mb + ne + oh) + t & (mc + nf + oi) + t\\\\\n", + " (pa + qd + rg) + u & (pb + qe + rh) + u & (pc + qf + ri)+ u\n", + "\\end{bmatrix}\\tag{3} $$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement initialization for an L-layer Neural Network. \n", + "\n", + "**Instructions**:\n", + "- The model's structure is *[LINEAR -> RELU] $ \\times$ (L-1) -> LINEAR -> SIGMOID*. I.e., it has $L-1$ layers using a ReLU activation function followed by an output layer with a sigmoid activation function.\n", + "- Use random initialization for the weight matrices. Use `np.random.rand(shape) * 0.01`.\n", + "- Use zeros initialization for the biases. Use `np.zeros(shape)`.\n", + "- We will store $n^{[l]}$, the number of units in different layers, in a variable `layer_dims`. For example, the `layer_dims` for the \"Planar Data classification model\" from last week would have been [2,4,1]: There were two inputs, one hidden layer with 4 hidden units, and an output layer with 1 output unit. Thus means `W1`'s shape was (4,2), `b1` was (4,1), `W2` was (1,4) and `b2` was (1,1). Now you will generalize this to $L$ layers! \n", + "- Here is the implementation for $L=1$ (one layer neural network). It should inspire you to implement the general case (L-layer neural network).\n", + "```python\n", + " if L == 1:\n", + " parameters[\"W\" + str(L)] = np.random.randn(layer_dims[1], layer_dims[0]) * 0.01\n", + " parameters[\"b\" + str(L)] = np.zeros((layer_dims[1], 1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters_deep\n", + "\n", + "def initialize_parameters_deep(layer_dims):\n", + " \"\"\"\n", + " Arguments:\n", + " layer_dims -- python array (list) containing the dimensions of each layer in our network\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n", + " Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])\n", + " bl -- bias vector of shape (layer_dims[l], 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(3)\n", + " parameters = {}\n", + " L = len(layer_dims) # number of layers in the network\n", + "\n", + " for l in range(1, L):\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) * 0.01\n", + " parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\n", + " ### END CODE HERE ###\n", + " \n", + " assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l - 1]))\n", + " assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))\n", + "\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]\n", + " [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]\n", + " [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]\n", + " [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]\n", + "b1 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.01185047 -0.0020565 0.01486148 0.00236716]\n", + " [-0.01023785 -0.00712993 0.00625245 -0.00160513]\n", + " [-0.00768836 -0.00230031 0.00745056 0.01976111]]\n", + "b2 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n" + ] + } + ], + "source": [ + "parameters = initialize_parameters_deep([5,4,3])\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]\n", + " [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]\n", + " [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]\n", + " [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]
**b1** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]
**W2** [[-0.01185047 -0.0020565 0.01486148 0.00236716]\n", + " [-0.01023785 -0.00712993 0.00625245 -0.00160513]\n", + " [-0.00768836 -0.00230031 0.00745056 0.01976111]]
**b2** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Forward propagation module\n", + "\n", + "### 4.1 - Linear Forward \n", + "Now that you have initialized your parameters, you will do the forward propagation module. You will start by implementing some basic functions that you will use later when implementing the model. You will complete three functions in this order:\n", + "\n", + "- LINEAR\n", + "- LINEAR -> ACTIVATION where ACTIVATION will be either ReLU or Sigmoid. \n", + "- [LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID (whole model)\n", + "\n", + "The linear forward module (vectorized over all the examples) computes the following equations:\n", + "\n", + "$$Z^{[l]} = W^{[l]}A^{[l-1]} +b^{[l]}\\tag{4}$$\n", + "\n", + "where $A^{[0]} = X$. \n", + "\n", + "**Exercise**: Build the linear part of forward propagation.\n", + "\n", + "**Reminder**:\n", + "The mathematical representation of this unit is $Z^{[l]} = W^{[l]}A^{[l-1]} +b^{[l]}$. You may also find `np.dot()` useful. If your dimensions don't match, printing `W.shape` may help." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: linear_forward\n", + "\n", + "def linear_forward(A, W, b):\n", + " \"\"\"\n", + " Implement the linear part of a layer's forward propagation.\n", + "\n", + " Arguments:\n", + " A -- activations from previous layer (or input data): (size of previous layer, number of examples)\n", + " W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n", + " b -- bias vector, numpy array of shape (size of the current layer, 1)\n", + "\n", + " Returns:\n", + " Z -- the input of the activation function, also called pre-activation parameter \n", + " cache -- a python dictionary containing \"A\", \"W\" and \"b\" ; stored for computing the backward pass efficiently\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " Z = np.dot(W, A) + b\n", + " ### END CODE HERE ###\n", + " \n", + " assert(Z.shape == (W.shape[0], A.shape[1]))\n", + " cache = (A, W, b)\n", + " \n", + " return Z, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z = [[ 3.1980455 7.85763489]]\n" + ] + } + ], + "source": [ + "A, W, b = linear_forward_test_case()\n", + "\n", + "Z, linear_cache = linear_forward(A, W, b)\n", + "print(\"Z = \" + str(Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Z** [[ 3.1980455 7.85763489]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 - Linear-Activation Forward\n", + "\n", + "In this notebook, you will use two activation functions:\n", + "\n", + "- **Sigmoid**: $\\sigma(Z) = \\sigma(W A + b) = \\frac{1}{ 1 + e^{-(W A + b)}}$. We have provided you with the `sigmoid` function. This function returns **two** items: the activation value \"`a`\" and a \"`cache`\" that contains \"`Z`\" (it's what we will feed in to the corresponding backward function). To use it you could just call: \n", + "``` python\n", + "A, activation_cache = sigmoid(Z)\n", + "```\n", + "\n", + "- **ReLU**: The mathematical formula for ReLu is $A = RELU(Z) = max(0, Z)$. We have provided you with the `relu` function. This function returns **two** items: the activation value \"`A`\" and a \"`cache`\" that contains \"`Z`\" (it's what we will feed in to the corresponding backward function). To use it you could just call:\n", + "``` python\n", + "A, activation_cache = relu(Z)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more convenience, you are going to group two functions (Linear and Activation) into one function (LINEAR->ACTIVATION). Hence, you will implement a function that does the LINEAR forward step followed by an ACTIVATION forward step.\n", + "\n", + "**Exercise**: Implement the forward propagation of the *LINEAR->ACTIVATION* layer. Mathematical relation is: $A^{[l]} = g(Z^{[l]}) = g(W^{[l]}A^{[l-1]} +b^{[l]})$ where the activation \"g\" can be sigmoid() or relu(). Use linear_forward() and the correct activation function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: linear_activation_forward\n", + "\n", + "def linear_activation_forward(A_prev, W, b, activation):\n", + " \"\"\"\n", + " Implement the forward propagation for the LINEAR->ACTIVATION layer\n", + "\n", + " Arguments:\n", + " A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)\n", + " W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n", + " b -- bias vector, numpy array of shape (size of the current layer, 1)\n", + " activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n", + "\n", + " Returns:\n", + " A -- the output of the activation function, also called the post-activation value \n", + " cache -- a python dictionary containing \"linear_cache\" and \"activation_cache\";\n", + " stored for computing the backward pass efficiently\n", + " \"\"\"\n", + " \n", + " if activation == \"sigmoid\":\n", + " # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " Z, linear_cache = linear_forward(A_prev, W, b)\n", + " A, activation_cache = sigmoid(Z)\n", + " ### END CODE HERE ###\n", + " \n", + " elif activation == \"relu\":\n", + " # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " Z, linear_cache = linear_forward(A_prev, W, b)\n", + " A, activation_cache = relu(Z)\n", + " ### END CODE HERE ###\n", + " \n", + " assert (A.shape == (W.shape[0], A_prev.shape[1]))\n", + " cache = (linear_cache, activation_cache)\n", + "\n", + " return A, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With sigmoid: A = [[ 0.96076066 0.99961336]]\n", + "With ReLU: A = [[ 3.1980455 7.85763489]]\n" + ] + } + ], + "source": [ + "A_prev, W, b = linear_activation_forward_test_case()\n", + "\n", + "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"sigmoid\")\n", + "print(\"With sigmoid: A = \" + str(A))\n", + "\n", + "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"relu\")\n", + "print(\"With ReLU: A = \" + str(A))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**With sigmoid: A ** [[ 0.96076066 0.99961336]]
**With ReLU: A ** [[ 3.1980455 7.85763489]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: In deep learning, the \"[LINEAR->ACTIVATION]\" computation is counted as a single layer in the neural network, not two layers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### d) L-Layer Model \n", + "\n", + "For even more convenience when implementing the $L$-layer Neural Net, you will need a function that replicates the previous one (`linear_activation_forward` with RELU) $L-1$ times, then follows that with one `linear_activation_forward` with SIGMOID.\n", + "\n", + "\n", + "
**Figure 2** : *[LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID* model

\n", + "\n", + "**Exercise**: Implement the forward propagation of the above model.\n", + "\n", + "**Instruction**: In the code below, the variable `AL` will denote $A^{[L]} = \\sigma(Z^{[L]}) = \\sigma(W^{[L]} A^{[L-1]} + b^{[L]})$. (This is sometimes also called `Yhat`, i.e., this is $\\hat{Y}$.) \n", + "\n", + "**Tips**:\n", + "- Use the functions you had previously written \n", + "- Use a for loop to replicate [LINEAR->RELU] (L-1) times\n", + "- Don't forget to keep track of the caches in the \"caches\" list. To add a new value `c` to a `list`, you can use `list.append(c)`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: L_model_forward\n", + "\n", + "def L_model_forward(X, parameters):\n", + " \"\"\"\n", + " Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation\n", + " \n", + " Arguments:\n", + " X -- data, numpy array of shape (input size, number of examples)\n", + " parameters -- output of initialize_parameters_deep()\n", + " \n", + " Returns:\n", + " AL -- last post-activation value\n", + " caches -- list of caches containing:\n", + " every cache of linear_relu_forward() (there are L-1 of them, indexed from 0 to L-2)\n", + " the cache of linear_sigmoid_forward() (there is one, indexed L-1)\n", + " \"\"\"\n", + "\n", + " caches = []\n", + " A = X\n", + " L = len(parameters) // 2 # number of layers in the neural network\n", + " \n", + " # Implement [LINEAR -> RELU]*(L-1). Add \"cache\" to the \"caches\" list.\n", + " for l in range(1, L):\n", + " A_prev = A \n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A, cache = linear_activation_forward(A_prev, \n", + " parameters['W' + str(l)], \n", + " parameters['b' + str(l)], \n", + " activation='relu')\n", + " caches.append(cache)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Implement LINEAR -> SIGMOID. Add \"cache\" to the \"caches\" list.\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " AL, cache = linear_activation_forward(A, \n", + " parameters['W' + str(L)], \n", + " parameters['b' + str(L)], \n", + " activation='sigmoid')\n", + " caches.append(cache)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " assert(AL.shape == (1, X.shape[1]))\n", + " \n", + " return AL, caches" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AL = [[ 0.0844367 0.92356858]]\n", + "Length of caches list = 2\n" + ] + } + ], + "source": [ + "X, parameters = L_model_forward_test_case()\n", + "AL, caches = L_model_forward(X, parameters)\n", + "print(\"AL = \" + str(AL))\n", + "print(\"Length of caches list = \" + str(len(caches)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**AL** [[ 0.0844367 0.92356858]]
**Length of caches list ** 2
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Now you have a full forward propagation that takes the input X and outputs a row vector $A^{[L]}$ containing your predictions. It also records all intermediate values in \"caches\". Using $A^{[L]}$, you can compute the cost of your predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - Cost function\n", + "\n", + "Now you will implement forward and backward propagation. You need to compute the cost, because you want to check if your model is actually learning.\n", + "\n", + "**Exercise**: Compute the cross-entropy cost $J$, using the following formula: $$-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} (y^{(i)}\\log\\left(a^{[L] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right)) \\tag{7}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost\n", + "\n", + "def compute_cost(AL, Y):\n", + " \"\"\"\n", + " Implement the cost function defined by equation (7).\n", + "\n", + " Arguments:\n", + " AL -- probability vector corresponding to your label predictions, shape (1, number of examples)\n", + " Y -- true \"label\" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)\n", + "\n", + " Returns:\n", + " cost -- cross-entropy cost\n", + " \"\"\"\n", + " \n", + " m = Y.shape[1]\n", + "\n", + " # Compute loss from aL and y.\n", + " ### START CODE HERE ### (≈ 1 lines of code)\n", + " cost = (-1 / m) * np.sum(np.multiply(Y, np.log(AL)) + np.multiply(1 - Y, np.log(1 - AL)))\n", + " ### END CODE HERE ###\n", + " \n", + " cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).\n", + " assert(cost.shape == ())\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 0.414931599615\n" + ] + } + ], + "source": [ + "Y, AL = compute_cost_test_case()\n", + "\n", + "print(\"cost = \" + str(compute_cost(AL, Y)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
**cost** 0.41493159961539694
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 - Backward propagation module\n", + "\n", + "Just like with forward propagation, you will implement helper functions for backpropagation. Remember that back propagation is used to calculate the gradient of the loss function with respect to the parameters. \n", + "\n", + "**Reminder**: \n", + "\n", + "
**Figure 3** : Forward and Backward propagation for *LINEAR->RELU->LINEAR->SIGMOID*
*The purple blocks represent the forward propagation, and the red blocks represent the backward propagation.*
\n", + "\n", + "\n", + "\n", + "Now, similar to forward propagation, you are going to build the backward propagation in three steps:\n", + "- LINEAR backward\n", + "- LINEAR -> ACTIVATION backward where ACTIVATION computes the derivative of either the ReLU or sigmoid activation\n", + "- [LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID backward (whole model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1 - Linear backward\n", + "\n", + "For layer $l$, the linear part is: $Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}$ (followed by an activation).\n", + "\n", + "Suppose you have already calculated the derivative $dZ^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial Z^{[l]}}$. You want to get $(dW^{[l]}, db^{[l]} dA^{[l-1]})$.\n", + "\n", + "\n", + "
**Figure 4**
\n", + "\n", + "The three outputs $(dW^{[l]}, db^{[l]}, dA^{[l]})$ are computed using the input $dZ^{[l]}$.Here are the formulas you need:\n", + "$$ dW^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial W^{[l]}} = \\frac{1}{m} dZ^{[l]} A^{[l-1] T} \\tag{8}$$\n", + "$$ db^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial b^{[l]}} = \\frac{1}{m} \\sum_{i = 1}^{m} dZ^{[l](i)}\\tag{9}$$\n", + "$$ dA^{[l-1]} = \\frac{\\partial \\mathcal{L} }{\\partial A^{[l-1]}} = W^{[l] T} dZ^{[l]} \\tag{10}$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Use the 3 formulas above to implement linear_backward()." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: linear_backward\n", + "\n", + "def linear_backward(dZ, cache):\n", + " \"\"\"\n", + " Implement the linear portion of backward propagation for a single layer (layer l)\n", + "\n", + " Arguments:\n", + " dZ -- Gradient of the cost with respect to the linear output (of current layer l)\n", + " cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer\n", + "\n", + " Returns:\n", + " dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n", + " dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n", + " db -- Gradient of the cost with respect to b (current layer l), same shape as b\n", + " \"\"\"\n", + " A_prev, W, b = cache\n", + " m = A_prev.shape[1]\n", + "\n", + " ### START CODE HERE ### (≈ 3 lines of code)\n", + " dW = np.dot(dZ, cache[0].T) / m\n", + " db = np.squeeze(np.sum(dZ, axis=1, keepdims=True)) / m\n", + " dA_prev = np.dot(cache[1].T, dZ)\n", + " ### END CODE HERE ###\n", + " \n", + " assert (dA_prev.shape == A_prev.shape)\n", + " assert (dW.shape == W.shape)\n", + " assert (isinstance(db, float))\n", + " \n", + " return dA_prev, dW, db" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dA_prev = [[ 2.38272385 5.85438014]\n", + " [ 6.31969219 15.52755701]\n", + " [ -3.97876302 -9.77586689]]\n", + "dW = [[ 2.77870358 -0.05500058 -5.13144969]]\n", + "db = 5.527840195\n" + ] + } + ], + "source": [ + "# Set up some test inputs\n", + "dZ, linear_cache = linear_backward_test_case()\n", + "\n", + "dA_prev, dW, db = linear_backward(dZ, linear_cache)\n", + "print (\"dA_prev = \"+ str(dA_prev))\n", + "print (\"dW = \" + str(dW))\n", + "print (\"db = \" + str(db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**dA_prev** [[ 2.38272385 5.85438014]\n", + " [ 6.31969219 15.52755701]\n", + " [ -3.97876302 -9.77586689]]
**dW** [[ 2.77870358 -0.05500058 -5.13144969]]
**db** 5.527840195
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2 - Linear-Activation backward\n", + "\n", + "Next, you will create a function that merges the two helper functions: **`linear_backward`** and the backward step for the activation **`linear_activation_backward`**. \n", + "\n", + "To help you implement `linear_activation_backward`, we provided two backward functions:\n", + "- **`sigmoid_backward`**: Implements the backward propagation for SIGMOID unit. You can call it as follows:\n", + "\n", + "```python\n", + "dZ = sigmoid_backward(dA, activation_cache)\n", + "```\n", + "\n", + "- **`relu_backward`**: Implements the backward propagation for RELU unit. You can call it as follows:\n", + "\n", + "```python\n", + "dZ = relu_backward(dA, activation_cache)\n", + "```\n", + "\n", + "If $g(.)$ is the activation function, \n", + "`sigmoid_backward` and `relu_backward` compute $$dZ^{[l]} = dA^{[l]} * g'(Z^{[l]}) \\tag{11}$$. \n", + "\n", + "**Exercise**: Implement the backpropagation for the *LINEAR->ACTIVATION* layer." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: linear_activation_backward\n", + "\n", + "def linear_activation_backward(dA, cache, activation):\n", + " \"\"\"\n", + " Implement the backward propagation for the LINEAR->ACTIVATION layer.\n", + " \n", + " Arguments:\n", + " dA -- post-activation gradient for current layer l \n", + " cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently\n", + " activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n", + " \n", + " Returns:\n", + " dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n", + " dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n", + " db -- Gradient of the cost with respect to b (current layer l), same shape as b\n", + " \"\"\"\n", + " linear_cache, activation_cache = cache\n", + " \n", + " if activation == \"relu\":\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dZ = relu_backward(dA, activation_cache)\n", + " ### END CODE HERE ###\n", + " \n", + " elif activation == \"sigmoid\":\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dZ = sigmoid_backward(dA, activation_cache)\n", + " ### END CODE HERE ###\n", + " \n", + " # Shorten the code\n", + " dA_prev, dW, db = linear_backward(dZ, linear_cache)\n", + " \n", + " return dA_prev, dW, db" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sigmoid:\n", + "dA_prev = [[ 0.08982777 0.00226265]\n", + " [ 0.23824996 0.00600122]\n", + " [-0.14999783 -0.00377826]]\n", + "dW = [[-0.06001514 -0.09687383 -0.10598695]]\n", + "db = 0.061800984273\n", + "\n", + "relu:\n", + "dA_prev = [[ 2.38272385 5.85438014]\n", + " [ 6.31969219 15.52755701]\n", + " [ -3.97876302 -9.77586689]]\n", + "dW = [[ 2.77870358 -0.05500058 -5.13144969]]\n", + "db = 5.527840195\n" + ] + } + ], + "source": [ + "AL, linear_activation_cache = linear_activation_backward_test_case()\n", + "\n", + "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"sigmoid\")\n", + "print (\"sigmoid:\")\n", + "print (\"dA_prev = \"+ str(dA_prev))\n", + "print (\"dW = \" + str(dW))\n", + "print (\"db = \" + str(db) + \"\\n\")\n", + "\n", + "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"relu\")\n", + "print (\"relu:\")\n", + "print (\"dA_prev = \"+ str(dA_prev))\n", + "print (\"dW = \" + str(dW))\n", + "print (\"db = \" + str(db))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output with sigmoid:**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dA_prev [[ 0.08982777 0.00226265]\n", + " [ 0.23824996 0.00600122]\n", + " [-0.14999783 -0.00377826]]
dW [[-0.06001514 -0.09687383 -0.10598695]]
db 0.061800984273
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output with relu**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dA_prev [[ 2.38272385 5.85438014]\n", + " [ 6.31969219 15.52755701]\n", + " [ -3.97876302 -9.77586689]]
dW [[ 2.77870358 -0.05500058 -5.13144969]]
db 5.527840195
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.3 - L-Model Backward \n", + "\n", + "Now you will implement the backward function for the whole network. Recall that when you implemented the `L_model_forward` function, at each iteration, you stored a cache which contains (X,W,b, and z). In the back propagation module, you will use those variables to compute the gradients. Therefore, in the `L_model_backward` function, you will iterate through all the hidden layers backward, starting from layer $L$. On each step, you will use the cached values for layer $l$ to backpropagate through layer $l$. Figure 5 below shows the backward pass. \n", + "\n", + "\n", + "\n", + "
**Figure 5** : Backward pass
\n", + "\n", + "** Initializing backpropagation**:\n", + "To backpropagate through this network, we know that the output is, \n", + "$A^{[L]} = \\sigma(Z^{[L]})$. Your code thus needs to compute `dAL` $= \\frac{\\partial \\mathcal{L}}{\\partial A^{[L]}}$.\n", + "To do so, use this formula (derived using calculus which you don't need in-depth knowledge of):\n", + "```python\n", + "dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL)) # derivative of cost with respect to AL\n", + "```\n", + "\n", + "You can then use this post-activation gradient `dAL` to keep going backward. As seen in Figure 5, you can now feed in `dAL` into the LINEAR->SIGMOID backward function you implemented (which will use the cached values stored by the L_model_forward function). After that, you will have to use a `for` loop to iterate through all the other layers using the LINEAR->RELU backward function. You should store each dA, dW, and db in the grads dictionary. To do so, use this formula : \n", + "\n", + "$$grads[\"dW\" + str(l)] = dW^{[l]}\\tag{15} $$\n", + "\n", + "For example, for $l=3$ this would store $dW^{[l]}$ in `grads[\"dW3\"]`.\n", + "\n", + "**Exercise**: Implement backpropagation for the *[LINEAR->RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID* model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: L_model_backward\n", + "\n", + "def L_model_backward(AL, Y, caches):\n", + " \"\"\"\n", + " Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group\n", + " \n", + " Arguments:\n", + " AL -- probability vector, output of the forward propagation (L_model_forward())\n", + " Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat)\n", + " caches -- list of caches containing:\n", + " every cache of linear_activation_forward() with \"relu\" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)\n", + " the cache of linear_activation_forward() with \"sigmoid\" (it's caches[L-1])\n", + " \n", + " Returns:\n", + " grads -- A dictionary with the gradients\n", + " grads[\"dA\" + str(l)] = ... \n", + " grads[\"dW\" + str(l)] = ...\n", + " grads[\"db\" + str(l)] = ... \n", + " \"\"\"\n", + " grads = {}\n", + " L = len(caches) # the number of layers\n", + " m = AL.shape[1]\n", + " Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL\n", + " \n", + " # Initializing the backpropagation\n", + " ### START CODE HERE ### (1 line of code)\n", + " dAL = dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n", + " ### END CODE HERE ###\n", + " \n", + " # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: \"AL, Y, caches\". Outputs: \"grads[\"dAL\"], grads[\"dWL\"], grads[\"dbL\"]\n", + " ### START CODE HERE ### (approx. 2 lines)\n", + " current_cache = caches[-1]\n", + " grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_backward(sigmoid_backward(dAL, \n", + " current_cache[1]), \n", + " current_cache[0])\n", + " ### END CODE HERE ###\n", + " \n", + " for l in reversed(range(L-1)):\n", + " # lth layer: (RELU -> LINEAR) gradients.\n", + " # Inputs: \"grads[\"dA\" + str(l + 2)], caches\". Outputs: \"grads[\"dA\" + str(l + 1)] , grads[\"dW\" + str(l + 1)] , grads[\"db\" + str(l + 1)] \n", + " ### START CODE HERE ### (approx. 5 lines)\n", + " current_cache = caches[l]\n", + " dA_prev_temp, dW_temp, db_temp = linear_backward(sigmoid_backward(dAL, caches[1]), caches[0])\n", + " grads[\"dA\" + str(l + 1)] = dA_prev_temp\n", + " grads[\"dW\" + str(l + 1)] = dW_temp\n", + " grads[\"db\" + str(l + 1)] = db_temp\n", + " ### END CODE HERE ###\n", + "\n", + " return grads" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dW1 = [[-0.09686122 -0.04840482 -0.11864308]]\n", + "db1 = -0.262594998379\n", + "dA1 = [[-0.71011462 -0.22925516]\n", + " [-0.17330152 -0.05594909]\n", + " [-0.03831107 -0.01236844]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess, AL, caches = L_model_backward_test_case()\n", + "grads = L_model_backward(AL, Y_assess, caches)\n", + "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n", + "print (\"db1 = \"+ str(grads[\"db1\"]))\n", + "print (\"dA1 = \"+ str(grads[\"dA1\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
dW1 [[-0.09686122 -0.04840482 -0.11864308]]
db1 -0.262594998379
dA1 [[-0.71011462 -0.22925516]\n", + " [-0.17330152 -0.05594909]\n", + " [-0.03831107 -0.01236844]]
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.4 - Update Parameters\n", + "\n", + "In this section you will update the parameters of the model, using gradient descent: \n", + "\n", + "$$ W^{[l]} = W^{[l]} - \\alpha \\text{ } dW^{[l]} \\tag{16}$$\n", + "$$ b^{[l]} = b^{[l]} - \\alpha \\text{ } db^{[l]} \\tag{17}$$\n", + "\n", + "where $\\alpha$ is the learning rate. After computing the updated parameters, store them in the parameters dictionary. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement `update_parameters()` to update your parameters using gradient descent.\n", + "\n", + "**Instructions**:\n", + "Update parameters using gradient descent on every $W^{[l]}$ and $b^{[l]}$ for $l = 1, 2, ..., L$. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters\n", + "\n", + "def update_parameters(parameters, grads, learning_rate):\n", + " \"\"\"\n", + " Update parameters using gradient descent\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " grads -- python dictionary containing your gradients, output of L_model_backward\n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " parameters[\"W\" + str(l)] = ... \n", + " parameters[\"b\" + str(l)] = ...\n", + " \"\"\"\n", + " \n", + " L = len(parameters) // 2 # number of layers in the neural network\n", + "\n", + " # Update rule for each parameter. Use a for loop.\n", + " ### START CODE HERE ### (≈ 3 lines of code)\n", + " for l in range(L):\n", + " parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n", + " parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n", + " ### END CODE HERE ###\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[ 1.72555789 0.3700272 0.07818896]\n", + " [-1.8634927 -0.2773882 -0.35475898]\n", + " [-0.08274148 -0.62700068 -0.04381817]\n", + " [-0.47721803 -1.31386475 0.88462238]]\n", + "b1 = [[-0.07593768]\n", + " [-0.07593768]\n", + " [-0.07593768]\n", + " [-0.07593768]]\n", + "W2 = [[ 0.71838378 1.70957306 0.05003364 -0.40467741]\n", + " [-0.54535995 -1.54647732 0.98236743 -1.10106763]\n", + " [-1.18504653 -0.2056499 1.48614836 0.23671627]]\n", + "b2 = [[-0.08616376]\n", + " [-0.08616376]\n", + " [-0.08616376]]\n", + "W3 = [[-0.88352436 -0.7129932 0.62524497]\n", + " [-0.02025258 -0.76883635 -0.23003072]]\n", + "b3 = [[ 0.08416196]\n", + " [ 0.08416196]]\n" + ] + } + ], + "source": [ + "parameters, grads = update_parameters_test_case()\n", + "parameters = update_parameters(parameters, grads, 0.1)\n", + "\n", + "print (\"W1 = \" + str(parameters[\"W1\"]))\n", + "print (\"b1 = \" + str(parameters[\"b1\"]))\n", + "print (\"W2 = \" + str(parameters[\"W2\"]))\n", + "print (\"b2 = \" + str(parameters[\"b2\"]))\n", + "print (\"W3 = \" + str(parameters[\"W3\"]))\n", + "print (\"b3 = \" + str(parameters[\"b3\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
W1 [[ 1.72555789 0.3700272 0.07818896]\n", + " [-1.8634927 -0.2773882 -0.35475898]\n", + " [-0.08274148 -0.62700068 -0.04381817]\n", + " [-0.47721803 -1.31386475 0.88462238]]
b1 [[-0.07593768]\n", + " [-0.07593768]\n", + " [-0.07593768]\n", + " [-0.07593768]]
W2 [[ 0.71838378 1.70957306 0.05003364 -0.40467741]\n", + " [-0.54535995 -1.54647732 0.98236743 -1.10106763]\n", + " [-1.18504653 -0.2056499 1.48614836 0.23671627]]
b2 [[-0.08616376]\n", + " [-0.08616376]\n", + " [-0.08616376]]
W3 [[-0.88352436 -0.7129932 0.62524497]\n", + " [-0.02025258 -0.76883635 -0.23003072]]
b3 [[ 0.08416196]\n", + " [ 0.08416196]]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7 - Conclusion\n", + "\n", + "Congrats on implementing all the functions required for building a deep neural network! \n", + "\n", + "We know it was a long assignment but going forward it will only get better. The next part of the assignment is easier. \n", + "\n", + "In the next assignment you will put all these together to build two models:\n", + "- A two-layer neural network\n", + "- An L-layer neural network\n", + "\n", + "You will in fact use these models to classify cat vs non-cat images!" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "c4HO0", + "launcher_item_id": "lSYZM" + }, + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Deep Neural Network - Application.ipynb b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Deep Neural Network - Application.ipynb new file mode 100644 index 0000000..79add88 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Deep Neural Network - Application.ipynb @@ -0,0 +1,1003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Neural Network for Image Classification: Application\n", + "\n", + "When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! \n", + "\n", + "You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation. \n", + "\n", + "**After this assignment you will be able to:**\n", + "- Build and apply a deep neural network to supervised learning. \n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first import all the packages that you will need during this assignment. \n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n", + "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n", + "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end.\n", + "- dnn_app_utils provides the functions implemented in the \"Building your Deep Neural Network: Step by Step\" assignment to this notebook.\n", + "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import time\n", + "import numpy as np\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "from PIL import Image\n", + "from scipy import ndimage\n", + "from dnn_app_utils import *\n", + "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "np.random.seed(1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Dataset\n", + "\n", + "You will use the same \"Cat vs non-Cat\" dataset as in \"Logistic Regression as a Neural Network\" (Assignment 2). The model you had built had 70% test accuracy on classifying cats vs non-cats images. Hopefully, your new model will perform a better!\n", + "\n", + "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n", + " - a training set of m_train images labelled as cat (1) or non-cat (0)\n", + " - a test set of m_test images labelled as cat and non-cat\n", + " - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).\n", + "\n", + "Let's get more familiar with the dataset. Load the data by running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code will show you an image in the dataset. Feel free to change the index and re-run the cell multiple times to see other images. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 0. It's a non-cat picture.\n" + ] + }, + { + "data": { + "image/png": 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HfI5GaVEQXFhksWdpmfs27PN1j0cca3sxJgM+H5lnYTIrlfBSouG+SR4Lb3kv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\n1IRFRDL6BcZD1YBtao6bAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example of a picture\n", + "index = 10\n", + "plt.imshow(train_x_orig[index])\n", + "print (\"y = \" + str(train_y[0, index]) + \". It's a \" + classes[train_y[0, index]].decode(\"utf-8\") + \" picture.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training examples: 209\n", + "Number of testing examples: 50\n", + "Each image is of size: (64, 64, 3)\n", + "train_x_orig shape: (209, 64, 64, 3)\n", + "train_y shape: (1, 209)\n", + "test_x_orig shape: (50, 64, 64, 3)\n", + "test_y shape: (1, 50)\n" + ] + } + ], + "source": [ + "# Explore your dataset \n", + "m_train = train_x_orig.shape[0]\n", + "num_px = train_x_orig.shape[1]\n", + "m_test = test_x_orig.shape[0]\n", + "\n", + "print (\"Number of training examples: \" + str(m_train))\n", + "print (\"Number of testing examples: \" + str(m_test))\n", + "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n", + "print (\"train_x_orig shape: \" + str(train_x_orig.shape))\n", + "print (\"train_y shape: \" + str(train_y.shape))\n", + "print (\"test_x_orig shape: \" + str(test_x_orig.shape))\n", + "print (\"test_y shape: \" + str(test_y.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, you reshape and standardize the images before feeding them to the network. The code is given in the cell below.\n", + "\n", + "\n", + "\n", + "
Figure 1: Image to vector conversion.
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_x's shape: (12288, 209)\n", + "test_x's shape: (12288, 50)\n" + ] + } + ], + "source": [ + "# Reshape the training and test examples \n", + "train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T # The \"-1\" makes reshape flatten the remaining dimensions\n", + "test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T\n", + "\n", + "# Standardize data to have feature values between 0 and 1.\n", + "train_x = train_x_flatten / 255.\n", + "test_x = test_x_flatten / 255.\n", + "\n", + "print (\"train_x's shape: \" + str(train_x.shape))\n", + "print (\"test_x's shape: \" + str(test_x.shape))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$12,288$ equals $64 \\times 64 \\times 3$ which is the size of one reshaped image vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Architecture of your model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you are familiar with the dataset, it is time to build a deep neural network to distinguish cat images from non-cat images.\n", + "\n", + "You will build two different models:\n", + "- A 2-layer neural network\n", + "- An L-layer deep neural network\n", + "\n", + "You will then compare the performance of these models, and also try out different values for $L$. \n", + "\n", + "Let's look at the two architectures.\n", + "\n", + "### 3.1 - 2-layer neural network\n", + "\n", + "\n", + "
Figure 2: 2-layer neural network.
The model can be summarized as: ***INPUT -> LINEAR -> RELU -> LINEAR -> SIGMOID -> OUTPUT***.
\n", + "\n", + "Detailed Architecture of figure 2:\n", + "- The input is a (64,64,3) image which is flattened to a vector of size $(12288,1)$. \n", + "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ of size $(n^{[1]}, 12288)$.\n", + "- You then add a bias term and take its relu to get the following vector: $[a_0^{[1]}, a_1^{[1]},..., a_{n^{[1]}-1}^{[1]}]^T$.\n", + "- You then repeat the same process.\n", + "- You multiply the resulting vector by $W^{[2]}$ and add your intercept (bias). \n", + "- Finally, you take the sigmoid of the result. If it is greater than 0.5, you classify it to be a cat.\n", + "\n", + "### 3.2 - L-layer deep neural network\n", + "\n", + "It is hard to represent an L-layer deep neural network with the above representation. However, here is a simplified network representation:\n", + "\n", + "\n", + "
Figure 3: L-layer neural network.
The model can be summarized as: ***[LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID***
\n", + "\n", + "Detailed Architecture of figure 3:\n", + "- The input is a (64,64,3) image which is flattened to a vector of size (12288,1).\n", + "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ and then you add the intercept $b^{[1]}$. The result is called the linear unit.\n", + "- Next, you take the relu of the linear unit. This process could be repeated several times for each $(W^{[l]}, b^{[l]})$ depending on the model architecture.\n", + "- Finally, you take the sigmoid of the final linear unit. If it is greater than 0.5, you classify it to be a cat.\n", + "\n", + "### 3.3 - General methodology\n", + "\n", + "As usual you will follow the Deep Learning methodology to build the model:\n", + " 1. Initialize parameters / Define hyperparameters\n", + " 2. Loop for num_iterations:\n", + " a. Forward propagation\n", + " b. Compute cost function\n", + " c. Backward propagation\n", + " d. Update parameters (using parameters, and grads from backprop) \n", + " 4. Use trained parameters to predict labels\n", + "\n", + "Let's now implement those two models!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Two-layer neural network\n", + "\n", + "**Question**: Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: *LINEAR -> RELU -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n", + "```python\n", + "def initialize_parameters(n_x, n_h, n_y):\n", + " ...\n", + " return parameters \n", + "def linear_activation_forward(A_prev, W, b, activation):\n", + " ...\n", + " return A, cache\n", + "def compute_cost(AL, Y):\n", + " ...\n", + " return cost\n", + "def linear_activation_backward(dA, cache, activation):\n", + " ...\n", + " return dA_prev, dW, db\n", + "def update_parameters(parameters, grads, learning_rate):\n", + " ...\n", + " return parameters\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### CONSTANTS DEFINING THE MODEL ####\n", + "n_x = 12288 # num_px * num_px * 3\n", + "n_h = 7\n", + "n_y = 1\n", + "layers_dims = (n_x, n_h, n_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: two_layer_model\n", + "\n", + "def two_layer_model(X, Y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=False):\n", + " \"\"\"\n", + " Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.\n", + " \n", + " Arguments:\n", + " X -- input data, of shape (n_x, number of examples)\n", + " Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\n", + " layers_dims -- dimensions of the layers (n_x, n_h, n_y)\n", + " num_iterations -- number of iterations of the optimization loop\n", + " learning_rate -- learning rate of the gradient descent update rule\n", + " print_cost -- If set to True, this will print the cost every 100 iterations \n", + " \n", + " Returns:\n", + " parameters -- a dictionary containing W1, W2, b1, and b2\n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + " grads = {}\n", + " costs = [] # to keep track of the cost\n", + " m = X.shape[1] # number of examples\n", + " (n_x, n_h, n_y) = layers_dims\n", + " \n", + " # Initialize parameters dictionary, by calling one of the functions you'd previously implemented\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " parameters = initialize_parameters(n_x, n_h, n_y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Get W1, b1, W2 and b2 from the dictionary parameters.\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " \n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + "\n", + " # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: \"X, W1, b1\". Output: \"A1, cache1, A2, cache2\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A1, cache1 = linear_activation_forward(X, W1, b1, 'relu')\n", + " A2, cache2 = linear_activation_forward(A1, W2, b2, 'sigmoid')\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute cost\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " cost = compute_cost(A2, Y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Initializing backward propagation\n", + " dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))\n", + " \n", + " # Backward propagation. Inputs: \"dA2, cache2, cache1\". Outputs: \"dA1, dW2, db2; also dA0 (not used), dW1, db1\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dA1, dW2, db2 = linear_activation_backward(dA2, cache2, 'sigmoid')\n", + " dA0, dW1, db1 = linear_activation_backward(dA1, cache1, 'relu')\n", + " ### END CODE HERE ###\n", + " \n", + " # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2\n", + " grads['dW1'] = dW1\n", + " grads['db1'] = db1\n", + " grads['dW2'] = dW2\n", + " grads['db2'] = db2\n", + " \n", + " # Update parameters.\n", + " ### START CODE HERE ### (approx. 1 line of code)\n", + " parameters = update_parameters(parameters, grads, learning_rate)\n", + " ### END CODE HERE ###\n", + "\n", + " # Retrieve W1, b1, W2, b2 from parameters\n", + " W1 = parameters[\"W1\"]\n", + " b1 = parameters[\"b1\"]\n", + " W2 = parameters[\"W2\"]\n", + " b2 = parameters[\"b2\"]\n", + " \n", + " # Print the cost every 100 training example\n", + " if print_cost and i % 100 == 0:\n", + " print(\"Cost after iteration {}: {}\".format(i, np.squeeze(cost)))\n", + " if print_cost and i % 100 == 0:\n", + " costs.append(cost)\n", + " \n", + " # plot the cost\n", + "\n", + " plt.plot(np.squeeze(costs))\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (per tens)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to train your parameters. See if your model runs. The cost should be decreasing. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.693049735659989\n", + "Cost after iteration 100: 0.6464283150388817\n", + "Cost after iteration 200: 0.6325086257948581\n", + "Cost after iteration 300: 0.6014932097069347\n", + "Cost after iteration 400: 0.5601652326734954\n", + "Cost after iteration 500: 0.5157934975899229\n", + "Cost after iteration 600: 0.47590238832247345\n", + "Cost after iteration 700: 0.43858640997112314\n", + "Cost after iteration 800: 0.3910072887566946\n", + "Cost after iteration 900: 0.3584220494573506\n", + "Cost after iteration 1000: 0.34299735904004847\n", + "Cost after iteration 1100: 0.3101904235095467\n", + "Cost after iteration 1200: 0.273966132872609\n", + "Cost after iteration 1300: 0.2370347086950205\n", + "Cost after iteration 1400: 0.18734248651597438\n", + "Cost after iteration 1500: 0.16864009816415473\n", + "Cost after iteration 1600: 0.14197411005689567\n", + "Cost after iteration 1700: 0.11292363594102996\n", + "Cost after iteration 1800: 0.09758444844752405\n", + "Cost after iteration 1900: 0.08554699338649566\n", + "Cost after iteration 2000: 0.07593496617351737\n", + "Cost after iteration 2100: 0.06794136245969253\n", + "Cost after iteration 2200: 0.06085759338231386\n", + "Cost after iteration 2300: 0.054887735653158576\n", + "Cost after iteration 2400: 0.04950829635846386\n" + ] + }, + { + "data": { + "image/png": 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ptE8k4zRvDs8+G4a39OkDDz0Ud0Qiks3STaAGpJoDcFtAI+8kY22yCUyaBP37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\n9i80s8vNbIKZLQXujMr/bGbzzGy5mS0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Cost after iteration 0** 0.693049735659989
**Cost after iteration 100** 0.6464283150388817
**...** ...
**Cost after iteration 2400** 0.04950829635846386
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good thing you built a vectorized implementation! Otherwise it might have taken 10 times longer to train this.\n", + "\n", + "Now, you can use the trained parameters to classify images from the dataset. To see your predictions on the training and test sets, run the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 1.0\n" + ] + } + ], + "source": [ + "predictions_train = predict(train_x, train_y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 1.0
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.72\n" + ] + } + ], + "source": [ + "predictions_test = predict(test_x, test_y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 0.72
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: You may notice that running the model on fewer iterations (say 1500) gives better accuracy on the test set. This is called \"early stopping\" and we will talk about it in the next course. Early stopping is a way to prevent overfitting. \n", + "\n", + "Congratulations! It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). Let's see if you can do even better with an $L$-layer model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - L-layer Neural Network\n", + "\n", + "**Question**: Use the helper functions you have implemented previously to build an $L$-layer neural network with the following structure: *[LINEAR -> RELU]$\\times$(L-1) -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n", + "```python\n", + "def initialize_parameters_deep(layer_dims):\n", + " ...\n", + " return parameters \n", + "def L_model_forward(X, parameters):\n", + " ...\n", + " return AL, caches\n", + "def compute_cost(AL, Y):\n", + " ...\n", + " return cost\n", + "def L_model_backward(AL, Y, caches):\n", + " ...\n", + " return grads\n", + "def update_parameters(parameters, grads, learning_rate):\n", + " ...\n", + " return parameters\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### CONSTANTS ###\n", + "layers_dims = [12288, 20, 7, 5, 1] # 5-layer model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: n_layer_model\n", + "\n", + "def L_layer_model(X, Y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=False): #lr was 0.009\n", + " \"\"\"\n", + " Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID.\n", + " \n", + " Arguments:\n", + " X -- data, numpy array of shape (number of examples, num_px * num_px * 3)\n", + " Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\n", + " layers_dims -- list containing the input size and each layer size, of length (number of layers + 1).\n", + " learning_rate -- learning rate of the gradient descent update rule\n", + " num_iterations -- number of iterations of the optimization loop\n", + " print_cost -- if True, it prints the cost every 100 steps\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + "\n", + " np.random.seed(1)\n", + " costs = [] # keep track of cost\n", + " \n", + " # Parameters initialization.\n", + " ### START CODE HERE ###\n", + " parameters = initialize_parameters_deep(layers_dims)\n", + " ### END CODE HERE ###\n", + " \n", + " # Loop (gradient descent)\n", + " for i in range(0, num_iterations):\n", + "\n", + " # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " AL, caches = L_model_forward(X, parameters)\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute cost.\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " cost = compute_cost(AL, Y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Backward propagation.\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " grads = L_model_backward(AL, Y, caches)\n", + " ### END CODE HERE ###\n", + " \n", + " # Update parameters.\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " parameters = update_parameters(parameters, grads, learning_rate)\n", + " ### END CODE HERE ###\n", + " \n", + " # Print the cost every 100 training example\n", + " if print_cost and i % 100 == 0:\n", + " print (\"Cost after iteration %i: %f\" % (i, cost))\n", + " if print_cost and i % 100 == 0:\n", + " costs.append(cost)\n", + " \n", + " # plot the cost\n", + " plt.plot(np.squeeze(costs))\n", + " plt.ylabel('cost')\n", + " plt.xlabel('iterations (per tens)')\n", + " plt.title(\"Learning rate =\" + str(learning_rate))\n", + " plt.show()\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will now train the model as a 5-layer neural network. \n", + "\n", + "Run the cell below to train your model. The cost should decrease on every iteration. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.771749\n", + "Cost after iteration 100: 0.673350\n", + "Cost after iteration 200: 0.648247\n", + "Cost after iteration 300: 0.620384\n", + "Cost after iteration 400: 0.568401\n", + "Cost after iteration 500: 0.520754\n", + "Cost after iteration 600: 0.469203\n", + "Cost after iteration 700: 0.487263\n", + "Cost after iteration 800: 0.358436\n", + "Cost after iteration 900: 0.347641\n", + "Cost after iteration 1000: 0.291955\n", + "Cost after iteration 1100: 0.273223\n", + "Cost after iteration 1200: 0.229250\n", + "Cost after iteration 1300: 0.196667\n", + "Cost after iteration 1400: 0.176585\n", + "Cost after iteration 1500: 0.157727\n", + "Cost after iteration 1600: 0.142742\n", + "Cost after iteration 1700: 0.139015\n", + "Cost after iteration 1800: 0.123863\n", + "Cost after iteration 1900: 0.111514\n", + "Cost after iteration 2000: 0.105953\n", + "Cost after iteration 2100: 0.098199\n", + "Cost after iteration 2200: 0.094213\n", + "Cost after iteration 2300: 0.087161\n", + "Cost after iteration 2400: 0.082077\n" + ] + }, + { + "data": { + "image/png": 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1VidSs0YVk0DNrLJI8KUvpRWMbr89LQv45S/DttvCbbc5kZo5gZpZ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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations=2500, print_cost=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Cost after iteration 0** 0.771749
**Cost after iteration 100** 0.673350
**...** ...
**Cost after iteration 2400** 0.082077
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 1.0\n" + ] + } + ], + "source": [ + "pred_train = predict(train_x, train_y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Train Accuracy**\n", + " \n", + " 1.0\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.84\n" + ] + } + ], + "source": [ + "pred_test = predict(test_x, test_y, parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Test Accuracy** 0.84
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats! It seems that your 5-layer neural network has better performance (84%) than your 2-layer neural network (72%) on the same test set. \n", + "\n", + "This is good performance for this task. Nice job! \n", + "\n", + "Though in the next course on \"Improving deep neural networks\" you will learn how to obtain even higher accuracy by systematically searching for better hyperparameters (learning_rate, layers_dims, num_iterations, and others you'll also learn in the next course). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Results Analysis\n", + "\n", + "First, let's take a look at some images the L-layer model labeled incorrectly. This will show a few mislabeled images. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Kddc7la23+v35SstNYXLHMA/bDaqO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\nGIFt7Da6r41nuthfeuOWyJL1XGTH99VvRUQsN2prb8K+PYQ2vvq8+s3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80/j+7ITzQdVW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kYc76i5loh5peHxarc5L6LuxADa3mei3WpTUz5xHPK4lpU+y1P39bKc18h8zvnVQtzTUDtD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\nMef1lIdqvHgdB3KDBlop9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_mislabeled_images(classes, test_x, test_y, pred_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A few type of images the model tends to do poorly on include:** \n", + "- Cat body in an unusual position\n", + "- Cat appears against a background of a similar color\n", + "- Unusual cat color and species\n", + "- Camera Angle\n", + "- Brightness of the picture\n", + "- Scale variation (cat is very large or small in image) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Test with your own image (optional/ungraded exercise) ##\n", + "\n", + "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n", + " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", + " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", + " 3. Change your image's name in the following code\n", + " 4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 1.0\n", + "y = 1.0, your L-layer model predicts a \"cat\" picture.\n" + ] + }, + { + "data": { + "image/png": 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/+loh43vVIdpc1v5axfrb5/UpqMf4LYNlzrrAzuE5U+NblmP3+W77W9wT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\nXfux7djxPr+H858m6pm+rUT9HNe6jZPtrOnYrU9YX41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GGGOMMcYYY4wxxhhjjDHGGGOM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mQlPesUgcOkxCRMYTeQirJd6/TITI94O9a3+ludklVT5G4o12n0VtJMrqRVdeMkkrJvuFTS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Z87h1oHGX8ZmUp8daeS7a7n09d0jGnGJD0rXntsI+WzszfsnaH/rME8Nq1txJO17YGMvz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\nEEIIIYQQQgghhBBCCCGEEEIIIfzwGC3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EkPjK5A/RznHOlRXtWYyvc9d8r6Q/65SWj4El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iwzLZcStPSNrav8+XpgvHbMWZ/vkzF6QGKV+9ZKG6n9jBypIfPKtXtA4vhxzLHm3rnBl4q9c77Z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\nw1ij3heJQWhLD8oQzo/6ZOpylkIa+9POWtd5HtwT9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K3vZZ61TMieSmzJbp7fdWnu4f/m\n33MeNFjm3sH2Y35dYuKosVznubVoEr9wf02P78hVr386JkOqT/29u1fUvp7Xx0Zq5O+BtfWzVFrM\ns/Rknn9cPsbGqqE/a3zvvrn7izqe1D19Pld/rnV+MwKpFwzczSMskDsHM503aiWINdZr+fhbuHrP\nNHLnNKJnI/dkV2sNI3r/Yd8ytlzr+GJydZyBeGeapunr154D2vN0v7MYsY24h2UpVda/1/GI/A5A\n7uNNHMXcyMg7lf12w+8++BMQ3O3e5zpH1n3o30quwlhX6vqoJ90YvPM3HTxXjZM1n+Xf6nsd3inc\nbsyT6n2U3+MxPkY94sAeff7Mmm9dA5xMrO9yANEbE39e9akyN/Yjvyt5HjvY51nXIvUF1KPcb7t5\n5+Z//W32Cz6TebT4Nhf7in73Jsd0itw+QxgogiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAI\ngiAIgiD45ZF/QBEEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEwS+Pmgv0T4rz/9Jr\n/AilUtXeU/DV7Yf6MZjfaDPy/dAYA7RRI+3d8+robxwlu4HrnxQ8jmp2pB9Cqa6ez3OUjs7RKz1w\nsQ3MyVBxmfcL6FlXQ73jzlvoN0GPtYJqh1TDq6HJ1r0wZwMqUdKNku5Y1z6Vz446aKT9W3BMSBfN\nyb8lHE3mCDuUOw9t8/yMx8eodXwzNsF36sZC/1Tjd1DtSv9mzc3QUjmM0f2h/1afgYPb57fG9XSr\nzzFiu4Vq+IMUUyiFL377UXO42qdv83EUqT+DbnUEI1v6nj1aQDPtKKo9nsc4O2jUhQ5UnBXp8vy4\nbeoUgTso1sleuCx9PNIcynBb52a8bZ++Pb/eXnoj0Fgut8/9Gf75WHosIBTYMKaN9MsSEzKWm/C+\npt8U2z73NW6k8QTd42y2cQa3+7zQdpGWERA7RPpJQ3s5PcSjeDyOvkc889va13Niv7gXpEN3cfAq\nNKaYt9BdCi932d4S8J7P42Nhi5e49HkQNqN/zmFjrAh2T9Jt7q9fynFl2AZ5ffDfjDsd2lnHuDOo\nfZfWz5Jjrzfamf5+f33t3y6k/cReQ4gWE8zuR+/H5UbofmIW0nbqGWhOQTXLfGCGDFGmN+oBmUpp\nh5baxjLe4X4q9fY0HbQ/RyufiZVns6xlG80xa12hXeWe8iz1aEibjDW3mkZY4o69plkmeB5sc8N7\nTmiBRh0S+9V6f051XHejr4LM7V80rz22rmu0+zPGJqXyTD8Enfj86S/fnl9eehuC89vg27atn9nv\nv//+7Xn9L/+lfH//o9sQzSWw16SFhSyvkI9Z4lKeJeUAtp12eK11l2fTjr63i+glfUd9xmpj1OaR\nHpoyeJA2+0TscMMebSwV1nUjgv6Z1Me0ORLft3pfbov63m99Cl23qaHssL2QP/ZPWeQaR6jWXe1K\nUddcnI9/BM/sfu/fSEyCzeYYr3uP8WRGkBGb38k6axt1v6NWZOjWhXib8SH9sQRzkOWV8ofYFTgw\nB+4V3ILKRCNFeu0vZqGlru3w39vVFOViu79Al8050f5QFxeJG/u33PfffvsNM4Lfmmo/LHGXxA71\n+d2xZr7nXp/YU8qlk62Xl36W7EcSHcDJFo9D9ge6tX3SM25Sn3V15XrNXAPthsZjiE05b8og8wyT\nAzEQbthfQmOW/l7sOeSX9k10AnOQOa/9LOW8TX3I1ogpB2hzsM9J8elTt9cuT+R4zv66OXE8zmmW\nmBv2dqv3S+QXZ3976XvnYjnxYbc65yXVPPW+Gb2U+xqOe+/vKbsS98+17L5+hc1c9Mpwb/XZiH1b\nax/u7qh4nzJTZpuLRzq4p4z1bzf4f8QgTJo2nNmuxqWcJ8FaAx6lRiryR1tnaqHuXN2z1ro4T40v\ntrm2e/srfBX88wv2mrJAMP9g3Ky5PWOKfk6vr2jPeynoxGr88CF5dB1HfGG+xbXLfRJkzuRGk/hU\nymtvwRitOTtpcgAJWiZvu+64o3NtOGvnhxmPLBKn1nEH4e/unteRnfwdphYnNVOJXWHzOQfW2KiX\nGKvNdXx/msIi8/3v7kEGdFMge2diUOS5bkebsUu0gSgjS03z/K3bw/OgjYX+sbaNitI5dfmTWAZx\nivPN8yvqzqil0f9JnfBEnWHv73ec67aZuOPxgsrE3wTTEvoMxqaNtRz6PN5tn7VfdH7b+RX3npKl\nNqC2k+6Z4i61RBc3SR6COBvySpPm/Jmbz2MNT+O6d9y/oba7wP6onaS/7e9lrhIj1fH0dNC2DNQk\nzV2t2GrjkzTP4whz+cw1jvwOZRT+Trp+7+qq7lveO/Bj8fjG3rq6jsTf5vc8zlc1qYm4My5ffxh0\nb8f2303p6u8dXA74nt/mOYzI5shvxEb6d9B643PbeHXcUZ37qD29+tu5kW/Fdtv+B87SjLUM7LW3\nac/nL7k8ZzxQ/63+u/qGccfVMxj51tVjrv6GYuT3nW5cV+9wsQDrgQfu/nlvwNoHIXqPcJVxzePa\nndxJLgkf7iyj1hxxT7rU+RPvPVk3YZ7L+umXL/2+ivHnypoI71UxN/o8pOMyz7lWS/1dgtReaz8n\ncZ25BjlNbYK5Nvt/HEtC+bOW601qz725nl99HqvRa1e3nt6wA9/6NPpqYzzK8l5vJPPQIRso9wyo\n60Of3Bx4TpQb1lnWh3og5e5or9MzaF2krkXq/rr7cvyH+00567P4LRRrUfu+TybkKxEGiiAIgiAI\ngiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIfnnkH1AEQRAEQRAEQRAEQRAEQRAEQRAEQRAE\nQRAEQRAEQRAEQRAEQfDLo+bB/ZOitXNqrVlaoBFcpdlyba6+n98Y62pfDh9FpWa/BdXJCMXT1f5H\naOGG5gkIVbmhrhzBI+2T/rehfDcUwa4fS4HOkUAzdRgqoNXQpPl9BCUSqVrB/bSas3Fw9GmLlQ/O\ns27hqOBO2ee35uT/VrUZmOrPwU+mfnT79R52QKe7btxRDNEIvuM8hPbb0NmO9G/tj/2ipov1/dd9\nuvM7L/7byJP6NNV69ghHbef0TNWX49U45A/P5UDn4CZRPg71+R68J9bwbT5uniN6NtL+KvbLa3hO\nS7nI65qW8rxoNNi+gU5R9IZ05nhe+CyUyzp/oVgXOs2awk5tIOjvSHNPau2VlOmdwm4++3O7g75x\nA23minHFAJFqkNTYjINqvuaZlIUmhlQKb3SDLVkd9Z+hq3YGapzEuKbNdrpJanvthVSZN/wFMk4q\nR8od9qWBepaywvhQdGLAV81GFlenWwt2T+JyM3/mEqTxxJ40OTNQKAvfaO0AH5c4K+l9fy9xM2MY\ntqa+k54dsow+wQw6HXh/GBlnbjhvnBs6GqD9/Pr161ThmGr70YwtIpVtg5xRik/ZQ/BnYj4LKDNJ\nl0rdXd5IxEaok13u6dpfpc2+Sm2+iizzGXSu8/N5ClUrVOhcaT8plx1CgU215DGZePImVL594OOB\nm/ckvTIDNfoq6Oby0t+/fEYT+Ji2Q3amWsa3rb//9OlTnx8Wur7+tc8Tc5BnQ5FLSmTOgb6KbdrJ\n3aZtIKcsaGTFdvEQINM4cEeNPQ/Y9vbgO0d8qaNJV59HnXCU6dwXPJ/1s8vnZhP30+yvRo+VHrhs\nIv5yNrGVtD9r+z8Zv2D3HOMKdfM0TY10yZBTrc2IltftDd2zs2nyfqAupxFTTVfN/V3ma/T1jvLd\nPastZfxddj9k21mvOtc3yuUnYjBZG7wm7CQp4gnRIfbTaJfQJSmqZ7RB3L/C9zIHODGW9aOIR7iP\nNveX3OgaJJRjLCbxF+0kvjVnuTbVrUdd6w3RBrZL08TaJy0m5tGx6vmdCweALEM+FtCk25hIwmDE\nykttY7lfQtvOfR+o08taGKhgQ1eZP30Y9Vt1S3TfjEd/uCMHsvMr3/p4mv18/frl6RyWbS3fE5SC\nJqbU+ZVrcN9qDLWV77mH0ifWtdwebKDERR3ramInF5cfz+8p6Bed/M5i0/q3XBvXz3NyNk3mc7FO\nRqHbjx7rMmY5acgG6m0L6zvMq0xe8ThltfV1rMmJc+9kHib2Y/9MDXhOK8/MmGSZ51w7H7FXjLkZ\na0x8bWJOtGFNT+oglHCpTdT1rcXYFRYIaAPmSWNFxhH0SbSH9Lf2ztDW4+u9cPrkn+sD9HeerDXQ\nz7MR2/NcTf5u82jWn/rjCV3UXI1dQqbl/U0bSvrLmM1s/MK5QsYZOxjZfJghvjXxiPmyHbB72IuJ\n8VKD3556m0VyOP4mAH1KvFrnfFJvNDVGqTda21u/Px52jnvBEz9rsze9vr72vuhLeAfP2sz5XFe2\nue/pfpqY8OI9UDOxw8xVzswLr0XmI+3d3YrOs+5zJDf9O1xc9Dx/dNhxgO43DiP9SHvYd57x3K7t\nOyF5AmvTznmauV19fu9d6NjvAgb2/eJvTviti1+knmb6HKkR/Iz7YuJqzZoYntuAHPkx0M3FM7bT\nMb8PsGH2wDldHffhD8+fr+JqLvFOjPxOb+R3ag4jv+sTX8ta0YDNsbLFNnzPYxrYUlsbGsR7fAZx\n9R6L4D3yyHkQLo4YqSGdUhfvfTLXdmO5cRknzy62cnv1hj243foeydinmZPph7mn3Pbc69/JuhrH\nyG9gZ1NXpMQOyXh95STY8LPwxdxB692NVLXQ3OTg5q5A+ll012dTayDY18vtU/ne3jNJ3Ixp4GBd\nXjl0fhf9hNSlBvSMdWetKdf66vTS7Y+9e3ywmfLfUhYZiDunur3cO8y1jlKn+e2+v+K5vqOhbm0v\nt2l9rHG+gTBQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQB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fjzfEjXwvZO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MZbrvYczJeZ9ED2rs5txgMudnep5SH5C4s/kD63eZuUfr/uV8AHBne/c1PNpKVwPcXQJ11DOm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2XTtWns4mDkyckQYxpndwejo1ZRk+fOtPb16PtRJ9hfn88eJ9qAOg+ibQ6mTtm8gDkRpwMn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\nE/YORfGo3ej5uHsfar+i960Nk8sfTI1DakKmj1uLnjsLd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Rf7kGa59qniYUyu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JePaYYHs5DuthaE8z+mMdpJZG3yltE6egfVvv3zGe8/Se+1NC4zTuef1uSuF8rMSK0AmR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\n3qV1uDqmcPcp8qx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7lwkLxryVNdNRahZYI4xPdZearPnGSHSL6wX53R2mAAdwljNX263b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3lut7a/+V7e/+GHH0p57JmZ2dOu7jwadaeNYgzFOOi33357v/7x55bn/eUvfyll0NxA3+dia8Lp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\nvo45KX4+60sDYi05Z493X+q/Mpe6LtPje1TfXZ2wHofjM95WI2hyhkHj/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mXrIzEObMlk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/PIr7E+vo6Id+jrVTFLKWG2MEDv++nWdNnGOQ7/uGYbiZ/RebY+olUi4xNcNtNvmTcb1yPtkR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\nhfUFrMsRNS4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DcDRbjj6dNJMDKLEcFa7QngFsT5CanjLkqJ7t/bL33btI4U3ZGus+hQqN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JG8iaPkXgD7e3qqcvT+EbltF3+fdbevc+06xiYj+sNOFu4v1jLE8tGZOicxu29y3Te25n5k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\n7frp0q7pkJHjD4yDIOu2g7/BXtJusbYysE4PBX/EmOsr3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/Wd9yuzqrWoe4Q76wjerubn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RXO2+ew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\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eiN9fYPz2yga6jJHfMsBPsBaQxPfW6n6dd9UN/qOq87lqx5yRsb77Hi9/D+C+T275XQPe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U8mxR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\njY8fgl7DmHdnWSZ9MG++geux1mb14Xy6ppF3Wc/iD6qPIbpRska7p3jQh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Z5vrof2Answ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8PhcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HI7vPcb5wn9P0ff98D/r38fa/CsR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\nKSkl1ysU9SxiAYjRYe2HlDEC9jh0UTdCEdui9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9uHqf2zl5+KTEfp/+Ja4rcMNcD1SuKi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\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ZDAnK5krqOw97kU+KXKJHbDnA/neoUzdY1wln4ImOeidxxzbTZ+tpJ+8uoUfFNfZphdow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s0QJp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MbWk/GbMcIZPV2vb1Jaj4gmeL76tg60tno3AmjrR1efvW4VzWiDsq3APVsLEscPJM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M31ybGVkhLTvTDbR+UcVXOdzaG/lP0Rszr6SYfLflT3x6/Cbiv/lBbYOhU/q0eX5HP/lncv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\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XDLGN2cDfPx0sTxidL7c2858ebzg/WS5RSilHxJQDctLtxuKxHXzDBrHA/Ql51dnesbuy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\nJ8//eDpe2jus39XLx9gmcBw35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J46mncXF23QLmEfKacV7ZhoizOgX3SU6ugzTrHuyjEh\nc6Pov/Rh1PFZ7TsfwPMcydHZgrp6mHnGcSzuY9P4XB3F+mxn2YBK1eUxPtjHz/FZurW4eOe6zklI\n32xdGFVPcyxDpejYg7ZSxW/1Cpvw1vzfx5Pho2+Gkj/WKVReQdvbdTG9LPv3fW/929hWMXdmfkmn\nx3FeKzSdQaPs6MZb1BfaOCcjxTrh8raR54oaUss6CKRNyBN1kfvI+VNbO+SFrv6iYgHqnJOnuI/T\nRZG3rMkjnZ1bzM3l7ULGRxFHsd0PJguKIl7JNaHqJk5mcTb8fTidLu3d7iYc81xEzQxnyfok65k3\nW6Mb7xqb/2ZjfnTsbfzj8Wi/Yw+7zqih93uj8C6oGXqZLuXUg4p8js+829i4a+p4jAvYh2Oqsyeo\nHxxzjV2lHAywMy+9neXdje1R62qb8Ls7+31GTn08GxV6S8pvyh/H6ex31kV7ULA3nY3Tbk0W69nb\nquPx5dLebu1sSFvP/TocbK6sRd3e3mJOPA97F9dG/aBsnqGjHGfiHJ6fw/dOeHbA3J7x++HZ1rvH\nevfQm9LCZ9uvrq4xMYaC3HMtbr3QxV+/fb20uef7nc1hh3Yp/gxenmz9T09Pl/btjdkTynsrfIzL\no517Qr0HlPQzfj/Bjg2j7e8N5k278fJi83x6tPVPe1v/u73N//Hzr/Ze0NFvsI/UAyYHtEMtfr97\n/87mzP18trkdXswePjy8t3nCng2II6be6xPrBQP0+nzGGWyoZ7a2DutxNWPs7/EIG8t96Wzf1dk/\nfbV9v7u7w3ttHMrjE/xWK/zlKHwwbbvKVfZbkw9fyxY1JHeHx/uBOI5oW9PFftK1WRcvYFz67RHy\nMsDO3t6bzK6KTUrc5n5N4g6T+ZnzE+IOqRX+kuhFbZBt3tW5+1gRN/F+5/zyEvevYz+tfPxv/45j\nivGMegH2grG7uivz+w7/0fNOGX3oP929HPpAVlwOhHn2nL+pX2G2Qiml7a3EfaC728OZuXyLY7q0\nHueNNTY4J55ZjfW+PMMmlUUs18QyMovaqJcR5Hcllk0mByo3dLWPKp7POLVxf+Zk2DBdf4plrkfM\nPU8Yx0Yv4wgfQ3Po9oExJOI62AaWOVWe87s1CL3263TSUyKsuYNXMbf0GWIceVcL8PzYdjGRsI2q\ndqP2VN7FULeYnXML3bPYh9nPjTl5XbgXK+5eV7Q5Jj59KHPD8xCy7+risHtYj6pnOpnjeavVjLE9\nOCEnU2e2RnYnnBPHYRxUlViPSyllrmlzGU+zU1yzamv4WygzXY/7Fop7Sn8mdG7XWVy0RZ434vye\njxa7Mq9ivtVubZzj2fZ9f/dwaQ/CF9K9VnOcv5/RZ8B5bzaIuYr5nmfE7kfaEtZNGjjbUsrpbGs7\nMZY/2nrqna3zNJzQxd73FXnlR+xR37IWbmewQZw9b+y8D8jHv3z5cmlvD/bej/cf7PcG9yPwhXvk\nSTX2Yg/Z6lxMge/OoPfv98jrO6ETvLek3+KVGW0DlaBnDAzbhtRuqVuU90m828VLjInh/2mXi4gL\nKta45HcNsZ5xDi5fEfe83q7G/bl2xnWs6TFvob2tXY0NeSpzG9bbOtT9AB8T2O/TIi5dU1tTNela\n9J9E3OW+EXP5IGr2/jbH+jBncNfulM04duV3n23HmIL3GrSBcYwzu+9HuBZMWX2ExgfoF+nL6bcW\nCsWcxv0n970nf47Pn0+zKjIN57CPy6X47VTNPSoh6J838EMu3+K5Mo53so/7Klfzvf5dKWVlmhgr\nYo3I6GbWzqHfNWtL1PuiYxaXPjfxfRpjXNoEhvEQFyfLUx3vSxE5OD8lnuc4lnNxirtjZbzHNnKp\nRnzLTl2Z+K44Tua72HbyR1uCM6jdgXMf7Oe69Trq8o/J4oWJdrmO6+UD7hUZ/9TIVaeBvsRiitqZ\nxjjar5FLTrynHyCnpZSRRYkrSAaKRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolE\nIpFIJBLfPfIPKBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIfPeIOdv/\noKjmqlRz7Wn+HOcNaUvt11lSKHIYUgpV4e+KXsfxs5AyRFD//I7+ztECoj26CfKBsE0KW0WpSyon\nRydFtieswVMwkdr2Os2kowgUdLmKGvStbUfBK+juwTpUBCNZ4f6QsnZJnimplibSKJIOFq9r4pc7\nejB3xKRRiulGp4FnzHlCHh1XT3w2wwTqVbHXjsoI7+I2qmcXvdBaQVU6xr+zf7OgJ1V0VO5s5VxL\n/DvpymZSiMWY3LbH5zdAbkhFxXlKClq+LD5iv+vnAX2gx6SrEvRTtaAbX0NZGxNA/4aGtJ/KtnCv\nwYnJ/fLzuE5dqmhuHQ35KlsUvkr2P4EmVMqoQKVexj6kXyzx+Opdrs8rr5K6Itrl+tLkmGv2xUHR\nTzob8LYh33pO7uzFOG+F01dBbf7aM2/9XZ+HtRW1vfYBBtJgumflPOP4glDU0rMQA9fnrfpdrtsG\ngmHW7/562dk6zlVONvx5jXQpyVFrllTiQsYnoSvXiaXXyZ+eM+eJMeE7GCmpdylK1T8/Zf9tBT32\nGjjKTUdrGMdjTnaErVcxsYwjHMXqGhsAKk7hz1R/Ys0ZE3+JveFe0M+zPYzncg3Spq+wY4o6mP0d\n1SlO2cWEAOXmMDOvKmG7msXZOLnB74L+1OmANCZx/8nFtH4+EwTbpaTMDxyFNB+ObZSWZWU/45xJ\n6h/rESJnd8tUcyDNK+0ne6+JD6/2WFDN1vG7iHGRe2l/zj7xaKfj9djXt6/nZO5sWhtf2UCX80IX\npwlUxrO1e9i3zcYolB3tNXUXgjxVsW2gzXR2HnMgBXtdx/9/J1wL85Zld9qK8/mMfpgraw2s2Tj6\n5vg8altCaVrSe8f2mlDxhaOCR91kHGJbPTXUXZtDQ7tCmmFRMzucxXyUsUPBkWc2owZESnlVD/vt\n3+oVlPfrc1qjW2vOxv0+xz5JjbOmTjj1kH0+a0zdZToz7geNM4Uc+3N4frm0SedN/djtdph/XEOg\njpKq/Le5WrtrzSZwX9ReE9R96qW3FdaH66Et6jpQVL8iX9fmxvmfERNNkPEeedgWlPKcZ7uxA/z4\n04+X9vFgZ7PbGA33dn9zaR9OB5so/frZ1n54sXFu7+8u7f3WzvX8+Gshuo3p4P7G3n082Vjfvn27\ntLlHNzc2v/cP9r7Hx8dLm/J16uEbQD0+x6Xa0kIGt43N7eeXn+33zvb0BnvHujAp3F8gKzWenShn\n6L9p0Qe1YBbPz0c7G5ayB/oUjLnf7y/t49Fo3XlOX5+fCkFbR3l8erJ+Nxj39vb20uYZUFdo6zvo\nTYPLj/PhFM719v39pU354HkMZ3uW5/Huzp49cp3Y631n82HtdcLcdtv4bL4d7DzazvpTXnfQRSff\nNpuy39kcDkc7yw1zD8y5lFIOeDfDtBE2vWrsmdbdp9lcvf21Z12seI89or2i7xE+hmdfIXpXur7m\nnknFBcqP3tJOznHM5sYXNei6RXyIdfUnxESs5S/CA3fPJPaI49bwk01r455hi6ln8q5I1k9tDuez\n6dwWssb5qCSl5lrEu86u/Gvz7EfIK353saKrlcS+nP7YoYrzEOrA7yFqiHP8blezQr420B5CRgbs\nNWWQsfLYw/+7/RUzxnvnEju649HspLpf4PAN/tVUcczJ9tNzXDNzdTLYpIoLplzi936wOc+jP7PJ\nKdT12pS7F2+QozhZoz9Hf+gK94X7yDMY59iGFKcfjKEZ68d3DZO7T4KdRJjJ3It1gMHlLTjX2s5D\n2QxXN4Js+Zw3vvsvxd+xym8TWFuaYhsy8puL8pdD2V61fq6HvzM+Uvms8klr/BnhdM7VwQ3uUVVQ\nErnstDBtzp+rnJe1V94jcxyR6zD3pCx3S6f5ZwxDLHcF8ZjTSxrKWZwNYjz/3Qfjjrh2zLxCyROh\nZELdD/RnYT8WcHfqrK8LP0mMsAPbNo7xVGzJfJDza+BXT6e41sUCWYczaLv497m38c/fzHc2iEsn\neOFJ3A0WURfl7o6MiZBHVoiJnuFHX07W3txYLjQvbhkPyG9q7DVztMfD86X9FX6vvbM4cLu13JMx\nTIWq/ft37y/tpxcb84icgXVV5oMNfO/zt682587eu7+18YcnG/PHnz7aWr59ubS/fbVx/uG//YOt\nC/r0+Yv1uf8bG2dAHORq/CPPEvaA13nIZ8YB8jFA/xhrnBa1TcQtDfK4kfIF3zYzx4K81MIYu28F\nZd4T37kwHulckVjUJPkd3BznPYx9OuTFLrZkjiH8zbzCEbkYjd86iu9HvB77sfxnqeLbBNo0cY/u\n6vls0//VwnbTh1NOcR71SBsImXUX+HzW3nWiv0T0w5x0hE8aC2Nx+kWuHXtSxz5yHrgPmCdzgxKf\n07L+yZiCQ/Guz8uUEyo04/eNk7gL50fJsBW12xfqWRwHqhhPfTOq8kjKDcc8wZe4e2fI3AyZGGl7\nRu/FLnNjvkEdgP187RsC+pimiXXT6QH6DxQ15vCon7pv8/q4Bk8ZV3F5cfeKcd19dHd9ojaPMoKr\nZbgAmXGvocUiW9YyxPm5xA3gvTD3s7gQyscXPBu+e3DfNUCX1X02t9R9j4i7Fbph+BL/LQYnS31F\nnoiYfjpPpT9e/w7kMuTqnolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgk\nEv8/Rf4BRSKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJ7x5/DQPh/+eo\nm6Y0TePo3EgA4ugFBdvc2+llr0PR/XKcDls9LShTHLMmKXBA/VUE1auihSflHymMKsGZSkqXGdwo\nfLbtPH1zOA6pjAQFMbGGRvCtUOdRV4ImU/wdkWfa039r5OmVQA07xXSPpFryNLSQl4YUeaAgHo1e\nRtFUOSpD0jK319W9FzRKii53ja4oCjSlo8VRUeEsyYbl+mO9izU6cZ9iuVBzVRS5jv2I85ti3Vd7\npOTU08St6CPexf3ialtxlo5GHbLo2PhI2UtaTUGju2aff3tfTKvm2lDBxlF6xZRmpAb/a+R0DbXt\nLGg5PX0c7AF+d1RqQufc/Bs247Pne2vRh1D0jpKCd/GfuEpPW4dmHf9OCmx6uTV2xhHVQTYVNThB\nqlbCUfO51wq6tTfGC2vgx4w1Ym3M4vup3+PnFW20Gn+VTQMUDaKPU0gVvYIWfYXNqdyY8bn6eaq4\nCTaGfr3EMrRmT0rxtI6klnQEiZKu9DqUtLhxRtpkoVuCmZg0mR18MknVHWXqihjB04TbvzagqXX0\np4Xxp9J1+K2wx+9jbGVbZGztno37kLaWlJjK9yjbqPaIFMETddrNP7YHSu8551nkIWvw1txLrd2d\nPWk1/8p5qBzLx2Duv5ToPzBOURTrjhpU2BOCNKGDkD/aEhlyy6VQ5lScdT3/dbSa0LR5OSPaNNKt\nrohzGF+4UUU+pHJkNx3KI6m0Bb1uAT2ryyVoVgUdtouVXHgPWUSnt9p/JdOTsu2vQNki7q/LLThv\noZtvtT9OK/+KHMvF+nGY7aByHS9bJk/92XJ2VxNx0weVMSjcZ2EzWCtYC2nT6vhsuAbuC+1ALc5b\n+S1fi4r12NGQO1p08/P39/eXdt9bzcLpJXXIUQhbn5Z57hTbCQXvy//rcgDqivN1zv5ef98orL07\n1xV2r65Am1zF50rab1czdPUY+meOQ3sAPTjHssIzYwz5gjn0goZ8v99f2oxLz4PYE9QZNjuryZVS\nSoN6CfWDub3yB8qmca4qzmEfznWzMd5vPkudVrWSgbTiwl9yfGUzx9HG4bPvP7y7tH8ZTV/53qa1\ngfoT6yMuEMAcYKtAu/54PFzaHeqZf34Kz9s7np+foleUn3786dLmPn798mjzpg9oaX/iOlg1xjrE\nuvtY23rub25sbui/29p5TD3kDLbhDs/eQH6Hs+1LjXyjQ41/guzPkDnuewMjOw025wG6fv/jny7t\n4/Fo/ekje39Om8bed3d3d2m/vLyEzyh55++UX8qm6w89u6GtwL5wnA7n/fT126VdQQ/2G9v3EWcz\nvNhe3Ox21qc323UivXwXxwJNF9dt+4m5LeyEsDE9zuaE9u39h0u7LPziz49fLu2HH3+w59GH58x8\nZZwps9a/xd2Hs8VonxBTUT5OR/v9/TvMG9hhrz9//hyO04hYhnjrnYjTdZ7rIPIQxhc4VxcfuCiV\nesnAyc9jnGP7w1pL05iMV20cp6l8W9XxGDd2zKVQvznjXLmn9HmuMsq4Dmvm+p0NaMUdIM9sRWxF\nL8H10k7o+xoV+3jM4py2LezJyDsFs4cT9IzxdMuwn3WzBjkAfCnj5hk2bZjjuvAI316EnztXtA6Y\nj4h7C+PP2j9xac208/E9ZwWfUnj2TdwmJtde3DEKfWcduhK/F+wp6wJncVdJHa3cGuz3xhVReD/J\nuBz31JD3CsGcqwtQnzhmieH8Ci8Q57hP3V6vjQ0T65axHXrtrk7fQbhNvdq/rMhn3bOwe2rDlC+R\n8wT89wf2LsZHf0k9NBrfyzr3WtyhCDlm7c33X34bI/JTcS+g4O4R3LPYF3c3pmpFkEFhu2tfvLPf\n8YJJ1c5FjMC4kQlX3VAX2Y7rOCP2nT61g21sYG8nxtVC7n8HzHUUdRFZPxb1NOfPWUt0+2LjqG8c\nmH9Qlrcd4m9KVA8bWCOn2Vte9eXF8k0fU8S1cPndGba0htwfz6hldOb7kYaUs8jxx4VycB/vbm4v\n7S3kYge5Pp5RIxjj9bSYSLWJv3liXLe5tb2735ofekbew2+1bjD+fWfPbuHnH/aQG+zd/a3F9DfY\n3y38fD1Zm3nYCee6xRx2iLl3LX2YzaffxDZ8C1dwi3yuZx69eIZ33tRNyviA86tEfkCbSzPuvlDB\nGTt9Z82NdhIxWFPHcQfhu9Bf2u9cV4c9en5+xu8mN+3W8nSukf6P9or6sd/beZdj/O2bywUZdyzX\nqL7Dgl2inZ0bnpNBxilO/2L71rAYRx/DOj0NDX3hJM6MtrSJfSHv1909gPiWpnYBK+Nb2nbGFy7r\ns/7FDYo+OofjuflcGk+7OIf2GnPioCIHrGq+i/vImroN00Am+H0Sfycq1KDVPS91lPkN69c8szPr\n7swXxR0svxskxGcizoaNc/w96/JrDJeeOxmnvKBuvbEz8LEvahDYX/bp+7hmz3NV3zhOzFFczIm5\ncR/VvfYc22rfxpi0n4XvQkzk8ra4Dk5QVrjlk/uKxz/LnLFueA9iTRdDUwYhF8XZK+iT+8ZPfN8z\nx+etajYTFjdMw5typWSgSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCLx\n3SP/gCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJxHePmHvlD4q6VKUu\nladjAgVK62g/reloFjFe62ipY3oyBUXPKqnZQeM4LeiFFO0kf+fSGlJFgbZoAl15JShj+G5Fwyep\nK5uY+shRWZEGirQ7Jd4j0mmpvZPUmAKqDyl+PQWROO953d8XOTo7RwGKdonP2NP8YX6kfiJ92sDf\n4/WsOVdFGaraPKe3Uox6+iLOWdFeQ74dG6aQD+xtvTgzpU9KZ1tF6+zGjCmeVNvNQbx3Qxosp07C\nzpSw+yrUK+bs32vPrnmvp69V71rS68bPVyWW2a42WifKpqfzVTSsglobUDTCCm/1GVLnhJ7J0QVF\nrN/D63b1zUJUSpnd84quXNG+keIwpmBe4w8UVaTTOVLkvULtZ3OD3V7D5i7e6+U97u/6CDo3hUbY\n4deghtXzvj6PN58ZUAs1q8TGr/H/RdgSapH3nTHtLunp/t/Yt7KkzBS2UkHSUr9RZpWvcvbKvfi6\n3Ll9xzrd9irbgD7jirhmjQ9Wc2tI8Vdfjw9KWaxBQlPJx3PivFWshfU7u3Eda/aFu7tGj11M2BjF\n8ZqYS42pYtphsJiLFItr4t7X5sRxO2WMxFy93tBWwJ+L/sw9CdIOj4KWmxhBN1pAh71mzoSKLdU4\ntVPvOFYszn7E9LJLw+rZWUn/izVMQjdXxCBrYr818kiQ+pj5H/25WxeeXWPDlS1SzxKrzptyCTl7\nzQaq+FXZffXsGj9XrThXj+vxyMycv7me866JcRSUPMk+oBZ2Ng37WWPOVUs6aMq6p6Ll3pGOmLS7\njh4ZYLwuazYYn7ZLyZGyadyjtiWdu815vzXqdZ/3xPS7pLJn3Oy8K/a6reM91bFS/N41sdXv9UTY\nn0K5sN5qf2eMw/OYRL6l4qK6jsc/nU6XNv3odru9tG9ubso17HZ2lhx/PNv4Sm+mwSjA2YNjUm7o\ndzn/qVDvrX+3sbim3di6Sillhq0fzzYP7gUn1QjbSqi94DlxPYfDIRxHnbHTe/xO+XDzh4q2tFHu\nnGztPp81nNHnZmt7+uvXXy7tLW0a1WOy+WwaO7+P7x8ubRc3DdDFhV6eji+2nobrsRfe3N1d2vf3\n1v78+fOl/fLt6dK+vb29tHl+29bkZRrimJV7zTWcBzvXFmHd+WDzf/dg6396OtqYZxuH9w417Mz5\naP03LWwjDw109M1MO2FdNpjcgfGOWO8OOvRq7X+MY/n7+/tL+8Q1QE8ZxyudO+JZtmlnaMeeD3be\nTBNmzLOBPRiw/sPp66XdYo/IcN+OsL3M/9B/OJkOla3pwXYPmyF8KmP03YY2wPp8+2rzrKFn1JmC\nfS7F6yllrVc+iTEC9oh1uQYyxf0a8LLzyfRj2tvvPDO+l7rFM6a+8nfaRj4LlXCyRb+ixqlgY53P\nXxEnu99xx0G7vYFtV7X8UkqZBuiyT7isLVLhYbK9YAKy2cR3qfI+U9zrUI9dfXPFHQp1pRZ3SOoe\nqxIL9rF7vEZfd2Bug18pE86W0sfD7/5uHtbue8Zy9sw00udbn9oVRK0Jd1tqzMPlFpQP9OE95Agf\nxnhM5q3Mt0TNu61sr3lmtLEu77RZOp1oRW5EeaK/nMRFCPPx252PA12sxVxKFGKbOraHE+4Va+Sw\nkzMb6M88htMWdSxfi+I8KY/C5nCv3X05azSqthLnzhXlz72W+4k4E7FcuyYFX9zrV3Msa06vRf1p\nTW3U6bJ7r6ip0+4z3qni/XJnTDlADXDgmMJG6e8vYhvlbLUYX/kwff9LW43uC583CT/pa4iYKzde\n1ihxBlW8R/PoFh32Z/5AwanEHIrItZ2ddN/P4Jz4XQnPjOck9tofDebPb3J4XwGf0qGuX+Nd00IH\nXFzHsUQfdwZNnJP2jJdg0hrEpnUH++Ni8bjWPo+xr6ZtHPrYD7vYBLHWSdQPufHM8wZRi2Gf82ht\nzp/x1571CObjlKeFDdwj925wOq6OIOKWp6dv4VxvOouhR8TKn55tTMbQ7x4sh+M5Hb9ZHka5291a\n/xvmHwPHfGdrQQzy7r39XiHPHU6oV2EOP3z4eGn/27df7V3qmzJRy9bfm6j6KvLOzudY/hHWV2Af\n8Iy3Y3Ed1seg0F387uRI1CRprVu3fr6X9jP2N+q+dM0dzarvrty3lOK+wvkFUYMV/Zdwa1Dft6C/\nyvUInoe6+3Cxvr+0x7PxfHgG1MsaNfi2NTljrME8hPH9psa9cMXv5iBbuO+YT4wLcP9Cv+vu1a7v\nW7XoI69v3NHG96H+WxH+ztoHal+Ip70t1ndr/wmVV7ad+YMWNm0ulGuMQztRx/o3oiagamY9ExH6\nThF3jAw8eH8v7kpo85dwayvX7cPIvEF9tchyh1iz28dJxVoYkrbI5WSUpzjucPNnLYA2E31a3slN\n1Bvc8+EBtpnDTRO/d8M8G7ewS7N3+Zmv006YR8U6I3y1M2PMmWi7Oc4Ux1SuFlBBptrYvvsYFTUL\n5rB1VeZa2/UlkoEikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSicR3j/wD\nikQikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQS3z3a613+QJjmUk1zmUH9\nOyqqJcnTY5BUToD6fU2f6i+Y26hofkHL4ihQQT2jaKrdroR6AAAgAElEQVQcZSNGHASN1xp6IUJT\nWsY0SKTmJXXOKOjG1rxXU6aB8gW0LZWjB47pLT0d74IOtIopfMjb46h9/NP2atLhzEY9N/aCpqiK\n6ckVLfwk6MMISScpqNHUs2vOr3Z8dDGdUiWOXs3htXmuWb+TI+qseh/p1hV9IzBOpPSK30tqLe4j\n+0+CJtVTSIVdHBw9EunQnCzivY4WLpY5vrYH/WRTxf2X8uFkkLRQQoko45ryT1BIk4IQVNEdaDmV\nLEtbrzjPF72i+ayhcFNUgwRZZ2kbeH6kzPZ7jlkKKrslFO2Zel8lBNVZBGFPKqGX+jz4Kuq3/T6J\n8V27eVtMoWR8le1Se7XiXWtBOk0Fyp2mtrvuS1QMsmptihJa/O71A90ddfUausfY/xdF7a5o6oUv\neM2WON8gaBHXxEWKgS4mTlwHzqFbQVvrqIn7OB4hGMeumQNxOlvc5MYfV8i6kkUcRrOUG/GIour1\noilorFfIUQVf5d8lfBWnLOjoCXkCpJSn73QPgP7d+a2CtpL9eK8cpfz0ttxgqX+NkC/asWGI8wAV\nZxMyF8Hv59Pp0t7v9+FcVVxDGm+Vt/q9i/2ltL38XehNI2IH2mRpt2Uu5LEq3+YAUyxTKqboSTXs\naKnjvMrpn6Nkh81BHNGAjr7QhtM2vtGer/Hyb40FVH9Hdy/s+fJ5J4+QnbHEVPVKF9344t1rctWJ\nFNXM7cVOvrXWwD0iNT3jVe7JBrTzXDt1ne2bbntpn4Vv4/jKNvS9PzOX0wjaYRWzuTNGm2NyDVvM\nSfl8QuWSKm58eXm5tHkGYO91+VzVXK+bUHYVja+OY0mJfL3mQLvlan6lFPX/a1MV2h9BC885kbNZ\njLmGOn6eYl2kbJ7g2yiPjahPKn3y9hZ2FfvL8yar+G63u7Rvbm7C91JuxgEx3oYU6SY3dR3reime\nwt7Fx9DfcbZnaAeoK+MY6zXlgvvbCFnmszwbvotzWBW7H3GuHd4r3rXd2tonxP3fPn26tN+/e3dp\n//j+g/Ufsb+Yw+Onz5f27c7iph8+vL+0x9Px0t6B1h6lqFKKP0PuF2VnGG09XzBvxm8//Pjx0v72\n5dulvWkpO/BVjKkYWyLGrfAs8XI6WB/sy08ff7i0O+j31y+P9jtp2KEs0xny1N3a+IyVTpQhazOu\naVyeFPvmwcnHBn0g94v8+vHp66V9Otr6P3wweaE9bHHm3OvzGbkn6z14135nMssYb6JvL3EM8vJi\ncvfh/s4GhU37/PN/XNo7/O7q3Jjz/sZkkbr1fHy+tE8nyDFsF30e9biFbHH+x6ON3/cmE+9uTSZe\nYAOWtfkP7+7D9zHnaBFbM8+fe8YjcU2W9q3bW5/j0fadNv3HH3+8tD9Bd6nrT09PNib2i3aSdoK2\nl3u32dg5NR2uUqHq3BPWTWrVLkBN+xzHFzN8GNfi6qiLGGRypiiO8XhmnMd0juP4ZhP7+eFs++ji\nXeYDJY4F3DgiL3bxgpPxEvY/wR56PyrqvzaMr3kyzxFxf4tg1OU8c+zjl7VNPuNiqjreoxm5LWOn\nGe0RdtydDRxlfzbdogOl73F3NCWO65zubrj+63eMbRXvXVNd/2TB+3jrv+nge6hp43XZqoV/KaWU\nmfE+ZQTC42s22Avqn7MtIlZETMV5j6w31sx5MU/Wk5gjUwTFnZnLmdpYlidVk8Qc3HnDrrDGiOtJ\npx/cW3c/yf0vcRz725y4tvh+jE13Hyjrs9BFd/Gwoo4l7roIlY+rZyn7jMXXfKOi2qr+sllxN+bH\n5L3g9TrDn19u71alS5rlFd+BuFpUxd9hB0StxdV1GsRyzOdnKpSoXTkffr22rWpm09liY4b69Bf8\nRormqaGOnjF/1pxak6dX62SIF1pRX3H1fMx1xs0D7Zuz4zj8uqXPx7uc+tk/np8tbr5B/rjdWtvF\neCLXZv5wQMxZbhB3iNpPD7/rvkmij9+Zzd/OiEtxaIevFrvWSDeY8z4dzH937cJfwua+fLW8+sOt\nxfEvyL2o1rfICdzaWBeBHvCOg2tmTjMjLnj/YHUB1lAoQ5SPDWrtB+TIN+9tLYx3Do+2rvubB5vD\n2c6VuQRrAkRdx/ZqdvE64kP6LQj+WFgDiuu5pXi/wvVXiCO8r7JnX5AnudzQ6d/171vcXUklbDdj\nQq6Z9yMq7xH+ZkC+sd/aeTj/J+6Xa3fpVIf9GQNv+W1deTvU3nFOzFtdjiVqespvuW/BsNfdBP9J\nx+jiddZEUEtlfZZ1RYzZbU2G+iNzO/jU2vq07jskTJnfmsVis8hHIbuMxZS/dzrqA4d6rsNnihiL\nmESOQj3YTKx32LPuXFEb9J9F4XcRX/GOsWnNZzDaP/MumxvvvjNErW+ws+ygZy62hK4wFmM+6u63\nkJ+wXtXxksbly4wbvAayhsF5E3z3oY/9LaHu11vWKIf4fmtA3cXdG/ErG8Y+ThVjmSP4LWbr8kjI\nFh6lXe1g3DtXh0NOze8S+L1woa2G7jasJ/G7DH8n4rKYKs6N6ANnyKwbd7TfB363DbluoR8Nct6G\nNnNGPIn43n37zpyjqsqI77CvIRkoEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQS\niUQikUh898g/oEgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQi8d3jOh/m\nHwhD35f+fJZ0difQI3lKdVCsof/LwWi/SNdFSjLStjj6PozJNilvBozzAlo4RXdYiqeSUTSebHeC\nZpP0NFwPf1f7MoE+h3MYzzH9+STotwhFCTmBJrwH5Q/n5miBxD4Qju7W0b9ybjFlJqldSU8zLbo7\netoupg72vD14B6kTQSXjaZpAbwZxHydQM5KSXNBgOvq7MaaK5FpI+6moOxW1sqIcVjqk4HQIz1IW\nHUUlKNmajR+fZ9MJPZ0E7aKiAyVdEmkjl9S+NmhM2URKWkfNJ88yph6rBL0eoWimScVFq+Qoe2Ed\nBqylF7SzLejMX7N1hO8XU9V66tWYMlzR0HLN1C3qKPfoAN/QCHo9ZdMI/17aovgsFT0Z6QgrccaO\nflmIoqKoVj61iP3/3b9Bk9eskEFnfvEsfamCp2KO5asWckB0XexXaMOdVFax7VXzIc2ioiZ0/H01\nxwynvDg/26vX9ExRGRNKVyZBZdyAvliN/yrlr7047i/GqVYNuYZOOu6jfAEpIdeM/9Y9X/brQXNL\nn6z6r3nfJKhauzaOG9W7lK1b8+yaMyCcrxJz6Eg1CI2dhB9ZBdB8kt5y7VxVm+Swbz0/vQaOH8eE\niiKeYw6jyRyfZawlqZhFHPsqPXswpor9VEyr9HUpo2t8nYsJudcidiJcbNLE+VPHXApjqjiebUeT\nLVDNlDkRQyqdYIxa4viWFK5OPipS1l4/Y0VzXkoppWHOweexNve70BW0vQ/H/vIs3XEzTlH+j743\npnlX6urmIOZMqHhB+RuV22g7DHprkVMv5d754em6bVW2njaEb1C5HvvTPri9qN9mV9V8lM4p28i2\nyyNXnIcD9pM5q5dddBf5+HJ8UkJzf7lOzlXVXfo+fseSej6a91vBufFsJlArO1lpLFaqXC0nrhu5\nmgtkfHdzE/b3c+A48T4oX8Wz6H7nU67HPCq+4PsO/THso3IX7qOKuzaI+xmXqnqds6WQa9qP4RzX\nJ0kvT01UczidLWf/5Zdfwj7v3r2zMc/YB/jLHjTRR9aUF76NVOc8s1NPCmhbzwk1YK+zNg++T8Us\n3FPWrfm7sksq7uB8qHN3u33Yf0DdlnTgpNhmzWKPeX7+5Vcb/+b20t7Aj/I89pj/HrWlCXIzHG0+\n3JPT4Om4b27w304mL9vWZITy1fd2ZjXO8uXp+dImlTidPn3p5GJZYSfRdrXKYnr55csna/9qMu5i\ncejNZmv7e3x5sd9bG79F/nR3Y+c9Ie/hPjSQ9edvT5f2DWSF8/kkdJFrXPoOyg77ff3yeGk/PDxY\nf9iTL9++2D/EfccobE4n6k+c94x9uf9g9uTrr58v7Q0Ok/uyhR5/fbT+1cj3wjYwBoGO7vfmn3pQ\n3D9/+2Z9bu3sVS7BGpirldN+dKb3p2V8CDsw48wG3kXhfU1N34j7gsZknKA9nHo7p9v93aVdo37x\n/Ayd3tq+K/93OJzC331ua+/d73GWW9svxhe9sKtn+JsbxBec2xFySZljzdrduQjb7uLexf9Pnrof\no4wMuPCaYNOZrc0i1nT3QBvcjUEnGsQCXD9rOZRT5uOM01xtAuOz1sWc1OVqWAzv+nzNgvIq+ogY\nj6oyjvH9r4s/FzZQxY6sXrj4GP5sPNv7WHyeMI8RNn0c4OfYH3Lt4ldIwkypqPg7Y1febcZxptvr\niX4U88H4tFH+bo/PIh7GWuTdacczgM3oEdcs8yq2VUrHmgf3TqRGa+JA5hw8pzLCd9bxnezz0WIB\nB97j1LF9G86i1lfH+S+OfnHG8Ausya2pCyN+of0n+t7XPF0NQ9THXKys6ugQ9xGxltu7Fd9i7Jou\n7CPzU3EPqfJudddHrKlxqPcOQ3x+ur6KdYm5sY78279VfW9kp0tT3t/M1Hd7VOVks7jjcEBt0OVS\nyOdolxjLDcJfyrubKrY/XafuyhG/wH42r9Ve/4y2wb0B8ycYq6XOdXiGc+3VNzqIC14OcZ2fe1dj\nfMrdCB1nDsA7aNY1Znf28JfwGcwfqQeUFcaHmzsb/wU5FnWLOkpV7DHmKPKQI/rsd8hNT+YjuM8N\nbP75BXFA8d819IjxDqyVYawj4oUDbN2Z9Te8bwtj/+WL5WGU8V1NX22y0uKcqEMF8dv+zuLv58+W\nCzb3lguO8FXMVe8eLGcYT9RR2E+6oTo++5Z5Du9KqDf8tmdGnMwaB9ojdLGtvf9jTOHsDG039tF9\nm9bEtXPmFhXttbtrF/6D3wCxRjD6Wks0H+WHfMwddpF3j/SXrYsJoffifsTFgWo+9IUwIMPCVrs7\nJ/ENyZq7cPUdGW0C8xvq8TCaTXDvwrclzCWbFnOA7k7wl9PM72F5OPBJ7htF05UZ8fdc0ffY+D3i\nwG1l9ZRhoP/2duzyXnU3hi2cFx9vuu/oRhUvxN+i+JfH3xby5VyDy+E2sU7MTu4QU0Gujyf4Fdgf\n1lCqhnkrlsLYld+UIZ6m7XJ5NH3+HMcd9Avc51vUosYB/bH/fR9/f/DnBZUI6g6UsfjM+0lxV+me\nHZnzsqYCmUWdhjWh/mTP8j6C9pb23fk/V8uI6zK1uwfH725doh7B79F4h0nTzvcyXeL3vH2cRy+f\nORy5Bls/VKL0J34YxzsL9OfdI+KRPYwOv1GlTXO5M+wbc8MJ/rnUdZnrOGaNkAwUiUQikUgkEolE\nIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCS+e+QfUCQSiUQikUgkEolEIpFIJBKJRCKR\nSCQSiUQikUgkEolEIpFIJBKJRCKR+O4Rc//9QbHrNmW/2TpKk7HENFOS+k/QUjl6UtKbCHpESfcD\nOPqQV2gpHd0uafhI/QXKQrZJw0OqGk+JVYe/ezpG0LKAavgMWq7N5jr1r6SILwZP23r9DBTUGskB\n5iiH55gay78rpn10NLXFUwqTcctTRTryrvDdnooyHp+0PdMsaL+EDK2iDGOfcJb6WUUvy7anxozp\nNtdQu7G/ly2cxeKMSUtGKKrPVXtK+ukS7wvBPo5mjAeOLo5WDutxEjTHz7LXrKhNudeUOa6FZ+am\nf329WndfoUWtKIOxPnLYSkpqDGWj1VRJyaYoCL2tjt+lzoDUXcoeVivGVLpYK2oz0rlR5iBnfO9r\nfo6UlWvsRi02myqqzmkNnK1wv1u7Efqq7I/ynUoOpG8T83S6Ur2iH2+Y85URLq2/Zq/XxFdrfp/m\n2PZyW2pStYkpO50gk5/YI0XhXoStc/4ez25ro00klA1UuluK18daUCSqdzi/5ShgQfEo5jHMnoYv\nwly4v7EtUlgTyzn7RvpF9HF2oubaqaN6fyMoO8n3NqBa/61fvF9rfKOy40oe1/hY14dcqrA608g4\n4m3x3rrfYzupnl2TM62hoF+T8y3/G/davcP1x37RXpEyXI1PGu8l3eVlHBcTilhghUx4ZaGcxfG3\ngvMxkJCuFrSia+TevwHP+nMdZkWfyVwsHEpinhnvitgJtMak9SWtehF5gpNrMR/KkEgFpf2hvk5C\nhuQ4q/JCe5Y5gKK7Xc7v7XmAoV0h4ypX4xpcLUPIkBr/rW3qtLJLnCfRCIGd0Z+U1jJ/b6/vraNc\nLn6PiM0GVOdTbKP9Xrfh72rNCqq/WhlV8ebmJuwzOI5q0CPDzrSYv4rXX06gIVd5NN5LWvSCNt/L\nZ1lXXPoFR/vtajPX6zcci+fP9zVjLAfso/RMtakT57PVDAup4EW9h+/dIO+eMU+O+XIymvpmsD6U\niffv34fv7VHbbDb2rqenJ+uD/d/sjJ67Wvz/DT0/P9tcYfg3O6OfboSsEW0b2zdCyeDj4+Ol3XWg\nCW9J4R7bUo7JegftwTvs6eFglOQvLy82JuUX8XcLe7PrbMxpY/OsoKPDaDK0AZX4xNo06Os3O5sb\nKcw5Jt9bio7BmkbEMFPsk/l7jXUy1mI67+ukOI8h1g/mOjQtN6CLn0ClvoGc9YjTetixDn343nmy\ncY444wE6zfdSPlwdVsTVjKfYPg2mx93dXSE6ZwdinThhbbSz3GuuYTiZDeEZb1p7F/Mkjt/Pti8t\n9ne3h+1q7b3jGTHCiHPa7i7tj+/MRn379u3SZozAd50hgOejze002bomkaffv3u4tKm7Y0/b8/XS\npl15OsLewh6WUsrDjz9e2l/Qr8K8adObDrFDYR+TKdrxCnbg+fyE31V8eL3O20BWRpHzPTzYfvl7\nMtvrI9eLF1B2dzs772Gy/iOnib3Co/Ke09VBoHO30KH5tfpRLWJQp7PXaxw97PIZbdoH5VcYLwx4\nF/2Wq2M5P4p6fHP9TtLl9fDNq+7J5rfl4BuVI4tSLW3v7+NA2yMXw2CveW9LW8e4q0y0y7xbQkzP\nOzTMiXlJJe5vWCOYV9w7zzPvBsU9JO+p6ziWmRGPMWYrFfqLu5uqig+kqVhrhv6xphw++edn6vgZ\nV5rxF73h7xNseo3103ZV/tIparoala8T8lHEMhy/FrZU1JaU7fUlFPZnTh3Pk/7C22HqYnzGtbBz\ny3lQh1x87L4DMdtVuzO+niepHHNNHUjleWty7TV36m/Fmrl5Oybu1sWYw7DwO+5qhrYI/h/n4Wsc\nPBtRP+5if+C/WRA1SQT47s6UtRnxTQ5rqZO4k3OxNeUV9rye45jb1SpdTBh/W6Du1XhnNFVxblNK\nKcOZdZHYprn+iDu77vbSdqfE+3Lx3QglmbHDFufKOI3SwVrt7OoUcV2R4++Zh7n7lOttrmuij8d5\njJDdBna4gawzhzueLR8YJqWLpTSYxhax2fM3i/cfj5Yf9LC/1cb2Yn9jce3DxuL1Bk5v3uJcYYtg\n6st2a+d0QP4/wiYX3L8xh3t/bzH6r2c74+GIGgTuDfbwE4yBJ+j3CXJ5REzRIW4sA+wVdZGfcrWM\nRyjVsQ2kYLL+W4r/drBjjom9nkY6UMgOigc1S6O8T8EaBuwF73Wca+f9CBx328S51Jp7znGMY901\ntSv13ZmC+r6zE7Hr5PwT4u3R+wXn5zkn1uKoyyXWZXdnyPtGjNOidska1zyxbs26nDWnEvvnqWA9\nkNm5Yqxk+jc76xvHFO4sYXyalvEbzmCMa9DKjyjMQlZ+A2tWsX9m7bLMsVxXIt9UbSenbRyzUtbU\nN2XdDWvSqPVZd7cunk2DWlcLb8twsh9iX6juDJ2s8MxoD/GPUcQ7TW1zqxqdZbk5uW++4rjLfX8p\nax+8X8b51fQlmDdyFxdHwQk0FedAmxbnc64uNY7h717jKH+0Pdan4ffCeJa2y39fDPs8cMxY/5rW\nn5PL0cwUufiNIf7g5sdaO22RNVveaW1hV2faTNYvrN2zrlHhvPnpeFs5O3UNyUCRSCQSiUQikUgk\nEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJROK7R/4BRSKRSCQSiUQikUgkEolEIpFIJBKJ\nRCKRSCQSiUQikUgkEolEIpFIJBKJ7x7t9S5/HLR1VbqmXlDYkX7S+k6OoiumMmo7UtgYvQdpOGtF\noTjEtH6Ksof070sKRUfVB2qRcSY9TUyLVwvaQUdbq2g8AUX5qygb18BTCmJMQWn5VrpKRY2p6CHn\nWZxZTXrgmO6H+/PbXD0pq/1O+ib2x98qVaSX4t8wkcIdZ0+qQfDfkBrM0ZZDNhXFoaRGI78Oz0+w\n2pDO1bWr+PemiSm6CNI+K5kgqGfLPqRAc2NxTxUTGQ9wjrvwd6Ur8q/UpC6uGMet5fo4ipp2plxS\nV9zSBf0pdW6FafhLbImzD+4MVtCQS0rdmNaKUHTget6KvljMZ6RcgsaLtLt4FX2EpBt3FM0x7Zcj\nk1yzLkGx/du4K6iuhC/174jp9khbv4YiT63B2Tr8rvbUTy32qQpqHNXH9a9iW7UGa/urWODtbY55\nndbZz4E0hfGam5r6Z89Sd+fYfTgaQeqxotsmKhGPOFf1NjZJTz+p/M5fMq44cu/CrsuFJAMXlOx8\nby3oGAkfiwpqV0FrT3ifhPFJWS981bzknH4DlrSws6P0jClpFdT6m0JKb/a3NmMqp0OOKppzWGGL\n/h/23qxHkiTL0hPdzMzdIzKzOotgT5MPzTcC8///DglihtPdVc2qjMXdzUw3PmS3yXc07nFTz+zB\n1ATuAQqQ0BQTleXuol6HNudubwX3vRjKzT02eY/P3uPz3tr/PWfj+jhKYcZRjZkrY3G3BlKPtiUe\nZ9ecF9KQlrDdOLvHtdDPMceSX3CeMlI4vqxd/Lr269v7FMx7Yk39gcmNHGWzoYufMO/e5PNrG8s1\n9XIxdk8p5Ulnet8Oy/nBgDjbSzD+nJdYXr/9DfXO6D4NBONvoUF2+otcGHOSOk0c7lqz1xrZ1Dz9\nfnuVOILP45xXJ+HsJNoSH5kzo5NoYzluNqrE/J+xL/Nt6gqpxHk2Q1f7c83sfzghz0dJwdGwEzbD\nor8EbTnXMk+xP15ANdyyBoHxZzmzWC7dHqq81vZofLbUKjd1EJeXtE2cGy4mFrhcSOdu/CeWTNso\ne8r5wD4fj8fwt2uJ6af32CLGWtep9nl4ery1D6f63peXl7DtfPn5cq7zGSsdPZWlg02iTLcbhTqd\nTvgXbOiOPOP3gGs7HKoesLZGOX0veE7Pz8+3NvdiHmNZYV1jYF0OqsLnp0M9y9cvOA/o0xG6zt+O\nl9p/vsJWHap9Uh19owYFZ9ri/AvrQDZ3hl5i/dRLynWHWHkBlTp9b7tAbzCdE+0ezmCALPY4v/PL\n66399FB1qDX27fp6DttPT0/hWs7n2oegDIkNwHvPsE/tB9VXygXfwXFfX+vaHo91/ZzrDLs0QV7o\n8w59bdOOUYe+rvVdC2RqmuqYtIf9ANuI+VMer1g/416x+ZC5AfrdQcY7yO4IeWqnKotfuFePOMvH\n+ttfPn2p78L+/PTHP97aL6Peofz5z/9ya89YM53+gn3soB9tV2W5bWqf67XuKV3jYuKxCevk+VFW\neJZyX4BxHh4e6nOTwxGUM+4X5fWXX365tf/ux7+7tV1s1TnbBTnojG3/618/1X/AaGzrF9yL1twJ\ncYMb/L5t6173Jl/Zvu/fsTCDxJ0b70ZniXFgM7FkzrnDvncyT3PemLS9HxFBC5ci4L7NkiPuqKO2\nZv9/nVX4vnGK8yHXn/GY1ClMvXmR+DOOp6UOy+drnBdr7Ry/DZ9qrNWau5hVk6+wj7RRY+uMvS1y\nD44JyR2mF4qZ85CrNTxHfDi5uwABc15Tj1m5TiPjyI1a+faB8WptzyU+vwbvklh3iW1v2zGWwbnS\nxrJ2JfvOuIxzrl3a9v7Zl1JKY2qULiddJSfg/SHv1u7XMd03Ie18/+zdNw7vrTX/njsz26eL87kF\n31m0ToZob9Y4f/h2TqynxXNdWu5pXBMjVtpfycMXdsKYvKcQixX+VnL71dlDdNllD/imOE9gPb5l\n8uXuME29ysVTy+ZbmnEx99OUEdrusfZHU3IFF6d0Uq9kLE6/yDyMNi22RfxeTD7tkTiKMVGNO66Q\nOc5ZvzkoeH6/Ru7qLyLrsOczahmsgU0buecZDm1dw3Go8x7G+o6HDzVnvGCdVFPObzzXeTz0NSfj\n2Twj9j0xl0R+xhjk2tR5fnqpOcrpD3+o7UOd/wtysvPX+q4VOcZhrWMyVmyHOh/WVnp+kzTXMflt\nIcP1paesxN+mUUZnuTfYGivqRBwLMZ9YzP235j2MwYyfYP7UUI/rzFCiK31v7CSg383tuIsy7aFl\nrG/uYniPw5jW3Oe1yL04Tbc/Wxvu9prf9Jyfa42SdWjaNDknug+Tk0o9sGfNDT9uaEPiM9D8L447\nrhP3Lj4DOlu9y2YtO46J6FYk1l3vy434TvqzjSy67w65Hlff09+y5oszoCOGz2CNgHlrizNjPD1L\nLIp5drXOsprvs8SFtUyecXdjfKR8Oy16UNGZvJV7uCDvZF48MQ+BTW76WAdKKWWW49Sa7u25qUcQ\nrfneSO6zJW+P76p75gPIzzp+p81yh7vjj6/ay7HE38DSDvfMZ6krFFGpcTMHXcM+8xzLRA9DL99s\nbbZZbDRq1SvqhozFpfYzx7EclyC61TAGZfw5og/rjfVxZ2oZTdfoN+B3kAwUiUQikUgkEolEIpFI\nJBKJRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCS+e+QfUCQSiUQikUgkEolEIpFIJBKJRCKRSCQS\niUQikUgkEolEIpFIJBKJRCKR+O7x23m+/wdgWsYyzVdLqUsqHGHaA80W6bRIE+Oo6oqhaCQ5kVIl\nksaq9l5B+Va2lIik/lpi+j/3brYdvalSJMY0t3i8lQMAACAASURBVEITg7mSdmgVWrmYBmkhhY2Z\nf4vfrr2hKST9FnhiHC3lamgmZQ6GUpdQek62db2O6kxp+0iDJfyKmBNpjjCPcHZKC7+H0tPSh+K3\nSv/2PgpJwlEiC+0VaHQWR6VmaEvn2dBEOlrfndhDV+r6E0oFXJsx6dc+ylfXnk1/5fEkhSn7gKrM\n0BUtjiZNKJHxvNyXG6UY2+gTRiNDMp9zrm2JdU7fFz7eBUeb6eVrB103dZHyjlHsXzSiv6N9dvpE\nLHyvo2y3FNgbTDEN/e+hCxYqeO4p/fCO8fdQ4YrMUt6VC+7uOKQGJ3WgxhH3YwdPaRk+tue93WdP\np/nbz8xSP+7AHhvt+izmPFahhYvRGj+tvjwOiVsXZ3HOFJt32sMtaK4XGRdtQ7u8533cxwZ0syKx\nxhi1a6xnraEmJoS2VnQOTUPFKPTFHHSHnWcs9w3vYPCu/XA6/j446ufZyBHpMYWqloOSvrCN85I9\nZya67uLDNY5F98Q1MmXz/HqttMwu7n0rHnaU6YujbbdtrgG2gvTePA+MzzN28YLMzey76w9W0V02\n2dpYQ3H72/TjV7Q71ltKKX3bhc+dmO6hbaVsih6gi/gVCYvc2cTnLd0Zr0usUcK2TJnrklw4npvq\n4vvyig60u9M39OEx3qu/LRdqzmyPfDGHpRkXCmwnQ+5dJoZ2v7X6V+if3pfPiR9l3UHkkr+t73Ux\n0Vv+SHJJcs+7fAK/XZo4FuLZsG40t3V8bu8e/+EsxTwxhq7Pm9bQqPegE8Y4rH2wBrj0sc0gpNbF\nfBGLZI7laiJbmeM/lVa9Ninhbn6kx+67un7XH6VR6cN9PPYxtTRVrkVZmXLAdV4ul1t7vFTfTp3+\nCp//4/HHOoeHOuZlrH1en19u7flrfdfhUNfO+gh/exhOtQvWTrrxZrNvPWRK9Ola1+ZqoBIHz3Ef\n7jtjB6nVStwYyxqxpUavc6jrZKw1v9S1cB8fnz7W9y5Vb6SWONfnbl3OJp8v51v7w4cP9beQj8+/\nfLq1bWy5bCyI+CHaa1OLLE3YX9eDMeVccaeAPo0oi8tDOWfELDPoyWGjFtiu1dTmeTYD5nDsjrf2\nZeb8Y5kg+Jy6Tll5wKYfQOs+nats9Y3qVod4+vla590/PtzaC2jhX16q7v/4Y7UVe+J+t7aHB7wL\ncdEZdmbEb48tdRH7yHxuqv0vqJ8dcXfFc7qOVQ96CNqHh6c6UcjQ169f6ziXOs7jh6qvf/3rX+s8\ncd5H7C119Bl7+xVnVkopp6c6j/7xsb67g+85Vrk4ov/DY20/PlYdX6Gzz8+vtzb9s8SBovvuDrCg\nT6xz7g6QNnae629t7ACT8fChrrHDOK/neq5izzkfU4M+HHAnB5s/VDWWWHFr80Un4A8lcqQvMXH5\n6VTPm++gbp2xTpfDc39HxHWmvCV3rC3tRhOvi/cGtIzqCtw+xHlbJ3Uf7hXmL66H8RSf+7zC1Su7\nA+LaGfLSIo4Yca8oMQjbkK8SP18lH6T/4767u4bannfUxthj5jxNPGzKeHpm0Ne2jfWJuemu2vQm\ntpJfUO5YK2OuwO1a4+eSuzC3pezzvdwMKk7Dc8ULWuprHJd22DuZsqlBSH0WW9Qzl+hju92v1Qa4\n2HWRWDyOvV098N/+493frPAfekyssd6vr4juYswZm9fuuEPZW9+M5uPGee+Yvn5hrSl/XHvwsdG5\nbbheZsblZr9MXiV1dFPX4hJWudegz8Bz6papZTSM19c4BxLZon+izeDdPPMH/Hq+xr6tkTPAu8we\nSk1njXMA9ZEqZ+xH/eB3L6yFjMiNunIM+3SMEUzeus3J63Ps0Uud2yiyz2+DaDMLnsOfm9yo7WJb\n5GLUGTIh32vgAKkHzBMaZz+lPgAfMXl/Rr/3+FDPYCx1fqcfat7wBbnIXz9/ubWvY43Rl1fkmB8R\n774ib0DeOuHbPKkzIX9kzthjkybEisMJOse4nDUIKZrVGJp125VyxtrVFH97ZL/h0YtavrhEmCQW\n24C1YX5fgPMfBnN/iNoM31GM32p4jxWb8dIxxuGdFgqIq7HDrvZPvOnD669vrT3fvRCtGX9CHVLW\nInsCndvapB0+ubUVbcyPds98nyU1BejQ2se54fKNY/0VTRPH8Vr/rWO2kKcGdV4JLeUbCn7VWLHM\n9KOwq2P8TRGhMUvYRbCVM1svN7GQfmeBmpuRu2VlvM74h5sUt9sGtWqTM0yMy2XO+L5TapuYM/Zi\ngm8YUUtrDvX8JnFKfC/tEH0SfT/jILlBqs9NLrFs9ITfcM/GpfVYJ+uJ7q6lZSxEuywJIesRyMFN\njMBjHeiruV+sNdOWwj6fBsoBpiZ5K3PBODbj3YLIK5bLHP8C/ZYaKQ6Ksc+8qn4PE74dh2+g/5Rv\nx1fmobUP62k94gXGP8vE80N/mOV+YOCMWgmOmPq0FL1/vYdkoEgkEolEIpFIJBKJRCKRSCQSiUQi\nkUgkEolEIpFIJBKJRCKRSCQSiUQi8d0j/4AikUgkEolEIpFIJBKJRCKRSCQSiUQikUgkEolEIpFI\nJBKJRCKRSCQSicR3j/5+l78dLNNc5nEqCyhEhDJzFV4SNNfwObEKtQ3obBz14R6aKVITtaR50f4T\nqGRIF9SsMWWO0AgJLSIphQzlnaMn59/SNJwr+8R0x0KLg71w+6JUn/Fv9QfmsVCex3S3RCvygXH4\n3pXn7ektha4NhzA76jKLmPpJCRl/O9wc3HNSAbqzcbSle9oT5JttUku2ps9yX43LVlhIZUm9W4WC\nicJgaNhI20rVAh0R+4jWCL0XxzRtQ/ulffiCuM0+M0WrjW2XjLmhNYpeJXvo+LYNtvTLjflH52Rq\nBxWeMxwqm/HzPXrjZNy9ixi62PXuoQTuTX9Svts5kLLdCaDBNxTjRu4aQzu4B3v2VOjK3nlmbA+G\nFlb5COMzUArhmC6uk/E5h/i1zvY4GV2mmEJwD43lW9hHD/0+fXdnQH+jcVptCvWcfUP8X4QO3Jyl\no2Uk3NkviIPszISasGJ945zaNpZrRyVusYdulpTQLna6/yYLK5t7pr9Dv/dQfdrxy/0Yx8nEt3Pi\nOt/pD53u23eZ37rzc/HbEsuEg6UvNnSme2zRe/3onvZbv/fPY/pRB+sPTHyo/uC+XBMSo5r+pH93\neZhIssSl8fhvyX4Edx7LLlrmUvqG9J613+zkjn5V1MAE2l38bqGgpw8nLS5zNfRh3MWwTuZp8o2l\nxOtSOww5M3OQc5rjM+N5DEL5zrMx8eEbUNWCDJpzkt5rnC+7ObWmrrFHd51NFxky9lBouJ3eA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BY7ou0gs1XazH7KO0WbGdWTo+r2OSnm4Q+lpDvXqfLfcN+SNNFunodNCO9KnULaOo0xzrCrGD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\nOFW78fSh2vHT8bFOjrJ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kIqgDNW2VD9YhJX4BJXuzmL3m+bFGzD4mLJVaqtgo2nnMoY1t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\nlthRhMpybWp85rOyLsz8F4/dfbmrh1Ku1ZDizkXq/fV6ivOQG3Ip7o+Le2k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+hzaGoI9n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Zg2kjRKDagTOSNSUVVB/eQI3zHn83yd2q0VdKukBnVUl9if+UwKMMgE+nB8Uj+1CymqSV+H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\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5f0NfyPh4RP40wu9WrJu4Wgbqh2O+jkBwr/vO+lAWqU/TZHb4pnd+9NmvumdxusLSBsdl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\nZZ0d8vYeMj5D/XDJeh2/o2KMWiOHh6/99ttvh/b9hw+G9r/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M7YvLy6H99RdfDO2//Mt/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\nKLsC5f2O7+byAmU4O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+/FFBnsYIr6YM97lz4ea6gYL360h3wiMl3fSu58/vBB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hi42MqDfi16GPyyzlkfdqKuZW9SqOX+C5kYdZuTm+pGIWxCA54vg9/Bafz3LoGeOLRu/PoO5J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\n9g+vfjW2//u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7eiNeN1t3G2Nr/m+xz1NiMHbnTg3fPMWvE8kEZZx6LivGV3PS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9nQP28vJfc6PsoenXEGu8vruf3261/N7d9d38ztFn6U733/QebZfvcfc/u3f/jj3N5fSu5M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\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83BQOrfbg7M/k7I/SeL68t6QYp0y71K4ppcWUt93cd6FyrvM2aiQFOKkyQZlGPa6EGhU0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sXo3vtnqmwhuZzwV9JsTQNDM1rztGkZXLs8z/QlmE/Cr7DLvEeLrHmB1+Vz5sYqwv/Sn3rEsd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\n34osMNY4nkW3jo8iyxv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qp+9MxzjqHhnvNWOE3KjV8r2wK5my+ek598xzYGNVDRC6S9s+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XSiaUSHtrATnnnKQudlDF0L6Ej9ID4tX+LsiXdnrczpuhIdTOHfmLA/wT4axKH1FvVArLNa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M2MMfzALPIt8gZnK/kqkzLdQglZxr1LyjGsj68f5XnzXHa6UU5yPHxyd9e48+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\nX0rOHL6Xdz8/lmeTDfY6OAd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f8N5iGK9NWGs9Ze5tG/cVC1buIZS9Jvov0TEPfu5UTo7n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CS9W6zkuz68+bZrbzdaz8mx/FgR5MfXq8UTz+r/DGegcDgcDofD4XA4HA6Hw+FwOBwOh8PhcDgc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\nrO1HyejFZ1917Y+obz00Gv9gJF90gpreC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BwOh8PhcDgcDofD4XA4HA6Hw+FwOByOXzzy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\neQfG33I/qyb32m3NfG+4JpBl1m9xNxGLK4kNVvg23oWPqk0OQ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HC3bY+9fYUx5Thg76DX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\nagmdyA+ok8Lnz5iH4H4HbVdE+8x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Ut17NNIYGFJU79L+Y6fnHQhRJ+1prt5lrXdKsn95y\nc9C7NfYmA3VcClqx4k5UcOtQNFOX6J/csc+lxrO4EPXPDHPcb6UDm1tRqX15rjV3zrn5XBRXZN4r\nQEFYPWt84xR7CcrG7QZ0VOd6ZjFT/6Tc2u7U5wJ0kjnWfQfKuHNQd6Up6aFBZQTasxz7WsH+TEht\nCr0fg0I4zEFfBJraHOOJILBpqncDUFqWpDyrZJ9IeUfb6NO/+3TVMSixQtBaee+gL9rrEpRNpLNL\nQQU4BdVZU2rONQzuEJ07aftIc07qsg62wqe0Bs05BLDGmEldyf2oSJWF75KKmmP2vkX6MFJ7g4qa\ntHukd65J8c7fa98GQhy9fU5BZRiFHkeZmqCSK3eSnbbxKUePAHVeDWq4AnYYzGiuxZz5DOlD96XG\nQxkPY9CTc/TekkJHB/ab+sHnM1Dqev2A2qyBPWwr0qVi70HVzZ3xKNidT4cegCauha54MhKFvc+T\nYp3stAH2Ney0dtR20tM1DrRvAW0LaNKH1hHPMI5oMDrGBS2oWkmpRwp36hDHzLnHkGOOk/Lt2brO\nmz3eJV9ecPJvoE4EhST3P3B8pv/vn71n8I2aNgR+mxTd3hxoG6HGESm38XyINvWbFsBbU6g6KTdJ\nI19VpLVHHAvbXoK6kbGu62jDqcmgDYacee+Cppa+5vcPwtZ3/e3Atxz63aN17NfLGnMesjkB7Yb3\nXew99IN+MQr6++FaUIcaPJMixqNf5Lw8fWqH9KD/ec6F4DN8l+1uQKd/7NuezRkYRwcKSeoQ1Zdr\nTdtSg6ISy+4ayFoNquvQ+TYBL6h/xh30u9A0UrWW8A4dBIrjJ3XnkEzT/9MOEwHtHuMRL34RwpPt\nbkIaBS4wbS73HI97stM/PuqQp0+eTPTLIP0iTS9tYEvjFfaPgTrtzSvA/g3JKNcUcUroxQWIIwZk\nhd9lvEPZrQao2X//ut7PUuUc9A2U2TTT7/TntEWtR5erboKEz/THXVyLGLE44zrPJjSMWfQz15dr\nGrBmgbmTXt2npkeOxVgJ7YR9YpwF7efAngUD+ufp7skzwZDse/re262nTvQxXkZHW4H4ogGVb4c9\njgbmE4A6mHLQkR0Z+leDirquQVsOJMhnuSxt0O//G8qZtx96N2aOyAGRGpoyB73k+rcnufDgcxBU\n5oku7Nd3Pu9AfcxcIen6dZ/xK7uhz2ebtMz/ZD74l+M4uS6Uy5h2RfWhIZ/t2UY3JMf9ukL66Abt\nsOmPj07teQADRKpvL1f3nB3teH9dxEsJBubGd09z8uMzXf/znv+gXRpYUy5kPRTX4RnSfnPMHGdJ\n2m7kwsxzStRKKOvM82hvw6j/KOPHDjhox7x1ZxrjrVd/jWcoxvXi44F4dKjmxBoBbabnA7yciXPp\nH89Q7DMEmjQ/bWHMjP75LvNCxiasu8LeBl6dyLkQtamQNQgqNgS4hQ/w8gnua9yfq5cldCgckC9m\nsV6I2y8TXIuQ+RbWIur67VLU9u9N2DGBUJPb7dUmIH/0x/ydcsBcpfViguG4w1ujmL6hP7Y+iRh6\nWn49aQgcN8fwx+SGBJ85rZv91POEX0cYyHkHZGuoHy/nGxh/6Pmkfv3+Jzll19/29mAgB+JDnqvy\n2tDXgXR2KERgDdSrh3m5KtoD//HH7NOQfHh1BM6MH/PyKrXpzxrX36e3xwPnD6f/zfe9ei6eZ14y\nJC+0P57sw0axlkGb4FXQmHvSHuaIxdv+OHYEe8I6hTdOTMzL973am/qPmJOxI8hTyfOUlnum3MsP\na/rl6dSWUJdHI+XCAex4lA7JRX+O3Q2c8dCXeHacA+r6c2TmwiOnNjOmYKSzym2uumqJw5VDod+7\nMfKTRj2NUeMPUbfFkYWbo27QYV6rqe535PQFqPnGOCOe4yx/E6KudhKvd4hNRxHONwut78qp1hsl\nGh/nv37S2e42xz2AK92DOB/rvHEKBx2U6r9Yag5dqv5L5GqMG/etFq+DXl6c6ex4NNa+rrc418Z8\nnyuN59P3747t+VjruPuss+w3L14f2zlkukVd/Hypexl7yDr1tan13d2jxsbaIOs4zjkXzpUDvr/V\n/Y2vXn15bCeV+np5rj14+3B3bB8Qa3ZLnU9PcZ8kPWgvRzuczyK2jD37oD24vNIezF+8OLYfHnQ3\ngecsQaQ+SyTY+wx2hnWWGOfCTuelJS6HXJwtNC/eHYK8poi/p6zVHiSLyV76/QvMZX8vmZichDi8\nF5CGmufVUvdVuj3sb6jziGmm5+nbYvjS+UJyEDLux1nJZq157p50z2Q5kU0LAz/SPLZY48DadbAh\nKezVeKK17jaSS9qcmnEQ2hnWPfZqCGwLFexnjIO4DGvOcQawVdFJXpxleB+OcruFfLE2yPieZzYl\nalHQLe/eBO5zDeWehwJ3lTaSLxbaZugnxT0W1pHHiDti716Dfs9g9/Zr3SEIESPUgZ4PkBdmPEuM\nJYus824xngTjL3bSJ95lC6Bzq0b+Ikn8Pbtf667TONNavEo0jnMYpg77PxvpmQPuOV2sZK867MEv\nL14e21eRvjUf404P1noMHb2YSife3t8c2y+vLo7tu7X0crHCnRzUfs6u1M/Nb+SfJrgvlsL3cLt3\nD7ca50iy8tXrr4/tGvv6kMtHLN5onLyrE5X9MWp9EmJT3mvoWQW5W2E/okL7H8EmvL/5cGy//oX8\nnNvCl2hqbo51ua0YL+B+B+7MXOKeYQp/e7WQL+Qd2Om11mWcwb/eaY/38Csd7rv9w+OnYzvEHF9c\nyS805cdjezXTeEaXusv39z/8cGzf/aDvvr5SbFLBiFeMUQM/plhC9nnunsPHNricMFtI958ftQcp\nYoc5+qw/ay2WF1q7xbd/dmw/fZJc15XGegZf+Fd/8c+O7c8P0hvK5le4S/z5QfrNPISgL1xMJDdn\nK8Tcj1pf5rOMs5mT5Gifz7UmxV52azWD3MMGznB39oCYII38uvZirHGMECunsHU5ZLxDUsN6KO9L\nxzxngv88wC57963o53DXukHtleeECWqaUcx6Fdqok3XQoU2uvWTdb4ozkbDRGJaIvzn3CeSpQq5d\nI45n3DEa804H4hfYqgLx4WmdJeH3WsWpdYqznxx1LRjv5QS5F3KFFLnLOe7Kuwy1SyTA21xyVGLO\nDfQshA8PJjzrc67b//G8EsZAYTAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgM\nBoPhZw/7AwqDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDD97/DRn7Z8Q\niueNOwSxR10Zg36krsVd8u2Xb47td8Xvju0UdHwBqH32z6KbeflSNFYXoEf699+/Pba/+jPRC+33\noo7JKlGS7D6LHupfX4lC6vF5608sEeXIBBRd+1pjnY5FaxXOzo7th7Voz7o9aC1nojppQYfSgJpp\nBkqetADFKqhnNhvQ3/w3okv6BGqtBtRr3aP24Fdf//LY/vxZ1FotKKQeHvTu46Oo8F6DUkjELs61\npDndar7c1xLr8HwrKpwctECHDehlQIWzP4AuL9X6dI1PVVMdIEdzrdfjo6iWpqCKLBute37Qt1vQ\nC60uRfdEmtgnUHdlpfokFc66kAwlI43nHvRxYHxzG1Cppef67kOhsXWPWuuqlVxmI9EFkfqwBPXR\nDOt1tpC87kCjQ3qo/U460eDvurJEu38AHaYr1c8//yWosZ7wjHPuANrP3V5r9PrVV/peTXpk7H+r\n+VyNtO43t6K4KkAVloDuMAZd5w5UVhPQvI0vtC77jcZdQO/JPjaaYu9B60iKxwCUUAVkIh1Bi0DB\nRzpFUvxy/LS3ZC/26NtJBV/79FsaMympfQ5Qn3q993Xv/QaCx/F5lFKk08SYilz7SsrmaSR959q5\nkPPXd2vST5OenbSZIehAvXdBNQw6Xv7ekFYc1McB+u8GFos+VAqmVQAAIABJREFUqYb9557NQMsY\ngFbssNP6kFrxlHY+Tvv3nO80oNBsS1AQY02DBEIOSj2Pwj6TX2kq0ISDrqsm/S/1APvdYA4daKwb\nUE57cgY59WjRo36abNKzFxjnCLrVQV75fHNCCfkHVIhxKENZprlUoOFssM6/fw7U1zGp5/RMjbEG\nDehWSWOO8ZEd29dd0OKRwpbPYI9pNzzdBU1hBXtIXaf/aEnFCOpxUruRmr7BtyrM3dNRrNU4hm2k\nnQfdKveScxxan1NDF2CfSdVOS8nx8XtEF/QLkkf5jm+nAWUCNhr6Rzpph5iFFN3cA44shA1s+Sfb\nXX//dStZLkrS3+r5cKxgZmhNSEEYOs4dugsa7hbzbUCiTFdw+o0WMs61ixDDePbE9etTAk5Wbz6k\nRg08Q3Ns1p5f7feLNWxCAL2h/tG5l9AJ0moHab/ukk7Z6xNIBr5L/WObKPJ+G8B+uM5DMciPjYN0\nkl0DH8N+IYMRdD9gP3jGW/eBeMQba9uvu2B59/aYFoSU4V68A/vpSEWdMH6Djg7okyffA5TcbmBs\nQ7ax7vz5NoG+ESLOiQb+Xw9ci47fIKU52iFihLDtp2ammnXdgFMeQAI/z/l7ehkwXu2fV8Tf0S5K\n5Hn8AObo6QFyg87R1+pV2rAkBTU2+mm7E32in+f+08cyfkOsTBpyjjuHDWk6ygHmRv/cMj9VO+58\nyuY/gJS6NWSQexNi1+jnmdvR1tFe0Z5HiI27AbsEpl3f5nf9Y3MDNoCoB2IC54ZjrcbTceSSni3q\nj0H5Oer4bMK4qP+7bNMPN54fRv9Yu+mUuRreRa5TR1phL2YbiHeICrTtnBep1hvmzvGAT3H9/dCP\nUNadcy4DzXSGuCBs6G+RrzA5xJ41cX9c64WHjPG8WEhtL6bHGFgvqArlm+jSdcgrKZuMFUn7TOrx\ntuhfOy9GZ342JPthv8zFsAfUacpEyvjrR3SL9prjOKAm7dUpwp8uvXvzxJhIS8350G54OjQQH1JO\nI6xFiNw2wO+MXashO8HnYZNz+C2uD2WIaJhUY/8ir7YCfUCMw72MIr9/zpmxAOHLKfW0Xwapr+FA\nXMTpsD5C/0Eb5cfuql8QHAP3gz7M013P9qr/ofzXsxNR/1p5NZqB73aQ9brQOGlj3Ik+pBhfhJiV\nc4uQ05VFv3xxjyO0GUfQyTAeS1B77ur+vQ88/4zfqVvok3E814h5QjgUW2Gt64E83cuNUuwBY13E\nJnzey2Xr/n06Te24voN1DsCPcxCvU9a6fnvVuv669XBM0R9H8ZnIi1f77er/3/YQ6kYy6um667ex\nxUCO6NntgXpY+CP/b7whX0oM/V4N5NVDaz207v7LA/bqj9hjBsX+mAfkMhpYl4Hiqx/r9j/TtsM1\n8j4M2eRTDM2/Qn2T/w9ETr8dqGF7+oFevJgFZ4b0K6x5Z6P+HK6DgQvwhTDGHuSo/dTMgoTAczeM\ncVhL8wy3xjBgxyovZmYsh3fxYV+O9czj06M31tlifmxz/6krPE8qG8pLv5yWXk0M53iIryYj5UCs\nOzSQZcp7lmrPmgPzLX13PNczn58+6t0L5FvwNxfL82Pb5YihoSsxzwZR251OdI68Q32PdyC8c4lY\n/jjiWe7l1bH9Nte59jrX2b9zvizw7KRZK3fpcBeAueTkSmO9mGi/Z7jLcLnQM+la88yw1qO239Z/\n+qwz5QXs+Bix5dlUNe8Msnl38/7YLg7qf3Gm8dCntojFr3H2z3OAi2v9/nQneT+b6R7Ks9NdAc6l\ng8yxpnOZSYZ4bsdzot1OfTrn3MdCZ+fZUt+uETCMcY/lCvOZXr84tv/du18f25OV9m+dw6/CJvzF\nK90B6m50dyVF/rFcaDxPa53f15DTs7nO+J9wf4Ex9xPuvVz/6gv1E2qNip3W4R8eJCuvE91DakLZ\ngBYnMCPo369e614Ua7573DE5w52n/cfPx/Y8gqxfvnLEhzvN4fCs9sUCd1p2Wuuo1LeLlnER4hna\nUuQNEWrE4xHO5WA/iz3u2KCWH3u1YOQlsIE5zotz5v/Y18lU7baA7YXf6gZcO+184vke+LmBc6UA\nuRAP2ei/c/xD0frxDn1yijrp8kr7tN3qLs4OcpGyLjWRjLQD+fLQGU++k70NMNYZ7nwl0LMG/s+r\nt+L8wt1LP8aUG/jCRSD5zXM9Px1hLti08Vjzvbn9dGyPxqhXob6wOaAu/KQ13OOOXoK7DlPM8ctQ\nNqk6CVFv4euW51qjeKJvb3Fv6/lRfq8M9Pv1hfY4YckGuvX2r//Lsf2LN7IV9V5yMJ9rrP/w67/T\nGEL4LYyzxB5PzvQ774OMOs3l/e9+OLZTjO0cdxpv7+XnXy41nsUr2eQ8wiSxqO/faS9fRNrjt7i7\n9xdff3tsx3vILu/uwfc759xhq/c/wp9fI15YZTM9jzuIoWcHNO7/4//6P4/tf/Wv/+WxnSSaz817\nrdfnM/X/+qvrY3tUoHZwJ9m8fa8YbzLR+vLMoqglf9/dy7aPXkmecETjDrhjur6TLC4QX1CPU8Q7\ni0l/nBkjz32BeI+1vjXGmZwr9nk4idfniWSE/iZGrdp16uuXv/qF+nqrta5mktln6NkM67jfaK2r\nrdaCcdftB90rpk48QIb+5j/+7bF9ibuLi4X8fwZ//uJSe//8iPmzLow65G4te7jEmQPrs/Ve+0of\nlsGnLuCP21Cy2FbI83AnLBrjHgqWPz2pN3aN3s9hZ7NYaz2BPkYd7t5CHlvc9aywFjPE1vsO82SZ\nkOVK3u+gLYVPbnjuAF/bItYoeRZeaj9WuBta4yyqwp1nno8X2Js0xlxwD7DEeUqIc1767BgxXgQb\nEyNnzZHj83zKOb8WzvJ6hTtDLRcJ8chqIr18vJUtHeOuzwK+Z4szpwwx/QRz+/wkndvDRybM/xHX\nuThwVTx8znMKY6AwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwfCzh/0B\nhcFgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAyGnz2CIarWPyUEQfBXzrl/\n/6/+h//ZzZdnHq0MKYV3B9HlLC5EFbWrRHty/lr0P+8/ifpweSkqnGdQK01B07cGzWQByqLvbkVz\nN7tW/7fPos75X9aiFFqNScHq3HQK+sNM1D7vP4hG6GKs3zPQMT6WoEM7F+0L12Lcav4taHsenWhu\nmqkoUNpS88lKUFG5X2nQ56I9+Zv73x7bm0Rj3pWi/Zq0WrAz0HWdxaA/bUQfc6hAYQfa0jYhPaTW\nkfQsUQyaulTvvl6BQupBz49C0UZ9AkXXI+jWPj2JUsY559KDaF7+2dWXx3axEWXMY6m+xteSR4/S\nGxRlDnL9kGoOH2v181+BCWk1V58XS8nd+rPkrnyUfLxZScZTzG2Ntcvm2tenXPuHLXDfvBS1W1eA\nhi3G/u1Ag3gAfW2kdTyQUgh06Z9uRSt2t/mgsY1AV9yARi8UXVXaaY7OOdfVoDkqtC5ffiO52xWi\n6KwqTXQ0eq12JjrQOtAcyoPmSQq0s5Xsxs2tdGgOqqGm095//iyde3GBfSKVPYxdDlo/Uiu3oEN7\n2IuWqwHlVgzWpdFIY74H3VEEqsgY1HwB9PIStnECyq0YslWBrqsADdQW6+acb2fbBtTVaJegQb5Y\niOZ3V0gpion2+22l+ccz6fgEdFoJ2gdQ/tGuvolFRfbNXuMZYY1+CPX8w0j9XIQa5/Jez7yvQJt1\nDlqqDlTGqfYm3GiOY9BsNaAi3mfa4/uddLcFvdcZ6JGnoKjswMFHuun8WTIxPgkV4pXeGYHSews5\nSkCRez3WHi8SPd+BOq8EpVcJ/jSOqan7KarZTwZbFNWgP99qDxYT2c8aVK2kNIsg11Hc//emHpU4\n0A78HkCPQ9CQci6kPyXVbAL9Lvu7d2HoU0smoPZNQDkaBNRxrVEEWahBsR7Bl+JVV9WgmAMtngOl\nYODU7vDyKgclJmIqUgTyY9h6V9SgOQePXovxr0mTCU65KNUakZrWwc+1rj825t5MMa9qxDgFNgZL\nkoJKM4Q9dM65GrJWJrDX2P/2WXYzJYP9SHM4cP+wT1mnfjJ8uixFy5lCL4MU33WkPhwQPPzcQQ4C\ntDPQDo5SUfNxTXPoOqm6a9IUg4a8ifVh5jMpxk866K721/04TrQr+JQk9mkKKZsx/i0BdSJ7q0EF\nTzrmDgLfRqCxBp0kwj1XYx1DCGoHisoMfmWFeKStJTcFKBRb6HQH+k2HuINClMFfjmBXSugQaSNr\nxDjpGLIFW1eWGhspMxlPloXmOEGs2JSk19V3I9I7O+fiTGOtQAdaQhbWmeaWksoZNNgj0G96Bgv2\ns4GfCDAH7n0FCnt+KycVJ3R6XWluDvThpOteIZ4usX3P2Ps9bE6KsY2x30mufjLMkTp6gAztETc1\nyM8cdB0q6uIDaLgLXxf3sNFTUqaDlrPGNwrMM4GfmFca97zAmLBPTxHy6JSUnpAD/D8mQuoxKEAL\nUJp+0UrG72vtJWlPUyRTEy2FJ2f0H7sQ+9oiZ8LajWhL1KWrY1BDo8+OPnsNHzzWM5zXI+oJzvm0\nqoyn3U5yPYF93zIPgJ/cYG4h1oWxdVL0yyPtsE8JLbuXpzQo6j9ssPAb0OKCUjfCHEvodAXa2mf4\nibLSOJegBr9AjJDgmX2ldd/DTrYzvcvY72kHmvqp5siYcL+VzE1TyaJzziVYX/o60mB3oNqlvtOf\np9ChEWI8PjMvNYf7QP3fQSeWyIUT1BEmoPrOc9S3MuShU+3BnjEqhH/Wqp/xQetOnaYsrmEPnkPt\nxwI56JSyqE+5EHTeBWS6Rlwawt7W2Isi8CmDY+zOBPnTBHYzAt/1Zqzv3TrEAuH5sc24qIXut4gX\n+EwI3SK9fAD9yFCvYh1kBFvN2HWNvLCA3tCWeJTh98phR7P+HJm6kof9OewCuuhAgR3AUtbwZ0WA\n/YNeJif5wBR7mFCh4BviDeoi8OcVZGR+jhrz44P6gT4lrInANgbI59fIh7YIpA4dYln4gBh15AD7\nt6RtD7XuOeK6EOaTtPPMq9wMNQHEeHGhsc1KzfF8rDrLDv7iATYjGCGmgH0qEU9x3Sbn6tM55wrU\nv5tc6zKLNdYY8dgBocMe375BnXiC9ZoO1KvGF6r/0+YUyE/foFa02qPGEWgt7mKNv0KttoNcXzn1\nEyFmcRM9/wB/3kFWWtSTmDs6nH2clXr+LIT+ofb2iKT3wx7nL6j9n3eSFfoa55zbQx/XOL/JEKe+\nnKp+w3WMMvoGUM8jtp4FemYMe8WYJ0dc6yLEzfB5zFES2M+g7s9/GbOwDZFzTcecVzrK/IF1qRRx\nMobv1bRmzJlY6wv7620V40zY/PQk9aeuMA5hLY7fY/rEdYlR2BnDb9NXf0Aew7VLsN8Z8zzYkDzH\nu5gz65ZcX9YAuaZeXc7154Le2LBPOXxt4Iv7Ef55LGVIexkipgiwT2PEpRHHFvh+i/Ub1lc6+M8K\n387hVwLWzrHfQ7F4Ufl1/j+AcQFlucE8k6j/mRY+mXvDMXD/vJoL7Ae3gN9yWF/OhWNgXF7Cj3o1\nQ3yLpQJvPIjFvD1zfg07w96uY/nhzVoxkncujmGcLeV7Hu9U6yugE8u5nqHd8OwVZJ+/00ZxvWhD\nKNc/ICd9jVhguUMdxMlvP8+QD6SKIeNO53lZoPOdfavzs09PypkuZ4hvc53J1dgDN9c56g4F9vFc\nexH7qbBXjzgUqEGxJr3T7/sCtuhceVyNWPbxQXHgFNcFXmY4L0ftsZpofFts/g61wYul7gf811jr\nvw61pn+DWjB92y9Hepe2el0j7looDnrY4RwdeSvH/E2iuPdbxMlzmIx7nKX9JsGZcqrxrxCDsLaU\nt35t6eaA82zI8i+mulsxvUMcgVz6/IWe2TcaR7WTMExhH1gSuU0UZ95EaqeFzpFvEGzdHFC7OpMs\nXyHnq25/OLYbnKFM5nq+3OOsBzKU4D7IdA4dhV05m2tvVo1sw/6d5vvv3qgd7zXhaydZCfeyw6PX\n6uddqLmvGw3uspF8O+dc9qA5r+E/219qntVU+/z1XO8vMKYt7mu830pmt7AJzAcvEBPODtqzlzgv\nL/da912nPh82WJe57NgBNvO56/fB377RXZL6SfJaQZ8Y7/ybz6gpXGru/7HSGD4uEN/Cz622mu9f\njrFnufbjuZAdulppLqsdTyacm+w5H+RPiBEc6jr7W9mE23P4jHvZ9C8WGtNXr18e2zny2QLnAguc\nRzCGTHCO3oxxxjGXz3CMcXB3ZYacPYSNfSyRP6HYPhrJBmYZzoPgYmrky4wF4BZdjJg5x34sMGbG\ntB0TCJx3tw0MkXNuipxpj3lWE+QZHB/y5THr4ohtqAcMNZcYK32ed2yCmt4GOfwOZzQN4qJDxfNo\nvXvbagwXj6hB47z1eaLB/XYrP+fuJItfx1qfBj7i70rcjUHN5VunOZ5h3d+WkuP/ctA9w69wP/Bq\np7F9jzV/fNS3nHNuu8FZ0Uw2dHatO0k5wtfHXO83DueksLOrM+nEhw/vju35UnZ/lGlu1R5xOXQr\nRg6/bhGLerUx6OVIfW5xVvCwxn3FMe7qIG9lXaqGTmzWWuspbN3ZufqpkYfU8Jc1zgyzg54PRrDV\n0PXJSz3zcPDvBBZOe5hg7WKcu5+PUJPdwGbCJ10jTpuwHoP7jm2n+dw/3GlMyGoW55LlGDXNy0jy\nm68lK9ux3v2Yar/3iN1f5FrfPyvlDyaIiXmm88lpnD886p7dF7jj9vxe9/0uv/nzY/sccd3f/ee/\n1QdQ0/u01R4sUZG/CiUrzcg/C36PGlqBI5IRbCNr8LyLwQvH6QPiFsRsTyiSNDjj2WL+337xxbEd\nIEwdx9LLz59wDzdjjVzzmSK+2MMn877N5YVi15cvtO7rJ41nNpeOIiR0WBKHsqKrW+n9ciXf/PlO\nd4HjELVNjKHdamwz3Ee6WOJMY6Ox/X4++vjV9TfHdoVYYFcop3vYSRensCH8Hs+LEzzT8S4D9GmL\n89b9Qe3VTDod4nyvRL08G6E2hnt6u04y1KDeeP2MMznc9WhbngMglkFcx1yb+egB93lTHl7BNvC8\nzbHuxxgKKd9pfLGYay2KRnpdYF1SnEvmWNMsVfw6x5qun7VGJc7xKONRxPtc0pUn3kVBwWf5Un74\ntz+81TOuc3cPH93/9r//r8459y+7rvsP7kdgDBQGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgM\nBoPBYDAYDAaDwWAwGH72sD+gMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwG\ng8Hws0f804/86eDN+Qt3eXnt/tN/+pvjb3/1L/75sf28EZ1PVYoCZT4SNUgFuq5XsX4PQJlynonG\nqt6Bvmgs+iwS2AWgD6tuRVn07Ytvju1RBLp4UDE559wzaCDjBrRvpG4BzWgArrfFQhQ+vwX9ews6\nmFdvRFeyedQaRZUoYOKxaFVq0HLNE/Xf7PX3Nk/PooMJQOe2A1Xp9StRjHX4/Qp0geW9fr/H2Jbn\nGnMAymxSwWeg+krH4qTZgtapBP1iE4ou5gq0nXefROdGGr0OtHOvFqI+cs65HPRdY+zTfqN3fvm1\n6Bi7mdb6gLWYg3LsYSPZ3D9oTC++FEXSN99gL9eSu/d3ovFKSKs90b5WoH5qQDUcTUHPA5pQqJBb\nLqQTd5DX85l+f1j309o3oOqejfV81WzxjL61WIg3qotA6wdquhovlLUGOpuC1sg510I2wS7sURXl\n2OcatEgdKBLLQvI+v8Q6HkD5Dpq4otGaukhrSrrjDDL7BSi9SDU/XcpGbaFzM1DtcS9vniSXpJm+\nhXyMQdd19fLVsT2aStc/30j+Eujf+YV0N8IePMD2pPhuAgq6eKTvPt6I3sk556ZL7fnjs+ZwthSV\n03Il+xuAMnUCGSer1eFJ8kgBm6+k+yNQPx0q9ZlQbvba1y1oT4NY+1cn+r0oSvyu+Yeg4Z6SxguU\n2eud9rgdqc9xBfrpWr8f4EvWoFFtYchmM+3rHBSjJXRgjzmSXp3vriLyfjnXrTSOx0fRKJKFfo79\nm6aghMxBwwobnUxhN0DXGYNibQQaVlKm5zBYFeizSdecgJKVVKLU0RBtsqo3mBgp4l0AmRigOXek\nQh+gmg/Qfwf/TUq2joYSlO0xqOnS2N8n12lMRSEZJ3V5GpOGVn2RPjYFdWcQq0+Pth5/k0saebhI\nb54p9pv07yV0om4qtEEXD4q5lhS2mG84+CfC2D/QKQd4N8Y+sZ8I8hFAiDr4uRJ9dvhU1Oj5iPLx\n+874H8dWBXsSgyM3BsV6jTUt4M9gNlzLv5cGn12Ywta1WuuugD3BHlMPKJtc6hAc5iFkq8YaFfCX\ncIuubLT3AdcaFOm0URBL14IKl/LUUc/QjtAn7QR/D05kqIHe1DViBMSXAQUee0mqwQTrntdaxxJx\nR4V3m4Cyo94j6hlAXQ9C0J9j/jQnDdeLnNMt1gjvhrABeQ55h+wT3I+g7VdMylNewlbBtiOVci0o\nppuuf49Pv11x/1q9X3prhDGRfhsKRd2nL+kS5G2UU3ZagrcV8ZWnZ2hTj2v4xRzjT7AHQUbqdNpD\ntWkyOTaOoaXfCmkP2SfHDL/A+dIGUo9HPgUo6VMz6FCHcTCGbjCmiPqLdY9Q5mg8Y6wmc+oGytU2\n/b635BjQT1HTh6ldQLZqLHxXdv2/o9Md3o0y5CfYpxJr7ekQ95K+mfNijAO1bCgTta/T9EkVKKG5\ntwXkooA/LxC/upj2HT4Pa12U9GegqJ4onpxBbpoA+w3a8hI6F2GNEsw5iaiLAn3SBraoS0Adi7g0\nhEGoYdthGlyKeJp6s4atatFu4Nv2AWoIc+WI06nWhHvsnG8DU4zb8+HwtxnW1Ks/wYe3A+8+bpXH\ndHPJR4o60x5xyggUv9OJvrs4U85HvbxDjefQaW9iCHMMHQ0qyhxsI2xvlMI3w0+7vfabJi2incc6\n7HeQV8jNZKZ92h0YN/hxYIYxJZAdqo3DHtSYWwW/vV6LBjvFnElXHTnaOvhFJnGIs0PPD1W9v9PG\nttAVzz9T19HPdqua0BI5Damod7SrrWKEJqHv0XhyxMyjkHEW4uoJcnPWzBAPt5BX55yrYNNCxmbQ\ng8rLMdXcgFp6i5pxVcheXcxBKQ9/tn3WGnXQyy6lz9cCsJ7UIOZcTZR7hbB7DWz754PqRpShMXLM\nkrEf5DUGzXsBPdih7pBFqpOxtrvvGJcxl0dMhLywga7HqLk8wQ4559wMdcw99qDbq1a0QB1sA3lc\nl9rMyy9eq5+16rY1ZQK6xVi8gI0qoR8F40kmNQjwC8arEC7G5SXWroPP6GLmcHp+h3wjcqSyl6wk\ntFEh8jnWXzDHLWLUNJY+BTX8IuxNcpILR4jHIvpSrNdug/oxZJblG4ijn9MxnsRaeDUejof2DeM+\nIP8o8XsAYx0NjIExdAzd9X7HundoBzTPrL8E/TE65a9FzFXDZjZx/7sB18f5eZWD/YkHch1Gjg2T\naZbQ6Ffot/HtZCBmcYxxmdtjLfhuC/2gvnJAHfMEx3Xvrw36tRvkLax1Jf15COWphX7T7zLf9+oU\nkAn2w8F1J3vmxfVMw7E3nfNjxz/AiwnRD9eR+f8o1fOUj26glto53w70Pc/6njuVx5+AV3vt+u1h\n0PaPzRuPl4/216taCHWHPruB+llz8q0CMV4DO7NLZK9DxKk8J80RXz6vZScT1P95HhGgRsetZ62r\naagreMbbS/zO9J9rijNGlENdC79CO7GHbD3hDHAcKa6JkAMdWOuhGEP+MsQ+bY4zxoP6fNgqPpg0\nkuPzUPGBc845nBV1rFcyx8S5wwRrXWEP9qxxwU+kiNli6D7zwZq1K9bm8UyJc+ennZ6PZ9qEKXLn\nmKaoUD8RdLpDLNNgnwLk2stMa11gb56etZfbVnnrKMEZUIf4BTowRZ+c4x5nwVVKO+Fcif9uef60\nU+w3xx68XOnsaoe4fI/bH7NrnYeO4WNubz4d2wf0vzyHnGo7XAwZihLYbuhfFdAX4oynxVzg59NM\ncnrA2fx0qnhscaZ+7jfIE6C7zGFbyN/qSeueQ6Y/Jbh7c4WcN1csfpmpn8mz+p9P/D0b/0rn4hW+\nscfxev72O43vo2Lx+eL62I6udW/i8yutb3anc+4Murtf46wdNvb2Vjn1FWoTXYg7M7i78uFR/VfQ\niTnO9ZO5JsOz0ArtMeSd55/5CrFcxthEejzCOWEEhz+C3LCmU8GnJpfKQdexfs8y3lNyro7hkxD/\nRBnmtka9B2eM47HGOl3JZk5GepcxYYX8nzFFlkquk6o/jiAa5I+sQqdcL9jhuuk/12A8xniBukiF\n4nmCFwexDov+vZoq+mTdJAo15gQ14qjz78OUyLEP8HVR6tfh/4DQMU7FuJme8o4Daz+cD2M8/F5h\nPhyPF+Jhjxln8nwk4DjR/65W/3mBfACxRovxr2GU1zj77qbS7wrxwjNzPkhRFcB5Yu9z5NoHPB8i\n1x5NYNycc+lYdnyHM9kPn94f2/Nr3Wm5PJffun2UH0pG0g+OiTH92Zn8WZFrDl9+qbtszV6///o/\n/7/H9uRK42R8TD+fI46YTGVbWLfmGSnvn+xgk2PsPZ/JD7L/odPvY8QsHz99OLa//Urz2t5AdnGf\nZz7GWQxk9PkZ94ucc6+/fnNsv3mtft999+7Y7pr+2D9EbS1kHRp389K55pCkqI8s9MzoifflNNbV\nSmtxMdJal6ixFgfd4XJl/3kx7X4U4B4ZfF690V2gKez/l9fa77d///fH9ptXerdErLR4IVmc4Nzg\n7I3WOdyo/wVs4Pb7m2P75Uv175xzj6XkqGi0Xrw106F+kyFGT3GvsQxgH5DrFaglXpyr3jjDvrLO\nPU01h/NzxRSzCeqQuBtKe/uE+34Z7lddI95Z4NxhhHeWdPprAAAgAElEQVTjM+1Hhpjz9Uy/R5ns\n1c2z9GYPG5iXust3/ULfrXC2SR/28rVqp5vPio9o/2kPnHMuibUWzDEPrMmjhn2OODtDbB3jrIT5\nhOdXePcvYH2lvxbFOKKF3MTeGbmGybo1LweF1GnWinjWgxiScXkY9Pt/xllppn0tSslTg/oF609x\nyDMjtLE+9UmcFQT9ez5FbDaZam9S+JIKyboXO8G3T+AbK/gn3rujbnVhf230sNczXCMXR25/gJ/8\nCRgDhcFgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAyGnz3sDygMBoPBYDAY\nDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMPzsEf/0I386uLu/c1XdutlctDiPoM++\nAx3fEhR5pGohVd0kBTUYqLES0Jg0B9GtXE9FT/PuoyiCgs+iqCLdyuXi5bG9ARXlNPZpT0iVSaq6\nyQTUO5FoRbpKNGOHEhRioCJJpqJPuc1FV5KXoKAq9OGbj5+P7a+u9K19Ljqij0+iL6xSUaOsA1Cd\ngkq1PKj/s7H2bLsX7c76XrRRCejcY1DN7kDPvQE17TQBJTd48fKdnmlBl/PhTrISgCrwfC76s/Wd\n5jgH9dHjCV3Xi6XeebyR3K1GosnZP+qdAhRMNShHK/wNUwOe5hEIpeqt6Gz+Nv/+2CbVWQ06mxdL\nUFeCHvEJ381B59pGWrux0x6sQR/aglKnBEVV3vIZcPxNRMFUgSLu829F3dWQ4w8UZqOJ2mPQOJIG\naXSmvQkbrXkDRifnfHpl0jTvQeEaghp0NgINXQPKrZ30ZnujvSSl0HQivWlAt0qK+Ket3k1K0PaB\nJqzFmj48iQ6NNF6XeL7BwuxA0ZiCBmsOarDVmX5//1G0XEUu+RiDNnC5FCXgHuuQYy6vQRlWQ+f2\noEqKGtC2znyKrslc32tA451GoJh9Fl3ZFWSE1Kgh6H8vlrI5DhR+AWxm60B5C+rjsNIzEfgqSZ07\nyrTfY/iSMeghSRtVgF54jX3qQOE6HoPqGbR1Ta613oK2rMn03RTPr9daq4QUyqA/2x+kx/tS6zBG\nPxOs2+SEWvLmIL9X15rzfHGph8CnuYE9mWBMNWgNK/qzmKR67BIUYjUpzUCfBtrPmDTyCei58K0I\n9G+kZCWfeQO6zq5DTIE9Jm1ZC9ue0Ffh+ar9aVpYtjmGkpSnDdqt9sU55+IQsu/oM0GZhvZ2qzbM\nsmtarBEo5jifDrobgD4tbEi3qvk8FZJBzg1MeF7/pIsvofd5Jd1q8K0049xBL4f+ObYQ/QegRY8c\nadQxX/AJ1jXpa0nNC1p3rE/kfNrcmnS+0BXKeAh9whRc63HeQg+wTwn8bQMq1RDzIS0w/WUAH9mC\ng5djcy0o6yPaQNIRQtdJDY51pBxEiIMijJNxske/SBp57AH3iXtMGlWHMXMMXJPfv69xeLYCbVIK\nYhl9useWY9UzBegxa4wpArU0eSw5hggxZAk6+hACX0MvSZTYIIamzEbQe9fJIHTw5wdHW4p1hM7V\noN4OQJceJlhDUokj7g+x30HbrycO++0tunOuRmxTDIyjw5xbjBuf8+jNw4DvQrc4DtiQCmOlbJIm\nu8bvpNbEUrsSa1Fgzw6gh2YI0oCelSF3ABkqa+gEVDqibadN0yNenzVf5jOOMioZyhK/BEF5b7ju\n0OscslZyfLAPB3wvxV7myIH2yGFzTIhy4DzfAPuOPunzyqG95LpzvSgHEDTGwNzjmDaD+gq9oazk\n9JeQ+xBUtimeyQdsWxr5VPD0Q2UjPeW6JOgrZz5EGl34rQ7fa2nrYR8S+MwOussYYY84O6cdh9wk\njNlINY92i/2mr4omil0r+g/sfYnfD1j3ysv9QSEMOxFQJ5r++HD/rHyABup8qTyvO7GBtMu7vfL/\nptWYohb0xYwFKlL76pkUcTZt8eVKOfl9i1wn4Jiw2MifHtfKeVP4vHCidknZ5x4wVnb9+kcdbbGv\nBSinN9izWaLcNEGOuEMNhSFCAApl0iDnqIe1kD/mP845FwSw79j+GDaacXAF2eHvE9Qy/P6xGF3/\n79xvxhEtfp8iH2S+Rfpl0nKzH+aS9MGsw9aMCWEPGsz3EXrQIhdeTDX3qkaOj5J3BLvCsZU15BWx\nEtyoc+4kbh6IF6NEdY71TmPdl5Kd1Uw6m4SovSInZ02F4LhrxiwwIW1NucMzsEWuZFyqZ57xe4Lx\n5KHWiHloEzBvhW5B5zLkwlvMq4JsMZ7MM/gIyHfTMH5W/1Pk8nusj3POPX3+pL7ghy4msle7CvNE\nPNME0uXbe9XLHajHM9Tpabvu1mv1iZz6fKoaVVQhx0L9qQyhE9jjAjIXY333WNMGvrkr4Z8QH4YZ\n6OgZZ0KOS9j2tNJDsdO7zCVokxinsObg5cutHzcWkCPmdxHm3OBMpcM3vDoC5ILqy7oL/VbJXAT7\nx3pPzfzUmwN+99wcaiIt42k9ksMfR/T/sI1+/Qm1DMgKfTPtOVOShnaVKRNrCLBtjBuizjeCcdev\nmxXrInimgSxHjmvRH8e3jL8xN37Lr8FAVnDOBrftWLKoUUulDwu8OlB/TYvPMEcMICvMe7w5toyB\n+/f1xN3od+4rfm9hA5gjd86PA1lr6bxYgJNQk3kA+2WbeXvT9s9nCByPF5sAXGuOs3P9/XspuD+Z\n3u8SjKGGnulohzDfOOrXh6F18OZ7MvUO68uYAubXBcxVoZsN5O7zk+rB8ynPtzS3vOjP2736etUf\ng3h1r4jri344FyzFAX0m8MdVgpwU63Jw/fpXwUb97ka+eQ97O50qbwu8WgZrUXomxXFHiJhlf7IO\nCNfdHrFDiDVNMekKPvm5Vv3701779OrqxbHd0Z8jL+GZYYc5lDgnrJjn4Zln5DqjVDHIdaB4NcI+\nMfcIxuqnwByrvea1wMFBHGuc2wDrw3gVcVMboGaPxG0OUT+seQcC52o4AM47v7a0Q0wy4XkMZDxG\n/rHPlYcmV7iLscA5W6D9WO+0l4f65CD6HzFFLXwPfY15Bo2cgfdJmMSORhpDAp+03SN3RB4251lo\nq/X63fsfju1gjNwOPu9xq3m9upBc/vlIY/4Oa/geNq10+la8UQy8qDX+K9T4M57POed++6B7NrdL\nrUuY6rkp6onX6GsVSO4+bnQe+nkj2RnlmkOSwg5grc8vdNdjMlOecHmhc/H3N8ornnEn6Wx+dmw/\n3upuSQS7tJzgPtPnO40B8U4IfdrvtKbfQebOl2+O7QPOELZrPb9c6WyWviBEXS0dy/D9diP52HTK\n815k/j5NYAdGOLeddOo3xxn2IuB9B8n4CDE3fSPvPuwrzXmKOw4dfAb1JsLaeXUT2PEQOWPCcxbU\n0Jhr8+huhLtKRMjYmEVGxoHMjRiz4buslRxwn4L//2OOrYNdSU/q68x7son2iT4pZB2Ifpt1QtZ5\nedaFdS8wJtZPeXbMuol3zomclH6Cd6f+P/beJNa2LM3v+tbuT3v79168F11mpSsry1SVwWVsC1xI\nGKkGCGSJARZIMLGNAEtIIBkjgZBggkCABfIAiRmyB5aRJVsgGQwjg+xyGZernJFZ2VRE5Gtvf9rd\nNwzi5fl+6+a5GWVHVkW8qO8/2vfcffZezdevdda/x/O7CPER7Q/6VZSUid2lt59g06ve5MhJ10us\nrzOcRKGloj+eoi6KPt7c6H63GvN3w5ys9QPBLFUZj7HHaNzoO2rsJ2mWGCOsiyfwMQX20XlxFNbc\nKqwnvXz5Ut8bqzxm2MfDdbWHp7ofKIZNH9r9MTHrhBn0eHQKO4P2+P3iPk6sOWz0mSFqBR32yrEm\nkmIP0tVS56noUat7qPb88Ez9gojI81fPd9cfrn5rd3001/sc5nYLWd6sVa+5TjipdKynB2pnpiO9\n7qZ6/STVd3WooaWIj6tr3a94eqj9ucRez77Q9jx55x1t/1LH/Xqp/rxeqd6Eh+rPfu07H2h7MpX3\nR29rHMG9eKe13vP0ex/urkfwN0vsAzvHntRX0NEj7Ed68UL3uImIuAO1ew+P1J+vrjX+zrBnqERM\nv8bYnWBPa47vtqLzurzVtgps0RZ9aCao4WJP0iSDb0MsPh1pLEp7Mp1iryRs5s2F6m69Vhv16KHK\nSox4rypU3l2n1xPsC8sgfw77dk+PEe/c6JiUOWrEMfInxEEJYrr53N/71WEv5nar81HBDsAsSZXr\nPaxJJ/BPDjmZwx7YADF6ANvCOljEAIDPhN+mvY3hcFpvDwhr8Nwvpv0PkLdQELjflvteioLPR/26\n5/4A1ICwDunVehgHwIdxDajv7sQXWGsZUH9rEWvllAXYw0E+ff8N6whxoLIcosBXb7AHFmu1rIMV\n2PvI9QgXR5Lg70+DMVAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPBYPjS\nw35AYTAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FgMBgMBoPhS4/o02/54uCyr2Xb\nl3JwqrQ7v7lQus5loZQxb58qdQfpbKagO+pA7Xb4lj6zrUAHtlKqj4tO6UkuWqW2efv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9t9DhvNgClfq5yWpU+h/UOwrsr5Y5zWDvvjI/a5bFT2qVuUZRfr/dse9q1A/jDRmJY04ZuF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\nGENGWftg3SEb7/8u17WTUSZBEEoyy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## START CODE HERE ##\n", + "my_image = \"my_image.jpg\" # change this to the name of your image file \n", + "my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat)\n", + "## END CODE HERE ##\n", + "\n", + "fname = \"images/\" + my_image\n", + "image = np.array(ndimage.imread(fname, flatten=False))\n", + "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1))\n", + "my_predicted_image = predict(my_image, my_label_y, parameters)\n", + "\n", + "plt.imshow(image)\n", + "print (\"y = \" + str(np.squeeze(my_predicted_image)) + \", your L-layer model predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") + \"\\\" picture.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**References**:\n", + "\n", + "- for auto-reloading external module: http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "TSPse", + "launcher_item_id": "24mxX" + }, + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Logistic Regression with a Neural Network mindset.ipynb b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Logistic Regression with a Neural Network mindset.ipynb new file mode 100644 index 0000000..6c39c00 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Logistic Regression with a Neural Network mindset.ipynb @@ -0,0 +1,1350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression with a Neural Network mindset\n", + "\n", + "Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.\n", + "\n", + "**Instructions:**\n", + "- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.\n", + "\n", + "**You will learn to:**\n", + "- Build the general architecture of a learning algorithm, including:\n", + " - Initializing parameters\n", + " - Calculating the cost function and its gradient\n", + " - Using an optimization algorithm (gradient descent) \n", + "- Gather all three functions above into a main model function, in the right order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages ##\n", + "\n", + "First, let's run the cell below to import all the packages that you will need during this assignment. \n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n", + "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n", + "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import h5py\n", + "import scipy\n", + "from PIL import Image\n", + "from scipy import ndimage\n", + "from lr_utils import load_dataset\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 2 - Overview of the Problem set ##\n", + "\n", + "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n", + " - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)\n", + " - a test set of m_test images labeled as cat or non-cat\n", + " - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).\n", + "\n", + "You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.\n", + "\n", + "Let's get more familiar with the dataset. Load the data by running the following code." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Loading the data (cat/non-cat)\n", + "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We added \"_orig\" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).\n", + "\n", + "Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = [1], it's a 'cat' picture.\n" + ] + }, + { + "data": { + "image/png": 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aMsuiW+/onNCoj3sjCp1YUyOxyBkgoLWE1sgX3FJnsQ0lyW6dsoC/5KzTzMrE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gAeuC3tT8zVQeaw8mrOqIO7fIiDKX9tRSu4bnL7NpA/K73S7UGwroSVEXZCUdl/6T6GeV8LZA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example of a picture\n", + "index = 25\n", + "plt.imshow(train_set_x_orig[index])\n", + "print (\"y = \" + str(train_set_y[:,index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:,index])].decode(\"utf-8\") + \"' picture.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. \n", + "\n", + "**Exercise:** Find the values for:\n", + " - m_train (number of training examples)\n", + " - m_test (number of test examples)\n", + " - num_px (= height = width of a training image)\n", + "Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training examples: m_train = 209\n", + "Number of testing examples: m_test = 50\n", + "Height/Width of each image: num_px = 64\n", + "Each image is of size: (64, 64, 3)\n", + "train_set_x shape: (209, 64, 64, 3)\n", + "train_set_y shape: (1, 209)\n", + "test_set_x shape: (50, 64, 64, 3)\n", + "test_set_y shape: (1, 50)\n" + ] + } + ], + "source": [ + "### START CODE HERE ### (≈ 3 lines of code)\n", + "m_train = train_set_y.shape[1]\n", + "m_test = test_set_y.shape[1]\n", + "num_px = train_set_x_orig.shape[1]\n", + "### END CODE HERE ###\n", + "\n", + "print (\"Number of training examples: m_train = \" + str(m_train))\n", + "print (\"Number of testing examples: m_test = \" + str(m_test))\n", + "print (\"Height/Width of each image: num_px = \" + str(num_px))\n", + "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n", + "print (\"train_set_x shape: \" + str(train_set_x_orig.shape))\n", + "print (\"train_set_y shape: \" + str(train_set_y.shape))\n", + "print (\"test_set_x shape: \" + str(test_set_x_orig.shape))\n", + "print (\"test_set_y shape: \" + str(test_set_y.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output for m_train, m_test and num_px**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**m_train** 209
**m_test** 50
**num_px** 64
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.\n", + "\n", + "**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\\_px $*$ num\\_px $*$ 3, 1).\n", + "\n", + "A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: \n", + "```python\n", + "X_flatten = X.reshape(X.shape[0], -1).T # X.T is the transpose of X\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_set_x_flatten shape: (12288, 209)\n", + "train_set_y shape: (1, 209)\n", + "test_set_x_flatten shape: (12288, 50)\n", + "test_set_y shape: (1, 50)\n", + "sanity check after reshaping: [17 31 56 22 33]\n" + ] + } + ], + "source": [ + "# Reshape the training and test examples\n", + "\n", + "### START CODE HERE ### (≈ 2 lines of code)\n", + "train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T\n", + "test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n", + "### END CODE HERE ###\n", + "\n", + "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n", + "print (\"train_set_y shape: \" + str(train_set_y.shape))\n", + "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n", + "print (\"test_set_y shape: \" + str(test_set_y.shape))\n", + "print (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**train_set_x_flatten shape** (12288, 209)
**train_set_y shape**(1, 209)
**test_set_x_flatten shape**(12288, 50)
**test_set_y shape**(1, 50)
**sanity check after reshaping**[17 31 56 22 33]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.\n", + "\n", + "One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel).\n", + "\n", + " \n", + "\n", + "Let's standardize our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train_set_x = train_set_x_flatten / 255.\n", + "test_set_x = test_set_x_flatten / 255." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What you need to remember:**\n", + "\n", + "Common steps for pre-processing a new dataset are:\n", + "- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)\n", + "- Reshape the datasets such that each example is now a vector of size (num_px \\* num_px \\* 3, 1)\n", + "- \"Standardize\" the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - General Architecture of the learning algorithm ##\n", + "\n", + "It's time to design a simple algorithm to distinguish cat images from non-cat images.\n", + "\n", + "You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!**\n", + "\n", + "\n", + "\n", + "**Mathematical expression of the algorithm**:\n", + "\n", + "For one example $x^{(i)}$:\n", + "$$z^{(i)} = w^T x^{(i)} + b \\tag{1}$$\n", + "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\\tag{2}$$ \n", + "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) = - y^{(i)} \\log(a^{(i)}) - (1-y^{(i)} ) \\log(1-a^{(i)})\\tag{3}$$\n", + "\n", + "The cost is then computed by summing over all training examples:\n", + "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)})\\tag{6}$$\n", + "\n", + "**Key steps**:\n", + "In this exercise, you will carry out the following steps: \n", + " - Initialize the parameters of the model\n", + " - Learn the parameters for the model by minimizing the cost \n", + " - Use the learned parameters to make predictions (on the test set)\n", + " - Analyse the results and conclude" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Building the parts of our algorithm ## \n", + "\n", + "The main steps for building a Neural Network are:\n", + "1. Define the model structure (such as number of input features) \n", + "2. Initialize the model's parameters\n", + "3. Loop:\n", + " - Calculate current loss (forward propagation)\n", + " - Calculate current gradient (backward propagation)\n", + " - Update parameters (gradient descent)\n", + "\n", + "You often build 1-3 separately and integrate them into one function we call `model()`.\n", + "\n", + "### 4.1 - Helper functions\n", + "\n", + "**Exercise**: Using your code from \"Python Basics\", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b)$ to make predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: sigmoid\n", + "\n", + "def sigmoid(z):\n", + " \"\"\"\n", + " Compute the sigmoid of z\n", + "\n", + " Arguments:\n", + " x -- A scalar or numpy array of any size.\n", + "\n", + " Return:\n", + " s -- sigmoid(z)\n", + " \"\"\"\n", + "\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " s = 1 / (1 + np.exp(-z))\n", + " ### END CODE HERE ###\n", + " \n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sigmoid(0) = 0.5\n", + "sigmoid(9.2) = 0.999898970806\n" + ] + } + ], + "source": [ + "print (\"sigmoid(0) = \" + str(sigmoid(0)))\n", + "print (\"sigmoid(9.2) = \" + str(sigmoid(9.2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**sigmoid(0)** 0.5
**sigmoid(9.2)** 0.999898970806
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 - Initializing parameters\n", + "\n", + "**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_with_zeros\n", + "\n", + "def initialize_with_zeros(dim):\n", + " \"\"\"\n", + " This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.\n", + " \n", + " Argument:\n", + " dim -- size of the w vector we want (or number of parameters in this case)\n", + " \n", + " Returns:\n", + " w -- initialized vector of shape (dim, 1)\n", + " b -- initialized scalar (corresponds to the bias)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " w = np.zeros(shape=(dim, 1))\n", + " b = 0\n", + " ### END CODE HERE ###\n", + "\n", + " assert(w.shape == (dim, 1))\n", + " assert(isinstance(b, float) or isinstance(b, int))\n", + " \n", + " return w, b" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w = [[ 0.]\n", + " [ 0.]]\n", + "b = 0\n" + ] + } + ], + "source": [ + "dim = 2\n", + "w, b = initialize_with_zeros(dim)\n", + "print (\"w = \" + str(w))\n", + "print (\"b = \" + str(b))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
** w ** [[ 0.]\n", + " [ 0.]]
** b ** 0
\n", + "\n", + "For image inputs, w will be of shape (num_px $\\times$ num_px $\\times$ 3, 1)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 - Forward and Backward propagation\n", + "\n", + "Now that your parameters are initialized, you can do the \"forward\" and \"backward\" propagation steps for learning the parameters.\n", + "\n", + "**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.\n", + "\n", + "**Hints**:\n", + "\n", + "Forward Propagation:\n", + "- You get X\n", + "- You compute $A = \\sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$\n", + "- You calculate the cost function: $J = -\\frac{1}{m}\\sum_{i=1}^{m}y^{(i)}\\log(a^{(i)})+(1-y^{(i)})\\log(1-a^{(i)})$\n", + "\n", + "Here are the two formulas you will be using: \n", + "\n", + "$$ \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T\\tag{7}$$\n", + "$$ \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)})\\tag{8}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: propagate\n", + "\n", + "def propagate(w, b, X, Y):\n", + " \"\"\"\n", + " Implement the cost function and its gradient for the propagation explained above\n", + "\n", + " Arguments:\n", + " w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n", + " b -- bias, a scalar\n", + " X -- data of size (num_px * num_px * 3, number of examples)\n", + " Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n", + "\n", + " Return:\n", + " cost -- negative log-likelihood cost for logistic regression\n", + " dw -- gradient of the loss with respect to w, thus same shape as w\n", + " db -- gradient of the loss with respect to b, thus same shape as b\n", + " \n", + " Tips:\n", + " - Write your code step by step for the propagation\n", + " \"\"\"\n", + " \n", + " m = X.shape[1]\n", + " \n", + " # FORWARD PROPAGATION (FROM X TO COST)\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A = sigmoid(np.dot(w.T, X) + b) # compute activation\n", + " cost = (- 1 / m) * np.sum(Y * np.log(A) + (1 - Y) * (np.log(1 - A))) # compute cost\n", + " ### END CODE HERE ###\n", + " \n", + " # BACKWARD PROPAGATION (TO FIND GRAD)\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " dw = (1 / m) * np.dot(X, (A - Y).T)\n", + " db = (1 / m) * np.sum(A - Y)\n", + " ### END CODE HERE ###\n", + "\n", + " assert(dw.shape == w.shape)\n", + " assert(db.dtype == float)\n", + " cost = np.squeeze(cost)\n", + " assert(cost.shape == ())\n", + " \n", + " grads = {\"dw\": dw,\n", + " \"db\": db}\n", + " \n", + " return grads, cost" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dw = [[ 0.99993216]\n", + " [ 1.99980262]]\n", + "db = 0.499935230625\n", + "cost = 6.00006477319\n" + ] + } + ], + "source": [ + "w, b, X, Y = np.array([[1], [2]]), 2, np.array([[1,2], [3,4]]), np.array([[1, 0]])\n", + "grads, cost = propagate(w, b, X, Y)\n", + "print (\"dw = \" + str(grads[\"dw\"]))\n", + "print (\"db = \" + str(grads[\"db\"]))\n", + "print (\"cost = \" + str(cost))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
** dw ** [[ 0.99993216]\n", + " [ 1.99980262]]
** db ** 0.499935230625
** cost ** 6.000064773192205
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### d) Optimization\n", + "- You have initialized your parameters.\n", + "- You are also able to compute a cost function and its gradient.\n", + "- Now, you want to update the parameters using gradient descent.\n", + "\n", + "**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\\theta$, the update rule is $ \\theta = \\theta - \\alpha \\text{ } d\\theta$, where $\\alpha$ is the learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: optimize\n", + "\n", + "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n", + " \"\"\"\n", + " This function optimizes w and b by running a gradient descent algorithm\n", + " \n", + " Arguments:\n", + " w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n", + " b -- bias, a scalar\n", + " X -- data of shape (num_px * num_px * 3, number of examples)\n", + " Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n", + " num_iterations -- number of iterations of the optimization loop\n", + " learning_rate -- learning rate of the gradient descent update rule\n", + " print_cost -- True to print the loss every 100 steps\n", + " \n", + " Returns:\n", + " params -- dictionary containing the weights w and bias b\n", + " grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n", + " costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n", + " \n", + " Tips:\n", + " You basically need to write down two steps and iterate through them:\n", + " 1) Calculate the cost and the gradient for the current parameters. Use propagate().\n", + " 2) Update the parameters using gradient descent rule for w and b.\n", + " \"\"\"\n", + " \n", + " costs = []\n", + " \n", + " for i in range(num_iterations):\n", + " \n", + " \n", + " # Cost and gradient calculation (≈ 1-4 lines of code)\n", + " ### START CODE HERE ### \n", + " grads, cost = propagate(w, b, X, Y)\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve derivatives from grads\n", + " dw = grads[\"dw\"]\n", + " db = grads[\"db\"]\n", + " \n", + " # update rule (≈ 2 lines of code)\n", + " ### START CODE HERE ###\n", + " w = w - learning_rate * dw # need to broadcast\n", + " b = b - learning_rate * db\n", + " ### END CODE HERE ###\n", + " \n", + " # Record the costs\n", + " if i % 100 == 0:\n", + " costs.append(cost)\n", + " \n", + " # Print the cost every 100 training examples\n", + " if print_cost and i % 100 == 0:\n", + " print (\"Cost after iteration %i: %f\" % (i, cost))\n", + " \n", + " params = {\"w\": w,\n", + " \"b\": b}\n", + " \n", + " grads = {\"dw\": dw,\n", + " \"db\": db}\n", + " \n", + " return params, grads, costs" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w = [[ 0.1124579 ]\n", + " [ 0.23106775]]\n", + "b = 1.55930492484\n", + "dw = [[ 0.90158428]\n", + " [ 1.76250842]]\n", + "db = 0.430462071679\n" + ] + } + ], + "source": [ + "params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n", + "\n", + "print (\"w = \" + str(params[\"w\"]))\n", + "print (\"b = \" + str(params[\"b\"]))\n", + "print (\"dw = \" + str(grads[\"dw\"]))\n", + "print (\"db = \" + str(grads[\"db\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
**w** [[ 0.1124579 ]\n", + " [ 0.23106775]]
**b** 1.55930492484
**dw** [[ 0.90158428]\n", + " [ 1.76250842]]
**db** 0.430462071679
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:\n", + "\n", + "1. Calculate $\\hat{Y} = A = \\sigma(w^T X + b)$\n", + "\n", + "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this). " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: predict\n", + "\n", + "def predict(w, b, X):\n", + " '''\n", + " Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n", + " \n", + " Arguments:\n", + " w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n", + " b -- bias, a scalar\n", + " X -- data of size (num_px * num_px * 3, number of examples)\n", + " \n", + " Returns:\n", + " Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n", + " '''\n", + " \n", + " m = X.shape[1]\n", + " Y_prediction = np.zeros((1, m))\n", + " w = w.reshape(X.shape[0], 1)\n", + " \n", + " # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n", + " ### START CODE HERE ### (≈ 1 line of code)\n", + " A = sigmoid(np.dot(w.T, X) + b)\n", + " ### END CODE HERE ###\n", + " \n", + " for i in range(A.shape[1]):\n", + " # Convert probabilities a[0,i] to actual predictions p[0,i]\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " Y_prediction[0, i] = 1 if A[0, i] > 0.5 else 0\n", + " ### END CODE HERE ###\n", + " \n", + " assert(Y_prediction.shape == (1, m))\n", + " \n", + " return Y_prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions = [[ 1. 1.]]\n" + ] + } + ], + "source": [ + "print(\"predictions = \" + str(predict(w, b, X)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **predictions**\n", + " \n", + " [[ 1. 1.]]\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "**What to remember:**\n", + "You've implemented several functions that:\n", + "- Initialize (w,b)\n", + "- Optimize the loss iteratively to learn parameters (w,b):\n", + " - computing the cost and its gradient \n", + " - updating the parameters using gradient descent\n", + "- Use the learned (w,b) to predict the labels for a given set of examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 - Merge all functions into a model ##\n", + "\n", + "You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.\n", + "\n", + "**Exercise:** Implement the model function. Use the following notation:\n", + " - Y_prediction for your predictions on the test set\n", + " - Y_prediction_train for your predictions on the train set\n", + " - w, costs, grads for the outputs of optimize()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):\n", + " \"\"\"\n", + " Builds the logistic regression model by calling the function you've implemented previously\n", + " \n", + " Arguments:\n", + " X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)\n", + " Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)\n", + " X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)\n", + " Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)\n", + " num_iterations -- hyperparameter representing the number of iterations to optimize the parameters\n", + " learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n", + " print_cost -- Set to true to print the cost every 100 iterations\n", + " \n", + " Returns:\n", + " d -- dictionary containing information about the model.\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # initialize parameters with zeros (≈ 1 line of code)\n", + " w, b = initialize_with_zeros(X_train.shape[0])\n", + "\n", + " # Gradient descent (≈ 1 line of code)\n", + " parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n", + " \n", + " # Retrieve parameters w and b from dictionary \"parameters\"\n", + " w = parameters[\"w\"]\n", + " b = parameters[\"b\"]\n", + " \n", + " # Predict test/train set examples (≈ 2 lines of code)\n", + " Y_prediction_test = predict(w, b, X_test)\n", + " Y_prediction_train = predict(w, b, X_train)\n", + "\n", + " ### END CODE HERE ###\n", + "\n", + " # Print train/test Errors\n", + " print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n", + " print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n", + "\n", + " \n", + " d = {\"costs\": costs,\n", + " \"Y_prediction_test\": Y_prediction_test, \n", + " \"Y_prediction_train\" : Y_prediction_train, \n", + " \"w\" : w, \n", + " \"b\" : b,\n", + " \"learning_rate\" : learning_rate,\n", + " \"num_iterations\": num_iterations}\n", + " \n", + " return d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to train your model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.693147\n", + "Cost after iteration 100: 0.584508\n", + "Cost after iteration 200: 0.466949\n", + "Cost after iteration 300: 0.376007\n", + "Cost after iteration 400: 0.331463\n", + "Cost after iteration 500: 0.303273\n", + "Cost after iteration 600: 0.279880\n", + "Cost after iteration 700: 0.260042\n", + "Cost after iteration 800: 0.242941\n", + "Cost after iteration 900: 0.228004\n", + "Cost after iteration 1000: 0.214820\n", + "Cost after iteration 1100: 0.203078\n", + "Cost after iteration 1200: 0.192544\n", + "Cost after iteration 1300: 0.183033\n", + "Cost after iteration 1400: 0.174399\n", + "Cost after iteration 1500: 0.166521\n", + "Cost after iteration 1600: 0.159305\n", + "Cost after iteration 1700: 0.152667\n", + "Cost after iteration 1800: 0.146542\n", + "Cost after iteration 1900: 0.140872\n", + "train accuracy: 99.04306220095694 %\n", + "test accuracy: 70.0 %\n" + ] + } + ], + "source": [ + "d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
**Train Accuracy** 99.04306220095694 %
**Test Accuracy** 70.0 %
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!\n", + "\n", + "Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 0, you predicted that it is a \"cat\" picture.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:4: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n" + ] + }, + { + "data": { + "image/png": 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AngB6IvStYRsdV77igR4waICJ5aWd2NHRVC/IiciMOGJp/ZKzVNZRFqr/ZRU1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example of a picture that was wrongly classified.\n", + "index = 5\n", + "plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n", + "print (\"y = \" + str(test_set_y[0, index]) + \", you predicted that it is a \\\"\" + classes[d[\"Y_prediction_test\"][0, index]].decode(\"utf-8\") + \"\\\" picture.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also plot the cost function and the gradients." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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tqLvvXcWxf3H3xnf9pIZ+9zv46Se46aa4KxERkcaqtsHjDeAvZtYqscLMWhNG\nHn0jE4VJ5m2yCZx+eri19rvv4q5GREQao9oGj/OAQcBnZjbJzCYBC6J152aqOMm8iy6CZcvCoGIi\nIiLZVttxPN4HegGXAO9Ey8XAVu4+PXPlSaZ17QqnnhqGUf/++7irERGRxqZWwcPMLgGOc/e73f2C\naLkHKDKzizJbomTaxRfDDz+ECeRERESyqbaXWs4APkyzfjr1PHKp1N1mm8Epp4RWjx9+iLsaERFp\nTGobPLoQ7mBJtZjazU4rWXbxxeG22ttvj7sSERFpTGobPBIdSVMNAj6vfTmSLd27w9ChcM018Nln\ncVcjIiKNRW2Dx93ADWY21Mw2j5Zi4Ppom+SBkSOhTRvYc09YsCDuakREpDGobfC4BrgXuA2YGy03\nE+ZWuSJDtUk969oVXnkFVq8O4ePTT+OuSEREGrra3k7r7n4RsBGwK7ADsKG7a+7TPNOjRwgf7vDL\nX4ZJ5EREROpLbVs8AHD3H9z9LXf/wN1/ylRRkl2bbx7CR5MmoeVj3ry4KxIRkYaqTsFDGo7u3eFf\n/4LmzUP4mDs37opERKQhUvCQ/9lss9Dy0bJluOzy8cdxVyQiIg2NgodUsOmmIXy0bRtaPj76KO6K\nRESkIVHwkLV07QovvwzrrRfCx6xZcVckIiINhYKHpLXJJqHlo0MH2GsvmDkz7opERKQhUPCQSnXu\nHFo+NtwwtHzMmBF3RSIiku8UPKRKG28cwsfGG4fwMX163BWJiEg+U/CQddpoI5g8Gbp0CZddPvgg\n7opERCRfKXhItXTqFMLHppuG8PHee3FXJCIi+UjBQ6qtY0eYNCkMNrb33vDOO3FXJCIi+UbBQ2pk\nww1h4sQwx8s++8C0aXFXJCIi+UTBQ2psgw1C+NhyyxA+SkvjrkhERPKFgofUSocO8NJLsPXWsO++\n8PbbcVckIiL5QMFDaq19e3jxRejTJ4SPqVPjrkhERHKdgofUyfrrwwsvwHbbwX77wZQpcVckIiK5\nTMFD6iwRPvr1g/33hzfeiLsiERHJVQoekhHrrQfPPw/9+4fw8dprcVckIiK5SMFDMqZdO3juOdhp\nJzjgAPj3v+OuSEREco2Ch2RU27bw7LOwyy5w0EHwwAPgHndVIiKSKxQ8JOPatIFnnoEjjoAhQ0Kn\n048+irsqERHJBQoeUi/atIGHHgr9PubOhe23h8svh59/jrsyERGJk4KH1KsDDwyz2Z53HowcCTvu\nCP/5T9xmSrJbAAAfQ0lEQVRViYhIXBQ8pN61aQNXXgllZWHQsV/8Ak47DZYsibsyERHJNgUPyZp+\n/cJttrfdBuPGQe/eUFKizqciIo2JgodkVZMmcNZZMHMm/PKXcMIJ4XLMnDlxVyYiItmg4CGx2GST\n0Orxz3/CrFnQty9ccQWsXBl3ZSIiUp8UPCRWhxwC06fDb38Lf/wjFBTA66/HXZWIiNQXBQ+JXdu2\ncM018Pbb0Lo1DBoULsd8+23clYmISKYpeEjO2HHHMMHczTeHMUB694ZHH1XnUxGRhkTBQ3JK06bh\nssuMGbD77nD88eFyzLx5cVcmIiKZkDPBw8yGm9k8M1tuZlPMbEAV+w4ys/+Y2VdmtszMZpjZedms\nV+rXppvC44/D+PFhALLttoOrr1bnUxGRfJcTwcPMjgOuBUYC/YF3gQlm1qmSQ34EbgZ+AfQG/gpc\nZmanZqFcyaLDDoMPP4Qzz4RLLgkz306ZEndVIiJSWzkRPIARwJ3uPtbdZwJnAsuA4nQ7u/s77v6o\nu89w90/d/WFgAiGISAPTrh1cdx289RY0bw677QbDh6vzqYhIPoo9eJhZc6AQmJRY5+4OTAQGVvMc\n/aN9X6mHEiVHFBTAm2/CDTfA2LHQsydcdRX8+GPclYmISHXFHjyATkBTYFHK+kVAl6oONLMFZrYC\nmArc6u7310+JkiuaNoVzzgmDjhUVhbE/ttwy3Anz009xVyciIuvSLO4C6mh3oB2wK3CVmX3s7o9W\ndcCIESNo3759hXVFRUUUFRXVX5WScV27wq23woUXwqhRYfbbv/8d/vQnOOkkaJbv/7JFRGJSUlJC\nSUlJhXVLly7N2PnNYx4kIbrUsgw42t3HJ60fDbR39yOreZ5LgcHu3qeS7QVAaWlpKQUFBXUvXHLK\nzJkhdDz2GPTqFcLIsceGuWFERKRuysrKKCwsBCh097K6nCv2/5bdfSVQCuyTWGdmFj2uyeDZTYGW\nma1O8kXv3mHul7Iy2HrrcBmmf/9wO64GIBMRyR2xB4/IdcBpZjbEzHoDdwBtgNEAZnaFmY1J7Gxm\nw8zsUDPbKlpOAS4AHoihdskh/fuHiedefx06doQjjoBdd4WJExVARERyQU4ED3cfB1wIjAKmAf2A\nA9x9cbRLF6Bb0iFNgCuifd8CzgJ+5+4js1a05LSBA2Hy5BA4zGC//WDvvTUBnYhI3HIieAC4+23u\n3sPdW7v7QHd/O2nbUHffO+nxLe6+vbuv5+4buPtO7n5XPJVLLttnnzD/y/jx8M03YQK6Qw6BadPi\nrkxEpHHKmeAhUl/Mwgio06bBI4/Axx+HMUF+/eswJ4yIiGSPgoc0Gk2awHHHwfTpcO+9MHUq9O0L\nJ5+sSehERLJFwUManWbNoLgYZs+GG2+ECRNgm21g2DD4/PO4qxMRadgUPKTRatkSfvtbmDMHLrsM\nHn00jIJ64YWwePG6jxcRkZpT8JBGr00b+P3vYe5cuOgiuOsu2HzzMCPuzJlxVyci0rAoeIhE2reH\nP/859Pf4v/+Dp5+GPn3g4IPhpZc0DoiISCYoeIik6NgR/vAHmD8fxoyBL76A/feHfv1Cp9QVK+Ku\nUEQkfyl4iFSiZUsYMiQMw/7yy9CzJ5x2GnTvHuaFWbgw7gpFRPKPgofIOpjBnnuGSy+zZ8Pxx8N1\n14V+ICefDO++G3eFIiL5Q8FDpAa22gpuugk++wwuvzy0hOy4YxiOffx4WLMm7gpFRHKbgodILXTo\nEG67nTMn3Ia7fHmYkG6bbeCWW+CHH+KuUEQkNyl4iNRBs2Zw7LFhPpg33oDCQjjvPOjWLdyiu2BB\n3BWKiOQWBQ+RDNl11zAXzNy5oRPqXXfBFluEPiFTpsRdnYhIblDwEMmw7t3h6qtDP5Abb4TSUhg4\nMCzjxsGqVXFXKCISHwUPkXrSrh0MHw6zZoWOp61bh0nqevYMt+POnRt3hSIi2afgIVLPmjSBww6D\nyZNh2jQ48EC44YYwL8xee8HYsfDjj3FXKSKSHQoeIlm0446h78fChfDAA2GMkJNOgk02Cf1CXn9d\nQ7OLSMOm4CESgzZtYPDg0Aoydy6cf36YD2bQoDA/zFVXweefx12liEjmKXiIxGyLLcLkdHPnwsSJ\nsNNO4XG3bnDIIfD44/DTT3FXKSKSGQoeIjmiSRPYZx948MEwMd1tt8HXX8Ovfw2bbgrnngvvvBN3\nlSIidaPgIZKDOnSAM84I439Mnw5Dh4YRUvv3D8vNN4dQIiKSbxQ8RHLcttvCNdeEUVDHj4cePUKf\nkK5dQ2vI88/D6tVxVykiUj0KHiJ5onnzcFvuk0/Cf/8LV14JM2fCwQeHQcsuuSTMnisikssUPETy\n0MYbw4gR8N578NZb8KtfwR13hEnqCgtDKJkzJ+4qRUTWpuAhksfMwl0wt94aOqQ++mgYGXXUKNhq\nK4UQEck9Ch4iDUSrVmGm3Mceg8WLw7wwW24Jf/1rCCEFBXDFFfDxx3FXKiKNmYKHSAPUtm3oeDpu\nHHz5ZQgjvXrBZZeFr/37w9/+Bh99FHelItLYKHiINHBt28Ixx4TLMIsXhwHJttkmBI+ttw7DuF9+\nuTqmikh2KHiINCJt2sDRR8Mjj4SWkCeeCEO0X3FFCCM77BBaRWbNirtSEWmoFDxEGqk2beCoo6Ck\nJLSE/OMfsN12YZ6Y3r2hX7/QP2TmzLgrFZGGRMFDRGjdGo48Eh5+OLSEPPkkbL89XH11aBHZfvtw\np8x772n2XBGpGwUPEamgdeswLshDD4WWkKeeCpdg/v738HXzzeHMM+GZZ2DZsrirFZF8o+AhIpVq\n1QqOOCJMXLd4Mbz4YmgZmTgRDj8cNtwQDjoIbrkF5s2Lu1oRyQcKHiJSLS1bwn77wY03httwZ84M\nd8b8/HMYRbVnzzCvzO9/D//6F6xcGXfFIpKLFDxEpMbMwl0w558PkyaFmXIffxx23RXGjoU994SN\nNoLjjguPFy+Ou2IRyRXN4i5ARPLf+uuH23SPPhrWrIGyMnj22bCcdFIIKjvvDIccEpYdd4Qm+rNH\npFHSj76IZFSTJmH+mJEjYepUWLgQ7rsPunULHVQLC2GzzeCUU8ItvN9/H3fFIpJNCh4iUq86d4aT\nTw7Dtn/1FUyeDCecAK+/HlpIOnaEffeF666Dd98NLSYi0nApeIhI1jRvDnvtFVo+ZswIs+Zeey00\nawaXXhouwXTuHCa7u/POMKGdxg0RaVjUx0NEYtOzJ5x9dlhWrIA33gidVSdNguHDYfVq6N4d9t4b\n9tknLJtsEnfVIlIXOdPiYWbDzWyemS03sylmNqCKfY80sxfN7EszW2pmr5vZ/tmsV0Qyq1Wr0Bpy\n2WUhgHzzTRik7KijoLQUfvMb6No13LJ79tlhYLMlS+KuWkRqKieCh5kdB1wLjAT6A+8CE8ysUyWH\n7AG8CBwEFAAvA8+Y2Q5ZKFdEsmD99eHQQ+H668NQ7YsWhXlldt8dnnsuDGTWqRMMGAAXXwwvvaSR\nVEXygXkOXEA1synAm+5+bvTYgAXATe5+dTXP8QHwiLtfVsn2AqC0tLSUgoKCDFUuInGZPz9ckpk8\nOXxdtAhatICBA8svywwYEPq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot learning curve (with costs)\n", + "costs = np.squeeze(d['costs'])\n", + "plt.plot(costs)\n", + "plt.ylabel('cost')\n", + "plt.xlabel('iterations (per hundreds)')\n", + "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Interpretation**:\n", + "You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 - Further analysis (optional/ungraded exercise) ##\n", + "\n", + "Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\\alpha$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Choice of learning rate ####\n", + "\n", + "**Reminder**:\n", + "In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\\alpha$ determines how rapidly we update the parameters. If the learning rate is too large we may \"overshoot\" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.\n", + "\n", + "Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "learning rate is: 0.01\n", + "train accuracy: 99.52153110047847 %\n", + "test accuracy: 68.0 %\n", + "\n", + "-------------------------------------------------------\n", + "\n", + "learning rate is: 0.001\n", + "train accuracy: 88.99521531100478 %\n", + "test accuracy: 64.0 %\n", + "\n", + "-------------------------------------------------------\n", + "\n", + "learning rate is: 0.0001\n", + "train accuracy: 68.42105263157895 %\n", + "test accuracy: 36.0 %\n", + "\n", + "-------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "image/png": 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BLL5rMd6e3qWfdM0as5vbmjVw9dVmOu5NNzk0gMyfDyNHwpkzUMIyA6IWs27K\ntHLlymKLYImaa9u2bQwaNIhJkyYRFhZGWloaY8eOJTQ01NVNE6JUhTaJ66G1rtJOYnV+jEdR/Vr1\nY8kflvD1719z/9L7ydNlLB3aty/8+CN8+60JGwMHQr9+Du2isFjM888/O+yUQgghhMtI8CjBze1u\nZv4d8/l8x+c8uuJRyuwVUgpuvBHWr4cVK+DiRbjuOlNWwvS8imrRwjwkeAghhKgNJHiU4q7ou/j4\nto95P/59xn87vuzwASaA3HIL/O9/sGSJ2f32mmvM2I/4+Cq1xWKRmS1CCCFqBwkeZbi/+/28Negt\nXv/ldaasKXurbhulzEyXrVthwQLYvx+uvBLuuANKWHugPCwWSEiACxcqdbgQQgjhNmRWy2U8cfUT\npGWl8eL3LxLkG8RTvZ4q34EeHnDPPWbmy2efmam33bpBmzZmlGhZj7Aw85y/TLPFYpYN2bDB3MER\nQgghaioJHuXw/LXPk5qZyrhV46jnU48HYx8s/8FeXjB6NIwYYdY/374dEhPNw/r6zBkzNqSoevWg\ncWM6Nm7MKu/GhP65MQwoI6wU2WJcCCGEcDcSPMpBKcW0/tNIy0zjoeUPEeQTxN2d767YSby9y14F\nLD29IJAUeajERIKPJOJzcA/852dTnpJS/Bz+/pfvTWncGBo1Mo/gYKctfiaEEEKURIJHOSmlePuW\nt0nLSmPkf0YS4B3AkA5DLn9gefn7F0xhKcHPr5slQ5J35u9Vl5kJZ8+WGlZITISDB2HTJvP63Lni\nJ/XwgJAQs0RqRR4lbDMuhBBClIcEjwrwUB7MGjqLi9kXuWvxXXwd9zU3tL6hWq5tsZhOkYQE6NUL\n85d/s2bmUR45OZCUVBBKkpJKfuzZU/D6/HnIK2Edk8DAioeV4GDZYreS9u3b5+omCAeS/z1FXSfB\no4K8PLz47I7PGPr5UG5bcBvfjvqW3s17O/26MTEQEGCm1fbqVYkTeHlBeLh5lFdeHiQnlx5SrI/E\nRNi9u+B9enrxc3l6Fu9dCQmBhg3No7TXDRuattdBjRo1IiAggMcff9zVTREO5uvrS1BQkKubIYRL\n1M0/0avI18uXJXcvYdC8Qdzy2S38cN8PdI/o7tRrentD794meIwf79RLFbDeigkJgXbtyn9cevrl\nw0pSEuzda3pVzp0zz1lZJZ8vKMg+kJQnsISEQP36NXoMS4sWLdi1axdnz54lOTmZpUuXEhgYiJ+f\n3+UPFm4QjQX4AAAgAElEQVQtKCiI0NBQu11zhagrJHhUUoB3AMtHLKf/3P4M+HQAa8espUOjDk69\npsUCM2eajgi3/vvU3x8iI82jvLQ2gcUaQgoHksKvrc9Hj9p/XtItIQ8Pc4unrHDSoEHJj+Bgk/Zc\nrEWLFrRo0YKkpCTi4+Px8fHBV8bY1AqXLl0iMzPT1c0QotpJ8KiCYL9gVo1cRb/Z/bjx0xtZO2Yt\nrRq0ctr1LBaYOBF++w06d3baZVxDKXMvKSCgYoEFTOhISys7sFhfnz0Lv/9eUJ6aakJPSQICSg8m\nZT2Cg82zj0/Vf5d8fn5+BAcHk5KSQlZpPUOiRgoODpZeLFGnSPCootCAUL4d9S2WTyz0n9uftWPW\n0jSoqVOu1auXGe6wdm3NDh7ffw9vvQUffuigHXetPRvBwdCqVcWOtYaW5OTyPU6cMMnP+j4lpfTg\n4u9fvpBS0qN+fbOOS37XVmBgIHFxcWRkZFTttxJux8/Pj0BZg0fUIeqye5BUE6XUY8CfgQhgK/CE\n1npzGfV9gJeAuPxjTgAva61nl1I/FoiPj48nNjbWwa2HQ8mHsHxiob5vfX66/ycaBTRy+DXAhI+o\nKLMYak2kNXTvDtu2mfC0erVZ+6zGKiu4pKSUL8yU9t+gUiaAFA0kJYWU0srq16+zg3OFEI6TkJBA\njx49AHporROqci63+BNJKXU38DrwELAJGAesUkq111qfLeWwxUBjYAywH2iCC/eeadWgFd+N+g7L\nJxYGzRvE6tGrCfYLdvh1LBazAKrWNXN26vLlJnS8/z689BJcf73pAanIZBu3Uri3pWXLih+fl2dW\nrU1JsX+kphYvs5afPGlmERUuz8kp/RqBgeUPKdZHUJD9+0K9L0IIURVu0eOhlNoAbNRaP5n/XgFH\ngbe01tNLqD8I+AyI0lonl/MaTu3xsNp6aivXzbmOzmGdWTVyFQHeAQ49/7JlMHSoWRusoncVXE1r\nuPpqcwfip5/MkiHXX2/uOHz/PUREuLqFNZTWkJFR/uBSUnlaGlxuhkW9evZhpLSQUlqZtdzXt2am\nZiHqsFrV46GU8gZ6AFOtZVprrZT6DihtgYwhwP+A55RSo4CLwDJggtbapTfBu0V045u4b7hx7o0M\nWziMZfcsw9fLcbMQ+vQxz2vX1rzg8d//wubN5hmgQwcTQK6/Hq67zoSPps4ZHlO7KWXSnL9/1dJb\nTo4JIKmp5lH4dVllp04VL8/NLf063t4lh5SgoMs/itYLCJAQI0QN4/LgATQCPIHTRcpPA6XNT40C\nLEAGcHv+Of4FhAAPOKeZ5dcrshfLRizjlvm3MOKLESy6axFeHo75qUNDITraBI+ytn5xN1rD5Mmm\nx6PwDrvt2sGPPxaEjx9+KP9irMLBvLwKphpXhXVqdEkhpaxAk5gIBw6Y14UfZfXKeniYnpjyhJby\nPGSqshBO5w7BozI8gDzgXq31BQCl1NPAYqXUo1prl0+Ov6H1Dfz7D/9m2MJhjF06ltm3z8ZDOeYe\nucVi/rKuSX76CdatM2M8iv4DtW3bgp6Pfv1M+Gje3DXtFA5QeGp0VQfvaG1uARUNI+V5HDlSEGqs\nj5JW1S3M29sEGWuYsb6u6PvCr91gPRgh3Ik7BI+zQC5Q9E+ocOBUKcecBI5bQ0e+XYACIjGDTUs0\nbtw4goPtB32OGDGCESNGVLDZlze4/WDmDZvHiC9GEOQTxDu3vINyQLfwtdfCe++ZfyA6ZDpqNZgy\nxcxmufXWkj+PirK/7fLDD6XulyfqEqXM4NjAQMcMAsrJgQsXyg4sFy+a5wsXCh5paXDsmP176+uS\nFq8rzMen4iHG+ggMLPm1jJMRTrRgwQIWLFhgV5ZS0o7oleTOg0uPYAaXziih/h+BN4EwrfWl/LKh\nwL+BeiX1eFTX4NKSfJzwMQ8uf5Dn+jzHtP7Tqhw+jhwxEyiWLIFhwxzUSCf65Re45hr4979h+PCy\n6x4+bMKH1iZ81LRxLKKOsQ7sLSmoVOX95f5ctt5iKi2clBZYLvdaAo0oRa0aXJrvDWC2Uiqegum0\nAcBsAKXUNKCp1vq+/PqfAX8FPlFKTcRMq50OfOwOt1mKeiD2AdKy0hi3ahx7kvbwQMwDDGwzEG/P\nynXBtmhhHj//XDOCx5Qp0KlT+drasqX9bZcff4TWrZ3eRCEqp/DAXkctSGMdI2MNIRcvVux1aqpZ\n6K6kOpfj6WkfXoo+Sisvz2cOXMlX1GxuETy01ouUUo2AlzG3WH4FBmqtE/OrRADNC9W/qJS6CXgb\n2AwkAQuBCdXa8Ap4qtdTNPBrwJsb3mTIgiGEBYZxb+d7Gd1tNN0jule4F8RiMQNM3V1CAnz9Ncyf\nX/5lIJo3N4HjhhsKxny0aePUZgrhPgqPkXHk6np5eQWBprTQUvh94WfrIzHR/r21TlnryFh5eVU8\nsBR+BAQUf2199veXdWZqELe41VIdXHmrpaitp7Yyd+tc5m+fz+mLp+kc1pnRXUcT1zWu3Mutv/8+\nPPaYWfiyXj0nN7gK7rgDtm+HXbsqvoDmiROm5+PiRRM+KrJBrhCiGmVlFQ8kJQWUytTJzi5fG/z9\nSw8mlwsu5Qk2np7O/Q3dnCNvtUjwcKGcvBz+u/+/zN06ly93f0l2XjY3Rd3E6G6juf2K28tcfOy3\n38y02m+/tZ+e6k527IAuXWDWLBgzpnLnOHnS9Hykppp1Pjo4dwNgIYS7yc42M5suXiz+XNrrinxe\n3k0X/fwKeqIKP6wB5XJllyv393fr7Q1q4xiPOsnLw4tb2t3CLe1uITkjmX//9m/mbJ1D3JI4gnyC\nuLPTndzX7T4sLS3FpuJ27GjW9Fi71n2DxyuvmDEbI0dW/hxNmhTcdrHOdrniCke1UAjh9ry9C5b3\nd4acnLKDzcWL5hZV4c8KP6xlp04VL7O+LmtBvcJ8fC4fUgICTEipbLkb3JaS4OEmGvg14MHYB3kw\n9kH2n9vPvG3zmLttLp/8+gktg1syqusoRnUbRfvQ9oC5DXztte47zmPPHli4EN59t+rLGISHm8DR\nv3/BCqedOjmkmUKIus7Lq2AVXWfJyio9sJSnzBp+zp61/8waiC5dMrOrysvae1ORAHP+vMN+DrnV\n4sa01qw7uo65W+eyaOciUjJT6B3Zm9HdRvOH6D/wybshTJhgxnm424DxMWPM0uj795v/jztCYqLp\n3Tl1yuxq27mzY84rhBA1nnXwcOEwUlJAKa3sMnUSUlPpYdbykDEe5VUTg0dh6dnpLN+7nLlb57Jy\n30o8PTzp02gIP7x5H2s/GcS117jP6ogHD5qBoK+9Bk895dhznz0LN91k1nJavRq6dnXs+YUQQhTn\nyDEeMv+ohvD39ucP0X/gq3u/4vjTx3m1/6ucYz/cexuDvm3Kk988SfyJeNwhSP797xASAg895Phz\nN2pkAkeLFmbcx6+/Ov4aQgghnEeCRw0UXi+ccb3H8esjW+gZv5WIU/ez6LdFXPnhlXT5Vxemr5vO\n8dTjLmnbsWPwySfwzDPmtqAzhITAd9+ZhcX69zdrhQghhKgZJHjUcLf06Mq5hTM4/ORRvon7hq7h\nXXnpx5do/mZzBnw6gPnb5nMxqxwrFjrIjBlmMPajjzr3Og0bmqnEbdua8BEf79zrCSGEcAwJHjWc\nxWIGG+/d7cWgtoP4bPhnnHrmFB8O+ZDM3ExG/mckEa9HMGbpGH44+AN5+jIbWlXBqVPwwQdmXEdQ\nkNMuY9OggRnAesUVJnxs2uT8awohhKgaCR41XK9eZjZY4Wm1wX7BPBD7AD/d/xMH/u8Az17zLD8f\n+Zkb5t5A65mtGf/f8aw+sJrMHMdua/PGG2bq7BNPOPS0ZQoOhlWrzGJqN90EGzZU37WFEEJUnASP\nGi4gAHr0KH09j9YNWzOh3wT2Pr6X9WPXc3Pbm5m3fR43fnojIdNDGPzZYN7e+DZ7k/ZWaWBqUpJZ\ns+OJJ8xtkOpUvz6sXGlmuAwYAOvXV+/1hRBClJ8Ej1rAumFcWblBKUXv5r15b/B7nHj6BFsf2crE\nfhNJz0nnz9/+mQ7vdCDqrSge+eoRvtz9JamZqRVqwz/+Ya7v6Omz5RUUBN98AzExMHCg2blXCCGE\n+5F1PGqBZctg6FCzfkarVhU//mLWRX489COr9q9i1f5V7E3ai5eHF70jezOwzUAGth1IbJPYYsu2\nWyUnm6XR//hHs3aHK128CEOGmPEeX38Nffu6tj1CCFEbyF4twk6fPuZ57drKBY9An0BubX8rt7a/\nFYCD5w/aQsjf1/2dv/7wVxoHNOamNjcxsM1ABrQZQES9CNvx77wDmZlmCq2rBQbCV1/BbbfBzTfD\nihVmmXUhhBDuQXo8aonOneGaa8ysEkfKzs1mw7ENrNy3klX7VxF/0sxb7RbejUFtB9G36UBG9utD\n3D0+vP22Y69dFenpcPvtJowtX25mvQghhKgcR/Z4SPCoJf70J7OL665dzr1O4sVEvj3wrekR2beK\n0xdPQ1Yg/dtcz7DOgxjYdiBtQ9o6txHllJEBw4aZ32XZMjPrRQghRMXJkumimGuvhd27zUZqztQ4\nsDH3drmXObfPYf+jJ2i4cAtXpU8gz/Mi41aNo93b7WjzVhseXfEoS3cvJS0zzbkNKoOfH/znP2Zp\n9SFDzLRbIYQQriVjPGoJi8U8r1tnbjFUh1kfe5C6tzuff9WdqKjnuJB1gR8O/mAbH/Kv//0LLw8v\n+jTvYxuk2j2ie6mDVJ3Bzw+WLIG77jIDcP/zHzP2QwghhGvIrZZapGVLuPNOeP11518rMxPatDG9\nCXPnllxn/7n9thDy/cHvuZB1gbDAMG6KuokBbQbQp3kfohpGoZRyenuzsuAPfzBTbr/4AgYPdvol\nhRCi1pBZLaJE1vU8qsOcOXDiBLzwQul12oS04dGQR3n0qkfJys3il6O/2ILI/O3zAQgPDKdPiz5c\nE3kNfVr0IbZJLD6ePg5vr48PLF4M99wDd9xhXg8d6vDLCCGEuAzp8ahF3n8fHnvMrKtRr57zrpOd\nDe3bQ8+esHBh5c5xLv0cG45tYN2Rdaw7uo5NxzeRnpOOn5cfVza9kj7N+9CneR96N+9No4BGDm17\nXJy55bJokRl8KoQQomzS4yFKZLFAbq7Zr+TGG513nc8+g0OHYOnSyp8jxD+EW9rdwi3tbgHMtN1f\nT/3KuqPrWH90PZ9u+5S/r/s7AB1CO9CneR+uaW56RTqEdqj07Rlvb9P+kSPNrZfvvy8YHyOEEML5\npMejFtEaGjc2vR6TJjnnGrm50KkTdOwIX37pnGsAaK05knKEdUfXse7IOtYfW8+209vI03mE+IeY\nEJIfRq5qehX+3v4VOn9ODlx/PRw5Alu3mp1uhRBClEx6PESJlDLTap05zmPxYti7F+bPd941wOwt\n07JBS1o2aMm9Xe4FIDUzlY3HNrL+6HrWHV3H1LVTSctKw9vDm9gmsbYw0qdFH7uVVUvi5QXz5kG3\nbvDww/D55+b3E0II4VxuEzyUUo8BfwYigK3AE1rrzaXU7Qf8UKRYA0201mec2lA3Z7HAhAlmFoeP\ng8do5uXBlCkwaBBceaVjz10e9X3rc1Obm7ipjVkJLDcvl+1nttuCyJJdS3hzw5sAtG7Q2m7QanTj\naDw9PO3O17KlWen17rvNFNv776/ubySEEHWPWwQPpdTdwOvAQ8AmYBywSinVXmt9tpTDNNAesK1Q\nVddDB5jgkZ4OCQnQq5djz710KezcaQaxugNPD0+6R3Sne0R3Hr3qUQCOpx63BZH1R9fz+Y7PycnL\nob5vfXpF9rINWu3ZrCdBvkG2KbaPP272vGnXzsVfSgghajm3GOOhlNoAbNRaP5n/XgFHgbe01tNL\nqN8P+B5oqLUu1/7tdWGMB5hZGw0awMSJMH68486rtenlqF8ffija1+TGLmVfYtPxTXZhJDkjGQ/l\nQdfwrlzZ5EqiQ3rw+p9jCdNd+WWtn8N7ioQQoqarVWM8lFLeQA9gqrVMa62VUt8Bvcs6FPhVKeUH\n7AAmaq3XO7WxNYC3N/TubcZ5ODJ4fPON6UVZvdpx56wOAd4BXNfqOq5rdR0AeTqP3Wd3s+7IOn45\n9gubTmzik18/IffmXI7leRL5SjSDY3sQ2ySWHk160C2iGwHeAa79EkIIUYu4PHgAjQBP4HSR8tNA\nh1KOOQk8DPwP8AX+CPyolOqptf7VWQ2tKSwWmDnTjMnwcMDq5FrD5Mkm0Fx/fdXP50oeyoNOjTvR\nqXEn/tjjjwBk5GSw7fQ2ps1O4MuNCfxcP5552+aRnZeNh/KgY6OOtiAS2ySW7hHdCfINcvE3EUKI\nmsnlt1qUUk2A40BvrfXGQuV/B/pqrcvq9Sh8nh+Bw1rr+0r5PBaI79u3L8HBwXafjRgxghEjRlTy\nG7if778328Bv3w6dO1f9fKtXm3VBvv66du9zkptrdrDduxc2J2RxMncHCScTiD8RT8KpBLae2kpm\nbiYKRfvQ9nZhJKZJDA38ZE6uEKLmW7BgAQsWLLArS0lJYc2aNeCAWy3uEDy8gUvAcK31skLls4Fg\nrXW51pZUSk0H+mit+5TyeZ0Y4wFw6RIEB8Nbb8Gf/lT1811/PaSlwebNtX/K6bFjZoptv35mT5fC\n3zc7N5tdZ3fZhZFfT/3KpexLALRp2IYeTXsQGxFLbBPzCA0IddE3EUIIx6lVYzy01tlKqXigP7AM\nbINL+wNvVeBU3TG3YOq8gADo0cOM86hq8Pj5Z/jxR7PEeG0PHQCRkfDRR2Y/lw8/hIceKvjM29Ob\nruFd6Rrelfu73w+YKb17kvaYIHIygfiT8Uz5fQoXsi4A0DK4pS2M9GhqekfCAsNc8M2EEMI9uDx4\n5HsDmJ0fQKzTaQOA2QBKqWlAU+ttFKXUk8BBYCfghxnjcT1wU7W33E1ZLGZRLK2rFhimTDG3a267\nzXFtc3fDhpnA8dRT0LcvXHFF6XU9PTxtY0ZGdRsFmAGsvyf9bgsiCScTmLF+BimZKQBE1o+0u03T\nNbwrzes3r5ZdeoUQwtXcInhorRcppRoBLwPhwK/AQK11Yn6VCKB5oUN8MOt+NMXcptkG9Ndar6m+\nVrs3iwVeew0OH4ZWrSp3js2bYdUqWLDAMYNUa5I33oA1a2DECLP3ja9v+Y/1UB50aNSBDo06MKKL\nGTuktebA+QN2YWTmxpmcSz8HmMXROod1pktYF9tzl/AuhPiHOOPrCSGEy1RqjIdSajSwUGudWaTc\nB7hHaz3XQe1zmLo0xgMgKQkaNYK5c2HUqMqdY+hQ2L0bfvsNPD0vX7+22bIFrr4anngCXn/d8ee3\n7kez/cx2tp/ezo7EHWw/vZ3dZ3eTnZcNQJN6TegS3oXOjTub57DOdGrcSab4CiGqlSPHeFQ2eORS\nwvLkSqlQ4IzW2u3+mqprwQPMLZJrrjHLglfU1q3QvTvMng33lThPqG544w145hnT8zNgQPVcMzs3\nm71Je9lxZgfbz2y3PR84fwAAhaJNSBu73pHOYZ1pF9oOLw+36MQUQtQy7jC4VGGWLC8qEkipfHOE\nI1ksZmBoZbzyCrRuDffe69Am1ThPPWVCx+jRsG0bhFXDuFBvT2+iw6KJDovmbu62lV/IusBvib+Z\nIJLfQ/JB/AecvmiWwPHx9KFjo47Fekhk/IgQwp1UKHgopbZgAocGViulcgp97Am0BlY6rnmiKiwW\neO89SEyExo3Lf9yuXfDvf5tjvb2d176awMPD9Pp07QoPPADLlrludk89n3r0bNaTns162pUnXkws\n1jvy5e4vbTNrZPyIEMKdVLTH48v85+7AKuBCoc+ygEPAF1VvlnCEa681z+vWwe23l/+4qVOhWbO6\nfYulsCZN4JNPYMgQePddeOwxV7fIXuPAxlzf+nqub12wrKzWmsMph+16R9YfXc+sLbPsxo9Yg0h0\nWDRXNLqCKxpdIYFECOFUFQoeWutJAEqpQ8DnRQeXCvfSooV5rF1b/uCxfz989hn84x8Vm8lR2w0e\nbHawfeYZs7iYI1aEdSalFK0atKJVg1YMbj/YVl7S+JEv93zJmxveROffPW0c0NgWQgo/Wga3xNPD\n7YZvCSFqmMoOLm2O2cvtWP77nsC9wG9a60oMZXS+uji4FGDkSLME+KZN5av/xz/C8uVw8CD4+zu3\nbTVNejr0zL/LsWlT7fp90rPT+f3c7+w+u9vusSdpj21lVl9PX9qHti8WSNqHtqeeTz0XfwMhhDO5\nw+DSz4APgE+VUhHAd5gdYuOUUhFa65er0ijhONaFxC5cgHqX+bvhyBGYM8fcaqlNf6k6ir+/6Q26\n6ip47jmzJH1t4e/tb1uVtbA8ncex1GPFAslHCR9x8kLBQsHN6zcvsZekSb0mMrBVCGGnssGjM2aF\nUYA/ANu11n2UUgOA9zALgQk3YLGYzc82bDAbvZVl+nSoXx8eeaR62lYTdeliFmZ74gkYOBBuvdXV\nLXIuD+VBi+AWtAhuwYA29vOJUzJS2JO0xy6QfHfgO97733u2cSRBPkF2QaRDaAeuaHQFbUPa4usl\n9/KEqIsqGzy8Aev4jhvJ32MF2A00qWqjhON07AihoWacR1nB4+RJs0fJhAmX7xmp6x57DFauhPvv\nNzsAR0S4ukWuEewXXOIsm+zcbA4mHyzWS7J873KSM5IBE2iiGkaZQBKaH0oadaBdSDvCAsOkl0SI\nWqyywWMn8IhSagVmf5QJ+eVNgSRHNEw4hlJmdsvatWXXe+018PMzAyhF2ZSCWbPMFNv77oNvvql7\nS8qXxdvTm/ah7Wkf2p7bOhRs8qO1JvFSYrFA8sWuLziUfMg2uLWeTz3ahrSlXUg72oa0tXsdUS9C\nQokQNVxlg8dzwH+A8cAcrfXW/PLbKLgFI9yExWJ6MrKywMen+OeJiWbNjqefhuDg6m9fTRQWZsbD\nDBoEM2fCuHGubpH7U0oRFhhGWGAYfVv2tfssPTudfef2sf/8fn5P+p195/ax7/w+NmzbwNHUo7Z6\ngd6BtjBSNJw0DWoqoUSIGqBSwUNr/WP+pm71tdbnC330AWbTNuFGLBYzIyMhAXr1Kv75m2+af8U/\n9VT1t60mGzjQBI6//AWuv94sMS8qx9/bny7hZmGzotKz0zlw/oAJI+f28fs5E0w+3/E5R1KO2HpK\nArwDaNOwDe1C29G2YX4wCW1nCyUeSrqlhHAHld7YQWudq5TyUkrlL1PFHq31Icc0SzhSTAwEBJjb\nLUWDx/nz8M478OijZiyIqJhp0+D7780utvHx5ncWjuXv7W9bQr6ojJwMDp4/aBdIfj/3O4t+W8SR\nlCPk6TxzDi9/2oS0Mb0jDQsCSduQtkTWj5RQIkQ1qlTwUEoFAm8DowHrf7G5Sqm5wBNaa+n1cCPe\n3tC7twke48fbf/bWW5CdbRbGEhXn6wsLFkCPHuZW1XvvubpFdYuflx8dG3ekY+OOxT7LzMnkYPLB\ngp6SpN/Zd34fS3Yv4VDyIVso8fX0tQslUQ2jaN2wNVENo2jVoBV+Xn7V/bWEqNUq2+PxBtAPGAKs\nyy+7FngLeB34U9WbJhzJYjFjEfLyCgZCpqaasocegvBw17avJuvY0dyueuQRM+ajIsvTC+fx9fK1\nTeMtKis3i0PJhwrGk+T3lCzds5TDKYfJySvYhqpJvSa2MNK6gQkk1uemQU1lNVchKqiywWM4cKfW\n+sdCZV8rpdKBRUjwcDsWC0ycCL/9VrDc97vvwsWLxXtBRMU99JCZYvvAA2aBsWbNXN0iURYfTx/b\nzJuicvNyOZ52nIPnD3Lg/AEOJh/kYLJ5vfrAaruF07w9vGnZoKUtjNiCSX5ICfEPkQGvQhRR2eAR\nAJwuofxM/mfCzfTqBV5e5nZL584mcLz+OowZA5GRrm5dzaeUWQela1cYPRq+/Vam2NZUnh6etkXT\n+rXqV+zz9Ox0DiUfsoWRg+dNMNlwbAMLdiwgNTPVVre+b327XhLrLZzWDVrTqkEr/L1liWBR91Q2\nePwCTFJKjdZaZwAopfyBl/I/E24mIMCMQ1i7Fv70J/jgAzOw9LnnXN2y2iM0FD791CzU9tpr8Oyz\nrm6RcAZ/b/9Sx5VorTmfcd4ukFh7TZbvXc6h5EO2VV3B3MYpegundUMTSpoFNcPb07s6v5oQ1aKy\nweMpYCVwTCllXcOjG2Y10wGlHiVcyrpvS0YGzJgBo0ZB69aublXtcsMNJnC8+KJ5feWVrm6RqE5K\nKUL8QwjxD+HKpsX/x8/Ny+VE2gm73pIDyeb5h0M/cCLthK2uh/KgaVBTWga3pGWDlua50OsWwS0I\n9Amszq8nhENUandaAKVUABAHWEdu7QLma63THdQ2h6qru9MWtmwZDB1q/mKcMQN274b2xW9xiyrK\nyoI+fSAlxaydIkvQi/JKz07ncMphDicf5nDKYY6kHLF7fzz1OLk611Y/1D+0xFDSsoEJJqH+oTLG\nRDiEy3enVUo9D5zSWn9YpHysUqqx1vrvVWmUcI4+fczz9Olwzz0SOpzFx8fsYhsTA08+CR9/7OoW\niZrC39u/1Jk4ADl5ORxPPV4QSvIDyeGUw3yz7xsOpxwmIyfDVj/QO5AWwS2KhZMWwS1oGdxSZuUI\nl6jsrZaHgbtLKN8JfA5I8HBDoaEQHQ07d5pbAcJ52rWDt9+GsWPNCqd/+IOrWyRqAy8PLxMiGrQs\n8XPrfjh2oST/eePxjSzauYjzGeftzhdZP7LYLRzrc2T9SLmdIxyussEjAjODpahEZHdatzZ2LBw+\nXDClVjjP/febKbYPPWRmFbVo4eoWidqu8H44JY0xAUjLTCt2C+dwymF+T/qd1QdWcyLthG0ZeoCG\nfg1pHtyc5vWbE1k/kub1m9M8uOB1ZP1ImZ0jKqSyweMo0Ac4WKS8D3CiePXLU0o9BvwZE2q2YlZA\n3VyO4/oAPwLbtdZ1c/BGBTz9tKtbUHcoZVYy7dYNRo6EH34AT+nVFi4W5BtU6hL0YBZXO5Z6jKMp\nRzdBwWkAACAASURBVDmaetTu9abjm/hi1xecvXTW7phQ/9DLhhNfL9/q+HqiBqhs8PgQ+IdSyhv4\nPr+sPzAds3JphSil7s4/7iHM7rbjgFVKqfZa67NlHBcMzAG+A2TtTeF2GjaE+fPhuuvMvi5//aur\nWyRE2Xw8fYhqGEVUw6hS66Rnp3M87XiJ4WT90fUcTT3KufRzdsc0DmhcZjhpVr8ZPp4lbJ8tap3K\nBo8ZQCjwLmD9f0oG8Het9bRKnG8c8L7Wei6AUuoR4FZgLCbMlOY9YD6QBwytxHWFcDqLxYypmTgR\n+vc3++YIUZP5e/vbNtkrzcWsi6WGkzWH13A09SjJGcl2x4QHhtuFk8j6kTQLakaz+s1oGtSUZkHN\nZMxJLVCp4KHNHNznlFKTgY5AOvC71jqzoufK7zXpAUwtfH6l1HdAqX9EK6XGAK0xU3onVPS6QlSn\nv/3NrGYaFwe//gr167u6RUI4V6BPYKnL0ltdyLpQ6m2d1QdXczz1OCmZKXbHBPsG06x+M5oFFYQR\n63trQAkPDJfZOm6ssj0eAGitLwCXHYdxGY0AT4ovwX4a6FDSAUqpdpigcq3WOk/mqQt35+Vlbrl0\n7w6PPWZWOBWirqvnU6/M6cNQ0HNyIu0Ex1OPczztuO15b9Je28JrhTf281SeRNSLuGxAqe8r/wJw\nhSoFD1dQSnlgbq+8pLXeby12YZOEKJeoKPjXv8xA00GDTO+HEKJs5ek5ydN5JF5MtIWSE2kn7ALK\nT4d/4kTaiWLjTur51LMLJCUFlIh6EXh51Li/Kt1apVcudVgDzK2WS8BwrfWyQuWzgWCt9bAi9YOB\n80AOBYHDI/91DjCgyK651uNigfi+ffsSHBxs99mIESMYMWKEo76SEGUaOdKsIvvrryaMCCGqR3p2\nerFQUtL7rNws2zEKRePAxjSp14QmQU1oWq8pTYKa2N4Xfq4tM3cWLFjAggUL7MpSUlJYs2YNOGDl\nUpcHDwCl1AZgo9b6yfz3CjgCvKW1nlGkrsKMKynsMeB6YDhwqKRl22XJdOEuUlLMqqbh4WbTPi/5\nx5QQbkNrzdlLZ22B5ETaCU6mnTTPF06aR9pJTl04ZbfhH0CIf4h9ICn0umlQQWCpiQNkXb5kuhO8\nAcxWSsVTMJ02AJgNoJSaBjTVWt+XP7D1t8IHK6XOABla613V2mohKiE42Iz3sFjg5ZfNQwjhHpQy\nPRyNAxvTLaJbqfXydB7n0s9xMq0gjBQOJwfOH2Dd0XWcTDtJeo79v4WDfILsektK60UJ9g2ulXvt\nuEXw0FovUko1Al7GrMfxKzBQa52YXyUCaO6q9gnhaL17w0svmSm2AwcW7KMjhKgZPJQHjQIa0Sig\nEV3Cu5RaT2tNamZq8XBiDSwXTrLl5BZOXjhJamaq3bH+Xv62IBJRL4LwwHAi6kWY1/X+v707j5Kr\nrPM//v5kIQvZyEKaJQwEAohCJI3KElkGhDPiyKqh0QPKvmtGR4mgDCCSMAOREEDA37CotIDoD9Cf\n4rCIIiKYBoYlLEoiISRNAkkgm4Tk+/vjqaIrne5Od7r73uqqz+uce6rq1l2+fU939afuc+/zjP5g\n3uhBo+nfp393/8hdpiyaWrLgphYrN2vXwsSJsHRput6jX2U0D5vZJlrx3goWLl/IguULPmjiKYaT\nxuWNLFy+kMYVjby54k3Wxbr11h3Wf9j6waSFkFIzqIZRA0fRt3ffDtdWiU0tZlWnd2+46SaYMAG+\n9z24+OK8KzKzPG2+2ebsOHxHdhy+Y5vLrV23lsUrF9O4IoWR4tS4vJGFK9Lz5958joXLF/LWqrc2\nWH/kwJEbhpMWgsqIASO6pT8UBw+zHH3kI/DNb6bu1D//+TR6sJlZW3r36s3oQamJZY/Re7S57Htr\n32PRikVN4aQkrDSuaGTeO/N48o0naVzeuEFnbb3Vm1Gbj6JmUA0DFnXdQIAOHmY5u+ACuOuuNIrt\nH/4AvXrlXZGZVYrNem+W+iUZss1Gl121ZhWNKxo/aNYpDSovLnmxy2py8DDLWf/+cOONcMABaTTb\ns87KuyIzq0YD+g5g+2Hbs/2w7Td4r2GrBmqn1HbJfvzdyqwM7L8/nHoqnH8+vP563tWYmXUfBw+z\nMnHFFbD55nDOOVAlN5uZWRVy8DArE8OGwTXXwD33wM9/nnc1Zmbdw8HDrIwccwx89rPprMfSpXlX\nY2bW9Rw8zMqIBNdeCytWpNtszcwqjYOHWZnZdtvUr8eNN0IaDNLMrHI4eJiVoTPPTOO5nHYarF6d\ndzVmZl3HwcOsDPXqlbpTf/XV1J26mVmlcPAwK1Mf/nDq12PqVHj++byrMTPrGg4eZmXsW9+CsWNT\n52Lr1m18eTOzcufgYVbG+vdPTS5/+hNcf33e1Zi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "learning_rates = [0.01, 0.001, 0.0001]\n", + "models = {}\n", + "for i in learning_rates:\n", + " print (\"learning rate is: \" + str(i))\n", + " models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n", + " print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n", + "\n", + "for i in learning_rates:\n", + " plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n", + "\n", + "plt.ylabel('cost')\n", + "plt.xlabel('iterations')\n", + "\n", + "legend = plt.legend(loc='upper center', shadow=True)\n", + "frame = legend.get_frame()\n", + "frame.set_facecolor('0.90')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**: \n", + "- Different learning rates give different costs and thus different predictions results.\n", + "- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). \n", + "- A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.\n", + "- In deep learning, we usually recommend that you: \n", + " - Choose the learning rate that better minimizes the cost function.\n", + " - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 7 - Test with your own image (optional/ungraded exercise) ##\n", + "\n", + "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n", + " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", + " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", + " 3. Change your image's name in the following code\n", + " 4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y = 0.0, your algorithm predicts a \"non-cat\" picture.\n" + ] + }, + { + "data": { + "image/png": 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pziFocZcALNeX/YM/vEafcIyBDUVFeLnugHD2k61ksLnYiUxKQmsd08TX719J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cYhu7o15aE4jTiNY8j9/VfbiGDu1f0PdXgweuek3Wf8rWJFx6J8UaUaOrwQOhSWbR+MDDM0Mmqc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wwG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\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V4T9uRuaT1gsHo87nV1kpj5hGzpH9pFFKQZiXWVWOonCrYD2HuwOnZdX8OpWaM2vKuqYT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TwrE8rZJlxu3/VkC0Fvv7t5yUq4Xq947+AS2W636UDQOcPQpxjqbkZJ0odVQqYVgiSeE8Bm\nvVnCrP4OsnM8Hl8w2kM/stlt2O92XK9XZEwK+6Yuubm5Y1psdF9++SVy2dG64FmtVvzw9geiX6h5\nIV3USimKssAYx7XrePXqNU8fHl/Ck4oy4Xmdc+z3t7x79xZrLdsFe316fma72ZDX5cK0uEsXUAgv\nv4N3abe7bjYMY8emaehjeLGgaa1fYGBlWb5YdFXUCCVf/p8gJQK9jO8d50uX0OVao0hK96IqOdze\nMi320t12j1KKfpxwLuKJlFWNm8ZEh2yqxdLWE0LiS0SfRr9p1B8o8jJZ+/oBGWGcJrRKRamPnkwI\nog9kSmODR/lIkSvyPIGwUpJo4PHxkTzPkDKnzAvmyS4WS5Pe8xEmaykytSRnanz0rNZrmlUquB6f\njqR8BsVoEtCrKAqc9yjnsd6jlX5R/4e/16UoBCJPpNZxGKiqRMb0y0oEIRK+Wgr8Yv/MsixphxZa\naoyRuiyTwHARAVZ5ErOGGCmyFBZmjWXsey6ntKaLUibHy1KMOOdodms+PI0ctjtccKzX63SRCkGM\ngirTiWzqks3Tec96tcJ5zzhPVGVFodKEpGu7NEWK8cWOnGcJBV7X9b/DVwDS6skr8jIVc69ub/Ah\npFXLcqknfoanrMsXPcOwaId0nhF9SIWiXNaceYHzhuAdlykVG8M0Ja1MXqeCOHq+/fpveXh4+Dtx\npND4EHCQ3DIpipUQ7ZKKGmiamrKMWOOxPrFMgvdIpYkirYusSxkoxhvatmW1WqG1ZtusaIqSS9+R\nKc3pdAIhWNcNz93XIDTfvPkaITeoXDDPI5vVFuMngg8YN+NJdFipt+gqJ0SDzDVVqdluS2KomcxI\nUAobDIFH5HyPt23SO8wlH9TIYbMhXg7MhaU2X1A3B5T25NKwy/6EqH/JX//qX3J/9xlKPHO4vSVY\nS60dx1EgfERquFkXdDNc5pmmyrFDcqWYqDhfLEMbkXmGGSzGRMp1TZM5JJZVKSmbDD9abncVnfEI\nBnSI5CpSbXL+9bcGfa3Q1cxmF2HWuKBwm5EQCmrjKLYV1WNPNxnyjUJlkXY09FYzeM/DvqTxkaAk\n1+NArhV5laZdypdcTyfE5sogNeOkmaNh7ifW24pKOxrtcQqGORKqnMn9dkUD/A4WDuM0cVDJH+38\nBMA8Wba7VQrfKbK0Z80Vj4/PZGXGMAxUZck4Dux2KSCrXq2YZ4NA8ptvvqeoqgSTalacz0dsSOuM\n7WbDfr/n+fmEUpJ5ntjvbhjHkWFO+30Rk7/6eDyjtCbPM4Z+XARkAW8tMcsQSFDQdtck2Bw6yrKk\nyFLBE5fDRUlJU695fjpTVhmz6Rj7Aa2SQFBnEh+SiElkmtv9A33XoZQi1zr93NttOswhwa2kgBCp\n6pKh61BCIJXiiy9+gplnhIJhmCjrlNk+DAOX05lXr17j5oHTKe1nN5sNQqTD2YfAuw8f6PqOn/30\npxRZmjo0TUPbp9/NmIm8KFFZ5M27twil2a1TNshsJ+4/fYUIaTe+2axeBHmPj89kmaKqUkBTXVZc\nr8fUxQCjsRRoNruk7TCz4f7ujtPlnCYtSiOVwEcSn2ARaA5DgmbVdZ0U8M5R1TWZUhjnuHt4hQiR\nVZMmLyEEhmGkyDTOpAtwaDv2u83LBTaOI84bRBSYOQF92vaZZvl+YwzGJhV+WZYMcwLWlFWNdwnZ\nHFyaZmQqI8tyAosCfvk9yrJgtV7jARECIToQgaquYQx459B5KmSnOdn63j4/Y0NEK4mdZ7phQOcp\n+EogyRbhrRaKvEgd9jBNC3o6da3/fzFevogyYwiJzUCyBVprEVIiSIWeRFBkGcMwobOMrIpIUyAn\nUHmWaItZRqFTiFmCFnnKvGAKEZELZhvp56e0NiCw3e/S+3kZ1yfaZ4HQmma7RQo49wmUpqXm3A7o\n5ectigKV6ZQuqiTNekVVlhiXYsfXTYMLqRh9Oh6p6pphngneszZzWruVJcUy3QlWJseDD0lIOs6Y\n6FMaa0g5ER+Fi2rR5nykTgqRouu1lC9TwjKvGGeDmSdCTCsPABkj1hgus3mBwG1Wa4aufwmQiy4i\nssRCcTEgDWnNtiR7fgxYUyoCERVLYnDLJFamiHohYPIJwltKxnHkcr2yXa+pi5wZwHtsCBxub5in\nkf7pSAwVRTUwjQqpXALl2SvGjMiqIBegMoWZRvarDdumQKiADLDKS0Zx5NKOIDSIO46XFqGPrPXn\n9OZC9DXVqkTnB/xUMkvN5vaZjDWy8kRxBAZOl1/TF4+sDwWf5Sua9YnvjpLbOKAyeN9P1JVGl5rN\nMNOZmfOzZ7VR2G5m1pJVk9FeDT5Kmizy3WQplteHbKBRerHfOrIativBtlHM/UhRVQyDx02Wvo08\nrHJCXhPEyEYZBhnYrwveX6HUDr2RjINFbSSvlGKYJWGyiI3k1greO8vQO7SVGCUZq5KjmWkqib9M\nrLeeUzeRuRWxGilzxwaJt4FxHomZxqkc4TVa9tjBsys+A77/re7Z37nCoapLLu2ZVbOjKAu8PbNa\nV1zbK0VZ012uNKuaGAOzG/n55z9hGkfOpwvOe47HI1kUvH37hrvDTfI1Z5JC1PRjyzh0rJo1c9eR\nSY23DuM9wVmmEQ6HW4ybmeeZu/t7rJnorxc2my273SoBaGJKmqybkvXhFpX3uDmglUhJhIv/GoAY\nMfOIIBCjTPRBmYiSZV1RlCW1FkRnqLSmdxPTbJF5xul8oilKnuMjr29f8eH5idtPPiFfgE1SykWI\nN6MKDUoxjRPrpiYvNE224vHpPWXd4CZP9I79/sCbN29QStKsGh6ffqDv+zTmFYLJe6KPZHmGjYb1\nOkX4fvfddyglFn+/RWqBj5a3j0/kOhUlzhj+5E/+mH/xz/45n77+jMP2hsfnR5TSrDYNGMPleqZZ\nrdnu9gSfpj/GzjhvacqGuCjui7wiChbrZpNWGsv4X4jkgfcEqoXuqFWOVoKBIU2VRMnt7T3t9cyX\nlfivAAAgAElEQVR2sybPcqq6TlAeraiyjBA9bdcipeT5dGG32eKdJwTD09N7QC7iTfAugkjd+vV6\nReeaGBNzYZrHJf8hMCxR3VMY2YZdym3wnnwR1QI4k4KgIim2MsYkjhNaIZf1UlmWlFXFr775hk8+\nfY2znu7S0raXFHW55FJIqQm5XKBfFUEqtk2Ni8kGqaJIGVZSMs8zuZQvuR3OOYoyT6wUndI+1WL/\nizHiTUhIYe9BSYILidtgZiJgfPq+jwj2/XrDJZtSYqWzyc6ZK7TSCyVRMU4GBNgYWO1q/qMf/4f8\nxZ//OZfTGWvm9DMLhbUOEVLcuM40xkw4Ajf7A3Y2uODSakHJdNAXObvVmkvXgfP4cab1yUJ8eU4F\nqS5KNIl30LUt+fJessHzenvAxoCJaZqkVdKhzLN5KcSSWDgRW9v2ghaKm5tbJmuYrKHQGYfDgegi\n82yZveP29pZ5HKl1yjKRJNEkPhJcTMWZTOcCIhDxyFyn54YAoXDKEtwMJrE5lJD4YNF5SRYySp1R\nZjlaBsgzrHc4t5ApY0J9Bx8gEyiWQiemNVY3dLQRtpst+92e9nqkHSfoBz788AOb7Zan01tuD5/w\npB4ZrjOZXhOixQ+STES8zIgqMPnIcJ6ocs3dzYYYM6KK7NaveDr9OWhLVgZwgklcUU0HriSTv4ef\nTqgsIs0O4khZBozfAd+RZ4Fs94cUjMSgyLMN/+Yvf+BH24abT9Y4d2Kyju4auKse+IsffsOP73Ie\n7nJGn/I+zCh4O3qureCw0fz62bDfC/brig/dQCE0s0+F5X4nyCqoVQLs0QpQjjg7RgfrQ0b1ONC5\nEbFVtEFzHWb6Z0+7gVoWlCLSZyO3eUk/eWwxsSoEfSc5hkBm4XCX089wHS23ZYFw4MeCvLQMThJt\nga4nVIyMXhF1xCFZyYxRwdHMbKTn9qAwVzhef7uiAX4HC4cQIlWWMQ4dKttQr1NiYF3XtG3/EhGd\nwqYUb968JfoUqNNUBbNNVrS7mzuqIoGBHI56WyNNOjC9C3TzyCoTHJ/OSCnIc40xI49P79nvt9zd\n3dC2F1bNmma1QRcZdoqUZYESkl6O5HnO6Xh8gfyEILDWo7Wkb1sAbm9vEVImEdrkWTWLp3m9ToyH\nD+9S1ycV3Tix3x84XY5sFupbqTO8izwenznc3NC1afJQNTV2NuRZxma75fs3v0FnOVVVUq8/Tg0U\n63VN2w8IJHVd8f7xHVIm3v1xGjDGsdls2GwPSYOhHajkKMnLAq/SQZO67AtjPyGVTI4ErdgsUB0p\nCvruA1999UuKoqKqC6w17Pc7VqsN33z7NZ++/oy7V5/y/PxIP7RkOgfSmmK3XtEOM94nBfrHIuGj\nI+HvO1o+agHyPPn0ITKM7YL6lYu1MjKMA1leImXG/uaWYehpyjKRMMeeqUvj4u7aEmXSEEQfEsGQ\nmLQyWpNlirJsqKtEexQCoo8EFzDeoITCkYoCay15oRmHCRcsImQIkeyLziWUslqyCfRySUPS9tRF\niZkmyrphu09AqrwqX4iCHxkAWZZTVkWyWdYVeule3TiSZYrRzpRViQoCYsR5n4qGPMc4R4gpjroo\nijSt8csKQ+ukK8iyZMksNEIK9BKw5JeO/SPq+iNy2tiUq1CvMoRavi4zrLdED2QSa5N2AiJVXZF7\nz/ly5q8fH3l4/ZpAWg/EmHb6SqVUyJW1GDOx3a7ppoHDbs88z0SR1ppSq4UoGbm0LZvdNvFJTic+\nv72j63qkzogebg83nE/PuLAEcfnAdp2EgjYmuE/XdUn7EpPDYbNe0/c9WirslCZS9ze37HYb5iFZ\nXf3kyaRaLLUDxllCSFTZGKBtB0IQXNuWm9sbLu2VTAgyLTBmpqqbF0cMgAsQlzhvFh3WR8ZDjMkd\nY4xJWS/KIKoKb1K6pxARFcAvYtWPfI+/z4WYlwTT9FlJz/tyubBdrfnr735g+3CXVj2rLd/+8l/w\nv/5f/z1/9p//t1y6NXf3FUM7pALIeGwuUhCYELx+eM15mKirht4MyDHHmJzT+cwPb5745KcNYipR\npcPYhnX9C4R3ZFLy4f1vKFFY/2vWTUaebRAioyzuCO7K0F/5ML/jsL3D+5nPP91SCcW7ruc8RW4C\n3DeKeXrPn/6k5jIPdHPD9TISbdr8X8YiUV+j4cvPC2YbOHVzWt05ybqE/S4io2B6CnwXTuR1QS9g\nuFo0Gh0irrc8B9jd7tDZzOmY6JSvv8j5zbOnGydMFGgkpop46Smziuejw2hLWR4YhyPH3uCsRglN\nd60pcjiOI2WVczl7yirQdo6H+z3STECgNzOzqjFdR+ElWWl5/51kc7fHmeff+p79nbNjfv7lF/gQ\n2KzXyaq0HFTWpJjaIsuwLqXEpQR7UncaA/M8oLRmmg3TNNN2HTc3N8mLPfWEEGnqNQD92LPd7Rmn\nYRlZR169erXs3x3dtcMHz5IcRBRJRBlDwNkUg9y2iQ55uVxSIbP4wIN3FEXOZrNl6CeUTiNHa8zi\nl0/jVWLEmNQt11XD6XTmcu1YrRs2q5TxIISgLBqU0hhj0x5RJKX66XTCuSTyrKsVu/2Ow+GGeTJs\ntls+/+xzjs9PiMUCdjmfKMqC2aQRtDGG/c0NQqZDTSrJ9XLCeUdZ5hR5xtt379Mu3XusNayaFcEn\ntfc8TeR5li5sFPvDIQnJYuR6vSCkwBrDNI1sN3ukyuj6IRUdIqnciyKnqWuu7ZW6SaAhneUIIRMK\nPCSKZCJUXtE6o1vWNqu6SRwBa1PhFmPq3EijXOssd7d31KsmWROXoCIzTyBE2uUCeZaYBEoqxmFI\nxEvvyfMiCeiy9H8a61ittsQI/TKpUCqxQiZj0mrDO2aTLsnNdr2IAdOz/xi1/dGOlyBQ6TK4XK6c\nTycEydNvnU12wLpGS5VonkqCiKy3C+ugqTlfL+RZThCgPropivxFyxNDIIiUgjkb83Lpf9THzFNK\nofzIhMiyLFn4SJwAZ1MBF0Lak3+cpikh0Ysbxi+wNB8jqW5IepzRJNaCUDJ9BpRMyG1rMdZTrxra\n9pqEkyEwT2l1UJbFy0QhL5KVsixLfv7zn9O1HfM8M80T93d35EXqnJumQWdpP6+15vb+jtPzEWOS\nO2ucE4sFKTjc3PHrb7/l4fUr+q5fSLQ1T89HZutwLrE9PopjjTFEISiKgnlKPBixFBVxITaGhZ2S\nCuyW4D1aKVbNmmEcadYrfEiprgLBOPT8k3/yv/H7f/ALvPMvmTAfi7lL24JK60ZjLXZ5nlIl8SMx\nIhBJg+L9i1Ay+ADL83PBv4DbEi5dvaDJPzo34oIft/PML//qlzw9PnN/s+Xt+yfWK8X/8j/9dwR5\nxFrB4fYOHzxSFIsg2zMYk4K8vOOwvaXWilxKqkyxXe3wQnDYwTh9ha4c++0dZoabmx+jtaDKS2KY\nacpAll1pqopVc0vrvuJQHjiNf8FsforcRupcokSJkhnBB/Liwjg4bjCwUhBgcJ55kpzmnMfjQFCa\nYVWivcXMnkpBUJGn6GkvgWoHmzyQF55+Fkytx3jBhxAJIqJ7mEREVwIdA3UOKvdINDc3HccPPcOU\nIWNOVAOFFagC6iJAnjM/ByQFUz+hsobz2TB3a4pyREWN7wPRBG4OFXKY2dYPdKZFhgykwHhHtQk4\nOfNkoYgF8zyQhkuS0xzxNlIKz9V43vwV8A+R43D76oGiLKhWFdE6ZjOx2+5S5Rw88zTz7v17IhIh\nIpvNOiGmZdoZl2VJnpfJ1rlqGKeB9nrB2ES+s87g/cgwJja/lhnRe4QqOT6fubnZsVnXeC9S9Kyb\niTFwc7hPAjRnQQi6y5nVeoPzKdr7cjyjtExizcMOBInBby2CJH4bxpHVek1ZVC/kQK0ku92Wb3/z\nLc1qzc9/9jOE1C+WwqfnE3d39xRVQdf3RO+p6hXHU/r6T774CcY4fvyjLzHWslqtKLOcvm/5/vvv\nKcqcrusBQbNq8N5xOp3J8oIiL3i4v09BLlpjjU02SDPzdHwmeM9mswVgvV4Dkffv3/LZZ5+S5xnB\np3F4cIG2bzm2z2w3WyKRh1ev6fqOaey5v79nngz9NDAOA0VRoJVGeJPisJ0nK5LobLvf0ncD1iY3\nyMcQKr3EdE/DxGF/k4onAkVRobPU1Yhlb5/AOJKHh1eUZUHEs15tKIuKvMgZpoF3Hz4Ql9XBYX/A\nzAZv3WIPFPRjj9IZbdfyfHyirCr6yeAilE0JMdGQPhYKCfwjcN5S1RXBe/ppTBHXC9Dp47QkaTwe\n8SHQdd1LNx9EsjzGTCVmgFYpTIo0cc4yze3dLT6mgKyUnZLIgz65/TmfTpjZEKPAhxRZHomM45jW\nE/ZjTHQqKggxCRlDYDQzeHAuMSu0krjgkSJdTvPyvMUS3hRjhAV3bGNA6YwoJMYtTIZoyGVGVpXE\n4Cnz7EXEKaQiLFOlvuvouo7d7sCHD+/xwaMzTV4kYFRRFnz66Sd89+vvOOx2SKXY7Xcppj7TKam1\nKBLfYCnULpcLeb6wQKxhGAf++I/+mL/++m9xNhWskM6MP/i930P6wNPzie1+TzdcX+Kv58VJIpTG\nLqAtv+R9aKXS71XmzNa92Cc3VYP1KVVzs13zfHymzDR1XtCezwQfmOeZ1w+vOJ3PdMOYLmSVnB1+\nnrm5u0EoxbCIw7M8S4yHZdInSemc56HFz8nSnISUEnL172RffHSJSClTYeHD8rOHhD9fph2/+vU3\nnMeOTx9eEzy8+/6f81df/zNMd087f8sf/eF/ibMTPmjmMKPzgIopnTW4mS9+/AVaycRxEZBnFT46\nmjyg8r/B2B33D/cEcULHkXl4S9e+o6kr3GRQeuawO3A6PRHMH6JXltPFsV09UGURESucGygqS4yW\nwkT6yXJzWFNmB2YvePfDSCcE+IC3gc4Hch/oVbpoQxV4daMpXaBeQ7XKMCZwcSnordIRmSt+ePZs\nBPRZYJ6hzASVyvBe8NQFug4ubcA62Nx5Yul520ZerUtMWTLNESVAZp7ZWILPaBrHOHt617OuJDLP\nWW+3SDUjneNSC759e+YgoaoAHVB5TTdPbBw0rMHMabLdQamhEAW1qnHR03eRt38T4R8ix6GsGrab\nPWPfE4Kjqiq+//57ttttEuFpz6effpoS57xhmgYECiEVu90BLZeUzFWNtQkQsztouiU7om1bnI3c\n36bEyo8iLGtG6lXBPBtOR0OIhtzndNeRh9evOJ2f0+W5pN1VRU5vJooso0EhditiUImGOCWb3tW3\nHPZ7xil1LneHG0JMawtdFNjoidbw/vTMp598ymF/wzCNiCipVzX92PFH/94fcu4vhNbycHeT4pBl\nQVXmDMOAsyNFWTKZmZvdjvfv3lKvU7bC7e0teZ7WM0M3MZqJcrVGSsFoRjbNhh/evcEbS16VjP1A\nWDIOfvqTLzkejxzbE34yQODYXri52fN0esJ4xzRMTOOiO1CCz+8+582bN9jZcj1f+dGPPiFsGt6+\nfctnn/2Ibrig64o8T2AplVfImCBNRRFwXvD8dCbGlHKpJC/BTZkumGbDzd2BIGLqDIko4xEoghPE\nRSF/OOzSfjiIhHNWOln+SAe7zgu2zfolZfCbb3+dgnqEZFU36bAWKulmnGNVb2iqBmevbOrUEfZt\nT1kWIEUS3ZFCxjIl0UjqTQox2213WGMYx4HZmEQKVSUgiVpiZgcuiTpX283igkgI7VzpxHoQUBYF\nx+Mz7cWzXjc8Xy4gRcKNW4twAqELhMyIJO0LgIsQjUtJsnh8TETLfnGoZFnBOM3Lui5nmEfyvMRY\n/3cTCOeWKclycckkBkyXUyroihgTYlkKrJSEKKnyFZOZ0HNOmSuCd0zDiLcfHRIC5wVeKXRRIhSs\nNusXt1FTN6xWDV999RVSwqeff8bj4yPzPNNIAS7yeHrPerNhGCaqJfBMaoWQGg8onb52e3vHv/nL\nf8tuvUFJgSFNdu52e8ZrR9vPFE2DUGBtQOuY1hMsWRHWIFSGDzHhtwFV5AztjPeS/XrDsbtyvl5Y\nr9eoTNO3E2Z2ib0hNO+fH3n96oG3b98idYa3M3GZEjRVjbcOiyeKwPnS4qyjrHN8FExjKrKNs+hc\nsVttGKaJvCxQKufUXtM5pi0VFVpI+mnEh8Dfzz6SWhECGJuQ3il/NAms//Tf/8eMveDxh6/ZbDL+\nn3/2P0DQrHczp+PEN3/zT/nkR3+Kj8/gKqYwUxZriDMPzR1z36EU6Cyyrg48PV9pTc8qmyhlxeb2\njvbyazbNj3j68Gtu9w1FAVgP+xug5DJ+zd2rP8XHM+dLS1XfkJWOQMO1H6iyGWFKymzNdnvGlwXv\njxe6eWSaLapYMYwdD/uKIDXmMmKLCqwl5jMbCZfZ0waJmCNy9kQTuV9HZil5fBe4PQi+/Lymv0S0\nHQkZfPg+crix7PYlWxk5S08sFfbJ080CP0Z+fNjy7vHC0MFup3n3IfDwqaLrPfc/cvzqq8CnO0W+\nC8y9R7mBsJoZlSCyRpxbboVC556rDWzqgsx0bFeatnPcrjUqaqKbCBquMlL3ksmN9KRpFPx2LIff\nuYnDJ59/hs4yjucL6/VqwQjnyRNt/JJsqWivHdM0I4SiWa1S9yQE5+sFlWVEkb7P+4CZ0rjy4y70\nY96DVKlDVCrRJXfbRDdUOnV5eZ6T5cWCQJ6ZFtuZmQ2X65Xb7YG27RitS6FOCEJMncP1mj7IiTsv\nqaoSIeWyvuiZ5+lFmX6zv+X1q9dkKme93lAUJW3XUZYF0zgjg2Cz3tA0a4YhcfKrZsXtzS377Q3H\n03Mi7YVAU9e8f3zEzGncejofE1Y3y5Fa0rYd1eKskEKyWa+IS6hYU9cE5NJ1S/K8oCoK6sXrv1mt\nOZ4vyUFgI2VeIhfKoYhxYR9MfPmTL/HeLmmg6fUuy4TXjiF1tGVZUZYZSkm22x3TNLHdb7hcTtzc\n3KKUWvQLvOgchEidVK41uZJIqbi2V5r1iixT1HVJ17XkWca6WRNFSGNvZ5kW0BFE+nFIBSZwOh6x\n1pI4sqkz/2jxvLu/x7nE5ljdHnj34T1l1SRaZdsu4+w5RTYHwXq9ASSr1QZiwDrHMI5kRXIzCFLA\nWSQitGBVN/zeT3/Kbrvj5rDnk9evIYLW6eLXmWYyU1p/xYjONDrTnNuRycyJhNq1hAU0Buny984w\nzxPOW+Z5Svtu0opAaY2PMFuL84FhGpmnJAbOioRm9j4kQaIzL9hjHwIuLBj0mFwFiU7pUnctJQKJ\ntQZnHd7ZFA4lZHJdZDlKKqRMxXXKsIhkuV7yWFKSrRDiJXDs/v4+WTqzjB9/+jlfffVXrJpVel2n\nkT/7s/+U7374jkgiibqQVj+egAmWLz7/EX3bI6XkcDgwTRPWpbCtTbNGy1QoaqkoygrvLMY7+q7D\nOsvt7R3Pz8+M04RcNCbhxZqanDvX65UQA4XOWNUNq9UaFjiYlIqyqhj6HplnbPd72rZDKIXO80TW\njAEpVcLPS4nKkqZmXmyaOivSRNQHbm9uEl5dZomuGUnTHmMpqpLL5bLEzifr70fhdBQRYw1SSUT8\nO7z1R83EC8+m7yi1pu9aKh34V//yLzFxYJ4MRbbiN98+84t/9I+ZjKTILdZIKu3JcwlB4UWJcY7J\nesqiYgwBbzQ/uvuMovpX4GeCUxCv7NYlZhpZla/wGLqpo6hrRuc5t4K2hWLV87Ot5s2Hv6UUDXnI\niGJC6zsu5wtvPzxy6lKo4Th7ZBkxGMqqwERPN1hQkigMu1pQFjl5WSch++ARSpLpwHZb4Z3j7dUi\nM0FF4IcnT5Q1Qnp2ZYbaanQGHZr+KlB1gFGjsgKXWzZz4ne4LENva7yNaGmxJrA6QDc1vN4EhuDR\nVmBkwTApCl0TZsMqc2wzg85L5qJCS8G1HVDUvG8NN5S8v/RonWOioJGCT2oNB0Vgoqo10Wq++8rB\nP8RVxf0nrwkRslxTFiXDkAJ78jxPWNhxSHGwmaapVyip8cEmIRoSL1hUxQ5nHWGJA+76gdV6RZFn\n1E35Mm34++O8j4dUspxZrm3L/cMrZuOwNv37uq4XEJJK9EJjkrCtrBJmOUakSnanj2Np52acD1jr\nscYsUKS0Q850zk9/8jParmOaDWa2TPPAdrcjxnS5yyjQec4w9sQYef3Jp0mJ7zxDP1DVFdZMtH3H\npW2p6+bv2AZA17VkeZZG6iGglEAKKJua49MRM07s726Zx4nH52fWmzXzNGONpe/al33v9XJBZUnM\nc3O44XQ5Y8zMzeGG8+mcxula8fx8ZJqnxAEQKgU1xcBsbLpUvF+cBJairBLMJkqMdVRlnWKZQ1Ls\n55nCO4tcDsKPPH9FSnasqirhdoVgmkZ2+z2b9RopFDpLwU1Pj4+MxiQ+hQ8JnywkbdsyTVMKllLp\nIhRSLvkMYQHpBPa7HcE6pmHm04fX4CNuATBBci1YG5YCsVqyHVJAk1jCorRSrFerhCWXgtvDgRj8\nAo/y6XKSkmkaubu/T0AsY5ASpExiSiGTUr4oK6QSNFWNlhKt0/tMiqQvGYbEEJFSJvCR1i9aCr0g\nrud5JixhVs5ZhnFknBL3YZ6TXkNrBUK86EbE4rbIsiyFMgFSJtR2jDHlLkj5gkYWEiKC5S+URf7i\n2NAqXarzNCfKqjWcT0dePbzi8enxBZ704f17DvsDHz48EoFXr18nu3VV89VffcXt3Q1E+OM/+kco\nJVP2h1RIIXh+fOLmcEgrs3lO2puuJdMZOtOUqwRsSgFYkfP5jM40VVWy2+95enxMRW5VczoeWa0a\nvLPEAHGJN79cLkCyLhMjcslN0VnGNCYWyOl8ZphGnEn6EyEV3hvaa4tSWXoOESAuqZ2pCVivNxzP\nF5qyXjDwO97+8C7ZuquS948f8C6wblKseVmXRCLGOCLJGSWVwjqb3ClKJQx18EiVQFsJ/qWx3qGl\n4sObD/g48Ddf/Y98/8OvsHNG5BE3l+RlpKkrymqF8BVZETm3I7u7HUhPzAXeAFKS68Dz+W1C0uee\n58evuX1YcbdtEHFGaYXMBP3Y4kODykvO/Qc261dUVeR6+Y778o6rkzSlxtieiOVyDVynb3DxwmAE\ns7CUZQZEhg7qrMSFCZFl7OscEcBpxew8vk/8m7xUyOCp6hLiknU0RNYqstaa3uZke8tWe9ZK0QtH\nNmYcZ0tuJWXhkWFLBvRioG+hN5pMw1pFbkTA5oGp0xSFwNuSDIPjFdMwc98onLYIGSl1xF4tXuSE\nrceNFpVb/oOf/Gf80S/+C/76+79gpT0PP61Z1TNZWfD+0ZD5jPfB4PskiK2rwPOT4d3fAv8QVxXe\nB25v9vTDdTnMpoSsnRMYSSgoi/JF9R9CGsE6M6GrPE0TjEOQ2AZSSyaTIoCt8wRnOJ56yiIBV/pF\nFyGlxcwBN8/oLBUSP//Zz/n6m29p1tuEl42RYUwH+zwN9G6k7a98cveAcYHZLI6DOV0azto0VShy\nlC6wPrC/2dN1AzLT3N3eUqiKyyVZTc0wYI0lhhQYFHxECpC5xgRL09RE7+j7id1ukzp6a2m7nkxr\nhumaIoNteq3quuRymbm/vUt7zmVsK4gvSYmjHbnf3/B8fsYMI7vdhqoq+M37d3z5k5/w4cOI8Y7j\n8cgnr15hrKUoCt68/R6ZF+zKRE4c5gmZZ2A9RVEmzv31yutXrxKwSSnyrETmKZ0xyzRVUzONhqpc\n8/u/9yOkyvnbv/klMKAVZHmJVoE81wiZsW5qsrpJNEydkS2H4A9v39LUNXm5Ynu4ScKtCOM8MfYD\nq9WK07UjIlhnJXY2nPvzi+BPkiiIxtoU1b0kRX68wJumYZgMMtN8OD2z2ezRRYmZLwny5AJaSjab\nFW2XlP3TIuj7gy++QAJD31OUJeOUCuEiy9ltNjyfT+RFWnn4GBBKcr526GU6Uxbl8h4nOXqU4nQ6\ngnNMIaKEYrWuObfXF12IFhpn08hSFZooE53QTjPD9cpq1aCITGOPyEoU4kVgd7lcWK02Sy5G6rLJ\nwnIBuQV6FF54BXjwi93Te8PgTBJxLtHezkypiG0q+j6yXdYx0QfMPLFZr5nmmVWzYfuzJFz+6U/X\nHM8nnDM06xU2evZ397in93z76295uLun7zpubm7Zb/ZMeuRv/vKX3H/2wOV05vf/4PeRSvPmu++x\nJqHmi7LgZn/D9rBn6HuC82RSU2jNSmWEJR0zz5ND5d27D0gRl8lXeg/JxaIZg0REh3OGPNdoLRnm\nNB2LMaIyTZnlfPLwisfHR+qiZHYzuZZkSjPYATsbyjzH+bjYnJfkUuMIQUMM/PDD97T9yPsf3oKS\nfP3rbwkmddFpggWbuqHMFVlZ0vU9zlr6fuTT15/gvKduGvIs49ReKUNqmFzwL06lECNaQF4UtN2J\nUpVUq4w//6df001XtFrj3YZyZRnGNb/8f/9v/pM/+2/wcuR0lWRlxPkBOUdykWN9z93NLQ83Napo\nmceKX/ziM/72rwdK8TO64Xu6dsbImqIp6SfL7c2a6AWrqmKe3yHNwCcHiKEnVz2z/im2/wveXy7s\nV4Jod8QiEviBefb0xjOPgoeH/5jn6/+BETAbwdWOzEOkWklqIRFVQBhPP0HrJXcxcj1DUQbKStFO\ngeMk2SDof5C8uTqiCLgssq4NuY/4IjDNgY04IZqC0OXowbJ9HXg+B2JT0geLbgtQjnZw7EuB0wqt\n37HONK3KWJcRosFaT7NXzEIwvYv86pvAqy8r/vf3/zM3rwuqWvF0DZTfWY5DjmFgXVcEP3NfKdpx\noFIRxS1KXADLb/Pnd27i8MmXX2CMQ0WWEXSBNTNFpuj7iXq1wQfButkyTyOb7RpnHUSJCYGmSSFY\nSmSpIx+mFIZUlnRtn5TLuWYc5rTPLQq67kpV1Dg3Y+zMtR0QBGZjyPMCJVUaYw8jWqex4tjPZP8f\neW8Sa9uS52d9sSJi9bs7+zS3e33my6yqrKzOJctgU5InFo0bhIxHxmOYMEJCMGbKABgwQfZ/KeUA\nACAASURBVIgpkiUDEpatAheScRVVriybdGW+bF537z392e3qV6yIYBD7XZmJVWJG1ZWe3uA8nXfO\nPfvsFfH//37fp8LtZCKE3rSM8JzgQ84FaJOSgZwnQjsjljEqilkv1wFuE0mapkFrRd+2YQceJ2HM\nPHYkcUKahADSfDYHEaETFR5AMqJpG8ZhZPu0YblcvrvpHasaZz1aJwgZ8fi0YTQDi7KgHyeEjMEE\nZfU4Gbq25Wy1AiepjqElMo4Tkxnf1TF9JCjKjHYwlOWcsRtomjqsB5KYOIkxbkTgEV6cRrCacpZj\n7URRlIzDyKIscd5ijKfM8qCdjmMiC+vzJdWpjpplGV1rQGi6rscME9W+YRp7PI6ubzHjSJykREnK\narWg71sSpYM6ehgAeHx4oMhziizl4fGGw3GP8yPeB3CQMQMqUgz9gIo1SknyJGCnhyGAsSKpEFEI\n25VlyjQZ3DcWSSGIZLBT6hPwaLFasihnbLZb9sc9RRF22OMp5Cu1pj4J16TUrM+WQbnuwg1rGk24\nGZ8CfjqJGY1BJwnjOKLimMViTlpkDKNBidBcMGZEnOqZ6pSi9zIovb0IQcZD2zJYixQRm/sbkjQ+\ntSgkk5nQWUpeFgjjgnJa6/D9W4+IQp4olRohA5QI77HTdGpCeIwJxlQhwoPQO0dVN8RxEg4YzjNO\nE2030HYD1nryNIYoCrbUvqdrG+pjD0rS9kGd3TUNQkmcFHRDw2gtL54943A8kJUFt9c35POSu5tb\nMqX5lV/7NX704x+zWMzRWvPhi5e8//IVP/rxj8nilCQNWu2qbZAyoe2adxeNYQjTl/p0yGjbNlAS\njeHT737K5mGLiiTH/Z7JjDx/7wWP94/vRFP2BA5bn9YLz1+8JE1jsjRFRgofRTgRhcNZ2+ERIfyM\nIDn9XSRZFiyeSuEjz3IVwoVlUbCcz8K/VzM2uwNmNCA8cZaSxjGbao/Sksh7Ep2ipebh4Y7iNAVr\nuo6uDZmmOElojhXXb9/imHj7+jN+8Ef/nK6vWGQrqvEI/XNU1KHihPPzM2R8SX+4pR8s0o0kaYbS\nMyKpKLKY89WC17cVIwIV/ZxSH3ncvkUma9phTprB1AvOn3+PeihQsmfyn1OqGdN4i1ZXWK9Bpnz+\ns695arZcra5o+q/5+mbDU7vHtxPOJVTjQOsivnxd4UXL5SLjetuTKcl6lqBFylAbxFJgHkVglbSO\n0QuS2CETxW5nOG4Bb7HOMTpBKjyLXLI4gySPkMbRKU8uQRZw2Fuct+goIrGC1aViNiqq+xErPEJN\nLC9m+HgAlbA5TAxuZOEnnl0lNBNcLgru7g2ffPsTdtWOcuaYzzzxLOawmxjaMYDPdpbizPJsPsMN\nNVZbTkgMOi+JlvBw0/P0BfBncVXx0ScfUcznVP1Ikeb0Q6j7HPZ7siwjScLof7d7Ii9Crz6ATgL5\nbhh7rAkSn3EK1a6qqkjiBK0DiOZYHcnynLwoaOpvKpVHVCzRsWI2nwWKIy7wEDhhhbs6TB3alsVy\nTlVVrFZnJ5KeJ0lTdofQpFBRqBOWZXkCuQiyNFQG57N5+H/pYOSzNjQH2rYlKwq0UiFFfRqpS6lI\nk4L1+vzdgSSJY5qq5unxkTQLiuBxMphpwtoQfItEGN3e3d+fcgIhoHY4Vu/MkZGKOFQVL1+8YGg7\nJje+qx9OU2ASFEVOVe2pqoq2aYl1yn67xU4TOgkPzslONG3LLA2wpuGbB7qUlGVOdlo7AQz9gJCK\ncrbgWB24OL9gPl/gvMV5WJ2dM1mIVUySpKE+KgJ7YLla4L0jSfS7G5qZJlbLFW4aQ8hs8jw9bRnG\nMNpbn62JVcLt7Q3D0FNXLUIqpIpROkGrmDRJ2Ww27A8HmqYJoTFnEVHwKjRdzawsiONwCE2TGGst\nZ2drutFQzgu8EOgkYXV2hneO51fPQm20KELV8pQLWK5CBTbW8YluOLLZPJKmGVEUkcUJkQioZDzB\nJirCrTSNE5aLJbOyeGdv3B8OxFrj8SyXSzKtwQfstJQhL+FOVkVxwq6XsxnWTOyPNXleYKeJRCti\nGcKQ1kwUeU5E4CV4Ag9C+DCmF87/v2qjznuGKfAchj5Ak/ph5FC1NF3L0PcM40CS51R1Q9cFe2PX\nDQzDSNu2dHVNrBRD16JExIcfvIdzE8JDWS5I45S+H/HeYa3j5dXzMD06HHjabrBmQmvN+++9z+Pj\nI2Pf8/z5cyIETd0gIsHnX3zOen1OmqUs5ovQhhHB6HnY7ynnMw7Hiqo60nUdaZryrU8+4VA3OGC9\nPufN6zc4GwLQfT8wKxc0dcW8yHHWv9PJe+fom5blbM5u+8TQD3ACrG33O3Ce6ljRjz1xoun7DqxF\nnw5YeZ6HaUxesFqdhSaM9wgtSbIUlSjSPGO5XKG1JFYaaybUSS3fdX0wXxpLlmXoNKUfRxKtQ2vn\nlHEwxhArzWG3J/ZLfvt3/nuq/RPCzjH2lmHyeJ6YhgRUy9u3Dzx79SnWjRgTJm0Cwfn6GVkiWa4W\nLFYr7rYdUgi+80pSV29Iz64QXuNkTlGekxdLRtvS1DsuV4o4GhAW7Jhh5JGD7+keX2PLjnr8iqfN\nG7bdSJrkrCPJIdVc1y1Sxsh+4j//D/8H/uAP/0cmIorIslwU1HXP02akVw7z4OFlhDlaXl6teWhr\n6jHCkNNOhvk8IVfBtbJeJOwOlny2Yhom0sjQRBG+1ox5RN87VAS5npP6AbTjsPE0QlMWEab0VF4y\nmB7dpuyqCTEY3n9PIbVjV0/0k+Ui19xuB6rbPdtxYpGkHKsJMVh0HPwtT58bfvU3L9g0DXE0UQ2O\nmJRyMSGspNYp86YhlQVf/rGBP4urir7vsCLCR5LHp3vWZxfsdjuSOA0hvWFkGMcggpk8fT/QixGp\nFGYcKOclWmuIIvbHDUkSfqmOx2AuTLIYqWJkIol1UP92XYdSmqIoOR73zGYLrNJ0Q08/9PRtx9lZ\nCGBlaY5S+t2B5XA4BE691rTtyOX5ZVBPu4Afts7Rtg1pmlGWJcZYsiIPwKnpFPZMw4Em8BImFDFE\nPnwdJtyKkyTh7u6efmiIZMTDwwN925FnOUkS07Ydk7Unb8cDSmm2223Y+04GKTK0jOnHMFmYrEPJ\niM32kfX5Off396fOvkSrNBDUViuOxyN1XVHXNUrFRKc6mpSSSAraoWfo+hDaUoqmaZFSIkXAE8c6\nZhwmZKIZhtOBSscIoYiV5sXzV+gkwXtBPwSIjTOW6+trlvM5zoW+eZpmeA91U1PkaZDXjBNxmlIm\nCWM/sFzNub+/Zxwm2rpheR4QxlVVcX93hxAgheLVy5ckecnj4z3Xb6/D1OIkX4rjkA3QSUyaZZgx\nVNl+/c/9Bj/90WdcXV7hvaduQgizrmvSLMa0A7/83V/k5uaaZ+fnbA6Hd1kCpSR5kZMXJWYY6YcO\nYwxuCr4OD7SDYXh84vxszdN2h1KSumnfidtAsNvuyF5ktG1L0zQM48jLVy+BwK1ISDkcj3gbMjmr\n1QohJdNkmeyEMxYbh/rlN+uj86vLMFE7tSJUooJaWynqrg31ZqWYfLhtF3FK72xgMigFJsiOhiFU\nOUNS3yJlindwfn52yvkY+r7n/uHuRE+N6IaO7XbHe++9R3OsmC3mfHn9NR9//DGvX7+m7RosnmPd\nhFXLMNJ3DXkZcjA//ewn5HnB5bNnwWppLBGS46EiiTPePNwBUJYl2bzkYbsBezp4pzHbB8NisSBL\nUqJTZVoJxcX6nNVq9U4U9/i4Idb6XT5jsVoyzzP6buDi4oK27UgzHfwbXUeS5iR5aDY4a9kfj2R5\nyPt89frLd6CoqmlOErLoVOsMcDKdBKLl3d0N8/mcuj4yUwv6sSPRMbM8XJjatiOPA/ROKcnQDmA9\nPva8ePECYwxvbq85W66AsI5QUtENA95aBmMwkwkXH+fJ04yhfmBoKxAuyM3sCxZFwmhfM4zhECno\nuLiI2UdrMvaIyfPi5Uu00jhraOsK18/xg8HikHpDUWhuHrd88uo5eXRgNV9xd/sFSZrz6mLNoa6Z\nz895eHyDkPeMrUT3r9ngeft6g0kipLtkkR24vqmpnylcbXmuQTSgM8l//Xf/Nr1ecrs/8r33Bbc3\nJhBK54qvd5LzbGDh4GYLP2r25EUgYbup5WKhGZsBPwraNqL3A9/9ds7D/RNTAZlbcb3dsyDU65Uv\nSYuO49TgZ55ESBI556ap8EJy+GJCLqGcXRAnGz6cgT9GuNZhB4H1EZdzzaPXLKOc17rlIlLsmpFl\n6nGTwNWGq/ci5MeCye45PIJTJYk8MD+PuNtIJqFZzx3SF9Q24k/650/dxOHixVUIB3nP1cWacTLk\nedjJtl1HqlMildC2A1JFpHFCnCTM5zNmi1kA7IwTxk5cPXsOTtB1NVmaU5Qp0zShdUw5z9Ey3ODq\npsZM/h2IZpom+qHncDyyXKw57qswzvUeiE6kyIJICmZFzmq5oO1M0Gp3DXmWMhrzjvQnlWK9Og+3\nzbMz0jRjso4szdjtDvRdG3rsiwWJjqnrljRLyPIMrTRmmohkxNP2iTSL6YbxXVVORIKuazkc9qED\nPk3EsWaaLKvliqY5GSqj0KjIiwytYrquZ7VaMg0DXd+T5BlFWaIiSd8b4jhFiAgpY+qmQgjIkoL5\nYsnucDjd9nu6cUT4ICQLMBrHcnEWfrmigO/NspyinHF+fk5ZBH2vE6eA5Dfd9/mS5fqMLE15ergj\nUYLJjQghyfKc1WrF5foqQJV0TJLlWOdIypzD4cD6bIUQks3mibo+cn6xpixLjiew0jiMeOfY7XYY\nM3Fze31CdgsiwiYpPSmYlZLIOFgIX756xdD3PDxuSXTCi+fPwsFQKP78b/4my9WC7dMD68srHh4f\naPvAtU+SNNyAT6TTkGMI4ccs1sznM+7uHmjant3uSD+GyuM0hje7fhwYzUg3jDgBm+2Gb3/r23Rd\nh8fztHni2fNnYaSdpNhpYrffEycxHsFiuQomTefoTUA5D8NInmdE+ECWVBFJlqKVZBwnkjT7hqkW\nLJiAOe35x3HEmLBGGNwU2BPW4oSgNyPeWqQPeYcgAwuHwVmZMpxgbNNkMEPHNBkOhy1NU5GmMXd3\nd5ipDxmb+YI3X79mtZiTnoKFfd9hTICW/eqv/TK/93/9HiKK+Gt/7a/x9LgBD1dXl2w3W4o0Z7vZ\n8PS05Vgd+KXv/gI//ewn/MK3P+Xt/Q113YK3xLOcyTuarguki3EiL0vM5DhWB9que0dcNKPBjj1d\n01A1NZvdhsidsNxuousbJh/cN0rrUzslZnJTsJdOI+b0oH728uXJHRGh4/id5MycjLd129C24f3A\n+1MTRirqrmGyFo9n8BPN0BNnKc0w0NcNQkY4FyqywePzGAK1KlAxlY/QUmLGMUxSgbbrcCLi8vKK\nvmqIfcT/9A/+KzaPI6bv8apBTBFW7ZCkJHHMLC1pm0AovJh9SCQ9STEnmV8wuJjBKVSc8ur5S37y\n8zdo5fnOJ5+gPRSFZ9d8TeRfsjv+BK1hXx+ZlxrTH7jZPNGaikP9wOFwy2fVkbiTRInDji1ybDBY\nDjXMtWcx0+hUYzPHvleYOiLJRj6cWbZbSVpEmN7y0WVEPhMMXiJrgZ7DB8+XdMYSpZBlEd0mYr1S\n9G6it56rT0ruHprT+57g9b7j+x+dszcdiyRmynue3jouVyvah56hg+IsRfuWyAjKueSsyEmmPdJ4\nelGy2XiO7cSs0GwOhvsbS2t7BmF4cXZFpVuWoyBSUB88IhPcHCbSpeD+a0eaw+zc4IucMoX0LCWa\nBh52A1oa6slx+8cO/iyuKj781rd5/uIlLgo0vflsxjD2tKYjEhEqkeg4NCFUJEnTGO8sZjTsj4H9\noLQiT3OUEvR9gxkmvAwIWdMbjJnI84zXb95QNe2JzhcxjiFjYC1ksUIIhY5j1us1sQ61wPDxUDfr\nh56qbmi7niROaeqaqqoDTGkaUVoHjsRiTV6W6NOYeXusWSwXtHWDd5Z9XXG2OntHl4wTTZ7PgshG\n+FMaPAhwjvsDIoowQ49nom4rpNIkaUaSJPTjiFIaY0baLgRI+2FAqZTFck7d16RFThRJbm5vyBcl\n3nmkUCgdsz8eGdqe+axkNB3Hw4GsyNCxoj4e8M7TtT3HQ8XVs2cIEZ1qsRHH/Z5IayY7MDnDMIxc\nrM9Du8VPTJMF4Wi6hjhOmZUznPe8fPmSfuiJiOhNj05i+q7nYn3FMIWwz9CNDKZn7HuSOKbvW+Ik\nJVUJuU6xCq7fvGG/27FarYiU4vMvv6A+Bgtg24SfUaCBcro9Ovwp2JcuZvydv/O3ebi/ZxhG/vq/\n9Veoq4YXL55TlBkff/Ix9w/3IS+TZQxtS991XN9ckyQpsdJ85zvfpR/6MA3oWuIk5uJ8TVXXeOeJ\nXBByeQTWhpaJUpI0zTFTh1QBN2xdMFF2bUeeZQF/LEMWZugHpFQnMJdg6HqGoQvrMSnJ0pR5WRIJ\nSde1lPMZcaRQUfhYkWYkcUyRF+H1mSS0bY/HY6bhZKINNss0TXFTEDfFSRamPxFIEWRszln6usP0\noTVkvQtwpTjGOkdVVXz2kx/z9voNjw+3bDb3HKoDvRlwUqA8IARSS9I4J4lDY2TC8/XbG75+/RUC\nwWw2I01CTmh1tuZuu2GoWtIs4/LiAhXB7d0t9w87Pv3Ot0M4sci4v7/l9esvWSznPH/+gn/+wx8y\nm5eBf6FTLs4vuLm5wU2O8/NzJue43z7Q9Q3Hw4Hdfkfd1Oy2G6q2CRpkrUhVOLBnOuH13W3IdIxT\nqHe704HYehKtaJvwwHeTeZd7OFueo7Tm7u6WJE0ZpjCFaseeejTYcQgHtckghUf4AKpyEdgx1Fyn\n0bDbbIjw+ChUZ5UMv4vdNJKomAnP2WJJdWyYODVZdDCvJklK1TUUWcbd19f4fiBTCb//e/873WZE\nRvfIacHgNc3+SH7mGWuBHyGKRtq+5dUHn4Z1S7fnww+/T1kk6CxGSag7w367ZdSWD86/wpgbIvGI\ntO8hso7+WDFPPJOa+Ppxy+ubN+D35PENrTGY6BHjB5R0NMOMZLFCypxe1CySGB2XpDpjt+u4OU64\nzuKcodpYxARp6snSCa8ETwe4+Wri4kJxOIwc9p6RDjfzdDuHiyyjVryYayI9Ur5IGbcdlU1Zl5rm\n0VGeJdw/HEmSCBt5MAUyGymVQLoCvRgYHkdconl8bXj+bM6+P7IfIU4i7BiRx5JqmjBtjBcTVzm0\nDSzOIgYa9ltNlkZYSloxkDrPVMDsAFJrNsrxfCmxacTz9N/j+voHzOcFWbnA6564KvniRwP8WTw4\nXLy4CjS+LpgDx34InnvnOT8/p207xsETxxnWj+g4DrRAJZGRZ5wGhJdE32CdEZjRcqx2jINBKnka\nqTlindC2NbOypBt69Mld37cD2In5aoW3jmHsiIRi6EcioYCISIp3qOm2bcE7psmwWi0xU9i1ZnnB\nfLagLEtm8xnFbIaxNtzym5ZxNBhjTzXRmM1mEw4LMkInCVIlWOe4vX4NwlNVddh9992JJyjC39Ew\nUuQ5Qz+yP2VB4jiQCpuq4tnlM+xkmaylrSskkqZquLw45/5xSxLHuClocSc7sV6tEJE/VfICnOh4\nOPDyxXu8fXuNjhN0HFO3DbFSIXB3OIR1C+GWNDsl8/sueDzyosA5yzAE9HJ8kv7MFwt0HJOmAa1s\nzHi6PVkmGw4NEQI/BTaiINQuJ2vJs5xJCKyZqPZ77u7u+P73v08URTw+Pr3TH5txZDThoTiYkfV6\nhfOeX/7lX+Zp88QnH3/CRx++z+/8o/+Nv/Xv/00uzs953GyomoYkTvjWh5/QtS1ffvU1F1eXFEWO\ncZaua3n/1ctQ5atbbm6vQ5hSK169eMHxeGR9dsZhtwv0Re84NhWz2YzHx0fUKVg52pGL9YrFfMbV\n+pxIwLwsuby4wDsb2h4nt0DbdaHSKcKaaJwMSoTv8RtL5DdIayHA2aCwljKYFe1JZw5gzURyepA4\nwgFKxQnewyyfUWY5aZaQJgkCwmvNurDakAqHYOzHwAlRIcA6TSPdOPCzn/2U29tr9vWWY3vEeMd0\nwi1rpZnlObPFHAQ8f/EeX3/1BjON7A/7wNKIINIqZB/ajqaqiYAvP/8cKzxFHKN0zNj1rC8u+err\n11xdXNB3zQlnPrGvDiRJzPvvfcA/+d3f5eMP36ftGnAwDiNj11OWJfP5nDxN2R+OKJ3ifcCcG2M4\nVgHq5IEiL1jOl/zs85+TpwVYy2y1oMhTjAsE0cgHXH5dVeG1Zw2TnRBRQEd77xnGUEFdngX6ZRQJ\nZmVJmSYoBIfj8aSGd+RpzjQOOOEw1hDJiH4c8XiklrR9d6KDWvqxpztxRca+YxhaTH3E4rHjcKpl\nK0zV0tQVP/3p58zykt3uCVzHZz/+Qz6/+TLUcZ1lEh0ygmIGsXyGHTuSFLwImP/nL95HRp5kVpIV\nc4JnTzD1HWergtv7B5qu55PnW+rdW5azhONQ0Y93GF1Ru2s6F9Hu7xGxwotHbu41R/+GWfICwTmP\nvWAdf4xNt5SyxjrD9mFC5YabuxafeOI+oSQmyzIGZhxrT9UYjmNCLGO0iHj+QcbdXcusFFy9nDNa\nh/cWlWTEynI4TrjYohLJ7ZeWKZK0b0eSImbfOaJO4EtH1AFOo0yLGr7F4+aROu7C68aacCiLLHkR\nkcqAn14VBbtDy+ZpIlt7uk4xTxyva08xL4k7z7azyAaKyJAWPefLhAbLYpZSJAXpec/SC/ox4/66\nRcWf8fLqP+Pm9Vfcv77DJ5b7W8PTl38ycqT4hjn+//c/QohfB/7wl/7cr5Pm+bvwW1kuwi9hFFF3\nNYv5nHF0AU6UqXe9cjOOPH/xDGMnjrsaISI22w2XV1c8Pj5xcb4KSXtjUJEMBwWtT+yBGOsMaZq+\nG8FKKYhkePh6NzH2E1lRnEx5AqIgLupPKN8iy2malqIsQsVPBRreolywXl9QFgWbzYYky1BCsNlu\n6U+EycXZAkmQz0RCkKQZy7Mz6qZht9ugIk/XtExTSOOnpxt5ksTUVUVWpHgnqNuG+Xx+Al0FqY2K\nBNaZd/TFLMtOD++Y3f4JlaZE1jMMA3mREUlFdKLZWQ95VmBHw3p9RpymfPbjf8HTZkeSpfSmJ9cZ\nXT8Qa800WLyYyE8CsjiJSZOENEmRSgXQlvMs5nOUijlUe5bLFevzSxwRaayCHGgcuX+4C9VIIZhM\nqI/1fdCCj+PIYjZnd9hTLlZ0h4qqPRKdZEzTaPDOk+QpxgfE7q/96q/wB3/wB/zWb/0WeZ7T9h0P\nd/f80i/9EkmaEkWW67e3xEmCNSOR1iGPkuaMJ1T4z7/4AhXHfPD+K97e3LBaLE6BQ4dXMfv9Puz4\nXfAUvPfee1SHI94H1HM5m/GzL7/g/VevTp6IMTAWtAyNGhGR5+EgKk58kbZtaduWYh5Il9YGoRJA\nO/RhvWKDElrHgZBpJ4dScWg1TCNJnL4jpEoVfr7frLog3I6HyeC8e5cpiQQMXR+aGO4bdTQcm46p\nD5W+CY+w7p0XoxsH2ram70e6vg2fbzLMZgsGMyI4MR/chPf29ACKMaOhKGYYOxIJQVmU9H3PYIbT\nIThht9kirKUoS9Yvn+G7AU8UJoVTkHQVcUqahWqrsROf/fQzsiRhPl+QFgXt8Ujb1SiVcLG+OHEy\nIsqyZL1YUjUt/TQxjgN1U4Xp32mdEEUhYzIOIziPNxN+mmicoSgKLq+u6LuOWIWMRJbPEFLQD32g\nf0YCN4XDQJZlaKVQUhIJxfX9DdvthjyJqdoW6zxZkZ/yBJ6ua7i7vyfSMUmWMkaQ5xnOucC0iRRJ\notGRRBCaTJmO2e23nOclDoEzgcJ7c3/Pmy++Yr9v+et/8z/g55/938yKmFgm/OiHv82/+Gc/xE57\nlHBMzmB9iYkqSl3S1CmLUuPFgJcHXn74nE8++Mtcvv8pKi4ZmxpkROwFq3XMP/4nf4RIU/7GX5jz\nR//8v2W23LNe/wLbuudnX92wfqmRBrq4ZaUkO/2M3dPXXBYr6hFc9h7n+gVf/PyfUfs7FlHDbB1j\nO0U6Uxz2R1wFpkjQvqfMY9IkZuhrrjewOWiWpWPsLC/fP8PrHYUq6cxE53uiDh4aWM0j9hvLsRW8\nPEvo9550NtBYSZEL9tVEkSuiccLJNdVuh0xKZDmnbd5SziOiZo3lwP7g6LA8W0a00vHtbM0P7zZc\nLSQ2slR1hhSG/dYwLxOqamCpFTKd6KQgm0X4G0eziph3CQ/aIzcGHXs6a/nw8hldvaXqDE4pqtpT\nSk2S9TzuPX/wvwDwG977H/yrnrd/6sKRTkiQGiXjUP0bDUme0Rwb8rwMI1mtGE2P1jHGWMYx3HC/\n/OI1caLxDuJYc35+RttWzGYlu82Wcj5jGDp8pEgyhfdw3DeUpSSLNcfjMSy7ZcQ8zxEySIl0rLm4\nfMbDwwPL1YL7hweSOKau62CHRLA/BtLdOARFtDq9ISVJgk7CQ0UiiLynN0PQYiNIdGBPxFLRnR5Q\ns3LB3c0tXVcjladqJvo2II6lgtuHJy4uzum7ltV8xhSB1gloGVTGUtK2A0kiQKlgcnQOMxoGM5Ik\nQVmdpglJkVHtDzx/9owokiF1Hevg+/ARQjhur98SVzAdLYv5iqJc8PrmLbP5nEwmAaCkFHlS0NtQ\nE03imH4cadqG73/v+xyPR7I0YzKnFL4No3BrbdjFyjBa1UqhTlXBvusZTgKpuuvREeRlybOr57x5\nfc3oRrwJxMoojUmEZBpNMKj6IKqSWvGv/8W/SKpi/uq//VdJ0/RdUPbZs2dsHp/QWUKRpcRpFlDA\ndqJrWtKsDAyAWGNd+Dqvrq4Yup6Ls3OOxyOL+RxrHd5OIZMzDhx2Yerz9PSElpIkTZAq3J4//da3\n3sm1vPUM1iImQSQkTkQc2w7hwBpzmhB4rq+veV99EPI700SZZqRZhtaatu/IkpSq2gatkwAAIABJ\nREFUbRj3w8nv4VEE4mOapERaY0cThFfGodKEOE7DxGLssTZwIqwxDFNoOHjvTmp4cQp5hsaEtRas\nwziLSDTShQnTcAo/juOAcxAnMdM04lTBaB3OCzwOMw7kSRymF8aiVYJwcNhtaCeL8GFa0vcd8/mc\nru+5u7sPEi/rWKzP+PnPf855uaAoZwxdR1bM0EnC+fqSH//4jylmJXXfIaXifH3O5YvnHLuW6zc3\n5FnMixcvsNZx/eYWayaeXz3j2x9/QDd0JDLGOUPTNuhIsX/aME4Tly+uGMZgUowTzc3Dl5RZweWz\nq9MFYqQsZ8RKo+KE1cUlFo+qjqH2uttizECsNI+PD2Q6HOKXsyXPLl8QOYGIPMc6VCTHYSSJY2Ip\nqeuaq6vnCCkZnCcXniRO2B/2SE/IMYxBH12kWXgN1geEVLTtCDIcwL++fstmv2N//wWb7cjv/s7/\nwYcfrYnkhFYrvvjqx7RmSyqXdP2AkwlmGFDZS9pmR7rYYqYUYXPGPuHhZuD73zvj7OID0jwj9p5i\nNSOZ4Pf/6T+knM05PI4cjn+f9SrC24/52eafcl9DsvyAu80dRq9YDgmPq4/Qh5QkVZCcMeze8lT/\nAcvLX+Ty2R1x2/H4FJHIDKKBh6cD3ZCQzQWxAJUmbI+Ox/uGD18WLBcDqYrQmUFdatJ4oBlhs60Y\nnWSMFEkMxkxsG0/nNJfvm5DJem6Z24zGTLST4Xye8JOv4NV5xHW1Rwv4pZcDP3z9lrYBOWq6ac+s\nVCwLie8EjbDEZLw9HFkVnm0zcX+IWc9bzhS4QrPtLelaYaxniBQaRzo5xIeS+kYiigVFd6C5dCQ2\np5Adx/7A62Gi9BmRbLl8teSu3pNUmuNRA+2f6Dn7p+7goCV4O9ANUyCfecdhX5GmOWkcU9U1WkYs\nsoSq78DJELI5bCnLMgCjkDgsZvBondIP/YlLEG49aVJgpiCLefb8nGkcGKeJyYRdp7EjfWMRcuLq\nxTO+/vo14/B4ekPrubi44PHukYuLNV03oJUm0ZKha4iUZLlcoHRClhZkeQHOM03hjduOnn1VETlP\nUmboJGZoB3rfU5ZzIqm4ubulbWt0HEJpnGyhi+WStzfXLIqMtt7TNC3OzQM+O+roxnAgWZQlWaoY\nho4yW5LFc2wSbpyJ0sg04e7+Dh1plsUZZ8UZh8MBqSCJQ5XvsN+CsywXK85XK0Y78nj/SFGUQW8e\nSUw9cuz3wauQJGgdU+8O6CSlG8PNP9aapgk11q5t0HFC03VEUU8RJzgZLA9jUyFdgVGCopzTdV/R\n1y3dMNB2PecXFzRtRZGVPD48slqd0fQVKB/Mk17RmoHWj+hIk6cpcaz56KOPSJMApjKRx/YNT9sn\nfCROds8YsxsoyznWe6qqwhoTsMf9hkgInh5bojSm7nqedluEd4BD64S7hzviOKaYzU5rtIHFYkXd\nhyqf857NZhfWbF1HVVXM53P2+z3OO6SQ2JOeu+8DhlzlOW1VB6jV6cDx+u0N5+cXxFqiszDBMZ1h\nlmSMLhDxEILBGDAe78NqY7SOFHD2VF01HbapglBpmuiHgcmO7yYSbgr+gkgGcqW3YE5hZQhU1lhp\n0kgzjgbrOPE+JoQTOCEQkUBGGjsJFA5rDcJHobgxGfppIi8LEhUIfrOioGoqVvMQnM3ShLOzFZ//\n/Kds6wYnBBeLkt/81b/ADz/7EdWxwvYj35ovObu8omtaxOQY2obv/MJ3ebi7pxAGn2VY5/n9//Mf\nc7ZccLZasN1u+PLLL9FKMy/KU204YTxZBs1pPZbnGc2x4uOPP+Rw2IN1fPvDT3h8eOCHP/gB8/WS\n5XrFVz//HJkl/Bt//i9wrGtWsyWTtTTbLVIp/GQ5tEeyJKFIThmicQqETRPhDlvKWcmHH73PYV8x\nWMfoDaYbTk6LAMt63AX09atXr3BmYpjllHlOUS6I3BTCtGNA4cdSvgPQtVVFXhR44ZnNggr8x8eB\nKFXkM4OfJqZJ8sdf/q9Yo8AKjHWYqSZyE5lPaKovicskIO+PR2KZojV0Y8P6/ILL52uqXY1XgrFu\niWREtZ0wxBz9LT+7/kMWseQtD/hDhMhKZP/IoRcUeodQ36PbfEW2/ohZ9QqXbDE6Zikkzf4nWGV5\nXgrK1LK721MUsEwj1itPlEqOm5h9PZIqeP9ccphCqHNMPYOViMFQ24jn84KfbVqk8igM/RE0sBIp\n4srSNRnCgkgn9kQkuUbEGtvGXF7s8ckZZ3rLNKb80U96splkOZOoecS8mnOIdvhp4nwl6TJH6Vr6\nQWBVjvItH7wE6UqGZEBEhtQKVqmg6z05EVYJaiGYtRHp0iCmW0wKUSextuaXf+Mv89M//Ce4ypNf\nduy2EX01kEaQFo65GP/Ez9k/dQcHKSXlYo7pB4SHaRjR0tNUW0aT4hwgNK3pKIsCgQxj9ixHKUWR\n53gnKOcFx92etm5I84yhrjHekWYhTT0NI7NswWG/J89DwyJPI6pDjZCgY4nD8eXnn/Peqw/YbB4x\nY890Gg8vVkv2xyMi8rRDzbJcMpvNGCdHnObMZgt0nNC2LVp4JjOyWJ2x3e8DstZ6dJzihXhX9YqV\nZrN9ZLs7IIRnuwvEuiSLcQ6++OpLFmcLzOhJ0zSsXJoaoTRxIlktVxgzMcsLvLd8+ul32Dxt8R7K\nWRomGqfb6WI+R0WK7ebhnb55GDsilaKiKPTC44yuD29G88WMNM3phwmpNK4fQk00XjCOI9V+f+Jp\nKJpjhVLqJFHSLFdnOAdlmr/bX+dFzOZhizMDU7Pj7vWX+KFDJhlCKYqiZGsqdJKS6oyH/RMfXj7D\nOBsS74D1LjQu1mu0lhRJziff+pAyK/j7/+Af8vy9V6F5QkTV1Xz+5jWzsiRLEqquY+x7CgHCC65v\nb8mK/HRQ8+xOCfnxhCafujYYLJ1DeMdgJyYXKrrN0NOfBFYIQTWG127XBWpiksTc3N68My1GQiCj\niLycMXpHLCIkIqyZhpHJTeRFMLzePj3go4g8zxB+wvQDpktBB9cAQqAjiVVhYmVtCDMOzYCIFFma\n0tUWY6d3KG0zGSZr6ccR58xpQtUCARAloygAqLQGAgvEThNJHHTKXVMTCQFS0XfB4vmNwdQ7eXJC\n9IymD+N5POPJhxJ5hxktdVORJkn4nS0K1CmL0jYNzk5EUlJ1gfgIEKuEH/zRH5KkKR++fB83GvwE\n3lgW5eydm+Xx7fVJRmfZHnfc1DdEccIkI5r9DhXHzIqS6KT+ns/mIczsXfjHGKIIvHFkccrj4xNt\n3/H24Y6r2Rl5nPDy/fdZLBe4yfLpdz5lezjw9ZvXmGGiqYJVdRsr8jynqaqAt5cwn88D+2O95FhV\nJyS05lDXxEkSLj7jwPawJS9yXr73CmTEZrvl06uLgOoWIf/zky8/x4+G84tLnPCUiwUCQaFj7vY7\n3H5H33YhM/H2DWVekGiNijXf/9d+i1RLUhXTjC1Fcsnr2x9x3FXkpWeoa2IxI3IF1u8QkcZFLYf9\ngqI/Z4oEvY/4jd/8S3zvu7/AsR/J4gCbG/qepJhzqHeUz1+ybhJe7zTzZcvBWLydsTKvuDXXzCLN\nIs4Yoy/Q+ft0jSDWW6rjz2l2FWf5xCQEkohBS1Iczy8jtvuWbJmw2w5Qlgy2oSgNzsFSKeY+oV8p\napOwq47YVpArx003MpPwuPOoMsX3Pc9eZewfeiKZkMQDiXY4u+B4e8ApQZxDs7M8u3zO/bQh94Kb\n7cizM7htLbGyaJHQDnt065i9n1NOE3/8Y8e3niXIuMcZx0oqZBmxfxyIrKXrzlie59zdXDOfafpp\nZLGKeNwJBuOJXcbWg29brl5YWqf53R/8I7JesL6UHI6ePHvFePwal8Hocupd9yd+zv6pOzg0Tbi1\nz8rAVMiyAiEkF5fnNG1PkZVBATy0pKlnf9ig45hoEjxtNjy7usLhubu7YzVfBIkMApnGCBdCT7Oi\npFjOqZqaJMkwk0NKi4olGEdehoaCw9I0NQ/3t5xfrjnsjhRFQV3X9NVIFCnOludsNg9MdmK/P5Lk\nBTpOKRcLdrsd2QlAFckIR+iiG+dZlmUYm2932PmCjz96wfXrr6nqGnO6QUqp2B2OxJ1mMSvQOuh0\nq+MOiow0y5nHGcK5QBdEIaUiS2copXBWnJwcnPIdE09P9yfVtCNbLrG1ZbVa4YFxCATFfgjj5v3j\n9mQ/lGyedmitqdv6lPQPP6/dIUwcIITrsiwowyMpw+FGRhw2DwjneaorLi8vWS6X7B/vUaajyFO6\nzRvkWJFpaIctzXGkPyhKaxGdRJqJZZpz8/ZnJElKmWd88Oqcjz/9LofDkcuLK/phYNc1jF6wN4aP\nf/EXUc6ikwQVa2azGd/73vc4Hg6Mfc9yPqeRkv3+xFvoetq+x3tPlqQMQ9hvAyfboiCPU8ZxZJoM\nw2iYZTnjMGC8I5YKRzjQGWdRUqNOpkbn/Tu+hU5ihq5jEhGurrHeEYtTcLFxoRqqZFgJuPD5Xr16\nSd10jNayXMyxCKyxZElC03Q4NxFF0Tt1trEmrE+Q3D0+IAl4aC9C9VVHCvA0Q48WEZO14AMlUwiB\nUgECZUzIm+RlgfOOYegZ7YTDM1kPNlBF7Ul9bq1lMgbsBM4jTght4YJZ01qH1JK6Dl/nMA7h8BMF\nb8vgJtIiR2iF0IrL1RqlQwPg/vYOqSXaeYa+J0sSjDPB/ZJkTNajM8Xd3R2Pj/dIGbFaLrl+uKUo\nS/reUKRZIDtOlkiCimMiCBjmKXyvUkqq6sBoAnhOaoUfDc9fviKynofNhtE77HbHbDZnd6yw3vP5\nzVuWxYzeT0zeEWtN1oUaNJLAOGm7AGobBsoiVJ+7LjRnqqpCq4SzszMu1mt2xwP76kjb95yVc6SI\niJVCSMnDZsN6vWbqh5PyW6KIODYVj/U9zWhYLufEaUKR55RnS2KVEEtFJKGuWtZFgalahJJ09QOH\n9oB3I8f9RKonXLRjUhGxT4n8GaPxyLhlefWC2SwlL2eYccPf+3v/Hb/y6/8uq2WBiARlUQQw2GCI\nDLjIs07OOTYHOt+Ruphq6sjnc54l59x2f0yaDmS2QIgZiK+J7IJnV0+82RVcFgI1tHzdDOTESDFR\nVQJRQlp6ds2eLIVYxhyHkXvp0MS8vW15PssxT2GFuNnuuHqhkDrFZz1RIklNwqGdUFJguwG9itju\nNA+3Bz76JKafLJOzLC8mdsd7FnlEvIi5uzOkWcIZnmE7ssgntsLx4fsFD9saPS/4lVeOapzYbiNe\nnmsqIUn2AyqX1PcJgzU4d81y7VmWENkZzd7w8LpnvpJAQzqDqIzpW2ijkZWIUUJweLAMeM7mr5Gq\n4OnYYMbqT0qbBv4/HByEEH8J+E+A3wCeA3/De/8/nz6mgP8C+DeBj4ED8NvAf+q9v/2XPkcC/JfA\n3wIS4B8A/5H3/uFf+m9WwH8D/DsECuvfBf5j733zr/r6pBAsT6GzRjQcjke01hyONWVeMjQDTkA5\nm3Os9pRlHqpiPkwrtNah00YU9LtFhjXBIZNmGcdjxSAkjR9ZzmeMbXcyAyq0jCnmBXVbMRlHMU//\nH+7eNFazLb/Petbaa8/7nc9Yc92pb98e3G53e0ic4DiCtmyDhPgSQQiJlA+MiuBLJCRkISERIRkI\nKJGCBEkE+EsMKAgSCxzbseWh22lfu2/37b59762qe2o6wzvveVhr8WG9fdNqOaQZLCGOVFLVObvO\nOaV6z95r/dfv9zz4nuTkeMZ6s2XQA72NiUcZdV7gCcXN1ZIsG6ECycnRKcZw2Pm7nVxRFAhtyCYp\neZ6TxhFaW+I0oygKJuMJo+MjbpZLnr18gRKOILlYLJwCfDAoZajLisXRjLqpuX164pTaBlQUECvp\nMgnGUOQF+3xLUexJkthxCTyF1k6WlGYTh8yuK4rSBcyWmy1JFJNvcgSeG49aMFY4G6B1EJ26rkE4\nguDJyQnWOIOb8gO0EHi+T1nuMYN1qGJP8tlPfZZ6vyaSkvWwY3n5EddPHxHHIUVdoXWPsZCN5+T7\nhqp1O1hvELRWgxmo8oKgrgntwOXTHcrzePLO7/ONs2OwAmM8Aj8ECcJT/Ngf/0mm8zkyU3x08ZRO\nawIr6aWlqSqSOEF0HXVROsNhUTBOMzSWMHYuC9/3ybc7lxnwBMLz8BMfK/iY8V9XFVJJrBQUbekQ\n2WWFlJJWaRpr3YJCGwI/cCIha92o/qBCtxoG6eqXVgqkBd8IhJX0eviY7jeZudeg9EOUcojpfugR\nxiIlLv/Q94iD6bCoanzfuS2scFwGc2jWDKJ3vzfaBSl99wALggDl+dRtSxC6Y5YklFSHf1PkB1jp\nYaxmGIxbRLQ96qCzbtsWrXvatiWKIjoNZVth7IBE8cYn3uT9999nn5e89dab7HYbJztSisloRN87\nx0zXOLx4kPoUTcmuKVFZyjTNUJ6kbWqsBBV4zoqpLdpKyrLk9OgIg6Y7EC0nozEgMMJSlQVpFONJ\nh+I2WrPZ7kgnY9q2ddMG5RGnCUh3VKOtwE9idNkglWR6NMdqQZAEbiqVRLRVxeuvvkaA5PLykvO7\ntxnHifNtHDgux8cnlFWH7luW10uOj49Z7/acnJzQG4M1hrpqOU5i6C3jdIK2gjhKWW+uaWoXDH72\n4jm2twSBTzKJCNIYoSW0HZPxmGCSEu4K+r4njgKMcZmRJHQTrHGWucbS0BNGITqG//1//q+oXj5m\n9uAOXTMwdC26O6cpR2RJQ8lTlD+iLTNu5J7FYo5tPLxE8JV3f403f+hfZLkvaDY7xrHPUDfstzl3\n7yeEIkOOptyaFJzevMXOf8qur5lVGx77a8aNj/FratMxzb6BF9/lhz/3Lr/y+w+ZdGu6iSZMQW99\n0mig390iCJ8dGm4wTiy6s+z2PW9kiq9vOgJjiLaKJCm5cwx9WmJTiwkET19WzD1B0NeICPath18Z\nohCunnnEJwF37va0dY+2grqAxSLg/AHsN4abXcutex5504GvOHk4ojAl3dby3suCT5xMyKnx0pBN\nUdM3BjMU+Fg2nUfWK+4EJcvQ53im2BeWm+uOTdNxJidM2oajGcRezJMnNelDiNsOz8KLDzXJsaYK\nYdL4fPjtnsVpyVkE69qjrPX3vQ74vzNxSIHfB/5r4H/8no8lwOeA/xD4GjAD/gvg7wI//F3X/ee4\nxcW/BOyBv4ZbGPyJ77rmF4BT4E8DAfC3gL8B/Nn/s2/OKXNLuqbBE74DCwlB3ZR0/UCn3U19t12T\nZhn7vOQ4jOnqhiCI2G1z+r4nCmOW6x12B8pAkqZs1i85OzshzwtSEVLtdxRNjS99fC0pyhw9aDwp\nWG3WjCb36LXlerllvpjSNa0LhnX9x3mKO3dP2G93TCdHVN3A0dHRYcfaMjQNwlrG0wlFsaeqGuIo\n5dbxMUVRMIoT7t25y3q7QyFoqxYThfzEn/iTVF3Pb/32P0TJADsYzm/fZTSZoIV0xyVhxmq1ou8L\nCiGR3t5NB7yAvMg5Oz+nLErCwIUvh2Eg3+3pTMcimIMZ6Nqeo6Mjtts1TVczOpodCHYxnh8QpzHb\n9YY4ThiGHj9UBJ3bhV88fcpsPkUKgzQt58dHhMrn6fMd6SgBYzDVmg/+4HdZrVYoFTh7n3YvbhkG\ntAN4XkRVltT96kDoXKCtoWwqQs9nOp3BINjvdwxBwPG9VyirkqIouLhau3Bp4BHrjn1ZoAfLy//h\nv0MPmjhJKduak9NTQumDhWSUce/zX6AwmuzkmCLPMZ5ks93SDoaRdXwN72APRUr6tsN27UEe1TMb\nj7haL5GhC6LJwAcJZe2aOlEQOEYBkjIvPoYIGaMRg0Ee1MZKKYzVtJ2rBfaDc54E0qNlAE8yGEfI\nLMotZhgoioLAU47OeWg6YDVgKOsKlGSo24MDoThQG90CZOh7wFL3A76nGLoW4fv0VY8IA4Iwomq6\nQxuidbZLJdFDT1E3FFIihOceSnFMHPi0DDR1Rdu25FXushLCNT5und7i5fvfpO8afvAHPs9+vaLK\n9zy8dxfd9bz6yitOJiUlRV6ijast3lxdEyjF7fQO1bZgPB7TlAXaD+ixSASmHbB+TzgOKYodnpQI\nPJq2xvcVQ+faPfuyoG2dr8YiEMqj7BqCMEJYDb5ks9lw9/4AuPpjnufc7NcMlTNqtn1POs4oVjvm\n8zl+7F5Lp3NHJs0Ox3Jd48Kp+W53MOiG7DY5T58+5c7ZFU8vnvCln/lZChqms2Peffkunu+mdUo5\nA3BZ5qRhhJIBx5MJZVPjB6fstzvqsuJkcYQZNO3Qk2Qpm82G8XwGfkCtOyJtMbYhCjxOjk6wRjMY\nt/GIgpC6rJlGKbubFVop4qqkaVeoyZTN9TVDF9F6hlm8Y6I6qmGPn5wzdAURArv32FQQDDnT9Ig/\n/+f/I6pOMEk8bGdRYUyWThGhBaHp+4bi+Ybbo5hH0T/CdBbRKpKjiOMd7PqKvkg5yWrWN1NGseDv\nX2WYDgKb0rYWv+8wylEr1XxH1EpiX9A3FkYjiq3m5fOKm2vNvVcecPn+EyYLWNke34duq7C15p1d\ny73FBDsy9KucwRecpFC1EJ54bK8k3VXB6QPJro/p+pq5J3je1zx/BL6MuT+LUWnL88Ln8rIjeavH\ni2LuLWrWWvI4r5i3sDix9HckaZdQVCO8tMCvS4qiZHp7RLnM6R/BMIL8ymX7oruQ1pJZ6lM3htlC\nsXnck76WMVxUTO5rxjIgTTsSBfoS4ixmt1Vko4Zh+v0vHP4f1TGFEIbvmjj8E675AvBl4L619pkQ\nYgzcAH/GWvs/Ha75BPBN4EettV8RQnwS+AauFvL24ZovAf8rcMdae/mHfJ3PA1/99Bd/EBVIJuMR\nfWcoqhqBu0nGccSgXbo7DEOMdCNY0w9IIQmjGHEQTpV54c5eDxhhpQ464c6ltTebzceWQjeezEmS\njLZ19bUoDhiNUrb7HZ708XwFxlLsy0M1sOPk5AiDRXk+aRITjyacnJygh56qLP/xue+BB5/vC0aj\nMUZYjo9PmUwmXL24ou9b5vM5L1++BAxShrz56U/zW7/9qwzdwKsPHrLbblC+oqgbhqYiCAKSOKIo\ncibzY7TuqcvKSa2kpKorPOmOfKR0xyR3b9/h+csXjkqnDUmWHqiALjy6LypnShwGhCeJosDlIuKM\n7XJN4Huu/hmHnJ26OpvvwTtf+33qsuLs5BQVhCSZayPs8y1lURElCaPRiDCMuLm5QZsB5Ul8PyKK\nIn7wcz/Eer1xBMzc7cKKumK/2XB8fApYmqZCAJvNhvVm4yickwnPnj8nG7kKbFmWnJ2ckiTOfJrv\n9ozHY6yA1WYNuOMUT7ibdOgHGGN4+fIlk+mCqql5461PMp1OybIxi8WCRxdPmS+OuFov6XuHBa6K\ngmbo8Q70v8EaYnziKMQcHtI2CA4+BgEHUqMeNF2niYOAIA7xA99ZX4VyFV7PI0kypNGYw2TjO0rz\nsqrQCIqiIItC2q4jTVPqriWLYoahQ3oeZePMi3Vdw8F3EIchRVlSNw3K8wiDAO+g2d4UOxbZmG1Z\nHhZ1HkjrArlpjB5czdIPQ6zBYd/7/mOyYn+wX3ZdR9XUIBVNU3K0WBCFEU9fXpIkEaen53RtT9+1\n+L5PliUY4bHdrJlOx/SdC0T3Q0+gPBgG0vGIsizdcUmSkI3GAI74qnySLCWOUqwVdF2DJxRXyxdU\nbUOaZXRNB2ZA46qnbafdz00Us9ltyJIUbTRJnHJy9zarF5fEfkjrmcPCvyUMAvZ5gR8nGDvgI6nK\ngizLKMuc49MTqrpFhQrPSPb7/NCyqPA6OD69zTrPmY7GzOZTfvVXfo0f+sIX+eb73+S1V15lMZu5\nY704pqr3xHGIJwRY+fFRWVEXmGFgl+dYTxBIxXq7+Zg1cfvWbeIg4Gq1ZJykPL+6xPcUyvfZ7XeM\nxmMW8zmr1Yq2bZn4IUOnCeOAX/q7f5uX1+8hrEVonzCIaQeN6fbYXuGpCO1XBGZCU225e/8VbGS4\nM7vF+Suv84nP/zRaSExfMEkXxNMU6p5f+IW/yfnte4wVrPV/ycn4BmEtFxvDfD5F2w2bQnA2CTHO\nxU2kDIUN2d3UvPYgYttZ2o2mERNOZq+zy3+HwkCQjBB5zlULY5lyuayJj0ZEYkeapHRVSZYlfPBB\nxcsePv/aiLgUXJoGGQXMRY8MBHXZUUqL0hF68An9PV4wResOmTQ0O0vizdg0JR2auYkZ7EAvLR8+\nbzhZwLYANRWcqoQokFR1g98OrHJBKn3aqCWYCsKdx7Ue8MqYwHS8KDXtAK+fRyQzweVVjRCCycSn\najtoI/x0YDzOuK4M5ionnIbIfYc/TRh2FWEQsu5q4ijD2IJ2H/Krv9jC/0fqmFPcpH97+PMPHb7u\nP/jOBdba94QQF8CPAV8BfhTYfGfRcHj75cPn+RHcBOMPfSvKhlk4Yb8rieOEOA5pmpYkGTkdrO9s\nhp5SmEOSXAhBHLvjirIsGY8zojihKEqyUcAoy9yNVSh8lbLfbpjPxm5kOFuwLxyWOcsihNBIaR3I\nppbkec6dew+4fPaUJM0IIjcFiZMezzNEYYwKEmexCwKaqkIK66AtCAevso5s6fshURR/3H545513\nuHPnDrtyj96sqfsGozVJKBn6jul0yvXl0kFrpEVIQ5JG2FA5OE1RYgbXlReAUg6Ckh/EXV3b0dYN\nYRojPI9Hjx9htGE8HtN2bnKTJSllJViv1wjhYaRHFIREUUiZ75gmPlWxodpfYkKfREny1ZIPVs+p\nqhqrLdPFjPF0ymq7g11OWBSEUYIVPnGacv/+A4SUXF/ecHxyhu/7HC0WdH1PWZZ845vvIhEkWcrZ\n2Rl939I3LUdHxyjlHyqbbrF4994DZvMjB7qx8MlPfoosS93OSkOWJDx9doExlk4PFFWFlIIkjLm5\nuiYMY0AgJpLL6ys8KTk6PqJrWjzb8eHX38YLQnczD2KEELxvDcW+IM4yvCjwYOBGAAAgAElEQVTk\n5NYZ82yCH0RI6ZFmGZe7NYXpsNLieZJ6s3TcAqUIw5CuH5wLxFNubJ4X+L5P03fEcXqoPkrHYdAa\nrHYP5mFgMPrjUbodDNvd7uMjDIC8dzyCru+o2pZRmtINA36gqJvK5SZwNGl18C3UXY3vB8xmM+p9\nQdPUhGGEHXqKqjkEJnOCg5tlv9+50GVTOpla72qvQki01egD3bLpO0fWrBtOT06dEG4ywRpL4AdO\n4OYrhLUUmy3TbERTlOjDsc7Qtex2BZ4VhInLCKSpE3rtNlsXns4yRqMRfd8xyeZ0g0ZrC54LYE6n\nU+azOS8uX2A6wX63Jx6lWCPZ3VyzWCzI0ozgcMQSeD5D0TjXQwjJZES9daKrNo4oh4a4tuAJeiBO\nE+remVdXN0vibERfu+yS8iR93+HZgT/42ttI9QdY6V5Lnq+wRvAbv/5rWCnYbx00zQ8U40NV/Md/\n/I/RdB2jZOTyI8PAfDp1bA/Po+oaPCGZT2e0dePCg8rjernkarvi+dNnnN27TRYlvPfBt4mzjAFD\nrwdOjo7ZbjbQDvhBiLAl++ISpUfs8oIorhDCQunhJylGWHbXAj9LSdKQ8fScbV4hTYl/9gadFvRd\ngRKS2eIE2RmK/YaEnr7vuFrv6X1FMrUEowFVTbh1+4z3vn7F4lZKGIHQEYtFQtML8jpHtg0nt1Jq\nTyK9BV18w1kIH9W/iy8VfmfYrnLoYHYrZFQNtIlisdhRlgF+23F9FUJdsTiDWx2MyOkWCW/6ipfr\nkNUwENiGfO/Rh4p7vkcfNXSBx81yy8OR4slzQ6wEL/SaB6OAPsl4drklyyTh1Md/FpCFHcE+JAsE\n2JKGjN7zyFTP+bFl6w8sRMbzq4qTTJB6HnFoMXvLJ+/PKfotyw8azsZnhKZmkihM2zFKU3rPUDQD\nAQZ7WbBY+Lx82TBaeFxfVsQB6L4mi0ANHU9f+EjffN8P9T/ShcMhy/BXgF+w1haHd58BnbV2/z2X\nXx0+9p1rrr/7g9ZaLYRYf9c1f+ibtdD1mjCMEUj63p2XWgbK0tEkkySlbXrC0AFvRtkIgcAaTZaN\nXNXScw6Ltu3Y7fbUzZ7Tk1u0bctsNme7XXN0dMygLVXbEqqAoiyZTMYsl0viIKHrG46Pj3j27IJx\nnDAaZxRFSRAoPGkZZemBOwHT+cyx7bWhP2CfVRBgLAzGUncdcZQiPZ80DejqnjCKuLp8ydH5Oc+e\nfuSS5Xrg7OyYr371d1kcz3jzrTe5ePQR00nmOPvGEqUxfugCdMpTzsSHwBhLnMYEYUBVlXjSVflU\n6DPKUsZZRlmU1G1LURSkcYxue9q+4fToiL7tmM8nfPvdd3n04jk/+AOfwfcUk8WMSezx+PEH5J2m\n6Qa63mG3O23p1oJQBSgr0LYni0P0oDk7PsYPA/K84OLiI87PbmOMy5/s9gWjScZYjYmigNl0Rtv0\n5MUeP/A5Oz1ls9kSBRHT2ZyyLDg+OeHunXts91u22w3WCPTQsVk7+qVAsdlsSdMxfd+SZCOyJOXZ\n82dIacmmIyw4oVmc4h15rFYrNpstfpQiVERZ7BG9ZZfvyeIEgWAyHdMPLXrX4hc+H7x4TjJKWW22\nbrfbtmSxa5CMZnOSdER2fMx0usAKyWg8QQrBvtxjBkNV18RxgrYQx5mzkQqomoYkjDFSoFDs9jvA\n+SCcwTFAK8NmV7mJmznUJD0PM7jWBEIcfk6cCbOuG5QVSCFIApdRqOpDg8JYunIPnUYYV2sd2pos\nG2EAoxWdGTCDdc2K3tBRMxhD2ziXhsvgSOf28DwiD/qm4/T4lOvLm4PBMwYr2O1yUC4061nLbHR4\neGMw0mPoWkKlmJ/eYjQe0TQVN0WJ9RWBH9DWDVIdIFfWMhuPaLvObR6UR93UH99EltfXlFVO3wwM\n2pLvK0ZZih/4rDZLJqMpxsacn57y0ZMLgusr/CjCSh9pNcZXBFmCsJpJkhEpRdnUbPIcfzZn0Joi\nz/GDAC2ko5tq7Y5JhoEgSPnL/8HP8Tf/27/F0xdXdPkGjSVOMgLlMPADAyejY9brG4zpODs9ZbVc\nkWYZeVXQ9x3GWNpeEoYhgbUIKakbl8uaTqd0Q8/V9RXT2Yyz2Kerahhca+wTr72BVAFtW5HnOb0f\n0LcdsRRoA7/yD/4XmnyLDiTRqCKpU9r9Bh2NGXaxa3N1N6TROcbbo+2IJPHJu5IgDTi7cw8/HhN6\nirLtEH3JNJ4g+j3zVJHrDY2UHPuK/Ubx8G7HfnnBgweSeNxTFh2+31BUETJU3JoEFJOIoTL4omc3\nPGVxnPL4esNrM6jkhMfvlJy8mrJ6uae5bLEqxIwMN5cxrW+YeJrsGMYTD7MRrBJIao3fwVNZEUSa\n09pwsYLeah4uUqgqdgzMdcytM817l5pRHHCz7kjujtgUOe9fdFR1zNTTZFPNnTuaDy9BxS1TJHaj\nmM80vtdSKIHVGdeXOfmop1ga2tZQl/DKmaaPQq6fb0jISBaatr2BzMOGkjoP0F3Dw5M/znL7ewx9\nxK2HA1eritNzWFeaOIbF2Yj1vma908wmA/FtKK/87/vZ/ke2cDgEJf8Obkrwb/5RfZ3vfVu/fMHm\nUrhamJAYazm7fc7D1x9iLVRFiZKS/W6H1gFpmqK1xZoOP1SYw9my7muUElhrSJIAzx9RVAVt3QGG\nME7ZFzVK9QQqOBwpaKqy5uT4hO1+h/ICimLPZJRgB03fNZiho2vd7sxYQxBEzBenSBkS+ortZo2S\nPkkWUveuHy+NQBo4XiwQwmOdb0i9wKnC/ZCrF8+xgwsxjedHVE3L/fv3kFLy5KPHjCYZdVMTKHf0\nURyOQdrWnTUrD9rGTSiKPGc0nfHJTz5geX3DS2MObouOvN4RCYgtTGZT+rpknIQ8f7nlo+tnxMqn\n208IBJyfnnBzdc14PObq5pqLi6cslysWxwtu37nHKMtIooB4NObm5ob9fu/05/sdWnhUfY1XVfiD\nZnF0SjoaM51MCcOIt99+m1cevoqQkjRLKPfgq4jaakajGdko5fz8lGfPniER+KHPF3/kRynKnJvr\nJdITzCYzkiTl0aMP+MIXv8jTp08py5JsPGez2fD6wzd48eIZQRDx6U99xgmK8pyzs1Oub67QWjOZ\njEFYur4nVCGekijPERuTOP7YWJrnOX7gxGVD72yC1WaHEB5gEZ6kB6wZuLp6gRSK8OUFerBOxHVw\nnLR9TxwmzI4XjCdjsmxMNplhjasIM/ToA4th3w0gBN1gQAx4nqAoS4TnMZtNafqesq4RWBgGtHFN\nD3toN7j0g0D6HmWRO9qosSilyILQ/Tu0IQti1uUGESg6Y8D3yasS5TkfwySZ0rYlSImfxnR1idEd\n6cg5MLb7HePRmLwoieKYct+Tjl2if7E45uXyCqskZV4xnoyxUjLoAWucs6TPnQp+GAaEkES+om1q\n2qZ2rY8wwPeda6MxhjAKGYwhUAqMdBViKdHdgBICXzkEO8Dx0SmPnjxmPp/TNi1V6cKwehjY7TYI\nYVmtl1xcXPDK7Tu03YAMDdIL6KqKdJTSVTV5UbDveqRSCOPcMWkQ4U1meMpzZE0pwVOEfkCWuhDt\nb/zWb/L6/Qecnt7DV5K6qSjLGk8quq5B+j6xH7KYT1C+x3g0puta8suSLI2xWUYQRPieO8rKd+7Y\nIT+EYOMoYMh7JuMxQ9cxCiPayFV9u77D6IFvf+s9XvvkGwSex/PHT7hzes5gNFEQsN08dYh+7aHs\nMYNXUNUJi6OIzgbU/YrzuyP2pcckDUA21JuGOBtxvXrJDx3fR1iNlR7KGrLxxAH2Og9ERig1D+9P\n0XrNrYXPzXONH0jKoEU+V0xPJZ0YI1SF7lKKqmCvNX6g2BYdm1XISdYy9WPKsuZyv8ZXHheXa37g\n9pSnhcXvB4bKcOZ1NNKifcnMKrqi59JX3JYtVyrmXrZAmQ6hAnbLnHv3FhR14YB3kXQyr6Bhtcnw\nvJwCyWUL848mfPSsYDIaEx8P3Jq1iE6RhIovfMrw4qJjmgjKSlCXmuxowbOLJXGac+/8iI+eL5nP\nYZplXNuBdtOzUh6eZxnailBKhj7D9gW90JyewuVSc7X6bS6vPZLU0i1r9p6HX1uiRNCheffLOS8f\nO8T3E21ptUUP7ff9nP0jWTh816LhLvCT3zVtALgEAiHE+HumDqeHj33nmpPv+ZweMP+ua/7Qtwev\nvcpkPMaPQi5vlggG4jBmvyuIkpj42I2Px7MMCURR4NwSQjrZTzeQjTKsVHjKJwgC2q4Haxn6njSN\n2e12jEN1GN2qQz2sZzybsFyu6PqW0WREqEJ8zwcBVlqMFkzGR1RVgycsNgjJsiMWs3PqpmS1vHFn\nrUo5HHOSoIcOD4/T0xPCMKBsGvqm4rrdIg/9+Du37qK1ZrfZMjQ9pw9foSpLoihCGsskG9EGPuvl\nzcfa57ZtsULgH3ae4zijaToCP6YtKr729tsEvsckVgQ+PP3o/YNUy+m0Petohy89yWBB+AGNgdWT\nRyAVw+DOg1/eXLLZ7phNF7z2iTepm5rJZEJRlHzw4WOub66QCE5OTvjSl77Eer0mSVPKqqJtBowV\n+H7E6fltttst4+mcP/ZjP44eBvK25M6d+xS7nOPFESrwP95Jl2XBa6+9RpIkPHr0iND3eP2znyWK\nIr78O7/L7Vt3CGOHVW6ahldfffVjIZPv+zx99pSHD19hv895+fwFnlIUZcXl1TXeoZbWHx5WvvJd\nqM74NE3HdDIlz3MXhPP9Q6bF7WatVSTTGev1iq7rP15cDMMAKJeH8Tzapj/wBAy+r1yAD4FpK64v\nNjwqSqR0xwaDla4iayye55NNpqTjCcLzuf/qq9RVT9d3SGPAaoxUYAxpHLPb74lDH4+AzlQOGW2t\nO/JKYuq2RhvrJm/7PWmWovvBoY/DCC1wu+bONRA8X30st2qaBtM2zEdTeqNp9UDsB0SDQ0oj4Ie/\n8HkuPnrKYjan7lr3OYLAuS88j9PFEVbAyXyBNhYjJXS1q+v5PnEQYvIdSZqCEDRVzdPrpw75rQfi\nNGGUZGRxTBD5TvyklKNQ9j1Ga6wxWG3o+pa2azFGo7Uhryse3LvPxbMn7ihGhfhC8OarrzKeTJlP\nFxij6QvDb/7mb/JTP/NT3L5zhySOmU3HbHY7KvYuP6NdlXX5cokxhtPjEzcF8H0kEhUodOeO3vq2\nwY9jLi4+4nOfeovB1kgJUThGWqeI97wpUimqomIycih7FQSstxvOTs7xlcT3HYJ9GFwLZJyNUNLj\n1sk5RZnT9T0FBWEQ4kkPYS2bfEUUhYxHGVobPv+5z3K92ZCEIadnZ9RNg9ARL2/eJQwsSXSK7gVa\nVww9hMrn6uolk3juMjtKgW4cOjxdUIklnjfnevkUL5ihRI8ZelcHbhpmacrX3/8WZtBYBpSMyeQ9\n1LAiCwa2foYvNvinkpvNQBDsGE8Vy03J2WkC+x1KWhI/ITiuePSBZHIq2dQSUXjY2BIWiptyD5VA\n+pp7R3PyoUQNEl91WN2hW4nNPZ6mkolsyLsXUIPuDdcaFsUGPRiisU+1HugbxW4Mom948iKm3nWc\nzQQLrrn/VkhhdqAUl08Mb7wRQp/T7QPGGQy9YPB6MoBqy+RIIqqQ3XrFLIFQeei24ZWHgpulR9I2\n1LnHnYcR/8KX/io//9//Rcaeu7dcXRkiL2DoLUPcsMsbxr5EdobKWtI2QCGZHfec35FUlWFxErHa\nWDYvOt5dfn+Zx//Xw5HftWh4BfhT1tr19/yd7ycc+SYuHPmF7wpH/nPA3+OfEo787Be/iAqV48ML\nQZIEbLd74mjkMNHGIqRH3bUESjKbTdht9kwmEzcRsAJjDJ7nowdL3zvNdOALEIa8KIjjlPEoYbV0\n0CLlC+LETS6SJKUsK6JAUVea6XxGrzvm4wWD0Rhjqeua85NTNvme09NbBEHIzfIZUluQTsmcZZkj\nVWpD0Q6cnp2itWa5XNLkBZ2E2PPpjOMo+EpRVxWep3jltVdZr1bcuXOHb7/3LTDuphjHIVprjubH\nXF5efux/aOqKxXSGxDkGXj6/oChysIZAurpd2/QYPGfJEwJjNF03oKSHlRLp+XTdQLFfMxqPP8bo\ntm0NQhJHMWVVcXR8DHagqmpOz27Rtg1t0zLKMqwxhEnMxZNHXF5e81M//TPMZlN85fHRk6fce/CQ\n+XzGkydP+NznPovn+TS14yVo06O1a3nYgzTr8vKabJyRRBHr5YrxdMZ2vWK/33Pnzj1ubm7YbFbc\nvXuXsix566232O12PH78mDRNWa5uSJPMURUljMZj0jRju9mwzfc8f/aM+XSG7/tI6VGWxYFf4DTU\nxliqqnKh3GEgDHzyoiCvCk7mC6T0Dtd37HYbBnNgQDQ1QnqYwTUn4B9Xhcu6IvYDFyz1fedGsa2b\nrg2a6fyIb33rXX72Z3+W1WpN2w6EQczV6oa6de0Li0ccB0wmU/w4xlM+da/RFvqupWk7fM9zkq40\noa1rfM/9TKnQodIBZuMJgx7YbXcEfoDWhki5c//pbIrFjcZ9z8cPQ4LIpy4dZKo/oMO7oUdYSLKU\nwVh03xFEEb7nmCNd3ZCMMjzPd6r5tkUIHOp6MCxvlgfAmUEbd/RmraXXBoNzO2TZiL7tXJUZp2o/\nOz6mqBpm4wlhEGCNoGkrDBohoKpq5idHXD57zvnZCbt8T9e0HE1mrqppYZKNMXpgMp2Rjad88MG3\n8ZXi3u3bGATL7Qq6gelizvVqyXKzJhu5wGZwEJShnTcHzx0RJWEEQJAm7Hc7lNWk6ZxtsSGOnWrc\nWqduV54PUmC1wVr7cYMMHD2z7zqkdFp5/xCyjoKQQTsS7a7YE8YRnnBVVOV7jtdhLUZb3n//29y+\ncxslwHSa/S6nqmpiP+GX/re/wW61RNsNgVww1AZrtyTDHJ10zI6m7PdrhBrTmR0JIzxvBkGBED6B\nGviL/85fIYqPqJqacZoxtD1iqHnnG1/j+fMbejPwg5+9Rzz8Q1rxVYzc8txEnJ+MuVwGmOIjzo8j\nNIZWL1iVa/qyYpoYqkLSIMn3PaGSRJOALOqYxhHffFQRZWOicE+vFGfCMkSSP/hAcuc2SAb62hL0\nhq2BwQa8IgKu7cCqMtzNYjZNSdEJmty5i5peo6YZrC0vjM/ReM/QwvHUEC1CVi975olhuYTJyGO7\n0ZzdC7h82TE6V1AJ6rJHBApTCaxR5FXN+TyiqAxJ7PE8r5kFCt0bhsHQC0EUWpSStOURWt8wP4V8\nJ5EkrJYFMrJ4Ex9vM+BPQvbPGmQMQSCJYx+hDZ7RGJWwuZH8zt/fw/cRjvy/vHAQQqTAa7i81O8B\n/x7wq8AaeImrVX4Ox1/47pzC2lrbHz7HX8fVMf8CkOMqm8Za+3EdUwjx93BTh38DV8f8b4CvWGv/\n1X/C9/V54KsP3vwkd+7dpqj3eL6HMh7GQpQ4Q99ut2exWGCtpqwLBD6eUiglSOKEpm0ZZyNubq6Y\nz4/o+4Hj4zn5rmC336C1RgrFbD6m6zTHx+dEUczFk2cYazg+OT7YGyVlVZPvS44Wc6qmQUjphEXD\nwDjOSLIR6STFVz7Xq2sC6bTAXhjSVCVtVRAHEY2UvPLqqzx+9IimbtB1gxeHzMYTTo7OWOdrp6fd\n75lOptx++DrPLp5w69YJ737jG4QqoG5KjO45OTmmryqO5zOqqqLIc/bbLVp3RFGIHizj2Zxit3cL\nEeXIf4PWNE2LNQ6XHMURVdWCHRxnfzQlyxJ2B6FYEAZIIem64XDDMwRBTFGWlGXJp976lHsY6IHR\nKKOuXB323oO7hFHA6dERq+WSUZrhBxH/2V/9eVaraz73mc/y5/6VP8tqteJbT5/xqU+8RWDhZDrj\ncr+jqQvKsuTk9Iw0zWiamidPPqJuOz7/2U/z4aMPGY1Hh/Gtpulbvvb1d/mRH/1Rnj654N6d23hh\nwNX1NUGgKLY5JyfHvP/BB8ymUxaLBav1lv1ui5SSj548IU4ismxK33VOxiQsZd1gjQsnbrYrhrZz\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xQjWr+K3f/N/Z3a9J8hTjwvc/tga82QcTp7CmkRG//Cu/wsHBIdc3V4xmoizKoCMegwTp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\n4hBFURRF0YH9D0xPRwXdbPcUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n", + "my_image = \"my_image.jpg\" # change this to the name of your image file \n", + "## END CODE HERE ##\n", + "\n", + "# We preprocess the image to fit your algorithm.\n", + "fname = \"images/\" + my_image\n", + "image = np.array(ndimage.imread(fname, flatten=False))\n", + "my_image = scipy.misc.imresize(image, size=(num_px, num_px)).reshape((1, num_px * num_px * 3)).T\n", + "my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n", + "\n", + "plt.imshow(image)\n", + "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") + \"\\\" picture.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**What to remember from this assignment:**\n", + "1. Preprocessing the dataset is important.\n", + "2. You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().\n", + "3. Tuning the learning rate (which is an example of a \"hyperparameter\") can make a big difference to the algorithm. You will see more examples of this later in this course!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, if you'd like, we invite you to try different things on this Notebook. Make sure you submit before trying anything. Once you submit, things you can play with include:\n", + " - Play with the learning rate and the number of iterations\n", + " - Try different initialization methods and compare the results\n", + " - Test other preprocessings (center the data, or divide each row by its standard deviation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bibliography:\n", + "- http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/\n", + "- https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "XaIWT", + "launcher_item_id": "zAgPl" + }, + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Planar data classification with one hidden layer.ipynb b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Planar data classification with one hidden layer.ipynb new file mode 100644 index 0000000..a532ff2 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/Planar data classification with one hidden layer.ipynb @@ -0,0 +1,1597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Planar data classification with one hidden layer\n", + "\n", + "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n", + "\n", + "**You will learn how to:**\n", + "- Implement a 2-class classification neural network with a single hidden layer\n", + "- Use units with a non-linear activation function, such as tanh \n", + "- Compute the cross entropy loss \n", + "- Implement forward and backward propagation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Packages ##\n", + "\n", + "Let's first import all the packages that you will need during this assignment.\n", + "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n", + "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n", + "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n", + "- testCases provides some test examples to assess the correctness of your functions\n", + "- planar_utils provide various useful functions used in this assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Package imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from testCases import *\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n", + "\n", + "%matplotlib inline\n", + "\n", + "np.random.seed(1) # set a seed so that the results are consistent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Dataset ##\n", + "\n", + "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X, Y = load_planar_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yc+ZM/Pz8uHQpDjtHX7RacyKjN1FUnFfu+LTMy1xJjOAf/5hc4ZwP1a1t27as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0AAAg\nAElEQVTg7hLG/ui3sbZ0Rq/XUl1bhPk1v4g6TSVRZ1cik8nw8Qjn0OkPkRCAoFv7iVxI2o69tScD\nus9CZXEjCVJ+USKHT33I1KlT2bBhAydOnCAu7ixtgx+gQytjWt5zCVuRy5VotNX06TytgSFg/Nup\n6NlhMjsOvER69imCfCMwN7MiInwa2/fNBoxx/AcPHmTixImAcfdBLpff1kRHDg4O/Otf/7pt9Zkw\n8Vdxx6IJJElSAuFAfSYNYdyG2A+Y9s9M3BXMmjWLN998kysZB9my9wW273+Rb36awZEzy+jYqS2n\nTp1EoWjaZpbL5UyaNJHUrGPUqBv7FFy/plQ278hmprQkJyeH/fv3Y23p3sgZ7TqWKkfMzSzJzTUq\n933++efo9QZ6dHiq3hAASLp6ADOlJb06PlVvCFzH270jAd49EAi6tp9AgHf3+vZkMjmBPj3p2nY8\nVWhp4duPti0f4GLyd5ibWVOnqcRMqWJwr7k8OnQ5jw5dztDe87G39eJozH/ZfWQRldUFaPV1hLUY\njkySUasup0+XGQ0MAQA351a0C3mIzZs3k52dzcKFC5EkiVa/OGpJyzmNvY03ZkpLPF0bZrG8jq21\nO072AeQVJdRfs7Z0xs25NblFPxs1/ktLmTNnDvY2diiVSsyVZgwbNoyLFy82+zcxYeJe5E6GFjoD\nciD/V9fzMfoPmDBxV/DKK69QUFDAxx9/xIRJjzLnxReIjY3l1KmTv+mA9vLLL2Nra8ne46+TnnMG\ng0GHwaAjPecMF5KN59W5BfFNPiuEICf/Aq7CKFxTUZVbH+v/a2rVZajrquvTDO/dsw9Pl/aY/crQ\nyC64iL9nV+TyphUd5TIFSoWqyeyPAEE+vVDIzTFTWlJTW0pW3jmsVM5IkoxBvf6Nu7MxPbQkSbg6\ntWRIr1cwN7emoiIDC3M79HoNLf37k5V3DjenEGysmtYRCPKJQK/Xs2fPHrKzszFXWqH6RRZLjbYG\npVKFJMmB5lfyMpmi0TtTKiyorS1FCMFri15l2fsf0KnKhom04j69Dyd/OkiH9u356quvmq3XhIl7\njbsytHDmzJmNPGbHjh3L2LFj/6Iemfin4+DgwIwZM25apqysDK1Wi5OTU70wT2ZmJrW11VRVV3Pk\nzLJ6mV8hDDg6OBEREcGF89/j7dGxPuzwOqmZxym7pj7XBRfO1BaSmXe2gaPhdRJT9wHU52TQaDUN\ndgSuYzDoUCgsmh2DTq/B0sKh0a7BdeRyM1QWdgiho6q2CBAUl12lVeCgRv0HMFNa0SpgEBcStyO/\ntrZQmdujN2gaiej8EqXCArlMQW1tLfb29tRpE6nTVNUr7VmpnDAYtNRpKikqvYKLY3CjOmrUZRSV\npuLv1e3G+HR15BcnIpcpsLBQQWUtr9MNZ+nGGf4w4csKEc+T459gwIABeHh4NKj36tWr7Ny5k6qq\nKoKDgxk5ciTm5s3LK5sw8VewadMmNm3a1OBaeXn5/1zfnTQGigA98OsUZG5A3s0e/OCDD0wOhCbu\nCoQQbNq0iQ/ee5+Ys0Z/HGtLK7r37MG8efN4+KHRuNUoeE30pBwNV4TxaECP4NvyVPz9/bl06Wf2\nRL1G68BhuDuHUaepIiXzKMlphzBDRmdcCMKOGIo4Hvsp+g6T8fPsgkwmR6utJfHqfi5e3oUjFhw6\ndIjp06fTtWsXvlyzEb1B12Bid7D1JafgIp1CH23ybFyrVVNVU4RWW4tS2djJTaOtprq2BLncDKXC\neF+nV+Ng69PsO3Kw9cGAgeo6Y1bIwpLL2Nt4kZJ5HL1e26TRUlCchN6gIzQ0lJkzZxId/ShJVw/Q\nLsQosBPs14fziTuwUjkTE7+JgT1fQvkLI8dg0HPm4lfIZYoGPhkXknei0dagUlmirq1lAmENDAEA\npSRnomjFbHGcOXPm1O8QVFVVMXnSJL7dsgWlJEclU1Kuq8XZwZFl/13BmDGNnUBNmPiraGqB/AsH\nwj/MHTMGhBBaSZJigUhgJ4Bk/DpFAs0HJ5swcZcghGDOnDksXboUL8kaM2RoMWBWo+PY/oP0378f\nGRKz6IqtZIYtZvhgXf98jUHHt998Q9Tx47y66FV+2L2ufmVvKZnRBw+OkstAfLhMOQogVG/Dsdj/\nEnPBGksLByqq89EZtAzFl1yqUdfWAkaNhBUrVnAxeWe90x1Ay4D+HDjxHqmZUQT5NtSvr1GXUViW\ngt6gIfHqftq2fKDRmBNS92Ew6KmpLcXexgsbK3eqagqorC5o9j1V1hQgQ0KOhKS04Hzyd3RtO56f\nU37i55SfGrWj12s5n7ydoKBg+vXrB4CHuzvnErehkJsR7D+AkIBIrmQcQ6OtpqQig+8PzSckcCCO\ndn5UVheQmLqX0oosWvoPoKa2lMLSKyRfPUBm3lnCwsIIDw9nw7p1dKJpsSY7yZwQ4cC+vXuv9UnP\nyAdGcOLYccaLlvQQ7pgLOblUs6MsjbFjx2JmZsZDDz3UZH1NcenSJT777DOOH4tCr9MR3CqEKVOm\nEBkZeVudGE2YuB3c6WOCpcCX14yC66GFlsCXd7hdEyZuSllZGUuWLOHgwYPIZDKGDh3K3LlzG2wH\n79u3j6VLl9IDN06IfHrjwQj8cZZUaIWBGApYTxJrSeQl0QnZrz7wEXiyo+4qaWlp7Ny1k+zsbOLi\n4hgxYgQPiwBsUHKUXJywoBodWgT34ccoAjitLaBGq8UBb3rhjjVK5shPMrRNGwDatGnDG2+8wbx5\n8ygtTyPIty8qc1tKyjKQy8w4fnYV+cVJBPlGYKa0IrcwnoTLu9FoqrBBybmELegNWloFDMLC3Iba\nugoSU/dyMXkn7s6hpGYdx9zMmpCASGLivyI5/SChQUNQ/Coxj16vJTl1P+G4EIoDa7VJ5Bclcubi\negK8e3I24VvKK3MICYjEUuVIUWkKl658T3lVNpu+2VM/KcadjaND+w7EXNrEucTt2Fi7oq6rQKdT\nIxBU1RQSE78JrukceGONDiXJaQdITjP6KPv5+bNixQqmT5/OpEmTkJCQ3cTfQI6EWl0HwI8//sjB\nw4eYTQfCpBt+Ih6SFVNFKBpJz4uzZvPggw/WHxE1x7lz55g+bRqnTp4EoC1OOGBO9KVUvv32W0Y/\nNJqNmzb+rkydJkz8WdxRY0AI8Y0kSc7AaxiPB84BQ4QQzYt6mzBxh1m+fDkzn38BnTCgQIYMOH36\nNG+98SZfrP2ScePGAbDs44/xk9uSpC+jMy5MpFX95KWUZPTAHQdhzjuc5TxFdPzVKtT62r9X7bXV\nvJeXF15eXkT2H8Dxo3GM1QcBkE4loTjiioptpDCLDjwsBTWoa4dIpdqg4emnn66/NnfuXAICAljy\n5hIOn/7I2C+lGUEt/ElOTiYjM5orGUcBkJCQSxLuri5UV9cgVVVxMek7LibvxExpiUZrzD9gb+PN\nwB4vkpC6h/OJ29AbdCjk5tTUlnLw1FJ6dJiEjZXx5K+qppCT57+koqaQKwoLzhmKkRkkDAjyi5Pq\nHfuuZp8gNet4fb+7du3G0qXrG+QmcHd3Jyc3h3Xr1vHhhx9SWlqKl08QKVdScKtTEi6cEYAzFrTG\ngfMUs5YkXnrpJUaMGIGdnR1hYWH1f5/u3buzZs0aLlJMe5wb/QaqhZZEyvB3NyoEfrH6CwLkdoQZ\nGjuMSpLEcOHLkvQ4jh07Rt++fZv4VRk5deoU/fv1Q6vW4I01z9MOR8mCdFFJgMGWDKrYsW0br7zy\nCu+//36z9Zgw8WdjkiM2cU+xbds2Hhn9MHaYMZZgOuCMHIlEyviay2RRzf6DB+jfvz9O9g60Lrfg\nOHn8h84ESLZN1rlYnMEec56T2jW4fkEU8SEXiImJaXCOt2/fPgYPHkw/PLlMObaYMZsOJFHGB5zD\nBxuG4UsAthSj5hDZnCKfV199lQULFjRqXwhBWloaVVVV+Pj4YGdnx+rVq3n/nXdJvGzUDLCytGLS\n5EksXLiQHTt2MPWZZ9DpDViY26LT1eHqFIydjRdX0o/w8JCPUCos0GirScs+RWV1AfnFSRSXpSGE\nHgdbXyRJRkl5GnK5gokTJ+Dk5MTuH34g6VICY0ULWmDHZcopo450KjhPCYMGDeKDDz74Q0l2oqOj\nuW/YcKqrqmhncMQKJUmKCvJ1VTz11FOsXLmyyZW6Xq/HWmWFg1bOK4RjLd3wWzAIwRoSiCaP/37y\nCVOnTqVzx05YnstjotS6yX7UCT3TOML69ev5v//7vybLCCEIax1KYXI6JaKWt+lJNVrWkshVKuvL\nyTBmhryano63t/fvfhcmTPwWd60C4R/FZAyYuNP4+fqSm5nNG7/yMAeoFToWcAqXVgEkJCTg7OCI\nX5mcOIr4lL6YSfIm6/xCJJBDNfOlzvXXNELPO7Lz2LXxI/bc2UZnxKtWrWLa1GkokNAYdLTDiVEE\nUoeer7lCKhX1ZX29vJm/cEGDXYHfgxCCjIwM6urq8PHxQaVSsWfPHoYOvZGYqV/XFzh57gtaBQ4m\n0KcXOw68hK9HZ3p3mtIgX0NxWRp7opbgYOeNnbUHAoFSoSIxdS/R0dFIkkSPHj2YShhdpV/7DMNG\nkcxJy1Jy8/PqVQd/L8XFxaxZs4adO3ZQU11DWNs2PDN16m/mOXj//fd5ac4cbDAjEm8CsKXkmnGV\nRiX+fn4kJSdjZmbGkMGDyToQxxzRvsm6skUV/+E0u3fvZtiwYU2WOXbsGH369MEPG2wx4xGCWEIs\nLqh4kEDa4EgdeqLJYxsp+LcI4uyF88TGxpKbm4urqyu9e/dGLm/6d2bCxG9xNysQmjBxy9TU1FBQ\nUICNjQ2Ojo4cOXKEzZs3U1paire3NxMnTqTNtbP0m5GVlUVWZha9cW9kCACoJAWDhA9fJyZSVlZG\n7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TGyYlL0pcyZM4dRo0bdlrY3bd5I3z792Hnw3/h7dcfR3o+qmkJSM49jMBiI\n7D6L84lbMQgdLYJbkHz5MgAF1JJIKa1p/Ls7QjYScElexoCI/oSGNtRMyMrKorS0FE9PT5ycnBo9\n/0/E2tqaFStW8OabbxITE4NGo0Eul6NSqWjRogUeHg2PXJ577jnWrFnDuXPnUCqVvD54MKNHj8bc\n/Iajp0ajYevWraxbu5a8nFzcPT0Y/8QTPPzww7fkS2Liz8GkQHiPkZWVxbDBQ4hP+Bk/uR0OeiUp\nlFOJFhkSBgTtwtqwdsN6OnTo8Fd3955nwIBILv9czIDus5u8X1VTyLZ9s9m2bdttmZBjYmLo0qUL\nvcOnEujdeDciNSuaqNhPUSoU9O3bl+dfeIEHHnjgltv9JSUlJbz11lt8+MFHaHValAoL3Jxb4Wwf\nSHZBHEWlaaxevZoJEyZQWlqKRqNh5AMjSDp3kSm61rTCHkmS0AsDx8ljHUnIkPDx9eHo8Si8vb0B\n+Omnn1j86mtEnzRKPctlMh54YASLX19MmzZtbuuY/ukUFBQwZOAgzl28QCu5I+56FXnyWhL1JXRo\n2449+/fdc6qQfwW3okBoMgbuIbRaLR3btSf/SjpTdaEEXhMRue4MuIFkwnGmUK6hRGXg1JnTtGrV\n6i/u9d1PXFwcp06dQi6X07dvX0JCQm5b3StWrOD555/nwcj3sLZsnNjpbMIWUrIOkJubg62t7S23\nN2DAAKKOneSxYSuQyRpvHBoMOr7+aQYzZjzD0qVLb7m9m5GXl8fbb7/N6tVfUFlp1BUYPHgI//73\ny/Tv379B2cLCQh647z5OnTmDp9wGJ70ZaVRQiRaVuQX/mjWTWbNm4exsfIdffvklkyZNoqVkTz+D\nJ05YkEElB+Q5VJgZOHDoYKOdlvz8fNatW0dKSgrW1taMHj26wS7avYBer6ekpARzc/P635sQgn59\n+nDhZCzP6cLqxYkA0kQFHysu0a57OIePHkWSJNRqNWlpacjlcgIDA28aZqzX6zl69ChZWVk4OTkR\nGRnZYDfCRENMxoCJ38WWLVt45JFHWEBn/KXGE8c2kcpeMniD7ryrOE//0fezefPmv6Cnfw8SEhKY\nMP4JTsfGILu2lS4w6sGvWfvlHzpLra6u5ptvviE+Ph5zc3OGDx9Or169qKyspGVwCHqdir6dn8dK\nZdzGFkKQln2S42c/Y/bsWbzzzju3PB69Xo+dnT0KyZaRkW81W27HgZcIae3Dl19+SUlJCd7e3vj7\n+99y+82h1WopKSnBysrqpkmIDAYD+/fvZ/PmzZSWluLj48P48ePp0qVLg3IFBQX4eHvTTevCk4Qg\n+8VkrhY6lsovIvk7kXQ5uf6I5K233mLhggXIDOApt6FCaCjWVRPRqxdbt2/HxaXpBFL/FCoqKnj/\n/fdZ+d9PyC8qBKB3z17MfnEOnp6edOvWjedpRwepscF6ThTxMRc4ePAgu3fv5vPPVlFWYYwE8fXy\n5vmZ/+Jf//pXI6Pg22+/5aXZc0jLzKi/5mTvwCvz5zFr1qx7ygj7vZiMARO/i0cffZS4bfuYZ2j6\n3ZaKOmZznCmEUoGWLfJUCgoL79nkQDcjNTWVrp07Y1Gp4yGdP+1wwgDEUMBWRRqOPu6cijnzu3wn\nvv76a6ZMeYbKygrsbT3Qamupqimlc3gXtm3fSklJCYMHD6GoqBAv13aYm9tRXHaF0vJsHnvsMTZs\n2HDLnvxgzATo5eWFTKbkkSEfYW7WeOKt01TxzY8zkMtl6PQ3Ikz69u3HkiVv3jbhozvJ22+/zYK5\n83nP0KORpDFAkijlbc6yf/9+IiMjWb58Oc899xzD8WMYvlhJSgzC6Ej7pSKZFm1bc/L06dvyN7gb\nKSsro2/vCJITk+ihdyUUR2rQckJeQKK+hD59+nAx+gzv6ro3MKyuYxCCOfITWDjZUV5cQh+9Ox1w\nRouBk+RzUsrnkUcfZePGjfWRHhs3bmTcuHF0lFwYJnzxxZpC1Bwgi8NkM2/ePF5//fU/+1Xc9dyK\nMXBvx9jcY5SWlOCgb/zxu449ZsiRqEFHADbo9Hpyc3P/xB7+fVi8eDGiso6XdR3oKLkgl2QoJRk9\nJHde0rUjMz2T5cuX/2Y9O3fuZMyYMRh0Zni7dSLQuy/D+ywmsvsckpKu0r9/JIGBgSQnJ/HRRx8R\nGGKPrVMVQ4f34dChQ2zatOm2TULXV2ZC6Pk55SdqaksoKk2lqqawvsyx2E8QGHBxbMWA7rMZMWAJ\nEeHTiD+fRt++/ThwGzIm3mliYmJogW2ThgBAS+yxlJsRGxtLXV0dry1cRB88eFgKwuraMzJJor3k\nzLO6MGLPnmXXrl1/5hD+VObMmUNq4mXm6jsyXgohXHIhQvLkRX17HiSAo0ePohSyJg0BML4rvTBQ\nWljEy/oOPCYFEyI50EZy4ikplKkijK+//rp+F1KtVvP8szPoJrkxQ7ShhWSHmSTHS7LiCSmEUQTw\n5ptvkpaW9ie+hX8+JmPgHqC4uJiUlBQ8vbzIUNQ0KSACkEkVegROWFCMMRnOvZqNrKqqipKSEgyG\nxgI41dXVbNq4if469yYnFFfJkm4GF1at/OymbZw6dYqHRo0GQKlQodZUcDbhW7bunUmtuozIbi9y\n9Woqa9euxc7OjhkzZnD48CFiY2PYuHEj/fr1u61bpa6urgQFtsDSwpGLybvYsvdf7D66iG37ZvP9\n4QXsj36XnIJ4QgIGMrDHi3i7tcfexosA7x4MjViAk10LnnxiAnq9/rb16U6gUCjQSs3viOoR6IUB\nhULB/v37KSwpZhC+TZZtIdkRJLdn3bp1d6q7fymlpaVsWL+BwXovvKWGO0WSJHE//jjLLCnUV1Mu\nmlYrLRK1VBu09BOe+Eo2je53llwJlTuxYtkyAL777juKy0oZKQKa/H0PxhdLmZLVq1ffhhGauI7J\nGPgHc/jwYQYNHIizszMtWrRg01cbKdBVs4eMRmWFEHxPOvaYEYYDR2S59Oze457LVf7dd9/Rp3cE\nNjY2ODk54ePpxWuvvUZV1Q0Z4Ly8POo0dQTQvMNeILZk5WQ3OzHm5eXRp09fbP6fvfMMj6rq2vB9\nZiaZ9EZ6Qhok1BBKIITehID0XgVEJCrKJ/jaqIIiIr6oNOlIB2kiHQUSegm9BQIkIZX03mZmfz8C\nwZhEXyAF9dzX5Q/3PmXtITNnnbXXepaJAz3af0GP9p/TtfVU+nf+FnfnFpy6vIL0zDiq2zdi9eo1\n5b3MUpEkie49XiUrJwkLMydaNBrLq21n0sZ3PEqlPjEJ11AolDSs3a/Ej7RSoaJJvUFEx0Sxb9++\nSrH3eenUqRNh2lQSRE6p85dJJE+noWPHjjx69AgAe8oWzrLTGhAfG1chtlY1ly9fJi8/jyaUnhOh\nkCSa6KqhRMFuwkuIXwkh2MY9dAgaUjKf4Ak+WitCQgqj2nfv3sVcZYi9VHo7bLWkxAVTwsLCnnNV\nMqUhOwP/QB49esSMGTPo2KED946FMJrajMALZ60R+ijYwX2mi7PcF4VJPFEik6Xc4AKP6I0HGwkj\nVJfC5KlTqngllcvnn39O7969eXTmJqOozdvUxzNewezPZtG2VWvS0go/ryfRkj/r25BMLsaGRmVm\nSn/22WdoNYLOLT7C0qx60biB2gz/hqOxt67DldCfMTNxrLStGo1Gw9at27C3rsOrbT7D1cEXfT1j\nHG3rE9DqU4yNrLG28ECtb1zq+dUs3NHXM+Ls2bOVYu/zMmTIEKpZWbFKGVpMWRPgkchhi+o+bVq1\nxsfHp6jePuZ3Dbj+SKwyB0fnp05zWFgYc+fOZcqUKaxevbqYI/l344nT92eZZYJC+fKjRPMDN7gv\n0skRGu6LdJZyg3MUOlQFlC0zXYCu6LtiYmJCjq6APFG6Iy2EIEMqwNi49L9Dmefjn5nx8i8lIiKC\nDz74gJ07dqDT6WiGLWO19dhLBDu5jxVqWuKABh0XSOBzQlAKCS0CPRRUx4TNynto0LFsyTK6detW\n1UuqNE6fPs3UqVPpjTs9de7w+MXXF1va65z4+voVPv74Y5YsWYK1tTVtW7ch6NQ1/LX2JfZKC4SW\nU6oEBgwaWOb9Nm/eikf1FhioS0YXJElBbfdXOHb+e9T6xtg52pfrWstiz549xMZG08a3DycuLiUy\nNgQhtEhIONk3xNTIlrz8jDLP1+k0aLUFL32eiZGRET//spuAzl34JO8czTU2WGNABJmcVyTgUt2F\nDZs2AtCxY0fsbWw5kBDBWOqVuNZtkcIDbRrfjxpFZmYmr48ezU/btmGg1MNUoSZJk8WEd9/j62/m\nMW7cuMpe6gvTqFEjDA0MuJD7iJ64l5jXCcElVTJde/TD2NiY1StWcr7gUdG8hakZS+Yu4cvPv+BM\ndDz1KSnqJITgvCqRDh07ANCzZ08mTpzIGeJoS8nI5D3SidKk069fv3JcqYzsDPxDCA8Px7+ZHwUp\nmdTQmXKPdIbgRQgJ7OQ+vXCnO64oHyvZDRderOcOJ6Q4evboiSRJ6HQ6fH19eeONN/4xEqNarZaD\nBw9y8+ZNDA0N6datG+7uJX/UFi5YgL3KhO4atxJzLpIpnbROrF3zI3PmzMHc3JxPp0wmICCA9dxh\ngKhR1BM+XeTzoyKUDKmAiRMnlmlXdnYWpo5li7CYGhfOxTy6zoefzn/GVT8fly5dwtDAlFOXlmNo\nYEGTeoOwMHUmIyue0Ae/kpYZixA6UtOjsTAr+SMdGRuCVleAj49Ppdj7IrRo0YIr166yYMECNq5b\nT2paPE6OTswYN5PAwMCiCho9PT2+mPMlY8aMwUCo6IEbFpIajdBxgUdsVN6jZbMWdOnShW4BXTkZ\nFMwoatNca4e+TkmSyOWXrHACAwNRqVSMGTOmilf+bJibm/PayJGsX7EaH601rr/b8xdCsJP7JGiy\nEEKwZMkS6kpWuGKCBh1x5HA7M5UfV68m8J23mTJ5Mg2FNb6SbbFr7CacCE0aK98vlLS2srKie7dX\n2XrgINW0BtTDqihCESUyWa66jbdXPbp06VK5H8Y/HLm08B9C3z59CfrlAKZaFRFkUAsLPpIaM0tc\nwAglk6RGJc7RCcE01QVa9glg69atVWB1xXLo0CHGvj6GyOgoDJX6FOg0aBH06tmLkaNGIoSgZs2a\neHt74+LkTL0YJQOl0tvnRolMpnGOoKCgIm37VatWMe7NN9FDQa3HfRtuK1LRN1Cz9aefyoyshISE\n0KpVG+yqedPW951Sj7n38CQnLy7F3NyCiIjwSknknDlzJp/NmIm1ZQ06+v8HPdVTcRetTsOxc98R\n8+gaFqZOdGrxEYa/i2qkpkdz+ORscvOzuHM3lJo1S/8c/64sXLiQj/7zIXl5ediqTMjQ5ZGpzaNb\nQFc2bNrI+fPn6dy5M/+HDw2k4m+/QgiWcZPLqmTCH0Zib185kZ7yIj09nQ7t2nHtyjWa6WyohxXZ\naDitfESYNoU33niDFStWMBhPOkvVi517X6QzT3mFt94bT0xMDFu2bKGusho+WisK0HFelUiEJo1P\nPvmElJQU9u3dS+TDhwCoFEo0Oi1uSnOctUYkKfK4pUumtqcXh377lerVq5dm7r+aFyktlCMDf2Py\n8/PZv38/V69eZdeuXRihRA/wovDBlCbyeEA640oJb0Jh8o+/xobdu36moKCA0NBQCgoK8PT0/FNx\nl4pEq9USFBREbGwsNjY2tG/fHj29pxn7Fy9e5Pvvv2fPrt3k5OVia2tL3/79+OKLLzAwMCg67tix\nY3R/9VVq6cyZii/uOjPyhJazxLP551/4+eddRfugTRs3oaCgAChbCe0Jv0+ce/311+nSpQvLly/n\nzJkzqJRKXmvfntGjR5epcb9+/XpGjhyJSmlIZMwF0jPjMTOx+8NnUMCNsH1IkoJDhw5WWkWHkZER\nOqGlqffwYo4AFCYINvN+jZ2/TiI9I4adh97HzdkfEyMbklPDeRh3EaWkQKCj56vd2XfwQIUKEVU2\n48ePZ8SIEWzevJmwsDBMTU3p27dvkWzxmjVrqK4yw1tTUldCkiS6CVfOauLp1KED127cKLMKRAhB\nZmYmkiRV2Xfwj5iZmXEsOJjvv/+eJYsWcyLmBgCd2nbk+w8msWTxYlxV5ryicS5xrodkRjutA6tX\nriQqJoaePXuyaMECdoRcRKlU0r5jB1qYmTH3q69AJ7BAjT4KCtDhoDMkUcolXpWLeR0XXJ0c+WTY\nMAYMGFDsuy5TPsiRgb8pa9eu5T8TJ/EoKRGlpEArdBihYhZ+XCGRdYTyMY35kotMoiH1pNLFb4JF\nDGu4jaOdPTHxhRnRRgaGvDZqJLNmzSqSb60MNmzYwCcffsTDmOiiMXsbW2bMmsm4ceNYtmxZYQhX\n6OOHHWqUXCGRB2RgoK9mx66ddO3aFYBmTXxJvXyf/+h8SrS2DRNpzCaEEXhhgZq9ioeEi3SsFYbM\n1jYrtV56l7jPrwbxxMbHPbfs740bN/D2boC7sz++9YZy4PgsNNp8mjUYgbNdQxQKJUmp4YTc2Exc\n4i2GDBnMxo0bn+tez8Onn37Kt/OXMKDL92Ues/vop6SmR+GIMTmSjlyhQSDIf1yqVw01uZIGWxcn\nrt+6+a/50W7bug15J+4wTird8RZCMIajAAwdOpTTJ06SkZGBh4cHb74VyKBBg/jxxx9Z+N333L57\nB4BGDXx47/3/Y+TIkS+N2t4TZ0VfX79IFtja0orWqRb0lEpuv0FhdOBzLlDa7/qUKVOY/cVseuJG\nJ5wxlvTIE1qOE8NWwmiINQ9V2dRt2ZQjx45W+Pr+7siRgX8Za9asYfTo0TTHjndpRqYo4Gsu0xIH\nLCU1zYUd27nHTu5jgJJQUqhXSjc3gFukoESiZryC4TRCjZKruUlsWL6ao7/+xonTpyrFIVixYgVj\nx47FF1tG4osTxsSTzeGEhwQGBnL9+nUWLVpEe+HIULyKHtg9ceeMiGNZ/k16dO/OiZMnMTU15fzF\nEN7Fu9Qe9zUlc2oLCy6QwH+kRtTXWTFLCiFKm8luHpSob44QGfyqjGHU62+8kP7/sGHDUCnV+PuM\nRqnUp3PLjwkOWcKxc9+hpzJEoVCRl5+BJCkYOHAA69evf+57PQ8qlQqFgjJbAQMoFBKtW7fm9IlT\naIQWSVIgSUrcnf0xNbIlJS2CxNgQMiMiWLZsGe+9916lrqGqsLa14ZJ0pcz5RxSWMSqAnZu20lzY\nYY45Vy7e4Y033uCtwLco0BRQF0vGUhcdgnNXoxg9ejTBwcGsXLnypXAIXsHjiwAAIABJREFUJEnC\n1LSkVkA82SwQV4knBzUKGmFDGxwxk/QRj2Nwf7Q/MTGRr7+aSw9c6fU7R0ItKelEdfSEgh8JZYCm\nBj8FHePWrVvUqVOnYhf4L0YuLfybkZeXxwfvT8Qfe8ZSFyfJhOqYoEPgSmFY0UBS8Q7ePCADHYIj\nRJNYSk11hMjgPI/ww45RUm1qS5a4S2b0ktz5VNuImAeRTJ06tcLXlJGRwfsT/o/WOPIW9fCQzApr\niSVTxkh1CcCFxYsWUQ2DYo7AE5pL9rTAHoUOpk+bRkREBACulPzReoIbZkXCSnqSkoGiBgC7Cecr\n5WWOiWjOiXhWi9t8qbhEHe96fPnll8+9xoSEBK5du4GrUzOUysJ2rkaGVgS0msyrbWdS37M77s7N\nAfjxxzVs2bKlSJq1smjbti1Z2ak8SgotdT41PYrk1If4+fmhEVpMJTWWJk70e+W/tGw0lga1etG2\n2Xv06DAbtZ4JMz+bWan2/xVZWVmsXr2aiRMn8sknnxAcHFyiLv55GTZsGA9EOvcel+v+kV+JQoFE\nDcyZJ1rQAzcukcgDkY4jRnhpTDFFj5ukcJc0/LHnfcmHMdRh9erVL62oUUFBAWpDA04TTxK51MUS\nO4zYQzifcoYwkcYFHmFpblHiQb5lyxaETkcnSt/7b4kDpuiRTj7AS1+y+ndHdgb+Zvzyyy8kpabQ\nHdciT9sQFUokEh8/3ABqS5ZMwxdfbMhDy+dc4IiIIkXkkShy2Cci+IpLqFEygpJd9uwkI9prHVi7\n5kcyMsouJysPtmzZQnZONr1wK/XtpxuuSAL8hV2ZkqctsKcAHYcOHy76gU/63efxR5LIxeh3gbFa\nFGaPT5gwAceWDVhLKD9wgwcOMPWz6QSdOP5cUYH4+HgWLVrEa6+9hhACpaKkYmE1Cze8vXpQv+ar\nAFXWC6JDhw7UrlWH8zfWkZOXXmwuvyCHs9fWYG/vUPSjniHyaOn7FoYGxXMaLEydaNbgNZKSk7h6\n9Wql2f9nbN26FUdHJ8aMGcOPq7eycMEy2rZti2+TpkXO44vQs2dPXJ2r8x1XuS6Siv4Gc4SG3eIB\nvxGFQPAW9dFHwbdcIY08PqYxs/BjktSQb2jJMLwIIpqd3AegpeRAbSyY/MmnL2xjRTB58mTi4+J5\nh/pMpylDJS/elOoxj5Y4Y8J8LnNUiuHNwHEltoxiY2OxUBqWKQutkhTYYUTqYz2PynaO/23I2wR/\nMx48eICRUh8H3VPBDUmS8BN2BBFDgHBBXypMhHOQjHmDevQRHkzlHBu4w3oK9yMlQKFQ0l3ngloq\nPXHOB2t254YTFhZGo0YlqxGg8G0rKCiIGzducPLkSc6eOkN+fh4NfHx465236dev35+2KIVCxTFb\nlQlWmtL3l00kPRBg8CcJfga/+1N2dHTExcmZo9HReGJR4tgUkcclEuhLjaKxjMdvHy1btuTbb78l\nOzub/Px8zM3Nnys8q9Fo+OCDD1i0aBE6nUBPZYQQWh7GXaSp93AUpWxfPIy7BEDDhg2f+X7lgSRJ\nbN+xjXbt2rPn2Ce4O7XEwtSZ9Kx4HkQfR6HUcXjPIXJyCqNMVmbVsTQrmTQG4OLQGJVKzYEDB2jQ\noEFlLqMEBw4cYMiQIbg4NKWT30BMjGwQQhCbcIPz19fQvn1HLl+++EJbQCqViiNBx6hV05P/iitY\nY4CFUBNFJnloscEAWwyxkNRcFAlEkslkmlBDeupIqSQFHXEmTeRxmCi6CleMJBXNsGNtbCgZGRml\nhuifl6ysLDZt2sTOnTvJysikVp3avPnmm0/2nP+SjIwMFi9cRFdRnSZS8TJZE0mPt0V9JnESB0dH\nWrZsSXBwMH5+fkW5BjY2NqRpc8kWGoykko8irdCRQA4GKJEkiVatWr34omXKRHa1/mZYWFiQqysg\nUxQUG++KC1kUsJBrJIunb8RpIp+t3KMAHePx5l28GY837XFC6HTo/kRbLI9CBbDFixfzxRdfFHvL\n02g0TJ06FUd7B1599VU+/PBDjv28n0YJatqlWRF78hqDBg1i4ICBaDSasm4BgLGxMRm6fDSidIUy\nIQQKJK6SVOY1rpGE6vGfs729PVOmT+MM8ewU94spmUWLLOZzGRP0aI1D0fgJYjFQq+nUqRNQmFlv\nYWHx3Pu048ePZ8GCBXh79mFAl4UM6rqIDn4Tyc5J5mbY/hLHZ+UkczX0ZwzUBjg7l/6ArQzq1q3L\npUsXeWf8OOJSznPq8goi4oIYNXoYly5dxM/PjzZt2mBsZIRKVXZyoEKhQl9PTX5+fiVaXzqTP52C\nbbVatG7yFiZGhbK6kiThaFufDn7/ISIinNWrV7/wfTw8PJg3/78A6KPADD0CcGEyvgjAiMI34HPE\n44ZpMUfg93TAmTy0XCGx2Pj58+df2MYnXLt2Da8aNXlz7JtEHDhL7vFQdqzagK+vL4GBgaX25Pgj\nQUFBZOVk0+p336PfYybp0xBrYqKj6dmzJ23btsXZwZFZs2ah1WoZOHAgOklwjOhSzz/PI9LIJ1KZ\nRbeuXfHw8HihNcv8OXJk4G9Gz549eefttzmuiaErrkXjTpIJ74oGLOIa/+EUNSULJB2EkYYKiXHU\no5H0VF+8kbDmHAmcluLpJdxLDb+fJg4FErvWbCRHaJgyZQqvdOzEhk0bmThxIhs3bMRf2HKaDFri\nwGvUKhI16q6DSySweNcuZs+ezbRp08pcU+/evZk+fTohJOCHXYn5GyRTgI5QUrkkEoqtAwolZH8j\nCkNJRbN2rXBwcOCNN94gPj6eadOmcUQRg4fOlAyRTzgZWKJmEg0xlvQQQnCZRPYqIhk39q1yCdHf\nvXuXpUuX0tR7OHU8OheNO9s3xNuzBxdvbiE+6Taeru1Q65sQl3iL2/cPk1+QhY1N2UJEFUlBQQG7\nd+9m27ZtpKam4uHhweHDh/D29i5W2gmF4do+ffuyccNm8vIzS211nJQaTnZOepVHBW7fvs3FSyG0\nb/Z/SKVEY0yN7XBxaMLq1WuYMGHCC99vwoQJ6OnpMeXTyVxMS+SGIpVdugeolEpu6VIpEDqy0FCN\nsh0pC0mNShR2DwUI4RESlFsDqLS0NDp37IQ6OY8vaY6tMAQJdBpBEDEsW7oMZ2dnpkz5czny7OxC\niWYTyu6EaooeVhjwIY3IQsOJlFg+mz6D27dvs379et5+5x0WLViInlDQFkf0JSUaoeMc8awlFCUS\ndq7OrJCbElU4sjPwN8POzo43xo5lxdJlWOjU+PF0H90AJYZKfaztrGjg35zt27fTBBtGUbuo9eoT\nJEmitbBnv4hkC2EMEjWLOQQXxCNOEEsXqjNQ64lG6LhEIhuCTtDSvwV374XxBnW4Sxpm6DPid47A\nExpJNrQTjiz87ns+/vhj9PX1S11TgwYNCOjchY2/HcNSq8ZLehrajxAZrOIW7phigh6LuEZb4YQ/\n9uij4CpJHOYhWnTkS4Lpn80oWt+UKVMYNmwYK1eu5MaNG+Tk5JB04gQZ2Tn8LMIxF/rcU2USrkml\nR7fuzJs3rzz+iVi7di0GBiZ4urYrMdewTn8MDSw5f30D0fGF2ecKSYVOaDAytMLD3a1cbHgWHj58\nSMArnbkZeht3pQUWWhXnVcEsXryYUSNHsnzFihJtkr/55hs2b9rMpVvb8GtQvPRNq9Nw6dZWnByd\nq1zSOi6usFzW3LTshltmJo7ExZ0pt3u+/fbbjB49mj179hAdHY21tTV16tTB19eXvYRTDTW3SEEn\nRKlOeJTIRIPACjWnRRw3SEFCom7duuVi348//khCYiJfieZYSU+dEoUk0R4nYkUW/503j0mTJmFo\nWHaDpie5I7dJpXEpjYx0QnCLFGpijrVkiDWFSb01hBnLNm5kxIgRfPPNN+Tn57N06VJ2KyKw1hmQ\nSDZZaDDUV/PBB5OYNGkSVlalV0PJlB+yM/A35NtvvyU5OZnlW7awSxVBdY0Rycp8wrVp+NTxZs/+\nfRw+fJgd27dzixRmcI4GwpqOOOMoPc01eNJ17zAPuUQCLYQ9apRcIpEw0miGLf0pVJJTSQqaYouN\nxoCZ9y5goTDAX2fPTh7QDLtSS/gAmmPHb8khHDx4EEtLS8zNzalfv36J8PvGzZvoFhDAnHPnqKmw\nxFFrQCzZ3CUNZ0x4jwaYoMcm7hJMDEcfhxYVgA4wNTZh88YNtG7duth13d3d+fzzz4v+Pzk5mVWr\nVrF960/EpqfjU6sxi8aNIyAg4IUTlIQQ7N+/n1WrVmFsYINKWdL5kSSJ2h6duHpnN7l5qYVrUCip\nWb0tdyOO8trI117IhmeloKCALq90JvHewyJxJiTQanScJI61a9dhbWPD119/Xew8W1tbFi5aSGBg\nIJnZCdR2fwUTYxtS0iK5/eAAqRnR7Nu3t4QTUdnY2hZGWtIzY0qIOz0hPTMWO7vS554XQ0NDBgwY\nUGxs5syZTJs2jVpYkEAu54inOcXVCIUQ7CUcI1QEE8MVkjCQVHTp8Wq5dRDdtvUnvKlWzBH4Pe1w\n4te0sxw7dqxIt6M0vL298WvalD0X71BPa1Ui9+gUccSTwyhqFxv3w46Dqmh+WLKEgIAAlixZwgcf\nfMC6deuIjo6mWrVq9O/fH19f3xdfrMz/jCw69DdFCMG5c+dYtWoV4eHhWFlZMXjwYDp37syQwYP5\nefduamJO/cfSoWeIJ4sC3qQeTR8n+6wXoZxWJ5KXl48TxqSQhwYdeWh5FTd6U/r2wVxxiXiy+UZq\nyfviBG1xpLdU+n5etMhkKueKjXnV9GTq9GkMHz682LhGo2H37t2sWb2aSyEXiYuN43Vq0wRb9H7n\nbOQLLceIZh+RWLo6MG3aNAYPHoyRUektTysDIQQffvgh8+bNw9DAHCEE/Tt/h0JRMumxoCCHrQff\nRafTIISODs0/IOTGOqyqGXPl6uVyTRL7K7Zt28aAAQOYTtNiuvNP2CXuc0gdS2xcHBYWJZMxd+zY\nwYzpn3Ht+tN8krZt2zF79he0aNGiQm3/XxBC0LhRE+Jj8+nU/D8ltgoyshLYffQjvv56Lu8/1sav\nSNasWcMXsz7n3v17KJDoRw3a4ICRpEecyOYXHnCaeAAsUZOvEJhYmXP63NlSe2o8Dz71vbG6kcJr\nUskqIoBsUcB4jrNlyxYGDiy72RbAhQsXaNumDTb5enTVVqcWlmSQTzAx/EYULXFgNLVLOP9bxF3C\nXFWEhd8vlzXJFCKLDv0LkSQJPz8//Pz8io1PnTqVvb/s4T0a0FB6KhbUT9RgFbdYxg1cRKEuwUlF\nPHl5GkbgRXupMGntkkhgAdfoiHOZZXzVMSGSwnJDF0y5RjK9Kd0ZuEYyEjAET+pgRQq5HLsXw4gR\nI4iOjuajjz4qOlalUtG3b1/69u3L6tWreWPMGGoLy2KOAIC+pKSTqM5vqli6devG66+//syfX3nz\n008/MW/ePJrWH45tNS/2Bk0jMvYCbk5+JY69GxmETluAqYk9mVmPOHJmHvXrefPLnt2V6ghAYcmd\nh9ICV13p922PE7vzwtmzZ08J5w2gb9++9OnTh9u3b5OUlISzs/NLJUMsSRKffzGLHj16cPLSchrV\n6Y+xYTWEEMQn3ebs1dU4OzlX2t/QqFGjGDlyJJcuXWLmZzPZvncP23X3MVTqkanJK4ywPX4/S1cU\n0LdfP+bOnVuun2nNWl6EhP6G0JQuLnWPwrLS/yVhz8nJiQ4dO7J/7z5+4EbRuILCCMMwvEq9RxYa\nDAxfDrllmUJkZ+AfRE5ODosWLKCDcCrmCADoSQpeF7W5QTKruUWcMg8TMzPUGdm00jztUPgkGegR\n2ZhT+h7/IymHPKElVeTRDkcWcI0zIo7mUvGQZ5LIZT8RNMWWTo8bmDhhTH2qsZ17fPLJJ/Tv358a\nNWqUuEefPn145+23+TU3iv6UnL9IAgmaLEaMGPFsH1IFMX/+tzjY1qVOjcKEQWf7Rpy+XKgaV93B\nF4WkQKvTcP/hSS7e3EoNlzZExp7H06smCxYsoGPHjlVSRx0XG0eONp/JnCEHDTYY0gZHmmGHnqTA\nXFKjQkFaWuliOlD4wH2ZleFeffVV1q9fz5tvjmPH4dNUs3AmvyCH9MwEvOt78/Punyut/wMUfl6N\nGzdm18+7iIuLY9euXaSlpeHu7k63bt0IDQ0lNzcXT0/Pom2O8mTs2LHs2LGDSySW2OvXCB17FZH4\n1PX+yxLDmJgY/Jv5kR6fRC/hhh2GxJDNbVK4Qyp6KEp9ocgVGi4qk5jQZ1R5LkvmBZGdgX8QFy5c\nICUtjRZ4lTqvJylpKmwJJoZBg4aQm5vLzZ+PFXvzroE51hhwmIfUFCVr7ONENtdIwtDIkMV5N3lb\nWwd/7FnOTW6JFFpgjyEqrpHEQR6iRGIQniVs6Y4bxxRxLF++nDlz5pSYt7Cw4KOPP2bGjBnoCwWd\nqI6RpCrKNN6gDKNb5640b978BT+1FycnJ4czZ07j3/Dp22XrxoEEhywm6PxCjAytMDGyIS09mryC\nTDycW2JtWYOwyCB27NhRbolhz8qZM2c4d/YsAg3NscMCNfdJZyW3CCKG94UPCeSgQfdSve0/D0OH\nDqVHjx5s2rSJa9euoVar6datG+3bt69SmV97e3sCAwOLjf2vdf7PS+fOnenZowdL9+6jh86V1jhi\nih53SGW3IoIHigwOLfj+Lz+XSZMmkRmfxBRNo2L5B71w5ztxhV95SA1hji82RdfKFRqWKm4h9BQl\n1i1TtcjOwD+IJzXdBn/yz2qAEgcHRzZs2MD48eM5LuUVy2pWSBK9hDsrucVmwugh3DB5XIIXRhor\nVaG4O7ux6sc19O3dhw9Tz9BAWOGOGWeI5zixAOgpVWi0GubRAktJXcIOtaTES2vK5cuXy7R16tSp\n5OXlMferuewnCjulMSkilwxNLn169Gbd+vUvhV77Ex0FpeJpJEVPz5AOfhNJSr3P/ajTJKeGk1eQ\nySv+H5Gdl8q5q2sYOHBglTkC6enpdO/2Kq5aY96lfrFqk3sijf9yhbWEokXgYGv3j+gdb2pqyptv\nvlnVZlQ5CoWCrT/9xKRJk1ixfAU78p/u23t5eHJg6U+0a9fuT6/x6NEjtm/bRj+Ne6mJiO9Qn/+T\nTrJEXMddZUFtjRlZaAhRJqLTU7Bj505cXFzKe2kyL0CFOAOSJLkCU4EOgD0QDWwAvhDiD2o5MuVG\n3bp1USoUXNcl0YGSwjVCCG6o0mjStC0AQ4YMYdGiRVwliYY83VZoKTmQLTRsIYwjROGutCALDbGa\nDLy96vHLvr24urpy914Yq1evZse2bWgzMulVuxavvPIKPj4+nD59monvv18ktFIauZKuSI2sNBQK\nBbNnz2b8+PGsX7+eyMhILC0tGTx4MPXqld4driowMTHBw6Mm0Y8u41H9adKcJElYW9bA2rIGJy8u\nIzktnFOXl5KVk0q/vv1Ys2ZNldm8bt06UlJT+UQ0L1F2WkMyp6/wYCN3EMCm7zZVeVWATPmiVqtZ\nuHAhM2fO5ODBg2RlZVGrVi1atWr1PznYN27coECjwYfSW3WrJCXthCPnrbKo59+cG1evY2Boyrt9\nXyMwMBBXV9dSz5OpOirqG16bQsXbscA9oD6wAjACPqyge/7rcXBwoGfPXhzYc5AmGhvM//BGfoo4\nIjVpLHv7bQBatGhB+7btWHXiNGO00IBqSJKERuhQo0QCmjb3o06dOhgaGtKzZ09eeeWVor1tS0tL\nJk6cyMSJE0vYYmdnx8SJEzkj4mhLyZKoJJFLqJTCe39SuvQER0dHPvzw5f2zkSSJd999h0mTPsDT\n5Sb2NsXf9h8l3eFB9Bl8fLzp2LEjw4cPx8fHp4qsLWTfvn3UxbLM8jJ/7NnAHQIDAxk8eHAlWydT\nWVhZWTFkyJBnPu+JENUTldLSyEeHmZkZv+zZ89z2yVQeFeIMCCEOAgd/NxQuSdI8IBDZGahQ5n87\nH/9TfnyefIlXNE7UxZJsNJwiluPEMWrUKDp3LkxykySJHbt20rd3H74LOoadygRrnZoYZQ4pmmyG\nDx/OypUryxQL+jPc3Nzo07s3O3bvw01rVqxsLVsUsEx5C0szS4YNG1Zua69KRo0axVdz5nL49Nd4\nurbFzbEZSAoiY85zN/IYLVu24NChgyWatVQVebm5GAhFocteCgYoUSC9UJ+EvLw8tm/fzpEjR9Bq\ntfj6+jJ8+PBKTdZ7HnQ6ndwU5y9o0qQJFmbmnEmPx6WU7qAaoeOCKomh3UZXgXUyz0Nl/sVbAMmV\neL9/Ja6urpw+d5ZO/XqwXfWAaZxjDhcJsxN8NferEn3RLSws+O3oEYKDg+nzxnDq9uvAqHfHceXK\nFdatW/dcjsATVqxciVeDusySLvCddI1fxAN+FLf5j/IMj4y07Nm3t9JL6SoCIQRDhw4jMSkJJ1tv\nImNDOHRqDodOziYsMhittoCBAwe8NI4AQAMfH+6oMigoox/ELVLQIfD29n6u658/fx53F1eGDRvG\n4TXbOL7+Z957912cHB3Ztm3bi5heIVy/fp3Ro0djYmKKUqnExcWVL774gtTU1Ko27aXE0NCQwLff\n4jdFNNdF8Z4hWqFjLaFk6PJ55513qshCmWelUkSHJEmqCVwAJgohVv3JcbLoUDmSkJDA3bt3MTAw\noEGDBlWy75uTk8P69etZ9sNSwh+EY25uxuBhQwkMDKzShjzlSVBQEO3ataNdswm4ODRBp9OQnlUo\nHGNqbMeZy6tJzb5FdHTUn+ZIVCahoaHUrl2b3rjTUyouZpMntMxTXsGolhNXrl975iTN8PBw6tWt\nC7lackQ+AnCUTGkubIggk8uKRH47coS2bduW44qen71799K3bz8M9M1wc2qJkYElSan3CY8+g5u7\nG8ePB5W7QuE/gfz8fHr36sX+Aweop6hGXZ0F2Wg4p0okWZfD6jVrXprS338LLyI69EzOgCRJXwIf\n/ckhAqgjhLjzu3OcgGPAESHEuL+4fmMgpE2bNiVCiUOGDHmuvS0ZmYpm9OjR7NpxiB7tviz1wZmW\nEcPPRz5mx44d9OnTpwosLJ3PPvuMGTNm0FSyo71wxBI190jjoDKaRL18jh47VkLU6n+hRYsWnD59\nGmtzN9yrt0KlUhMdd5mHcRepixVZCg012/ly+LdfK2BVz0ZiYiKurq5Ym9ehdZO3USqfJlOmZ8bx\n6+kvad3Wn3379lahlS8vGo2GTZs2sXjhIm7cuIFaX5/uvXry3nvvldn2XKZ82LRpE5s2bSo2lpaW\nRnBwMFSCM1ANykgffcp9IYTm8fGOwFHglBDiLzeP5MiAzN+RV155hTs30mjX7N0yj9nwy+t8+918\nxo8fX4mW/TWrVq1i9qzPuRf+oGjslY6dmDP3q+f6Dh49epQOHTrQsHY/vL16FnOOYhNucOT0N3gL\ncy6TRHx8fIWI6jwLc+fOZfLkKfTt9C0G6pJbVmGRxzl1aTl3796lZs2aVWChjMz/TqXJEQshkuBP\nmsr/jscRgSPAeaDq9WJlZCoIGxsbLueGlTmfmZ2IVqfB2tq6zGOqitdff51Ro0Zx5coV0tPTcXNz\ne6Gyr++//x4zE4cSjgCAg009arq14054MAhISkqqcmfgyJEj2FerV6ojAODm2IxTl5YTFBQkOwMy\n/2gqSmfAkcKtgQcUVg/YPvlhEELEV8Q9ZWSqiqFDh7Jp0ybik0Kxq1ay+cvt+4cxNjahe/fuVWDd\nX6NQKMotpHvsaBAezmWr+rk5+RH64FcUkuKl2IcvrBwobCaVlBrOvcjjZOcmo69vgoeTPzZWnkiS\nhFZbdgmdjMw/gYrKKHsF8Hj838PHYxKFOQUl27jJyPyN6dq1K76+TTkeshD/hmNxtPFGkiQKNHmE\nPjjMzXv7mTlzJiYm//zGLDqhQ5LK/oorHktft2vX7qXoUd+sWTOCgv7LsfMLiIw5j5GBFRZmTqQk\n3iYsIggzYzuEeP6qChmZvwtyC2MZmXIgISGB3r36cOr0ScxN7TA0sCA1PYq8/GwmTZrE3LlzXwrp\n5Iqmc+cuXLl4j4BW00udD7mxhZv39nP69KnnSk4sbyIjI3Fzc0NCiX/D13F39keSFNx7eIKrobvI\nzE4AwMjImJEjX2P69OkvRURDRqY0XiRnQFbWkJEpB2xsbDhx8jhBQUGMGDmAVwKa8eFHk7h//z5f\nf/11lTsCV69e5a233qJRw8Y0aeLLJ598Qnh4eLnf5913x/Mo6R53I46VmEtKDef2g8MMHDjgpXAE\ngCJxoSb1BlHDpRUKhZJLt37i1KXlKBRKjAyt0NczAZ0+K1aspllTP2JjY6vYahmZ8kcWHJeRKSck\nSaJNmza0adOmqk0pxuzZs5k8eTImxlbYW3uj02n4dv4C5s37hnXr1par3HD37t0JDAzkhx9+IPrR\nFdyd/FEp1UTFX+H+w+P4+HizfPnycrvfi7J9+3aUSj1quhT+mz1Kvsv1u3tQqQzIzknG1ckPUyMb\nklIjiIq7SHR0DIGBgfz8889VbLmMTPkiOwMyMv9gtm7dyuTJk2lQqzcNvHqiUBR+5Qs0eZy9uobh\nw0fg6elZbm1zJUli8eLF+Pr68t9v5hN0fiEANta2fPTxf/joo49eqtyJ5ORkDNUm6OkZAhB6/zAK\nhQozY3s6+X+Agdqs6NiU9IccOjmHX37ZQ1xcHPb29lVltoxMuSNvE8jI/EMRQvDl7Dk42TWgYe2+\nRY4AgJ5KTYuGb2BiVI35878t1/tKksSoUaP4cs5sAgICqF/fG5+GPnh6er503Q9dXFzIykkjO6dQ\nKT0u8TY6nYbWTQKLOQIAlmbVaeo9DCF0/PLLL1VhroxMhSE7AzIy/1CuXbvG5SuXqFG99G0LhUKJ\nu1NLdu7cWa73zcjIoGPHTvTq1YuQc3fITbfi5tUYRo4ciU+Dhjx8+PCvL1JJDBgwAAO1muth+wDQ\naPOoZuGOualjqce7OjRFIam4cOFCZZopI1PhvFxuuoyMzAsjhGDZEyGXAAAgAElEQVTWrFnMmvU5\nQJmCOgBqfTNyc3MQQpRbkuPrr4/hzJlzdPL/EEfb+kXjKelRHDv3X/z9WzB48CAMDAzo2rUrLVq0\nqLIESzMzM2Z9PotJkyYhdFr09YzQUxmWebxCoUKhUJGbm1uJVsrIVDxyZEBG5h/GnDlzmD59OrVc\nO6FU6vMoKbTMYx8lh1KjRs1yexjfu3eP7du30bjOkGKOAEB6Ziy5eVlER0exYtl6vp2/iFatWtGs\nqR+RkZHlcv/n4f3332f+/PlEJ54jKyeJhOS75Bdkl3psYsp9NNpcHBwcKtlKGZmKRXYGZGT+QaSn\np/P5519Qt0YATeoPwcO5Bbcf/Fa0J/57klLDiYw9z1tvBZbb/Xfv3o1KqY+7s3+x8ZhH1wk+vxAH\nm3r07vg1vTp8Td9O39LR/wPuhIbToX1H0tPTy82OZ0GSJP7v//6P2NgY6tati1an4crtHfxRg0Wr\nLeDizS1IkoIuXbpUia0yMhWF7AzIyDwnCQkJ7Nixgy1btnDnzp2/PqES2LVrFzk52dSpEQCAT60+\nKCQl+49/zt2IIHLzMsjOSeZG2H5+O/MVPj4+jBv3p81En4nMzEz09Q1RKfWLjV++vR2bal60aToe\nM5NC0R5JUuBk24AOfh/yIPwBq1evLjc7ngcTExNmzpwJCG7dP8SRM/8lOv4qaRkx3I86xf7jM3mU\nfAdbW7uXpv2yjEx5IecMyMg8IxkZGUyYMIH16zdQUJBfNN6+fQeWLv0BT0/PKrMtLi4OA7UxxoaF\nUr9GhpYEtJ7C2as/cvryKk6zEigU2xk+fATff/8dRkZG5XZ/T09PsrJTSc+MxcykMJSenhlLYso9\n2jV9r0iO+PeYmdjh4tCEVatWM2HChHKz5Xno1asXzZv7ExJykdSMKH47M69ozlBtgRA6vv/+2yKx\nIhmZfwqyMyAj8wzk5ubSuXMXLl28QgOvvrg7t0Cl1Ccq/jKXQn6mRYuWnDt3Fnd39yqxz8bGhty8\nbLJzUsjNTyczOxF9PSPaN5tAdm4KiSn3uXB9Pf0G9OTHH9eU+/179+6NlVU1roTuolXjQCRJIjs3\nDQALM6cyzzM3cSI27lS52/OsqFQq9u/fx/DhI9i7dw96egboqdRk56Shpxb8sOxHBg4cWNVmysiU\nO7IzICPzDKxevZqzZ8/StfVUrC1rFI17OLfA0cab/SemM3nyFDZu3FAl9vXp04dx48axN2g6OXmp\nReOGagvqe3bH2LAa2blpvPnmmxVyfwMDAxYs+J5hw4ah0eRSz7M7ekoDANIy44qiBX8kPSsOGxub\nCrHpWbGwsGDPnl+4desWu3btIisrCy8vL/r371+uURQZmZcJ2RmQkXkGfvhhKdUdGhVzBJ5goDbF\ny/UVtm3bxqJFC7G0tKx0+86ePYtWq8XC1Ab/RmOwtnAnKyeJ0Ae/cf76epRKFZ06vULLli0rzIah\nQ4eir6/PBx98yIHjs4DC/ICb9/bjbOeD9IetgszsRCJjLzBnwuwKs+l5qFOnDnXq1KlqM2RkKgV5\n40tG5hm4e/cutpZeZc7bVatFQUE+ERERlWhVIVqtlrFjx+FgU48uLT/F2c4HA7UZ1SzcadHoDRrV\nGYBWq+Grr+ZUeF1///79uX8/jGPHjrFu3TqmT5/Go6RQTl5aXlTZIIQgLvE2v535GkcHB8aMGVOh\nNsnIyJSNHBmQkfkDeXl5bN++neDgYIQQNG/enEGDBmFkZISxsQk5eWWXwOXmFe6PGxsbV5a5RRw+\nfJiHDyPo1mY6CoWyxHzdGl24/eAgmzZtqpQW4QqFoljWfY0aNRg3bhw7fj2DiVE1snPS0GjzAHB0\nrsuOHTsYNWoUSmVJ22VkZCoWOTIgI/M7Tp06haurG8OGDWP71v3s2HaIMWPG4OTkzKFDh+jXrw8R\nsafRagtKPT8sMpjatetSs2bNSrYcbt++jZ5KTTULj1LnlUp9qlnU4Nat25VsWSHDhw8nNjaWTp06\nkp75CCvz6jTzHkFzn9FkpugzduxYBg4YiEajqRL7ZGT+zciRARmZx9y9e5fOnbtgauRMzw4TsDAt\nzH7PyIrn/PX19OzZi/Xr17F69RpOXFyCf8Ox6D/udqfTabl+dy+RsSGs/WptlcjrGhkZodEWoNHk\nFnXh+yMFBZkYGZUtt1vRBAcHc+jQIXzrD6XuYy0EAC+39jyMvciunxfw3XffMWnSpCqzUUbm34js\nDMjIPGbevHlI6NO+6fvFHqamxna09X2PfcensnnzZrZt+4mBAwex4/AEHG0bolLqE5d0ncysZKZP\nn86IESOqxP5u3bqhUEjce3iS2h6dSsynZkQTn3SXXr2mV4F1hXz77XfYVfMs5gg8obpDY9yd/Pn+\nuwW8//77VV7Lr9VqOXToENeuXUOtVhMQEECtWrWq1CYZmYpC3iaQkQF0Oh3r16/Hw7lNqW/VSqUe\nNau3Z9euXXTo0IF798L45NOPcKiuwNwmm2HDB3D58mVmzJhR+cY/xtnZmUGDBnMldBtxCTeLzWVm\nJ3Ly4hKqO7vQv3//KrFPCEFwcDDVHZqWeYyrkx+RDyOqtFcBwNGjR/HwqEm3bt2YPm0mH3zwIbVr\n16Zr124kJiZWqW0yMhWBHBmQkaFQTCg7OxszY7syjzEzsUer1ZKSkoKzszMzZsyo0od/aSxd+gPR\n0dEcCpqDnbUXVmZuZOUkExV/CTtbOw4cPIxara4y+4ROh0IqO0HwyZxOp6ssk0pw+vRpAgK6Ym1R\nk25tplPNwgOdTkN49FmOHlmPh0cNqlWzxsjIkI4dO/Dmm29Sv379v76wjMxLjBwZkJEBDA0NMTMz\nJyU9qsxjUtKj0NPTx8rKqhItezZMTEz49dfDbN++nUa+NdDpPcTRRY/58//Lrds3qVu3bpXZJkkS\nvk2bEvXoUpnHPIwNwdbGjurVq1eiZcX5+ONPMDN2pH2ziVhb1kCSJCRJIir+Enn52SiECanJBdy+\nFcqCBQvw9vamYcNGHDx4sMpslpF5UeTIgIwMhQ+qUaNGsmL5Gup5dsNA37TYfEFBDmGRRxk4cOBL\nr0KnUqno27cvffv2rWpTSvDuu+MZNmwYD6LP4O7UvNhcQvJd7kUd56OP/sP27dv59ddfKSgooHHj\nxrz22muVIuJ0//59goODaN3kLZRKvaLxy7d38jD2Is28R3Dtzi8IBN5ePbCxqkl2bip3HvxGQEAA\nS5curTB1RxmZikT6Y5vOqkSSpMZASEhISKXUQcvI/J7IyEgaN24COmN86w3H1soLSZJITLlPyM2N\nZObEcP78OVmV7gXQ6XS89tprbNy4EVfHZrg5NUehUBEVd5EHUSepW68uCQkJxMREY23lhkqhT0LK\nffT09FixYjnDhg2rUPuCg4Np27YtvTp8hblpoXRygSaPbQcn4OXWnsSUe2TnphDQagqGBuZF5wkh\nOHd1LWEPjxEWFoabm1uF2ikjUxoXL16kSZMmAE2EEBef5Vw5MiAj8xgXFxeOHj1Cv34DOHjiC0yM\nrZAkBRmZibhUd+XnPb++1I5ASEgIS5Ys4dKFEPTVarq+2o2xY8fi4FB6P4CqQKFQsHbtWvz9/Zk/\n/zuOnfsOAHs7B96b8C4rV65CT2FJz/azsTBzBiAnN42Qm5sZMWIENjY2dO7cucLsexJ9yMxJKHIG\nEpLvUKDJxtbKixthe2nj+04xRwAKI0uN6w0mPOY0y5YtY/bsl0taWUbmr5BzBmRkfoe3tzc3b15n\nyZIl9O3XnVGjh7B7927uP7iHn59fVZtXKkIIPv74Y3x9ffn5x80YX4lHd+4Bsz+bRQ0PD/bv31/V\nJhZDoVDwzjvvcPduKFFRUURERPAwKhI9PT1yc/Jp7zepyBEAMDQwp2WjsdhW8+Szz2ZWqG3169fH\npbort+8f5knUVKMtbFOdkf0IgOr2pUct9VRq7K3rsWHDRl6miKuMzP+CHBmQkXmMEIIVK1bwxRez\niYgIBwrf+O7du4eHhwf16tWrUvsePHjA7t27yczMxMvLi549e6JWq1m+fDlfffUVA6lJZ011FI8F\nj7J1BazIv03fPn24eu0anp6eVWr/H5EkCSenp22N161bj6ujf4l8jcJjFXi5deL4qcVERkbi4uJS\nYTbVqOnB0aNHuXBjIz61+mJh6ghAemYMADqho6x6CJ1Oy8PISBYtWsT48eMrxEYZmYpAdgZkZB4z\ndOhQNm/ejKuTH51bDMbAwJyE5LucOnGA5s39OX48mIYNG1a6XZmZmbwxZgxbf/oJPUmJoUKPNE0O\n1pZWfLdwAXO/nEMzyY4Aij8gjSQ9AnV1+VB7lkWLFvHtt99Wuu3PQnJyMg41bMucNzUqnEtKSqow\nZwAK+0qYmzpx+/5h7oYfw8bKEz2VETGPrgMSEdFnqenapsR5eflZxCRcx8qsOl9++RVvvfWW3GdB\n5m+DvE0gIwN88sknbN68mUZ1BtDW9x3sbepiYeqEp2s7AlpNR62yon//AQQEdKVx4yb07NGT7du3\nV7iOvlarpVePnuzevpMRwovvdC2Zr/XnC/xwT9Vj2LBh3At/QGtRel6AvqTET2PD9q0/Vaid5YGj\noyMp6Q/LnE9Jf4gkSRWeA2Fra4tCIejb6Rvqeb6KnsoQ22qe5Oalo6dSc/HWT6RnxhU7R6fTcObK\nKhCChnX6ERMTxfnz5yvUThmZ8kSODMj867l58yZz5szBQG1OvZpdS8zr6xni7dWbY+e+Iy1Zh6WZ\nC+ce3uaXPf3x82vO/v37Kqzsbf/+/Rw5dpRJNKSe9FTfwEEyZpyoSwq5hJGGGfplXsMMPbKzMyrE\nvvJkzJjXmTZtBj61emNiZFNsTqvNJzT8EF26BGBvb1+hdgwfPpxVq1aRkhGFT63eReMp6VGcvfoj\nCcl32X10MjWqt8TGsrC0MCwyiOycZNr4vlOU75CR8fJ/5jIyT5AjAzL/ehYvXoxSqYejTX0UitL9\nYyfbBgDUcuuEf8PX6dJyKgGtpnD1yg2GDq24creVK1birjSnnmSFRujIFgXoHienKSSJbo+3Bu6Q\nWuY17ijS8fLyqjAby4vAwECcnZz49fRXRMVdQicKVQiTUh9w5Nx/ycpJYNasik0gBGjXrh3t23fg\n1KUfCI8+i06nBcDY0ApHm3oIoUOSJB5EneHU5RVcvr2d7NxUXJ38qGbpQUJyGAAeHqV3j5SReRmR\nIwMy/3qOB59ArWdCgSanzGOezOnE020B22pe+NYbwYEDi7l+/Xq5SdLm5+ezadMmfljyAxdCQpC0\nOj7iNInkIhAYS/q0FvYE4EIdCqMFBxVR+OnsMJH0il3rjkjlmkhk1Vtzy8W2isTS0pKg4GMMHDCI\nI2fnY6A2QaXUIzM7BSen6hw4sB9fX98Kt0OSJHbt2sngwUPYv38RxkYWGBlakpYei1ZXQLt27QgK\nCkZfzwgvtwDMTBxJz4wlLDKYmEfXMDAwoV279tSoUaPCbZWRKS9kZ0BGBjA0sCT60VVy89IxUJuV\nmL/38CQgcfbKWjSaPOrU6AKAi6MvBtdN2LVrV7k4A9nZ2bz6aneOHTuKs10DnO2aEB59ljwTC5q4\ntcfIwIrElDCORgRxTpvA66LwjT/PUMGc3Mt011anAdXIRctp4tireEibFq0rXKynvHBxceHM2dOc\nO3euSIGwUaNGdOvWjf9v777Dq6jSB45/z72pJKSHkAIJSegtAQIEJICAgrqoK4LsuiC7i2VVLKuy\n7qooq6uiP7B31r4oFmBVECmhKyUJLYQQIKQQakivt5zfHzdEQhIWUHJvuO/neXh47sydmXfOczPz\nzplTXFxa7nLl4+PD0qXfkZaWxsKFCykuLiYyMpLJkyczbFgS/j4dGDPkb7i7edVv06vzdfyw8VlK\ny48wZ86SFotViF+DJAPC6SUNH8a/53+E0eDK+pQ3GZEwo8HMhccL97Fj7yKiwgfh4e7D1t2f4uri\nSWxkEkaDCx7uXpSXl/8qsTz00ENs3LiJq4b+HR/v9ny94q90ikhkaPx0DAZby/So8IF0j7ma5ev+\nyfzqTIIDAlmZvJr77p3BO+vW1u/L3c2dKVNvY968ebi5Nd+mwBENHDiQgQMH2jsM4uPjiY+Pr/+8\nePFi8vJyuXb47AaJAICHe1sS46axbP0/ZWZD0epIMiCc3l133cXrr79Oh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data:\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have:\n", + " - a numpy-array (matrix) X that contains your features (x1, x2)\n", + " - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n", + "\n", + "Lets first get a better sense of what our data is like. \n", + "\n", + "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n", + "\n", + "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The shape of X is: (2, 400)\n", + "The shape of Y is: (1, 400)\n", + "I have m = 400 training examples!\n" + ] + } + ], + "source": [ + "### START CODE HERE ### (≈ 3 lines of code)\n", + "shape_X = X.shape\n", + "shape_Y = Y.shape\n", + "m = Y.shape[1] # training set size\n", + "### END CODE HERE ###\n", + "\n", + "print ('The shape of X is: ' + str(shape_X))\n", + "print ('The shape of Y is: ' + str(shape_Y))\n", + "print ('I have m = %d training examples!' % (m))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**shape of X** (2, 400)
**shape of Y**(1, 400)
**m** 400
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Simple Logistic Regression\n", + "\n", + "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "# Train the logistic regression classifier\n", + "clf = sklearn.linear_model.LogisticRegressionCV();\n", + "clf.fit(X.T, Y.T);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now plot the decision boundary of these models. Run the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n" + ] + }, + { + "data": { + "image/png": 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y2enuxGG28pGrkX80l9GmPSQpY4rvTt3HJlo4PzaP59oqubduOwAfUM8H1HOm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\nkffagxwoXkV64sUopSip2ERNQy6pl/3XaB+CcS1h0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241bqaUuhF43t11CCGEEBPYTVrr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\nhBBixCRQCCGEEGLE/h/96Dt9FJOxrwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the decision boundary for logistic regression\n", + "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n", + "plt.title(\"Logistic Regression\")\n", + "\n", + "# Print accuracy\n", + "LR_predictions = clf.predict(X.T)\n", + "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y, LR_predictions) + np.dot(1 - Y,1 - LR_predictions)) / float(Y.size) * 100) +\n", + " '% ' + \"(percentage of correctly labelled datapoints)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 47%
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Neural Network model\n", + "\n", + "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n", + "\n", + "**Here is our model**:\n", + "\n", + "\n", + "**Mathematically**:\n", + "\n", + "For one example $x^{(i)}$:\n", + "$$z^{[1] (i)} = W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n", + "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n", + "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n", + "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n", + "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n", + "\n", + "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large \\right) \\small \\tag{6}$$\n", + "\n", + "**Reminder**: The general methodology to build a Neural Network is to:\n", + " 1. Define the neural network structure ( # of input units, # of hidden units, etc). \n", + " 2. Initialize the model's parameters\n", + " 3. Loop:\n", + " - Implement forward propagation\n", + " - Compute loss\n", + " - Implement backward propagation to get the gradients\n", + " - Update parameters (gradient descent)\n", + "\n", + "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 - Defining the neural network structure ####\n", + "\n", + "**Exercise**: Define three variables:\n", + " - n_x: the size of the input layer\n", + " - n_h: the size of the hidden layer (set this to 4) \n", + " - n_y: the size of the output layer\n", + "\n", + "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: layer_sizes\n", + "\n", + "def layer_sizes(X, Y):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- input dataset of shape (input size, number of examples)\n", + " Y -- labels of shape (output size, number of examples)\n", + " \n", + " Returns:\n", + " n_x -- the size of the input layer\n", + " n_h -- the size of the hidden layer\n", + " n_y -- the size of the output layer\n", + " \"\"\"\n", + " ### START CODE HERE ### (≈ 3 lines of code)\n", + " n_x = X.shape[0] # size of input layer\n", + " n_h = 4\n", + " n_y = Y.shape[0] # size of output layer\n", + " ### END CODE HERE ###\n", + " return (n_x, n_h, n_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The size of the input layer is: n_x = 5\n", + "The size of the hidden layer is: n_h = 4\n", + "The size of the output layer is: n_y = 2\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = layer_sizes_test_case()\n", + "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n", + "print(\"The size of the input layer is: n_x = \" + str(n_x))\n", + "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n", + "print(\"The size of the output layer is: n_y = \" + str(n_y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**n_x** 5
**n_h** 4
**n_y** 2
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 - Initialize the model's parameters ####\n", + "\n", + "**Exercise**: Implement the function `initialize_parameters()`.\n", + "\n", + "**Instructions**:\n", + "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n", + "- You will initialize the weights matrices with random values. \n", + " - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n", + "- You will initialize the bias vectors as zeros. \n", + " - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: initialize_parameters\n", + "\n", + "def initialize_parameters(n_x, n_h, n_y):\n", + " \"\"\"\n", + " Argument:\n", + " n_x -- size of the input layer\n", + " n_h -- size of the hidden layer\n", + " n_y -- size of the output layer\n", + " \n", + " Returns:\n", + " params -- python dictionary containing your parameters:\n", + " W1 -- weight matrix of shape (n_h, n_x)\n", + " b1 -- bias vector of shape (n_h, 1)\n", + " W2 -- weight matrix of shape (n_y, n_h)\n", + " b2 -- bias vector of shape (n_y, 1)\n", + " \"\"\"\n", + " \n", + " np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = np.random.randn(n_h, n_x) * 0.01\n", + " b1 = np.zeros(shape=(n_h, 1))\n", + " W2 = np.random.randn(n_y, n_h) * 0.01\n", + " b2 = np.zeros(shape=(n_y, 1))\n", + " ### END CODE HERE ###\n", + " \n", + " assert (W1.shape == (n_h, n_x))\n", + " assert (b1.shape == (n_h, 1))\n", + " assert (W2.shape == (n_y, n_h))\n", + " assert (b2.shape == (n_y, 1))\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]\n", + "b1 = [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]\n", + "W2 = [[-0.01057952 -0.00909008 0.00551454 0.02292208]]\n", + "b2 = [[ 0.]]\n" + ] + } + ], + "source": [ + "n_x, n_h, n_y = initialize_parameters_test_case()\n", + "\n", + "parameters = initialize_parameters(n_x, n_h, n_y)\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[-0.00416758 -0.00056267]\n", + " [-0.02136196 0.01640271]\n", + " [-0.01793436 -0.00841747]\n", + " [ 0.00502881 -0.01245288]]
**b1** [[ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]]
**W2** [[-0.01057952 -0.00909008 0.00551454 0.02292208]]
**b2** [[ 0.]]
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 - The Loop ####\n", + "\n", + "**Question**: Implement `forward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "- Look above at the mathematical representation of your classifier.\n", + "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n", + "- You can use the function `np.tanh()`. It is part of the numpy library.\n", + "- The steps you have to implement are:\n", + " 1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n", + " 2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n", + "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: forward_propagation\n", + "\n", + "def forward_propagation(X, parameters):\n", + " \"\"\"\n", + " Argument:\n", + " X -- input data of size (n_x, m)\n", + " parameters -- python dictionary containing your parameters (output of initialization function)\n", + " \n", + " Returns:\n", + " A2 -- The sigmoid output of the second activation\n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters['W1']\n", + " b1 = parameters['b1']\n", + " W2 = parameters['W2']\n", + " b2 = parameters['b2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Implement Forward Propagation to calculate A2 (probabilities)\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " Z1 = np.dot(W1, X) + b1\n", + " A1 = np.tanh(Z1)\n", + " Z2 = np.dot(W2, A1) + b2\n", + " A2 = sigmoid(Z2)\n", + " ### END CODE HERE ###\n", + " \n", + " assert(A2.shape == (1, X.shape[1]))\n", + " \n", + " cache = {\"Z1\": Z1,\n", + " \"A1\": A1,\n", + " \"Z2\": Z2,\n", + " \"A2\": A2}\n", + " \n", + " return A2, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852\n" + ] + } + ], + "source": [ + "X_assess, parameters = forward_propagation_test_case()\n", + "\n", + "A2, cache = forward_propagation(X_assess, parameters)\n", + "\n", + "# Note: we use the mean here just to make sure that your output matches ours. \n", + "print(np.mean(cache['Z1']), np.mean(cache['A1']), np.mean(cache['Z2']), np.mean(cache['A2']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + "
-0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n", + "\n", + "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n", + "\n", + "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n", + "\n", + "**Instructions**:\n", + "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n", + "$- \\sum\\limits_{i=0}^{m} y^{(i)}\\log(a^{[2](i)})$:\n", + "```python\n", + "logprobs = np.multiply(np.log(A2),Y)\n", + "cost = - np.sum(logprobs) # no need to use a for loop!\n", + "```\n", + "\n", + "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: compute_cost\n", + "\n", + "def compute_cost(A2, Y, parameters):\n", + " \"\"\"\n", + " Computes the cross-entropy cost given in equation (13)\n", + " \n", + " Arguments:\n", + " A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n", + " \n", + " Returns:\n", + " cost -- cross-entropy cost given equation (13)\n", + " \"\"\"\n", + " \n", + " m = Y.shape[1] # number of example\n", + " \n", + " # Retrieve W1 and W2 from parameters\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " W1 = parameters['W1']\n", + " W2 = parameters['W2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute the cross-entropy cost\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " logprobs = np.multiply(np.log(A2), Y) + np.multiply((1 - Y), np.log(1 - A2))\n", + " cost = - np.sum(logprobs) / m\n", + " ### END CODE HERE ###\n", + " \n", + " cost = np.squeeze(cost) # makes sure cost is the dimension we expect. \n", + " # E.g., turns [[17]] into 17 \n", + " assert(isinstance(cost, float))\n", + " \n", + " return cost" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cost = 0.692919893776\n" + ] + } + ], + "source": [ + "A2, Y_assess, parameters = compute_cost_test_case()\n", + "\n", + "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**cost** 0.692919893776
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the cache computed during forward propagation, you can now implement backward propagation.\n", + "\n", + "**Question**: Implement the function `backward_propagation()`.\n", + "\n", + "**Instructions**:\n", + "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation. \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "- Tips:\n", + " - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n", + " $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: backward_propagation\n", + "\n", + "def backward_propagation(parameters, cache, X, Y):\n", + " \"\"\"\n", + " Implement the backward propagation using the instructions above.\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing our parameters \n", + " cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n", + " X -- input data of shape (2, number of examples)\n", + " Y -- \"true\" labels vector of shape (1, number of examples)\n", + " \n", + " Returns:\n", + " grads -- python dictionary containing your gradients with respect to different parameters\n", + " \"\"\"\n", + " m = X.shape[1]\n", + " \n", + " # First, retrieve W1 and W2 from the dictionary \"parameters\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " W1 = parameters['W1']\n", + " W2 = parameters['W2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve also A1 and A2 from dictionary \"cache\".\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A1 = cache['A1']\n", + " A2 = cache['A2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Backward propagation: calculate dW1, db1, dW2, db2. \n", + " ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n", + " dZ2= A2 - Y\n", + " dW2 = (1 / m) * np.dot(dZ2, A1.T)\n", + " db2 = (1 / m) * np.sum(dZ2, axis=1, keepdims=True)\n", + " dZ1 = np.multiply(np.dot(W2.T, dZ2), 1 - np.power(A1, 2))\n", + " dW1 = (1 / m) * np.dot(dZ1, X.T)\n", + " db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)\n", + " ### END CODE HERE ###\n", + " \n", + " grads = {\"dW1\": dW1,\n", + " \"db1\": db1,\n", + " \"dW2\": dW2,\n", + " \"db2\": db2}\n", + " \n", + " return grads" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dW1 = [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]\n", + "db1 = [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]\n", + "dW2 = [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]\n", + "db2 = [[ 0.06589489]]\n" + ] + } + ], + "source": [ + "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n", + "\n", + "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n", + "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n", + "print (\"db1 = \"+ str(grads[\"db1\"]))\n", + "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n", + "print (\"db2 = \"+ str(grads[\"db2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected output**:\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**dW1** [[ 0.01018708 -0.00708701]\n", + " [ 0.00873447 -0.0060768 ]\n", + " [-0.00530847 0.00369379]\n", + " [-0.02206365 0.01535126]]
**db1** [[-0.00069728]\n", + " [-0.00060606]\n", + " [ 0.000364 ]\n", + " [ 0.00151207]]
**dW2** [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]
**db2** [[ 0.06589489]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n", + "\n", + "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n", + "\n", + "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n", + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: update_parameters\n", + "\n", + "def update_parameters(parameters, grads, learning_rate=1.2):\n", + " \"\"\"\n", + " Updates parameters using the gradient descent update rule given above\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " grads -- python dictionary containing your gradients \n", + " \n", + " Returns:\n", + " parameters -- python dictionary containing your updated parameters \n", + " \"\"\"\n", + " # Retrieve each parameter from the dictionary \"parameters\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = parameters['W1']\n", + " b1 = parameters['b1']\n", + " W2 = parameters['W2']\n", + " b2 = parameters['b2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Retrieve each gradient from the dictionary \"grads\"\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " dW1 = grads['dW1']\n", + " db1 = grads['db1']\n", + " dW2 = grads['dW2']\n", + " db2 = grads['db2']\n", + " ## END CODE HERE ###\n", + " \n", + " # Update rule for each parameter\n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " W1 = W1 - learning_rate * dW1\n", + " b1 = b1 - learning_rate * db1\n", + " W2 = W2 - learning_rate * dW2\n", + " b2 = b2 - learning_rate * db2\n", + " ### END CODE HERE ###\n", + " \n", + " parameters = {\"W1\": W1,\n", + " \"b1\": b1,\n", + " \"W2\": W2,\n", + " \"b2\": b2}\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]\n", + "b1 = [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]\n", + "W2 = [[-0.01041081 -0.04463285 0.01758031 0.04747113]]\n", + "b2 = [[ 0.00010457]]\n" + ] + } + ], + "source": [ + "parameters, grads = update_parameters_test_case()\n", + "parameters = update_parameters(parameters, grads)\n", + "\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[-0.00643025 0.01936718]\n", + " [-0.02410458 0.03978052]\n", + " [-0.01653973 -0.02096177]\n", + " [ 0.01046864 -0.05990141]]
**b1** [[ -1.02420756e-06]\n", + " [ 1.27373948e-05]\n", + " [ 8.32996807e-07]\n", + " [ -3.20136836e-06]]
**W2** [[-0.01041081 -0.04463285 0.01758031 0.04747113]]
**b2** [[ 0.00010457]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n", + "\n", + "**Question**: Build your neural network model in `nn_model()`.\n", + "\n", + "**Instructions**: The neural network model has to use the previous functions in the right order." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: nn_model\n", + "\n", + "def nn_model(X, Y, n_h, num_iterations=10000, print_cost=False):\n", + " \"\"\"\n", + " Arguments:\n", + " X -- dataset of shape (2, number of examples)\n", + " Y -- labels of shape (1, number of examples)\n", + " n_h -- size of the hidden layer\n", + " num_iterations -- Number of iterations in gradient descent loop\n", + " print_cost -- if True, print the cost every 1000 iterations\n", + " \n", + " Returns:\n", + " parameters -- parameters learnt by the model. They can then be used to predict.\n", + " \"\"\"\n", + " \n", + " np.random.seed(3)\n", + " n_x = layer_sizes(X, Y)[0]\n", + " n_y = layer_sizes(X, Y)[2]\n", + " \n", + " # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n", + " ### START CODE HERE ### (≈ 5 lines of code)\n", + " parameters = initialize_parameters(n_x, n_h, n_y)\n", + " W1 = parameters['W1']\n", + " b1 = parameters['b1']\n", + " W2 = parameters['W2']\n", + " b2 = parameters['b2']\n", + " ### END CODE HERE ###\n", + " \n", + " # Loop (gradient descent)\n", + "\n", + " for i in range(0, num_iterations):\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n", + " A2, cache = forward_propagation(X, parameters)\n", + " \n", + " # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n", + " cost = compute_cost(A2, Y, parameters)\n", + " \n", + " # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n", + " grads = backward_propagation(parameters, cache, X, Y)\n", + " \n", + " # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n", + " parameters = update_parameters(parameters, grads)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Print the cost every 1000 iterations\n", + " if print_cost and i % 1000 == 0:\n", + " print (\"Cost after iteration %i: %f\" % (i, cost))\n", + "\n", + " return parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:26: RuntimeWarning: divide by zero encountered in log\n", + "/home/jovyan/work/Week 3/Planar data classification with one hidden layer/planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n", + " s = 1/(1+np.exp(-x))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W1 = [[-4.18494056 5.33220609]\n", + " [-7.52989382 1.24306181]\n", + " [-4.1929459 5.32632331]\n", + " [ 7.52983719 -1.24309422]]\n", + "b1 = [[ 2.32926819]\n", + " [ 3.79458998]\n", + " [ 2.33002577]\n", + " [-3.79468846]]\n", + "W2 = [[-6033.83672146 -6008.12980822 -6033.10095287 6008.06637269]]\n", + "b2 = [[-52.66607724]]\n" + ] + } + ], + "source": [ + "X_assess, Y_assess = nn_model_test_case()\n", + "\n", + "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\n", + "print(\"W1 = \" + str(parameters[\"W1\"]))\n", + "print(\"b1 = \" + str(parameters[\"b1\"]))\n", + "print(\"W2 = \" + str(parameters[\"W2\"]))\n", + "print(\"b2 = \" + str(parameters[\"b2\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**W1** [[-4.18494056 5.33220609]\n", + " [-7.52989382 1.24306181]\n", + " [-4.1929459 5.32632331]\n", + " [ 7.52983719 -1.24309422]]
**b1** [[ 2.32926819]\n", + " [ 3.79458998]\n", + " [ 2.33002577]\n", + " [-3.79468846]]
**W2** [[-6033.83672146 -6008.12980822 -6033.10095287 6008.06637269]]
**b2** [[-52.66607724]]
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.5 Predictions\n", + "\n", + "**Question**: Use your model to predict by building predict().\n", + "Use forward propagation to predict results.\n", + "\n", + "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n", + " 1 & \\text{if}\\ activation > 0.5 \\\\\n", + " 0 & \\text{otherwise}\n", + " \\end{cases}$ \n", + " \n", + "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: predict\n", + "\n", + "def predict(parameters, X):\n", + " \"\"\"\n", + " Using the learned parameters, predicts a class for each example in X\n", + " \n", + " Arguments:\n", + " parameters -- python dictionary containing your parameters \n", + " X -- input data of size (n_x, m)\n", + " \n", + " Returns\n", + " predictions -- vector of predictions of our model (red: 0 / blue: 1)\n", + " \"\"\"\n", + " \n", + " # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n", + " ### START CODE HERE ### (≈ 2 lines of code)\n", + " A2, cache = forward_propagation(X, parameters)\n", + " predictions = np.round(A2)\n", + " ### END CODE HERE ###\n", + " \n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions mean = 0.666666666667\n" + ] + } + ], + "source": [ + "parameters, X_assess = predict_test_case()\n", + "\n", + "predictions = predict(parameters, X_assess)\n", + "print(\"predictions mean = \" + str(np.mean(predictions)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**predictions mean** 0.666666666667
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost after iteration 0: 0.693048\n", + "Cost after iteration 1000: 0.288083\n", + "Cost after iteration 2000: 0.254385\n", + "Cost after iteration 3000: 0.233864\n", + "Cost after iteration 4000: 0.226792\n", + "Cost after iteration 5000: 0.222644\n", + "Cost after iteration 6000: 0.219731\n", + "Cost after iteration 7000: 0.217504\n", + "Cost after iteration 8000: 0.219454\n", + "Cost after iteration 9000: 0.218607\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VF9jJyWoUdpy8xV4c4mKnrR6A88hkJnHcyfcor45q7a83UkmDO6HQTyEdfDqZ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\nZofFcmPyFB4v3c4KZXSjDRQTNyZNYXa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MzuScm9iw689UN64lI2kBBp0v8bCxbRuW4U6m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QTq6OefveWMNcexYDCDYW9c4MjmE1cr7wzUs4xW4qWRGtxYFR2/3k/golHRcJ4ji/P24pXwTUFK\nyes99TzWWopBb2XqmMtQBrMZctKn0dJRQkvHFgx6K+u2LkRRtKSMP52c2VeiM0XiFPSxyfS3V+N2\nNfLxyuG1/dImn7dHm3nzrqWktYp7e9aSKmx0Sx8BgkwefcmQMVZozGUsXvpbbqlfi08NIYAUrYmT\n7ElcFJ87lLVxvJBhsPCnvJk831HJCncbKpGMoStkIVqh0CEH+IAGCgx2Kv1uprBTtJbQTS9+7mAC\no0SklP2pMp3fs5G/tFdEBUWUg+JQgjL3WSJcSrnkq3UnSpRD57XuOmr9/fySKWQNWqK8I+t4nRpO\nZmdgXB+RKfMUhgcnJg++9hBinkhnk+zknZ7G/QoKIQRP583kY2cL/+muxS/DXGnL54qEAixfkr74\nTcYVCnBnw3pKvU70ehtWc+KQmNiBzZKEL9BPyNWAojUw+ftPDCsrPtDTTH97NeeTi5SSt6jDpLej\n11tw9bfSuPIV4rLHD9mNA+gtsUz8/hN0li/D3bwdS08z4aYyRuaePrSPVqNnZP6ZLNvwZ75LESrw\nXqie13saeK2nngsd2fwkddRxFYyYY7RxX1bECXhRTwN/aClhE50kSBN19JGkNXJhfDa/bymhHS/p\nRERVOU7iMFDMziBQjVCYJVP4h7d8jxig/eEKBRhQQyTpTGii8RgnJMfviBflhMGrhlje18FbPfWM\nJG5ITADkE4mD6MVP3OBqXBY2FARr6eAsds4QrKUdDYJMIscnYaYq3HtAfTgcU9ffJB5sKaEy4EVK\nlezUKVQ2LMHd347dGlm7V9Uwdc2rkWoYY2o2xefegTFmuGN+V+VK9ELLKBnHA2xgXNH5jCs6H0Uo\ndPZU8dHKP1C//GXy51877DhFqyd59HySR8+nae0buJpKB0uj70QdfD2ZJKxCx3gZz52sxIKW13vq\nKfH08lTejGN2KeSrcJ4ji7HmON5zNuEMBbjAlMmZsRnohcJf2yt4Mbyd6+QoEoQJLyEGCBJERc/O\ngGQXAYxCs0cM0N5oD3h5uGUrK/s7kECy1sgPkov26sMS5fjm+JHoUU5IVvd1csH2T/lV40baQz5K\n6OY5WUpo8IFSSAwGFBZSgXuwzoSKxIyW16ji37KSDbKThbKSN6hhLunECD1+GWYTnYw2x+6v+ROS\nrqCPZe42igsWAJCcUIzFlMDi5Q9QVv0BNY3LWbz8QXrdDaRMOIsJl/9hDzEBgKoiEKyhA7M+ZkhM\nACQ6ChiRdQodpZ/vty/xI2aghkNsrXqXHVnrgeAAZVXvUUgcVhERDA5hJB87o3FwF5No8Hu4YPsn\nPNi0mSb/8Zeclmu0cUNKMb/IGM+F8TlYNFp0isID2ZPpVAb4GSu5WS5lCS34UXmVaoKDv5l62ccn\nNHF6TNp+44cgkil1U+0qyvtdXEkRtzCOrJCd3zZv5lNX69G41CjHENEZiijHPGEpWepu462eBkKo\nzLQmcZ4jC78a5u6G9YyQsVxJIbEYWEkb/6CcJEycTx6N9BNApQY3t7OcZGmmnQFMaJhKMh/TyGIa\n0SAwoSUFE0tlC5/SjEeEuCwhaga0O7+ecS6y/BNSE0dT17yS8rpPOH3mHWzY9irrS/+NlCpCKBR9\n6xZSxp2+z/M48qdS+8WL1OLGqLcNiYkdGA12wkHvPo6OYIpNJfuky9iy/KVIrRFrCs3tW5DhALew\nsxhgWKp04iUbGyNELKfJDBbLRpY5O1jibufP+bPINli/2gfzDWCMOY7/jpzPZ642OoNe8o12WgMD\n/LGtjNW0E4ueJjzkG2wHlGX0qauV5uAA9zGd9MF4pLEyHh8hXuyoZP5ePDiiHL9EBUWUY5p6fz83\n1KzEFQ6gAg4MbPRs56+DWRhhKbmOUVgG/4nOJo162ccHNNAs+9lINymYicNALW4ysTKfdMYSz8tU\nYBAa/l4wG78M80zbdl7ur0QC480OfpEy/WtLPfw6aA4M8Fp3HVVeNwk6A//jyGaCxTH0/it/vozN\ni2Ix+T0oWj0tHVuYNu4qPln5MB8uf5DUpDHEWFNx9jWTM+eq/YoJAGtSLmkTF1C98V3od9PVW01C\nXETAhcMBappWEHMAZb9zTr4Me1oRbVsW0+txYk4twNVYQj19pEsLflT+SzVOAkOxNA4MBFH5DdP4\njbqOFzoq9+v+eTxhUrTD6sEATLMl8oGzib5wkKvNI5hnTzkgT5Zyn4tUzENiAiJxRRNlIv/wl+9R\n2yTK8U1UUEQ5ZglLyW01q/GEQ6Rg5mbGkyRM9Eo/T8ktvN3TSLww7mFfnIWNAM14DAFsQS0GVcPF\nFPAnSllNO5U4WUglWiG4P2vyUNT/IznT8KohVMlxHVC5N7YO9HJL7Wq0UmEkcZTgZLGrhVtSR3FR\nfC53L7h+sL4vaA0WUid8iy3rFzEWycwJ11Be+zG1TSvRGKyMveg3OPIOrNxx5oyLcdZtwuts5cPl\nD1KYPRdF0dLQvhGPt5sJJ/30gM7jyJs81KZUw5S/+ygvlH3OS1QQRiKBKykkS9hQpWQl7RQQg1Xo\nmSVT+Kyv+VA+tuOGbIOVHyYfvO9JvNZADz68MoRJ7PzNNOMhTqOPiokTjBNr1IzyjWK9p4u2sA+A\niyggSUTqTsQJA5fJQu5nPe1ygFY8Q+mfAJvpIldv5fkRs9k24OTWujXcr64nFRMaBG4CXODI4rtJ\nI4Yqde5gb5Ukj3eklDzSvJVUaeEOJmAUWqSULKSSJ9rKWf3tu9m9VmXe3KsRQqF043uEQ36EUEgo\nOpnCM29AazywpQMpVcpeuxe9q4fvyEKWhlsor/0EVUbqWMRkjEFvdXzJWfZEKBpGnvtT0qdeQPP6\nt+ks+xytqtKDn89kEytpo4Y+bmM8ECm8pjuOMj6OJmfFZvB8eyXPs40rZCE2dKylgy9o4TJH3iGd\nM6iqVPndGISGXIM16uD5DeLEGz2jHNPsKIpkVDSDVSojxDPcNTGBQXGh0fN4eDPnyVwcGFlBKxvp\n4p6kyMOi2BzLvwvn8oGziQa/h3P0mZwdl0GcNuq/toP2oJcKv5sbGINx8F+mEILzZC4fyya6q9eS\nOu6MYccoGi35868l+6TL8Lna0Vsd6M0HZg2+A2dDCX2dtdzJRHrxU4Wb3LSZ5GXOon+gmy2Vb7Hl\n379g8tVPohxCNoYtpYCRC26l4LTrqF3yIh9s+YhwOEASJm5lHKOEgxbpYSmtLIiNZiQcCok6I/dl\nTeTexk3cLpejRSGIyhxbMt9L3Hsl2/3xTm8DT7Zuo18NAZCuM3NP5oSDqm0S5esjKiiiHHGklJR6\nnazu60QrFObak3GrQcoGXMRq9cyxJ9McGOCp1m1DJZinWhJYMLjOq0NhJW1cxM4BaiVtCODBrCn8\npaOc5z3bAHBoDNyRPIYzd3lAxGr1XJJwaP+WTgR21LXU7Jb0pdmRMqgOT8ncFa3BjDUp95Da9XQ1\nAJFMnP8T68hMnsTsKT8eej/Rkc87n/+SroqVJBXPOaQ2In20MOKM68k/7UdULn6ats0fsFDUYKeR\ncnoxx6XzvcQRez3Wr4Z5saOKt3obcIcDFJti+V7SCGZ9Ax1NjxSz7Sm8NfJUlrjb6A+HGG9xMPJL\nbOurfG5e6KhkXV8XKhKViEV+AJUxxHEeeXgJ8UawhptqVvFY7nTW9HfiDAUYbY7ltJg0DAdZdyfK\nkScqKKIcUUJS5b6mTXzsasWKjhAqz3WUAxGhEETFqmgJSRWHNHElRYDkE08TDw9spchop9rXx/s0\n4JIBiomjGhdLaGGqJYExljj+mDuDzqAPTzhIhsFyXBkWHQ3SdCay9VYWB5sYIx1Dn9+HNCAQxB1g\nPMTBEA76adsUce+vwEWb7Oek1OHtOGKysZgT6W+v+kqCYgeKolB45o0kjJhBx7YldAV9ZCflozXZ\nuE1nwJE3hYc/Xzi0v5SSG2pWUuFzM5d0kjGx1tvBnfVruT9rMqfYU75yn44XrBrdfg3gdqXS6+ZH\nNSuwSx0aFMKEmUc6ZrQspZVK3BjRUCBiyJd2fsIybqyNeIjEYWRRbwP/6qzmqdwZQ3VvohwbRAVF\nlCPKmz0NfOpq5VqKmUEKYVTepp53qONWxpOAkRfUbZTj4lbGEy8iA8R0mczP5SpGmWIxCy0bvT2s\nop0VtKEgmGZN4KHsaUPtJOqMJEYHl0NCCIH9/J9R+uq9/EJZyzg1jnrhoYpesmZdgtGeeNjbbNnw\nDt6uBuIx8ALbMAgtvX1Nw/bxBfrw+nrRWw4+jmJfCCGIz59KfP5U6lf8m/plLw2ZYgmh4fyik3gi\nLMgyWHnP2cQ2n4vrGcMUEZmRmC8zeJzNPNW6jTmDpcA3DfSwzevEoTEwx56C+QQL6D1Ynu+oIE4a\nmE8GL1PBr5lGhojE3ZwmM/kVa3ibOn7MGHyEUZHMJo3LGYFOaGiW/Twc2MQTbWX8OnPSl7QW5WgS\nvfOjHFHe621kIonMEpF0PQUNF8hc1g6Kg6tFMdfK0dzBckrpYQ4Rt0mz0DFWxlPhdfNcwUl0B32s\n6evEqGiYak3Eqj3+HA6/Du5ecD0ADmDCVY/StOYNVrdVobdnM2rij0konHVE2u0q/YwpJHEheTxD\nCZ3Sx/aaj0iIzSMrbSpen5NVm19AqmHi8qcc/vYrV1G39J+MHXEuo0csQEqVzeVvsn37Yi4FUsae\ngbWzFgMaJrFTUClCMFum8WxwK7fWraZkwIlfhtEgCCGxtZbxQNZkJlmjlTr3xbr+Ls4mm2pc5GEf\nEhMABqFhukxmCZGsm1WD7rXfoQCdiCxxpAsrZ8hM3nDV4E8PR5c+jiGigiLKEcUVCpLLcLdJIQSJ\n0kQ/QQBi0WNCixP/sP068ZI0GDwZrzPyLceBTalG+XKMn13IbQ8Pn7K3pRRQfN6BpWl+VdRQADMa\nkoSJX8mpVODiFbWKL9Y9jSI0qDKMYXB4ctZtwuI4vEGTrZveJ8FRwMRRFw1tmzrmclo7t6JR9LRv\n/ZhOrQFQGSCEdZfU5BK6AWj2+JhLOvX0sY1eTiODFtXDXQ3reL3o1BMu9fhAMSoa+sNBjGjoI4iU\nclgmh4sAejS0ywGW04oeDUaGi4ZYDISQBKWKgaigOFaILjZHOaKMNseykU6CMjy0rVf6KcdJPpGK\nk5W4GCBED35CUiUkVRbLBqpwDQVmRjl83L3g+iExERhwUfnRs6x44jKWP3YRpW8+MBQseSSJyZ/C\nGtGFS/oRQlAkYrmWYhRgvIzjWop5lFnEKib8fd2Hvf2AuwuHfXilVyEEjphstFoDeZkng5SoSBZS\ngX/w/m2U/ayijXHE8xumcbEo4KdiIueRw2c0823y8aghPndHbaf3xWmxaSyllRzsdODlfRpQB23T\ny2UvK2ilCx93sYo2BhggxCZ21qRUpWQpLRQY7FhOwDTvY5notxHliHJlYgHX9S3nQbmBuTIdH2E+\npAENgiRMfCwbeYc64jR6vgi3sI4OAAYIcbEjJxr4dhjZsbyxg3DAy+aXf0aor5fCrFPQaY1UNS5l\n0z/vYOJVj2KOP3JiLnPahXSVLeEe7zpOkskEUVlOKylYuJZRmISWJtlPrzpASmLOYW/fkpxLc20J\nYTWEZvChFAr5ae0sJTttGjHWVGoalyGJTLtvoguHNNJMpO7H6WQOM206gywWUUcz/ZjQ0hPy763Z\nKMDVSYVs7O/hRf92YtDzGtUspgGL1NHKADZFh0cNohcaTo9JpSXg5U8DpcyWqSRhZh3t1ODm9ylT\nox4VxxhRQRHliDLCZOeJ3Ok821bO3we2owC5BhtdQcmzaikKglPsydyeNobukJ+l7nYEMNueTL7R\n/nV3/7hgwrdCnK3cvMf2tq2f4O1p5tx59xNri5QVH5l3Bos+u5uGla8w8pzbj1if9NZ48s+4nvoV\nC/mopwVFKASDYXKx0Ug/ndLLG6Iesz3liMRxZEy9gA3blvLxyocYnf8tVBmmtPJdQiEfI3NPZ+3W\nl7Ak5pI0eh61n7+AV4YHxYQCqAQZnkobGEy+7cDLACGKviRt8kTGrtHxXP4sPnhGOu8AACAASURB\nVHa1sMHTjVcNoUXBqtEx1ZrAbHvysEwtvxrmxc4q3ulpxB0OMMocx6NJ05lqTfgaryLK3ogKiihH\nnLFmB8/kzcSrhtAg0CsaQlKlI+jDqtFhHzQtitMaKIiKiMPK7rMSu+Ks30JSfNGQmADQ60zkpk+n\nqn7NEeuTlCoV7z9JW8liTMZY9DozXm8v1pQRrOlpYXlgAwCOzPGMPfsWlCMQgGtNzmfM/95L1UfP\n8unqRwGwmOKZOOpiSireoqVjC8Xn/Zyk4tkkj5lPb91GBBCbM4mSl3/O2z31FMlYTEKLKiX/pRot\ngs9oZrQplimW6MNufxgUDQviMg8o1dSgaLguuYjrkouOQs+ifBWigiLKQVPv7x8qIhWvM5CuNyMQ\nZBgszLen7jNtbldba61QSNObj1aXTzj2JyR2oNEb8fndewTFef1utAbTEetbe8kntJUsZuaEqynI\nivhLVDcuZ8XGv1B41s3YUgvRGW0Y7Ef2oezIncjUH/yZ3rqN1H7xD/rbKllb8i/05jgKz7qZpOLZ\nAOjNMSSPmjt03Ihv3UzJK7/gjtBKimQMdbhxEkABTopJ4tbU0XutYSGlpNLnpifkp8BoJyGa5nzY\nWd3XyX+6a2nye8gwWPhOQi7TrIc/7TnK3okKiigHxYb+bm6vX4NZahlJHGVeF5/RhgUtA4T4U9t2\nHsuZzghTdKbh6+JAxARA0qi5lGz9hO01HzEy7zSEUGjtLKOueTVZJ11yxPpXu+RFEh0jGJE9d2hb\nQdZsqhuX0br5Q1LHn3nE2t4dIQSO3Ek4cifhc3cQ9nsxOdJR9pOhYU8fyeRrnqVl4zs0dNRhtCUw\npnAmj5au3GdmR5Pfwz2NGyj3uQFQEJwbl8FtaWOiRmyHiTd76nmoZSt52BlNPBVBJ7f2r+FnaWM5\nz5H15SeI8pWJCoooB4yUkodbtpIjbdzGBPRCg5SSf1HBUlq5hyn8PbydXzZu4OURp0QrDR4CUkpW\n9HXwkasFrxpisiWBBXGZB5SCeKBCYgdxORNJn3wua9f/i7KaD9BqjbjcTcRmjSNj6gWHegn7xdPV\nQNDrwurYs7Kl1RRPV9u6PWZMjhZG+4HbaRtjksibe/WwbffnTwXggXefGbY9JFVurVtDOCi5hfGk\nYWY9nbzWW41No+fHKXt+FqqUvNvbyNu9jfSGAowyx3B5Qj6FJ3Bshl8Ns7KvA3c4yGhz7LAYq4Fw\niKdbtzOHVL7LSIQQSCn5G9t4um0bZ8SmYxz0q/CpYZa42+gIeskz2JhhS0Kzn/vNq4b40NnMZk8P\nVo2OM2PTo7VF9kFUUEQ5YBoCHuoD/dzCOPSDJjNCCP5H5vIZzbQwwCWM4PeBjZR5ndEf3UEipeSh\nlhLe6m0kCytWdDzVt403eup5Jm/mPgua7c1TAiDk66d+5X/o2rYUNRwgLnciWbMuxeyIxEwIIcg/\n9YckjpxN5/alqKEg6blXkTBiBuIImQX1d9QgpUpT2yZ8fjdGQ+Sh4Av00dC6jnDIh7enGZC0lXxE\nwOPEmlxAythT0Ros+z/5McLdC67n89+ZWDH2EQBW9HXQEhzgXqaSJWwAnEkWfTLI6931XJ00Yg9z\npodaSljU28h44hlDPJtdXVznWsHjudOYYDnxTLM2ebr5RcMGnOHA0La5thTuyZyAQdFQMtDLgAxx\nJllDYlQIwZkyi+VqG1sHepliTWCb18lP69bSGw5gHpxVzTPYeDRn2l6ddntDfm6oWUVDoJ987DgJ\n8HpPPdckFXJ10t7rv5zIRAVFlANGDuaKKwxX8zteq0gSB6uAukIBohwcGzzdvNXbyFUUMVdEHvqt\n0sODgfX8raOS29PG7HHM3Quuh4f3PFc46GfjP+/A72onL2MWRr2N6poVbKpex8SrHsUUF3EkFUIQ\nkzGamIzRR/TadmAYdJBUhIb3vriXwpz5gKC89hNCgw+Lju1LqV/+EnqdBZsliZrSz2la/RrjL/vd\nUL+Pdeb+3AsLruc99Qle+ocHE5ohMbGDQmJ5T9bjDAVI1u+MWanyuVnU28iVFDJPRFJ3vy3z+QMb\neKp1G38tOPmoXsvXTV84yJ3168hUrdzJJBIwsoZ2Xuwr5y8dFdyYUox2UETsnn2z47VWCEJS5a76\n9cSFjfyMSSQJM9XSxTP+rTzYtIVHc6ft0faf28rpCfi5j+mkCQuqlCyiluc7KphjT44Gke9GdPEu\nygGTZbCSrjPzIQ2EBusfSCl5j3o0CMYQP2SVG02bO3g+d7eRhIlT2PnQTBUWTiaNz1zDjZLuXnD9\nPpc4/P09rPnztQz0NBIOB6htWgVCcM4pv0EndNSveOWIXsf+iMkcjTEmGYkkEPSycdtrbCj7D16f\nE6vUojdYaVixkLyMWVx0xh9ZMOdeLjjtIXSqQuWHT39t/T5UzlZuZsmsb+MlTIPsG/ZeJU7MQkus\nVj9s+6q+ToxomL3LfaATCvPJYJvPdcKJ9U9cLXjVMNcxmhRhRisUZolUTiODRT2RsWic2UGcRs+b\n1A6NTUGpsoha4jUGxpjjWNPfSWfIx3cZSZKIBITnixguII/Vnk46gt5h7Uop+djVwjzSSROR2TFF\nCM4hBxs6PnG1HN0P4htAdIYiyl6RUvKhs5l3htZwY7k0IY9b00bz8/p1/B+rGSUd1OKmnj6mkcQi\nallCMxc6sqMR7IM0BwZ4tbuWsgEnsRo95zgymW1L3muMQFCq6FH2eM+AhuDgILkvT4kdSKmy8R+3\nIoJBZk64hlhbBo1t6ympeBtF0ZKbPpOqulWH9yIPAjUYQKMz4g91kZEyAYGgqW0jIOkjgCNzIj1V\na5gy5jI0g+nEVnMCYwvPY+Wm5wkOuNCZv1li1ZE/DbM9iWf6yrhcFpCGhfV08AENXBKfu8dyh1YI\nwkjCyGED9A6vi/2t9x+PdAR9xKAnVgxf8svChkcN4VPDWDU6fpY+lv9r2MDPWEmetFONCw9B7k+f\njFYo9A4KsRSGZ5elDr52hgIk6YZnN/mlioXhacsaBCa0+NThsyFRooIiym741TC9IT8vdlaxqLeR\n0TjIxc4XgXY+cDYz3ZrAj1JGUjHgpNrvRkgVW0jLGrWDOI2ea+MLuSKx4Ou+jGOCCq+LG2tXoVEF\no4mnjQHu6l/P5Ql5XJ9SvMf+062JvN3bSJnsYZSIVNj0yCDLaWWGLfGAgi57azfg7+vijJPuJiUh\nEuyX6MgnHA6yrfpDMlMnofkaxV7r5g8Y6Gni7Dm/Ii4mEnnv6mvm3c/vIWn0fAJ9XSiKBr12+MBu\n1EeWC8KhAN+0snCKRsuYS+5n2xsP8Fjn5sg2BAviMvbqrTDHnsJTbdtYRC3/K/MRQuCWARbTyFRL\nAlbN8E9ASkmp10mD30OG3sxYc9xx5SBZYLTTi58G2Tds2WgzXSRrTUP227PtKbxQMJs3eupp8nuY\nb0jlQkc2ucbIMSMHZ03X08EMdsYcraMDs9CSuVuMjhCCyZZ4lnlamSvThoqTldJDB16mRgvA7UFU\nUEQBIKiq/Kl9O2/1NOAdrFtwGSOYSzpPU0I/QdKxUN8/wMr+bZxsS+ZvBSejFQqqlHjVMCZFE83s\n2IUnWsuIUw3cxWRMIvJTe1fW8VJXDQviMsk2WIftP9uezCRzPI8PbGaKTMKGnnV00G/Q4rroXg7E\ntcPdUo6i6EiOH/6gSk8ex7aaD6ltWkXGjG8frks8aLorV5HkKGRL+Zs0tW8CBBnJE0hJHEV/bwta\nsx1VDVHduJwR2acAg0ZYdZ+iCA0G2zdzEDfFpTHx+0/S315NwNOLNSmXn3/xn73um6Y386PkkTzb\nvp1NdJEkTWzHiUmj4ZbUUcP27Q76+Hn9esp8zqFtI40x/C57yl6DDL+JzLYlk6W38GRgC/8j80jC\nxGraWUU7dySOGRJPvSE/r3bX8omrlYCqohcKYeTQefKNdmbbknmxbzttcoAc7JTQzWc0c01i4TCf\nnB1cl1zEjbUruVeuZZpMphcfK2lnsiWe6dYDzwo6UYgKiihAJKr8A2cTk0iklQE68TKXdJbRyha6\nuYVxjBMRo6GNspOn+kr4wNnMOXGRmgYnQmVFvxrmC3cbtf5+0vTm/Zp49YeDbBzo4fuMHBITEKn5\n8A71LHO3k504XFBohcLDOVN5tbuOxc5m6lU3huJTGDHjYkxxqQfUR4M9CVUN4upvGeaA2e2sAwR6\nm4PMaV+foFDDQbp6qjGbHEwe9R0kUF77CV6fE1NiJo7cyfRUrWXVpr/R3rWNWHsGjS3r6HTWYInP\nQnyDPRuEENhSds7e7Zhx2j3NFOCKxHzGmGN5p7eJ3pCfy015nO/IIn43kXBv4yZafV5uYzxFxFKB\ni+d9Zdxcu4qXjpPUbZ2i8Mfc6fyuqYS/ebYBYFd03JRUzPmD/hKecIjra1bSGwgwjwxMaFjW38qP\nq1fwXP5JQ7MU92ZO5Om2bbzX24hPhonV6Lk+YSSXJuQNa7M1MIA7HCTfaOPZvFm82FHJ554mrBod\nV8Xlc3lC/gm39HQgHP9PgShfSnvAy3vOJuIxspZIQJhKJGtjDe2MJX5ITABMFImMknF87GzhnAOw\nzj2WcYcCDKhhknTG/Q6+TX4Pt9StpjXoJQ4DTvw807qN+7Mm0Rz00hX0UWSKYZo18UsHmv29a1A0\nXJGYT9n3Hjmo6wh63VR/8hfay5YghMLSdc8wa+IPiLWl09C2ni0VbyGEwqSrHkdr+PocSoVGh6Lo\nOHvOrzDoI4IqP/NkXv/4djQ6E8mj59O04j8Ij4uu5g00Na9BJyMiQigKbSUfkTzm1G+0sNidfQmL\nCZb4/aaI1vv72TDQzY8ZwxgR2W80Dq6URTwZKOGuhnX8LmvKcbH8kaQz8WjuNLqCPtzhIOl687DY\nk/edTTQFBriPaaQOBlDOk+ncI9fwj84qfpU5EYiUTr89bQw3phTjDgeJ0+qHjMWqfW4WdtWwoq8D\nVzgIgFXRclViAfdnTT4uPscjTVRQnICEpMqnrlaW93UggESdEUmkwufPmEgsBu5iFe9Rj58wsezp\nf2BGh0+N/OjCUrK6v5Mqn5skrZFTYlL2On14tJBSsnmgl89crQSlygxbIifZkoc96NsCAzzcspVV\n/Z1IwIDCJGs8d6SNJUW/p+30fU2bUYNwH9NJFxa6pY9n1BJ+UreGMBIrOvoJUmS080jONOK0BiaY\nHXw80MQUmTQ0S/ExjfgIc7I9ea99n1VyeyTl8GCuVw2z8R+3Efa4mFT8bUCwpfxN3l1yz9A+ikbH\nmIt+jc789aa5qQEvWamThsQEgNFgIzNlEt2hLrQGM+OvfIiqxc/SXbM2cgwKhcQQ6Oyh/L3HcdZv\noWjBbcfdAH/3guv3OluxL9oCkfskh+HpqDlEvuNlfR2s93QzxZpAWEqqB1068432Yb8FTzjI2v4u\nJDDZmjBUW+dYJEFn3GvA90ZPN4XEDIkJAKPQMlUmsc7Tscf+BkVD4i6C5ENnE79t2owVHZnY8OFC\nAMWqg2fat2NWtFwQn31Erul4IiooTjACapg769ex1tNFHnYkko9oQRApyVwkImZU58kcFlGHFS0t\neOiRPhwi8kPukl620MUVtny6gz5uq1tDlb9vyH77idYyHsqZyuivwdhKSsmjraW83lNPAkYMaHir\nt4Eplnj+kD0Vg6LBq4a4sWYV/pDKlRQRi4GltLCyv5MrKpfw1/yTyDHuHKQb/P1s9fZyA2NIHxyw\n4oWRK2QR97GOHzKaaSRRiYtnfCU82lLKfVmTuCl1FDfVrORuuYqxMp52BqjExaXxuXvET8DgP9WD\nFBMA1Z8+j9fZyrdm/5JER8RspzjvTD5Yeh8ubweZs75DxuRzUXZLT/w60JpsePp79tju8XajjYk8\nCI0xyYy56F5KXvkl1JUxghjW0oGKRCBoL/2MpNHzcOROOtrdP+Lsbxlkd3IMVgSwlR7msXN5ayvd\nAMRjYIm7jYBUeaR5K22hyL2VojVxQ2ox481xLHe388e2bfgG46b0QuH6lJFcFJ97mK/syGJRtPTg\nZ7vsxYFhKC3URWAoaHNf9IeD/KF5K9NI5mqK0QoFjwzyMJvowMt0knipq5rzHVnHnYg93EQFxQnG\n272NrPd0czsTGD2YSbBOdvAMW0lgp/I/X+SRL2N4ki0A3MtaTpapqEhW0oZDZ+BCRw4PNG2my+/n\nbiZTIGLolF7+opZxV/16/ls0H51ydKemV/V38npPPZdTyDzSUYSgVPbwR89mXu2u44rEfD5yttAW\n8vIAM0geHHjGy3geYiM10s2f2sv5XfaUoXO6Bg2Xdph27WDHa0FkfbyQWBbIHF5xV9EXDjLSFMML\nBbN5ZTBtNF6r57txk5hrH+5qOfNv45j330MzK5JqOFK10xA7JCYANBotI/PPYPmGP5M+ccExISYA\nksfMZ/s7j1BZ/zkFWXOQQFX953R0l1N80s+G9pNqmN76TWRjYb3Sw6TiS0hJHEVHdyUbyl6h6qNn\nmXbdX76+CzkA1HCQkLcPrcm+39oge+PuBdcz/jwn3/nhy/vcJ1lv4vSYNP7tqiQgw4wkjgqcvE4N\nU0iM/BEI+rmrfh0jieMqRtJHgJdCFfyyMVLRVUEwghiuoRgNCu/KOh5vLSNDb2ambe+zaMcaQVXF\nFQrQiZc/sBGAUTKOk0llDe1cHVu43+NX9nXgk2EuomBo+cMidJwjc3iaEmaQzOpgB141vM+YqSgR\nop/OCcYnrlbGET8kJgAmk4gRDatoY5ZMGVLhehRCSG5MLmZZXzvLfC3ohYbTY9L4bmIBYamyor+D\n7zKSAhFJyUoUJr4nR/J/4dWs6u9gtn1PS+gjyUfOFjKwMJ/0oesYLRxMlUksdjZzRWI+5T4XaViG\nxAREBMEkmUgFTpb3dRCWcmhaOM9gwyg0rJbtZO0yvbyadgSQy85lhFTMqEj6wkFsGh0ZBsteHS53\ncPeC6+G/B3+dAY8TNRwgHAwQDvw/e/cd3lZ9NXD8e7VlWZL3jvceibN3SMJMQpmFNtBJW0rTlpdC\nR5rSSedbWjre0tLSQssIFGhKGAECGSRkObETx47jGe+9bdna9/1DjhNnD9uS7d/neXgefH0lHV05\n0tFvnDOITeHAZreg1Zwa8u3rb0ahVI9J++8rFZa5nO6aQvYe/gcFx/8DyFitPUTOuInQ9KWnThzq\nxVBDH3OzPkN64nUABJnjUKt0fFTwVwY6G4bLiPsSt8tJ9e4XaCp4C6fNglpnJGr2x4hb9MnLKml+\nZHMAR84o432m70RPp3igm387KpDx1EiYRzi5BPNniol06QlAy4NMx43MD8lDiYJ7SCEA7fCi63os\n5Eoh3CunUkYPv28qmTAJxZPNJezrb+PjJDGdEOro4yXKeZpjzPIL5pMhFx5tsQ/VeNEx8rXRD/1c\nSz8mhXq4F4hwfiKhmGLsbhfmM3byS5JEtGygmC6e4Ajz5XDasbKVOsJUOv7UUoI0tJRwACfBKi0h\nah1V1j5kzi4UEz70zb3zEiv6uWSZXpcdSYbtvc0cH+wmUKVldWAMseeYGriQAbcTE5qzhiaNaKh2\n9VJt7cPictKBFZvsQiudepNoxIIeFYNDBYROMijVrA1J5Jm2cvplB1kEUUUv71NPOoGESqdGLg7Q\nSpBSS9hFtuxdbiOv4efXUUf5e0/SXesZOZJOxi/L7DvyD+ZP/xxajT+NbUcprtyCPih6zPpyXAlJ\nUpC66n+InHET7RWeAlshKQswRqaNeM0kSYEpOoPehmNEheWMuI+TPw+01/pkQlHx3pM0F71PZuKN\nhAal0NJ+nON7XsJptZB83f2XfX+nl/E+vGXkW7ZOoeS38fO4v/IjHG43uYRgxcVTHGOhfyhdTjvp\nBKKSFOyWm2hhYHgdEMBsOZRfU8CbVJNLCJIkkSKb2W1vwim7x7QTarN9kN19LbhlmYXGsLPqQFwK\ni8vJ6121rCGeVZJnjUM0Bsyyhl9zmM+EJaNVKIerZ57r+cw2hKAA3qeejxEPeJqzfUA9/qjZTzOf\nDk6eFDtmxppIKKaY+cZQXrKeoF0eJGTog7BVHqCWfq43R1Jp7efvthJ0kpIZhiD297dxM3GsxvOP\n9W1q+FtrGal6M7MNwRgVag66W0klYPgxDtEGQIbeTKW1lyprH+Fq/VkFd2RZ5pWOal5or6TdaUOJ\nhIxMHEbasPJCeyUbomewKjDmkp/fLEMw/9dXQrM8QMTQCMSA7CSPVhQy3Fvx4fC5z1DCPXIqBlTs\np4VdNKFDyeJzdB/8QlgK/koVG9ur2OVswqhQE67UccLRw2b5BNH4k08be2nmobDM874RX2kiAZ6d\nHEdeXI9WoWPxzC+hURsorf6AxtajKJUa6poPU9f0IGq1Hpu9HwkF/pEXHu71BkmSMEWnY4o+u8vm\n6eKX3Evhy9+jo7sKk/+pb8vt3VUAaM2+VwfA1ttO09GtzM1aS0aSpw17bORstBojRwr+S+yiT6C5\nwkqfqxUPwpqz11fEaA38I3kpL3VUcbCvHT+FigcDM7gtMI4f1xdQbu1FlmVO0Es0huFkAoaKN8lh\nvEgZsizjRqaYThy46Xc5zyoLPlqea6vgry2lKJCQkPh98zHWhiTy1fD0y1qn0OIYxCa7yWTkeq10\nApGAI5ZOXu2oZndfKzIy8/1DWReRPqJTaYRGz9qQRF5or6JC7iYOE4dpp55+AG40R/H5UNEI7FKI\nhGKKuSs4gXe7G/iJI4/5cgQyMvtoIVyt4+GoHExKNQMuJxqFgkdr80nAyB1S0vDt7yCJYrmTzZ21\nLDKGcU9oIk+1lGKX3cwgmFr6eYda5htCeKqllH39bcO3TdIa+UXcHKI1ng/6F9ureLLlOEuIpJ5+\nerHzbWYRJulxyG7+xXF+1VDIAmPoeTttnmlN4DQ2ddbwc/shlsqR6FDyEc30YUfrUvIAWSRj5i1q\n2EEDebSiRIETNyoklBLnbCctSRKfDEnk7uAELG4nfgoVVreLPzUfY0t3DTbZTZhKxyOhWdwedO7V\n4FeTTAA0Fb6H02rhlut/gp/Ok8BFR+Ty1o4f0N3XgCy70OsCsdn7kCQFCoUarX/QRe51fDisfbQc\n/QBLWzVaYwgR069HZ77wkHpgfC7mmGwOFD2PWqUnIiSD1s5y9hf+C1NUOv5hiRe8vTf0tVSA7CYu\namSjqfjoeRw+/iqW1hNo4nOv6jHOtXAzQqPnocgsOKNcyR1B8Xy9dx/PUYoG5TlH5poZwICacnp4\nh1raGMRfocJ/jNYLHOxv5y8tpawmjpuJQ4HEB9Szsb2SdL2Z68yX3gAuRK1DjUQlvaSc9qWmil5k\n4OWOE+jdKu4mCQUS2/sb+ErVXv6RtISY00ZEvhKeTrzWyKaOGvY5mghWa7nXP4k1gTHnXEAtnJtI\nKKaYAJWGpxIX8Vx7Jbt7WpAkuMU0jU+FJg1vFzu58KjNPkgUZw9DRmGgfaiRzqdDktBICv7VWsGH\n7kbUSKwwR2J3uzjQ18EDZJFDMNX08S/bcb5Tnce/UpbhkN0811bJtcRwOwl8nV18ijTChkZN1JKC\nT8gp7KOFHb3N5/2QPpNBqeLJxIX8vbWMbd2ebaPZfoG0Wgb5MhnMlkI9cZNGuKznJSoIV+vQKhQs\nMIZxV3D8WfX8T6eQJIxD18mgVPHt6On8T2QWFrcTs1JzzhoULz91D0c2B5x1/HLIskxfYxkhgUnD\nyYQnHgWxUXPoLm1AkhT46QJIibuGgcFuKmp3EJ65/KoedzRY2ms5+uJ6HIN9xCpMNMsW6va9QsZt\n6wlJWXjB22be9l2KN/2Ubft/O3zMGJ5M5q3f9ckV9xo/z2vTa2nGT3/qW3Nvv6e5m9pwdX8Hpzuz\nTfq5zPIP5puR2fyx+Ri2oWH/ZznOvXIqfqg4RBs7aMCFzC/Jx2/oI2FtSOKYTXds7qwlBgN3kjj8\nGq4ijqNyB2901l5WQmFSqrkhIJrN3ScwympmEEItfTxHKWaFBpvbyfeYg0nyjLQskiP5nnsfL3VU\n8c2oU1NpkiSxOjCG1ZcxGiqcTSQUU1CwWsdDkVmebzTn8H53I8+1VVBh60NFHyGynpuJQykpsMku\niunkGr1nsaUMVFr76HE70KPEjcx7PZ5tqPeQyjzJ8y00g0A+J6fzK3sBhy2dBKg09LkdzCUMO25k\nwHTG2g49SlQoGHA5h/sV1Nj6iVT7kWsIOu+cZqBKyzejcobfMLb3NHHA0k4aI9/MpxPCS1SwPno6\ns66iLr9WoTyrwROcVlNi8xXfNbLbRd2B12g4uBm7pQutxojb7URx2la47t56tGoDNkc//QOdlFZv\nw2brJXHFF/EL9n7hsfK3f0eg1cW3WEigrMUmu/irfIyiN35D4NeeR6k5/3oTjSGA3Ht/TV9zOYMd\n9egCIjBFZ/hkMgFgjErDEDyN/UefY/mcr2E2RtHVW0de8UaM4ckYQka3lsHJ9RUX2mZ6e3Ac1wVE\nkdffTqGlk/921nKQVjQoGMSFv6SiX3YCYMfJ3cEJfHoM+/F0Om1EYjjrNYzEQLWj57Lv7xtRWfS7\nHPy9r2T4WJLWSJCkwWDVDicTAH6Silw5hEJL15U/AeG8REIhjPDfzhp+3VjEDIL5NKnU0M9mTlBK\nFyvlGN6lFqvk4hNDK6ff6KpjS3c9nyOdxUTgRuZ5ythFE4mMLKKUhGfuuMkxQOzQcGMLA6RgJhI/\nPqSJmXLocKKwnxZsuEjVm/hK1V6ODp56E0jUGvnfuDlEai5e9fHkYq9SupjNqXn3UrqQ8PROGG1X\nWlPiTJXb/k5D/mZS45ZjiA2hoOQV9h55htlZn0St1FFeu5OaxjySpi2msm43xoQc9AHhhGWtxD80\n/uqfyFWy9rTQ01TKWrIIHOoWqZWUrJWT+bZjLx2VeYRlLL3gfUiShCkyFZMPrgc5kyRJZNy2gaP/\n/j6vb1uPVmvEZutDZ45g+q3fGbNE6GL1K4xKNSvNkaw0R/LZsGR29DZjN81WswAAIABJREFUcTnJ\nNQSRqTNTaeunx2UnWWcas3UTJ6X7BfDGQC0DshO/oYJvDtnFUTqY63f5ib1OUnJncDzTtAbsspul\nxnBmGoLZUHuIeuvZ/wY7sA6PMgqjSyQUwjCH283TLWUsJoL7OPUtMFb253nKOE43KVoTT0TNG55X\nfKuzjhmEsEzyDFMqgbvkJHbTRDGdI7ZUFuEpaJSgNRKi1rHAP5T/9p8gBn/uJIk/cZSfc4g5chhN\nWNhDMytNkbzUfoLaQQsPMYNMAqmkh2dsx/luzSGeSV5y0TfpZJ2JWX7BPDdQilOWScbMMTp5hUqu\nMUacszLmlbqamhJnsvd30ljwJjPTP05O6scA0GvN7D3yDJW1u1EolLjdTlLiluNyOVDr/Mm8db1P\nfXt3DVVz7MQ6YiGwEc+Hlstx9UmXrzGExDLv/r/RXraXwe4m/IKiCU5ZgGIcPsQ2rFnH9jt3s/e+\nwvOeE6jSnjWFmKIfvwqqHw+KY3NnLb9y53OTHIsSia3U0SvZz+qpcTG9TjvfrMmjeLAbDQrsuNna\n3civ4uawOjCG9X2HeE+u41qikZD4iCaK6GRD4PQxenZTm0gohGF1dgtdLjtLiBzxobSESJ6njK+G\ne5ronP67bpedrDNK//pLGoJkLa9zAqUskUMwJ+jlNSqZrg8kY6iN8Pro6XzjxH4esx/EhBoZqKWP\neqmfEJWWLwalstwUwdrynXyRDKYP9StII5BPy2n8xnaY4sFusi+hIudjsbP4UV0BT1mKh48tM4az\nIWb03liutKbE+fQ1VyC7XSTEnFpnkBy3DLMxii27fkJEcAYp8ctp76qkuOZtklZ+yaeSCcdgH1Xb\n/g7Av6nk31QyWw7lPjLYTRMgERCbc+E7maAUKg1hmdd45bFXvLYE1iy5rDLe4ylC48cfExbwRGMx\nfxs8BkCazsQTkfNG7L64FI83FlEzaOERcskkkFYG+bvrGN+pOcirqSu4KyielzrLeYtqFEj0YGdV\nQDSrAsRaibEwYRIKSZLigO8DK4EIoAF4AfiZLMsOb8Y2WfgNzct3M7J+RDc2ACI1fmd9YGX7BZDf\n08YdciKaoZXjPbKdPuyk6ExsslbxCpUALPYPY0PMjOH7CFXreDZlKXv7Wqmw9hGm1rHijA6ehy2e\nUY34M6ZPTvYvaHVYL+m5Bag0/C5hPjW2fhrtA8Rq/Yd3m1ytq929cT4qvec5Wgba8fc71ZzN4fQ8\n58a2ozS2HUWp1hG3+F6i59w6JnFcqeOv/xJr7TG+SAbJBHCcLl6inO9zgE6sROWuRh9waV1Uhct3\nOWW8x1ua3sxfkhbR5bThluWzuqheil6Xg+29zXyS5OFCfeH4cZ+cyQbXPvb2t/FQVBarAmPY2duM\njMxiYzhZ+gCfSrwnkwmTUADpeKocfwmoBLKBpwE/4NtejGvSiNDoma4P5PXBEyTKJkIlPQOygxcp\nx6hQs9B49r7/e0KT2NHbzK/kfJbL0Z4hR+owKNX8On4uCiTq7P2EqfSEn2NqQSUpWGqKOG9FzVit\nASUShXSM2HFydKhfQeJlbumK0/qP6jawsUomAExRafgFRpNX/CLL5z6Iv18IfZY2Dh7biCEkjoxb\nv4vT2ochNN6rHUTPxdJeS2fNYR4ga3hhbhh6kD27DGIXrSV+yT1ejnJq8OXE4lK3g59Lt9OGG/ms\nnWhh6FEi0T6UeKfpzaTpr6z2h3B5JkxCIcvyu8C7px2qliTpceABREIxar4bM50HT+xnvXMvMfjT\nygBI8LNps89ZejZZZ+IPCQv4U1MJzwweRwIW+ofx9cgMgobeLAJUV14LIWioYuamripcsptMgqig\nh/9SxWL/sBFNvMbTWCYSJ0mSgoxbv0Phy4/yn62PYPALxjLQgcYQyPQ7H8MQ4v0dHOcz2NkAeAoM\nne7kTpuAadmTqgX5RHAp20wnkgi1HpNCTYG7nUxOvccU0oELmTSdSCLG24RJKM4jADi7daFwxWK1\n/ryQcg1bexqotPYRqvbMN4ZeYEgy2y+QPyctwuJyopSkUa95/3BkFkokNndV8xpVKJC4zhzJNy/Q\nI2Os6LbfwcOPj09/Ell201K8A4fVAshYBtpR6Uykf+ybGHxgB8eF6AI9UxnldI/YWVOOZ1ugLmB0\nrqHb6aC9bA+W9hq0plDCMpahuoISzlPFpWwznSg0CiX3hCbyl5ZS3LLMTEKox8KbVDPTL4gcL3Q7\nnuombEIhSVIy8DXgYW/HMtkYlCpuu8RCUmfebixoFEq+FZ3DlyPSabIPEKbWXdVQ6ZUYrinx+Pg9\nZsPB16nP20Ru+h0kxS5lYLCDvKKNHNv0c+Y98DRqL43OXAr/0HgCYrL5V0M5sgwpmCmhi5ekSkIS\n56EfhYTC2tNK4YvrGextwSzp6ZWtVG//B1l3/RhzTOYoPIuxIcsyTUfeofHQm1h7mvELjiVm3h0X\n3T47mnx5GuRyfCrEUwHzhbZKtrsbUCNxrTmKb0RliXUSXiDJsuzdACTpF8B3LnCKDGTIslx22m2i\ngR3ANlmWv3yR+58FHFq2bBlm88ghsLVr17J27dorintPzs1XdDth4hmP6Y1z2f/nzxNlTmXJrFMN\npQat3by69Rskrfwi0bNv8Upcl8pu6eb467+kq+7o8LHg+Fmk3frtq06GZFkm769fwt7Tikt24Sep\nWSCHUYOFer3MvK/+c1y2aV6Jqp3PUrfvFWKj5hIamExjWxFNrUdJvu4Bomd/bNzjudg204nA4XbT\n5rRiVqox+OjrPl4WHX3zim+7ceNGNm7cOOJYT08PH374IcBsWZbzL3R7XxiheBx45iLnVJ38H0mS\nooBtwO6LJROne+KJJ5g1a9aVRShMSaNZU+JyyW4X1t5WwhNHJq56XQBGQziDXU1eietyaAwBTL/n\nl1jaqhnsbsYvKHrUKndWbf87g91NJMUuJTpsOu1dVeyseo9sORDbYDudJ/IJSZ4/Ko81muz9ndQf\n+A8z0u9gRtptAGQm3cS+I89wYtfzREy/AaV6fEfffH2b6aVQKxRjUqBuqjnXl+z8/Hxmz559Sbf3\nekIhy3IHDC3Zv4ihkYltQB5w31jGJUxto11T4nJJCiV6cwRN7SWkxC0fPm4Z7KSvv4UwH2zbfT6G\n0PhRXfPhsg/SVLCFzKRVzMn2vPnFR8/H5B/OviPPAuAc7B21xxtNPfXFyG4XqXErho9JkkRq/ArK\na3bQ31qFOTrDK7FNlmkQwXsmzDLroZGJHUANnl0dYZIkhUuSdOGWhYJwGTasWee1KY4zRc+7ner6\nvRwqfpmevkYaW4vYtv8JVDp/wrNWXPwOJilLRx0up5WEmAUjjsdHnyoAZoq6cGt0b1EMLW622vtw\nuZ309rdgs/djtfUBoBzFqq1XasOadei23+HtMIQJyOsjFJfheiBx6L+6oWMSnjUWo7utQJiSfCWR\nOClq5hocAz0c3/8axRVvAWAIjmX6HT+d0jsZ1EMFv/osrQQHJAwf77O0AGCOyfaJpmjnEhiXi1pv\nYmfeH7HZ+4dbzatVevyCYka9ediVevjxCLiEMt6CcLoJk1DIsvxP4J/ejkOYfHwtkThJkiTil9xL\nzJxb6WupRKU14B+eNOVXr+sDIjHHZJFf8gpmYzSBphgsgx3sP/IsKo0fOXf/2NshnpdCpSYkfSlN\nBW+RErecuKh59PY3k1/yCjZLF4ef/xY6cxhRs272iZ0qk2F9hTB+JkxCIQijbTxrSlwNlc6fwLgZ\n3g7jvNxOB21lH9HfXIHaEEB45gq0xitvB38p0tY8zNGXvscb2zeg1wcyaO1GrfUn5xOPobyCMs7j\nRXa76CzfR9K0pSzM9SwDk2UXLqcNFVrc3e10t9XQWrKT1JseJHLGjV6O2EOsrxAuhUgohCknd5WT\n1YoHx7WmxGRl6++kcOMGBjrr8PcPZ3Cwi5pdz5Nxy3cISV148Tu4QvqACOZ88S+0l+/F0laDzhxG\naPpSnytBfibHQA+2/g6mZXp2nLllN3sOP4NKpcHptGI2RuF2O7E7Bih/70mfe04isRAuRCQUwpTi\nq9MbE1XF1j8jD/Rx8/KfEmSOxe4YYE/B05S88WsWfPWfY1p8S6FSE5axDLyzKeKKqHT+KFQaunvr\niY2cTVdPDYPWTnRaE6uX/RizMRK37Kbg2L8prniblqIPvFKb4mI2TJJqm8LomjC7PAThauSucopk\nYpQ5bRY6yveRk/IxgsyxAGjUfsyf/lncLjvtpXvG9PFddiu1+14h/5/f4NAzX+fEh//C4aPbRU9S\nqDSEZ62kqOJtapsO4XI7AYmMxBsxGz3lyhWSgtz0O1AptXTXFXk34AvwpR1Rgm8QIxTCpCfe9EaX\nLMt0VB6g6ci7yLIbg37kegmd1ohKqcFp7R+zGFwOG4UvbaC/pZJpkbNRqlXU5r1OW8kuZn76cdR+\nvtsYKmnlF7F2t7DjwO/xfKeT0WpGdsBVKNSolBrcTrtXYrwcYhpEOEkkFMKkJRKJ0SfLMuXv/h9N\nR94hwBSLSqmlsm43MREzh3ef1DYdwum0YY7JGrM4Worep7e5jNVLf0hIYCLg2Ub6xo7vU7njWZDd\nWFqr0BpDiJy5muCkuWMWy+VSavTkfOIx+hpLaS/fR/3+Vymr3k5S7FKUCs9bcn1zAVZ7H8FDTdYm\ngg1r1vG2+w8c3iI+VqYq8coLk5JIJsZGd20hTUfeYcGMz5Mav4Kquo/Ynf8UH+x7nLioefT0NXK8\n+n2CEudijEobszg6Kg4QGZI1nEwAGA1hhAUl03h0Kwa/EKJCs+jsrKXo1R+RsOyzxC68e8ziuVyS\nJGGKTsc/PJHG/Dfp6q3lze2PkhCzkP6BdqrqdgMSYVkrvR3qZVmteBDWiNGKqUokFMKkIhKJsdVe\nuht/Q9hwOfDEaYtRKNQcOPovGluPotYZiZ5zC3GL7x3TehmSpMAtu0Yck2WZ9q4qwoNTuW7ht1EO\nNYk6VPwyx3Y/T3jOdWj9g8YspiuhUGmIXfQJTux8FqfLRlHFWygVKmQJguJnYYpI9naIV0RMg0xN\nYlGmMCnott8xoZMJt9NB7d5/k/fX+9n7x3sp3vQz+porvB3WWdwuF0qlZkSyEB89j7T4a1Fq/Fj4\n4EYSl9835g2uQlIX0dJ+nKa24uFjDc2HsTssZCWvGU4mAHJSb0Z2u+iqOjSmMV2pafM/TtLKL+FQ\nuHE6rThlJ5G5q8i8fYO3Q7tqooz31CJGKIQJb8OadRO6poQsuyne9FO6qgtIjF6EITSI6sYDHH7+\nW8xY+wtM0b7TlyIocTbHCt+lrimf8JA0NGoDNruFirpdBCfNHbcqnmFZy2k9tpOte/6XiNAMlAo1\nDS2eNuky8ohzZXnoZx8tMCpJEjFzbyN69sdwDPai0hpQqDTeDmvUiDLeU4dIKIQJayKPSJyuq/oI\nnVUHWTHvIaZFegoe5aR+jLd3PcaJD//JjLW/8HKEpxhC41HrzWw/8DsA9FozTrcTlAoyl9w7bnEo\nlGqy7/ohLUXbaC/7CLfLReKK+2gpfJeiireIDM1CpdQgyzKFpa8jKVQEJfrOwsxzkRRKNIZAb4cx\nZkQZ78lPJBTChDNc6XKS6KouwE8fTEzEzOFjSqWGlNhrOHD0X8huF5LC+/3vnNZ+Cl/agEZSk5vz\nKTRqA2XV22jvqiR99XfwG+eW6gqlmsgZN44oT22MSOboKz/gP+9/k6iQLDp7a+nurSNp5RfRGALG\nNT7h3MT6islLrKEQJpQNa9ZNqmQCQKnS4HTZzlpkaHcMoFCqwUeagTUf3Yrd0sWNizeQkXgDSdMW\nc+Pi72I2RtNa9IG3wwMgIDaH2Z/7A0EZi2h3d6KOjCPz9kcJz77W26EJZxDrKyYfMUIhTAgTeXpj\nsKuJhvw3sbSeQGsKJTL3JszRp+pFh6YvpWbPRgpL/0tu+h1IkoLe/maOn9hKaPpSJMk38v7exjJC\nA5Px9wsZPqZQqIiNnM3x2u1ejGwkv+BppNzwVfqayqn44CmObfopAKboDJKuvR9TZKqXIxROOrm+\nQoxWTA4ioRB83kROJnrqj3H0399HpVATEZxOx4mjHC56n5Qbv0ZU7ioADKFxxC/9NEd3PUdV/V4M\n+iDaOsvRmcNJuOZz3n0Cp1H7megeKMYtu1GcluT0WVp9rjLlYFcjRzZ+F5NfGItn3g+SREnVuxRu\n/C6zPveHcZ+eES5MTINMDiKhEHzWRE4k4GRVyT8RaIzh+oXfRq3SIctu9h35J5Xv/5XQtCWo9Z7m\nWXGLPklA3AxairbhtPaTNHMF4dnX+lSnyYic62jMf5NDRRvJzfg4SqWa6vq9VDfsI2H5570d3gj1\nB19HrdRw0+INqNV6AGIjZ7Ppg2/RcPB1Um6Y2H9bk9WGNevY8Us9e3J+4+1QhCsgEgrB5yw6+gjL\n1w96O4yrNtjViKW9mnnzv4FapQM8BZlyM+6kvGY7nVUHCc9aMXy+OTpjxFSIrzFGpJB07f2UbHua\nsprtKBQqHI4BQtOXEjPnVm+HN0J/UznRoTnDyQSAWqUjOnQ6rU3lXozs3NxOB/V5m2gt3obTNoB5\nWjaxC+/GEBrv7dDG3fL1gyDKeE9I4tUSfMqGNetgEiQTALLbs8jyZH+Gk5RDOzZO/n4iiZlzKyEp\nC2kr3Y3baScwPhdjZNq41Z+4VGo/Mz2dTWcd7+lvQh3kW1szZdlN0Ws/obv2CAlRC/ALCuREzX4K\nKh4h55M/x9HfgbWnBb/gWAITZvrMmpqxJsp4TzwioRB8wkSf3jgXv+AY9OYIjlW+S0RIJoqhRKK4\nYguSQklgwiwvR3hldOYwps3z7dX5ETNupPg/j1FYupnM5JuQgGOV79DeVUHW8ke9Hd4InVWH6KrO\nZ+WCR4gJnwFATuqtvLH9exzduAGX04pCocbtdqBU6wlMnE3sgo9jjEjxcuTjQ6yvmDhEQiF43WRM\nJsAzvZF03Zcp3vRTNm/fQFRYDp09NbR2lBK35F6f6ysxmQQnzyd24d0c3vtvCstfR0LC5bIzbcFd\nBKcsAMDtctJZlYe1uxl90DSCEmZ6pd5H14kC/A1hRIdNHz6mUqpxOq3464NJiF7I4eOvYfKPJDQo\nmebaEgrKHibj1vWEpi0e93i9ZcOadcy4pZtPfPlFb4cinIdIKASvmayJxOmCk+eRe++vqT+widq2\nY2iNoWQu3TClPgi8QZIkEpZ9lvDs6+ioOAB4XouTuzsGOhso+vcPGOxpRqnU4HLZMQTHkn33j9GZ\nwsY1VoVKjdNlQ0ZGGqoP3tJ+HKu9l6VzvsKOA38kMWYRi2d9ydMUze1iZ94fqXjvzwQnz0ehnDpv\n40c2B3BEbDP1WVPnL1HwGVMhkTidKSqNzNvWezuMKya7XbQe20lr8TZctgHM8TOInvUxNBNghMUv\nKBq/ebePOCbLMsc2/Ry1W8HKa35CcEA8bZ0VfHjoSY5v/jW5n/r1uMYYmr6Euv2vUlz+FtkpNyNJ\nEh09NQDY7BYczgFyUm8ZXjuhUCjJTvkYdbt+TE/dUQLjZ17o7iclMQ3im0RCIYyrqZZMAFh7W+mp\nK/LMfyfMGvNOnKNJlmWOv/k4rSUfki4FYZbVHG7eRGvhVmZ8+rfozOP7bX409DWWYmmv5vpF3yE4\nIB6A0KBk5mStZWfeHxnoqMMveNq4xWOMSEGp0VNQ8gqVtbvQ6wNpaS8FoK3T03H2zDWvJxfBlr37\nf8z+7O9R6fzHLV5fIhIL3yISCmFcTMVEQpbdlG35A81H34ehDphqnZG0mx8hOMk3GlXJspuO8v1D\nuzYcBCXOJjxrxXC3y67qw7SWfMj9ZLKACJCgW7bx44FDVO96jvSbH/HyM7h8dksnAAHGmBHHT/5s\n6+8c14RClmVc9kEykm7C4RjA7hhkbs49NLcdo/TE+ygVagrL3mDxzC96pjxkN0Xlb6LVGLH3dVJ3\n4D8kLPvMuMXrizaIaRCfIBIKYUxNlpoSl8vtcnLkxe/Q23icWZmfIDV+BVZbD3lFL3Js08+Ze/9T\n6ExhOAZ6sA/0oDOHj/vIhSy7Of7G47SW7GSaZEKHgrKyj2gu2ELO2p+j0vrRUbGPEIWB+e7w4dsF\nSFqukSPYUrZ3XOMdLYawRECirvkQqfErh4/XNR9CUqgwhMSNazySJGEInoZloJ3l8071qYkMzaKu\nOR9QUFW3m/bOCsKC02jpOE7/QBvL5qyjoeUojaV7pnxCAWK0wheIhEIYM5OppsTlqvnoRfqayomP\nXkB2yhoANGo/ls1exyvv/Q+N+W9i7W6hvWwPsuxGpfEjet7txC365LjVGego30dryc5Tow/ACXr5\nZWsB9XmbiF9yL8jyOW8rISFz7t/5On1ABGGZ13Cg6AUGbb2EBibT3F5CccXbRObe5JWupDHz76T0\n7d+x78izJMcuxTLYScHx19Aagpj52d9R9J/HsHU209VbS0hgEktnP0BIYBJNbccmZD2TsSQSC+8R\nCYUw6qbi9MbpZLeLxvy3kJAICUwc8Tu1Wo/JP5LmwveRXC7mZn+KQFMMdc35HNv9IpKkIG7RJ8cl\nzrbju5kmmYaTCYAEycR8OYzDx3YSv+ReglMWcLTgLfJoZR6eUYoe2c4OqZng1EXjEudYSFv1P6j1\nRoqOvIXLaUOp1hMz7zbil3rnm3549nU4rf1UffQSZdXbADBFpjF9zQ/QGoOJyl1F2Tt/ID56PkqF\nmj5LKxqNP9WN+wmbcYNXYvZ1ooz3+BMJhTCqpnoyAeC09uO09WM0hNPQUkhG4o3Di+gGrN109dQi\nyy5Wzn+YmIhcAMJD0pFlN+UHNjFt3h3DaxhG22B3Ey3F23EM9GDpbEAhO/kLRTiRySaIRUSgQ4nb\naQMgMH4moWlL+EvpbnbSTICspkDqQNYbSFty75jEOB4UKg3J1z1AwrLPYbd0ofEP8upiWUmSiJl7\nO5G5qxlor0Wp9RvRwEyp8UOSFJTX7ECvDeBY5RYUkhK1IcDni4x5kyjjPb7EFRZGhUgkTlHp/NHo\nzRj9QmlsK2JPwdOkxi9n0NZLQcmrnFyguffw35k//bPERs0BICZiFiVV72HtbRuTbpgtRdsoffsJ\nVCotOm0Alv5mLMg4zKGolDoKOsvYJjXSJdsISL4e8HzQZdzybYKK5tBctI0G+wCh8YuJnn0rWmPw\nqMc43pQaHXpNpLfDGKZUazFGjqyA6bD2Ufr2E0yLmM2imV9Ao/ajs6eW9/f+Gr/wxAmxfdfbRBnv\n8SESCuGqiETibJJCSdTcW6n+8Dmiw2dQ15xPZd0uADRqf5bM+jJGQziFZa+z8+CfWL3shwQHxNPV\nU4OkUKEZg1bgtr4OSrf8nsSYxcyf/hkaWgvZmfdHls35GvHR8wDo6D7Bll2PISmVZC/4+IjnEzH9\neiKmXz/qcQkX1166B7fLzvzpn0Gj9nSfDTLHkpP6MQ4WvYjTNuBTXWl9mVhfMbamRpcZYUyIZOL8\nYhfcxbR5d9DUfgy7wwKAQR/E3av+j4SYhYQEJrJ87oPodWaOV22lrimfI2WvE5ax9JJrCljaaijf\n+meOvvIDKj74GwOdDec9t+34LiQk5ubci0qlpabxAMEBicPJBEBwQAIJMQvRGIPHvVqkr5HdLuTz\nLEgdb05rPyqlBp3WOOK4QR+MLLtx2Qe8FNnEJd67xoYYoRAu28tP3cORzeO/En4ikSQFiSvuY9qC\nu+hvqaTo1R8THT4DxWk7OBQKJRHBGVTVf0Rl3S4Cpk0n+fqvAGDtbcPtsKEPjDxnf4m20o8o2fwr\ntBojIQEJtDVso6ngbbLu/AFBCWdXTnTaBlCrdahVnnbeLpfTU3La7RzRDVWt1IHbPdqXY8LoPFFA\n7a7n6GkqRaXWE5ZzLQnLPoNKa/BaTKaYTJxOG7VNh4iL8tQvkWWZqro96EzhYsrjConRitEnEgrh\nkg3XlNjs7UgmDrXeiC4gArfLTktHGbIsDy/QdMtuWjpK0RiDybxlPcaoNAY66jj6yg/pbSgBQGcM\nJWH55wjLXD58n26nnfJ3/khMeC7L5nwVpUKF02Vn2/4nKH/nD8z78tNnJSHmaVnUfPQC9S2HMftH\n0j/QRldvLS+88QWiwrKZlfkJ9FoTJxr3EZx1zbhdH1/SeSKfon//gETJzC2k0emw8n7Bu/Q3lpL7\n6d+cM7Eb6Gygq7oApUpLcMp81HrTJT9eb2MpNR9tpKeuCJXWj7DslcQu+ATWniZsfR0YQuPRmUIx\nRaUTlDiH3flP0dZZgdkYRU1THo0thaTf/MiUaWc+VkRiMXpEQiFckqlcU+JqnZzf7ulrYE/B02Sn\n3IyMm8LS17EMthM7cy2m6HQcg70UblyPXunP0tnr0Gr8KaveTskbv0ap9Sc4ybN4s7u2EIe1j5np\ndw6PLqiUGmak3ca7u39GX3M5pqj0ETEExE4nMH4mO/P+D4WkQKsxMif7HhSSiuMntvL2hz9GqdSg\n0GiJnX/X+F4gH1H74XMkSWbWyzNRDCV9OXIwv2zOp6PiACGpC4fPlWU3Fe/9mcbDbyNJSmTZjeI9\nNSk3fpWInOsu+Dhup52qnc/SeOgNTIYIpiffzIC1i4q812nKfxun3TNFhqQgPHM5qTd9nczbNlC9\n+wXKC9/DYe3DEBJPxi3rCctYOmbXY6rZsGYd2+/czd77Cr0dyoQlEgrhgsRc49VT600EJ8+nr6aI\nmqa84QWaCoUaSake3vbXXLgVp3WA669/DD+dZ0opMjSLd3b/jLp9rwwnFO6hQkZK5chtjqqhn90u\n51kxSJJE1h2PUvjS97C0VLH6mh+h13q+TSdNW8ym97+FwhTA9LseQ2sKGYOr4Ntkt4ue5jJuJW04\nmQBIlQIIkvzoqS8ekVA0FrxN4+EtzM3+FKnxy3E4rRwqfpnSLb/HGJGMITQeALfTQWfVQewD3Z7j\nIfEUvvx9ehuOYTZGs2bZj5CRqa7fhyWkg/qWAlLiVpKTejP1zYcmmR8uAAAXfElEQVQ5eGwjSo2e\nlBvWkbTiPhKXfx7Z7UShVI/3JZoSVry2BNYsEaMVV0gkFMJ5iWRi9KTcsI7CjRsY6GpApzVjc/Qj\nS5B1+4bhEYz+thMEByQMJxMwVJ8gfAZHq94ePhYQk4VCpeVY5Rbm5XwaSZKQZZmSyndR64yYIlPP\nGYNSrUOh0hAdNmM4mQBPsa3YqDk09FVOyWQCAEmBSq2j02Edcdgmu7DgwHTGGormw+8QGzWHjCRP\nUSmlUsOC3M/T0FZIU+FWkq/9Er2Nxyn+z8+GeodIgIxfSBwD7TVIkoKU2GXY7P2899Ev6LW0EGCK\nQa3SUV6znbCgZNITr8PusFBY+AYJ13wOldYPSZKQRDIx5sQ0yJURCYVwFpFIjD6tMYTZ9/2J9rKP\n6GuuQOMfTHjWcjSGwFPn+AfTajmIy2VHqTxV2Kqztxat/6maDyqdPwnLPkPptr/R2VtLWGAKzR0l\ndHRVkbbqoQsWxVLp/OnvbTrreP9AOyr91OxYCZ7ELSx7Je8f3kqOHEyKFIBNdvEy5dhl54g1LOBp\nIBYYN3Lxq1Khwuwfhb2/A5fDStGrP8GsD2XR/G9hMkRQ23SIXYeeJDw4g86eaqz2PvKKXsDhsnHL\nip8TYIrG6bKz/8iz7D38d6LCsgkPTsN9/DXs/R1ia6gXiMTi8oiEQhgmdm+MLYVKTVjm8rM+nE6K\nmH4D9Xmb+KjgaeZkrUWjMVBevZ2ahgMkX3f/iHNj5t6GLiCCxkNvUNV6EH1wDDnX/5SghJn0NVfQ\nUrQNx2AvpphMIrJWotToPI+Rcz1Fr/2Y4ootZCTegCRJVNbuprG1kNRV/zPWl8CnJSz7LP2NZfyi\nJZ8QyUAfduyyk+Qbv4o+cGTxK//wROqaC5ieesvwoshBaw9tneW420voqSvCMdjDkkWPYvL3lCyP\nj57H4ZJXkSRIiFnI8ar3cbqszM76JAEmTyEzlVLD3Jx7OdGwj+rGAzgcgyiUGjT+E7+I2ES2Yc06\nfvvNZqwr/uPtUHya5Ct7rceKJEmzgEOHDh1i1qxZo3a/e3JuHrX78gViVMI3tJZ8SNmW3+NyWDk5\nTB6Zu4qUG9Zd0mr+uv2vUbXjHygValxuByChVOuYsfbnGCNTPdsNt/+d+rxNaDT+SJICm62X8Oxr\nSVv90JTfMSC7XbSX76On/hhqnT9hmcvPSiYAumqOUPjyo0SH5ZAWf61naqLsdQYGO8nNuJP2rhNU\nN+xjRvodzEi7bfh2+wv/SemJbaxc8DB5hc/TN9CCXmtGpdIxL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Build a model with a n_h-dimensional hidden layer\n", + "parameters = nn_model(X, Y, n_h = 4, num_iterations=10000, print_cost=True)\n", + "\n", + "# Plot the decision boundary\n", + "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + "plt.title(\"Decision Boundary for hidden layer size \" + str(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
**Cost after iteration 9000** 0.218607
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 90%\n" + ] + } + ], + "source": [ + "# Print accuracy\n", + "predictions = predict(parameters, X)\n", + "print ('Accuracy: %d' % float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100) + '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
**Accuracy** 90%
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n", + "\n", + "Now, let's try out several hidden layer sizes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n", + "\n", + "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for 1 hidden units: 67.5 %\n", + "Accuracy for 2 hidden units: 67.25 %\n", + "Accuracy for 3 hidden units: 90.75 %\n", + "Accuracy for 4 hidden units: 90.5 %\n", + "Accuracy for 5 hidden units: 91.25 %\n", + "Accuracy for 20 hidden units: 90.0 %\n", + "Accuracy for 50 hidden units: 90.25 %\n" + ] + }, + { + "data": { + "image/png": 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IiIiIiIiMILErr2DFvTnhDuMQKjSKiIiIiIiIiIiMEHcsWw73hjuKgalHo4iIiIiIiIiI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UPMIBit//F7G5i1CpTyxrS6E4XR3PX/YPgCullO8M/lwshJgD/A74ADh1yz0KheIwr3TVssHR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\ngg48mPJmAWBr3sfW5++i/tOXcNeVU7v672x9/i4c7Rd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eR67SkFhy+ZjMx5w1naaNr9HY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ll5CI/LQU76Daik4Y/lWo2erJSrGOisZ6CtWuGE01PErCuJWfgFjhx/m1e33kv+\n0VeQ8ZIWf+lIkREgJW4larUOR1eTgmkFwTeJHY2CMA5G+uAIPkH2elCrtJ+6rlZrkb1uBRIN62+t\npmrnM3RW5qHS6LCnLyVu8S1oTf6KZRpPeksg2bc8Qn9bDUM9LdTseZmeumI8Xg9q9cnHeTwuACSV\n+jSvJAiCIAjCRwzbrufuR8OUjiGMkez1AKD+WHELQKMa/m/via9PNo9riJoPX6K56F3cjj6sEenE\nLP4CAdEzFMkz3iRJIn7Z7UTNvZbepjLcjj5KNv4aj9c16nFerwdZ9uJ1u07zSoIgnI7Y0SgI50n0\nwfEd/a3VNOS9idZopamtmPauqpGv9Q20Ud2wn8CkuYpkG2iv5fAzP8BRf5ysxDWkRC6hrWgb+c/d\nh8fpUCTTRDEHxxCYMAed2YYsyxQcexVZlgFwuvopKn8TSVKjM4spmYIgCIJwJuvWrBVFRh/jdvTR\nXPQejp4WJJWa4vJNI1/zyl6Kj7+D3hKMxR436dlk2cuR9Q9St289cUHZZCdfg9TdScEL6+iszp/0\nPBNJa/QjMH7WifWmRHH5JhzOXmB4QExh2et4vW4MAaHKBhUEHyR2NArCeRBHVHyD1+OmdNMfaT6y\nDUlSIcteJJWGt3c+SGz4PNRqLVUN+1Cb/Iiae50iGat3v4heY+Kq5b9AqzUCkBi9hI3vr6P5yFYi\nctfgcTpwdDehNdvQTYNdjmZ7HO2luyk+vom65sME+EXS1FaM2+NEb7UrHU8QBEEQpjSxDvU9rUd3\ncOytP+JxOUbWpEfK36S1s5zggHgaWo/Q1VtPxtX3KnKyo7Mqn87qPC5acA9RJ1oMZSSu4p1dv6Rq\n+3+w3f47ZFkeGZBitEX4fB9Jg38oIDMw1MWGLT8g3J5Bd18T3b31AFgj05UNKAg+SBQaBeEciIWd\nb6ndt56W4u0szPkyCdFLcAz1sCf/SepbCmgaqEGSIDR3NdHzrkNntimSsbumgKSIBSNFRoAAayQh\ngSl0VB1mqLeD+gOv4XENIkkqglMWkXz5t9Ea/RTJOx7Csi6h5sOXCPSLwqgPwDHUg79fJK0dZUQv\n+JzS8QRBEARhShIte3zTYGcjJa//lpiw2cydcSt6nYXymu3sLXiKXm8vve0FmEJjyL7y24odU+6q\nKcBotBEZMnPkmkqlJilmKR8efoL24weoeO+fDHTUAmAOiibx0m9hi80+3UtOeQb/UIIS59FTU0RY\ncAZDzj4MOj8GdRYM9mj8QhOVjigIPkcUGgVhjESR0fc05W0iKWYpybErADAbA1k2+1u8vPl7hGSu\nmBJTptU6Iw5nz6hrsizjcPbi6XLRXvYhmYlXEB2WS0dPDYePvsKR9b8g+9bf+OydZL1fMDM+9zOO\nvv4oHd3DDc8llYboBTcSnr1K4XSCIAiCMPWsW7MW1iudQjgXTYVb0GoMLJ719ZGhI6nxF9PaUU5T\nfxVzv/EPhROCWmfA5XLg8bpGDUZxDPUgqTQUb3gIuy2JBfPvBmSKyt+i6OWfM+uO/8McHKNc8POU\nuuZuSjb+mtqqAyPXrJHpZFy7TsFUguC7RKFREM6SuHvsu4b62rHFRY+6ptUasZhDGOptVyjVaPaM\n5VTufonEqMWE2TOQZS8lFZvp6W1APWggI3EVszNvAiAkKAU/k52te35HT30J/lEZCqc/d7bYbOZ/\n69901RTidQ1ijcwQvRkFQRAE4RTEzW7f5uzrwM8cOqqAB2Dzj6G6+aBCqUYLSV9G1fanySt+idmZ\nX0Cl0tDVU0dxxWb0Vjsqp5NLFvwAtXp4qGK4PZNXtv6Q+gOvkbLqOwqnP3daox8zb3qI/tYqBtrr\nMNjCxU5GQTgPotAoCGfhQr177HW7GOioQ2MwY7CGKB3nnFlCEqhtyiM1/pKR3X+9/c109dRiD71a\n4XTDouddT3d1AZt3P4K/NQqXe5CBgXZCMlfScmQbUaG5ox4fEZKFJKnoa6nw6UIjgEqtITA+97Mf\nKAiCIAgXqAu9yDjY2YjXPYQpKFqR3oXjwRySQEXRe/QPtmM2BgHDp1dqmw5hDolXON0wY0A4SZd8\nnZJ3H6Oifg8mg43O7hpMgVGo1BrCrCkjRUYYnpgdEZxJa0ulgqnHj9keh1mBITyCMN2IQqMgnMGi\nwntYcd+g0jEUUWkUIVsAACAASURBVH9wI9W7nsc1OHyc1z9qBqlXfB+jLVzhZGMXvfBGil/9JdsP\n/pXkmOUMOrrIL30VgyWY0IwVSscDQK01MPMLD9NWtofOyoOoNHpS0pZiCoqmtWQ7nd3VhAWnjTy+\nq6cOWfai9wtWMLUgCIIgCBPJsO36C3qqdF/zcUo3/Yne5nIA9JYg4lfcSWjmSoWTjV3YjIuo3fMy\nm3f/muyUa9Dr/Sir+oCW9mPMuOFnSscbETn7agJiZtJU9B5uRy8pkdcSkr6cko2/oaOtGlmWR27c\ny7JMe081ujDfPTYtCML4E4VGQTiNdWvWwgVaZGwq3EL5u4+RFLucxOglDAx2kHf0FQpeWMecr/4N\ntdagdMQxsacuJvWKu6ja/jTVH+4FICAmm4xV30GtM37GsyePpFJjT12MPXXxqOv21CXkl76KxWQn\nMiyHrp46duU9jt4vmMCEOQqlFQRBEARhIq1bsxYeVTqFcpz9nRS8cD8WvY3lc7+DXmfhWOVWjr7x\nKFqjn8+tgTQGC9k3/4qyd/7MzkOPAWCwhpB21Q8JSpqncLrRzPY4Eld+edS1iNzVFL78Mw4Wv0BW\n8tWATEHpa3R11zJz1TeUCSoIwpQkCo2CcAoX+vGU2j3/JSZ8DotyvjJyLSggnle3/ojWkh2EzbxU\nwXRjN9TbjqOnBWtUOhqDhbAZl2CNTPvsJ04RyZet5cgrD7Nt3x9Hrhn87Mz43M9RqcWvcUEQBEGY\nbi70tShAY8FmvK4hLl1xLwa9FYDQoDTe3tlJ7d71Pldo9Hpc9DaXo7MGY09fji02h9AZF/nMWi4w\nYQ7xK+6kZPt/KC7fBAwP8UtY+VVscaIFjiAIJ/nGbzVBmCRiUQey18NARx2ROZePum61hOFnCaWv\ntUqZYOeop/4ohS89AF4Pgf7xtPUcpLnwXTKuu5+gxLlKxzsrGoOFmTf/kt7GY/S1VKK3BGGLn+Uz\nC1NBEARBEM7Ohdy255P6W6sICkgYKTICSJJEZMhMjlRtUTDZ2HlcDgpf+inddUcIDIjH7RmitOQD\neuqPkLL6+yNHkae6mPmfIyzzIjoqDwISgQmz0ZltSscSBGGKEZ9SBeEEUWQ8QVKhMwXQ0VUFsctH\nLjucvfQPtBNi9Z2egLIsc+zN3xNgDufiBT9ArzPjdg/xwYE/c+zNP7Bg7VOoNNrPfqEpQJIkrBFp\nWCN8ZyemIAiCIAhn70Ju23Mqer9gWivy8HhcowaQtHdXofeh9ShA3f5X6W0o5fIl9xMalIosyxyv\n2cHuw/8kKGURwUnzlY541nSWQMKyfOt0kyAIk0uldABBUNrCJ2aKIuPHSJJEeO4aSqvfp7TqPdwe\nJz19jew48DcktZqQzIuUjnjW+loqGOisJyftBvQ6MwAajZ5ZGTfhGuymqyZf4YSCIAiCIAjihvep\nhM+8HKdrgJ2HHqN/sB2Xa5CisjeobTxIeO4VSscbk9biD4iLnEdoUCowvN5Oil2GzT+W1pLtCqcT\nBEEYX2JHo3BBW7dmLaxXOsXk8nrcIHtRaXSnfUzMwhtxdDWyJ/9J9uQ/CYDWaCXz+gfQmfwnKen5\n87qGANDrLKOu67XDRUfPia/7GvfQAK0l2xnoqMNoCyckYwUavVnpWIIgCIIgjFHOajdXqL6rdIxJ\nJ8tevC4nKq3+tMeGTUFRpF35Q0o3/R/Vm/cNX5RURM65hvDsVZOY9vx53U70Wsunruu1Zlw+uh6V\nZZmu6nw6q/JQaXTY05ZiDhbTpwVBEIVG4QJ1Ifa/cXS3ULHtX7SVfYjs9RAQnUX88jtOORRFpdaQ\nduU9xCy8ie76YrQGC7b42ai1egWSnztLaCIa/fCEwoU5Xx5ZyB6r2oqk0uAfPUPhhGPX11JJ4Ys/\nwTnYjZ85lPr+Fqq2P03WTQ/iF5asdDxBEARBEM7Si4/dwrqNAUrHmFSy10PNnpdpOPg6zoEu9H7B\nRM29jsg515yy4BiSvpTAhNl0VBzA6x4iICYbg3+IAsnPT0BcDpXH9pCVejUGnR8AnT11NLcfJTHX\n9yY2ez0uijf8kvbj+zAabXg8Tqp3PUfcktuIXXyz0vEEQVCYKDQKF5wLsf+Na7CX/GfvReX2Miv9\n82g1Bo5VbSP/hR+Te/vvsIQknPJ5pqAoTEFRk5x2/Ki1euKXf5GyzX+ld6CFCHsmLR1l1DfnE7v4\nFp/anQnDd46Pvv5bzForVy7+KRZTMAODHWzb/ydKXn2Eud94HEkSHTEEQRAEYapbt2YtbFQ6xeQr\nf/cxGg9vIiV2JfbAJBrbijn+3uO4HL3EL739lM/R6E2EpC+b5KTjK3rB52kr3c3r7/+ExKgluN0O\njtftxBQUQ9iMi5WON2Z1+1+lo/IgK+Z+l+jw2XhlD4XHXqNg5zMExGbjH5WhdERBEBQkPpEKF5QL\ntf9NU8FmnP2drFpyP5lJV5ASdxFXLH0Ak95GzYcvKx1vQkXkriHzuvsZ1ENR5Tv00E/qFXcRu/gW\npaONWV/zcfrbqpmd8QUspuEm6CZjIHMyb2awu4me+hKFEwqCIAiC8Fku1PXoUE8bDYc3MSvjJuZn\nf4mE6MUszv0aM5Kvon7fBtxD/UpHnDDGgDByb/891sTZlNbvoKrtMKE5q8i+5RHUOqPS8caspWgr\n8RELiImYgyRJqFUastOuw2IOobloq9LxBEFQmNjRKFwQLtQF3Ue660sICUodKU4BqNU6YsPnUF63\nV8FkkyM4ZRHBKYuUjnHe3I4+AMzGoFHXP/pzdZ34ui+SZZmh3jZUGp3P7TQVBEEQhLNxIbbu+bie\nxmMge0mIWjjqekLUQorKXqevuYKAmCyF0k08oy2ctDV3KR1jXLgcfZiDRq9HJUmFyRA4sl71VS5H\nL56hQfR+QUgqtdJxBMEniUKjMO1d6EVGAK3BQv9gJbIsj+p/0zfYhtZoVTCZMBaW0ERUah0VdbvI\nTf/cyPWK2l1IKg1WH+3R2H58HxXvPcFARy0AAdFZJF22VjQUFwRBEKaNC7F1zydpjcO9CfsG2jAa\nTvam7BtoG/V1YeqzRmVQVb+PmSlXo1YPD5js7W+mtaOMhBzfPOY+1NdB+ea/0la+F2Qver9gYpfc\nSvjMy5SOJgg+RxydFqathU/MFEXGE0KzLqG3r4n8YxvweFzIspfKuj3UNOwnNMv3+sJcqLRGP6Lm\nXUdh6UZ2HvoH5TU72J33L/JK1hMxaw06S6DSEcesu+4IR9Y/iL/anxXzvsfi3K/j7Wqn4Pn7cPZ3\nAcM7OR09rciyV+G0giAIgjB2Yj06zD8qE4N/KPuKnqF/sAOAnr5mDha/iCUkEVNwrMIJhbMVs/Am\n+gc7eGvHgxytfJfC0td5e+fD6K12wrIuVTremHk9LgpeWEd/bQnzsm7jogX3EG5NonTT/9F85L2R\nxzi6W/A4HQqnFYSpT+xoFKaldWvWwnqlU0wdAdEziF1yKwU7n6WkYjNqtRaHo5vg1MVEzr5K6XjC\nGMQtvR2tyZ/6/a9SUbsTvSWYhBV3EjXvOqWjnZOaPS8TYI3i4gX3oDpxPCUiJItX3r2H2v0bGGyv\no/34vhN3lu2EZV9G2IyLMfiHKpxcEARBEM4sZ7WbK1TfVTrGlCGp1GRc82MKX/4pr2y5G5MpiP7+\nNvSWQLKu+skpp04LU5NfaCLZN/+Kyu1Psa/gP6jUWoJTF5Ow4k40epPS8casrXQPA+21rFn+C4IC\n4gCIDJmJ2+OketcLDHY103DgNVyOXiS1lqCk+UTNuRZrRIo4Xi0IpyAKjcK0cqH3vjmTuMW3YE9d\nQtuxXXg9LgITZmONzBCLOh8jSRJRc64hcvbVyB43klrj03+G/U3HSY1cOlJkBDAa/Am2JdCU9xZa\ntZ55M26jpeMYNQ0Hqd75LNU7nyUgJpvUNXdhsNoVTC8IgiAIpyZ2MZ6aX3gy877xL1pKPsDR1URU\nUBT2tKWotQalowljZI1MI/vmX+H1uJFUKiTJdw9L9jWXYzIFjxQZYXjNHRs+h7q8f1C981nSEy7F\nqA+g+Pgm2o7tpO3YTvR+wSRe/HXsqYuVCy8IU5AoNArThuh989nMwTGi7900IUkSkkZ7xsf0Nh9n\noK0avTUE/6jMKVmQ1JptdPXWj7rm9Xro7K7F7Rpg9cqfUNeUR1X9PjKTriAuch7dfY0cKnmZwhcf\nYM5X/iLuJAuCIAhTxsInZrJy/RKlY0xpGr2JiJzVSscQxolKfeaSgnOgm67qfCSVGltc7pTc8agz\n23A4uhly9qHXWUaut3dXI0kqMhJXkxy7nNff/wnBAfEsSbkGtUrDkeNvU/zaI+Tc8mv8ozIU/A4E\nYWoRhUZhWhB3jQXhJJejl+INv6KrJn/kmtkex4wbfjrljhyH56yibPNfOVqxheTYFbg9TvJKXsLp\n6sfPEoq/JZwtFY+QEreS2Zk3ARAUEI/VHMZb239O+/F9BCcv/Ix3EQRBEISJJ1r3CMJoNXteomrH\ns8heNwBqrZHky9cSmnmRwslGC8lYQeX2p9h56DEWZt+JwRBAbeMBjlW+iyx7iY2Yw7HKd9FqDFy8\n4AdoNHoA7EEpvL7tfur2bxCFRkH4GFFoFHyaKDAKZ+JxDgISat2FdRyn9K3/Y6DpOCvmfpfwkCza\nuyrYffhfHFn/ILPu/H9TamdjeM4q6g9uZF/h0xw48vzIwBeVSsPAQAe9A604hnqIDJk56nnBtgR0\nOgv9bTWi0CgIgiAoTqxJhdPxelx4XA40esuUWoNNtLbS3VR+8BQZSavJTFyNx+sir2Q9R9/8A6ag\nGPzCkpSOOEJnDiB28a1UffAk/938fdQqLR6vC73WwpCrj+7eBrp66wkNSh0pMgKoJBUR9kyqW48o\nmF4Qph5RaBR8lljQCafT21RO5XuP01lbBEBgbA4JF38Nsz1O2WCTYKi3jbayPSzMuZOYiDkAhAWn\nszD7y2zZ/Qg99cX4R2UqnPIkt6Ofwc5G0hNXYzEGoVZriQ6bRUNrIbsOPc6hIy+gUetp66ogOnzW\nyPN6+5txOvvobTiG1+1C9RnHyAVBEARhIoiBL8LpeJyDVHzwJC0FW3C7hzD62YlefDPh2ZcrHW1S\n1B98g5CgVOZk3jxybXHu12juOEbj4U34rfqOguk+rbfhKFZrBJkJq3G6B7DbkrFZo3lx01oOFr9E\nUEAc7V1VeGUvqo/1o2ztLMc11M9Aex2moCgFvwNBmDp8t2OrcMFaVHiPKDKeI9dAN46eVmRZVjrK\nhBnoqKfguR9hqqvmDtL4IqnoasrJf/ZeHD0tSsebcEM9bYBMUEDCqOvBtkQAHN1T62fg6G5C9rpJ\niFpAeuJlpMStxGjwJy5iPiBT31qI2zPEkbK3KK/ejsvtoL2rkg/2/wWNWk/H8QOUvP4bpb8NQRAE\n4QJk2Ha9KDKeA6/biaO7GY/LoXSUCSPLMsWvPERb3ttc4Q7nm2Qyo1ei9O0/0XB4k9LxJsVQTwvB\nn1iPqlRqgv3jpuSafKC9jojgTJJil5GRuAp7YCIajY4wewZu2UV9cz59A63sznucvoE2Bh3dHCh6\nnrbOCtQemcPP/GBKfl+CoASxo1HwKWLgy7kZ7GygbPNf6azKA8BkiyR+xR0EpyxSONnYyLKXgfY6\nJJUaoy3ilMdP6va/iskD98u5GKThX3Fz5RDude6l/uAbJK788mTHnlRGWziSSkNDSyGB/icH/zS0\nFABMuWFAej87kqSipaOMoID4keutHWUApF+3jqGeNhrz3mL34X+y+/A/ATAbg7l8yf1099az89Bj\n9DYfxy80UZHvQRAEQbjwrFuzFh5VOoVv8XrcVO14moZDb+JxDaLWGgjLXkXC8jt87mTCUF8HroFu\njLbwU07M7qkvoaP6MN8mi1mSHYB5hKKRi8nb8SzhMy+b9sPsTMGxNLQWIcvekYnULvcQzR2lBGdd\nrHC6TzMEhNHSWY4syyOfMbxeD129dQSlLiEkbQlNBVuoLNtNRe0uYLjVz6yMG0mOXcGGrfdSt/81\nki7+mpLfhiBMCaLQKPgEw7brufvRMKVj+CRHTyuHnroLvdrIwpyvoNdZKKt+nyMbfknWjQ8SGJ+r\ndMSz0n58PxWb/85ATxMAlsBoEld9m4DoGaMe119XTI43cKTICGCWtGTJAZTWFU9qZiVoTf6EZV3C\n4cJXAJmIkJm0dR7nUMnL2GJzsEyxYpzOHIA9fRmHj65HpzERGTqT9q5K9hT8B4s9nqCEuUiSRGB8\nLvse+yoZiVcQEZJJWHAGKpUamzWa3Yf/RXftEVFoFARBECaFOFkzdrIsc2TDL+ms2E9m0hWEBqfR\n2l5G0aE3cTv6SFtzl9IRz4qzr4PSTX+iveIAIKPRGoicfwOxi74wUkwD6G08hlZSkyMHj3r+fEL4\ncKCAod62KTegb7xFzbuO/Ofu4/39/4+MxFV4PC4KSl/D5XESOWuN0vE+JXL2VRS+/FP2FjxJZtIa\nPF4X+Uc3MDDYQersK/ELTSQwYTaHnroLoxNS4y8h3J6BQW8FIDo0h5YTbZsE4UInCo3ClCfuGJ87\nZ38nh578Hh7nAJdf8gsspuHFTnRYLpt2PETtnpdGFRrdQwMge9EYLEpFPqXexjKOrP8F6dhYRTYe\nZN7srKHoxQeY9eU/YwqMHHmsxuRPs1TzqddolobQmP0nM7Ziki75JkgSeQWvcKj4JUAiOHkhKVdM\nzeNdyZf9D0edDnbl/WPkml9YMhnXrRu5o6zRmQAJf78wIkKyRh43ONSN1+tGYzBPdmxBEAThArOo\n8B5WiJM1YybLMqVv/4mO4/uYlXEjM5KHi0yRITMx6K3sK3qWuKW3YrCGAMM7H91D/WgNlim160/2\neih68QGk9ibuIJUIzBx0tfDOzmdRqTXELLhx5LEaoxWX7KGLIQI5ueOxhUEkSYVGP/3XLQHRM0i/\n+kdUbP0HtTsfBoZPVWV9/n8x2iIUTvdpgQmzSbr0Wxx//9+UVm0DQKO3kHbVD0fdzNaarODqJT5q\nwajn9w92oDFPrc9QgqAUUWgUpjRxx/j8VG1/Gs9QP0EB8SNFRgBJUhETPouC8jcAGGiv5fjWx+mo\nPAiANSKNhJVfwT8qQ5Hcn1S3fwPBkpHve7PQnLhbnCbb+KF3Dw2H3iDpkm+MPDYk+zKO1vyWLdSy\nkkhkYDM1VNNN5szLFPoOJpdKoyXl8m8Tv/R2Brsa0fsFo/cL/uwnKkSjNzHjhgcYaK+jv60agzUE\nS1jSqKPxWpM/gQmzyS99DbstmQBrJE7XAHsL/oNaaxSTpwVBEIQJJdr3nLvuuiM0FWwGICZ8zqiv\nxYTPYV/h0/Q1V6Az2ajc8TRNhzfhdg6gMwUQOe86ouddP2q3oFI6q/LobaviPmaRIgUAkIQ/TtnL\nrr2vEDX3elTq4Y/XwckLqdAa+bf7GF+V0/GXdJTL3WyUaghOXjjlbupPlJD0pQSnLKS/tQpJrcEc\nHDMl/ixPJ3LWlYRmXkRXbSGSpCYgZsanjsaHZl5Eyeu/obRqG8mxywE4XrOTprZi0ubfo0RsQZhy\nRKFRmJJEgXF8tB7dQaB/HL39zXg8LtTqk/1vunrq0JltOPs6yH/uR+hVRubPvAONWsvRqq0UvHA/\nuV/8PZaQ+DO8w+QYaK5gnjdgpMgIoJfUZMj+lDZXjHpsSPpyeuqKeT7vTTZIVcjAkOwiev4NBCXO\nm+TkytKa/NGafGcXpyko6ozT+pIv/x8Knl/Hxm0/xs8vnIGBdmQg/dr70OhNkxdUEARBuKCIden5\naT26A6PBxqCjk+7eeqyWk0eGu3rrAdCZbRzb9Efaju4kPeFygm0JNLYWUfr+v/E4B4lfertS8Uf0\ntVahl7Qky6PXVjMJYpujHld/F3rr8I1djd5E2nX3U/LKg9zj3o1Z0tErO/ALiiXpsgvr/yeVWoNf\nWJLSMc6aRm8iOGn+ab9uT19KV3U+e/L/zeFjG5AkicHBTkJnXEJI5orJCyoIU5goNApTjljMjR+v\nx0VIYDLtncfZk/9vZmfejE5r5HjtLirr9xC//A4aDm/C6xxi1SUPYzzRYyQucj6vbVtH7d7/kn7V\nDxX+LkBvtVPVObqg6JVlqlQD6Kz2UdclSSL5srWE56ymvXwvkqQiKHnBlBuCIoydZ2gAv/AUPC4H\nQ94hAhJnk3TxNzFYp+5uTUEQBMF35ax2i6nS48DrdqHXWTAbA9lf9BxGQwDBtgQ6umvYW/AUZnsc\nar2ZluL3WZB9JylxKwGIjZiLVmPi6P5XiZ53g+I3FQ1+wQzJLloYJJSTWWroRa3WojGO3qUYGJ/L\nvP95itaS7Tj7OokJSyIoce6UOg4ujJ3XNYTOascYEIF7qB+9fzCZq/6HoOQFpxxUKQgXIlFoFKYM\n0fdm/NnicqlryWd+9h3sK3yGirrdqCQ1Hq+LgJiZhM28lJKNvyYsOH2kyAigVuuIDsulqqFQkdyD\nXY20Ht2J1zWELT6X4MyVHKs6xI/5kAT8mYudAjpo8faRk3vFKV/DEhI/JXZjCuOjp+Eo+c+vw6iz\nkhg2j96BFupK96DRGkm7UhxTEQRBEMbXi4/dwrqNAUrHmBYC43MpLniHJbPXUnBsA29t/zkatQG3\nx4FaayTnxp/SXVMADN/s/ri4yPkcKX+TgbYarJFpk5rb43LQenQng50NGG0R2BLmotGZeMh5kDjZ\nj1kEo0PNm1ItIVmXnnL6tNbgR0Tu1Bt8Ipwbr9tJwQv309dSQVz4PDQBeqoa9lG+5e/4hSdP6VZF\ngjCZRKFRmBJE35uJEbfsdg4/80MKy94gJXYFbV0VtHdVYgqKRfZ62P2nmwHoVRvo6KomMCB25Lk9\nfc3DzY4nWd3+DRx/719oNDrUah3Vu59HrTWgktRIQXEU9Lfw4WAhkqQi+dK1U6aPpDCxKrb9mwBL\nOKuW/ASNWgdAWfUHfHj4X0TMvgpreIrCCQVBEITpYt2atbBR6RTTR1DyQvyjZrD78OPERcwnwC+S\nxrZiVOiwJcwm7+m78bqHkCQVRWVvkJv++ZGdYb39zQCT3gqmv7WKwhcfYKi/E6PRxuBgByqNHq97\nCItfJC0aI093liJJKgJis0lc+dVJzScoo/nINnoa/z979x0f1XXmf/xzZkYa9d5QLyAQIDqYjrFx\nA3cnjkvs9LJO4sTOZuOQ6niT2Fkn2WSz/sXZdMfduGDHlWZEr6KJKgkV1Huvc39/DJYtC2xAoBmk\n7/v14g9d3Xv1zFwkPXrOOc85wtKFPyIq3L1BzKTMG3j13R9QsvUF94aMIqJCo3iW39qbuf/ROE+H\nMWwFxaQz9a5fU7zlOQqLd+FwBhA//Xoq9ryJs8di7pQvYuHiwLE3eGPDQ1y78KcEB8VytOhdTlTm\nknn10C4Xaq44Sv6aPzE+42qmjLsFu92HN3IeorWthmuW/JyggCgsy0XuoRfZd2QlYcmThzQ+8Yze\nrg4aS/czZ8rn+4qMABnJC9iZ9yx1+TtUaBQRkfNCLXzOP5vdQfatD1K67SUq8tbR29VO+JhL6Gio\noCF/B9mjlxEWnEBx2Q72H30Nu82XyeNupK6xmJ15zxGaOAH/8FFDFq9lWeS98jD+jkCWLvkewYGx\nHC5cy9a9f2Xe1C+TkTwfgNqGQt7c8DOCR43F7jtwNqMMP3UFO4iJzOwrMgIE+IeTFj+bomPbQYVG\nEUCFRvGg5cvugUc9HcXwFxid0q/P4qHXf4OPzZer5/8AH4c7KUqJn8mKt+9n5brv43D40t3dTtyk\nK4mbdMWQxlqxbxUB/hFMm3AbNmPD5eqhtuE408bf2rdrtjE2JmXewKHCVVQf3kDK3NuGNEYZesZm\nA2Ojp7er33HL1YvL1Yuxq9eRiIgMjlr4XFh2Hz9S5t1Oyjz3aprG0jxyn/wOi2d9i6RR0wB3Pmqz\nOdh75GUOHV9FZ2cT/mHxQ94ipbn8CG21Jcyb+12CA90b11TXHSYsJLGvyAgQGZZGeuJcThzKIW2h\n5zerkQvP2Bz0uroHHO/p7cLYVVoReY++G8QjNFrsOXVHtpIaN72vyAjg6xNIQuxkKloKGTX5aiLS\npxMUm/ERd/lovd0dVB/MoaW6EGdwNLETL8P3DJa89LQ3ExgQhe3k7tKW5cKyevvFCmCz2bHbfXD1\ndJ3qNjLM2By+RKbP4GDB26TGz8LfLwzLsth/9DV6etqJzpzn6RBFROQiphY+Q69879vYbA4S46b0\nO56acAn5JTlEZS8mNGE8kWNmYzvHAo5lWTSWHqAufxsYO9GZcwkeNeZjr+tubwYgODCm71ivqwdf\nx8DNaHwcfspHR5DosXPJO/QwxWU7SI6fAUBdYzGFZVtImHmTh6MT8R4qNMqQmvOXSSxeMf/jT5QL\norO5lu7OFppaKgZ8rrGlDGdwFMlzbh3U12ivL2Pv08vpaK4hODiO1tYaijb8kwk3/5Dw1CkfeW1w\n/DgKDm2gubWK4MAY7HZfYiIyOVK4moykedhPLps9XraNjo5GItKnDyrWkciyLBqKcqk5ugVjDJGj\nZxOWMtnrd8lLv+yL7Hnyu7y46juMisqiua2axuYTpMy7g4DIRE+HJyIiFykNfntGc9kRXK4eWtvr\n+latACdzVEPqvDtw+AWd/gYfw3L1cuhfv6Yqbx3+/uG4XL2UbHmOhGnXkbHkKx+Z9wTHZWBsDgpL\nt5CdeR0A8TET2Zz7V2obCokMc2822NHZTMGJLYSPveS095LT62ispGLfKrpaagmKHU3M+Es9vrP4\nx4nKnEtU5lzWbf8dUeEZOBx+VNYcJDAqlaRLbvZ0eCJeQ4VGGTLLl90DKzwdxcjW2VILWFTWHiIv\n/03Gpi0By0Ve/pvUNxaTMObGU17X1dpA7bGtWK4ewlOnfWSfnMOv/zcOl40bL3+EkKA4Orqaydnx\n/zi48hFm3/N3bA7f014bl305J7a/xFsbf8749KvwcQTQ1tFAS1sVr6z5HqkJs2lpq6aobBtRmXMJ\nSdBGMGfDL8v3bwAAIABJREFUcvVycOV/UX04h0hbIGCxd+erxGRdyrhr78fYhm4JsuXqpaOpGocz\nEB//4I89PyAigemf/z1lua/TXHYIZ1Qmk7K/RniK+nSKiMjZm3JND0ttQ9uLWt5n9XZht/mwOfcv\nzJv6JQL8w6mqPcLeIy/jExB6yiKj5eqltmAHnY2V+EckEZ46GXNyFcyHVexbRVXeu8yb9hXSE+di\nYXG4cBXbd/2TsNTJRI2Zc9rYfAPDiZ+6lN27XqCtvY6YqLGUVx8E4I2ch8hImo+Pw4+CE1tw2SyS\nZg9ukH4kqj60gUMrf4kPNqKNP8f2vE3ppmfIvvMR/MOGrh8nQFdrPa6ebpwh0R878G5sdsbf8ADV\nhzdSfSiHrt5uMqZ8hbiJS9SnU+QDVGiUC049b7yHf/gojM1BVGgqO/Y/xZ5DL2JZFj29nQDETFg8\n4Jqy3Dc49s4fsFy9GGOwLBcJM24g47IvDfhl3NFYRWPpARZMv4eQIPcmP36+wVwy6S5eXv1d6gp2\nEpV5+sTO4Qxk8p2/pGDNn9iR9ywG9/JpgNb2Og4eX4VfaAzpiz9P/LTrvH4Wnrep3L+G6sM5fIUJ\nzHK5lwNtoZL/O7iOiPTpxE687LTXWq5e6gp309lUiX9EImHJk875/S/PfZOijU+5C9/GRmTGLMZc\neQ/O4EjaG8rpbmskIDJ5wKi2b2AYqfPuOKevKSIi8p5nH7+D5SvDPB3GiBYYm46ts5ua+nxWvHMf\nTp9AOrqasdkcxGQN7BHeVlvK/ud/THtjBTabA5erh6DoNCZ+8ic4g6MGnF+5fzUJsZPISHK3VzEY\nstKvpKB0E5X713xkoREg47Iv4hMQSsH2Vzh8fDW4s1Jcrh4Kyrbg8A0gYuxskufcil9o7Pl4S0aM\nno4WDv/r10yzIvkCWTixU0U7j7bu4eib/8Ok237+kde31ZbQULIfu68/kRmzznkWZGt1EUfffozG\n0v0ABEQkkr74C0SOnkVPZytttSX4BoYPeL7GZicmayExWQvP6euKjAQqNMoFpZ433sXHL5j4KddQ\nlvv6ydmMFm0d9VTUHiQofiwhcaP7nd9cfpSjb/2ezNTFTMn6BA67k0MF77Brx7MExaQTl72k3/k9\nnS0ABPpH9Dse4B/p/nxHy8fG6BcSzdhl99F04iBO42TG+NsICoim8MRm9h1ZSWz2FSSqB8o5qdq/\nhiwTySW8nzDNIY71lFN1YM1pC41tdSc48MKDtNWfwBgbluUiKDaDiZ/4Cc6giFNecyqu3h7yV/8f\nZbtfIz1xLmkT59LSVsPeI6+w56kH8AkMo+lEHuBuGp8w40ZSF9x52tkKIiIiZ2v5sntgpaejkMSZ\nN7HnyH8QFzWBsJAEOjqbqG08TnNbDckfWoJqWRZ5L/0MH5eNyxb9lIjQFKrqjpCz8/9x6LVfMfn2\nXwy4f09HC4FB6QOOB/pF0nQG+aix2UmZexvdbY2U736dqVmfIDF2KvVNxew48DS2gDDGXPU15Sjn\noOboFnp7OrmTMTiNezVNjPHnOiuZvxbtoau1Ad/AgQMBlquXI2/+jop9q8AYsCzsPv6Mu/bbHzmR\n4VTqi/ZwYMVPCXSGM3/aV/Bx+HOocBUHXnyI6HELqDm6ua/3ZnjKFMYuu++UBW0ROTUVGuWC8Ft7\nM/c/GufpMOQU0i/7Ahg4mvsWrt4uMDaiM+eRefXXB5xbvvctAgOimDXpM30btEwcs4yK2oOU574x\noNAYEJGIj18w+SUbiYnM7DueX7wBgJDEM1vqXH14I50tdVxzcvk1wNSQT9De0UjJ9pdInHmjZjOe\ng96uNsIsH/eg/AeEWb5UdLad8pr3kntHt4ulC39CZFgalTUHydn1OIdf+xWTbvvZGX3tzuZa9jyz\nnI76MpLipjF/+lf7PhcVns6/3v0RrrZmFsy4h5DAOIrKtrF/87PYff1Inv3Jc37NIiIi71E/Ru8R\nmpDF+BuXk7/6j5Tn7wMgICKJSZ96CP/w+H7nNp04SGttMVfOW05kWCoAsZFjmT7+NnJ2PkZ7fdmA\na0KTJlCct5Fp4z+Fr497xlt7RwNl1fuIn3XqVkEf1tPZRsWet5g45lomjF7qvm/wKPycoby98ec0\nFO9TC5dz0NvVjh0bgfj0Ox6K0/357nZgYKGxZPtLVO5fwyWTPsvo5AV0dDWzfd8/yXvlYWZ9+f/w\nC40ZcM2HWZZF/urHObHzVWw2B1fN/z7+zhAAEuKm8Nq6H1J1cD2TMq8nOX4GDc0n2JX3HPue+zHT\nP/e7IW0zJHIxU6FRzrvly+6BRz0dhZyOze7D6CVfJXX+p2lvqMAZHIlvYPgpz+1sriEsOKGvyPie\n8OAk6iq3D7y3w5fkeXdwdPXjdHY1kxA7mdqGQo4WvUvsxMsJiEg4oxjbaksJCIjsKzK+Jy46i2PF\n79Lb1e71zaK9UWjqZHKrXqXR6iLUuHtlNlid5Jo6YlMXnfKaphMHaa0p4sp53yMq3D0zIC56PNMn\nfIoNO/9Ae315v56dPZ1tVOWtpbn8KD4BocRlLyEgMomjb/8vVmsLluUieZR7l76u7lYOHH2dovId\n2Gw++PkGERuRSYB/BJFhqXR1t3F8+8skzbpZiZ2IiJwztfHxTlGZc4gcPYvW2hKMsREQmXTKgeSu\nlloAwkP6b/4WHpIEQGdL3YBCY+Ksm6nKe5d/rf8JY1Mvw+Xq5dDxVdicASRMu/aM4utsrqG3p5NR\n0RP6HY+NHAvG0FZbokLjOQhNzqYXF5upYAHu52ZZFjmU4R8UhV/IqQuG5bteJz1pHmPT3CtwAv0j\nmDftyzz/1jep2LeK1Pnvt9exLIuG4r3UHN6I5eohImMmkRmzqDmymRM7XyU8JAmnbwj+zhAsy+L4\nia0cLlxFW3stNmMjNmocEaEpRISmEBQQzZs5D1FXuJvIjBkX/g0SGQZUaJTzSiPFFw+HXxDBH1oq\n/WFBsRmUFb1CZ1cLTl93U26Xq5fSqlwC4wYuRwFInHE9Dqc/JVteoDh3B74B4STPu43ks2iU7R8W\nR1t7LS1tNf12IqyuO4avf6iaLZ+jhOk3ULV3FQ927ORSKw4LWGcqMP5BJEy77pTXdDa/l9wn9Tse\nEZLi/nxLbV+hsaOpij1PPUBHUxURoanUt9dQsu1FMi77IrXHtnHJpLvZffB5GppP0N3TwVsbfkFz\naxVpibOJj55IQekmXs/5KUsX/oQAvzDiY7I5cnwN3e3Np1xCIyIi8nHUxse7GZudoOjUjzwnMMad\nc5aU72J0yvt98UoqdmFsDgIjkwZc4x82iil3/pLCd//OzgPPgLERNWY2aZd+9rQD7B/mDI7EZveh\nuu6ou7h4Uk19AVgW/mFavXUugqJTicm6lL8fXM8xq5FEgthlajhs1TPu0n8/7eByZ0stEcnJ/Y75\nOPwIDoqlq6Wm75hluTjyxm+p2LeKoMAY7DYfyve8RUTadACiIzKJCE2mpHwXLlcv+46+yp5DLzIq\neiJjUy/nRNVe3tn0S+ZPd28kFB0+GofDj7aaIhUaRc6QCo1yXmikeHiKn7KUsp2v8dbGX5A95jp8\nHE4OFrxDU3M5k6/71mmvi8u+grjsK7BcvWBsZ73MOXrcAgrf/Tvv7vg9sybe6e7RWLqZI8fXkDzv\ndvXDOUfO4Egm3/1rjuf8k9eObAYDEWPmMG7hXfieptdiUEwaAKUVu8lIXtB3/L3kPuADyf2xdx7H\n1t3DjZf9kpCgWHp7u9m67x8cW/NnwCI4MJbRyYs4VPgOHZ2NNDSf4NpLf9pXxJw4Zhkr136fvGOv\nM2PiHdTU5+PwDcDhF3jh3hQRERm2NAA+PAREJBCVOY+t+56gvbOB6PDRlFcfYP+x14mfuhSfgNBT\nXhcYncrET/z4ZD5qzjp/dDgDiZ1wGXsOvILTN5ikuKnUNRazdd8/CAhPIDx16vl4eSPS2GX3ERid\nwo7dr7OxtZDgmDQmzP06UWNmn/aaoJh0Sip3My79yr6/LVraamhoLCYj5pq+82qObKZi3yrmTvki\nGckLMMZQWrmHtVt/gzM4mviQ0YxOXsjhwtVs3P0nik5sIXvMdUwd727VMyXrFtbveIyd+58hNX4W\nTS2V9PR0aNMfkbOgQqMMmkaKhy9ncCSTb/85R99+jJydjwEQEJ7AhFt+SOgZ9Fs81+WuDmcA2bc+\nyMGXH+aNnIfc9zI2Rk2+mpQ5nzqne4qbf1gcWdf9+xmfHxCZSNSYuWzd9w/aO5uIjhhNedV+9h17\njZgJl9F0Ig+7bwBBMenU5m9j1sQ7CQlyJ2J2uw8zJtxGfvEG7L6BFJZu5pLJn6GppZz8kg2Mip7Y\nb6ZkgH8EqQmzKK3cQ2hwPHn5bxE//Vpsdp/ThSciIjKACozDz7hr7yd/9f+xZ/9KXL1d2H38SJx1\nI6kL7v7YawfTfiVjyZfp6Wxjc+6f2XzyWFB0GhNv+r7augyCze4gec6tJM858xVPyXM+yYGXfsb6\nnY8xJnkRHZ2N7DnyCr6B4ThDoqk5uoWQhCyq8tYRGZ7eb/ZrYuxkkuKmU954lNKqPczM/jSzJ3+W\nrXv/jmW5yMq4qu9cY2xkpV9JUdk2Cko2cbDwbZxBUUSOvuS8vgciw5kKjXLOtOHLyBAUm8HUu35F\nZ3MNrt4e/EJjB7URS0tlPqU7XqGt6ji+IVHET11GRPr0AecFx41h5pf/SGNpHt3tTYSMGoszRLu9\necK4a7/NsVWPs/vACixXD3aHH4GxGVTuX0PlvncAcAZFguXC72RD7fc4HP44HL6EpEwk/2gOPb2d\npCbMpq6xmO6egQMUXd3tNLdWsjn3L8RkLSJt4WeG5DWKiMjwoCLj8GT38SPz6m+QvvgLdLXW4wyK\nHFQrnZ7OVk7sfJW6Y9vcy6oz5xA/bRl2n/73tPv4Mf7GB2ivv5uWqkKcwZEEjxqrTQk9ICpzLmOX\n3sfx9U9QtHkr4J7l6Gqp48CKnwJgbA6cIVFE+A6cfejnDMbu40+vq4U3NjzEuNQlpCZcQmHpZrp7\nOvrlsN09HQBsyv0T/qFxTLz1QWwODXyLnCkVGuWcaMOXkccZPPgiX13BTva98CC+Pv4YDK3Vx6nL\n307M+EsZd+23B5xvbHbCkrMH/XVlcOy+foxd+k0yLvsCnS111BXsoGDtn5k89iZGpyyivaOBHQee\nprq1gUOFq0mOn9m3gVDRia10d7eTPPtTRI2eQ/GmZyjatQ1j7LR31LNy7fcZk7yIMSmLqG8qobh8\nB1Fj55G64K5Tbh7U3d5M5YG1dDRWEBCZREzWIm0MJCIiauMzQjicAYP+vd/T0ULuP79DW/0J/HxD\n6Oxqobn8CKXbX2b65/8H34CBfaH9w+MHbDgjQy8uewmxExbTXl9Ob1c7e55+gKjQNGbMvA8/ZwhH\njq9l35GVlJtqmlurCA50byzT0dVMUfkOIicsJH7KNeSv/Qvb9j0BWIDhjfU/JTl+JuMzrsLPGcqe\nIy/jDIokc9n9hCdnD5i9arl6qSvYSUPxXuy+AcSMX3TGm16KjAQqNMpZ0SxGOVeW5eLYO38g0C+M\n1vZaUuJnEhmWTlnVXioOrCEwJo2kWTd7Okz5CA6/IBx+QeS9+J+kJsxm8ribAPeuf5fOupcX3voW\nVbWHeTPnIZJHzaCppYL80g1EZc4leFQmIfFjiRo7n71PP0BLVSHxsZMBix0HniL30It097QREp/F\n2Gu+dcpZCo2leex//ie4ejoJCozhRMtKijY8xaTbfkZgVPKA80VEZGRQGx85G6U7V9JeX47B4OsT\nwNjUy2nrqONY8Xpy//kdZn7xD1oW7cWMzU5AZCJFm57BWBaLZ92Lr4+7n/fUrE/Q2FxGadVeXs95\nkNFJC7HbfThWnIPLZkiadQt+oTFMuvWnFG16huM5TxAWkkhESBJFZVs5WrQOm80BNkP2rQ+dslVU\nb1c7+57/CY2l+wkMjKarq5WijU+RcfmXSJxxw1C/HSJeSYVGOWOaxTg89XS20dPZijMo4pRJVc3R\nzZTtfI2Ohgr8o5JInHkT4SmTz/rrtNWW0t5QhsHG1KxPkJ15PQATRi9l0+4/UbTxaRKmXYvN4Tvo\n1yTnh6uni46manwCQvDxC+473t5YQUziZf3O9fMNJjQkHhMZQ093J3uOvoKPfygp8+4g6ZJb+pYY\nlee+Tkt1IUsX/ojIMPdGMzX1+byR8xAxWYsYu/S+Uy5NsVy9HHzlESKCE1k08xv4O0Noaatm9dbf\ncPi1XzH1M/+tZUwiIiOQlkoPD67eHrpa6/DxC8bu6z/g8+315ZRsfZ6Gon3Yff2JmbCYhHPs41x3\ndCtO30D8fENYuvBH2O3u3DMlfhbvbHqY2vxtRI2ZM+jXJOeHZVl0NlUD4AyJ7sv3OhoqCA1O6Csy\nvicmMpPS6r1EZM3nyOEcLFcvERkzSZ1/J36h7hmOnc01FG14kgmjlzFt/K0YY+ju6eTtjb+gubuB\nKZ9+FP+wU2/+cnzj07RUHOWKuQ8wKno8vb1d7Dr4PAdX/5HwlCkERqdcwHdD5OKgQqOcESVxw09X\nawPH3vkDNUc2Ylku/IKjSZ53O6Mmv98MuWTbixSs/TPRkZmkR0+jvCaPvc8sZ9y13yZ2wmUfcfeB\n3ksKLFyMSV3c73hm6mXkl+TQWl1E8Kgx5+cFyjmzLBfFm5+jdNtL9HS2YGx2osfOZ/SV/4aPXzAB\n4QlU1BxkXPoVfde0dzTQ2HSCtGlXkDjzptPeu+boZpJip/YVGQGiwjNIiJ1Mc0vtafvf1BftpbOl\nhpnT78X/ZA+doIBopmV9krVbf0NbbYlmNYqIjCBTrulhqe1eT4chg2RZFqXbXqR06wq62hux2X2I\nGb+YjMu/1LdEuq22hN1P/Ds+xkFK/CzaOxooXPdXGopymXjLj8569qFlWbR3NDJh9NK+IiPAqOjx\nBAVE01C0V4VGL9FQsp/8d/5AS3Uh4N5JfPQV/0ZY0kT8IxKoyltHR2czfs73B8TLq/MIiEgk86qv\nk3nV109539r87QBkZ17f9zeKj8PJxDFLeXf77zG20+9QXrV/NZnJlzIq2j3b0W73Zdr4T1FQupnK\nvLWkL/rs+XjpIhc1FRrlI6nfzfBkuXrZ9+wP6G6qZcaE2wkKjKGwdDNH3vwdxu4gbuLldLc3c3z9\nE2SlX8XM7Dvd11kucnb+gYLVfyJ63IIBo8i9XR1UHlhN/fFcbA5forMWEpkxC2MM/hGJ+AZF0tVS\nS1dXC36+7ycEnV1NANh8nEP3JshpFW9+juM5/yQr/QqS4qZR31TCniOvcGDFQ0y+4xESZt3EkTd+\ny/b9TzImeRFtHQ3sOvg8dl9/Yide/tE3t4BTzDw02MCC5opjFG95jqbSPBzOQILjxxESP5aerjbA\nvUz7gwL9IwF3vyURERkZnn38DpavHNhHTy4+JVtfoPDdv5GZupjEuGnUNxaz/9BrdDZVkf2p/8QY\nQ2HOE/g5Arh20YN9s9dKK3JZs/XX1BXsJHL0rAH3rS/aQ+X+NfR0thKakEXc5Cv7VmdEZs6htbqQ\nzq7+uYPL1UN3Tzs2n3PfZEbOn9aaYvY990MiQ1KYMdM9qHAg/w32PfdDpn3mt8RlX0HJludZs/XX\nTM36JH7OEI4eX8uJytxT9n7vx7IABqyGMbgLjL2dbRSu/zvVB3Nw9XQRFDeakPhxBEQl09PZOiAf\ntdsc+DtDlY+KnKRCo5yW+t0MX7XHtp5cvvpjosIzAEiKm4rL1UPxxmeInXAZjSX7cPV2MX701X3X\nGWNjfMZVHD+xhZaKfEISxvV9rqejhT1PPUBLzXFiI8fR1d3Kgbx1xGUvIfOab7lnLl59LwdWPMj2\n/U+xcMbX8XE46ehsYvehFwmKTiMgMmnI3wvpz9XTRem2l8hKv4KZ2Z8GIC56PMFBcazZ8isaSw8Q\nl30F3W0NHNn0LAfz3wIgICKJ7E89RGt1EZX7V7t3Ck/IYtTkq/Dxf38Xv8gxsyjKeYr6phLCQ9zP\nu66xmNKqXEZNuYbcf36HQP8IkiOzOX5iG5V1q6jcv+rk1Yb8ko1MGH1N3/3yS3Kw+/gTGJ06JO+P\niIh41vJl98BKT0ch54Orp5vSrSsYm7aESybdDUBi7GRCg+NZt+23NJcfJiR+HPUFO5k0+tp+S2QT\nYicTHBRHXcH2AYXGwvVPULz5GUKCRhHoH8nx/H9Qtus1Jt/5S/xCokmccQMndrzCoYJ3SI2fRXho\nMi7LxZ7DL9PZ1UJM1sIhfR/k1E7sXInTEcgVc7+L4+TM0/jYSby0+juc2PEymVffS/atD3H4tV/x\nzqaHAbD7+JN+6ecJS55EYc4TtFYV4BsUxajJVxEcN7rv3hEZM7DegQPHXmfKOHeP+N7eLg4UvElg\nVCqHXvsV7XWlpMVfQlXtEWqPbaX2mHuna5vdl4LSzYxLv8LdzxGobThOQ1MJcYm3DuVbJOK1VGiU\nAbThy/DXXHEMf//wviLje5LjZ1C88w/0drVjTv7i7O3t6ndOz8mPjb3/MpWizc/RUV/BdYseIjzU\nvYT1WNF6NuW6Zz9GpM8gMmMGGUu+Sv6qP/D8W98gLDiBusZi7L5+ZN/4n+qx5wU6mqrp6WwhMW5a\nv+MJMdnYbA5aqwoIS5pI8uxbiZ96LS2Vx7D7BhAUm0HRpqcp2vAkwUFxBAdEU1T4JGU7X2PynY/g\nH+b+mRI/9Vqq83L417s/ISluKgAlFbsJjE6ltbKAsOB4rpzzXV5Z8z1Cg+OZO+XzhAbHU1y+k5yd\nf2DngWdobCkjOnw05dUHOH5iC6kL79bO0yIiI4Ba+QwvHY2VdHc0kzJqRr/jiXFTMcZOc8UxQuLH\nYWz2vvzzfRa9vd0YW//VNS1VhRRvfoYp427pWxbb0lbDGzkPUbjur2Rd/x84nAFM+8x/k/vP7/Dq\nuh8QHppCR2cT7R31pC64i6CYNMTzWiryiY+e2FdkBHDYfUmIzqaysgCAkPixzPjS47RUHqO3q52g\n2NG015ex48/3QK+L2MhM6su2Up77BplXf6OvRZRfSAwpc29j78anKKvaT3hIIieq9tHR1Uz89Oso\n3fYiSxf+mILSTbR21DF/2ldJSZhFc0sF7+54jLrGIt7Y8J9kJM2no6ORQ8dXExSdRvTYBR55r0S8\njQqN0o82fBkZfAPD6OhsGtDTpLG5DLuPH3YfJ2Epk3A4g9h9cAULpn8Vm81BT08new6/jF9oHEGx\n/YuUNYdyyEia11dkBMhIXsCB/DeoPrSBiHR3EpkwbRmRGTMo3/s2nY1VpExaQNykK/ENCB2aFy8f\nyScgBGOz09BU0td7BqCxpRyXqwffoMi+Yw5nAGHJkwB3/6SiDU8yaeyNTB57E8YYWtvreGPDQxSs\n/TMTbvp+3zWT73yEst2vUXtkCwApC+4gbvLVbP7d7Vwy6bOcqN5He2cDV83/HiFBowBITbiEppZK\n9hx+iZLa/Rwrehf/sFGMuerrjJr8/qxbEREZftTKZ3jyCQgBY6OhpYy4D+QczS2VWFYvvoHhAESN\nnceRw+sYnbyQ4ED3Zh6Hj6+hrb2WzHHz+t2z+vAGfH2DmDhmWd8AdlBAFOPSlpB7+CXGWS6MseEf\nFsesr/yJ6oPraSjZR4AzkJgJiwkZlTlEr14+jjM4kvrq0gHH65pK8I16f6MWYwzBce/3eD/61u8J\n9oviyrkP4PQNxOXqZcuev3HsnT8QlTmnb6VN6vw7CYrNoDz3TcpbigkZPY3xM2+kaNOzREeMJjwk\niWNF65mQcQ3pSXMBCAtJ5PLZ9/PiO/fTbjrZtvcf2H38iBl/KWmLPnPaXuMiI40KjQK4e93sUa+b\nESMmaxEF6/7Gxt1/ZM7kz+PvF0pJxW4OFrxF3OQrMTY7dpudMVd9nUOv/pIVq75NVGgaVfVH6e7t\nZOItP8aY/k2SXb3dOOz9eywaY3A4nLh6u/sd9wuNJW3BXRf8dcrZ8/ELJnrsfPYceZngoDgSYrJp\naqlg4+7/wzcwgsiMgX2QAKoPb8THx5/sMdf1JfaB/hGMT7uSHXnPsm/Fg7i6OghNmkj81GUkz76V\n5NnvLy+xXL0Ym4PunjZ6ejtx2P0IDuw/szoyLBXL6mXKpx/FGRx51s3fRUTk4qNWPsOXj38IUWNm\ns+fwy4QFxRMblUVLWxWbcv+Eb0AYkRkzAUhdcBeNRXt5Zc0DxEWNp72zkfrGIuKnLiMkYXy/e1q9\n3djtPhjTP0fwcfhhuXrdvflOLqCx+ziJm3QFcZOuQLzPqClXs+/5H7PzwLNkZ14PwP6jr1LXUMjE\nJZ895TUdjVU0Vxxl0cyv4/R1L7W32exMHf9JjhW/S94rD4PLhTMkmvipy4gaM5uoMbP73cPm8KWj\np43O7jZ6ejuICO2/i3RQQBROZzAxEy4jefYnwdi0KkvkQ1RoFPW6GYF8AkIZf+NyDr7yMC+8/S0c\nDl96ejoJT5lK2sLP9p0Xk7WAgMhEynPfoKWhguikJcRPXYp/ePyAe0akT6fg2CYmjFnat9FLdd1R\nausLGDfv9LsQi/cZfeW/cWDFQ6zZ8iuMzY7l6sU3MIKJt/zotCO1rt5ubDYfbB8q/jkcfmC5MDXV\nhPhHcmLrCir2vMWUT/8XfqEfGI222YkeO4+DBe8wNesT9PR2UFl7mLio9/uAnqjcg49fML5B4Soy\nioiMAFoqPfyNuerr7H/+x7y96WEcDn96etrx8Q89mXO4l8w6gyKY9tnfUr7nLRqK9+GITGHiks8S\nkTFzQIEnPG06JVtXUFy+nZR49+BoT08nR4rWEZ46RfnDRSQifQZpCz9DXs4THDj2OhgwGFIX3k1k\nxoxTXvPe5IYPT3547+OWE0dIjJ1MzfED7D6wtt9y6vfEZC1g3/5VlFbsxN8ZRmnlHpLj3/96NfX5\ndHY2Exidqv9PIqdxURYajTG+wDZgEjDFsqy9Hg7poqUEbuSKzJjB7K/9nZrDm/o27ghJyBqQsAXF\npDGkmd4eAAAgAElEQVTmyv7/TyxXLw0l++ntbCU4fhzOoAiS597G7mPbWLn2+6QlXEJXVxuFZVsI\nic8iepz6lVxMfPyCmXzHIzSdOEhLZT6+Qe6ZjB+1HCQibTrFm56hsHRL3/KSnt4uDheuIjgwjmWL\nHgSgrb2e13MepPDdv5N1/X/0u0f6pZ8nt+w/2LT7z/g4/Hl3+++YNv5WQoMTKC7bwaHCVaQuvGvA\nbuciIp6inPTCmHJND0tt93o6DBkCvgGhTL3719Qf3+PeuCM4kqgxs7F/aOdnh18QSZfcQtIlt/Q7\n3lZbSmtNEX4hMQTFjSYseRKRY2azfsdjJMdvJ8g/kuNl22nvamLKjR+zE7F4neQ5txI78TLq8ndg\nYRGZMRNncNRpz/cPH4V/aBwHC95hVEw2tpMrsA4VvA3AsoU/JjQ4HstysSn3Lxxb9TjR4+bjcL6/\n0VB42nTiJl3Jlj1/w88ZyrHid3E4nKTGz6KppYLdh1cQGJncN+NWRAa6KAuNwC+BUiDb04FcrNTr\nRgAczsCzXi7SeOIgh155hI7masA9Ey1+6rVkXP5Fpt79G0q2PE9h4S7sPk6S5txK0sybVRi6CBlj\nCE0cT2ji+I88r7utkeMbnqT64HqMzc6GXY9zrCSHyNAUisq209pey1XzlvedH+AfztjUy9hzZCWW\nZfUrbDtDopj+uf+hYt8qGo7n0lp9nM25fwUsHL4BpMy7w71ERUTEeygnPc+effwOlqudz4hijI2I\ntKlEpE0942t6Ots49NqjfTsBAwTHjWH8TcsZf8P3KNv1KpX71lDZVEBI6gSyZn+SwOjUCxC9XGjO\n4ChGTfnoftyWZVG+503KdrxCR3MN5U3VrHjnftIT5lDfVEJZ1V5SRs0kNNi9KssYG1PG3UR+8Xrq\nC3f1mxRhjCHz6nuJHjuPyrz12KvyOVq8vq9YGZE2ncxrvqnZjCIf4aIrNBpjrgGuAG4Blno4nIuS\net3Iuepub2L/8z8mPDCeyxZ+jQD/CPKLc9i98wWcIVEkzbqZzKu/4ekwZYj0dnWQ+9R36WmuJzN5\nEb4+ARwrXk9lzSHqWsux+wXgdAUTHTGm33XG2LEs1ynv6XAGkjjjBhJn3ABAV0sdXW2N+IeNwu7r\nd8prREQ8QTnp+ad2PnKmjr79vzQV7WX+tK8QHzOJ2oZCtuz9OwdeeIhpn/sdiTNvInGmWveMFMc3\n/JPiTc+QEj+LmPELqag5SEnFLg4Xr8Mv1L2BUGbakn7X2E728bQsa8D9jDFEpM/o28yyt7uT9voy\nfPxDcAZHDjhfRPq7qAqNxphY4I/A9YAqZWfJb+3N3P9o3MefKHIalQfW4uru5NJZ38Tf6d6xLTvz\nOppaKzix81WSZt3s4QhlKFUeWE1bXSnXX/pzwkISABiXtoSV635AYMp44iZfxd5nlnP8xFbSEt2N\ntju7WjlStJbIU/RVcvV0U304h/rje7D7OIkZv4jQxAn4BkUM+WsTEfkoyknPP7XzkTPV1dpA9cH1\nzJx4J+lJ7l2nE2InMW/KF3h708M0lh4gLGmih6OUodLd1kjJlhfIzryeqVmfACAr4yq273+SI8Xv\nMun2X7Dzr18nL/8NYiMzsdnsWJbF/qP/wmb3ITx1yoB7NpUfoerAGno62whNnEDM+EUExaQN9UsT\nuWhdVIVG4K/AY5Zl7TbGpHzs2dJn+bJ74FFPRyEXu46GCoICY/qKjO+JiRhDfnEOluUasBu1DF8N\nxfuIicjsKzIC+Pj4k5Ywm6PFGxl33XeIHreAnJ3/j4LSjQT4RVBcsROXsRi38O5+9+rpbGPf09+j\nqfIYybZQWugmd/e/SJx1MxmLvzDUL01E5OMoJz1PNBAuZ6uzqRrLchEdMbrf8fdWUHQ0VIAKjSNG\nU9lhLFcPY1Iu7Xd8TMqlHMx/i5aqAkYv+Sp5L/+clWuXMyp6AjUNBdTWF5C++Av4+Pf/u6Zo0zMc\nz3mCMJs/ofhyZP9qyra9RPadj+AbEDqEr0zk4uXxQqMx5hfAdz/iFAvIAq4GgoBH3rv0Aoc2bGiE\nWM6XgMhEylpeo7W9lkD/95cNlFfn4R82SkXGEcbu609rZ+OAXovtnQ3Yff0xxpB13XcIT5lC5YE1\nNLQWEDlhIYkzb8I/rP8flcWbn6Gj6jg/YAbpVgguy+JtSnhu24tEjZlNaOKEoX55IjLCKCcdehoI\nl3PhFxaLsTkor84jMuz9WWblNXkABEQkeio08QC7rz8AHZ2NBAW8v1FMR0dj3+fDUyYz5dP/Ren2\nlymtPoIzPIaJl989YEOXlurjHM95gmtJ5UZXGjZjKKWFh+tzOb7+H2oRJXKGPF5oxJ1e/PVjzikE\nFgNzgM4PLbfbYYx50rKsz33UDe677z5CQ/uPQNx+++3cfvvtZx/xRUIFRjnfYsYvpmjDU6zZ+hum\nZd3q7tFYksPxE1sYc+XXPB2eDLGY8Zeyd+/b5OW/yfiMqzDGRnl1HoWlW0iaeyvg3ixo1JSrP7aJ\nd83+tSyw4kg37lFlmzFcaSWx2lZGVd67KjTKiPL000/z9NNP9zvW2NjooWhGlAuek47EfPR0lKfK\nufLxDyEuewm5+1/EZrOTEDOZ2oYCdhx4hpD4LILjx3o6RBlCoYnjcQZHsePAMyye9U2cvkF0dDax\n6+DzBIQnEBznnvkaEj+O8Tc88JH3qj74LoHGyfVWKraTP98TTRCXWfG8mbeOMVd9fUDrH5HhajD5\nqMcLjZZl1QK1H3eeMeYbwPc/cCgeeAu4Fdj2cdf/5je/Ydq0aeca5kVHyZtcCA5nANm3/YxDr/4X\nq7e4pyDYffxIXXAXo6Zc4+HoZKiFJU8iceZN7Nz+NHkFb+Hj8KOpuYywpOyz7tfZ291JEP3/+LYZ\nQyA+dHZ3nM+wRbzeqQpPu3btYvr06R6KaGQYipx0pOWjpzLnL5NYvGK+p8OQi9zoJV/BcvWwc/8z\n7Nj/FADhqdMYd+23VQgaYYzNzrjrvsP+53/CC29/i9DgBBqaSrH5+JJ960/PasVVb1cn/saBg/7X\nBONDb3fn+Q5dxKsNJh/1eKHxTFmWVfrBj40xrbiXqhRYllXmmai8j/rcyIUWFJ3K9M/9ntbq4/R0\ntBAUm4HDGeDpsMQDjDFkXPZFosfOp+rQelw9XSSlfY7I0ZdgbPazuldo2hQ2HcnlKisJP+P+1VRo\nNVFsNTI2ZfKFCF9E5JwoJz13y5fdAys8HYUMBzaHL2OX3kfqws/QXleKMzga//BRng5LPCQsaSKz\nvvxHKvavor2+gtSIxcRmLznrnophqZM5sPMVDlDHBOPejLDb6iXHVBCWNElFbJEzdNEUGk9j4F70\nI9SUa3pYartXfW5kSBhjtPOa9AlJGEdIwrhB3SNl3h3kFuzkRz07mGfF0kI3OaaCkOgMYsYtPE+R\niohcMMpJP4ZW28iF4AyKwBkU4ekwxAv4BkWQPPvWQd0jMn0G4UnZ/LZ0H/OtOMLxZbOpptrWyaRF\nd3/8DUQEuIgLjZZlFQFnN2VmmHr28TtYvjLM02GIiJyzwOhUptz1K4o2PsXrhbux+/gRM+EGUuZ+\nCpvDx9PhiYiclnLSj6cio4hcDIzNzoRP/oTiLc+zde8qerrqCU2ayOR5dxA8aoynwxO5aFy0hUZx\nW77sHljp6ShERAYvMDqV8Tcu93QYIiJynqilj4hcbOw+fqQtuIu0BXd5OhSRi5YKjRcxjQ6LiIiI\niDdavuwetfQREREZgVRovAipwCgiIiIi3ujZx+9gj1r6iIiIjFgqNF5kVGQUEREREW+klj4iIiKi\nQuNFQj1uRERERMRbaTBcREREQIVGrzflmh6W2u5VjxsRERER8Tpz932bSx9o93QYIiIi4iVUaPRi\nzz5+B8vV40ZEREREvNDyZfeAiowiIiLyASo0ein1uBERERERb6Wl0iIiInIqKjR6ISVuIiIiIuKN\n5vxlEotXzPd0GCIiIuKlVGj0IiowioiIiIi3Wr7sHljh6ShERETEm9k8HYC4qcgoIiIiIt5KuaqI\niIicCc1o9DC/tTdz/6Nxng5DREREROSUVGQUERGRM6VCo4f09bd51NORiIiIiIgMpAFxEREROVsq\nNHrAs4/fwfIVYZ4OQ0RERETklJYvu0cD4iIiInLWVGgcYsuX3QMrPR2FiIiIiMipaam0iIiInCsV\nGoeQkjYRERER8VZ9rX1EREREzpEKjUNABUYRERER8WbLl90DKzwdhYiIiFzsVGi8gKZc08NS272e\nDkNERERE5LQ0KC4iIiLni83TAQxXzz5+h4qMIiIiIuLVVGQUERGR80kzGi8AbfgiIiIiIt7Mb+3N\n3P9onKfDEBERkWFGhcbzSCPCIiIiIuLtli+7Bx71dBQiIiIyHGnp9HmiIqOIiIiIeDvlrCIiInIh\naUajiIiIiMgwN+cvk1i8Yr6nwxAREZFhToVGEREREZFhbPmye2CFp6MQERGRkUBLp0VEREREhikt\nlRYREZGhpBmNIiIiIiLDzJRrelhqu9fTYYiIiMgIoxmNIiIiIiLDyLOP36Eio4iIiHiEZjSKiIiI\niAwTy5fdAys9HYWIiIiMVJrRKCIiIiIyDKgfo4iIiHiaCo0iIiIiIiIiIiIyaCo0ioiIiIiIiIiI\nyKCp0CgiIiIiIiIiIiKDpkKjiIiIiIiIiIiIDJoKjSIiIiIiIiIiIjJoKjSKiIiIiIiIiIjIoDk8\nHcBwse7hpZ4OQURERERGMOWjIiIi4mma0SgiIiIiIiIiIiKDpkKjiIiIiIiIiIiIDJoKjSIiIiIi\nIiIiIjJoKjSKiIiIiIiIiIjIoKnQKCIiIiIiIiIiIoOmQqOIiIiIiIiIiIgMmgqNIiIiIiIiIiIi\nMmgqNIqIiIiIiIiIiMigqdAoIiIiIiIiIiIig6ZCo4iIiIiIiIiIiAyaCo0iIiIiIiIiIiIyaCo0\nioiIiIiIiIiIyKCp0Cjn7Omnn/Z0CPIBeh7eQ8/Cu+h5eA89CxE53/RzxbvoeXgXPQ/voWfhXfQ8\nLiwVGuWc6ZvTu+h5eA89C++i5+E99CxE5HzTzxXvoufhXfQ8vIeehXfR87iwVGgUERERERERERGR\nQVOhUURERERERERERAZNhUYREREREREREREZNIenAxgCfgAHDx70dBzDTmNjI7t27fJ0GHKSnof3\n0LPwLnoe3kPP4tx9II/x82Qccs6Uj14g+rniXfQ8vIueh/fQs/Aueh7n5kzzUWNZ1oWPxoOMMXcA\nT3o6DhEREZHz4E7Lsp7ydBBydpSPioiIyDDykfnoSCg0RgJXAceBDs9GIyIiInJO/IBU4C3Lsmo9\nHIucJeWjIiIiMgycUT467AuNIiIiIiIiIiIicuFpMxgREREREREREREZNBUaRUREREREREREZNBU\naBQREREREREREZFBU6FRREREREREREREBk2FRjlvjDG+xphcY4zLGDPJ0/GMRMaYFGPMn4wxBcaY\nNmPMUWPMT4wxPp6ObaQwxnzNGFNojGk3xmwxxsz0dEwjjTHme8aYbcaYJmNMpTHmJWNMpqfjEjdj\nzAMnf0/82tOxiMjwpJzUs5SPep7yUe+gnNR7KR+9sFRolPPpl0ApoK3MPWccYIAvAeOB+4CvAj/z\nZFAjhTHmU8CvgB8DU4E9wFvGmCiPBjbyLAD+B7gEWAL4AG8bY/w9GpVw8g+dL+P+3hARuVCUk3qW\n8lEPUj7qVZSTeiHloxeesSz9/pXBM8ZcAzwK3ALkAVMsy9rr2agEwBjz78BXLcsa7elYhjtjzBZg\nq2VZ3zz5sQFKgN9ZlvVLjwY3gp1MrKuAhZZlbfB0PCOVMSYI2An8G/BDYLdlWfd7NioRGW6Uk3on\n5aNDR/mo91JO6nnKR4eGZjTKoBljYoE/Ap8G2j0cjgwUBtR5Oojh7uRyoOnA6veOWe6RnFXAHE/F\nJYD7e8BC3wee9r/Aq5ZlrfF0ICIyPCkn9WrKR4eA8lGvp5zU85SPDgGHpwOQYeGvwGOWZe02xqR4\nOhh5nzFmNPB1QKM0F14UYAcqP3S8Ehg79OEI9I3i/zewwbKsPE/HM1IZY24DpgAzPB2LiAxrykm9\nkPLRIaV81EspJ/U85aNDRzMa5ZSMMb842Rz1dP96jTGZxph7gSDgkfcu9WDYw9aZPo8PXZMAvAE8\na1nWXzwTuYjHPYa7P9Rtng5kpDLGJOJOrO+0LKvb0/GIyMVFOan3UD4qMijKST1I+ejQUo9GOSVj\nTCQQ+TGnFQLPAdd+6Lgd6AGetCzrcxcgvBHnDJ9HgWVZPSfPjwfWApv0DIbGyaUqbcAtlmWt/MDx\nvwGhlmXd5KnYRipjzO+B64AFlmUVezqekcoYcwPwItDL+3/423EvHeoFnJaSERE5DeWk3kP5qPdT\nPuqdlJN6nvLRoaVCowzKyZGBkA8cigfewt2Ae5tlWWUeCWwEOzlyvAbYDtylH5hD5zTNt4txN9/+\nL48GN8KcTOhuABZZllXg6XhGMmNMIPDhJYx/Aw4CD1uWdXDIgxKRYUc5qXdRPuo5yke9i3JS76B8\ndGipR6MMimVZpR/82BjTinuEoEAJ3dA7OXK8DvfI/n8AMe7cAizL+nCvFjn/fg38zRizE9gG3AcE\n4P4lJkPEGPMYcDtwPdB6cnMAgEbLsjo8F9nIZFlWK+6dX/uc/F1Rq6RORM4X5aTeQ/moxykf9RLK\nSb2H8tGhpUKjXAgasfScK4D0k/9KTh4zuJ+J3VNBjRSWZT1njIkCfgrEArnAVZZlVXs2shHnq7j/\nz6/70PHPAf8Y8mjkVPR7QkSGgn7WeIbyUQ9SPupVlJN6N/2OuEC0dFpEREREREREREQGTbtOi4iI\niIiIiIiIyKCp0CgiIiIiIiIiIiKDpkKjiIiIiIiIiIiIDJoKjSIiIiIiIiIiIjJoKjSKiIiIiIiI\niIjIoKnQKCIiIiIiIiIiIoOmQqOIiIiIiIiIiIgMmgqNIiIiIiIiIiIiMmgqNIqIiIiIiIiIiMig\nqdAoIiIiIiIiIiIig6ZCo4jIeWCMiTPGPGmMOWyM6TXG/NrTMYmIiIjIyKF8VES8gQqNIiLnhxOo\nAh4Ccj0ci4iIiIiMPMpHRcTjVGgUETkDxpgoY0y5MeaBDxyba4zpNMYstiyryLKs+yzL+ifQ5MFQ\nRURERGQYUj4qIhcDh6cDEBG5GFiWVWOM+TzwsjHmbeAI8A/gd5ZlrfVsdCIiIiIy3CkfFZGLgQqN\nIiJnyLKsN4wxfwSeAnYALcByz0YlIiIiIiOF8lER8XZaOi0icna+g3uQ5hPAHZZldXs4HhEREREZ\nWZSPiojXUqFRROTsjAbicf/8TPNwLCIiIiIy8igfFRGvpaXTIiJnyBjjAzwBPAMcBv5sjJloWVaN\nZyMTERERkZFA+aiIeDsVGkVEztzPgRDgG0AbsBT4K3AdgDFmMmCAICD65MddlmUd9Ey4IiIiIjLM\nKB8VEa9mLMvydAwiIl7PGLMIeBu41LKszSePpQC5wAOWZT1ujHEBH/6hWmRZVvrQRisiIiIiw43y\nURG5GKjQKCIiIiIiIiIiIoOmzWBERERERERERERk0FRoFBERERERERERkUFToVFEREREREREREQG\nTYVGERERERERERERGTQVGkVERERERERERGTQVGgUERERERERERGRQVOhUURERERERERERAZNhUYR\nEREREREREREZNBUaRUREREREREREZNBUaBQREREREREREZFBU6FRREREREREREREBk2FRhERERER\nERERERk0FRpFRERERERERERk0FRoFBERERERERERkUFToVFEREREREREREQGTYVGERERERERERER\nGTQVGkVERERERERERGTQVGgUERERkf/P3n2HyVWW/x9/PzPbey9JNpteIRUCCaGEKllFikqzooKs\nvy9SRDEo2L+KgAXlS1dAQEBUIlJEDCJJCCQhhfS6JZvtvc7OzPP740yW3c1usmGzO7O7n9d1zZXs\nmXPO3GfOJHPv/TQRERERkX5ToVFERERERERERET6TYVGERERERERERER6TcVGkVERERERERERKTf\nVGgUERERERERERGRflOhUURERERERERERPpNhUYRERERERERERHpNxUaRUREREREREREpN9UaBQR\nEREREREREZF+U6FRRDoYY/YbYx7rw35fNMb4jTFjj9c55fgxxnzMGPO+MabFGOMzxiT041x/MMbs\nO57xiYiIiByJctLhQTmpyMikQqPIMGWM+UIg8ZrXy/NvGmM2ddvsB2wfTm/7uN+hfQedMebMwPVf\nGozXDxZjTArwLNAM5AOfA5r6cUqL87kYNMaYrwQ+n6XGmFZjzF5jzGPGmNzBjENERET6TzmpclKG\naE7amTEmzBizNXAvbw5WHCJDQViwAxCRAXWkhKqn56YSxC/wARCUhDLITgbigO9aa1cch/N9hcFv\nlJoL7AVeBGqA8cC1QJ4xZra1tnSQ4xEREZH+UU468gyHnLSzG4AcRua9FDkmKjSKSAdrbXuwYzjO\nTLADOBbGmChrbWs/T5MZ+LOuv/EAWGt9gO94nOsYXvPr3bcZY14E1gKfB+4azHhERERkcCknDS7l\npF0ZYzKA7wE/A34UjBhEhhINnRaRDj3NXWOMmWGM+bcxptkYU2SMuZ1e/u8wxnw3sE+TMeYNY8yM\nXvZLNMb8yhhTGBgau8sY8y1jjOm0T+6hoQnGmK8aY3YH9n3XGHPScbzmbxpjVhpjKgPXuNYYc1m3\nfd40xmzo5fgdxphXOv1sjDE3GmM+CMxHU2qMecAYk9TtuP3GmOXGmPONMe8ZY1pweu0dKdZPB+Jr\nNsZUGGOeNMaM6vT8CuAPgR/XBt6/XuciMsbEBe7DvsB7W2aM+acxZk6nfbrMh2OMWRE4b0+Pz3fa\n76j3+BgVBP5MOuJeIiIiMuQpJ1VOGmI56c+AbcBTx3CMyIilHo0iw1+iMSa12zYDhPewb5ehAMaY\nTOBNnCTupzhzrFwLHNbCaYz5EXA78BLwCjAP+Gf31zHGRANvAdnAA0ARsAj4XyAL6D7nydU4wy4e\nCMT3beAFY8yEQMtmf92AM0T3j0AEcAXwnDHm49baQ8nak8BDxpgZ1tqtna7lZGAy8INO53sIp9fd\nY8CvcYb9/g8wxxhzWqeYLTANeBp4MHDcjt6CNMZ8MXDONcBtOK3ENwKLjDFzrbX1wI8D5/gq8F1g\nP7DnCNf+IHApcB9O8pQKLAamA4eS2O5zH/0YeLjbeT4HnA+UB2I91nvc2zWnAG4gF7gjEMcbfTlW\nREREQo5y0iNTThqCOakxZgHO+7gIDZsW6RtrrR566DEMH8AXcOa2OdJjU7dj9gGPdfr5lzhDFOZ3\n2paKM2+eDxgb2JaGk+i92O18Pw68TudzfheoByZ02/engAcYHfg5N3BsOZDQab9PBF576VGu/8zA\n8ZceZb/Ibj+7gU3A6522JeAktD/ttu+vA9cSHfh5ceA1L++233mB7Vd0e699wLl9uJdhQClOohXR\nafvSwHnv7HbffcC8Ppy3BvjNUfb5PbD3CM8vAtqAh471HvchvpZOn9Vy4OvB/nelhx566KGHHnoc\n20M5qXLSPpw3ZHNSnILqk90+CzcH+9+VHnqE8kNDp0WGNwtcD5zbw6P76n49uRB4x1q7ruOE1lZx\n+LCBc3Faie/rtv1XPZzzU8B/gTpjTOqhB05PtTDgjG77/8k6LaOH/Ben9XtCH+I/Kmtt26G/B4aS\nJAdeY16nfepxWpiv7LSvC/gM8FdrbUuna6sF3uh2be8DjcCSbi+/z1r7rz6EeRKQAdxvrfV0iutl\nYDuQ18fL7a4WOMUYk/1RDjbGZAF/BtYDnedVPNZ73JuP4XwGbwYKgdiPEqeIiIgEnXLSo1BOGno5\nqTHmS8BMnN6rItJHGjotMvy9Z61d332jMaYGpyX4SHKBd3rY3n04RW7gz92dN1prKwOv09lk4ESg\noofzWpzkpbOibuesDUypktx72H1njPk4zvCaOUBkp6e6r3T4BPAZY8xia+3bOC3CGThDWA6ZjDOH\nYHkPL9XTte3rYb+e5AaO39nDc9uB0/p4nu6+hTN/TpExZh3wMvCEtfaocRlj3MBzOAn2pbbrpO3H\neo97ZK39T+CvrxljlgMfGGMarbX39+V4ERERCSnKSY9AOWlo5aTGmHicno93WWtL+ngdIoIKjSIy\n+FzA68DP6XkFvu6JS29z3vR79T5jzOk4rcJv4rSyHwTagWvo1FIc8BpOsvZZ4O3An6V0nTPQBZQB\nV/USX/ckp6WHfQaNtfZ5Y8xbwCU489l8E/i2MeYSa+1rRzn8buAU4Bxr7cFuzx3rPe5LrHuNMe/j\nzI+kQqOIiIj0l3LSDyknPdytOL1jnzPGHCpg5wT+TA5sK7HDb4V0kX5ToVFEjqQApyWwu2k97Edg\n3/2HNhpj0ji8lXcPEGetXXGcYuyPS3ESqwustd5DG40xX+6+o7XWb4x5GviCMeY24JPAg9bazpNC\n7wHOAVZ1Hv5yHBTgJEdTcRLQzqby4ft/zKy1ZTiTYz8QuF/v47Sm95rUGWOuAL4B3BBoSe9uoO5x\nNM7k6CIiIjKyKCcNUE76oQHOSXNwPjNbu223gbiWAXPp29B/kRFFczSKyJG8DJxqjDnp0AZjTDpO\n62hn/wK8OCvZdXZTD+d8DlhojDm/+xPGmMTA8IfB4sNJFjoaXYwx43AStp48CaTgrIwXy+HzAj0X\nONcd3Q80xriNMYkfMc61OC3XXzPGdKyYaIy5EGc1vpeO9YTGGJcxJqHzNmttJVBC1+E63Y87AWeF\nvyestb/tZbePfI8D71NSD9sX4Ax9ea+3Y0VERGTYUk7alXLSAc5JcRbYuQS4uNPjWpxC6+8DP/d1\nyLnIiKIejSLDW3+HctwFfA5njrxf46xy91WcFuJZh3YKzHtzN3CbMeYlnGRwLs5iHt2HZvwCuE1W\neBcAACAASURBVAh4yRjzB2AdToI0C6c1dxxQ3c+4O/uUMWZ6D9v/APwDZ6GR1wItw5lAPrCLTtd3\niLV2gzHmA+DTwFZr7YZuz79ljHkQ532YA/wTZ9jLFJzJqG8A/nKsF2Ct9Rpjvg08BrxljHkGyAqc\nby+HT3Del/seDxQbY/4MbMSZGPw8nEm+bz7Ccb/HSYTfNsZc3e25VYG5dPpzj+Nw5ud5FtgCNAWO\n+yLOioQ/7sO1iYiISGhRTqqctDchmZMG3tMu72unIdRbrLV/78O1iYxIKjSKDG/2GJ+3nbdZa0uN\nMWfhrNz3baAK+D+ceWAe6XKgtbcbY1qArwFn4UzYfT5O4tT5nC3GmDNwhht8GidprMeZI+UOoK63\nePqwvaf9Lu/luRXW2hXGmGuA24Bf4rRKfgsYTw9JXcATOMnuEz2+oLXXG2PWAtcBP8FpVd8f2H/l\nR7iGQ+d93BjTFIj1ZzgFuBeA27qtgHjo3EfTDPwO5x5dgtPDfTdwvbX2oSOcLw0nQXuwh3N+CWfV\nwmO5xz3F9TDOaoiX4QyXLsFpqf+JtbawD9cmIiIioUU5qXLS3oRqTtqbPr9XIiOV6TqVg4iIHIkx\n5hvAPcA4a21xsOMRERERkZFHOamIhCoVGkVEjoExZiNQYa09N9ixiIiIiMjIpJxUREKVhk6LiByF\nMSYGZzLuJcAJOHO9iIiIiIgMGuWkIjIUqEejiMhRBCZ+3oezGMnvrLWHreAnIiIiIjKQlJOKyFCg\nQqOIiIiIiIiIiIj0myvYAYiIiIiIiIiIiMjQN+znaDTGpAIXAPuB1uBGIyIiIvKRRAHjgNestVVB\njkWOkfJRERERGQb6lI8O+0IjTlL3VLCDEBERETkOrgaeDnYQcsyUj4qIiMhwccR8dCQUGvcD/PGP\nf2T69OlBDmV4uemmm/jlL38Z7DAkQPcjdOhehBbdj9Che/HRbdu2jc9+9rMQyGtkyNkPykcHgv5f\nCS26H6FF9yN06F6EFt2Pj6av+ehIKDS2AkyfPp158+YFO5ZhJTExUe9pCNH9CB26F6FF9yN06F4c\nFxp2OzQpHx0g+n8ltOh+hBbdj9ChexFadD/67Yj5qBaDERERERERERERkX5ToVFERERERERERET6\nTYVGERERERERERER6TcVGuUju/LKK4MdgnSi+xE6dC9Ci+5H6NC9EJHjTf+vhBbdj9Ci+xE6dC9C\ni+7HwDLW2mDHMKCMMfOAdevWrdNknyIiIjIkrV+/nvnz5wPMt9auD3Y8cmyUj4qIiMhQ19d8VD0a\nRUREREREREREpN9UaBQREREREREREZF+U6FRRERERERERERE+k2FRhEREREREREREek3FRpFRERE\nRERERESk31RoFBERERERERERkX5ToVFERERERERERET6TYVGERERERERERER6TcVGkVERERERERE\nRKTfVGgUERERERERERGRflOhUURERERERERERPotLNgBDBerTvx4sEMQERGRIFm0+aVghyCifFRE\nRGQEC5V8VD0aRUREREREREREpN9UaBQREREREREREZF+U6FRRERERERERERE+k2FRhERERERERER\nEek3FRpFRERERERERESk31RoFBERERERERERkX5ToVFERERERERERET6TYVGERERERERERER6TcV\nGkVERERERERERKTfVGgUERERERERERGRflOhUURERERERERERPpNhUYRERERERERERHpNxUaRURE\nREREREREpN9UaBQREREREREREZF+Cwt2ACIiIiIiIiIiInLsluXlA/BmcMPooB6NIiIiIiIiIiIi\nQ8yhImMoUY9GERERERERERGRIWLOhV6Wum4Idhg9Uo9GERERERERERGRIeDZB68K2SIjqNB43CzL\ny2fR5luCHYaIiIiIiIiIiAwzCx+bxbK8fDYuTwp2KEekQuNxdNZtLSE5Pl5EREREhr9lefnKRUVE\nRIahZXn5LHlhcbDD6BMVGgfAsrx8olZcGuwwRERERGQEWpaXz8LHZgU7DBERETkOhlojogqNA+Tm\nu7NYlpfPnAu9wQ5FREREREaYJS8sHnK/mIiIiMiH5lzoHZLf5So0DrClrht49sGrgh2GiIiIiIxA\nmkdcRERk6FmWlx/SC74ciQqNg2Dj8iT1bhQRERGRoNA84iIiIkPDoQVfhjIVGgfRUtcNQ/4DIyIi\nIiJDk+YRFxERCV1DacGXI1GhMQg0hEVEREREgkHziIuIiISW4dCLsTMVGoNEQ1hEREREJFg0j7iI\niEjwDZdejJ2p0BhkKjaKiIiISDAcmkd84WOzgh2KiIjIiDNc60EqNIaAZXn5w/YDJiIiIiKhbckL\ni5WLioiIDJI5F3qH9feuCo0hZDh/0EREREQktCkXFRERGVjL8vJZ6roh2GEMqLBgByBdHUrwfvqP\n+4MciYiIiIiMNMpFRUREjr+Fj80adnMx9kY9GkOUWpRFREREJFiUi4qIiBwfw3HBlyNRoTGELcvL\nJ2rFpcEOQ0RERERGIM0jLiIi0j8j8Xt0SBcajTG3GWP8xph7gx3LQLn57qwR+cEUkeGrsr2V4rYm\nfNYGOxQRkeNiuOekykVFZLhp9nkpaGukydce7FBkmBrJjXVDdo5GY8zJwLXAxmDHMhiW5eVz7zdL\naV3yl2CHIiLykRS2NXLXgc2831wNQGZYNNdnTeW8pNFBjkzko4lacSk3350FwJvBDUWCaKTkpJq7\nUUSGg3a/n/8r286L1YW0Wh8RxsXSpDHckD2DSJc72OHJMDFSC4yHDMkejcaYOOCPwFeA2iCHM2gO\n9W6cc6E32KGIiByTBl87/7P3HQ42t/JVZnATsxnjjeP7xRtY1VAe7PBEjtmyvPyOIqOMXCMxJx3p\nvzyJyND2y4Mf8ELVfs63OXyLueTZcbxcU8xPi4d1W5EMkjkXevU9yRAtNAK/A/5urf13sAMJhqWu\nG3j2wauCHYaISJ+9UlNMjc/DN5nDQpPFiSaVfE5gCok8Wb472OGJ9NmzD16lBFI6G5E56UgeDiYi\nQ1e1t42Xaoq5lIlcbCYwzSTzCTOOK5jMG/UHOeBpDnaIMoQ9++BVLHXdEOwwQsKQKzQaY64A5gDf\nCXYswbRxeRLL8vJZtPmWYIciInJUu1rrGUc8KSaqY5sxhjmks6u1PoiRifTNwsdmsSwvn43Lk4Id\nioQI5aRopI2IDCn7WhvwYZlLWpftc0nHAnuUk8pHoBzxcEOq0GiMGQP8CrjaWqtZW4GzbmtRi7KI\nhLy08EjKaKbd+rpsL6KRtLCoXo4SCQ3L8vJZ8sLiYIchIUQ56YeWum5QLioiQ0JauJNzFtHYZXtx\n4GflpHKslCP2bKgtBjMfSAfWG2NMYJsbOMMY8/+ASGt7Xsb0pptuIjExscu2K6+8kiuvvHIg4x00\nWixGREJZXlIOT1Xs5VG2caWdTAzhvE0Jayjl66nTgx2eSI8Wbb6Fs25rGfTXfeaZZ3jmmWe6bKur\nqxv0OOSIPlJOOpzz0WV5+ay47G1WX7Mp2KGIiPQoNzKOWdHJPNuym0QbwSQS2U8Df2QHkyITmB6d\nePSTiAQM90a2/uSjppe6XEgyxsQCud02/wHYBvzMWruth2PmAevWrVvHvHnzBiy2s257ecDOfay0\nGqCEqkZfO5uba4g0bmbFJhNmhlSnaumnf9WV8L/Fm2i1PlwY/Fjyksbw7dGzcHf8ni4SGo41eXzz\nZ0sHKBLH+vXrmT9/PsB8a+36AX0xOapjzUlHWj6qXFRCmd9atrTU0OBrZ3p0EslhkcEOSQZReXsL\nt+x/j71tDbgx+LDkhMdy97iTGRMZG+zwZAhY+NiskO3FGCr56JDq0WitbQK2dt5mjGkCqnoqMo5U\ny/LyleBJyHmqYg+PVuymze/M5ZQaHs13R5/Igrj0IEcmg+XcxFFMjoznmap9ePw+PpmSy+zYlGCH\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U0OT0RMxxFq385fgF/PbgNh5q3QrAybFp3Jg9g3caKwDo3t3BHchRLU5Oaq1lW0sd7zVW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RpFRERE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\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DHaNOIS6gj6pw8cyDV7Narcwi0o80lDr0Pbh/I08UbiOBKCaQzBmks6a5iO/vfo8vjZjM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rbs+Q3NpbsITbtEkVxjyRgUhUqlZVf+09Q3H8Fmiaaqfi/NbWVoDJbTdpxOBmJ3oyAIgjAW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XN2Iip69A5xfEvziOFg2l1TtP+XxZ9Q4AcqMyfVHeBUkx2/h9wjzC/Uw8QzFvKOXMDQjh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Vlfcf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\nAIvFQWzkzCH3nRS3lK37/oLX3YfeaDmHV396b/qeYt9bZ/62+FERZqIVH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This may take about 2 minutes to run\n", + "\n", + "plt.figure(figsize=(16, 32))\n", + "hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]\n", + "for i, n_h in enumerate(hidden_layer_sizes):\n", + " plt.subplot(5, 2, i + 1)\n", + " plt.title('Hidden Layer of size %d' % n_h)\n", + " parameters = nn_model(X, Y, n_h, num_iterations=5000)\n", + " plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n", + " predictions = predict(parameters, X)\n", + " accuracy = float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100)\n", + " print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Interpretation**:\n", + "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n", + "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticable overfitting.\n", + "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optional questions**:\n", + "\n", + "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n", + "\n", + "Some optional/ungraded questions that you can explore if you wish: \n", + "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n", + "- Play with the learning_rate. What happens?\n", + "- What if we change the dataset? (See part 5 below!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**You've learnt to:**\n", + "- Build a complete neural network with a hidden layer\n", + "- Make a good use of a non-linear unit\n", + "- Implemented forward propagation and backpropagation, and trained a neural network\n", + "- See the impact of varying the hidden layer size, including overfitting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice work! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Performance on other datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+fJjt27ejlKJBgwb3/bwQo9FI2q3WnmiNGYucTirEI04OLhMPpcuXL/P5558z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\nU2PWrl37iCMTNYUkF0IIIYrJyMjAwtwKC4vape5jZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Datasets\n", + "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n", + "\n", + "datasets = {\"noisy_circles\": noisy_circles,\n", + " \"noisy_moons\": noisy_moons,\n", + " \"blobs\": blobs,\n", + " \"gaussian_quantiles\": gaussian_quantiles}\n", + "\n", + "### START CODE HERE ### (choose your dataset)\n", + "dataset = \"noisy_moons\"\n", + "### END CODE HERE ###\n", + "\n", + "X, Y = datasets[dataset]\n", + "X, Y = X.T, Y.reshape(1, Y.shape[0])\n", + "\n", + "# make blobs binary\n", + "if dataset == \"blobs\":\n", + " Y = Y % 2\n", + "\n", + "# Visualize the data\n", + "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congrats on finishing this Programming Assignment!\n", + "\n", + "Reference:\n", + "- http://scs.ryerson.ca/~aharley/neural-networks/\n", + "- http://cs231n.github.io/neural-networks-case-study/" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "neural-networks-deep-learning", + "graded_item_id": "wRuwL", + "launcher_item_id": "NI888" + }, + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/images/2layerNN_kiank.png b/Deep Learning Notebooks by Andrew NG/Neural Networks and Deep Learning/images/2layerNN_kiank.png new file mode 100644 index 0000000..817bd2b Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Neural 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Deep Learning/images/sgd_bad.gif differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/Building a Recurrent Neural Network - Step by Step - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/Building a Recurrent Neural Network - Step by Step - v1.ipynb new file mode 100644 index 0000000..cf3b40d --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/Building a Recurrent Neural Network - Step by Step - v1.ipynb @@ -0,0 +1,2139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building your Recurrent Neural Network - Step by Step\n", + "\n", + "Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy.\n", + "\n", + "Recurrent Neural Networks (RNN) are very effective for Natural Language Processing and other sequence tasks because they have \"memory\". They can read inputs $x^{\\langle t \\rangle}$ (such as words) one at a time, and remember some information/context through the hidden layer activations that get passed from one time-step to the next. This allows a uni-directional RNN to take information from the past to process later inputs. A bidirection RNN can take context from both the past and the future. \n", + "\n", + "**Notation**:\n", + "- Superscript $[l]$ denotes an object associated with the $l^{th}$ layer. \n", + " - Example: $a^{[4]}$ is the $4^{th}$ layer activation. $W^{[5]}$ and $b^{[5]}$ are the $5^{th}$ layer parameters.\n", + "\n", + "- Superscript $(i)$ denotes an object associated with the $i^{th}$ example. \n", + " - Example: $x^{(i)}$ is the $i^{th}$ training example input.\n", + "\n", + "- Superscript $\\langle t \\rangle$ denotes an object at the $t^{th}$ time-step. \n", + " - Example: $x^{\\langle t \\rangle}$ is the input x at the $t^{th}$ time-step. $x^{(i)\\langle t \\rangle}$ is the input at the $t^{th}$ timestep of example $i$.\n", + " \n", + "- Lowerscript $i$ denotes the $i^{th}$ entry of a vector.\n", + " - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the activations in layer $l$.\n", + "\n", + "We assume that you are already familiar with `numpy` and/or have completed the previous courses of the specialization. Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first import all the packages that you will need during this assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from rnn_utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Forward propagation for the basic Recurrent Neural Network\n", + "\n", + "Later this week, you will generate music using an RNN. The basic RNN that you will implement has the structure below. In this example, $T_x = T_y$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
**Figure 1**: Basic RNN model
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how you can implement an RNN: \n", + "\n", + "**Steps**:\n", + "1. Implement the calculations needed for one time-step of the RNN.\n", + "2. Implement a loop over $T_x$ time-steps in order to process all the inputs, one at a time. \n", + "\n", + "Let's go!\n", + "\n", + "## 1.1 - RNN cell\n", + "\n", + "A Recurrent neural network can be seen as the repetition of a single cell. You are first going to implement the computations for a single time-step. The following figure describes the operations for a single time-step of an RNN cell. \n", + "\n", + "\n", + "
**Figure 2**: Basic RNN cell. Takes as input $x^{\\langle t \\rangle}$ (current input) and $a^{\\langle t - 1\\rangle}$ (previous hidden state containing information from the past), and outputs $a^{\\langle t \\rangle}$ which is given to the next RNN cell and also used to predict $y^{\\langle t \\rangle}$
\n", + "\n", + "**Exercise**: Implement the RNN-cell described in Figure (2).\n", + "\n", + "**Instructions**:\n", + "1. Compute the hidden state with tanh activation: $a^{\\langle t \\rangle} = \\tanh(W_{aa} a^{\\langle t-1 \\rangle} + W_{ax} x^{\\langle t \\rangle} + b_a)$.\n", + "2. Using your new hidden state $a^{\\langle t \\rangle}$, compute the prediction $\\hat{y}^{\\langle t \\rangle} = softmax(W_{ya} a^{\\langle t \\rangle} + b_y)$. We provided you a function: `softmax`.\n", + "3. Store $(a^{\\langle t \\rangle}, a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}, parameters)$ in cache\n", + "4. Return $a^{\\langle t \\rangle}$ , $y^{\\langle t \\rangle}$ and cache\n", + "\n", + "We will vectorize over $m$ examples. Thus, $x^{\\langle t \\rangle}$ will have dimension $(n_x,m)$, and $a^{\\langle t \\rangle}$ will have dimension $(n_a,m)$. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: rnn_cell_forward\n", + "\n", + "def rnn_cell_forward(xt, a_prev, parameters):\n", + " \"\"\"\n", + " Implements a single forward step of the RNN-cell as described in Figure (2)\n", + "\n", + " Arguments:\n", + " xt -- your input data at timestep \"t\", numpy array of shape (n_x, m).\n", + " a_prev -- Hidden state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)\n", + " Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)\n", + " Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " ba -- Bias, numpy array of shape (n_a, 1)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " Returns:\n", + " a_next -- next hidden state, of shape (n_a, m)\n", + " yt_pred -- prediction at timestep \"t\", numpy array of shape (n_y, m)\n", + " cache -- tuple of values needed for the backward pass, contains (a_next, a_prev, xt, parameters)\n", + " \"\"\"\n", + " \n", + " # Retrieve parameters from \"parameters\"\n", + " Wax = parameters[\"Wax\"]\n", + " Waa = parameters[\"Waa\"]\n", + " Wya = parameters[\"Wya\"]\n", + " ba = parameters[\"ba\"]\n", + " by = parameters[\"by\"]\n", + " \n", + " ### START CODE HERE ### (≈2 lines)\n", + " # compute next activation state using the formula given above\n", + " a_next = np.tanh(np.dot(Waa, a_prev) + np.dot(Wax, xt) + ba)\n", + " # compute output of the current cell using the formula given above\n", + " yt_pred = softmax(np.dot(Wya, a_next) + by)\n", + " ### END CODE HERE ###\n", + " \n", + " # store values you need for backward propagation in cache\n", + " cache = (a_next, a_prev, xt, parameters)\n", + " \n", + " return a_next, yt_pred, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_next[4] = [ 0.59584544 0.18141802 0.61311866 0.99808218 0.85016201 0.99980978\n", + " -0.18887155 0.99815551 0.6531151 0.82872037]\n", + "a_next.shape = (5, 10)\n", + "yt_pred[1] = [ 0.9888161 0.01682021 0.21140899 0.36817467 0.98988387 0.88945212\n", + " 0.36920224 0.9966312 0.9982559 0.17746526]\n", + "yt_pred.shape = (2, 10)\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "xt = np.random.randn(3,10)\n", + "a_prev = np.random.randn(5,10)\n", + "Waa = np.random.randn(5,5)\n", + "Wax = np.random.randn(5,3)\n", + "Wya = np.random.randn(2,5)\n", + "ba = np.random.randn(5,1)\n", + "by = np.random.randn(2,1)\n", + "parameters = {\"Waa\": Waa, \"Wax\": Wax, \"Wya\": Wya, \"ba\": ba, \"by\": by}\n", + "\n", + "a_next, yt_pred, cache = rnn_cell_forward(xt, a_prev, parameters)\n", + "print(\"a_next[4] = \", a_next[4])\n", + "print(\"a_next.shape = \", a_next.shape)\n", + "print(\"yt_pred[1] =\", yt_pred[1])\n", + "print(\"yt_pred.shape = \", yt_pred.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **a_next[4]**:\n", + " \n", + " [ 0.59584544 0.18141802 0.61311866 0.99808218 0.85016201 0.99980978\n", + " -0.18887155 0.99815551 0.6531151 0.82872037]\n", + "
\n", + " **a_next.shape**:\n", + " \n", + " (5, 10)\n", + "
\n", + " **yt[1]**:\n", + " \n", + " [ 0.9888161 0.01682021 0.21140899 0.36817467 0.98988387 0.88945212\n", + " 0.36920224 0.9966312 0.9982559 0.17746526]\n", + "
\n", + " **yt.shape**:\n", + " \n", + " (2, 10)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 - RNN forward pass \n", + "\n", + "You can see an RNN as the repetition of the cell you've just built. If your input sequence of data is carried over 10 time steps, then you will copy the RNN cell 10 times. Each cell takes as input the hidden state from the previous cell ($a^{\\langle t-1 \\rangle}$) and the current time-step's input data ($x^{\\langle t \\rangle}$). It outputs a hidden state ($a^{\\langle t \\rangle}$) and a prediction ($y^{\\langle t \\rangle}$) for this time-step.\n", + "\n", + "\n", + "\n", + "
**Figure 3**: Basic RNN. The input sequence $x = (x^{\\langle 1 \\rangle}, x^{\\langle 2 \\rangle}, ..., x^{\\langle T_x \\rangle})$ is carried over $T_x$ time steps. The network outputs $y = (y^{\\langle 1 \\rangle}, y^{\\langle 2 \\rangle}, ..., y^{\\langle T_x \\rangle})$.
\n", + "\n", + "\n", + "\n", + "**Exercise**: Code the forward propagation of the RNN described in Figure (3).\n", + "\n", + "**Instructions**:\n", + "1. Create a vector of zeros ($a$) that will store all the hidden states computed by the RNN.\n", + "2. Initialize the \"next\" hidden state as $a_0$ (initial hidden state).\n", + "3. Start looping over each time step, your incremental index is $t$ :\n", + " - Update the \"next\" hidden state and the cache by running `rnn_cell_forward`\n", + " - Store the \"next\" hidden state in $a$ ($t^{th}$ position) \n", + " - Store the prediction in y\n", + " - Add the cache to the list of caches\n", + "4. Return $a$, $y$ and caches" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: rnn_forward\n", + "\n", + "def rnn_forward(x, a0, parameters):\n", + " \"\"\"\n", + " Implement the forward propagation of the recurrent neural network described in Figure (3).\n", + "\n", + " Arguments:\n", + " x -- Input data for every time-step, of shape (n_x, m, T_x).\n", + " a0 -- Initial hidden state, of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)\n", + " Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)\n", + " Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " ba -- Bias numpy array of shape (n_a, 1)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + "\n", + " Returns:\n", + " a -- Hidden states for every time-step, numpy array of shape (n_a, m, T_x)\n", + " y_pred -- Predictions for every time-step, numpy array of shape (n_y, m, T_x)\n", + " caches -- tuple of values needed for the backward pass, contains (list of caches, x)\n", + " \"\"\"\n", + " \n", + " # Initialize \"caches\" which will contain the list of all caches\n", + " caches = []\n", + " \n", + " # Retrieve dimensions from shapes of x and Wy\n", + " n_x, m, T_x = x.shape\n", + " n_y, n_a = parameters[\"Wya\"].shape\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # initialize \"a\" and \"y\" with zeros (≈2 lines)\n", + " a = np.zeros((n_a, m, T_x))\n", + " y_pred = np.zeros((n_y, m, T_x))\n", + " \n", + " # Initialize a_next (≈1 line)\n", + " a_next = a0\n", + " \n", + " # loop over all time-steps\n", + " for t in range(T_x):\n", + " # Update next hidden state, compute the prediction, get the cache (≈1 line)\n", + " a_next, yt_pred, cache = rnn_cell_forward(x[:,:,t], a_next, parameters)\n", + " # Save the value of the new \"next\" hidden state in a (≈1 line)\n", + " a[:,:,t] = a_next\n", + " # Save the value of the prediction in y (≈1 line)\n", + " y_pred[:,:,t] = yt_pred\n", + " # Append \"cache\" to \"caches\" (≈1 line)\n", + " caches.append(cache)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # store values needed for backward propagation in cache\n", + " caches = (caches, x)\n", + " \n", + " return a, y_pred, caches" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a[4][1] = [-0.99999375 0.77911235 -0.99861469 -0.99833267]\n", + "a.shape = (5, 10, 4)\n", + "y_pred[1][3] = [ 0.79560373 0.86224861 0.11118257 0.81515947]\n", + "y_pred.shape = (2, 10, 4)\n", + "caches[1][1][3] = [-1.1425182 -0.34934272 -0.20889423 0.58662319]\n", + "len(caches) = 2\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(3,10,4)\n", + "a0 = np.random.randn(5,10)\n", + "Waa = np.random.randn(5,5)\n", + "Wax = np.random.randn(5,3)\n", + "Wya = np.random.randn(2,5)\n", + "ba = np.random.randn(5,1)\n", + "by = np.random.randn(2,1)\n", + "parameters = {\"Waa\": Waa, \"Wax\": Wax, \"Wya\": Wya, \"ba\": ba, \"by\": by}\n", + "\n", + "a, y_pred, caches = rnn_forward(x, a0, parameters)\n", + "print(\"a[4][1] = \", a[4][1])\n", + "print(\"a.shape = \", a.shape)\n", + "print(\"y_pred[1][3] =\", y_pred[1][3])\n", + "print(\"y_pred.shape = \", y_pred.shape)\n", + "print(\"caches[1][1][3] =\", caches[1][1][3])\n", + "print(\"len(caches) = \", len(caches))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **a[4][1]**:\n", + " \n", + " [-0.99999375 0.77911235 -0.99861469 -0.99833267]\n", + "
\n", + " **a.shape**:\n", + " \n", + " (5, 10, 4)\n", + "
\n", + " **y[1][3]**:\n", + " \n", + " [ 0.79560373 0.86224861 0.11118257 0.81515947]\n", + "
\n", + " **y.shape**:\n", + " \n", + " (2, 10, 4)\n", + "
\n", + " **cache[1][1][3]**:\n", + " \n", + " [-1.1425182 -0.34934272 -0.20889423 0.58662319]\n", + "
\n", + " **len(cache)**:\n", + " \n", + " 2\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You've successfully built the forward propagation of a recurrent neural network from scratch. This will work well enough for some applications, but it suffers from vanishing gradient problems. So it works best when each output $y^{\\langle t \\rangle}$ can be estimated using mainly \"local\" context (meaning information from inputs $x^{\\langle t' \\rangle}$ where $t'$ is not too far from $t$). \n", + "\n", + "In the next part, you will build a more complex LSTM model, which is better at addressing vanishing gradients. The LSTM will be better able to remember a piece of information and keep it saved for many timesteps. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Long Short-Term Memory (LSTM) network\n", + "\n", + "This following figure shows the operations of an LSTM-cell.\n", + "\n", + "\n", + "
**Figure 4**: LSTM-cell. This tracks and updates a \"cell state\" or memory variable $c^{\\langle t \\rangle}$ at every time-step, which can be different from $a^{\\langle t \\rangle}$.
\n", + "\n", + "Similar to the RNN example above, you will start by implementing the LSTM cell for a single time-step. Then you can iteratively call it from inside a for-loop to have it process an input with $T_x$ time-steps. \n", + "\n", + "### About the gates\n", + "\n", + "#### - Forget gate\n", + "\n", + "For the sake of this illustration, lets assume we are reading words in a piece of text, and want use an LSTM to keep track of grammatical structures, such as whether the subject is singular or plural. If the subject changes from a singular word to a plural word, we need to find a way to get rid of our previously stored memory value of the singular/plural state. In an LSTM, the forget gate lets us do this: \n", + "\n", + "$$\\Gamma_f^{\\langle t \\rangle} = \\sigma(W_f[a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}] + b_f)\\tag{1} $$\n", + "\n", + "Here, $W_f$ are weights that govern the forget gate's behavior. We concatenate $[a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}]$ and multiply by $W_f$. The equation above results in a vector $\\Gamma_f^{\\langle t \\rangle}$ with values between 0 and 1. This forget gate vector will be multiplied element-wise by the previous cell state $c^{\\langle t-1 \\rangle}$. So if one of the values of $\\Gamma_f^{\\langle t \\rangle}$ is 0 (or close to 0) then it means that the LSTM should remove that piece of information (e.g. the singular subject) in the corresponding component of $c^{\\langle t-1 \\rangle}$. If one of the values is 1, then it will keep the information. \n", + "\n", + "#### - Update gate\n", + "\n", + "Once we forget that the subject being discussed is singular, we need to find a way to update it to reflect that the new subject is now plural. Here is the formulat for the update gate: \n", + "\n", + "$$\\Gamma_u^{\\langle t \\rangle} = \\sigma(W_u[a^{\\langle t-1 \\rangle}, x^{\\{t\\}}] + b_u)\\tag{2} $$ \n", + "\n", + "Similar to the forget gate, here $\\Gamma_u^{\\langle t \\rangle}$ is again a vector of values between 0 and 1. This will be multiplied element-wise with $\\tilde{c}^{\\langle t \\rangle}$, in order to compute $c^{\\langle t \\rangle}$.\n", + "\n", + "#### - Updating the cell \n", + "\n", + "To update the new subject we need to create a new vector of numbers that we can add to our previous cell state. The equation we use is: \n", + "\n", + "$$ \\tilde{c}^{\\langle t \\rangle} = \\tanh(W_c[a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}] + b_c)\\tag{3} $$\n", + "\n", + "Finally, the new cell state is: \n", + "\n", + "$$ c^{\\langle t \\rangle} = \\Gamma_f^{\\langle t \\rangle}* c^{\\langle t-1 \\rangle} + \\Gamma_u^{\\langle t \\rangle} *\\tilde{c}^{\\langle t \\rangle} \\tag{4} $$\n", + "\n", + "\n", + "#### - Output gate\n", + "\n", + "To decide which outputs we will use, we will use the following two formulas: \n", + "\n", + "$$ \\Gamma_o^{\\langle t \\rangle}= \\sigma(W_o[a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}] + b_o)\\tag{5}$$ \n", + "$$ a^{\\langle t \\rangle} = \\Gamma_o^{\\langle t \\rangle}* \\tanh(c^{\\langle t \\rangle})\\tag{6} $$\n", + "\n", + "Where in equation 5 you decide what to output using a sigmoid function and in equation 6 you multiply that by the $\\tanh$ of the previous state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - LSTM cell\n", + "\n", + "**Exercise**: Implement the LSTM cell described in the Figure (3).\n", + "\n", + "**Instructions**:\n", + "1. Concatenate $a^{\\langle t-1 \\rangle}$ and $x^{\\langle t \\rangle}$ in a single matrix: $concat = \\begin{bmatrix} a^{\\langle t-1 \\rangle} \\\\ x^{\\langle t \\rangle} \\end{bmatrix}$\n", + "2. Compute all the formulas 1-6. You can use `sigmoid()` (provided) and `np.tanh()`.\n", + "3. Compute the prediction $y^{\\langle t \\rangle}$. You can use `softmax()` (provided)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: lstm_cell_forward\n", + "\n", + "def lstm_cell_forward(xt, a_prev, c_prev, parameters):\n", + " \"\"\"\n", + " Implement a single forward step of the LSTM-cell as described in Figure (4)\n", + "\n", + " Arguments:\n", + " xt -- your input data at timestep \"t\", numpy array of shape (n_x, m).\n", + " a_prev -- Hidden state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " c_prev -- Memory state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wf -- Weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " bf -- Bias of the forget gate, numpy array of shape (n_a, 1)\n", + " Wi -- Weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " bi -- Bias of the update gate, numpy array of shape (n_a, 1)\n", + " Wc -- Weight matrix of the first \"tanh\", numpy array of shape (n_a, n_a + n_x)\n", + " bc -- Bias of the first \"tanh\", numpy array of shape (n_a, 1)\n", + " Wo -- Weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " bo -- Bias of the output gate, numpy array of shape (n_a, 1)\n", + " Wy -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " \n", + " Returns:\n", + " a_next -- next hidden state, of shape (n_a, m)\n", + " c_next -- next memory state, of shape (n_a, m)\n", + " yt_pred -- prediction at timestep \"t\", numpy array of shape (n_y, m)\n", + " cache -- tuple of values needed for the backward pass, contains (a_next, c_next, a_prev, c_prev, xt, parameters)\n", + " \n", + " Note: ft/it/ot stand for the forget/update/output gates, cct stands for the candidate value (c tilde),\n", + " c stands for the memory value\n", + " \"\"\"\n", + "\n", + " # Retrieve parameters from \"parameters\"\n", + " Wf = parameters[\"Wf\"]\n", + " bf = parameters[\"bf\"]\n", + " Wi = parameters[\"Wi\"]\n", + " bi = parameters[\"bi\"]\n", + " Wc = parameters[\"Wc\"]\n", + " bc = parameters[\"bc\"]\n", + " Wo = parameters[\"Wo\"]\n", + " bo = parameters[\"bo\"]\n", + " Wy = parameters[\"Wy\"]\n", + " by = parameters[\"by\"]\n", + " \n", + " # Retrieve dimensions from shapes of xt and Wy\n", + " n_x, m = xt.shape\n", + " n_y, n_a = Wy.shape\n", + "\n", + " ### START CODE HERE ###\n", + " # Concatenate a_prev and xt (≈3 lines)\n", + " concat = np.zeros((n_a + n_x, m))\n", + " concat[: n_a, :] = a_prev\n", + " concat[n_a :, :] = xt\n", + "\n", + " # Compute values for ft, it, cct, c_next, ot, a_next using the formulas given figure (4) (≈6 lines)\n", + " ft = sigmoid(np.dot(Wf, concat) + bf)\n", + " it = sigmoid(np.dot(Wi, concat) + bi)\n", + " cct = np.tanh(np.dot(Wc, concat) + bc)\n", + " c_next = ft * c_prev + it * cct \n", + " ot = sigmoid(np.dot(Wo, concat) + bo)\n", + " a_next = ot * np.tanh(c_next)\n", + " \n", + " # Compute prediction of the LSTM cell (≈1 line)\n", + " yt_pred = softmax(np.dot(Wy, a_next) + by)\n", + " ### END CODE HERE ###\n", + "\n", + " # store values needed for backward propagation in cache\n", + " cache = (a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters)\n", + "\n", + " return a_next, c_next, yt_pred, cache" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a_next[4] = [-0.66408471 0.0036921 0.02088357 0.22834167 -0.85575339 0.00138482\n", + " 0.76566531 0.34631421 -0.00215674 0.43827275]\n", + "a_next.shape = (5, 10)\n", + "c_next[2] = [ 0.63267805 1.00570849 0.35504474 0.20690913 -1.64566718 0.11832942\n", + " 0.76449811 -0.0981561 -0.74348425 -0.26810932]\n", + "c_next.shape = (5, 10)\n", + "yt[1] = [ 0.79913913 0.15986619 0.22412122 0.15606108 0.97057211 0.31146381\n", + " 0.00943007 0.12666353 0.39380172 0.07828381]\n", + "yt.shape = (2, 10)\n", + "cache[1][3] = [-0.16263996 1.03729328 0.72938082 -0.54101719 0.02752074 -0.30821874\n", + " 0.07651101 -1.03752894 1.41219977 -0.37647422]\n", + "len(cache) = 10\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "xt = np.random.randn(3,10)\n", + "a_prev = np.random.randn(5,10)\n", + "c_prev = np.random.randn(5,10)\n", + "Wf = np.random.randn(5, 5+3)\n", + "bf = np.random.randn(5,1)\n", + "Wi = np.random.randn(5, 5+3)\n", + "bi = np.random.randn(5,1)\n", + "Wo = np.random.randn(5, 5+3)\n", + "bo = np.random.randn(5,1)\n", + "Wc = np.random.randn(5, 5+3)\n", + "bc = np.random.randn(5,1)\n", + "Wy = np.random.randn(2,5)\n", + "by = np.random.randn(2,1)\n", + "\n", + "parameters = {\"Wf\": Wf, \"Wi\": Wi, \"Wo\": Wo, \"Wc\": Wc, \"Wy\": Wy, \"bf\": bf, \"bi\": bi, \"bo\": bo, \"bc\": bc, \"by\": by}\n", + "\n", + "a_next, c_next, yt, cache = lstm_cell_forward(xt, a_prev, c_prev, parameters)\n", + "print(\"a_next[4] = \", a_next[4])\n", + "print(\"a_next.shape = \", c_next.shape)\n", + "print(\"c_next[2] = \", c_next[2])\n", + "print(\"c_next.shape = \", c_next.shape)\n", + "print(\"yt[1] =\", yt[1])\n", + "print(\"yt.shape = \", yt.shape)\n", + "print(\"cache[1][3] =\", cache[1][3])\n", + "print(\"len(cache) = \", len(cache))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **a_next[4]**:\n", + " \n", + " [-0.66408471 0.0036921 0.02088357 0.22834167 -0.85575339 0.00138482\n", + " 0.76566531 0.34631421 -0.00215674 0.43827275]\n", + "
\n", + " **a_next.shape**:\n", + " \n", + " (5, 10)\n", + "
\n", + " **c_next[2]**:\n", + " \n", + " [ 0.63267805 1.00570849 0.35504474 0.20690913 -1.64566718 0.11832942\n", + " 0.76449811 -0.0981561 -0.74348425 -0.26810932]\n", + "
\n", + " **c_next.shape**:\n", + " \n", + " (5, 10)\n", + "
\n", + " **yt[1]**:\n", + " \n", + " [ 0.79913913 0.15986619 0.22412122 0.15606108 0.97057211 0.31146381\n", + " 0.00943007 0.12666353 0.39380172 0.07828381]\n", + "
\n", + " **yt.shape**:\n", + " \n", + " (2, 10)\n", + "
\n", + " **cache[1][3]**:\n", + " \n", + " [-0.16263996 1.03729328 0.72938082 -0.54101719 0.02752074 -0.30821874\n", + " 0.07651101 -1.03752894 1.41219977 -0.37647422]\n", + "
\n", + " **len(cache)**:\n", + " \n", + " 10\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 - Forward pass for LSTM\n", + "\n", + "Now that you have implemented one step of an LSTM, you can now iterate this over this using a for-loop to process a sequence of $T_x$ inputs. \n", + "\n", + "\n", + "
**Figure 4**: LSTM over multiple time-steps.
\n", + "\n", + "**Exercise:** Implement `lstm_forward()` to run an LSTM over $T_x$ time-steps. \n", + "\n", + "**Note**: $c^{\\langle 0 \\rangle}$ is initialized with zeros." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: lstm_forward\n", + "\n", + "def lstm_forward(x, a0, parameters):\n", + " \"\"\"\n", + " Implement the forward propagation of the recurrent neural network using an LSTM-cell described in Figure (3).\n", + "\n", + " Arguments:\n", + " x -- Input data for every time-step, of shape (n_x, m, T_x).\n", + " a0 -- Initial hidden state, of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wf -- Weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " bf -- Bias of the forget gate, numpy array of shape (n_a, 1)\n", + " Wi -- Weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " bi -- Bias of the update gate, numpy array of shape (n_a, 1)\n", + " Wc -- Weight matrix of the first \"tanh\", numpy array of shape (n_a, n_a + n_x)\n", + " bc -- Bias of the first \"tanh\", numpy array of shape (n_a, 1)\n", + " Wo -- Weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " bo -- Bias of the output gate, numpy array of shape (n_a, 1)\n", + " Wy -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " \n", + " Returns:\n", + " a -- Hidden states for every time-step, numpy array of shape (n_a, m, T_x)\n", + " y -- Predictions for every time-step, numpy array of shape (n_y, m, T_x)\n", + " caches -- tuple of values needed for the backward pass, contains (list of all the caches, x)\n", + " \"\"\"\n", + "\n", + " # Initialize \"caches\", which will track the list of all the caches\n", + " caches = []\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from shapes of x and Wy (≈2 lines)\n", + " n_x, m, T_x = x.shape\n", + " n_y, n_a = parameters[\"Wy\"].shape\n", + " \n", + " # initialize \"a\", \"c\" and \"y\" with zeros (≈3 lines)\n", + " a = np.zeros((n_a, m, T_x))\n", + " c = a\n", + " y = np.zeros((n_y, m, T_x))\n", + " \n", + " # Initialize a_next and c_next (≈2 lines)\n", + " a_next = a0\n", + " c_next = np.zeros(a_next.shape)\n", + " \n", + " # loop over all time-steps\n", + " for t in range(T_x):\n", + " # Update next hidden state, next memory state, compute the prediction, get the cache (≈1 line)\n", + " a_next, c_next, yt, cache = lstm_cell_forward(x[:,:,t], a_next, c_next, parameters)\n", + " # Save the value of the new \"next\" hidden state in a (≈1 line)\n", + " a[:,:,t] = a_next\n", + " # Save the value of the prediction in y (≈1 line)\n", + " y[:,:,t] = yt\n", + " # Save the value of the next cell state (≈1 line)\n", + " c[:,:,t] = c_next\n", + " # Append the cache into caches (≈1 line)\n", + " caches.append(cache)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # store values needed for backward propagation in cache\n", + " caches = (caches, x)\n", + "\n", + " return a, y, c, caches" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a[4][3][6] = 0.73162451027\n", + "a.shape = (5, 10, 7)\n", + "y[1][4][3] = 0.95087346185\n", + "y.shape = (2, 10, 7)\n", + "caches[1][1[1]] = [ 0.82797464 0.23009474 0.76201118 -0.22232814 -0.20075807 0.18656139\n", + " 0.41005165]\n", + "c[1][2][1] -0.855544916718\n", + "len(caches) = 2\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(3,10,7)\n", + "a0 = np.random.randn(5,10)\n", + "Wf = np.random.randn(5, 5+3)\n", + "bf = np.random.randn(5,1)\n", + "Wi = np.random.randn(5, 5+3)\n", + "bi = np.random.randn(5,1)\n", + "Wo = np.random.randn(5, 5+3)\n", + "bo = np.random.randn(5,1)\n", + "Wc = np.random.randn(5, 5+3)\n", + "bc = np.random.randn(5,1)\n", + "Wy = np.random.randn(2,5)\n", + "by = np.random.randn(2,1)\n", + "\n", + "parameters = {\"Wf\": Wf, \"Wi\": Wi, \"Wo\": Wo, \"Wc\": Wc, \"Wy\": Wy, \"bf\": bf, \"bi\": bi, \"bo\": bo, \"bc\": bc, \"by\": by}\n", + "\n", + "a, y, c, caches = lstm_forward(x, a0, parameters)\n", + "print(\"a[4][3][6] = \", a[4][3][6])\n", + "print(\"a.shape = \", a.shape)\n", + "print(\"y[1][4][3] =\", y[1][4][3])\n", + "print(\"y.shape = \", y.shape)\n", + "print(\"caches[1][1[1]] =\", caches[1][1][1])\n", + "print(\"c[1][2][1]\", c[1][2][1])\n", + "print(\"len(caches) = \", len(caches))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **a[4][3][6]** =\n", + " \n", + " 0.172117767533\n", + "
\n", + " **a.shape** =\n", + " \n", + " (5, 10, 7)\n", + "
\n", + " **y[1][4][3]** =\n", + " \n", + " 0.95087346185\n", + "
\n", + " **y.shape** =\n", + " \n", + " (2, 10, 7)\n", + "
\n", + " **caches[1][1][1]** =\n", + " \n", + " [ 0.82797464 0.23009474 0.76201118 -0.22232814 -0.20075807 0.18656139\n", + " 0.41005165]\n", + "
\n", + " **c[1][2][1]** =\n", + " \n", + " -0.855544916718\n", + "
\n", + " **len(caches)** =\n", + " \n", + " 2\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You have now implemented the forward passes for the basic RNN and the LSTM. When using a deep learning framework, implementing the forward pass is sufficient to build systems that achieve great performance. \n", + "\n", + "The rest of this notebook is optional, and will not be graded." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Backpropagation in recurrent neural networks (OPTIONAL / UNGRADED)\n", + "\n", + "In modern deep learning frameworks, you only have to implement the forward pass, and the framework takes care of the backward pass, so most deep learning engineers do not need to bother with the details of the backward pass. If however you are an expert in calculus and want to see the details of backprop in RNNs, you can work through this optional portion of the notebook. \n", + "\n", + "When in an earlier course you implemented a simple (fully connected) neural network, you used backpropagation to compute the derivatives with respect to the cost to update the parameters. Similarly, in recurrent neural networks you can to calculate the derivatives with respect to the cost in order to update the parameters. The backprop equations are quite complicated and we did not derive them in lecture. However, we will briefly present them below. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Basic RNN backward pass\n", + "\n", + "We will start by computing the backward pass for the basic RNN-cell.\n", + "\n", + "
\n", + "
**Figure 5**: RNN-cell's backward pass. Just like in a fully-connected neural network, the derivative of the cost function $J$ backpropagates through the RNN by following the chain-rule from calculas. The chain-rule is also used to calculate $(\\frac{\\partial J}{\\partial W_{ax}},\\frac{\\partial J}{\\partial W_{aa}},\\frac{\\partial J}{\\partial b})$ to update the parameters $(W_{ax}, W_{aa}, b_a)$.
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deriving the one step backward functions: \n", + "\n", + "To compute the `rnn_cell_backward` you need to compute the following equations. It is a good exercise to derive them by hand. \n", + "\n", + "The derivative of $\\tanh$ is $1-\\tanh(x)^2$. You can find the complete proof [here](https://www.wyzant.com/resources/lessons/math/calculus/derivative_proofs/tanx). Note that: $ \\sec(x)^2 = 1 - \\tanh(x)^2$\n", + "\n", + "Similarly for $\\frac{ \\partial a^{\\langle t \\rangle} } {\\partial W_{ax}}, \\frac{ \\partial a^{\\langle t \\rangle} } {\\partial W_{aa}}, \\frac{ \\partial a^{\\langle t \\rangle} } {\\partial b}$, the derivative of $\\tanh(u)$ is $(1-\\tanh(u)^2)du$. \n", + "\n", + "The final two equations also follow same rule and are derived using the $\\tanh$ derivative. Note that the arrangement is done in a way to get the same dimensions to match." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rnn_cell_backward(da_next, cache):\n", + " \"\"\"\n", + " Implements the backward pass for the RNN-cell (single time-step).\n", + "\n", + " Arguments:\n", + " da_next -- Gradient of loss with respect to next hidden state\n", + " cache -- python dictionary containing useful values (output of rnn_cell_forward())\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradients of input data, of shape (n_x, m)\n", + " da_prev -- Gradients of previous hidden state, of shape (n_a, m)\n", + " dWax -- Gradients of input-to-hidden weights, of shape (n_a, n_x)\n", + " dWaa -- Gradients of hidden-to-hidden weights, of shape (n_a, n_a)\n", + " dba -- Gradients of bias vector, of shape (n_a, 1)\n", + " \"\"\"\n", + " \n", + " # Retrieve values from cache\n", + " (a_next, a_prev, xt, parameters) = cache\n", + " \n", + " # Retrieve values from parameters\n", + " Wax = parameters[\"Wax\"]\n", + " Waa = parameters[\"Waa\"]\n", + " Wya = parameters[\"Wya\"]\n", + " ba = parameters[\"ba\"]\n", + " by = parameters[\"by\"]\n", + "\n", + " ### START CODE HERE ###\n", + " # compute the gradient of tanh with respect to a_next (≈1 line)\n", + " dtanh = (1- a_next**2) * da_next\n", + "\n", + " # compute the gradient of the loss with respect to Wax (≈2 lines)\n", + " dxt = np.dot(Wax.T, dtanh)\n", + " dWax = np.dot(dtanh, xt.T)\n", + "\n", + " # compute the gradient with respect to Waa (≈2 lines)\n", + " da_prev = np.dot(Waa.T, dtanh)\n", + " dWaa = np.dot(dtanh, a_prev.T)\n", + "\n", + " # compute the gradient with respect to b (≈1 line)\n", + " dba = np.sum(dtanh, 1, keepdims=True)\n", + "\n", + " ### END CODE HERE ###\n", + " \n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dxt\": dxt, \"da_prev\": da_prev, \"dWax\": dWax, \"dWaa\": dWaa, \"dba\": dba}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradients[\"dxt\"][1][2] = -0.460564103059\n", + "gradients[\"dxt\"].shape = (3, 10)\n", + "gradients[\"da_prev\"][2][3] = 0.0842968653807\n", + "gradients[\"da_prev\"].shape = (5, 10)\n", + "gradients[\"dWax\"][3][1] = 0.393081873922\n", + "gradients[\"dWax\"].shape = (5, 3)\n", + "gradients[\"dWaa\"][1][2] = -0.28483955787\n", + "gradients[\"dWaa\"].shape = (5, 5)\n", + "gradients[\"dba\"][4] = [ 0.80517166]\n", + "gradients[\"dba\"].shape = (5, 1)\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "xt = np.random.randn(3,10)\n", + "a_prev = np.random.randn(5,10)\n", + "Wax = np.random.randn(5,3)\n", + "Waa = np.random.randn(5,5)\n", + "Wya = np.random.randn(2,5)\n", + "b = np.random.randn(5,1)\n", + "by = np.random.randn(2,1)\n", + "parameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"ba\": ba, \"by\": by}\n", + "\n", + "a_next, yt, cache = rnn_cell_forward(xt, a_prev, parameters)\n", + "\n", + "da_next = np.random.randn(5,10)\n", + "gradients = rnn_cell_backward(da_next, cache)\n", + "print(\"gradients[\\\"dxt\\\"][1][2] =\", gradients[\"dxt\"][1][2])\n", + "print(\"gradients[\\\"dxt\\\"].shape =\", gradients[\"dxt\"].shape)\n", + "print(\"gradients[\\\"da_prev\\\"][2][3] =\", gradients[\"da_prev\"][2][3])\n", + "print(\"gradients[\\\"da_prev\\\"].shape =\", gradients[\"da_prev\"].shape)\n", + "print(\"gradients[\\\"dWax\\\"][3][1] =\", gradients[\"dWax\"][3][1])\n", + "print(\"gradients[\\\"dWax\\\"].shape =\", gradients[\"dWax\"].shape)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients[\"dWaa\"][1][2])\n", + "print(\"gradients[\\\"dWaa\\\"].shape =\", gradients[\"dWaa\"].shape)\n", + "print(\"gradients[\\\"dba\\\"][4] =\", gradients[\"dba\"][4])\n", + "print(\"gradients[\\\"dba\\\"].shape =\", gradients[\"dba\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dxt\"][1][2]** =\n", + " \n", + " -0.460564103059\n", + "
\n", + " **gradients[\"dxt\"].shape** =\n", + " \n", + " (3, 10)\n", + "
\n", + " **gradients[\"da_prev\"][2][3]** =\n", + " \n", + " 0.0842968653807\n", + "
\n", + " **gradients[\"da_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWax\"][3][1]** =\n", + " \n", + " 0.393081873922\n", + "
\n", + " **gradients[\"dWax\"].shape** =\n", + " \n", + " (5, 3)\n", + "
\n", + " **gradients[\"dWaa\"][1][2]** = \n", + " \n", + " -0.28483955787\n", + "
\n", + " **gradients[\"dWaa\"].shape** =\n", + " \n", + " (5, 5)\n", + "
\n", + " **gradients[\"dba\"][4]** = \n", + " \n", + " [ 0.80517166]\n", + "
\n", + " **gradients[\"dba\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Backward pass through the RNN\n", + "\n", + "Computing the gradients of the cost with respect to $a^{\\langle t \\rangle}$ at every time-step $t$ is useful because it is what helps the gradient backpropagate to the previous RNN-cell. To do so, you need to iterate through all the time steps starting at the end, and at each step, you increment the overall $db_a$, $dW_{aa}$, $dW_{ax}$ and you store $dx$.\n", + "\n", + "**Instructions**:\n", + "\n", + "Implement the `rnn_backward` function. Initialize the return variables with zeros first and then loop through all the time steps while calling the `rnn_cell_backward` at each time timestep, update the other variables accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rnn_backward(da, caches):\n", + " \"\"\"\n", + " Implement the backward pass for a RNN over an entire sequence of input data.\n", + "\n", + " Arguments:\n", + " da -- Upstream gradients of all hidden states, of shape (n_a, m, T_x)\n", + " caches -- tuple containing information from the forward pass (rnn_forward)\n", + " \n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradient w.r.t. the input data, numpy-array of shape (n_x, m, T_x)\n", + " da0 -- Gradient w.r.t the initial hidden state, numpy-array of shape (n_a, m)\n", + " dWax -- Gradient w.r.t the input's weight matrix, numpy-array of shape (n_a, n_x)\n", + " dWaa -- Gradient w.r.t the hidden state's weight matrix, numpy-arrayof shape (n_a, n_a)\n", + " dba -- Gradient w.r.t the bias, of shape (n_a, 1)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Retrieve values from the first cache (t=1) of caches (≈2 lines)\n", + " (caches, x) = caches\n", + " (a1, a0, x1, parameters) = caches[0]\n", + " \n", + " # Retrieve dimensions from da's and x1's shapes (≈2 lines)\n", + " n_a, m, T_x = da.shape\n", + " n_x, m = x1.shape\n", + " \n", + " # initialize the gradients with the right sizes (≈6 lines)\n", + " dx = np.zeros((n_x, m, T_x))\n", + " dWax = np.zeros((n_a, n_x))\n", + " dWaa = np.zeros((n_a, n_a))\n", + " dba = np.zeros((n_a, 1))\n", + " da0 = np.zeros((n_a, m))\n", + " da_prevt = np.zeros((n_a, m))\n", + " \n", + " # Loop through all the time steps\n", + " for t in reversed(range(T_x)):\n", + " # Compute gradients at time step t. Choose wisely the \"da_next\" and the \"cache\" to use in the backward propagation step. (≈1 line)\n", + " gradients = rnn_cell_backward(da[:,:, t] + da_prevt, caches[t])\n", + " # Retrieve derivatives from gradients (≈ 1 line)\n", + " dxt, da_prevt, dWaxt, dWaat, dbat = gradients[\"dxt\"], gradients[\"da_prev\"], gradients[\"dWax\"], gradients[\"dWaa\"], gradients[\"dba\"]\n", + " # Increment global derivatives w.r.t parameters by adding their derivative at time-step t (≈4 lines)\n", + " dx[:, :, t] = dxt\n", + " dWax += dWaxt\n", + " dWaa += dWaat\n", + " dba += dbat\n", + " \n", + " # Set da0 to the gradient of a which has been backpropagated through all time-steps (≈1 line) \n", + " da0 = da_prevt\n", + " ### END CODE HERE ###\n", + "\n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dx\": dx, \"da0\": da0, \"dWax\": dWax, \"dWaa\": dWaa,\"dba\": dba}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradients[\"dx\"][1][2] = [-2.07101689 -0.59255627 0.02466855 0.01483317]\n", + "gradients[\"dx\"].shape = (3, 10, 4)\n", + "gradients[\"da0\"][2][3] = -0.314942375127\n", + "gradients[\"da0\"].shape = (5, 10)\n", + "gradients[\"dWax\"][3][1] = 11.2641044965\n", + "gradients[\"dWax\"].shape = (5, 3)\n", + "gradients[\"dWaa\"][1][2] = 2.30333312658\n", + "gradients[\"dWaa\"].shape = (5, 5)\n", + "gradients[\"dba\"][4] = [-0.74747722]\n", + "gradients[\"dba\"].shape = (5, 1)\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(3,10,4)\n", + "a0 = np.random.randn(5,10)\n", + "Wax = np.random.randn(5,3)\n", + "Waa = np.random.randn(5,5)\n", + "Wya = np.random.randn(2,5)\n", + "ba = np.random.randn(5,1)\n", + "by = np.random.randn(2,1)\n", + "parameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"ba\": ba, \"by\": by}\n", + "a, y, caches = rnn_forward(x, a0, parameters)\n", + "da = np.random.randn(5, 10, 4)\n", + "gradients = rnn_backward(da, caches)\n", + "\n", + "print(\"gradients[\\\"dx\\\"][1][2] =\", gradients[\"dx\"][1][2])\n", + "print(\"gradients[\\\"dx\\\"].shape =\", gradients[\"dx\"].shape)\n", + "print(\"gradients[\\\"da0\\\"][2][3] =\", gradients[\"da0\"][2][3])\n", + "print(\"gradients[\\\"da0\\\"].shape =\", gradients[\"da0\"].shape)\n", + "print(\"gradients[\\\"dWax\\\"][3][1] =\", gradients[\"dWax\"][3][1])\n", + "print(\"gradients[\\\"dWax\\\"].shape =\", gradients[\"dWax\"].shape)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients[\"dWaa\"][1][2])\n", + "print(\"gradients[\\\"dWaa\\\"].shape =\", gradients[\"dWaa\"].shape)\n", + "print(\"gradients[\\\"dba\\\"][4] =\", gradients[\"dba\"][4])\n", + "print(\"gradients[\\\"dba\\\"].shape =\", gradients[\"dba\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dx\"][1][2]** =\n", + " \n", + " [-2.07101689 -0.59255627 0.02466855 0.01483317]\n", + "
\n", + " **gradients[\"dx\"].shape** =\n", + " \n", + " (3, 10, 4)\n", + "
\n", + " **gradients[\"da0\"][2][3]** =\n", + " \n", + " -0.314942375127\n", + "
\n", + " **gradients[\"da0\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWax\"][3][1]** =\n", + " \n", + " 11.2641044965\n", + "
\n", + " **gradients[\"dWax\"].shape** =\n", + " \n", + " (5, 3)\n", + "
\n", + " **gradients[\"dWaa\"][1][2]** = \n", + " \n", + " 2.30333312658\n", + "
\n", + " **gradients[\"dWaa\"].shape** =\n", + " \n", + " (5, 5)\n", + "
\n", + " **gradients[\"dba\"][4]** = \n", + " \n", + " [-0.74747722]\n", + "
\n", + " **gradients[\"dba\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 - LSTM backward pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.1 One Step backward\n", + "\n", + "The LSTM backward pass is slighltly more complicated than the forward one. We have provided you with all the equations for the LSTM backward pass below. (If you enjoy calculus exercises feel free to try deriving these from scratch yourself.) \n", + "\n", + "### 3.2.2 gate derivatives\n", + "\n", + "$$d \\Gamma_o^{\\langle t \\rangle} = da_{next}*\\tanh(c_{next}) * \\Gamma_o^{\\langle t \\rangle}*(1-\\Gamma_o^{\\langle t \\rangle})\\tag{7}$$\n", + "\n", + "$$d\\tilde c^{\\langle t \\rangle} = dc_{next}*\\Gamma_i^{\\langle t \\rangle}+ \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * i_t * da_{next} * \\tilde c^{\\langle t \\rangle} * (1-\\tanh(\\tilde c)^2) \\tag{8}$$\n", + "\n", + "$$d\\Gamma_u^{\\langle t \\rangle} = dc_{next}*\\tilde c^{\\langle t \\rangle} + \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * \\tilde c^{\\langle t \\rangle} * da_{next}*\\Gamma_u^{\\langle t \\rangle}*(1-\\Gamma_u^{\\langle t \\rangle})\\tag{9}$$\n", + "\n", + "$$d\\Gamma_f^{\\langle t \\rangle} = dc_{next}*\\tilde c_{prev} + \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * c_{prev} * da_{next}*\\Gamma_f^{\\langle t \\rangle}*(1-\\Gamma_f^{\\langle t \\rangle})\\tag{10}$$\n", + "\n", + "### 3.2.3 parameter derivatives \n", + "\n", + "$$ dW_f = d\\Gamma_f^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{11} $$\n", + "$$ dW_u = d\\Gamma_u^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{12} $$\n", + "$$ dW_c = d\\tilde c^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{13} $$\n", + "$$ dW_o = d\\Gamma_o^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{14}$$\n", + "\n", + "To calculate $db_f, db_u, db_c, db_o$ you just need to sum across the horizontal (axis= 1) axis on $d\\Gamma_f^{\\langle t \\rangle}, d\\Gamma_u^{\\langle t \\rangle}, d\\tilde c^{\\langle t \\rangle}, d\\Gamma_o^{\\langle t \\rangle}$ respectively. Note that you should have the `keep_dims = True` option.\n", + "\n", + "Finally, you will compute the derivative with respect to the previous hidden state, previous memory state, and input.\n", + "\n", + "$$ da_{prev} = W_f^T*d\\Gamma_f^{\\langle t \\rangle} + W_u^T * d\\Gamma_u^{\\langle t \\rangle}+ W_c^T * d\\tilde c^{\\langle t \\rangle} + W_o^T * d\\Gamma_o^{\\langle t \\rangle} \\tag{15}$$\n", + "Here, the weights for equations 13 are the first n_a, (i.e. $W_f = W_f[:n_a,:]$ etc...)\n", + "\n", + "$$ dc_{prev} = dc_{next}\\Gamma_f^{\\langle t \\rangle} + \\Gamma_o^{\\langle t \\rangle} * (1- \\tanh(c_{next})^2)*\\Gamma_f^{\\langle t \\rangle}*da_{next} \\tag{16}$$\n", + "$$ dx^{\\langle t \\rangle} = W_f^T*d\\Gamma_f^{\\langle t \\rangle} + W_u^T * d\\Gamma_u^{\\langle t \\rangle}+ W_c^T * d\\tilde c_t + W_o^T * d\\Gamma_o^{\\langle t \\rangle}\\tag{17} $$\n", + "where the weights for equation 15 are from n_a to the end, (i.e. $W_f = W_f[n_a:,:]$ etc...)\n", + "\n", + "**Exercise:** Implement `lstm_cell_backward` by implementing equations $7-17$ below. Good luck! :)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm_cell_backward(da_next, dc_next, cache):\n", + " \"\"\"\n", + " Implement the backward pass for the LSTM-cell (single time-step).\n", + "\n", + " Arguments:\n", + " da_next -- Gradients of next hidden state, of shape (n_a, m)\n", + " dc_next -- Gradients of next cell state, of shape (n_a, m)\n", + " cache -- cache storing information from the forward pass\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dxt -- Gradient of input data at time-step t, of shape (n_x, m)\n", + " da_prev -- Gradient w.r.t. the previous hidden state, numpy array of shape (n_a, m)\n", + " dc_prev -- Gradient w.r.t. the previous memory state, of shape (n_a, m, T_x)\n", + " dWf -- Gradient w.r.t. the weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWi -- Gradient w.r.t. the weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWc -- Gradient w.r.t. the weight matrix of the memory gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWo -- Gradient w.r.t. the weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " dbf -- Gradient w.r.t. biases of the forget gate, of shape (n_a, 1)\n", + " dbi -- Gradient w.r.t. biases of the update gate, of shape (n_a, 1)\n", + " dbc -- Gradient w.r.t. biases of the memory gate, of shape (n_a, 1)\n", + " dbo -- Gradient w.r.t. biases of the output gate, of shape (n_a, 1)\n", + " \"\"\"\n", + "\n", + " # Retrieve information from \"cache\"\n", + " (a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters) = cache\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from xt's and a_next's shape (≈2 lines)\n", + " n_x, m = xt.shape\n", + " n_a, m = a_next.shape\n", + " \n", + " # Compute gates related derivatives, you can find their values can be found by looking carefully at equations (7) to (10) (≈4 lines)\n", + " dot = da_next * np.tanh(c_next) * ot * (1 - ot)\n", + " dcct = (dc_next * it + ot * (1 - np.square(np.tanh(c_next))) * it * da_next) * (1 - np.square(cct))\n", + " dit = (dc_next * cct + ot * (1 - np.square(np.tanh(c_next))) * cct * da_next) * it * (1 - it)\n", + " dft = (dc_next * c_prev + ot *(1 - np.square(np.tanh(c_next))) * c_prev * da_next) * ft * (1 - ft)\n", + " \n", + " # Code equations (7) to (10) (≈4 lines)\n", + " ##dit = None\n", + " ##dft = None\n", + " ##dot = None\n", + " ##dcct = None\n", + " concat = np.concatenate((a_prev, xt), axis=0)\n", + "\n", + " # Compute parameters related derivatives. Use equations (11)-(14) (≈8 lines)\n", + " dWf = np.dot(dft, concat.T)\n", + " dWi = np.dot(dit, concat.T)\n", + " dWc = np.dot(dcct, concat.T)\n", + " dWo = np.dot(dot, concat.T)\n", + " dbf = np.sum(dft, axis=1 ,keepdims = True)\n", + " dbi = np.sum(dit, axis=1, keepdims = True)\n", + " dbc = np.sum(dcct, axis=1, keepdims = True)\n", + " dbo = np.sum(dot, axis=1, keepdims = True)\n", + "\n", + " # Compute derivatives w.r.t previous hidden state, previous memory state and input. Use equations (15)-(17). (≈3 lines)\n", + " da_prev = np.dot(parameters['Wf'][:, :n_a].T, dft) + np.dot(parameters['Wi'][:, :n_a].T, dit) + np.dot(parameters['Wc'][:, :n_a].T, dcct) + np.dot(parameters['Wo'][:, :n_a].T, dot)\n", + " dc_prev = dc_next * ft + ot * (1 - np.square(np.tanh(c_next))) * ft * da_next\n", + " dxt = np.dot(parameters['Wf'][:, n_a:].T, dft) + np.dot(parameters['Wi'][:, n_a:].T, dit) + np.dot(parameters['Wc'][:, n_a:].T, dcct) + np.dot(parameters['Wo'][:, n_a:].T, dot)\n", + " ### END CODE HERE ###\n", + " \n", + " # Save gradients in dictionary\n", + " gradients = {\"dxt\": dxt, \"da_prev\": da_prev, \"dc_prev\": dc_prev, \"dWf\": dWf,\"dbf\": dbf, \"dWi\": dWi,\"dbi\": dbi,\n", + " \"dWc\": dWc,\"dbc\": dbc, \"dWo\": dWo,\"dbo\": dbo}\n", + "\n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradients[\"dxt\"][1][2] = 3.23055911511\n", + "gradients[\"dxt\"].shape = (3, 10)\n", + "gradients[\"da_prev\"][2][3] = -0.0639621419711\n", + "gradients[\"da_prev\"].shape = (5, 10)\n", + "gradients[\"dc_prev\"][2][3] = 0.797522038797\n", + "gradients[\"dc_prev\"].shape = (5, 10)\n", + "gradients[\"dWf\"][3][1] = -0.147954838164\n", + "gradients[\"dWf\"].shape = (5, 8)\n", + "gradients[\"dWi\"][1][2] = 1.05749805523\n", + "gradients[\"dWi\"].shape = (5, 8)\n", + "gradients[\"dWc\"][3][1] = 2.30456216369\n", + "gradients[\"dWc\"].shape = (5, 8)\n", + "gradients[\"dWo\"][1][2] = 0.331311595289\n", + "gradients[\"dWo\"].shape = (5, 8)\n", + "gradients[\"dbf\"][4] = [ 0.18864637]\n", + "gradients[\"dbf\"].shape = (5, 1)\n", + "gradients[\"dbi\"][4] = [-0.40142491]\n", + "gradients[\"dbi\"].shape = (5, 1)\n", + "gradients[\"dbc\"][4] = [ 0.25587763]\n", + "gradients[\"dbc\"].shape = (5, 1)\n", + "gradients[\"dbo\"][4] = [ 0.13893342]\n", + "gradients[\"dbo\"].shape = (5, 1)\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "xt = np.random.randn(3,10)\n", + "a_prev = np.random.randn(5,10)\n", + "c_prev = np.random.randn(5,10)\n", + "Wf = np.random.randn(5, 5+3)\n", + "bf = np.random.randn(5,1)\n", + "Wi = np.random.randn(5, 5+3)\n", + "bi = np.random.randn(5,1)\n", + "Wo = np.random.randn(5, 5+3)\n", + "bo = np.random.randn(5,1)\n", + "Wc = np.random.randn(5, 5+3)\n", + "bc = np.random.randn(5,1)\n", + "Wy = np.random.randn(2,5)\n", + "by = np.random.randn(2,1)\n", + "\n", + "parameters = {\"Wf\": Wf, \"Wi\": Wi, \"Wo\": Wo, \"Wc\": Wc, \"Wy\": Wy, \"bf\": bf, \"bi\": bi, \"bo\": bo, \"bc\": bc, \"by\": by}\n", + "\n", + "a_next, c_next, yt, cache = lstm_cell_forward(xt, a_prev, c_prev, parameters)\n", + "\n", + "da_next = np.random.randn(5,10)\n", + "dc_next = np.random.randn(5,10)\n", + "gradients = lstm_cell_backward(da_next, dc_next, cache)\n", + "print(\"gradients[\\\"dxt\\\"][1][2] =\", gradients[\"dxt\"][1][2])\n", + "print(\"gradients[\\\"dxt\\\"].shape =\", gradients[\"dxt\"].shape)\n", + "print(\"gradients[\\\"da_prev\\\"][2][3] =\", gradients[\"da_prev\"][2][3])\n", + "print(\"gradients[\\\"da_prev\\\"].shape =\", gradients[\"da_prev\"].shape)\n", + "print(\"gradients[\\\"dc_prev\\\"][2][3] =\", gradients[\"dc_prev\"][2][3])\n", + "print(\"gradients[\\\"dc_prev\\\"].shape =\", gradients[\"dc_prev\"].shape)\n", + "print(\"gradients[\\\"dWf\\\"][3][1] =\", gradients[\"dWf\"][3][1])\n", + "print(\"gradients[\\\"dWf\\\"].shape =\", gradients[\"dWf\"].shape)\n", + "print(\"gradients[\\\"dWi\\\"][1][2] =\", gradients[\"dWi\"][1][2])\n", + "print(\"gradients[\\\"dWi\\\"].shape =\", gradients[\"dWi\"].shape)\n", + "print(\"gradients[\\\"dWc\\\"][3][1] =\", gradients[\"dWc\"][3][1])\n", + "print(\"gradients[\\\"dWc\\\"].shape =\", gradients[\"dWc\"].shape)\n", + "print(\"gradients[\\\"dWo\\\"][1][2] =\", gradients[\"dWo\"][1][2])\n", + "print(\"gradients[\\\"dWo\\\"].shape =\", gradients[\"dWo\"].shape)\n", + "print(\"gradients[\\\"dbf\\\"][4] =\", gradients[\"dbf\"][4])\n", + "print(\"gradients[\\\"dbf\\\"].shape =\", gradients[\"dbf\"].shape)\n", + "print(\"gradients[\\\"dbi\\\"][4] =\", gradients[\"dbi\"][4])\n", + "print(\"gradients[\\\"dbi\\\"].shape =\", gradients[\"dbi\"].shape)\n", + "print(\"gradients[\\\"dbc\\\"][4] =\", gradients[\"dbc\"][4])\n", + "print(\"gradients[\\\"dbc\\\"].shape =\", gradients[\"dbc\"].shape)\n", + "print(\"gradients[\\\"dbo\\\"][4] =\", gradients[\"dbo\"][4])\n", + "print(\"gradients[\\\"dbo\\\"].shape =\", gradients[\"dbo\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dxt\"][1][2]** =\n", + " \n", + " 3.23055911511\n", + "
\n", + " **gradients[\"dxt\"].shape** =\n", + " \n", + " (3, 10)\n", + "
\n", + " **gradients[\"da_prev\"][2][3]** =\n", + " \n", + " -0.0639621419711\n", + "
\n", + " **gradients[\"da_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dc_prev\"][2][3]** =\n", + " \n", + " 0.797522038797\n", + "
\n", + " **gradients[\"dc_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWf\"][3][1]** = \n", + " \n", + " -0.147954838164\n", + "
\n", + " **gradients[\"dWf\"].shape** =\n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWi\"][1][2]** = \n", + " \n", + " 1.05749805523\n", + "
\n", + " **gradients[\"dWi\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWc\"][3][1]** = \n", + " \n", + " 2.30456216369\n", + "
\n", + " **gradients[\"dWc\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWo\"][1][2]** = \n", + " \n", + " 0.331311595289\n", + "
\n", + " **gradients[\"dWo\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dbf\"][4]** = \n", + " \n", + " [ 0.18864637]\n", + "
\n", + " **gradients[\"dbf\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbi\"][4]** = \n", + " \n", + " [-0.40142491]\n", + "
\n", + " **gradients[\"dbi\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbc\"][4]** = \n", + " \n", + " [ 0.25587763]\n", + "
\n", + " **gradients[\"dbc\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbo\"][4]** = \n", + " \n", + " [ 0.13893342]\n", + "
\n", + " **gradients[\"dbo\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Backward pass through the LSTM RNN\n", + "\n", + "This part is very similar to the `rnn_backward` function you implemented above. You will first create variables of the same dimension as your return variables. You will then iterate over all the time steps starting from the end and call the one step function you implemented for LSTM at each iteration. You will then update the parameters by summing them individually. Finally return a dictionary with the new gradients. \n", + "\n", + "**Instructions**: Implement the `lstm_backward` function. Create a for loop starting from $T_x$ and going backward. For each step call `lstm_cell_backward` and update the your old gradients by adding the new gradients to them. Note that `dxt` is not updated but is stored." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm_backward(da, caches):\n", + " \n", + " \"\"\"\n", + " Implement the backward pass for the RNN with LSTM-cell (over a whole sequence).\n", + "\n", + " Arguments:\n", + " da -- Gradients w.r.t the hidden states, numpy-array of shape (n_a, m, T_x)\n", + " dc -- Gradients w.r.t the memory states, numpy-array of shape (n_a, m, T_x)\n", + " caches -- cache storing information from the forward pass (lstm_forward)\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradient of inputs, of shape (n_x, m, T_x)\n", + " da0 -- Gradient w.r.t. the previous hidden state, numpy array of shape (n_a, m)\n", + " dWf -- Gradient w.r.t. the weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWi -- Gradient w.r.t. the weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWc -- Gradient w.r.t. the weight matrix of the memory gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWo -- Gradient w.r.t. the weight matrix of the save gate, numpy array of shape (n_a, n_a + n_x)\n", + " dbf -- Gradient w.r.t. biases of the forget gate, of shape (n_a, 1)\n", + " dbi -- Gradient w.r.t. biases of the update gate, of shape (n_a, 1)\n", + " dbc -- Gradient w.r.t. biases of the memory gate, of shape (n_a, 1)\n", + " dbo -- Gradient w.r.t. biases of the save gate, of shape (n_a, 1)\n", + " \"\"\"\n", + "\n", + " # Retrieve values from the first cache (t=1) of caches.\n", + " (caches, x) = caches\n", + " (a1, c1, a0, c0, f1, i1, cc1, o1, x1, parameters) = caches[0]\n", + " \n", + " ### START CODE HERE ###\n", + " # Retrieve dimensions from da's and x1's shapes (≈2 lines)\n", + " n_a, m, T_x = da.shape\n", + " n_x, m = x1.shape\n", + " \n", + " # initialize the gradients with the right sizes (≈12 lines)\n", + " dx = np.zeros((n_x, m, T_x))\n", + " da0 = np.zeros((n_a, m))\n", + " da_prevt = np.zeros(da0.shape)\n", + " dc_prevt = np.zeros(da0.shape)\n", + " dWf = np.zeros((n_a, n_a + n_x))\n", + " dWi = np.zeros(dWf.shape)\n", + " dWc = np.zeros(dWf.shape)\n", + " dWo = np.zeros(dWf.shape)\n", + " dbf = np.zeros((n_a, 1))\n", + " dbi = np.zeros(dbf.shape)\n", + " dbc = np.zeros(dbf.shape)\n", + " dbo = np.zeros(dbf.shape)\n", + " \n", + " # loop back over the whole sequence\n", + " for t in reversed(range(T_x)):\n", + " # Compute all gradients using lstm_cell_backward\n", + " gradients = lstm_cell_backward(da[:, :, t], dc_prevt, caches[t])\n", + " # Store or add the gradient to the parameters' previous step's gradient\n", + " dx[:,:,t] = gradients[\"dxt\"]\n", + " dWf += gradients[\"dWf\"]\n", + " dWi += gradients[\"dWi\"]\n", + " dWc += gradients[\"dWc\"]\n", + " dWo += gradients[\"dWo\"]\n", + " dbf += gradients[\"dbf\"]\n", + " dbi += gradients[\"dbi\"]\n", + " dbc += gradients[\"dbc\"]\n", + " dbo += gradients[\"dbo\"]\n", + " # Set the first activation's gradient to the backpropagated gradient da_prev.\n", + " da0 = gradients[\"da_prev\"]\n", + " \n", + " ### END CODE HERE ###\n", + "\n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dx\": dx, \"da0\": da0, \"dWf\": dWf,\"dbf\": dbf, \"dWi\": dWi,\"dbi\": dbi,\n", + " \"dWc\": dWc,\"dbc\": dbc, \"dWo\": dWo,\"dbo\": dbo}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradients[\"dx\"][1][2] = [-0.00173313 0.08287442 -0.30545663 -0.43281115]\n", + "gradients[\"dx\"].shape = (3, 10, 4)\n", + "gradients[\"da0\"][2][3] = -0.095911501954\n", + "gradients[\"da0\"].shape = (5, 10)\n", + "gradients[\"dWf\"][3][1] = -0.0698198561274\n", + "gradients[\"dWf\"].shape = (5, 8)\n", + "gradients[\"dWi\"][1][2] = 0.102371820249\n", + "gradients[\"dWi\"].shape = (5, 8)\n", + "gradients[\"dWc\"][3][1] = -0.0624983794927\n", + "gradients[\"dWc\"].shape = (5, 8)\n", + "gradients[\"dWo\"][1][2] = 0.0484389131444\n", + "gradients[\"dWo\"].shape = (5, 8)\n", + "gradients[\"dbf\"][4] = [-0.0565788]\n", + "gradients[\"dbf\"].shape = (5, 1)\n", + "gradients[\"dbi\"][4] = [-0.15399065]\n", + "gradients[\"dbi\"].shape = (5, 1)\n", + "gradients[\"dbc\"][4] = [-0.29691142]\n", + "gradients[\"dbc\"].shape = (5, 1)\n", + "gradients[\"dbo\"][4] = [-0.29798344]\n", + "gradients[\"dbo\"].shape = (5, 1)\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "x = np.random.randn(3,10,7)\n", + "a0 = np.random.randn(5,10)\n", + "Wf = np.random.randn(5, 5+3)\n", + "bf = np.random.randn(5,1)\n", + "Wi = np.random.randn(5, 5+3)\n", + "bi = np.random.randn(5,1)\n", + "Wo = np.random.randn(5, 5+3)\n", + "bo = np.random.randn(5,1)\n", + "Wc = np.random.randn(5, 5+3)\n", + "bc = np.random.randn(5,1)\n", + "\n", + "parameters = {\"Wf\": Wf, \"Wi\": Wi, \"Wo\": Wo, \"Wc\": Wc, \"Wy\": Wy, \"bf\": bf, \"bi\": bi, \"bo\": bo, \"bc\": bc, \"by\": by}\n", + "\n", + "a, y, c, caches = lstm_forward(x, a0, parameters)\n", + "\n", + "da = np.random.randn(5, 10, 4)\n", + "gradients = lstm_backward(da, caches)\n", + "\n", + "print(\"gradients[\\\"dx\\\"][1][2] =\", gradients[\"dx\"][1][2])\n", + "print(\"gradients[\\\"dx\\\"].shape =\", gradients[\"dx\"].shape)\n", + "print(\"gradients[\\\"da0\\\"][2][3] =\", gradients[\"da0\"][2][3])\n", + "print(\"gradients[\\\"da0\\\"].shape =\", gradients[\"da0\"].shape)\n", + "print(\"gradients[\\\"dWf\\\"][3][1] =\", gradients[\"dWf\"][3][1])\n", + "print(\"gradients[\\\"dWf\\\"].shape =\", gradients[\"dWf\"].shape)\n", + "print(\"gradients[\\\"dWi\\\"][1][2] =\", gradients[\"dWi\"][1][2])\n", + "print(\"gradients[\\\"dWi\\\"].shape =\", gradients[\"dWi\"].shape)\n", + "print(\"gradients[\\\"dWc\\\"][3][1] =\", gradients[\"dWc\"][3][1])\n", + "print(\"gradients[\\\"dWc\\\"].shape =\", gradients[\"dWc\"].shape)\n", + "print(\"gradients[\\\"dWo\\\"][1][2] =\", gradients[\"dWo\"][1][2])\n", + "print(\"gradients[\\\"dWo\\\"].shape =\", gradients[\"dWo\"].shape)\n", + "print(\"gradients[\\\"dbf\\\"][4] =\", gradients[\"dbf\"][4])\n", + "print(\"gradients[\\\"dbf\\\"].shape =\", gradients[\"dbf\"].shape)\n", + "print(\"gradients[\\\"dbi\\\"][4] =\", gradients[\"dbi\"][4])\n", + "print(\"gradients[\\\"dbi\\\"].shape =\", gradients[\"dbi\"].shape)\n", + "print(\"gradients[\\\"dbc\\\"][4] =\", gradients[\"dbc\"][4])\n", + "print(\"gradients[\\\"dbc\\\"].shape =\", gradients[\"dbc\"].shape)\n", + "print(\"gradients[\\\"dbo\\\"][4] =\", gradients[\"dbo\"][4])\n", + "print(\"gradients[\\\"dbo\\\"].shape =\", gradients[\"dbo\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dx\"][1][2]** =\n", + " \n", + " [-0.00173313 0.08287442 -0.30545663 -0.43281115]\n", + "
\n", + " **gradients[\"dx\"].shape** =\n", + " \n", + " (3, 10, 4)\n", + "
\n", + " **gradients[\"da0\"][2][3]** =\n", + " \n", + " -0.095911501954\n", + "
\n", + " **gradients[\"da0\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWf\"][3][1]** = \n", + " \n", + " -0.0698198561274\n", + "
\n", + " **gradients[\"dWf\"].shape** =\n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWi\"][1][2]** = \n", + " \n", + " 0.102371820249\n", + "
\n", + " **gradients[\"dWi\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWc\"][3][1]** = \n", + " \n", + " -0.0624983794927\n", + "
\n", + " **gradients[\"dWc\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWo\"][1][2]** = \n", + " \n", + " 0.0484389131444\n", + "
\n", + " **gradients[\"dWo\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dbf\"][4]** = \n", + " \n", + " [-0.0565788]\n", + "
\n", + " **gradients[\"dbf\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbi\"][4]** = \n", + " \n", + " [-0.06997391]\n", + "
\n", + " **gradients[\"dbi\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbc\"][4]** = \n", + " \n", + " [-0.27441821]\n", + "
\n", + " **gradients[\"dbc\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbo\"][4]** = \n", + " \n", + " [ 0.16532821]\n", + "
\n", + " **gradients[\"dbo\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations !\n", + "\n", + "Congratulations on completing this assignment. You now understand how recurrent neural networks work! \n", + "\n", + "Lets go on to the next exercise, where you'll use an RNN to build a character-level language model.\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "xxuVc", + "launcher_item_id": "X20PE" + }, + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM.png new file mode 100644 index 0000000..c2e333d Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM_rnn.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM_rnn.png new file mode 100644 index 0000000..fbb9190 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/LSTM_rnn.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/clip.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/clip.png new file mode 100644 index 0000000..d7685cc Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/clip.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/initial_state.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/initial_state.png new file mode 100644 index 0000000..b8ec43d Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/initial_state.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn.png new file mode 100644 index 0000000..c0ab647 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_cell_backprop.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_cell_backprop.png new file mode 100644 index 0000000..5bb04f4 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_cell_backprop.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_step_forward.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_step_forward.png new file mode 100644 index 0000000..260db60 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Building a Recurrent Neural Network - Step by Step/images/rnn_step_forward.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/Dinosaurus Island -- Character level language model final - v3.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/Dinosaurus Island -- Character level language model final - v3.ipynb new file mode 100644 index 0000000..4e8fbb8 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/Dinosaurus Island -- Character level language model final - v3.ipynb @@ -0,0 +1,1202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Character level language model - Dinosaurus land\n", + "\n", + "Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to give names to these dinosaurs. If a dinosaur does not like its name, it might go beserk, so choose wisely! \n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "Luckily you have learned some deep learning and you will use it to save the day. Your assistant has collected a list of all the dinosaur names they could find, and compiled them into this [dataset](dinos.txt). (Feel free to take a look by clicking the previous link.) To create new dinosaur names, you will build a character level language model to generate new names. Your algorithm will learn the different name patterns, and randomly generate new names. Hopefully this algorithm will keep you and your team safe from the dinosaurs' wrath! \n", + "\n", + "By completing this assignment you will learn:\n", + "\n", + "- How to store text data for processing using an RNN \n", + "- How to synthesize data, by sampling predictions at each time step and passing it to the next RNN-cell unit\n", + "- How to build a character-level text generation recurrent neural network\n", + "- Why clipping the gradients is important\n", + "\n", + "We will begin by loading in some functions that we have provided for you in `rnn_utils`. Specifically, you have access to functions such as `rnn_forward` and `rnn_backward` which are equivalent to those you've implemented in the previous assignment. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from utils import *\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 1 - Problem Statement\n", + "\n", + "### 1.1 - Dataset and Preprocessing\n", + "\n", + "Run the following cell to read the dataset of dinosaur names, create a list of unique characters (such as a-z), and compute the dataset and vocabulary size. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['\\n', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n", + "There are 19909 total characters and 27 unique characters in your data.\n" + ] + } + ], + "source": [ + "data = open('dinos.txt', 'r').read()\n", + "data= data.lower()\n", + "chars = list(set(data))\n", + "print(sorted(chars))\n", + "data_size, vocab_size = len(data), len(chars)\n", + "print('There are %d total characters and %d unique characters in your data.' % (data_size, vocab_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The characters are a-z (26 characters) plus the \"\\n\" (or newline character), which in this assignment plays a role similar to the `` (or \"End of sentence\") token we had discussed in lecture, only here it indicates the end of the dinosaur name rather than the end of a sentence. In the cell below, we create a python dictionary (i.e., a hash table) to map each character to an index from 0-26. We also create a second python dictionary that maps each index back to the corresponding character character. This will help you figure out what index corresponds to what character in the probability distribution output of the softmax layer. Below, `char_to_ix` and `ix_to_char` are the python dictionaries. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: '\\n', 1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z'}\n" + ] + } + ], + "source": [ + "char_to_ix = { ch:i for i,ch in enumerate(sorted(chars)) }\n", + "ix_to_char = { i:ch for i,ch in enumerate(sorted(chars)) }\n", + "print(ix_to_char)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Overview of the model\n", + "\n", + "Your model will have the following structure: \n", + "\n", + "- Initialize parameters \n", + "- Run the optimization loop\n", + " - Forward propagation to compute the loss function\n", + " - Backward propagation to compute the gradients with respect to the loss function\n", + " - Clip the gradients to avoid exploding gradients\n", + " - Using the gradients, update your parameter with the gradient descent update rule.\n", + "- Return the learned parameters \n", + " \n", + "\n", + "
**Figure 1**: Recurrent Neural Network, similar to what you had built in the previous notebook \"Building a RNN - Step by Step\".
\n", + "\n", + "At each time-step, the RNN tries to predict what is the next character given the previous characters. The dataset $X = (x^{\\langle 1 \\rangle}, x^{\\langle 2 \\rangle}, ..., x^{\\langle T_x \\rangle})$ is a list of characters in the training set, while $Y = (y^{\\langle 1 \\rangle}, y^{\\langle 2 \\rangle}, ..., y^{\\langle T_x \\rangle})$ is such that at every time-step $t$, we have $y^{\\langle t \\rangle} = x^{\\langle t+1 \\rangle}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Building blocks of the model\n", + "\n", + "In this part, you will build two important blocks of the overall model:\n", + "- Gradient clipping: to avoid exploding gradients\n", + "- Sampling: a technique used to generate characters\n", + "\n", + "You will then apply these two functions to build the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - Clipping the gradients in the optimization loop\n", + "\n", + "In this section you will implement the `clip` function that you will call inside of your optimization loop. Recall that your overall loop structure usually consists of a forward pass, a cost computation, a backward pass, and a parameter update. Before updating the parameters, you will perform gradient clipping when needed to make sure that your gradients are not \"exploding,\" meaning taking on overly large values. \n", + "\n", + "In the exercise below, you will implement a function `clip` that takes in a dictionary of gradients and returns a clipped version of gradients if needed. There are different ways to clip gradients; we will use a simple element-wise clipping procedure, in which every element of the gradient vector is clipped to lie between some range [-N, N]. More generally, you will provide a `maxValue` (say 10). In this example, if any component of the gradient vector is greater than 10, it would be set to 10; and if any component of the gradient vector is less than -10, it would be set to -10. If it is between -10 and 10, it is left alone. \n", + "\n", + "\n", + "
**Figure 2**: Visualization of gradient descent with and without gradient clipping, in a case where the network is running into slight \"exploding gradient\" problems.
\n", + "\n", + "**Exercise**: Implement the function below to return the clipped gradients of your dictionary `gradients`. Your function takes in a maximum threshold and returns the clipped versions of your gradients. You can check out this [hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.clip.html) for examples of how to clip in numpy. You will need to use the argument `out = ...`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### GRADED FUNCTION: clip\n", + "\n", + "def clip(gradients, maxValue):\n", + " '''\n", + " Clips the gradients' values between minimum and maximum.\n", + " \n", + " Arguments:\n", + " gradients -- a dictionary containing the gradients \"dWaa\", \"dWax\", \"dWya\", \"db\", \"dby\"\n", + " maxValue -- everything above this number is set to this number, and everything less than -maxValue is set to -maxValue\n", + " \n", + " Returns: \n", + " gradients -- a dictionary with the clipped gradients.\n", + " '''\n", + " \n", + " dWaa, dWax, dWya, db, dby = gradients['dWaa'], gradients['dWax'], gradients['dWya'], gradients['db'], gradients['dby']\n", + " \n", + " ### START CODE HERE ###\n", + " # clip to mitigate exploding gradients, loop over [dWax, dWaa, dWya, db, dby]. (≈2 lines)\n", + " for gradient in [dWax, dWaa, dWya, db, dby]:\n", + " np.clip(gradient, -maxValue, maxValue, out=gradient)\n", + " ### END CODE HERE ###\n", + " \n", + " gradients = {\"dWaa\": dWaa, \"dWax\": dWax, \"dWya\": dWya, \"db\": db, \"dby\": dby}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gradients[\"dWaa\"][1][2] = 10.0\n", + "gradients[\"dWax\"][3][1] = -10.0\n", + "gradients[\"dWya\"][1][2] = 0.29713815361\n", + "gradients[\"db\"][4] = [ 10.]\n", + "gradients[\"dby\"][1] = [ 8.45833407]\n" + ] + } + ], + "source": [ + "np.random.seed(3)\n", + "dWax = np.random.randn(5,3)*10\n", + "dWaa = np.random.randn(5,5)*10\n", + "dWya = np.random.randn(2,5)*10\n", + "db = np.random.randn(5,1)*10\n", + "dby = np.random.randn(2,1)*10\n", + "gradients = {\"dWax\": dWax, \"dWaa\": dWaa, \"dWya\": dWya, \"db\": db, \"dby\": dby}\n", + "gradients = clip(gradients, 10)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients[\"dWaa\"][1][2])\n", + "print(\"gradients[\\\"dWax\\\"][3][1] =\", gradients[\"dWax\"][3][1])\n", + "print(\"gradients[\\\"dWya\\\"][1][2] =\", gradients[\"dWya\"][1][2])\n", + "print(\"gradients[\\\"db\\\"][4] =\", gradients[\"db\"][4])\n", + "print(\"gradients[\\\"dby\\\"][1] =\", gradients[\"dby\"][1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected output:**\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " **gradients[\"dWaa\"][1][2] **\n", + " \n", + " 10.0\n", + "
\n", + " **gradients[\"dWax\"][3][1]**\n", + " \n", + " -10.0\n", + "
\n", + " **gradients[\"dWya\"][1][2]**\n", + " \n", + "0.29713815361\n", + "
\n", + " **gradients[\"db\"][4]**\n", + " \n", + "[ 10.]\n", + "
\n", + " **gradients[\"dby\"][1]**\n", + " \n", + "[ 8.45833407]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 - Sampling\n", + "\n", + "Now assume that your model is trained. You would like to generate new text (characters). The process of generation is explained in the picture below:\n", + "\n", + "\n", + "
**Figure 3**: In this picture, we assume the model is already trained. We pass in $x^{\\langle 1\\rangle} = \\vec{0}$ at the first time step, and have the network then sample one character at a time.
\n", + "\n", + "**Exercise**: Implement the `sample` function below to sample characters. You need to carry out 4 steps:\n", + "\n", + "- **Step 1**: Pass the network the first \"dummy\" input $x^{\\langle 1 \\rangle} = \\vec{0}$ (the vector of zeros). This is the default input before we've generated any characters. We also set $a^{\\langle 0 \\rangle} = \\vec{0}$\n", + "\n", + "- **Step 2**: Run one step of forward propagation to get $a^{\\langle 1 \\rangle}$ and $\\hat{y}^{\\langle 1 \\rangle}$. Here are the equations:\n", + "\n", + "$$ a^{\\langle t+1 \\rangle} = \\tanh(W_{ax} x^{\\langle t \\rangle } + W_{aa} a^{\\langle t \\rangle } + b)\\tag{1}$$\n", + "\n", + "$$ z^{\\langle t + 1 \\rangle } = W_{ya} a^{\\langle t + 1 \\rangle } + b_y \\tag{2}$$\n", + "\n", + "$$ \\hat{y}^{\\langle t+1 \\rangle } = softmax(z^{\\langle t + 1 \\rangle })\\tag{3}$$\n", + "\n", + "Note that $\\hat{y}^{\\langle t+1 \\rangle }$ is a (softmax) probability vector (its entries are between 0 and 1 and sum to 1). $\\hat{y}^{\\langle t+1 \\rangle}_i$ represents the probability that the character indexed by \"i\" is the next character. We have provided a `softmax()` function that you can use.\n", + "\n", + "- **Step 3**: Carry out sampling: Pick the next character's index according to the probability distribution specified by $\\hat{y}^{\\langle t+1 \\rangle }$. This means that if $\\hat{y}^{\\langle t+1 \\rangle }_i = 0.16$, you will pick the index \"i\" with 16% probability. To implement it, you can use [`np.random.choice`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.choice.html).\n", + "\n", + "Here is an example of how to use `np.random.choice()`:\n", + "```python\n", + "np.random.seed(0)\n", + "p = np.array([0.1, 0.0, 0.7, 0.2])\n", + "index = np.random.choice([0, 1, 2, 3], p = p.ravel())\n", + "```\n", + "This means that you will pick the `index` according to the distribution: \n", + "$P(index = 0) = 0.1, P(index = 1) = 0.0, P(index = 2) = 0.7, P(index = 3) = 0.2$.\n", + "\n", + "- **Step 4**: The last step to implement in `sample()` is to overwrite the variable `x`, which currently stores $x^{\\langle t \\rangle }$, with the value of $x^{\\langle t + 1 \\rangle }$. You will represent $x^{\\langle t + 1 \\rangle }$ by creating a one-hot vector corresponding to the character you've chosen as your prediction. You will then forward propagate $x^{\\langle t + 1 \\rangle }$ in Step 1 and keep repeating the process until you get a \"\\n\" character, indicating you've reached the end of the dinosaur name. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: sample\n", + "\n", + "def sample(parameters, char_to_ix, seed):\n", + " \"\"\"\n", + " Sample a sequence of characters according to a sequence of probability distributions output of the RNN\n", + "\n", + " Arguments:\n", + " parameters -- python dictionary containing the parameters Waa, Wax, Wya, by, and b. \n", + " char_to_ix -- python dictionary mapping each character to an index.\n", + " seed -- used for grading purposes. Do not worry about it.\n", + "\n", + " Returns:\n", + " indices -- a list of length n containing the indices of the sampled characters.\n", + " \"\"\"\n", + " \n", + " # Retrieve parameters and relevant shapes from \"parameters\" dictionary\n", + " Waa, Wax, Wya, by, b = parameters['Waa'], parameters['Wax'], parameters['Wya'], parameters['by'], parameters['b']\n", + " vocab_size = by.shape[0]\n", + " n_a = Waa.shape[1]\n", + " \n", + " ### START CODE HERE ###\n", + " # Step 1: Create the one-hot vector x for the first character (initializing the sequence generation). (≈1 line)\n", + " x = np.zeros((vocab_size, 1))\n", + " # Step 1': Initialize a_prev as zeros (≈1 line)\n", + " a_prev = np.zeros((n_a, 1))\n", + " \n", + " # Create an empty list of indices, this is the list which will contain the list of indices of the characters to generate (≈1 line)\n", + " indices = []\n", + " \n", + " # Idx is a flag to detect a newline character, we initialize it to -1\n", + " idx = -1 \n", + " \n", + " # Loop over time-steps t. At each time-step, sample a character from a probability distribution and append \n", + " # its index to \"indices\". We'll stop if we reach 50 characters (which should be very unlikely with a well \n", + " # trained model), which helps debugging and prevents entering an infinite loop. \n", + " counter = 0\n", + " newline_character = char_to_ix['\\n']\n", + " \n", + " while (idx != newline_character and counter != 50):\n", + " \n", + " # Step 2: Forward propagate x using the equations (1), (2) and (3)\n", + " a = np.tanh(np.dot(Wax, x) + np.dot(Waa, a_prev) + b)\n", + " z = np.dot(Wya, a) + by\n", + " y = softmax(z)\n", + " \n", + " # for grading purposes\n", + " np.random.seed(counter+seed) \n", + " \n", + " # Step 3: Sample the index of a character within the vocabulary from the probability distribution y\n", + " idx = np.random.choice(list(range(vocab_size)), p = y.ravel())\n", + "\n", + " # Append the index to \"indices\"\n", + " indices.append(idx)\n", + " \n", + " # Step 4: Overwrite the input character as the one corresponding to the sampled index.\n", + " x = np.zeros((vocab_size, 1))\n", + " x[idx] = 1\n", + " \n", + " # Update \"a_prev\" to be \"a\"\n", + " a_prev = a\n", + " \n", + " # for grading purposes\n", + " seed += 1\n", + " counter +=1\n", + " \n", + " ### END CODE HERE ###\n", + "\n", + " if (counter == 50):\n", + " indices.append(char_to_ix['\\n'])\n", + " \n", + " return indices" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling:\n", + "list of sampled indices: [12, 17, 24, 14, 13, 9, 10, 22, 24, 6, 13, 11, 12, 6, 21, 15, 21, 14, 3, 2, 1, 21, 18, 24, 7, 25, 6, 25, 18, 10, 16, 2, 3, 8, 15, 12, 11, 7, 1, 12, 10, 2, 7, 7, 11, 5, 6, 12, 25, 0, 0] \n", + "\n", + "list of sampled characters: ['l', 'q', 'x', 'n', 'm', 'i', 'j', 'v', 'x', 'f', 'm', 'k', 'l', 'f', 'u', 'o', 'u', 'n', 'c', 'b', 'a', 'u', 'r', 'x', 'g', 'y', 'f', 'y', 'r', 'j', 'p', 'b', 'c', 'h', 'o', 'l', 'k', 'g', 'a', 'l', 'j', 'b', 'g', 'g', 'k', 'e', 'f', 'l', 'y', '\\n', '\\n']\n" + ] + } + ], + "source": [ + "np.random.seed(2)\n", + "_, n_a = 20, 100\n", + "Wax, Waa, Wya = np.random.randn(n_a, vocab_size), np.random.randn(n_a, n_a), np.random.randn(vocab_size, n_a)\n", + "b, by = np.random.randn(n_a, 1), np.random.randn(vocab_size, 1)\n", + "parameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"b\": b, \"by\": by}\n", + "\n", + "\n", + "indices = sample(parameters, char_to_ix, 0)\n", + "print(\"Sampling:\")\n", + "print(\"list of sampled indices:\", indices, '\\n')\n", + "print(\"list of sampled characters:\", [ix_to_char[i] for i in indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected output:**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " **list of sampled indices:**\n", + " \n", + " [12, 17, 24, 14, 13, 9, 10, 22, 24, 6, 13, 11, 12, 6, 21, 15, 21, 14, 3, 2, 1, 21, 18, 24,
\n", + " 7, 25, 6, 25, 18, 10, 16, 2, 3, 8, 15, 12, 11, 7, 1, 12, 10, 2, 7, 7, 11, 5, 6, 12, 25, 0, 0]\n", + "
\n", + " **list of sampled characters:**\n", + " \n", + " ['l', 'q', 'x', 'n', 'm', 'i', 'j', 'v', 'x', 'f', 'm', 'k', 'l', 'f', 'u', 'o',
\n", + " 'u', 'n', 'c', 'b', 'a', 'u', 'r', 'x', 'g', 'y', 'f', 'y', 'r', 'j', 'p', 'b', 'c', 'h', 'o',
\n", + " 'l', 'k', 'g', 'a', 'l', 'j', 'b', 'g', 'g', 'k', 'e', 'f', 'l', 'y', '\\n', '\\n']\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Building the language model \n", + "\n", + "It is time to build the character-level language model for text generation. \n", + "\n", + "\n", + "### 3.1 - Gradient descent \n", + "\n", + "In this section you will implement a function performing one step of stochastic gradient descent (with clipped gradients). You will go through the training examples one at a time, so the optimization algorithm will be stochastic gradient descent. As a reminder, here are the steps of a common optimization loop for an RNN:\n", + "\n", + "- Forward propagate through the RNN to compute the loss\n", + "- Backward propagate through time to compute the gradients of the loss with respect to the parameters\n", + "- Clip the gradients if necessary \n", + "- Update your parameters using gradient descent \n", + "\n", + "**Exercise**: Implement this optimization process (one step of stochastic gradient descent). \n", + "\n", + "We provide you with the following functions: \n", + "\n", + "```python\n", + "def rnn_forward(X, Y, a_prev, parameters):\n", + " \"\"\" Performs the forward propagation through the RNN and computes the cross-entropy loss.\n", + " It returns the loss' value as well as a \"cache\" storing values to be used in the backpropagation.\"\"\"\n", + " ....\n", + " return loss, cache\n", + " \n", + "def rnn_backward(X, Y, parameters, cache):\n", + " \"\"\" Performs the backward propagation through time to compute the gradients of the loss with respect\n", + " to the parameters. It returns also all the hidden states.\"\"\"\n", + " ...\n", + " return gradients, a\n", + "\n", + "def update_parameters(parameters, gradients, learning_rate):\n", + " \"\"\" Updates parameters using the Gradient Descent Update Rule.\"\"\"\n", + " ...\n", + " return parameters\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: optimize\n", + "\n", + "def optimize(X, Y, a_prev, parameters, learning_rate = 0.01):\n", + " \"\"\"\n", + " Execute one step of the optimization to train the model.\n", + " \n", + " Arguments:\n", + " X -- list of integers, where each integer is a number that maps to a character in the vocabulary.\n", + " Y -- list of integers, exactly the same as X but shifted one index to the left.\n", + " a_prev -- previous hidden state.\n", + " parameters -- python dictionary containing:\n", + " Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)\n", + " Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)\n", + " Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " b -- Bias, numpy array of shape (n_a, 1)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " learning_rate -- learning rate for the model.\n", + " \n", + " Returns:\n", + " loss -- value of the loss function (cross-entropy)\n", + " gradients -- python dictionary containing:\n", + " dWax -- Gradients of input-to-hidden weights, of shape (n_a, n_x)\n", + " dWaa -- Gradients of hidden-to-hidden weights, of shape (n_a, n_a)\n", + " dWya -- Gradients of hidden-to-output weights, of shape (n_y, n_a)\n", + " db -- Gradients of bias vector, of shape (n_a, 1)\n", + " dby -- Gradients of output bias vector, of shape (n_y, 1)\n", + " a[len(X)-1] -- the last hidden state, of shape (n_a, 1)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Forward propagate through time (≈1 line)\n", + " loss, cache = rnn_forward(X, Y, a_prev, parameters)\n", + " \n", + " # Backpropagate through time (≈1 line)\n", + " gradients, a = rnn_backward(X, Y, parameters, cache)\n", + " \n", + " # Clip your gradients between -5 (min) and 5 (max) (≈1 line)\n", + " gradients = clip(gradients, maxValue=5)\n", + " \n", + " # Update parameters (≈1 line)\n", + " parameters = update_parameters(parameters, gradients, learning_rate)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return loss, gradients, a[len(X)-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loss = 126.503975722\n", + "gradients[\"dWaa\"][1][2] = 0.194709315347\n", + "np.argmax(gradients[\"dWax\"]) = 93\n", + "gradients[\"dWya\"][1][2] = -0.007773876032\n", + "gradients[\"db\"][4] = [-0.06809825]\n", + "gradients[\"dby\"][1] = [ 0.01538192]\n", + "a_last[4] = [-1.]\n" + ] + } + ], + "source": [ + "np.random.seed(1)\n", + "vocab_size, n_a = 27, 100\n", + "a_prev = np.random.randn(n_a, 1)\n", + "Wax, Waa, Wya = np.random.randn(n_a, vocab_size), np.random.randn(n_a, n_a), np.random.randn(vocab_size, n_a)\n", + "b, by = np.random.randn(n_a, 1), np.random.randn(vocab_size, 1)\n", + "parameters = {\"Wax\": Wax, \"Waa\": Waa, \"Wya\": Wya, \"b\": b, \"by\": by}\n", + "X = [12,3,5,11,22,3]\n", + "Y = [4,14,11,22,25, 26]\n", + "\n", + "loss, gradients, a_last = optimize(X, Y, a_prev, parameters, learning_rate = 0.01)\n", + "print(\"Loss =\", loss)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients[\"dWaa\"][1][2])\n", + "print(\"np.argmax(gradients[\\\"dWax\\\"]) =\", np.argmax(gradients[\"dWax\"]))\n", + "print(\"gradients[\\\"dWya\\\"][1][2] =\", gradients[\"dWya\"][1][2])\n", + "print(\"gradients[\\\"db\\\"][4] =\", gradients[\"db\"][4])\n", + "print(\"gradients[\\\"dby\\\"][1] =\", gradients[\"dby\"][1])\n", + "print(\"a_last[4] =\", a_last[4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Expected output:**\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " **Loss **\n", + " \n", + " 126.503975722\n", + "
\n", + " **gradients[\"dWaa\"][1][2]**\n", + " \n", + " 0.194709315347\n", + "
\n", + " **np.argmax(gradients[\"dWax\"])**\n", + " 93\n", + "
\n", + " **gradients[\"dWya\"][1][2]**\n", + " -0.007773876032\n", + "
\n", + " **gradients[\"db\"][4]**\n", + " [-0.06809825]\n", + "
\n", + " **gradients[\"dby\"][1]**\n", + " [ 0.01538192]\n", + "
\n", + " **a_last[4]**\n", + " [-1.]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 3.2 - Training the model " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the dataset of dinosaur names, we use each line of the dataset (one name) as one training example. Every 100 steps of stochastic gradient descent, you will sample 10 randomly chosen names to see how the algorithm is doing. Remember to shuffle the dataset, so that stochastic gradient descent visits the examples in random order. \n", + "\n", + "**Exercise**: Follow the instructions and implement `model()`. When `examples[index]` contains one dinosaur name (string), to create an example (X, Y), you can use this:\n", + "```python\n", + " index = j % len(examples)\n", + " X = [None] + [char_to_ix[ch] for ch in examples[index]] \n", + " Y = X[1:] + [char_to_ix[\"\\n\"]]\n", + "```\n", + "Note that we use: `index= j % len(examples)`, where `j = 1....num_iterations`, to make sure that `examples[index]` is always a valid statement (`index` is smaller than `len(examples)`).\n", + "The first entry of `X` being `None` will be interpreted by `rnn_forward()` as setting $x^{\\langle 0 \\rangle} = \\vec{0}$. Further, this ensures that `Y` is equal to `X` but shifted one step to the left, and with an additional \"\\n\" appended to signify the end of the dinosaur name. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(data, ix_to_char, char_to_ix, num_iterations = 35000, n_a = 50, dino_names = 7, vocab_size = 27):\n", + " \"\"\"\n", + " Trains the model and generates dinosaur names. \n", + " \n", + " Arguments:\n", + " data -- text corpus\n", + " ix_to_char -- dictionary that maps the index to a character\n", + " char_to_ix -- dictionary that maps a character to an index\n", + " num_iterations -- number of iterations to train the model for\n", + " n_a -- number of units of the RNN cell\n", + " dino_names -- number of dinosaur names you want to sample at each iteration. \n", + " vocab_size -- number of unique characters found in the text, size of the vocabulary\n", + " \n", + " Returns:\n", + " parameters -- learned parameters\n", + " \"\"\"\n", + " \n", + " # Retrieve n_x and n_y from vocab_size\n", + " n_x, n_y = vocab_size, vocab_size\n", + " \n", + " # Initialize parameters\n", + " parameters = initialize_parameters(n_a, n_x, n_y)\n", + " \n", + " # Initialize loss (this is required because we want to smooth our loss, don't worry about it)\n", + " loss = get_initial_loss(vocab_size, dino_names)\n", + " \n", + " # Build list of all dinosaur names (training examples).\n", + " with open(\"dinos.txt\") as f:\n", + " examples = f.readlines()\n", + " examples = [x.lower().strip() for x in examples]\n", + " \n", + " # Shuffle list of all dinosaur names\n", + " np.random.seed(0)\n", + " np.random.shuffle(examples)\n", + " \n", + " # Initialize the hidden state of your LSTM\n", + " a_prev = np.zeros((n_a, 1))\n", + " \n", + " # Optimization loop\n", + " for j in range(num_iterations):\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Use the hint above to define one training example (X,Y) (≈ 2 lines)\n", + " index = j % len(examples)\n", + " X = [None] + [char_to_ix[ch] for ch in examples[index]] \n", + " Y = X[1:] + [char_to_ix[\"\\n\"]]\n", + " \n", + " # Perform one optimization step: Forward-prop -> Backward-prop -> Clip -> Update parameters\n", + " # Choose a learning rate of 0.01\n", + " curr_loss, gradients, a_prev = optimize(X, Y, a_prev, parameters, learning_rate = 0.01)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " # Use a latency trick to keep the loss smooth. It happens here to accelerate the training.\n", + " loss = smooth(loss, curr_loss)\n", + "\n", + " # Every 2000 Iteration, generate \"n\" characters thanks to sample() to check if the model is learning properly\n", + " if j % 2000 == 0:\n", + " \n", + " print('Iteration: %d, Loss: %f' % (j, loss) + '\\n')\n", + " \n", + " # The number of dinosaur names to print\n", + " seed = 0\n", + " for name in range(dino_names):\n", + " \n", + " # Sample indices and print them\n", + " sampled_indices = sample(parameters, char_to_ix, seed)\n", + " print_sample(sampled_indices, ix_to_char)\n", + " \n", + " seed += 1 # To get the same result for grading purposed, increment the seed by one. \n", + " \n", + " print('\\n')\n", + " \n", + " return parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell, you should observe your model outputting random-looking characters at the first iteration. After a few thousand iterations, your model should learn to generate reasonable-looking names. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration: 0, Loss: 23.087336\n", + "\n", + "Nkzxwtdmfqoeyhsqwasjkjvu\n", + "Kneb\n", + "Kzxwtdmfqoeyhsqwasjkjvu\n", + "Neb\n", + "Zxwtdmfqoeyhsqwasjkjvu\n", + "Eb\n", + "Xwtdmfqoeyhsqwasjkjvu\n", + "\n", + "\n", + "Iteration: 2000, Loss: 27.884160\n", + "\n", + "Liusskeomnolxeros\n", + "Hmdaairus\n", + "Hytroligoraurus\n", + "Lecalosapaus\n", + "Xusicikoraurus\n", + "Abalpsamantisaurus\n", + "Tpraneronxeros\n", + "\n", + "\n", + "Iteration: 4000, Loss: 25.901815\n", + "\n", + "Mivrosaurus\n", + "Inee\n", + "Ivtroplisaurus\n", + "Mbaaisaurus\n", + "Wusichisaurus\n", + "Cabaselachus\n", + "Toraperlethosdarenitochusthiamamumamaon\n", + "\n", + "\n", + "Iteration: 6000, Loss: 24.608779\n", + "\n", + "Onwusceomosaurus\n", + "Lieeaerosaurus\n", + "Lxussaurus\n", + "Oma\n", + "Xusteonosaurus\n", + "Eeahosaurus\n", + "Toreonosaurus\n", + "\n", + "\n", + "Iteration: 8000, Loss: 24.070350\n", + "\n", + "Onxusichepriuon\n", + "Kilabersaurus\n", + "Lutrodon\n", + "Omaaerosaurus\n", + "Xutrcheps\n", + "Edaksoje\n", + "Trodiktonus\n", + "\n", + "\n", + "Iteration: 10000, Loss: 23.844446\n", + "\n", + "Onyusaurus\n", + "Klecalosaurus\n", + "Lustodon\n", + "Ola\n", + "Xusodonia\n", + "Eeaeosaurus\n", + "Troceosaurus\n", + "\n", + "\n", + "Iteration: 12000, Loss: 23.291971\n", + "\n", + "Onyxosaurus\n", + "Kica\n", + "Lustrepiosaurus\n", + "Olaagrraiansaurus\n", + "Yuspangosaurus\n", + "Eealosaurus\n", + "Trognesaurus\n", + "\n", + "\n", + "Iteration: 14000, Loss: 23.382339\n", + "\n", + "Meutromodromurus\n", + "Inda\n", + "Iutroinatorsaurus\n", + "Maca\n", + "Yusteratoptititan\n", + "Ca\n", + "Troclosaurus\n", + "\n", + "\n", + "Iteration: 16000, Loss: 23.288447\n", + "\n", + "Meuspsangosaurus\n", + "Ingaa\n", + "Iusosaurus\n", + "Macalosaurus\n", + "Yushanis\n", + "Daalosaurus\n", + "Trpandon\n", + "\n", + "\n", + "Iteration: 18000, Loss: 22.823526\n", + "\n", + "Phytrolonhonyg\n", + "Mela\n", + "Mustrerasaurus\n", + "Peg\n", + "Ytronorosaurus\n", + "Ehalosaurus\n", + "Trolomeehus\n", + "\n", + "\n", + "Iteration: 20000, Loss: 23.041871\n", + "\n", + "Nousmofonosaurus\n", + "Loma\n", + "Lytrognatiasaurus\n", + "Ngaa\n", + "Ytroenetiaudostarmilus\n", + "Eiafosaurus\n", + "Troenchulunosaurus\n", + "\n", + "\n", + "Iteration: 22000, Loss: 22.728849\n", + "\n", + "Piutyrangosaurus\n", + "Midaa\n", + "Myroranisaurus\n", + "Pedadosaurus\n", + "Ytrodon\n", + "Eiadosaurus\n", + "Trodoniomusitocorces\n", + "\n", + "\n", + "Iteration: 24000, Loss: 22.683403\n", + "\n", + "Meutromeisaurus\n", + "Indeceratlapsaurus\n", + "Jurosaurus\n", + "Ndaa\n", + "Yusicheropterus\n", + "Eiaeropectus\n", + "Trodonasaurus\n", + "\n", + "\n", + "Iteration: 26000, Loss: 22.554523\n", + "\n", + "Phyusaurus\n", + "Liceceron\n", + "Lyusichenodylus\n", + "Pegahus\n", + "Yustenhtonthosaurus\n", + "Elagosaurus\n", + "Trodontonsaurus\n", + "\n", + "\n", + "Iteration: 28000, Loss: 22.484472\n", + "\n", + "Onyutimaerihus\n", + "Koia\n", + "Lytusaurus\n", + "Ola\n", + "Ytroheltorus\n", + "Eiadosaurus\n", + "Trofiashates\n", + "\n", + "\n", + "Iteration: 30000, Loss: 22.774404\n", + "\n", + "Phytys\n", + "Lica\n", + "Lysus\n", + "Pacalosaurus\n", + "Ytrochisaurus\n", + "Eiacosaurus\n", + "Trochesaurus\n", + "\n", + "\n", + "Iteration: 32000, Loss: 22.209473\n", + "\n", + "Mawusaurus\n", + "Jica\n", + "Lustoia\n", + "Macaisaurus\n", + "Yusolenqtesaurus\n", + "Eeaeosaurus\n", + "Trnanatrax\n", + "\n", + "\n", + "Iteration: 34000, Loss: 22.396744\n", + "\n", + "Mavptokekus\n", + "Ilabaisaurus\n", + "Itosaurus\n", + "Macaesaurus\n", + "Yrosaurus\n", + "Eiaeosaurus\n", + "Trodon\n", + "\n", + "\n" + ] + } + ], + "source": [ + "parameters = model(data, ix_to_char, char_to_ix)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Conclusion\n", + "\n", + "You can see that your algorithm has started to generate plausible dinosaur names towards the end of the training. At first, it was generating random characters, but towards the end you could see dinosaur names with cool endings. Feel free to run the algorithm even longer and play with hyperparameters to see if you can get even better results. Our implemetation generated some really cool names like `maconucon`, `marloralus` and `macingsersaurus`. Your model hopefully also learned that dinosaur names tend to end in `saurus`, `don`, `aura`, `tor`, etc.\n", + "\n", + "If your model generates some non-cool names, don't blame the model entirely--not all actual dinosaur names sound cool. (For example, `dromaeosauroides` is an actual dinosaur name and is in the training set.) But this model should give you a set of candidates from which you can pick the coolest! \n", + "\n", + "This assignment had used a relatively small dataset, so that you could train an RNN quickly on a CPU. Training a model of the english language requires a much bigger dataset, and usually needs much more computation, and could run for many hours on GPUs. We ran our dinosaur name for quite some time, and so far our favoriate name is the great, undefeatable, and fierce: Mangosaurus!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 - Writing like Shakespeare\n", + "\n", + "The rest of this notebook is optional and is not graded, but we hope you'll do it anyway since it's quite fun and informative. \n", + "\n", + "A similar (but more complicated) task is to generate Shakespeare poems. Instead of learning from a dataset of Dinosaur names you can use a collection of Shakespearian poems. Using LSTM cells, you can learn longer term dependencies that span many characters in the text--e.g., where a character appearing somewhere a sequence can influence what should be a different character much much later in ths sequence. These long term dependencies were less important with dinosaur names, since the names were quite short. \n", + "\n", + "\n", + "\n", + "
Let's become poets!
\n", + "\n", + "We have implemented a Shakespeare poem generator with Keras. Run the following cell to load the required packages and models. This may take a few minutes. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading text data...\n", + "Creating training set...\n", + "number of training examples: 31412\n", + "Vectorizing training set...\n", + "Loading model...\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "from keras.callbacks import LambdaCallback\n", + "from keras.models import Model, load_model, Sequential\n", + "from keras.layers import Dense, Activation, Dropout, Input, Masking\n", + "from keras.layers import LSTM\n", + "from keras.utils.data_utils import get_file\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from shakespeare_utils import *\n", + "import sys\n", + "import io" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To save you some time, we have already trained a model for ~1000 epochs on a collection of Shakespearian poems called [*\"The Sonnets\"*](shakespeare.txt). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's train the model for one more epoch. When it finishes training for an epoch---this will also take a few minutes---you can run `generate_output`, which will prompt asking you for an input (`<`40 characters). The poem will start with your sentence, and our RNN-Shakespeare will complete the rest of the poem for you! For example, try \"Forsooth this maketh no sense \" (don't enter the quotation marks). Depending on whether you include the space at the end, your results might also differ--try it both ways, and try other inputs as well. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1\n", + "31412/31412 [==============================] - 290s - loss: 2.5641 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print_callback = LambdaCallback(on_epoch_end=on_epoch_end)\n", + "\n", + "model.fit(x, y, batch_size=128, epochs=1, callbacks=[print_callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Write the beginning of your poem, the Shakespeare machine will complete it. Your input is: One Love\n", + "\n", + "\n", + "Here is your poem: \n", + "\n", + "One Love the carve, oud bearing maly,\n", + "with ink falels me all trife' sey pely,\n", + "why of offore self-race spuse doth by kees,\n", + "not your have i mon chech should she wod,\n", + "in whensiigh nand it dellens a weld, ditnor shilun,\n", + "in himter felten stornt to sifpe thought,\n", + "thee is, mahe betier thite the shall ever looh.\n", + "\n", + "sow god thumser to youl of choudcless spere.\n", + " noe thou in whereoed to mer as one'er,\n", + "but mode my vier" + ] + } + ], + "source": [ + "# Run this cell to try with different inputs without having to re-train the model \n", + "generate_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The RNN-Shakespeare model is very similar to the one you have built for dinosaur names. The only major differences are:\n", + "- LSTMs instead of the basic RNN to capture longer-range dependencies\n", + "- The model is a deeper, stacked LSTM model (2 layer)\n", + "- Using Keras instead of python to simplify the code \n", + "\n", + "If you want to learn more, you can also check out the Keras Team's text generation implementation on GitHub: https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py.\n", + "\n", + "Congratulations on finishing this notebook! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**References**:\n", + "- This exercise took inspiration from Andrej Karpathy's implementation: https://gist.github.com/karpathy/d4dee566867f8291f086. To learn more about text generation, also check out Karpathy's [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/).\n", + "- For the Shakespearian poem generator, our implementation was based on the implementation of an LSTM text generator by the Keras team: https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "1dYg0", + "launcher_item_id": "MLhxP" + }, + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/clip.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/clip.png new file mode 100644 index 0000000..d7685cc Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/clip.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dino.jpg b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dino.jpg new file mode 100644 index 0000000..f7d96fc Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dino.jpg differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dinos3.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dinos3.png new file mode 100644 index 0000000..e303856 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/dinos3.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/mangosaurus.jpeg b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/mangosaurus.jpeg new file mode 100644 index 0000000..1c6dde9 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/mangosaurus.jpeg differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/rnn.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/rnn.png new file mode 100644 index 0000000..c0ab647 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/rnn.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/shakespeare.jpg b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/shakespeare.jpg new file mode 100644 index 0000000..fed49ee Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Dinosaur Island -- Character-level language model/images/shakespeare.jpg differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/Jazz improvisation with LSTM - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/Jazz improvisation with LSTM - v1.ipynb new file mode 100644 index 0000000..dff3ad3 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/Jazz improvisation with LSTM - v1.ipynb @@ -0,0 +1,1033 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Improvise a Jazz Solo with an LSTM Network\n", + "\n", + "Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment. \n", + "\n", + "**You will learn to:**\n", + "- Apply an LSTM to music generation.\n", + "- Generate your own jazz music with deep learning.\n", + "\n", + "Please run the following cell to load all the packages required in this assignment. This may take a few minutes. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "import IPython\n", + "import sys\n", + "from music21 import *\n", + "import numpy as np\n", + "from grammar import *\n", + "from qa import *\n", + "from preprocess import * \n", + "from music_utils import *\n", + "from data_utils import *\n", + "from keras.models import load_model, Model\n", + "from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector\n", + "from keras.initializers import glorot_uniform\n", + "from keras.utils import to_categorical\n", + "from keras.optimizers import Adam\n", + "from keras import backend as K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Problem statement\n", + "\n", + "You would like to create a jazz music piece specially for a friend's birthday. However, you don't know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM netwok. \n", + "\n", + "You will train a network to generate novel jazz solos in a style representative of a body of performed work.\n", + "\n", + "\n", + "\n", + "\n", + "### 1.1 - Dataset\n", + "\n", + "You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio('./data/30s_seq.mp3')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have taken care of the preprocessing of the musical data to render it in terms of musical \"values.\" You can informally think of each \"value\" as a note, which comprises a pitch and a duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a \"value\" is actually more complicated than this--specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playng multiple notes at the same time generates what's called a \"chord\"). But we don't need to worry about the details of music theory for this assignment. For the purpose of this assignment, all you need to know is that we will obtain a dataset of values, and will learn an RNN model to generate sequences of values. \n", + "\n", + "Our music generation system will use 78 unique values. Run the following code to load the raw music data and preprocess it into values. This might take a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X: (60, 30, 78)\n", + "number of training examples: 60\n", + "Tx (length of sequence): 30\n", + "total # of unique values: 78\n", + "Shape of Y: (30, 60, 78)\n" + ] + } + ], + "source": [ + "X, Y, n_values, indices_values = load_music_utils()\n", + "print('shape of X:', X.shape)\n", + "print('number of training examples:', X.shape[0])\n", + "print('Tx (length of sequence):', X.shape[1])\n", + "print('total # of unique values:', n_values)\n", + "print('Shape of Y:', Y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have just loaded the following:\n", + "\n", + "- `X`: This is an (m, $T_x$, 78) dimensional array. We have m training examples, each of which is a snippet of $T_x =30$ musical values. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t. \n", + "\n", + "- `Y`: This is essentially the same as `X`, but shifted one step to the left (to the past). Similar to the dinosaurus assignment, we're interested in the network using the previous values to predict the next value, so our sequence model will try to predict $y^{\\langle t \\rangle}$ given $x^{\\langle 1\\rangle}, \\ldots, x^{\\langle t \\rangle}$. However, the data in `Y` is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed to the LSTM later. \n", + "\n", + "- `n_values`: The number of unique values in this dataset. This should be 78. \n", + "\n", + "- `indices_values`: python dictionary mapping from 0-77 to musical values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Overview of our model\n", + "\n", + "Here is the architecture of the model we will use. This is similar to the Dinosaurus model you had used in the previous notebook, except that in you will be implementing it in Keras. The architecture is as follows: \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "We will be training the model on random snippets of 30 values taken from a much longer piece of music. Thus, we won't bother to set the first input $x^{\\langle 1 \\rangle} = \\vec{0}$, which we had done previously to denote the start of a dinosaur name, since now most of these snippets of audio start somewhere in the middle of a piece of music. We are setting each of the snippts to have the same length $T_x = 30$ to make vectorization easier. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Building the model\n", + "\n", + "In this part you will build and train a model that will learn musical patterns. To do so, you will need to build a model that takes in X of shape $(m, T_x, 78)$ and Y of shape $(T_y, m, 78)$. We will use an LSTM with 64 dimensional hidden states. Lets set `n_a = 64`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n_a = 64 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here's how you can create a Keras model with multiple inputs and outputs. If you're building an RNN where even at test time entire input sequence $x^{\\langle 1 \\rangle}, x^{\\langle 2 \\rangle}, \\ldots, x^{\\langle T_x \\rangle}$ were *given in advance*, for example if the inputs were words and the output was a label, then Keras has simple built-in functions to build the model. However, for sequence generation, at test time we don't know all the values of $x^{\\langle t\\rangle}$ in advance; instead we generate them one at a time using $x^{\\langle t\\rangle} = y^{\\langle t-1 \\rangle}$. So the code will be a bit more complicated, and you'll need to implement your own for-loop to iterate over the different time steps. \n", + "\n", + "The function `djmodel()` will call the LSTM layer $T_x$ times using a for-loop, and it is important that all $T_x$ copies have the same weights. I.e., it should not re-initiaiize the weights every time---the $T_x$ steps should have shared weights. The key steps for implementing layers with shareable weights in Keras are: \n", + "1. Define the layer objects (we will use global variables for this).\n", + "2. Call these objects when propagating the input.\n", + "\n", + "We have defined the layers objects you need as global variables. Please run the next cell to create them. Please check the Keras documentation to make sure you understand what these layers are: [Reshape()](https://keras.io/layers/core/#reshape), [LSTM()](https://keras.io/layers/recurrent/#lstm), [Dense()](https://keras.io/layers/core/#dense).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "reshapor = Reshape((1, 78)) # Used in Step 2.B of djmodel(), below\n", + "LSTM_cell = LSTM(n_a, return_state = True) # Used in Step 2.C\n", + "densor = Dense(n_values, activation='softmax') # Used in Step 2.D" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each of `reshapor`, `LSTM_cell` and `densor` are now layer objects, and you can use them to implement `djmodel()`. In order to propagate a Keras tensor object X through one of these layers, use `layer_object(X)` (or `layer_object([X,Y])` if it requires multiple inputs.). For example, `reshapor(X)` will propagate X through the `Reshape((1,78))` layer defined above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "**Exercise**: Implement `djmodel()`. You will need to carry out 2 steps:\n", + "\n", + "1. Create an empty list \"outputs\" to save the outputs of the LSTM Cell at every time step.\n", + "2. Loop for $t \\in 1, \\ldots, T_x$:\n", + "\n", + " A. Select the \"t\"th time-step vector from X. The shape of this selection should be (78,). To do so, create a custom [Lambda](https://keras.io/layers/core/#lambda) layer in Keras by using this line of code:\n", + "``` \n", + " x = Lambda(lambda x: X[:,t,:])(X)\n", + "``` \n", + "Look over the Keras documentation to figure out what this does. It is creating a \"temporary\" or \"unnamed\" function (that's what Lambda functions are) that extracts out the appropriate one-hot vector, and making this function a Keras `Layer` object to apply to `X`. \n", + "\n", + " B. Reshape x to be (1,78). You may find the `reshapor()` layer (defined below) helpful.\n", + "\n", + " C. Run x through one step of LSTM_cell. Remember to initialize the LSTM_cell with the previous step's hidden state $a$ and cell state $c$. Use the following formatting:\n", + "```python\n", + "a, _, c = LSTM_cell(input_x, initial_state=[previous hidden state, previous cell state])\n", + "```\n", + "\n", + " D. Propagate the LSTM's output activation value through a dense+softmax layer using `densor`. \n", + " \n", + " E. Append the predicted value to the list of \"outputs\"\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: djmodel\n", + "\n", + "def djmodel(Tx, n_a, n_values):\n", + " \"\"\"\n", + " Implement the model\n", + " \n", + " Arguments:\n", + " Tx -- length of the sequence in a corpus\n", + " n_a -- the number of activations used in our model\n", + " n_values -- number of unique values in the music data \n", + " \n", + " Returns:\n", + " model -- a keras model with the \n", + " \"\"\"\n", + " \n", + " # Define the input of your model with a shape \n", + " X = Input(shape=(Tx, n_values))\n", + " \n", + " # Define s0, initial hidden state for the decoder LSTM\n", + " a0 = Input(shape=(n_a,), name='a0')\n", + " c0 = Input(shape=(n_a,), name='c0')\n", + " a = a0\n", + " c = c0\n", + " \n", + " ### START CODE HERE ### \n", + " # Step 1: Create empty list to append the outputs while you iterate (≈1 line)\n", + " outputs = []\n", + " \n", + " # Step 2: Loop\n", + " for t in range(Tx):\n", + " \n", + " # Step 2.A: select the \"t\"th time step vector from X. \n", + " x = Lambda(lambda x: X[:,t,:])(X)\n", + " # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)\n", + " x = reshapor(x)\n", + " # Step 2.C: Perform one step of the LSTM_cell\n", + " a, _, c = LSTM_cell(x, initial_state=[a, c])\n", + " # Step 2.D: Apply densor to the hidden state output of LSTM_Cell\n", + " out = densor(a)\n", + " # Step 2.E: add the output to \"outputs\"\n", + " outputs.append(out)\n", + " \n", + " # Step 3: Create model instance\n", + " model = Model([X, a0, c0], outputs)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to define your model. We will use `Tx=30`, `n_a=64` (the dimension of the LSTM activations), and `n_values=78`. This cell may take a few seconds to run. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = djmodel(Tx = 30 , n_a = 64, n_values = 78)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now need to compile your model to be trained. We will Adam and a categorical cross-entropy loss." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)\n", + "\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, lets initialize `a0` and `c0` for the LSTM's initial state to be zero. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "m = 60\n", + "a0 = np.zeros((m, n_a))\n", + "c0 = np.zeros((m, n_a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets now fit the model! We will turn `Y` to a list before doing so, since the cost function expects `Y` to be provided in this format (one list item per time-step). So `list(Y)` is a list with 30 items, where each of the list items is of shape (60,78). Lets train for 100 epochs. This will take a few minutes. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "60/60 [==============================] - 5s - loss: 125.9040 - dense_1_loss_1: 4.3549 - dense_1_loss_2: 4.3491 - dense_1_loss_3: 4.3446 - dense_1_loss_4: 4.3425 - dense_1_loss_5: 4.3457 - dense_1_loss_6: 4.3444 - dense_1_loss_7: 4.3403 - dense_1_loss_8: 4.3426 - dense_1_loss_9: 4.3420 - dense_1_loss_10: 4.3363 - dense_1_loss_11: 4.3399 - dense_1_loss_12: 4.3542 - dense_1_loss_13: 4.3405 - dense_1_loss_14: 4.3315 - dense_1_loss_15: 4.3349 - dense_1_loss_16: 4.3365 - dense_1_loss_17: 4.3502 - dense_1_loss_18: 4.3426 - dense_1_loss_19: 4.3398 - dense_1_loss_20: 4.3456 - dense_1_loss_21: 4.3334 - dense_1_loss_22: 4.3307 - dense_1_loss_23: 4.3370 - dense_1_loss_24: 4.3398 - dense_1_loss_25: 4.3419 - dense_1_loss_26: 4.3336 - dense_1_loss_27: 4.3417 - dense_1_loss_28: 4.3389 - dense_1_loss_29: 4.3489 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0000e+00 - dense_1_acc_2: 0.0000e+00 - dense_1_acc_3: 0.0333 - dense_1_acc_4: 0.0167 - dense_1_acc_5: 0.0500 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.0833 - dense_1_acc_8: 0.0667 - dense_1_acc_9: 0.0833 - dense_1_acc_10: 0.0833 - dense_1_acc_11: 0.1000 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.0333 - dense_1_acc_14: 0.0833 - dense_1_acc_15: 0.1000 - dense_1_acc_16: 0.0833 - dense_1_acc_17: 0.0333 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.0833 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.0833 - dense_1_acc_27: 0.0167 - dense_1_acc_28: 0.1000 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 2/100\n", + "60/60 [==============================] - 0s - loss: 122.6568 - dense_1_loss_1: 4.3326 - dense_1_loss_2: 4.3046 - dense_1_loss_3: 4.2773 - dense_1_loss_4: 4.2703 - dense_1_loss_5: 4.2588 - dense_1_loss_6: 4.2670 - dense_1_loss_7: 4.2432 - dense_1_loss_8: 4.2369 - dense_1_loss_9: 4.2453 - dense_1_loss_10: 4.2128 - dense_1_loss_11: 4.2108 - dense_1_loss_12: 4.2624 - dense_1_loss_13: 4.2184 - dense_1_loss_14: 4.2120 - dense_1_loss_15: 4.2054 - dense_1_loss_16: 4.2141 - dense_1_loss_17: 4.2386 - dense_1_loss_18: 4.2264 - dense_1_loss_19: 4.1898 - dense_1_loss_20: 4.2158 - dense_1_loss_21: 4.1903 - dense_1_loss_22: 4.1631 - dense_1_loss_23: 4.2017 - dense_1_loss_24: 4.2174 - dense_1_loss_25: 4.2308 - dense_1_loss_26: 4.1616 - dense_1_loss_27: 4.2144 - dense_1_loss_28: 4.2066 - dense_1_loss_29: 4.2285 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0500 - dense_1_acc_3: 0.1333 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.2167 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.0833 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1000 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.2333 - dense_1_acc_27: 0.0667 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 3/100\n", + "60/60 [==============================] - 0s - loss: 117.0733 - dense_1_loss_1: 4.3111 - dense_1_loss_2: 4.2544 - dense_1_loss_3: 4.1883 - dense_1_loss_4: 4.1658 - dense_1_loss_5: 4.1323 - dense_1_loss_6: 4.1614 - dense_1_loss_7: 4.0850 - dense_1_loss_8: 4.0316 - dense_1_loss_9: 4.0027 - dense_1_loss_10: 3.8873 - dense_1_loss_11: 3.8779 - dense_1_loss_12: 4.1530 - dense_1_loss_13: 3.9607 - dense_1_loss_14: 3.9360 - dense_1_loss_15: 3.9995 - dense_1_loss_16: 4.0009 - dense_1_loss_17: 4.1242 - dense_1_loss_18: 4.1254 - dense_1_loss_19: 3.8774 - dense_1_loss_20: 4.0043 - dense_1_loss_21: 3.9801 - dense_1_loss_22: 3.8202 - dense_1_loss_23: 3.8966 - dense_1_loss_24: 3.9966 - dense_1_loss_25: 4.1391 - dense_1_loss_26: 3.7823 - dense_1_loss_27: 4.0445 - dense_1_loss_28: 3.9607 - dense_1_loss_29: 4.1739 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1667 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.1667 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1500 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.0833 - dense_1_acc_14: 0.1000 - dense_1_acc_15: 0.0667 - dense_1_acc_16: 0.0667 - dense_1_acc_17: 0.0333 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.0667 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.0500 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0333 - dense_1_acc_29: 0.0167 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 4/100\n", + "60/60 [==============================] - 0s - loss: 112.9154 - dense_1_loss_1: 4.2889 - dense_1_loss_2: 4.2037 - dense_1_loss_3: 4.0946 - dense_1_loss_4: 4.0680 - dense_1_loss_5: 3.9878 - dense_1_loss_6: 4.0213 - dense_1_loss_7: 3.9193 - dense_1_loss_8: 3.7682 - dense_1_loss_9: 3.8235 - dense_1_loss_10: 3.6754 - dense_1_loss_11: 3.7804 - dense_1_loss_12: 4.0816 - dense_1_loss_13: 3.8364 - dense_1_loss_14: 3.7726 - dense_1_loss_15: 3.8076 - dense_1_loss_16: 3.8271 - dense_1_loss_17: 3.9546 - dense_1_loss_18: 3.8845 - dense_1_loss_19: 3.7412 - dense_1_loss_20: 3.9397 - dense_1_loss_21: 3.8654 - dense_1_loss_22: 3.7902 - dense_1_loss_23: 3.7795 - dense_1_loss_24: 3.7457 - dense_1_loss_25: 3.9795 - dense_1_loss_26: 3.6115 - dense_1_loss_27: 3.7478 - dense_1_loss_28: 3.8803 - dense_1_loss_29: 4.0392 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1500 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.0500 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1167 - dense_1_acc_15: 0.1333 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0667 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1167 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 5/100\n", + "60/60 [==============================] - 0s - loss: 110.2597 - dense_1_loss_1: 4.2705 - dense_1_loss_2: 4.1568 - dense_1_loss_3: 4.0205 - dense_1_loss_4: 4.0005 - dense_1_loss_5: 3.8821 - dense_1_loss_6: 3.9264 - dense_1_loss_7: 3.8413 - dense_1_loss_8: 3.6637 - dense_1_loss_9: 3.7376 - dense_1_loss_10: 3.5831 - dense_1_loss_11: 3.7226 - dense_1_loss_12: 4.0011 - dense_1_loss_13: 3.7122 - dense_1_loss_14: 3.6388 - dense_1_loss_15: 3.7068 - dense_1_loss_16: 3.7367 - 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dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 94/100\n", + "60/60 [==============================] - 0s - loss: 5.9052 - dense_1_loss_1: 3.7502 - dense_1_loss_2: 1.1397 - dense_1_loss_3: 0.3089 - dense_1_loss_4: 0.0863 - dense_1_loss_5: 0.0507 - dense_1_loss_6: 0.0358 - dense_1_loss_7: 0.0314 - dense_1_loss_8: 0.0284 - dense_1_loss_9: 0.0278 - dense_1_loss_10: 0.0235 - dense_1_loss_11: 0.0260 - dense_1_loss_12: 0.0221 - dense_1_loss_13: 0.0198 - dense_1_loss_14: 0.0205 - dense_1_loss_15: 0.0211 - dense_1_loss_16: 0.0217 - dense_1_loss_17: 0.0203 - dense_1_loss_18: 0.0212 - dense_1_loss_19: 0.0213 - dense_1_loss_20: 0.0226 - dense_1_loss_21: 0.0225 - dense_1_loss_22: 0.0214 - dense_1_loss_23: 0.0200 - dense_1_loss_24: 0.0215 - dense_1_loss_25: 0.0222 - dense_1_loss_26: 0.0235 - dense_1_loss_27: 0.0240 - dense_1_loss_28: 0.0238 - dense_1_loss_29: 0.0268 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 95/100\n", + "60/60 [==============================] - 0s - loss: 5.8725 - dense_1_loss_1: 3.7469 - dense_1_loss_2: 1.1300 - dense_1_loss_3: 0.3034 - dense_1_loss_4: 0.0847 - dense_1_loss_5: 0.0497 - dense_1_loss_6: 0.0350 - dense_1_loss_7: 0.0308 - dense_1_loss_8: 0.0278 - dense_1_loss_9: 0.0272 - dense_1_loss_10: 0.0231 - dense_1_loss_11: 0.0254 - dense_1_loss_12: 0.0216 - dense_1_loss_13: 0.0194 - dense_1_loss_14: 0.0201 - dense_1_loss_15: 0.0207 - dense_1_loss_16: 0.0213 - dense_1_loss_17: 0.0199 - dense_1_loss_18: 0.0207 - dense_1_loss_19: 0.0209 - dense_1_loss_20: 0.0222 - dense_1_loss_21: 0.0220 - dense_1_loss_22: 0.0210 - dense_1_loss_23: 0.0196 - dense_1_loss_24: 0.0211 - dense_1_loss_25: 0.0218 - dense_1_loss_26: 0.0230 - dense_1_loss_27: 0.0235 - dense_1_loss_28: 0.0234 - dense_1_loss_29: 0.0263 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 96/100\n", + "60/60 [==============================] - 0s - loss: 5.8412 - dense_1_loss_1: 3.7437 - dense_1_loss_2: 1.1198 - dense_1_loss_3: 0.2988 - dense_1_loss_4: 0.0832 - dense_1_loss_5: 0.0488 - dense_1_loss_6: 0.0344 - dense_1_loss_7: 0.0302 - dense_1_loss_8: 0.0273 - dense_1_loss_9: 0.0267 - dense_1_loss_10: 0.0227 - dense_1_loss_11: 0.0249 - dense_1_loss_12: 0.0212 - dense_1_loss_13: 0.0190 - dense_1_loss_14: 0.0197 - dense_1_loss_15: 0.0203 - dense_1_loss_16: 0.0208 - dense_1_loss_17: 0.0195 - dense_1_loss_18: 0.0203 - dense_1_loss_19: 0.0204 - dense_1_loss_20: 0.0217 - dense_1_loss_21: 0.0216 - dense_1_loss_22: 0.0206 - dense_1_loss_23: 0.0193 - dense_1_loss_24: 0.0206 - dense_1_loss_25: 0.0213 - dense_1_loss_26: 0.0225 - dense_1_loss_27: 0.0230 - dense_1_loss_28: 0.0229 - dense_1_loss_29: 0.0258 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 97/100\n", + "60/60 [==============================] - 0s - loss: 5.8096 - dense_1_loss_1: 3.7406 - dense_1_loss_2: 1.1099 - dense_1_loss_3: 0.2939 - dense_1_loss_4: 0.0817 - dense_1_loss_5: 0.0478 - dense_1_loss_6: 0.0337 - dense_1_loss_7: 0.0296 - dense_1_loss_8: 0.0267 - dense_1_loss_9: 0.0262 - dense_1_loss_10: 0.0222 - dense_1_loss_11: 0.0244 - dense_1_loss_12: 0.0208 - dense_1_loss_13: 0.0186 - dense_1_loss_14: 0.0193 - dense_1_loss_15: 0.0199 - dense_1_loss_16: 0.0204 - dense_1_loss_17: 0.0191 - dense_1_loss_18: 0.0199 - dense_1_loss_19: 0.0200 - dense_1_loss_20: 0.0213 - dense_1_loss_21: 0.0212 - dense_1_loss_22: 0.0202 - dense_1_loss_23: 0.0189 - dense_1_loss_24: 0.0202 - dense_1_loss_25: 0.0209 - dense_1_loss_26: 0.0221 - dense_1_loss_27: 0.0226 - dense_1_loss_28: 0.0224 - dense_1_loss_29: 0.0253 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 98/100\n", + "60/60 [==============================] - 0s - loss: 5.7808 - dense_1_loss_1: 3.7374 - dense_1_loss_2: 1.1009 - dense_1_loss_3: 0.2898 - dense_1_loss_4: 0.0802 - dense_1_loss_5: 0.0469 - dense_1_loss_6: 0.0330 - dense_1_loss_7: 0.0290 - dense_1_loss_8: 0.0262 - dense_1_loss_9: 0.0258 - dense_1_loss_10: 0.0218 - dense_1_loss_11: 0.0239 - dense_1_loss_12: 0.0204 - dense_1_loss_13: 0.0183 - dense_1_loss_14: 0.0189 - dense_1_loss_15: 0.0195 - dense_1_loss_16: 0.0200 - dense_1_loss_17: 0.0187 - dense_1_loss_18: 0.0195 - dense_1_loss_19: 0.0196 - dense_1_loss_20: 0.0209 - dense_1_loss_21: 0.0208 - dense_1_loss_22: 0.0198 - dense_1_loss_23: 0.0185 - dense_1_loss_24: 0.0198 - dense_1_loss_25: 0.0205 - dense_1_loss_26: 0.0217 - dense_1_loss_27: 0.0221 - dense_1_loss_28: 0.0219 - dense_1_loss_29: 0.0249 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 99/100\n", + "60/60 [==============================] - 0s - loss: 5.7520 - dense_1_loss_1: 3.7342 - dense_1_loss_2: 1.0915 - dense_1_loss_3: 0.2857 - dense_1_loss_4: 0.0789 - dense_1_loss_5: 0.0461 - dense_1_loss_6: 0.0324 - dense_1_loss_7: 0.0285 - dense_1_loss_8: 0.0257 - dense_1_loss_9: 0.0253 - dense_1_loss_10: 0.0214 - dense_1_loss_11: 0.0234 - dense_1_loss_12: 0.0200 - dense_1_loss_13: 0.0179 - dense_1_loss_14: 0.0185 - dense_1_loss_15: 0.0191 - dense_1_loss_16: 0.0196 - dense_1_loss_17: 0.0183 - dense_1_loss_18: 0.0191 - dense_1_loss_19: 0.0193 - dense_1_loss_20: 0.0205 - dense_1_loss_21: 0.0204 - dense_1_loss_22: 0.0194 - dense_1_loss_23: 0.0181 - dense_1_loss_24: 0.0194 - dense_1_loss_25: 0.0201 - dense_1_loss_26: 0.0213 - dense_1_loss_27: 0.0216 - dense_1_loss_28: 0.0215 - dense_1_loss_29: 0.0245 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n", + "Epoch 100/100\n", + "60/60 [==============================] - 0s - loss: 5.7237 - dense_1_loss_1: 3.7312 - dense_1_loss_2: 1.0825 - dense_1_loss_3: 0.2813 - dense_1_loss_4: 0.0776 - dense_1_loss_5: 0.0452 - dense_1_loss_6: 0.0317 - dense_1_loss_7: 0.0280 - dense_1_loss_8: 0.0252 - dense_1_loss_9: 0.0248 - dense_1_loss_10: 0.0211 - dense_1_loss_11: 0.0230 - dense_1_loss_12: 0.0196 - dense_1_loss_13: 0.0176 - dense_1_loss_14: 0.0182 - dense_1_loss_15: 0.0188 - dense_1_loss_16: 0.0192 - dense_1_loss_17: 0.0180 - dense_1_loss_18: 0.0188 - dense_1_loss_19: 0.0189 - dense_1_loss_20: 0.0201 - dense_1_loss_21: 0.0200 - dense_1_loss_22: 0.0190 - dense_1_loss_23: 0.0178 - dense_1_loss_24: 0.0191 - dense_1_loss_25: 0.0197 - dense_1_loss_26: 0.0209 - dense_1_loss_27: 0.0212 - dense_1_loss_28: 0.0211 - dense_1_loss_29: 0.0241 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit([X, a0, c0], list(Y), epochs=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should see the model loss going down. Now that you have trained a model, lets go on the the final section to implement an inference algorithm, and generate some music! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Generating music\n", + "\n", + "You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music. \n", + "\n", + "#### 3.1 - Predicting & Sampling\n", + "\n", + "\n", + "\n", + "At each step of sampling, you will take as input the activation `a` and cell state `c` from the previous state of the LSTM, forward propagate by one step, and get a new output activation as well as cell state. The new activation `a` can then be used to generate the output, using `densor` as before. \n", + "\n", + "To start off the model, we will initialize `x0` as well as the LSTM activation and and cell value `a0` and `c0` to be zeros. \n", + "\n", + "\n", + " \n", + "\n", + "\n", + "**Exercise:** Implement the function below to sample a sequence of musical values. Here are some of the key steps you'll need to implement inside the for-loop that generates the $T_y$ output characters: \n", + "\n", + "Step 2.A: Use `LSTM_Cell`, which inputs the previous step's `c` and `a` to generate the current step's `c` and `a`. \n", + "\n", + "Step 2.B: Use `densor` (defined previously) to compute a softmax on `a` to get the output for the current step. \n", + "\n", + "Step 2.C: Save the output you have just generated by appending it to `outputs`.\n", + "\n", + "Step 2.D: Sample x to the be \"out\"'s one-hot version (the prediction) so that you can pass it to the next LSTM's step. We have already provided this line of code, which uses a [Lambda](https://keras.io/layers/core/#lambda) function. \n", + "```python\n", + "x = Lambda(one_hot)(out) \n", + "```\n", + "[Minor technical note: Rather than sampling a value at random according to the probabilities in `out`, this line of code actually chooses the single most likely note at each step using an argmax.]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: music_inference_model\n", + "\n", + "def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):\n", + " \"\"\"\n", + " Uses the trained \"LSTM_cell\" and \"densor\" from model() to generate a sequence of values.\n", + " \n", + " Arguments:\n", + " LSTM_cell -- the trained \"LSTM_cell\" from model(), Keras layer object\n", + " densor -- the trained \"densor\" from model(), Keras layer object\n", + " n_values -- integer, umber of unique values\n", + " n_a -- number of units in the LSTM_cell\n", + " Ty -- integer, number of time steps to generate\n", + " \n", + " Returns:\n", + " inference_model -- Keras model instance\n", + " \"\"\"\n", + " \n", + " # Define the input of your model with a shape \n", + " x0 = Input(shape=(1, n_values))\n", + " \n", + " # Define s0, initial hidden state for the decoder LSTM\n", + " a0 = Input(shape=(n_a,), name='a0')\n", + " c0 = Input(shape=(n_a,), name='c0')\n", + " a = a0\n", + " c = c0\n", + " x = x0\n", + "\n", + " ### START CODE HERE ###\n", + " # Step 1: Create an empty list of \"outputs\" to later store your predicted values (≈1 line)\n", + " outputs = []\n", + " \n", + " # Step 2: Loop over Ty and generate a value at every time step\n", + " for t in range(Ty):\n", + " \n", + " # Step 2.A: Perform one step of LSTM_cell (≈1 line)\n", + " a, _, c = LSTM_cell(x, initial_state=[a, c])\n", + " \n", + " # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)\n", + " out = densor(a)\n", + "\n", + " # Step 2.C: Append the prediction \"out\" to \"outputs\". out.shape = (None, 78) (≈1 line)\n", + " outputs.append(out)\n", + " \n", + " # Step 2.D: Select the next value according to \"out\", and set \"x\" to be the one-hot representation of the\n", + " # selected value, which will be passed as the input to LSTM_cell on the next step. We have provided \n", + " # the line of code you need to do this. \n", + " x = Lambda(one_hot)(out)\n", + " \n", + " # Step 3: Create model instance with the correct \"inputs\" and \"outputs\" (≈1 line)\n", + " inference_model = Model([x0, a0, c0], outputs)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return inference_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to define your inference model. This model is hard coded to generate 50 values." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, this creates the zero-valued vectors you will use to initialize `x` and the LSTM state variables `a` and `c`. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x_initializer = np.zeros((1, 1, 78))\n", + "a_initializer = np.zeros((1, n_a))\n", + "c_initializer = np.zeros((1, n_a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement `predict_and_sample()`. This function takes many arguments including the inputs [x_initializer, a_initializer, c_initializer]. In order to predict the output corresponding to this input, you will need to carry-out 3 steps:\n", + "1. Use your inference model to predict an output given your set of inputs. The output `pred` should be a list of length 20 where each element is a numpy-array of shape ($T_y$, n_values)\n", + "2. Convert `pred` into a numpy array of $T_y$ indices. Each index corresponds is computed by taking the `argmax` of an element of the `pred` list. [Hint](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html).\n", + "3. Convert the indices into their one-hot vector representations. [Hint](https://keras.io/utils/#to_categorical)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: predict_and_sample\n", + "\n", + "def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, \n", + " c_initializer = c_initializer):\n", + " \"\"\"\n", + " Predicts the next value of values using the inference model.\n", + " \n", + " Arguments:\n", + " inference_model -- Keras model instance for inference time\n", + " x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation\n", + " a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell\n", + " c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel\n", + " \n", + " Returns:\n", + " results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated\n", + " indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.\n", + " pred = inference_model.predict([x_initializer, a_initializer, c_initializer])\n", + " # Step 2: Convert \"pred\" into an np.array() of indices with the maximum probabilities\n", + " indices = np.argmax(pred, 2)\n", + " # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )\n", + " results = to_categorical(indices, num_classes=None)\n", + " ### END CODE HERE ###\n", + " \n", + " return results, indices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "np.argmax(results[12]) = 15\n", + "np.argmax(results[17]) = 36\n", + "list(indices[12:18]) = [array([15]), array([22]), array([31]), array([11]), array([51]), array([36])]\n" + ] + } + ], + "source": [ + "results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)\n", + "print(\"np.argmax(results[12]) =\", np.argmax(results[12]))\n", + "print(\"np.argmax(results[17]) =\", np.argmax(results[17]))\n", + "print(\"list(indices[12:18]) =\", list(indices[12:18]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: Your results may differ because Keras' results are not completely predictable. However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above, you should very likely observe a sequence of indices that are not all identical. Moreover, you should observe that: np.argmax(results[12]) is the first element of list(indices[12:18]) and np.argmax(results[17]) is the last element of list(indices[12:18]). \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **np.argmax(results[12])** =\n", + " \n", + " 1\n", + "
\n", + " **np.argmax(results[12])** =\n", + " \n", + " 42\n", + "
\n", + " **list(indices[12:18])** =\n", + " \n", + " [array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.3 - Generate music \n", + "\n", + "Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your `predict_and_sample()` function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time). \n", + "\n", + "Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so lets use it in our implementation as well. \n", + "\n", + "Lets make some music! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to generate music and record it into your `out_stream`. This can take a couple of minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting new values for different set of chords.\n", + "Generated 51 sounds using the predicted values for the set of chords (\"1\") and after pruning\n", + "Generated 50 sounds using the predicted values for the set of chords (\"2\") and after pruning\n", + "Generated 51 sounds using the predicted values for the set of chords (\"3\") and after pruning\n", + "Generated 50 sounds using the predicted values for the set of chords (\"4\") and after pruning\n" + ] + } + ], + "source": [ + "out_stream = generate_music(inference_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To listen to your music, click File->Open... Then go to \"output/\" and download \"my_music.midi\". Either play it on your computer with an application that can read midi files if you have one, or use one of the free online \"MIDI to mp3\" conversion tools to convert this to mp3. \n", + "\n", + "As reference, here also is a 30sec audio clip we generated using this algorithm. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "IPython.display.Audio('./data/30s_trained_model.mp3')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations!\n", + "\n", + "You have come to the end of the notebook. \n", + "\n", + "\n", + "Here's what you should remember:\n", + "- A sequence model can be used to generate musical values, which are then post-processed into midi music. \n", + "- Fairly similar models can be used to generate dinosaur names or to generate music, with the major difference being the input fed to the model. \n", + "- In Keras, sequence generation involves defining layers with shared weights, which are then repeated for the different time steps $1, \\ldots, T_x$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations on completing this assignment and generating a jazz solo! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**References**\n", + "\n", + "The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim's github repository.\n", + "\n", + "- Ji-Sung Kim, 2016, [deepjazz](https://github.com/jisungk/deepjazz)\n", + "- Jon Gillick, Kevin Tang and Robert Keller, 2009. [Learning Jazz Grammars](http://ai.stanford.edu/~kdtang/papers/smc09-jazzgrammar.pdf)\n", + "- Robert Keller and David Morrison, 2007, [A Grammatical Approach to Automatic Improvisation](http://smc07.uoa.gr/SMC07%20Proceedings/SMC07%20Paper%2055.pdf)\n", + "- François Pachet, 1999, [Surprising Harmonies](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.5.7473&rep=rep1&type=pdf)\n", + "\n", + "We're also grateful to François Germain for valuable feedback." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "EG0F7", + "launcher_item_id": "cxJXc" + }, + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_seq.mp3 b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_seq.mp3 new file mode 100644 index 0000000..40b0d5b Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_seq.mp3 differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_trained_model.mp3 b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_trained_model.mp3 new file mode 100644 index 0000000..f6862a3 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/30s_trained_model.mp3 differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/training_example.mp3 b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/training_example.mp3 new file mode 100644 index 0000000..08c63c5 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/data/training_example.mp3 differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/jazz.jpg b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/jazz.jpg new file mode 100644 index 0000000..d94675d Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/jazz.jpg differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/model.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/model.png new file mode 100644 index 0000000..c6ac7b7 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_gen.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_gen.png new file mode 100644 index 0000000..bd1efe0 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_gen.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_generation.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_generation.png new file mode 100644 index 0000000..833baf3 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week1/Jazz improvisation with LSTM/images/music_generation.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/Emojify - v2.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/Emojify - v2.ipynb new file mode 100644 index 0000000..250c89c --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/Emojify - v2.ipynb @@ -0,0 +1,1612 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Emojify! \n", + "\n", + "Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier. \n", + "\n", + "Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than writing \"Congratulations on the promotion! Lets get coffee and talk. Love you!\" the emojifier can automatically turn this into \"Congratulations on the promotion! 👍 Lets get coffee and talk. ☕️ Love you! ❤️\"\n", + "\n", + "You will implement a model which inputs a sentence (such as \"Let's go see the baseball game tonight!\") and finds the most appropriate emoji to be used with this sentence (⚾️). In many emoji interfaces, you need to remember that ❤️ is the \"heart\" symbol rather than the \"love\" symbol. But using word vectors, you'll see that even if your training set explicitly relates only a few words to a particular emoji, your algorithm will be able to generalize and associate words in the test set to the same emoji even if those words don't even appear in the training set. This allows you to build an accurate classifier mapping from sentences to emojis, even using a small training set. \n", + "\n", + "In this exercise, you'll start with a baseline model (Emojifier-V1) using word embeddings, then build a more sophisticated model (Emojifier-V2) that further incorporates an LSTM. \n", + "\n", + "Lets get started! Run the following cell to load the package you are going to use. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from emo_utils import *\n", + "import emoji\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Baseline model: Emojifier-V1\n", + "\n", + "### 1.1 - Dataset EMOJISET\n", + "\n", + "Let's start by building a simple baseline classifier. \n", + "\n", + "You have a tiny dataset (X, Y) where:\n", + "- X contains 127 sentences (strings)\n", + "- Y contains a integer label between 0 and 4 corresponding to an emoji for each sentence\n", + "\n", + "\n", + "
**Figure 1**: EMOJISET - a classification problem with 5 classes. A few examples of sentences are given here.
\n", + "\n", + "Let's load the dataset using the code below. We split the dataset between training (127 examples) and testing (56 examples)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train, Y_train = read_csv('data/train_emoji.csv')\n", + "X_test, Y_test = read_csv('data/tesss.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "maxLen = len(max(X_train, key=len).split())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to print sentences from X_train and corresponding labels from Y_train. Change `index` to see different examples. Because of the font the iPython notebook uses, the heart emoji may be colored black rather than red." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I am proud of your achievements 😄\n" + ] + } + ], + "source": [ + "index = 1\n", + "print(X_train[index], label_to_emoji(Y_train[index]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Overview of the Emojifier-V1\n", + "\n", + "In this part, you are going to implement a baseline model called \"Emojifier-v1\". \n", + "\n", + "
\n", + "\n", + "
**Figure 2**: Baseline model (Emojifier-V1).
\n", + "
\n", + "\n", + "The input of the model is a string corresponding to a sentence (e.g. \"I love you). In the code, the output will be a probability vector of shape (1,5), that you then pass in an argmax layer to extract the index of the most likely emoji output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get our labels into a format suitable for training a softmax classifier, lets convert $Y$ from its current shape current shape $(m, 1)$ into a \"one-hot representation\" $(m, 5)$, where each row is a one-hot vector giving the label of one example, You can do so using this next code snipper. Here, `Y_oh` stands for \"Y-one-hot\" in the variable names `Y_oh_train` and `Y_oh_test`: \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Y_oh_train = convert_to_one_hot(Y_train, C = 5)\n", + "Y_oh_test = convert_to_one_hot(Y_test, C = 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what `convert_to_one_hot()` did. Feel free to change `index` to print out different values. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 is converted into one hot [ 1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "index = 50\n", + "print(Y_train[index], \"is converted into one hot\", Y_oh_train[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the data is now ready to be fed into the Emojify-V1 model. Let's implement the model!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 - Implementing Emojifier-V1\n", + "\n", + "As shown in Figure (2), the first step is to convert an input sentence into the word vector representation, which then get averaged together. Similar to the previous exercise, we will use pretrained 50-dimensional GloVe embeddings. Run the following cell to load the `word_to_vec_map`, which contains all the vector representations." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've loaded:\n", + "- `word_to_index`: dictionary mapping from words to their indices in the vocabulary (400,001 words, with the valid indices ranging from 0 to 400,000)\n", + "- `index_to_word`: dictionary mapping from indices to their corresponding words in the vocabulary\n", + "- `word_to_vec_map`: dictionary mapping words to their GloVe vector representation.\n", + "\n", + "Run the following cell to check if it works." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the index of cucumber in the vocabulary is 113317\n", + "the 289846th word in the vocabulary is potatos\n" + ] + } + ], + "source": [ + "word = \"cucumber\"\n", + "index = 289846\n", + "print(\"the index of\", word, \"in the vocabulary is\", word_to_index[word])\n", + "print(\"the\", str(index) + \"th word in the vocabulary is\", index_to_word[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement `sentence_to_avg()`. You will need to carry out two steps:\n", + "1. Convert every sentence to lower-case, then split the sentence into a list of words. `X.lower()` and `X.split()` might be useful. \n", + "2. For each word in the sentence, access its GloVe representation. Then, average all these values." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: sentence_to_avg\n", + "\n", + "def sentence_to_avg(sentence, word_to_vec_map):\n", + " \"\"\"\n", + " Converts a sentence (string) into a list of words (strings). Extracts the GloVe representation of each word\n", + " and averages its value into a single vector encoding the meaning of the sentence.\n", + " \n", + " Arguments:\n", + " sentence -- string, one training example from X\n", + " word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n", + " \n", + " Returns:\n", + " avg -- average vector encoding information about the sentence, numpy-array of shape (50,)\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Step 1: Split sentence into list of lower case words (≈ 1 line)\n", + " words = (sentence.lower()).split()\n", + "\n", + " # Initialize the average word vector, should have the same shape as your word vectors.\n", + " avg = np.zeros((50,))\n", + " \n", + " # Step 2: average the word vectors. You can loop over the words in the list \"words\".\n", + " for w in words:\n", + " avg += word_to_vec_map[w]\n", + " avg = avg / len(words)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return avg" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "avg = [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983\n", + " -0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867\n", + " 0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767\n", + " 0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061\n", + " 0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265\n", + " 1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925\n", + " -0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333\n", + " -0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433\n", + " 0.1445417 0.09808667]\n" + ] + } + ], + "source": [ + "avg = sentence_to_avg(\"Morrocan couscous is my favorite dish\", word_to_vec_map)\n", + "print(\"avg = \", avg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **avg= **\n", + " \n", + " [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983\n", + " -0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867\n", + " 0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767\n", + " 0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061\n", + " 0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265\n", + " 1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925\n", + " -0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333\n", + " -0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433\n", + " 0.1445417 0.09808667]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "#### Model\n", + "\n", + "You now have all the pieces to finish implementing the `model()` function. After using `sentence_to_avg()` you need to pass the average through forward propagation, compute the cost, and then backpropagate to update the softmax's parameters. \n", + "\n", + "**Exercise**: Implement the `model()` function described in Figure (2). Assuming here that $Yoh$ (\"Y one hot\") is the one-hot encoding of the output labels, the equations you need to implement in the forward pass and to compute the cross-entropy cost are:\n", + "$$ z^{(i)} = W . avg^{(i)} + b$$\n", + "$$ a^{(i)} = softmax(z^{(i)})$$\n", + "$$ \\mathcal{L}^{(i)} = - \\sum_{k = 0}^{n_y - 1} Yoh^{(i)}_k * log(a^{(i)}_k)$$\n", + "\n", + "It is possible to come up with a more efficient vectorized implementation. But since we are using a for-loop to convert the sentences one at a time into the avg^{(i)} representation anyway, let's not bother this time. \n", + "\n", + "We provided you a function `softmax()`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(X, Y, word_to_vec_map, learning_rate = 0.01, num_iterations = 400):\n", + " \"\"\"\n", + " Model to train word vector representations in numpy.\n", + " \n", + " Arguments:\n", + " X -- input data, numpy array of sentences as strings, of shape (m, 1)\n", + " Y -- labels, numpy array of integers between 0 and 7, numpy-array of shape (m, 1)\n", + " word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n", + " learning_rate -- learning_rate for the stochastic gradient descent algorithm\n", + " num_iterations -- number of iterations\n", + " \n", + " Returns:\n", + " pred -- vector of predictions, numpy-array of shape (m, 1)\n", + " W -- weight matrix of the softmax layer, of shape (n_y, n_h)\n", + " b -- bias of the softmax layer, of shape (n_y,)\n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + "\n", + " # Define number of training examples\n", + " m = Y.shape[0] # number of training examples\n", + " n_y = 5 # number of classes \n", + " n_h = 50 # dimensions of the GloVe vectors \n", + " \n", + " # Initialize parameters using Xavier initialization\n", + " W = np.random.randn(n_y, n_h) / np.sqrt(n_h)\n", + " b = np.zeros((n_y,))\n", + " \n", + " # Convert Y to Y_onehot with n_y classes\n", + " Y_oh = convert_to_one_hot(Y, C = n_y) \n", + " \n", + " # Optimization loop\n", + " for t in range(num_iterations): # Loop over the number of iterations\n", + " for i in range(m): # Loop over the training examples\n", + " \n", + " ### START CODE HERE ### (≈ 4 lines of code)\n", + " # Average the word vectors of the words from the i'th training example\n", + " avg = sentence_to_avg(X[i], word_to_vec_map)\n", + "\n", + " # Forward propagate the avg through the softmax layer\n", + " z = np.dot(W, avg) + b\n", + " a = softmax(z)\n", + "\n", + " # Compute cost using the i'th training label's one hot representation and \"A\" (the output of the softmax)\n", + " cost = - np.sum(Y_oh[i] * np.log(a))\n", + " ### END CODE HERE ###\n", + " \n", + " # Compute gradients \n", + " dz = a - Y_oh[i]\n", + " dW = np.dot(dz.reshape(n_y,1), avg.reshape(1, n_h))\n", + " db = dz\n", + "\n", + " # Update parameters with Stochastic Gradient Descent\n", + " W = W - learning_rate * dW\n", + " b = b - learning_rate * db\n", + " \n", + " if t % 100 == 0:\n", + " print(\"Epoch: \" + str(t) + \" --- cost = \" + str(cost))\n", + " pred = predict(X, Y, W, b, word_to_vec_map)\n", + "\n", + " return pred, W, b" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(132,)\n", + "(132,)\n", + "(132, 5)\n", + "never talk to me again\n", + "\n", + "(20,)\n", + "(20,)\n", + "(132, 5)\n", + "\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(Y_train.shape)\n", + "print(np.eye(5)[Y_train.reshape(-1)].shape)\n", + "print(X_train[0])\n", + "print(type(X_train))\n", + "Y = np.asarray([5,0,0,5, 4, 4, 4, 6, 6, 4, 1, 1, 5, 6, 6, 3, 6, 3, 4, 4])\n", + "print(Y.shape)\n", + "\n", + "X = np.asarray(['I am going to the bar tonight', 'I love you', 'miss you my dear',\n", + " 'Lets go party and drinks','Congrats on the new job','Congratulations',\n", + " 'I am so happy for you', 'Why are you feeling bad', 'What is wrong with you',\n", + " 'You totally deserve this prize', 'Let us go play football',\n", + " 'Are you down for football this afternoon', 'Work hard play harder',\n", + " 'It is suprising how people can be dumb sometimes',\n", + " 'I am very disappointed','It is the best day in my life',\n", + " 'I think I will end up alone','My life is so boring','Good job',\n", + " 'Great so awesome'])\n", + "\n", + "print(X.shape)\n", + "print(np.eye(5)[Y_train.reshape(-1)].shape)\n", + "print(type(X_train))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the next cell to train your model and learn the softmax parameters (W,b). " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 --- cost = 1.95204988128\n", + "Accuracy: 0.348484848485\n", + "Epoch: 100 --- cost = 0.0797181872601\n", + "Accuracy: 0.931818181818\n", + "Epoch: 200 --- cost = 0.0445636924368\n", + "Accuracy: 0.954545454545\n", + "Epoch: 300 --- cost = 0.0343226737879\n", + "Accuracy: 0.969696969697\n", + "[[ 3.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 4.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 1.]\n", + " [ 4.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 2.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 0.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 4.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 2.]\n", + " [ 1.]\n", + " [ 1.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 4.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 4.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 4.]\n", + " [ 0.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 0.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 3.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 2.]\n", + " [ 4.]\n", + " [ 1.]\n", + " [ 1.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 1.]\n", + " [ 2.]\n", + " [ 1.]\n", + " [ 1.]\n", + " [ 3.]\n", + " [ 1.]\n", + " [ 0.]\n", + " [ 4.]\n", + " [ 0.]\n", + " [ 3.]\n", + " [ 3.]\n", + " [ 4.]\n", + " [ 4.]\n", + " [ 1.]\n", + " [ 4.]\n", + " [ 3.]\n", + " [ 0.]\n", + " [ 2.]]\n" + ] + } + ], + "source": [ + "pred, W, b = model(X_train, Y_train, word_to_vec_map)\n", + "print(pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output** (on a subset of iterations):\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Epoch: 0**\n", + " \n", + " cost = 1.95204988128\n", + " \n", + " Accuracy: 0.348484848485\n", + "
\n", + " **Epoch: 100**\n", + " \n", + " cost = 0.0797181872601\n", + " \n", + " Accuracy: 0.931818181818\n", + "
\n", + " **Epoch: 200**\n", + " \n", + " cost = 0.0445636924368\n", + " \n", + " Accuracy: 0.954545454545\n", + "
\n", + " **Epoch: 300**\n", + " \n", + " cost = 0.0343226737879\n", + " \n", + " Accuracy: 0.969696969697\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Your model has pretty high accuracy on the training set. Lets now see how it does on the test set. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1.4 - Examining test set performance \n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set:\n", + "Accuracy: 0.977272727273\n", + "Test set:\n", + "Accuracy: 0.857142857143\n" + ] + } + ], + "source": [ + "print(\"Training set:\")\n", + "pred_train = predict(X_train, Y_train, W, b, word_to_vec_map)\n", + "print('Test set:')\n", + "pred_test = predict(X_test, Y_test, W, b, word_to_vec_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Train set accuracy**\n", + " \n", + " 97.7\n", + "
\n", + " **Test set accuracy**\n", + " \n", + " 85.7\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random guessing would have had 20% accuracy given that there are 5 classes. This is pretty good performance after training on only 127 examples. \n", + "\n", + "In the training set, the algorithm saw the sentence \"*I love you*\" with the label ❤️. You can check however that the word \"adore\" does not appear in the training set. Nonetheless, lets see what happens if you write \"*I adore you*.\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.833333333333\n", + "\n", + "i adore you ❤️\n", + "i love you ❤️\n", + "funny lol 😄\n", + "lets play with a ball ⚾\n", + "food is ready 🍴\n", + "not feeling happy 😄\n" + ] + } + ], + "source": [ + "X_my_sentences = np.array([\"i adore you\", \"i love you\", \"funny lol\", \"lets play with a ball\", \"food is ready\", \"not feeling happy\"])\n", + "Y_my_labels = np.array([[0], [0], [2], [1], [4],[3]])\n", + "\n", + "pred = predict(X_my_sentences, Y_my_labels , W, b, word_to_vec_map)\n", + "print_predictions(X_my_sentences, pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Amazing! Because *adore* has a similar embedding as *love*, the algorithm has generalized correctly even to a word it has never seen before. Words such as *heart*, *dear*, *beloved* or *adore* have embedding vectors similar to *love*, and so might work too---feel free to modify the inputs above and try out a variety of input sentences. How well does it work?\n", + "\n", + "Note though that it doesn't get \"not feeling happy\" correct. This algorithm ignores word ordering, so is not good at understanding phrases like \"not happy.\" \n", + "\n", + "Printing the confusion matrix can also help understand which classes are more difficult for your model. A confusion matrix shows how often an example whose label is one class (\"actual\" class) is mislabeled by the algorithm with a different class (\"predicted\" class). \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(56,)\n", + " ❤️ ⚾ 😄 😞 🍴\n", + "Predicted 0.0 1.0 2.0 3.0 4.0 All\n", + "Actual \n", + "0 6 0 0 1 0 7\n", + "1 0 8 0 0 0 8\n", + "2 2 0 16 0 0 18\n", + "3 1 1 2 12 0 16\n", + "4 0 0 1 0 6 7\n", + "All 9 9 19 13 6 56\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(Y_test.shape)\n", + "print(' '+ label_to_emoji(0)+ ' ' + label_to_emoji(1) + ' ' + label_to_emoji(2)+ ' ' + label_to_emoji(3)+' ' + label_to_emoji(4))\n", + "print(pd.crosstab(Y_test, pred_test.reshape(56,), rownames=['Actual'], colnames=['Predicted'], margins=True))\n", + "plot_confusion_matrix(Y_test, pred_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "**What you should remember from this part**:\n", + "- Even with a 127 training examples, you can get a reasonably good model for Emojifying. This is due to the generalization power word vectors gives you. \n", + "- Emojify-V1 will perform poorly on sentences such as *\"This movie is not good and not enjoyable\"* because it doesn't understand combinations of words--it just averages all the words' embedding vectors together, without paying attention to the ordering of words. You will build a better algorithm in the next part. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Emojifier-V2: Using LSTMs in Keras: \n", + "\n", + "Let's build an LSTM model that takes as input word sequences. This model will be able to take word ordering into account. Emojifier-V2 will continue to use pre-trained word embeddings to represent words, but will feed them into an LSTM, whose job it is to predict the most appropriate emoji. \n", + "\n", + "Run the following cell to load the Keras packages." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "np.random.seed(0)\n", + "from keras.models import Model\n", + "from keras.layers import Dense, Input, Dropout, LSTM, Activation\n", + "from keras.layers.embeddings import Embedding\n", + "from keras.preprocessing import sequence\n", + "from keras.initializers import glorot_uniform\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - Overview of the model\n", + "\n", + "Here is the Emojifier-v2 you will implement:\n", + "\n", + "
\n", + "
**Figure 3**: Emojifier-V2. A 2-layer LSTM sequence classifier.
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Keras and mini-batching \n", + "\n", + "In this exercise, we want to train Keras using mini-batches. However, most deep learning frameworks require that all sequences in the same mini-batch have the same length. This is what allows vectorization to work: If you had a 3-word sentence and a 4-word sentence, then the computations needed for them are different (one takes 3 steps of an LSTM, one takes 4 steps) so it's just not possible to do them both at the same time.\n", + "\n", + "The common solution to this is to use padding. Specifically, set a maximum sequence length, and pad all sequences to the same length. For example, of the maximum sequence length is 20, we could pad every sentence with \"0\"s so that each input sentence is of length 20. Thus, a sentence \"i love you\" would be represented as $(e_{i}, e_{love}, e_{you}, \\vec{0}, \\vec{0}, \\ldots, \\vec{0})$. In this example, any sentences longer than 20 words would have to be truncated. One simple way to choose the maximum sequence length is to just pick the length of the longest sentence in the training set. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 - The Embedding layer\n", + "\n", + "In Keras, the embedding matrix is represented as a \"layer\", and maps positive integers (indices corresponding to words) into dense vectors of fixed size (the embedding vectors). It can be trained or initialized with a pretrained embedding. In this part, you will learn how to create an [Embedding()](https://keras.io/layers/embeddings/) layer in Keras, initialize it with the GloVe 50-dimensional vectors loaded earlier in the notebook. Because our training set is quite small, we will not update the word embeddings but will instead leave their values fixed. But in the code below, we'll show you how Keras allows you to either train or leave fixed this layer. \n", + "\n", + "The `Embedding()` layer takes an integer matrix of size (batch size, max input length) as input. This corresponds to sentences converted into lists of indices (integers), as shown in the figure below.\n", + "\n", + "\n", + "
**Figure 4**: Embedding layer. This example shows the propagation of two examples through the embedding layer. Both have been zero-padded to a length of `max_len=5`. The final dimension of the representation is `(2,max_len,50)` because the word embeddings we are using are 50 dimensional.
\n", + "\n", + "The largest integer (i.e. word index) in the input should be no larger than the vocabulary size. The layer outputs an array of shape (batch size, max input length, dimension of word vectors).\n", + "\n", + "The first step is to convert all your training sentences into lists of indices, and then zero-pad all these lists so that their length is the length of the longest sentence. \n", + "\n", + "**Exercise**: Implement the function below to convert X (array of sentences as strings) into an array of indices corresponding to words in the sentences. The output shape should be such that it can be given to `Embedding()` (described in Figure 4). " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: sentences_to_indices\n", + "\n", + "def sentences_to_indices(X, word_to_index, max_len):\n", + " \"\"\"\n", + " Converts an array of sentences (strings) into an array of indices corresponding to words in the sentences.\n", + " The output shape should be such that it can be given to `Embedding()` (described in Figure 4). \n", + " \n", + " Arguments:\n", + " X -- array of sentences (strings), of shape (m, 1)\n", + " word_to_index -- a dictionary containing the each word mapped to its index\n", + " max_len -- maximum number of words in a sentence. You can assume every sentence in X is no longer than this. \n", + " \n", + " Returns:\n", + " X_indices -- array of indices corresponding to words in the sentences from X, of shape (m, max_len)\n", + " \"\"\"\n", + " \n", + " m = X.shape[0] # number of training examples\n", + " \n", + " ### START CODE HERE ###\n", + " # Initialize X_indices as a numpy matrix of zeros and the correct shape (≈ 1 line)\n", + " X_indices = np.zeros((m, max_len))\n", + " \n", + " for i in range(m): # loop over training examples\n", + " \n", + " # Convert the ith training sentence in lower case and split is into words. You should get a list of words.\n", + " sentence_words = X[i].lower().split()\n", + " \n", + " # Initialize j to 0\n", + " j = 0\n", + " \n", + " # Loop over the words of sentence_words\n", + " for w in sentence_words:\n", + " # Set the (i,j)th entry of X_indices to the index of the correct word.\n", + " X_indices[i, j] = word_to_index[w]\n", + " # Increment j to j + 1\n", + " j = j + 1\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return X_indices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to check what `sentences_to_indices()` does, and check your results." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 = ['funny lol' 'lets play baseball' 'food is ready for you']\n", + "X1_indices = [[ 155345. 225122. 0. 0. 0.]\n", + " [ 220930. 286375. 69714. 0. 0.]\n", + " [ 151204. 192973. 302254. 151349. 394475.]]\n" + ] + } + ], + "source": [ + "X1 = np.array([\"funny lol\", \"lets play baseball\", \"food is ready for you\"])\n", + "X1_indices = sentences_to_indices(X1,word_to_index, max_len = 5)\n", + "print(\"X1 =\", X1)\n", + "print(\"X1_indices =\", X1_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **X1 =**\n", + " \n", + " ['funny lol' 'lets play football' 'food is ready for you']\n", + "
\n", + " **X1_indices =**\n", + " \n", + " [[ 155345. 225122. 0. 0. 0.]
\n", + " [ 220930. 286375. 151266. 0. 0.]
\n", + " [ 151204. 192973. 302254. 151349. 394475.]]\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's build the `Embedding()` layer in Keras, using pre-trained word vectors. After this layer is built, you will pass the output of `sentences_to_indices()` to it as an input, and the `Embedding()` layer will return the word embeddings for a sentence. \n", + "\n", + "**Exercise**: Implement `pretrained_embedding_layer()`. You will need to carry out the following steps:\n", + "1. Initialize the embedding matrix as a numpy array of zeroes with the correct shape.\n", + "2. Fill in the embedding matrix with all the word embeddings extracted from `word_to_vec_map`.\n", + "3. Define Keras embedding layer. Use [Embedding()](https://keras.io/layers/embeddings/). Be sure to make this layer non-trainable, by setting `trainable = False` when calling `Embedding()`. If you were to set `trainable = True`, then it will allow the optimization algorithm to modify the values of the word embeddings. \n", + "4. Set the embedding weights to be equal to the embedding matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: pretrained_embedding_layer\n", + "\n", + "def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n", + " \"\"\"\n", + " Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.\n", + " \n", + " Arguments:\n", + " word_to_vec_map -- dictionary mapping words to their GloVe vector representation.\n", + " word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)\n", + "\n", + " Returns:\n", + " embedding_layer -- pretrained layer Keras instance\n", + " \"\"\"\n", + " \n", + " vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n", + " emb_dim = word_to_vec_map[\"cucumber\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n", + " \n", + " ### START CODE HERE ###\n", + " # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\n", + " emb_matrix = np.zeros((vocab_len, emb_dim))\n", + " \n", + " # Set each row \"index\" of the embedding matrix to be the word vector representation of the \"index\"th word of the vocabulary\n", + " for word, index in word_to_index.items():\n", + " emb_matrix[index, :] = word_to_vec_map[word]\n", + "\n", + " # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \n", + " embedding_layer = Embedding(vocab_len, emb_dim)\n", + " ### END CODE HERE ###\n", + "\n", + " # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \"None\".\n", + " embedding_layer.build((None,))\n", + " \n", + " # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n", + " embedding_layer.set_weights([emb_matrix])\n", + " \n", + " return embedding_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weights[0][1][3] = -0.3403\n" + ] + } + ], + "source": [ + "embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n", + "print(\"weights[0][1][3] =\", embedding_layer.get_weights()[0][1][3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **weights[0][1][3] =**\n", + " \n", + " -0.3403\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Building the Emojifier-V2\n", + "\n", + "Lets now build the Emojifier-V2 model. You will do so using the embedding layer you have built, and feed its output to an LSTM network. \n", + "\n", + "
\n", + "
**Figure 3**: Emojifier-v2. A 2-layer LSTM sequence classifier.
\n", + "\n", + "\n", + "**Exercise:** Implement `Emojify_V2()`, which builds a Keras graph of the architecture shown in Figure 3. The model takes as input an array of sentences of shape (`m`, `max_len`, ) defined by `input_shape`. It should output a softmax probability vector of shape (`m`, `C = 5`). You may need `Input(shape = ..., dtype = '...')`, [LSTM()](https://keras.io/layers/recurrent/#lstm), [Dropout()](https://keras.io/layers/core/#dropout), [Dense()](https://keras.io/layers/core/#dense), and [Activation()](https://keras.io/activations/)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: Emojify_V2\n", + "\n", + "def Emojify_V2(input_shape, word_to_vec_map, word_to_index):\n", + " \"\"\"\n", + " Function creating the Emojify-v2 model's graph.\n", + " \n", + " Arguments:\n", + " input_shape -- shape of the input, usually (max_len,)\n", + " word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n", + " word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)\n", + "\n", + " Returns:\n", + " model -- a model instance in Keras\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Define sentence_indices as the input of the graph, it should be of shape input_shape and dtype 'int32' (as it contains indices).\n", + " sentence_indices = Input(shape=input_shape, dtype=np.int32)\n", + " \n", + " # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\n", + " embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n", + " \n", + " # Propagate sentence_indices through your embedding layer, you get back the embeddings\n", + " embeddings = embedding_layer(sentence_indices) \n", + " \n", + " # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\n", + " # Be careful, the returned output should be a batch of sequences.\n", + " X = LSTM(128, return_sequences=True)(embeddings)\n", + " # Add dropout with a probability of 0.5\n", + " X = Dropout(0.5)(X)\n", + " # Propagate X trough another LSTM layer with 128-dimensional hidden state\n", + " # Be careful, the returned output should be a single hidden state, not a batch of sequences.\n", + " X = LSTM(128)(X)\n", + " # Add dropout with a probability of 0.5\n", + " X = Dropout(0.5)(X)\n", + " # Propagate X through a Dense layer with softmax activation to get back a batch of 5-dimensional vectors.\n", + " X = Dense(5, activation='softmax')(X)\n", + " # Add a softmax activation\n", + " X = Activation('softmax')(X)\n", + " \n", + " # Create Model instance which converts sentence_indices into X.\n", + " model = Model(sentence_indices, X)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to create your model and check its summary. Because all sentences in the dataset are less than 10 words, we chose `max_len = 10`. You should see your architecture, it uses \"20,223,927\" parameters, of which 20,000,050 (the word embeddings) are non-trainable, and the remaining 223,877 are. Because our vocabulary size has 400,001 words (with valid indices from 0 to 400,000) there are 400,001\\*50 = 20,000,050 non-trainable parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) (None, 10) 0 \n", + "_________________________________________________________________\n", + "embedding_3 (Embedding) (None, 10, 50) 20000050 \n", + "_________________________________________________________________\n", + "lstm_3 (LSTM) (None, 10, 128) 91648 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 10, 128) 0 \n", + "_________________________________________________________________\n", + "lstm_4 (LSTM) (None, 128) 131584 \n", + "_________________________________________________________________\n", + "dropout_4 (Dropout) (None, 128) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 5) 645 \n", + "_________________________________________________________________\n", + "activation_2 (Activation) (None, 5) 0 \n", + "=================================================================\n", + "Total params: 20,223,927\n", + "Trainable params: 20,223,927\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using `categorical_crossentropy` loss, `adam` optimizer and `['accuracy']` metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's time to train your model. Your Emojifier-V2 `model` takes as input an array of shape (`m`, `max_len`) and outputs probability vectors of shape (`m`, `number of classes`). We thus have to convert X_train (array of sentences as strings) to X_train_indices (array of sentences as list of word indices), and Y_train (labels as indices) to Y_train_oh (labels as one-hot vectors)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)\n", + "Y_train_oh = convert_to_one_hot(Y_train, C = 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fit the Keras model on `X_train_indices` and `Y_train_oh`. We will use `epochs = 50` and `batch_size = 32`." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "132/132 [==============================] - 2s - loss: 1.6063 - acc: 0.2500 \n", + "Epoch 2/50\n", + "132/132 [==============================] - 1s - loss: 1.5827 - acc: 0.3712 \n", + "Epoch 3/50\n", + "132/132 [==============================] - 1s - loss: 1.5617 - acc: 0.3561 \n", + "Epoch 4/50\n", + "132/132 [==============================] - 1s - loss: 1.5480 - acc: 0.3712 \n", + "Epoch 5/50\n", + "132/132 [==============================] - 1s - loss: 1.5066 - acc: 0.4242 \n", + "Epoch 6/50\n", + "132/132 [==============================] - 1s - loss: 1.4802 - acc: 0.4394 \n", + "Epoch 7/50\n", + "132/132 [==============================] - 1s - loss: 1.4200 - acc: 0.5076 \n", + "Epoch 8/50\n", + "132/132 [==============================] - 1s - loss: 1.3913 - acc: 0.5152 \n", + "Epoch 9/50\n", + "132/132 [==============================] - 1s - loss: 1.3794 - acc: 0.5682 \n", + "Epoch 10/50\n", + "132/132 [==============================] - 1s - loss: 1.3192 - acc: 0.6061 \n", + "Epoch 11/50\n", + "132/132 [==============================] - 1s - loss: 1.3099 - acc: 0.5985 \n", + "Epoch 12/50\n", + "132/132 [==============================] - 1s - loss: 1.2381 - acc: 0.6818 \n", + "Epoch 13/50\n", + "132/132 [==============================] - 1s - loss: 1.2051 - acc: 0.7424 \n", + "Epoch 14/50\n", + "132/132 [==============================] - 1s - loss: 1.1538 - acc: 0.7879 \n", + "Epoch 15/50\n", + "132/132 [==============================] - 1s - loss: 1.1818 - acc: 0.7273 \n", + "Epoch 16/50\n", + "132/132 [==============================] - 1s - loss: 1.1137 - acc: 0.7879 \n", + "Epoch 17/50\n", + "132/132 [==============================] - 1s - loss: 1.1251 - acc: 0.7803 \n", + "Epoch 18/50\n", + "132/132 [==============================] - 1s - loss: 1.0874 - acc: 0.8106 \n", + "Epoch 19/50\n", + "132/132 [==============================] - 1s - loss: 1.0984 - acc: 0.8106 \n", + "Epoch 20/50\n", + "132/132 [==============================] - 1s - loss: 1.0785 - acc: 0.8485 \n", + "Epoch 21/50\n", + "132/132 [==============================] - 1s - loss: 1.0431 - acc: 0.8864 \n", + "Epoch 22/50\n", + "132/132 [==============================] - 1s - loss: 1.0329 - acc: 0.8864 \n", + "Epoch 23/50\n", + "132/132 [==============================] - 1s - loss: 1.0048 - acc: 0.9167 \n", + "Epoch 24/50\n", + "132/132 [==============================] - 1s - loss: 0.9825 - acc: 0.9318 \n", + "Epoch 25/50\n", + "132/132 [==============================] - 1s - loss: 0.9913 - acc: 0.9242 \n", + "Epoch 26/50\n", + "132/132 [==============================] - 1s - loss: 0.9696 - acc: 0.9545 \n", + "Epoch 27/50\n", + "132/132 [==============================] - 1s - loss: 0.9977 - acc: 0.9242 \n", + "Epoch 28/50\n", + "132/132 [==============================] - 1s - loss: 1.0870 - acc: 0.8030 \n", + "Epoch 29/50\n", + "132/132 [==============================] - 1s - loss: 0.9474 - acc: 0.9545 \n", + "Epoch 30/50\n", + "132/132 [==============================] - 1s - loss: 0.9819 - acc: 0.9242 \n", + "Epoch 31/50\n", + "132/132 [==============================] - 1s - loss: 0.9829 - acc: 0.9318 \n", + "Epoch 32/50\n", + "132/132 [==============================] - 2s - loss: 0.9443 - acc: 0.9697 \n", + "Epoch 33/50\n", + "132/132 [==============================] - 2s - loss: 0.9318 - acc: 0.9773 \n", + "Epoch 34/50\n", + "132/132 [==============================] - 2s - loss: 0.9290 - acc: 0.9773 \n", + "Epoch 35/50\n", + "132/132 [==============================] - 2s - loss: 0.9442 - acc: 0.9697 \n", + "Epoch 36/50\n", + "132/132 [==============================] - 2s - loss: 0.9217 - acc: 0.9848 \n", + "Epoch 37/50\n", + "132/132 [==============================] - 2s - loss: 0.9213 - acc: 0.9848 \n", + "Epoch 38/50\n", + "132/132 [==============================] - 2s - loss: 0.9189 - acc: 0.9924 \n", + "Epoch 39/50\n", + "132/132 [==============================] - 2s - loss: 0.9291 - acc: 0.9773 \n", + "Epoch 40/50\n", + "132/132 [==============================] - 2s - loss: 0.9145 - acc: 0.9924 \n", + "Epoch 41/50\n", + "132/132 [==============================] - 2s - loss: 0.9153 - acc: 0.9924 \n", + "Epoch 42/50\n", + "132/132 [==============================] - 2s - loss: 0.9158 - acc: 0.9924 \n", + "Epoch 43/50\n", + "132/132 [==============================] - 2s - loss: 0.9213 - acc: 0.9848 \n", + "Epoch 44/50\n", + "132/132 [==============================] - 2s - loss: 0.9136 - acc: 0.9924 \n", + "Epoch 45/50\n", + "132/132 [==============================] - 2s - loss: 0.9147 - acc: 0.9924 \n", + "Epoch 46/50\n", + "132/132 [==============================] - 2s - loss: 0.9142 - acc: 0.9924 \n", + "Epoch 47/50\n", + "132/132 [==============================] - 2s - loss: 0.9126 - acc: 0.9924 \n", + "Epoch 48/50\n", + "132/132 [==============================] - 2s - loss: 0.9134 - acc: 0.9924 \n", + "Epoch 49/50\n", + "132/132 [==============================] - 2s - loss: 0.9136 - acc: 0.9924 \n", + "Epoch 50/50\n", + "132/132 [==============================] - 2s - loss: 0.9129 - acc: 0.9924 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your model should perform close to **100% accuracy** on the training set. The exact accuracy you get may be a little different. Run the following cell to evaluate your model on the test set. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32/56 [================>.............] - ETA: 0s\n", + "Test accuracy = 0.785714294229\n" + ] + } + ], + "source": [ + "X_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)\n", + "Y_test_oh = convert_to_one_hot(Y_test, C = 5)\n", + "loss, acc = model.evaluate(X_test_indices, Y_test_oh)\n", + "print()\n", + "print(\"Test accuracy = \", acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should get a test accuracy between 80% and 95%. Run the cell below to see the mislabelled examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected emoji:😞 prediction: I am upset\t⚾\n", + "Expected emoji:😞 prediction: work is hard\t😄\n", + "Expected emoji:😞 prediction: work is horrible\t😄\n", + "Expected emoji:🍴 prediction: any suggestions for dinner\t😄\n", + "Expected emoji:😄 prediction: you brighten my day\t😞\n", + "Expected emoji:😞 prediction: she is a bully\t❤️\n", + "Expected emoji:😞 prediction: I am upset\t⚾\n", + "Expected emoji:😞 prediction: My life is so boring\t❤️\n", + "Expected emoji:😄 prediction: will you be my valentine\t❤️\n", + "Expected emoji:😞 prediction: go away\t⚾\n", + "Expected emoji:😞 prediction: yesterday we lost again\t⚾\n", + "Expected emoji:🍴 prediction: I did not have breakfast 😄\n" + ] + } + ], + "source": [ + "# This code allows you to see the mislabelled examples\n", + "C = 5\n", + "y_test_oh = np.eye(C)[Y_test.reshape(-1)]\n", + "X_test_indices = sentences_to_indices(X_test, word_to_index, maxLen)\n", + "pred = model.predict(X_test_indices)\n", + "for i in range(len(X_test)):\n", + " x = X_test_indices\n", + " num = np.argmax(pred[i])\n", + " if(num != Y_test[i]):\n", + " print('Expected emoji:'+ label_to_emoji(Y_test[i]) + ' prediction: '+ X_test[i] + label_to_emoji(num).strip())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can try it on your own example. Write your own sentence below. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "not feeling happy ❤️\n" + ] + } + ], + "source": [ + "# Change the sentence below to see your prediction. Make sure all the words are in the Glove embeddings. \n", + "x_test = np.array(['not feeling happy'])\n", + "X_test_indices = sentences_to_indices(x_test, word_to_index, maxLen)\n", + "print(x_test[0] +' '+ label_to_emoji(np.argmax(model.predict(X_test_indices))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously, Emojify-V1 model did not correctly label \"not feeling happy,\" but our implementation of Emojiy-V2 got it right. (Keras' outputs are slightly random each time, so you may not have obtained the same result.) The current model still isn't very robust at understanding negation (like \"not happy\") because the training set is small and so doesn't have a lot of examples of negation. But if the training set were larger, the LSTM model would be much better than the Emojify-V1 model at understanding such complex sentences. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations!\n", + "\n", + "You have completed this notebook! ❤️❤️❤️\n", + "\n", + "\n", + "**What you should remember**:\n", + "- If you have an NLP task where the training set is small, using word embeddings can help your algorithm significantly. Word embeddings allow your model to work on words in the test set that may not even have appeared in your training set. \n", + "- Training sequence models in Keras (and in most other deep learning frameworks) requires a few important details:\n", + " - To use mini-batches, the sequences need to be padded so that all the examples in a mini-batch have the same length. \n", + " - An `Embedding()` layer can be initialized with pretrained values. These values can be either fixed or trained further on your dataset. If however your labeled dataset is small, it's usually not worth trying to train a large pre-trained set of embeddings. \n", + " - `LSTM()` has a flag called `return_sequences` to decide if you would like to return every hidden states or only the last one. \n", + " - You can use `Dropout()` right after `LSTM()` to regularize your network. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Congratulations on finishing this assignment and building an Emojifier. We hope you're happy with what you've accomplished in this notebook! \n", + "\n", + "# 😀😀😀😀😀😀\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to Alison Darcy and the Woebot team for their advice on the creation of this assignment. Woebot is a chatbot friend that is ready to speak with you 24/7. As part of Woebot's technology, it uses word embeddings to understand the emotions of what you say. You can play with it by going to http://woebot.io\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "RNnEs", + "launcher_item_id": "acNYU" + }, + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/data_set.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/data_set.png new file mode 100644 index 0000000..bec3009 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/data_set.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/dataset_kiank.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/dataset_kiank.png new file mode 100644 index 0000000..0acb762 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/dataset_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emb_kiank.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emb_kiank.png new file mode 100644 index 0000000..7942f43 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emb_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/embedding1.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/embedding1.png new file mode 100644 index 0000000..f615ae7 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/embedding1.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emo_model.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emo_model.png new file mode 100644 index 0000000..1e9a253 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emo_model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifier-v2.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifier-v2.png new file mode 100644 index 0000000..e9fd9ab Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifier-v2.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifierv1.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifierv1.png new file mode 100644 index 0000000..3bee972 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojifierv1.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojify_model.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojify_model.png new file mode 100644 index 0000000..4da0864 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojify_model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojiss.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojiss.png new file mode 100644 index 0000000..43a2263 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/emojiss.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/image_1.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/image_1.png new file mode 100644 index 0000000..6ec3be0 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/image_1.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/woebot.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/woebot.png new file mode 100644 index 0000000..fc66b03 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Emojify/images/woebot.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/Operations on word vectors - v2.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/Operations on word vectors - v2.ipynb new file mode 100644 index 0000000..1001c69 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/Operations on word vectors - v2.ipynb @@ -0,0 +1,764 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Operations on word vectors\n", + "\n", + "Welcome to your first assignment of this week! \n", + "\n", + "Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings. \n", + "\n", + "**After this assignment you will be able to:**\n", + "\n", + "- Load pre-trained word vectors, and measure similarity using cosine similarity\n", + "- Use word embeddings to solve word analogy problems such as Man is to Woman as King is to ______. \n", + "- Modify word embeddings to reduce their gender bias \n", + "\n", + "Let's get started! Run the following cell to load the packages you will need." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from w2v_utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, lets load the word vectors. For this assignment, we will use 50-dimensional GloVe vectors to represent words. Run the following cell to load the `word_to_vec_map`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "words, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've loaded:\n", + "- `words`: set of words in the vocabulary.\n", + "- `word_to_vec_map`: dictionary mapping words to their GloVe vector representation.\n", + "\n", + "You've seen that one-hot vectors do not do a good job cpaturing what words are similar. GloVe vectors provide much more useful information about the meaning of individual words. Lets now see how you can use GloVe vectors to decide how similar two words are. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Cosine similarity\n", + "\n", + "To measure how similar two words are, we need a way to measure the degree of similarity between two embedding vectors for the two words. Given two vectors $u$ and $v$, cosine similarity is defined as follows: \n", + "\n", + "$$\\text{CosineSimilarity(u, v)} = \\frac {u . v} {||u||_2 ||v||_2} = cos(\\theta) \\tag{1}$$\n", + "\n", + "where $u.v$ is the dot product (or inner product) of two vectors, $||u||_2$ is the norm (or length) of the vector $u$, and $\\theta$ is the angle between $u$ and $v$. This similarity depends on the angle between $u$ and $v$. If $u$ and $v$ are very similar, their cosine similarity will be close to 1; if they are dissimilar, the cosine similarity will take a smaller value. \n", + "\n", + "\n", + "
**Figure 1**: The cosine of the angle between two vectors is a measure of how similar they are
\n", + "\n", + "**Exercise**: Implement the function `cosine_similarity()` to evaluate similarity between word vectors.\n", + "\n", + "**Reminder**: The norm of $u$ is defined as $ ||u||_2 = \\sqrt{\\sum_{i=1}^{n} u_i^2}$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: cosine_similarity\n", + "\n", + "def cosine_similarity(u, v):\n", + " \"\"\"\n", + " Cosine similarity reflects the degree of similariy between u and v\n", + " \n", + " Arguments:\n", + " u -- a word vector of shape (n,) \n", + " v -- a word vector of shape (n,)\n", + "\n", + " Returns:\n", + " cosine_similarity -- the cosine similarity between u and v defined by the formula above.\n", + " \"\"\"\n", + " \n", + " distance = 0.0\n", + " \n", + " ### START CODE HERE ###\n", + " # Compute the dot product between u and v (≈1 line)\n", + " dot = np.dot(u, v)\n", + " # Compute the L2 norm of u (≈1 line)\n", + " norm_u = np.sqrt(np.sum(u**2))\n", + " \n", + " # Compute the L2 norm of v (≈1 line)\n", + " norm_v = np.sqrt(np.sum(v**2))\n", + " # Compute the cosine similarity defined by formula (1) (≈1 line)\n", + " cosine_similarity = dot / np.dot(norm_u, norm_v)\n", + " ### END CODE HERE ###\n", + " \n", + " return cosine_similarity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cosine_similarity(father, mother) = 0.890903844289\n", + "cosine_similarity(ball, crocodile) = 0.274392462614\n", + "cosine_similarity(france - paris, rome - italy) = -0.675147930817\n" + ] + } + ], + "source": [ + "father = word_to_vec_map[\"father\"]\n", + "mother = word_to_vec_map[\"mother\"]\n", + "ball = word_to_vec_map[\"ball\"]\n", + "crocodile = word_to_vec_map[\"crocodile\"]\n", + "france = word_to_vec_map[\"france\"]\n", + "italy = word_to_vec_map[\"italy\"]\n", + "paris = word_to_vec_map[\"paris\"]\n", + "rome = word_to_vec_map[\"rome\"]\n", + "\n", + "print(\"cosine_similarity(father, mother) = \", cosine_similarity(father, mother))\n", + "print(\"cosine_similarity(ball, crocodile) = \",cosine_similarity(ball, crocodile))\n", + "print(\"cosine_similarity(france - paris, rome - italy) = \",cosine_similarity(france - paris, rome - italy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **cosine_similarity(father, mother)** =\n", + " \n", + " 0.890903844289\n", + "
\n", + " **cosine_similarity(ball, crocodile)** =\n", + " \n", + " 0.274392462614\n", + "
\n", + " **cosine_similarity(france - paris, rome - italy)** =\n", + " \n", + " -0.675147930817\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you get the correct expected output, please feel free to modify the inputs and measure the cosine similarity between other pairs of words! Playing around the cosine similarity of other inputs will give you a better sense of how word vectors behave. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Word analogy task\n", + "\n", + "In the word analogy task, we complete the sentence \"*a* is to *b* as *c* is to **____**\". An example is '*man* is to *woman* as *king* is to *queen*' . In detail, we are trying to find a word *d*, such that the associated word vectors $e_a, e_b, e_c, e_d$ are related in the following manner: $e_b - e_a \\approx e_d - e_c$. We will measure the similarity between $e_b - e_a$ and $e_d - e_c$ using cosine similarity. \n", + "\n", + "**Exercise**: Complete the code below to be able to perform word analogies!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: complete_analogy\n", + "\n", + "def complete_analogy(word_a, word_b, word_c, word_to_vec_map):\n", + " \"\"\"\n", + " Performs the word analogy task as explained above: a is to b as c is to ____. \n", + " \n", + " Arguments:\n", + " word_a -- a word, string\n", + " word_b -- a word, string\n", + " word_c -- a word, string\n", + " word_to_vec_map -- dictionary that maps words to their corresponding vectors. \n", + " \n", + " Returns:\n", + " best_word -- the word such that v_b - v_a is close to v_best_word - v_c, as measured by cosine similarity\n", + " \"\"\"\n", + " \n", + " # convert words to lower case\n", + " word_a, word_b, word_c = word_a.lower(), word_b.lower(), word_c.lower()\n", + " \n", + " ### START CODE HERE ###\n", + " # Get the word embeddings v_a, v_b and v_c (≈1-3 lines)\n", + " e_a, e_b, e_c = word_to_vec_map[word_a], word_to_vec_map[word_b], word_to_vec_map[word_c]\n", + " ### END CODE HERE ###\n", + " \n", + " words = word_to_vec_map.keys()\n", + " max_cosine_sim = -100 # Initialize max_cosine_sim to a large negative number\n", + " best_word = None # Initialize best_word with None, it will help keep track of the word to output\n", + "\n", + " # loop over the whole word vector set\n", + " for w in words: \n", + " # to avoid best_word being one of the input words, pass on them.\n", + " if w in [word_a, word_b, word_c] :\n", + " continue\n", + " \n", + " ### START CODE HERE ###\n", + " # Compute cosine similarity between the vector (e_b - e_a) and the vector ((w's vector representation) - e_c) (≈1 line)\n", + " cosine_sim = cosine_similarity((e_b - e_a), (word_to_vec_map[w] - e_c))\n", + " \n", + " # If the cosine_sim is more than the max_cosine_sim seen so far,\n", + " # then: set the new max_cosine_sim to the current cosine_sim and the best_word to the current word (≈3 lines)\n", + " if cosine_sim > max_cosine_sim:\n", + " max_cosine_sim = cosine_sim\n", + " best_word = w\n", + " ### END CODE HERE ###\n", + " \n", + " return best_word" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to test your code, this may take 1-2 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "italy -> italian :: spain -> spanish\n", + "india -> delhi :: japan -> tokyo\n", + "man -> woman :: boy -> girl\n", + "small -> smaller :: large -> larger\n" + ] + } + ], + "source": [ + "triads_to_try = [('italy', 'italian', 'spain'), ('india', 'delhi', 'japan'), ('man', 'woman', 'boy'), ('small', 'smaller', 'large')]\n", + "for triad in triads_to_try:\n", + " print ('{} -> {} :: {} -> {}'.format( *triad, complete_analogy(*triad,word_to_vec_map)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **italy -> italian** ::\n", + " \n", + " spain -> spanish\n", + "
\n", + " **india -> delhi** ::\n", + " \n", + " japan -> tokyo\n", + "
\n", + " **man -> woman ** ::\n", + " \n", + " boy -> girl\n", + "
\n", + " **small -> smaller ** ::\n", + " \n", + " large -> larger\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you get the correct expected output, please feel free to modify the input cells above to test your own analogies. Try to find some other analogy pairs that do work, but also find some where the algorithm doesn't give the right answer: For example, you can try small->smaller as big->?. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations!\n", + "\n", + "You've come to the end of this assignment. Here are the main points you should remember:\n", + "\n", + "- Cosine similarity a good way to compare similarity between pairs of word vectors. (Though L2 distance works too.) \n", + "- For NLP applications, using a pre-trained set of word vectors from the internet is often a good way to get started. \n", + "\n", + "Even though you have finished the graded portions, we recommend you take a look too at the rest of this notebook. \n", + "\n", + "Congratulations on finishing the graded portions of this notebook! \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Debiasing word vectors (OPTIONAL/UNGRADED) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following exercise, you will examine gender biases that can be reflected in a word embedding, and explore algorithms for reducing the bias. In addition to learning about the topic of debiasing, this exercise will also help hone your intuition about what word vectors are doing. This section involves a bit of linear algebra, though you can probably complete it even without being expert in linear algebra, and we encourage you to give it a shot. This portion of the notebook is optional and is not graded. \n", + "\n", + "Lets first see how the GloVe word embeddings relate to gender. You will first compute a vector $g = e_{woman}-e_{man}$, where $e_{woman}$ represents the word vector corresponding to the word *woman*, and $e_{man}$ corresponds to the word vector corresponding to the word *man*. The resulting vector $g$ roughly encodes the concept of \"gender\". (You might get a more accurate representation if you compute $g_1 = e_{mother}-e_{father}$, $g_2 = e_{girl}-e_{boy}$, etc. and average over them. But just using $e_{woman}-e_{man}$ will give good enough results for now.) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g = word_to_vec_map['woman'] - word_to_vec_map['man']\n", + "print(g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you will consider the cosine similarity of different words with $g$. Consider what a positive value of similarity means vs a negative cosine similarity. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "print ('List of names and their similarities with constructed vector:')\n", + "\n", + "# girls and boys name\n", + "name_list = ['john', 'marie', 'sophie', 'ronaldo', 'priya', 'rahul', 'danielle', 'reza', 'katy', 'yasmin']\n", + "\n", + "for w in name_list:\n", + " print (w, cosine_similarity(word_to_vec_map[w], g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, female first names tend to have a positive cosine similarity with our constructed vector $g$, while male first names tend to have a negative cosine similarity. This is not suprising, and the result seems acceptable. \n", + "\n", + "But let's try with some other words." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print('Other words and their similarities:')\n", + "word_list = ['lipstick', 'guns', 'science', 'arts', 'literature', 'warrior','doctor', 'tree', 'receptionist', \n", + " 'technology', 'fashion', 'teacher', 'engineer', 'pilot', 'computer', 'singer']\n", + "for w in word_list:\n", + " print (w, cosine_similarity(word_to_vec_map[w], g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do you notice anything surprising? It is astonishing how these results reflect certain unhealthy gender stereotypes. For example, \"computer\" is closer to \"man\" while \"literature\" is closer to \"woman\". Ouch! \n", + "\n", + "We'll see below how to reduce the bias of these vectors, using an algorithm due to [Boliukbasi et al., 2016](https://arxiv.org/abs/1607.06520). Note that some word pairs such as \"actor\"/\"actress\" or \"grandmother\"/\"grandfather\" should remain gender specific, while other words such as \"receptionist\" or \"technology\" should be neutralized, i.e. not be gender-related. You will have to treat these two type of words differently when debiasing.\n", + "\n", + "### 3.1 - Neutralize bias for non-gender specific words \n", + "\n", + "The figure below should help you visualize what neutralizing does. If you're using a 50-dimensional word embedding, the 50 dimensional space can be split into two parts: The bias-direction $g$, and the remaining 49 dimensions, which we'll call $g_{\\perp}$. In linear algebra, we say that the 49 dimensional $g_{\\perp}$ is perpendicular (or \"othogonal\") to $g$, meaning it is at 90 degrees to $g$. The neutralization step takes a vector such as $e_{receptionist}$ and zeros out the component in the direction of $g$, giving us $e_{receptionist}^{debiased}$. \n", + "\n", + "Even though $g_{\\perp}$ is 49 dimensional, given the limitations of what we can draw on a screen, we illustrate it using a 1 dimensional axis below. \n", + "\n", + "\n", + "
**Figure 2**: The word vector for \"receptionist\" represented before and after applying the neutralize operation.
\n", + "\n", + "**Exercise**: Implement `neutralize()` to remove the bias of words such as \"receptionist\" or \"scientist\". Given an input embedding $e$, you can use the following formulas to compute $e^{debiased}$: \n", + "\n", + "$$e^{bias\\_component} = \\frac{e \\cdot g}{||g||_2^2} * g\\tag{2}$$\n", + "$$e^{debiased} = e - e^{bias\\_component}\\tag{3}$$\n", + "\n", + "If you are an expert in linear algebra, you may recognize $e^{bias\\_component}$ as the projection of $e$ onto the direction $g$. If you're not an expert in linear algebra, don't worry about this.\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def neutralize(word, g, word_to_vec_map):\n", + " \"\"\"\n", + " Removes the bias of \"word\" by projecting it on the space orthogonal to the bias axis. \n", + " This function ensures that gender neutral words are zero in the gender subspace.\n", + " \n", + " Arguments:\n", + " word -- string indicating the word to debias\n", + " g -- numpy-array of shape (50,), corresponding to the bias axis (such as gender)\n", + " word_to_vec_map -- dictionary mapping words to their corresponding vectors.\n", + " \n", + " Returns:\n", + " e_debiased -- neutralized word vector representation of the input \"word\"\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Select word vector representation of \"word\". Use word_to_vec_map. (≈ 1 line)\n", + " e = None\n", + " \n", + " # Compute e_biascomponent using the formula give above. (≈ 1 line)\n", + " e_biascomponent = None\n", + " \n", + " # Neutralize e by substracting e_biascomponent from it \n", + " # e_debiased should be equal to its orthogonal projection. (≈ 1 line)\n", + " e_debiased = None\n", + " ### END CODE HERE ###\n", + " \n", + " return e_debiased" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "e = \"receptionist\"\n", + "print(\"cosine similarity between \" + e + \" and g, before neutralizing: \", cosine_similarity(word_to_vec_map[\"receptionist\"], g))\n", + "\n", + "e_debiased = neutralize(\"receptionist\", g, word_to_vec_map)\n", + "print(\"cosine similarity between \" + e + \" and g, after neutralizing: \", cosine_similarity(e_debiased, g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: The second result is essentially 0, up to numerical roundof (on the order of $10^{-17}$).\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **cosine similarity between receptionist and g, before neutralizing:** :\n", + " \n", + " 0.330779417506\n", + "
\n", + " **cosine similarity between receptionist and g, after neutralizing:** :\n", + " \n", + " -3.26732746085e-17\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 - Equalization algorithm for gender-specific words\n", + "\n", + "Next, lets see how debiasing can also be applied to word pairs such as \"actress\" and \"actor.\" Equalization is applied to pairs of words that you might want to have differ only through the gender property. As a concrete example, suppose that \"actress\" is closer to \"babysit\" than \"actor.\" By applying neutralizing to \"babysit\" we can reduce the gender-stereotype associated with babysitting. But this still does not guarantee that \"actor\" and \"actress\" are equidistant from \"babysit.\" The equalization algorithm takes care of this. \n", + "\n", + "The key idea behind equalization is to make sure that a particular pair of words are equi-distant from the 49-dimensional $g_\\perp$. The equalization step also ensures that the two equalized steps are now the same distance from $e_{receptionist}^{debiased}$, or from any other work that has been neutralized. In pictures, this is how equalization works: \n", + "\n", + "\n", + "\n", + "\n", + "The derivation of the linear algebra to do this is a bit more complex. (See Bolukbasi et al., 2016 for details.) But the key equations are: \n", + "\n", + "$$ \\mu = \\frac{e_{w1} + e_{w2}}{2}\\tag{4}$$ \n", + "\n", + "$$ \\mu_{B} = \\frac {\\mu \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n", + "\\tag{5}$$ \n", + "\n", + "$$\\mu_{\\perp} = \\mu - \\mu_{B} \\tag{6}$$\n", + "\n", + "$$ e_{w1B} = \\frac {e_{w1} \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n", + "\\tag{7}$$ \n", + "$$ e_{w2B} = \\frac {e_{w2} \\cdot \\text{bias_axis}}{||\\text{bias_axis}||_2^2} *\\text{bias_axis}\n", + "\\tag{8}$$\n", + "\n", + "\n", + "$$e_{w1B}^{corrected} = \\sqrt{ |{1 - ||\\mu_{\\perp} ||^2_2} |} * \\frac{e_{\\text{w1B}} - \\mu_B} {|(e_{w1} - \\mu_{\\perp}) - \\mu_B)|} \\tag{9}$$\n", + "\n", + "\n", + "$$e_{w2B}^{corrected} = \\sqrt{ |{1 - ||\\mu_{\\perp} ||^2_2} |} * \\frac{e_{\\text{w2B}} - \\mu_B} {|(e_{w2} - \\mu_{\\perp}) - \\mu_B)|} \\tag{10}$$\n", + "\n", + "$$e_1 = e_{w1B}^{corrected} + \\mu_{\\perp} \\tag{11}$$\n", + "$$e_2 = e_{w2B}^{corrected} + \\mu_{\\perp} \\tag{12}$$\n", + "\n", + "\n", + "**Exercise**: Implement the function below. Use the equations above to get the final equalized version of the pair of words. Good luck!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def equalize(pair, bias_axis, word_to_vec_map):\n", + " \"\"\"\n", + " Debias gender specific words by following the equalize method described in the figure above.\n", + " \n", + " Arguments:\n", + " pair -- pair of strings of gender specific words to debias, e.g. (\"actress\", \"actor\") \n", + " bias_axis -- numpy-array of shape (50,), vector corresponding to the bias axis, e.g. gender\n", + " word_to_vec_map -- dictionary mapping words to their corresponding vectors\n", + " \n", + " Returns\n", + " e_1 -- word vector corresponding to the first word\n", + " e_2 -- word vector corresponding to the second word\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Step 1: Select word vector representation of \"word\". Use word_to_vec_map. (≈ 2 lines)\n", + " w1, w2 = None\n", + " e_w1, e_w2 = None\n", + " \n", + " # Step 2: Compute the mean of e_w1 and e_w2 (≈ 1 line)\n", + " mu = None\n", + "\n", + " # Step 3: Compute the projections of mu over the bias axis and the orthogonal axis (≈ 2 lines)\n", + " mu_B = None\n", + " mu_orth = None\n", + "\n", + " # Step 4: Use equations (7) and (8) to compute e_w1B and e_w2B (≈2 lines)\n", + " e_w1B = None\n", + " e_w2B = None\n", + " \n", + " # Step 5: Adjust the Bias part of e_w1B and e_w2B using the formulas (9) and (10) given above (≈2 lines)\n", + " corrected_e_w1B = None\n", + " corrected_e_w2B = None\n", + "\n", + " # Step 6: Debias by equalizing e1 and e2 to the sum of their corrected projections (≈2 lines)\n", + " e1 = None\n", + " e2 = None\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return e1, e2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "print(\"cosine similarities before equalizing:\")\n", + "print(\"cosine_similarity(word_to_vec_map[\\\"man\\\"], gender) = \", cosine_similarity(word_to_vec_map[\"man\"], g))\n", + "print(\"cosine_similarity(word_to_vec_map[\\\"woman\\\"], gender) = \", cosine_similarity(word_to_vec_map[\"woman\"], g))\n", + "print()\n", + "e1, e2 = equalize((\"man\", \"woman\"), g, word_to_vec_map)\n", + "print(\"cosine similarities after equalizing:\")\n", + "print(\"cosine_similarity(e1, gender) = \", cosine_similarity(e1, g))\n", + "print(\"cosine_similarity(e2, gender) = \", cosine_similarity(e2, g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "cosine similarities before equalizing:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **cosine_similarity(word_to_vec_map[\"man\"], gender)** =\n", + " \n", + " -0.117110957653\n", + "
\n", + " **cosine_similarity(word_to_vec_map[\"woman\"], gender)** =\n", + " \n", + " 0.356666188463\n", + "
\n", + "\n", + "cosine similarities after equalizing:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **cosine_similarity(u1, gender)** =\n", + " \n", + " -0.700436428931\n", + "
\n", + " **cosine_similarity(u2, gender)** =\n", + " \n", + " 0.700436428931\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Please feel free to play with the input words in the cell above, to apply equalization to other pairs of words. \n", + "\n", + "These debiasing algorithms are very helpful for reducing bias, but are not perfect and do not eliminate all traces of bias. For example, one weakness of this implementation was that the bias direction $g$ was defined using only the pair of words _woman_ and _man_. As discussed earlier, if $g$ were defined by computing $g_1 = e_{woman} - e_{man}$; $g_2 = e_{mother} - e_{father}$; $g_3 = e_{girl} - e_{boy}$; and so on and averaging over them, you would obtain a better estimate of the \"gender\" dimension in the 50 dimensional word embedding space. Feel free to play with such variants as well. \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations\n", + "\n", + "You have come to the end of this notebook, and have seen a lot of the ways that word vectors can be used as well as modified. \n", + "\n", + "Congratulations on finishing this notebook! \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**References**:\n", + "- The debiasing algorithm is from Bolukbasi et al., 2016, [Man is to Computer Programmer as Woman is to\n", + "Homemaker? Debiasing Word Embeddings](https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf)\n", + "- The GloVe word embeddings were due to Jeffrey Pennington, Richard Socher, and Christopher D. Manning. (https://nlp.stanford.edu/projects/glove/)\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "8hb5s", + "launcher_item_id": "5NrJ6" + }, + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/1-hot-vector.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/1-hot-vector.png new file mode 100644 index 0000000..6f63eb1 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/1-hot-vector.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/cosine_sim.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/cosine_sim.png new file mode 100644 index 0000000..78ab131 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/cosine_sim.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/equalize10.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/equalize10.png new file mode 100644 index 0000000..e53ab36 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/equalize10.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/lookup.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/lookup.png new file mode 100644 index 0000000..4f40a4f Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/lookup.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutral.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutral.png new file mode 100644 index 0000000..6d1bdaf Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutral.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize.png new file mode 100644 index 0000000..3d3ba04 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize_kiank.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize_kiank.png new file mode 100644 index 0000000..42d7924 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week2/Word Vector Representation/images/neutralize_kiank.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/Neural machine translation with attention - v2.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/Neural machine translation with attention - v2.ipynb new file mode 100644 index 0000000..5367802 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/Neural machine translation with attention - v2.ipynb @@ -0,0 +1,1147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Machine Translation\n", + "\n", + "Welcome to your first programming assignment for this week! \n", + "\n", + "You will build a Neural Machine Translation (NMT) model to translate human readable dates (\"25th of June, 2009\") into machine readable dates (\"2009-06-25\"). You will do this using an attention model, one of the most sophisticated sequence to sequence models. \n", + "\n", + "This notebook was produced together with NVIDIA's Deep Learning Institute. \n", + "\n", + "Let's load all the packages you will need for this assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply\n", + "from keras.layers import RepeatVector, Dense, Activation, Lambda\n", + "from keras.optimizers import Adam\n", + "from keras.utils import to_categorical\n", + "from keras.models import load_model, Model\n", + "import keras.backend as K\n", + "import numpy as np\n", + "\n", + "from faker import Faker\n", + "import random\n", + "from tqdm import tqdm\n", + "from babel.dates import format_date\n", + "from nmt_utils import *\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Translating human readable dates into machine readable dates\n", + "\n", + "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", + "\n", + "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 - Dataset\n", + "\n", + "We will train the model on a dataset of 10000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [00:01<00:00, 8412.91it/s]\n" + ] + } + ], + "source": [ + "m = 10000\n", + "dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('9 may 1998', '1998-05-09'),\n", + " ('10.09.70', '1970-09-10'),\n", + " ('4/28/90', '1990-04-28'),\n", + " ('thursday january 26 1995', '1995-01-26'),\n", + " ('monday march 7 1983', '1983-03-07'),\n", + " ('sunday may 22 1988', '1988-05-22'),\n", + " ('tuesday july 8 2008', '2008-07-08'),\n", + " ('08 sep 1999', '1999-09-08'),\n", + " ('1 jan 1981', '1981-01-01'),\n", + " ('monday may 22 1995', '1995-05-22')]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've loaded:\n", + "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", + "- `human_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", + "- `machine_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `human_vocab`. \n", + "- `inv_machine_vocab`: the inverse dictionary of `machine_vocab`, mapping from indices back to characters. \n", + "\n", + "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (10000, 30)\n", + "Y.shape: (10000, 10)\n", + "Xoh.shape: (10000, 30, 37)\n", + "Yoh.shape: (10000, 10, 11)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y.shape:\", Y.shape)\n", + "print(\"Xoh.shape:\", Xoh.shape)\n", + "print(\"Yoh.shape:\", Yoh.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now have:\n", + "- `X`: a processed version of the human readable dates in the training set, where each character is replaced by an index mapped to the character via `human_vocab`. Each date is further padded to $T_x$ values with a special character (< pad >). `X.shape = (m, Tx)`\n", + "- `Y`: a processed version of the machine readable dates in the training set, where each character is replaced by the index it is mapped to in `machine_vocab`. You should have `Y.shape = (m, Ty)`. \n", + "- `Xoh`: one-hot version of `X`, the \"1\" entry's index is mapped to the character thanks to `human_vocab`. `Xoh.shape = (m, Tx, len(human_vocab))`\n", + "- `Yoh`: one-hot version of `Y`, the \"1\" entry's index is mapped to the character thanks to `machine_vocab`. `Yoh.shape = (m, Tx, len(machine_vocab))`. Here, `len(machine_vocab) = 11` since there are 11 characters ('-' as well as 0-9). \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets also look at some examples of preprocessed training examples. Feel free to play with `index` in the cell below to navigate the dataset and see how source/target dates are preprocessed. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source date: 9 may 1998\n", + "Target date: 1998-05-09\n", + "\n", + "Source after preprocessing (indices): [12 0 24 13 34 0 4 12 12 11 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36\n", + " 36 36 36 36 36]\n", + "Target after preprocessing (indices): [ 2 10 10 9 0 1 6 0 1 10]\n", + "\n", + "Source after preprocessing (one-hot): [[ 0. 0. 0. ..., 0. 0. 0.]\n", + " [ 1. 0. 0. ..., 0. 0. 0.]\n", + " [ 0. 0. 0. ..., 0. 0. 0.]\n", + " ..., \n", + " [ 0. 0. 0. ..., 0. 0. 1.]\n", + " [ 0. 0. 0. ..., 0. 0. 1.]\n", + " [ 0. 0. 0. ..., 0. 0. 1.]]\n", + "Target after preprocessing (one-hot): [[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "index = 0\n", + "print(\"Source date:\", dataset[index][0])\n", + "print(\"Target date:\", dataset[index][1])\n", + "print()\n", + "print(\"Source after preprocessing (indices):\", X[index])\n", + "print(\"Target after preprocessing (indices):\", Y[index])\n", + "print()\n", + "print(\"Source after preprocessing (one-hot):\", Xoh[index])\n", + "print(\"Target after preprocessing (one-hot):\", Yoh[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Neural machine translation with attention\n", + "\n", + "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", + "\n", + "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", + "\n", + "\n", + "### 2.1 - Attention mechanism\n", + "\n", + "In this part, you will implement the attention mechanism presented in the lecture videos. Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", + "\n", + "\n", + " \n", + " \n", + "
\n", + "
\n", + "		 \n", + "
\n", + "
\n", + "
**Figure 1**: Neural machine translation with attention
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here are some properties of the model that you may notice: \n", + "\n", + "- There are two separate LSTMs in this model (see diagram on the left). Because the one at the bottom of the picture is a Bi-directional LSTM and comes *before* the attention mechanism, we will call it *pre-attention* Bi-LSTM. The LSTM at the top of the diagram comes *after* the attention mechanism, so we will call it the *post-attention* LSTM. The pre-attention Bi-LSTM goes through $T_x$ time steps; the post-attention LSTM goes through $T_y$ time steps. \n", + "\n", + "- The post-attention LSTM passes $s^{\\langle t \\rangle}, c^{\\langle t \\rangle}$ from one time step to the next. In the lecture videos, we were using only a basic RNN for the post-activation sequence model, so the state captured by the RNN output activations $s^{\\langle t\\rangle}$. But since we are using an LSTM here, the LSTM has both the output activation $s^{\\langle t\\rangle}$ and the hidden cell state $c^{\\langle t\\rangle}$. However, unlike previous text generation examples (such as Dinosaurus in week 1), in this model the post-activation LSTM at time $t$ does will not take the specific generated $y^{\\langle t-1 \\rangle}$ as input; it only takes $s^{\\langle t\\rangle}$ and $c^{\\langle t\\rangle}$ as input. We have designed the model this way, because (unlike language generation where adjacent characters are highly correlated) there isn't as strong a dependency between the previous character and the next character in a YYYY-MM-DD date. \n", + "\n", + "- We use $a^{\\langle t \\rangle} = [\\overrightarrow{a}^{\\langle t \\rangle}; \\overleftarrow{a}^{\\langle t \\rangle}]$ to represent the concatenation of the activations of both the forward-direction and backward-directions of the pre-attention Bi-LSTM. \n", + "\n", + "- The diagram on the right uses a `RepeatVector` node to copy $s^{\\langle t-1 \\rangle}$'s value $T_x$ times, and then `Concatenation` to concatenate $s^{\\langle t-1 \\rangle}$ and $a^{\\langle t \\rangle}$ to compute $e^{\\langle t, t'}$, which is then passed through a softmax to compute $\\alpha^{\\langle t, t' \\rangle}$. We'll explain how to use `RepeatVector` and `Concatenation` in Keras below. \n", + "\n", + "Lets implement this model. You will start by implementing two functions: `one_step_attention()` and `model()`.\n", + "\n", + "**1) `one_step_attention()`**: At step $t$, given all the hidden states of the Bi-LSTM ($[a^{<1>},a^{<2>}, ..., a^{}]$) and the previous hidden state of the second LSTM ($s^{}$), `one_step_attention()` will compute the attention weights ($[\\alpha^{},\\alpha^{}, ..., \\alpha^{}]$) and output the context vector (see Figure 1 (right) for details):\n", + "$$context^{} = \\sum_{t' = 0}^{T_x} \\alpha^{}a^{}\\tag{1}$$ \n", + "\n", + "Note that we are denoting the attention in this notebook $context^{\\langle t \\rangle}$. In the lecture videos, the context was denoted $c^{\\langle t \\rangle}$, but here we are calling it $context^{\\langle t \\rangle}$ to avoid confusion with the (post-attention) LSTM's internal memory cell variable, which is sometimes also denoted $c^{\\langle t \\rangle}$. \n", + " \n", + "**2) `model()`**: Implements the entire model. It first runs the input through a Bi-LSTM to get back $[a^{<1>},a^{<2>}, ..., a^{}]$. Then, it calls `one_step_attention()` $T_y$ times (`for` loop). At each iteration of this loop, it gives the computed context vector $c^{}$ to the second LSTM, and runs the output of the LSTM through a dense layer with softmax activation to generate a prediction $\\hat{y}^{}$. \n", + "\n", + "\n", + "\n", + "**Exercise**: Implement `one_step_attention()`. The function `model()` will call the layers in `one_step_attention()` $T_y$ using a for-loop, and it is important that all $T_y$ copies have the same weights. I.e., it should not re-initiaiize the weights every time. In other words, all $T_y$ steps should have shared weights. Here's how you can implement layers with shareable weights in Keras:\n", + "1. Define the layer objects (as global variables for examples).\n", + "2. Call these objects when propagating the input.\n", + "\n", + "We have defined the layers you need as global variables. Please run the following cells to create them. Please check the Keras documentation to make sure you understand what these layers are: [RepeatVector()](https://keras.io/layers/core/#repeatvector), [Concatenate()](https://keras.io/layers/merge/#concatenate), [Dense()](https://keras.io/layers/core/#dense), [Activation()](https://keras.io/layers/core/#activation), [Dot()](https://keras.io/layers/merge/#dot)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Defined shared layers as global variables\n", + "repeator = RepeatVector(Tx)\n", + "concatenator = Concatenate(axis=-1)\n", + "densor = Dense(1, activation = \"relu\")\n", + "activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook\n", + "dotor = Dot(axes = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can use these layers to implement `one_step_attention()`. In order to propagate a Keras tensor object X through one of these layers, use `layer(X)` (or `layer([X,Y])` if it requires multiple inputs.), e.g. `densor(X)` will propagate X through the `Dense(1)` layer defined above." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: one_step_attention\n", + "\n", + "def one_step_attention(a, s_prev):\n", + " \"\"\"\n", + " Performs one step of attention: Outputs a context vector computed as a dot product of the attention weights\n", + " \"alphas\" and the hidden states \"a\" of the Bi-LSTM.\n", + " \n", + " Arguments:\n", + " a -- hidden state output of the Bi-LSTM, numpy-array of shape (m, Tx, 2*n_a)\n", + " s_prev -- previous hidden state of the (post-attention) LSTM, numpy-array of shape (m, n_s)\n", + " \n", + " Returns:\n", + " context -- context vector, input of the next (post-attetion) LSTM cell\n", + " \"\"\"\n", + " \n", + " ### START CODE HERE ###\n", + " # Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states \"a\" (≈ 1 line)\n", + " s_prev = repeator(s_prev)\n", + " # Use concatenator to concatenate a and s_prev on the last axis (≈ 1 line)\n", + " concat = concatenator([a, s_prev])\n", + " # Use densor to propagate concat through a small fully-connected neural network to compute the \"energies\" variable e. (≈1 lines)\n", + " e = densor(concat)\n", + " # Use activator and e to compute the attention weights \"alphas\" (≈ 1 line)\n", + " alphas = activator(e)\n", + " # Use dotor together with \"alphas\" and \"a\" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)\n", + " context = dotor([alphas, a])\n", + " ### END CODE HERE ###\n", + " \n", + " return context" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will be able to check the expected output of `one_step_attention()` after you've coded the `model()` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement `model()` as explained in figure 2 and the text above. Again, we have defined global layers that will share weights to be used in `model()`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n_a = 64\n", + "n_s = 128\n", + "post_activation_LSTM_cell = LSTM(n_s, return_state = True)\n", + "output_layer = Dense(len(machine_vocab), activation=softmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can use these layers $T_y$ times in a `for` loop to generate the outputs, and their parameters will not be reinitialized. You will have to carry out the following steps: \n", + "\n", + "1. Propagate the input into a [Bidirectional](https://keras.io/layers/wrappers/#bidirectional) [LSTM](https://keras.io/layers/recurrent/#lstm)\n", + "2. Iterate for $t = 0, \\dots, T_y-1$: \n", + " 1. Call `one_step_attention()` on $[\\alpha^{},\\alpha^{}, ..., \\alpha^{}]$ and $s^{}$ to get the context vector $context^{}$.\n", + " 2. Give $context^{}$ to the post-attention LSTM cell. Remember pass in the previous hidden-state $s^{\\langle t-1\\rangle}$ and cell-states $c^{\\langle t-1\\rangle}$ of this LSTM using `initial_state= [previous hidden state, previous cell state]`. Get back the new hidden state $s^{}$ and the new cell state $c^{}$.\n", + " 3. Apply a softmax layer to $s^{}$, get the output. \n", + " 4. Save the output by adding it to the list of outputs.\n", + "\n", + "3. Create your Keras model instance, it should have three inputs (\"inputs\", $s^{<0>}$ and $c^{<0>}$) and output the list of \"outputs\"." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):\n", + " \"\"\"\n", + " Arguments:\n", + " Tx -- length of the input sequence\n", + " Ty -- length of the output sequence\n", + " n_a -- hidden state size of the Bi-LSTM\n", + " n_s -- hidden state size of the post-attention LSTM\n", + " human_vocab_size -- size of the python dictionary \"human_vocab\"\n", + " machine_vocab_size -- size of the python dictionary \"machine_vocab\"\n", + "\n", + " Returns:\n", + " model -- Keras model instance\n", + " \"\"\"\n", + " \n", + " # Define the inputs of your model with a shape (Tx,)\n", + " # Define s0 and c0, initial hidden state for the decoder LSTM of shape (n_s,)\n", + " X = Input(shape=(Tx, human_vocab_size))\n", + " s0 = Input(shape=(n_s,), name='s0')\n", + " c0 = Input(shape=(n_s,), name='c0')\n", + " s = s0\n", + " c = c0\n", + " \n", + " # Initialize empty list of outputs\n", + " outputs = []\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Step 1: Define your pre-attention Bi-LSTM. Remember to use return_sequences=True. (≈ 1 line)\n", + " a = Bidirectional(LSTM(n_a, return_sequences=True))(X)\n", + " \n", + " # Step 2: Iterate for Ty steps\n", + " for t in range(Ty):\n", + " \n", + " # Step 2.A: Perform one step of the attention mechanism to get back the context vector at step t (≈ 1 line)\n", + " context = one_step_attention(a, s)\n", + " \n", + " # Step 2.B: Apply the post-attention LSTM cell to the \"context\" vector.\n", + " # Don't forget to pass: initial_state = [hidden state, cell state] (≈ 1 line)\n", + " s, _, c = post_activation_LSTM_cell(context, initial_state=[s, c])\n", + " \n", + " # Step 2.C: Apply Dense layer to the hidden state output of the post-attention LSTM (≈ 1 line)\n", + " out = output_layer(s)\n", + " \n", + " # Step 2.D: Append \"out\" to the \"outputs\" list (≈ 1 line)\n", + " outputs.append(out)\n", + " \n", + " # Step 3: Create model instance taking three inputs and returning the list of outputs. (≈ 1 line)\n", + " model = Model([X, s0, c0], outputs)\n", + " \n", + " ### END CODE HERE ###\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to create your model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = model(Tx, Ty, n_a, n_s, len(human_vocab), len(machine_vocab))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get a summary of the model to check if it matches the expected output." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "____________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "====================================================================================================\n", + "input_2 (InputLayer) (None, 30, 37) 0 \n", + "____________________________________________________________________________________________________\n", + "s0 (InputLayer) (None, 128) 0 \n", + "____________________________________________________________________________________________________\n", + "bidirectional_1 (Bidirectional) (None, 30, 128) 52224 input_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "repeat_vector_1 (RepeatVector) (None, 30, 128) 0 s0[0][0] \n", + " lstm_1[0][0] \n", + " lstm_1[1][0] \n", + " lstm_1[2][0] \n", + " lstm_1[3][0] \n", + " lstm_1[4][0] \n", + " lstm_1[5][0] \n", + " lstm_1[6][0] \n", + " lstm_1[7][0] \n", + " lstm_1[8][0] \n", + "____________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 30, 256) 0 bidirectional_1[0][0] \n", + " repeat_vector_1[0][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[1][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[2][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[3][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[4][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[5][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[6][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[7][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[8][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 30, 1) 257 concatenate_1[0][0] \n", + " concatenate_1[1][0] \n", + " concatenate_1[2][0] \n", + " concatenate_1[3][0] \n", + " concatenate_1[4][0] \n", + " concatenate_1[5][0] \n", + " concatenate_1[6][0] \n", + " concatenate_1[7][0] \n", + " concatenate_1[8][0] \n", + " concatenate_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "attention_weights (Activation) (None, 30, 1) 0 dense_1[0][0] \n", + " dense_1[1][0] \n", + " dense_1[2][0] \n", + " dense_1[3][0] \n", + " dense_1[4][0] \n", + " dense_1[5][0] \n", + " dense_1[6][0] \n", + " dense_1[7][0] \n", + " dense_1[8][0] \n", + " dense_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "dot_1 (Dot) (None, 1, 128) 0 attention_weights[0][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[1][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[2][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[3][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[4][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[5][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[6][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[7][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[8][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[9][0] \n", + " bidirectional_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "c0 (InputLayer) (None, 128) 0 \n", + "____________________________________________________________________________________________________\n", + "lstm_1 (LSTM) [(None, 128), (None, 131584 dot_1[0][0] \n", + " s0[0][0] \n", + " c0[0][0] \n", + " dot_1[1][0] \n", + " lstm_1[0][0] \n", + " lstm_1[0][2] \n", + " dot_1[2][0] \n", + " lstm_1[1][0] \n", + " lstm_1[1][2] \n", + " dot_1[3][0] \n", + " lstm_1[2][0] \n", + " lstm_1[2][2] \n", + " dot_1[4][0] \n", + " lstm_1[3][0] \n", + " lstm_1[3][2] \n", + " dot_1[5][0] \n", + " lstm_1[4][0] \n", + " lstm_1[4][2] \n", + " dot_1[6][0] \n", + " lstm_1[5][0] \n", + " lstm_1[5][2] \n", + " dot_1[7][0] \n", + " lstm_1[6][0] \n", + " lstm_1[6][2] \n", + " dot_1[8][0] \n", + " lstm_1[7][0] \n", + " lstm_1[7][2] \n", + " dot_1[9][0] \n", + " lstm_1[8][0] \n", + " lstm_1[8][2] \n", + "____________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 11) 1419 lstm_1[0][0] \n", + " lstm_1[1][0] \n", + " lstm_1[2][0] \n", + " lstm_1[3][0] \n", + " lstm_1[4][0] \n", + " lstm_1[5][0] \n", + " lstm_1[6][0] \n", + " lstm_1[7][0] \n", + " lstm_1[8][0] \n", + " lstm_1[9][0] \n", + "====================================================================================================\n", + "Total params: 185,484\n", + "Trainable params: 185,484\n", + "Non-trainable params: 0\n", + "____________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "Here is the summary you should see\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Total params:**\n", + " \n", + " 185,484\n", + "
\n", + " **Trainable params:**\n", + " \n", + " 185,484\n", + "
\n", + " **Non-trainable params:**\n", + " \n", + " 0\n", + "
\n", + " **bidirectional_1's output shape **\n", + " \n", + " (None, 30, 128) \n", + "
\n", + " **repeat_vector_1's output shape **\n", + " \n", + " (None, 30, 128) \n", + "
\n", + " **concatenate_1's output shape **\n", + " \n", + " (None, 30, 256) \n", + "
\n", + " **attention_weights's output shape **\n", + " \n", + " (None, 30, 1) \n", + "
\n", + " **dot_1's output shape **\n", + " \n", + " (None, 1, 128) \n", + "
\n", + " **dense_2's output shape **\n", + " \n", + " (None, 11) \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using `categorical_crossentropy` loss, a custom [Adam](https://keras.io/optimizers/#adam) [optimizer](https://keras.io/optimizers/#usage-of-optimizers) (`learning rate = 0.005`, $\\beta_1 = 0.9$, $\\beta_2 = 0.999$, `decay = 0.01`) and `['accuracy']` metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### START CODE HERE ### (≈2 lines)\n", + "opt = Adam(lr = 0.005, beta_1=0.9, beta_2=0.999, decay = 0.01)\n", + "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last step is to define all your inputs and outputs to fit the model:\n", + "- You already have X of shape $(m = 10000, T_x = 30)$ containing the training examples.\n", + "- You need to create `s0` and `c0` to initialize your `post_activation_LSTM_cell` with 0s.\n", + "- Given the `model()` you coded, you need the \"outputs\" to be a list of 11 elements of shape (m, T_y). So that: `outputs[i][0], ..., outputs[i][Ty]` represent the true labels (characters) corresponding to the $i^{th}$ training example (`X[i]`). More generally, `outputs[i][j]` is the true label of the $j^{th}$ character in the $i^{th}$ training example." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s0 = np.zeros((m, n_s))\n", + "c0 = np.zeros((m, n_s))\n", + "outputs = list(Yoh.swapaxes(0,1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now fit the model and run it for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1\n", + "10000/10000 [==============================] - 49s - loss: 15.4064 - dense_2_loss_1: 1.0319 - dense_2_loss_2: 0.8296 - dense_2_loss_3: 1.6019 - dense_2_loss_4: 2.6215 - dense_2_loss_5: 0.6991 - dense_2_loss_6: 1.1022 - dense_2_loss_7: 2.5118 - dense_2_loss_8: 0.8281 - dense_2_loss_9: 1.6111 - dense_2_loss_10: 2.5692 - dense_2_acc_1: 0.5660 - dense_2_acc_2: 0.7592 - dense_2_acc_3: 0.3628 - dense_2_acc_4: 0.0974 - dense_2_acc_5: 0.8998 - dense_2_acc_6: 0.4708 - dense_2_acc_7: 0.1080 - dense_2_acc_8: 0.8650 - dense_2_acc_9: 0.2831 - dense_2_acc_10: 0.1226 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit([Xoh, s0, c0], outputs, epochs=1, batch_size=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While training you can see the loss as well as the accuracy on each of the 10 positions of the output. The table below gives you an example of what the accuracies could be if the batch had 2 examples: \n", + "\n", + "
\n", + "
Thus, `dense_2_acc_8: 0.89` means that you are predicting the 7th character of the output correctly 89% of the time in the current batch of data.
\n", + "\n", + "\n", + "We have run this model for longer, and saved the weights. Run the next cell to load our weights. (By training a model for several minutes, you should be able to obtain a model of similar accuracy, but loading our model will save you time.) " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.load_weights('models/model.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now see the results on new examples." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source: 3 May 1979\n", + "output: 1979-05-03\n", + "source: 5 April 09\n", + "output: 2009-03-05\n", + "source: 21th of August 2016\n", + "output: 2016-08-01\n", + "source: Tue 10 Jul 2007\n", + "output: 2007-07-10\n", + "source: Saturday May 9 2018\n", + "output: 2018-05-09\n", + "source: March 3 2001\n", + "output: 2001-03-03\n", + "source: March 3rd 2001\n", + "output: 2001-03-03\n", + "source: 1 March 2001\n", + "output: 2001-03-01\n" + ] + } + ], + "source": [ + "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", + "for example in EXAMPLES:\n", + " \n", + " source = string_to_int(example, Tx, human_vocab)\n", + " source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)\n", + " prediction = model.predict([source, s0, c0])\n", + " prediction = np.argmax(prediction, axis = -1)\n", + " output = [inv_machine_vocab[int(i)] for i in prediction]\n", + " \n", + " print(\"source:\", example)\n", + " print(\"output:\", ''.join(output))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also change these examples to test with your own examples. The next part will give you a better sense on what the attention mechanism is doing--i.e., what part of the input the network is paying attention to when generating a particular output character. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Visualizing Attention (Optional / Ungraded)\n", + "\n", + "Since the problem has a fixed output length of 10, it is also possible to carry out this task using 10 different softmax units to generate the 10 characters of the output. But one advantage of the attention model is that each part of the output (say the month) knows it needs to depend only on a small part of the input (the characters in the input giving the month). We can visualize what part of the output is looking at what part of the input.\n", + "\n", + "Consider the task of translating \"Saturday 9 May 2018\" to \"2018-05-09\". If we visualize the computed $\\alpha^{\\langle t, t' \\rangle}$ we get this: \n", + "\n", + "
\n", + "
**Figure 8**: Full Attention Map
\n", + "\n", + "Notice how the output ignores the \"Saturday\" portion of the input. None of the output timesteps are paying much attention to that portion of the input. We see also that 9 has been translated as 09 and May has been correctly translated into 05, with the output paying attention to the parts of the input it needs to to make the translation. The year mostly requires it to pay attention to the input's \"18\" in order to generate \"2018.\" \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Getting the activations from the network\n", + "\n", + "Lets now visualize the attention values in your network. We'll propagate an example through the network, then visualize the values of $\\alpha^{\\langle t, t' \\rangle}$. \n", + "\n", + "To figure out where the attention values are located, let's start by printing a summary of the model ." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "____________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "====================================================================================================\n", + "input_2 (InputLayer) (None, 30, 37) 0 \n", + "____________________________________________________________________________________________________\n", + "s0 (InputLayer) (None, 128) 0 \n", + "____________________________________________________________________________________________________\n", + "bidirectional_1 (Bidirectional) (None, 30, 128) 52224 input_2[0][0] \n", + "____________________________________________________________________________________________________\n", + "repeat_vector_1 (RepeatVector) (None, 30, 128) 0 s0[0][0] \n", + " lstm_1[0][0] \n", + " lstm_1[1][0] \n", + " lstm_1[2][0] \n", + " lstm_1[3][0] \n", + " lstm_1[4][0] \n", + " lstm_1[5][0] \n", + " lstm_1[6][0] \n", + " lstm_1[7][0] \n", + " lstm_1[8][0] \n", + "____________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 30, 256) 0 bidirectional_1[0][0] \n", + " repeat_vector_1[0][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[1][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[2][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[3][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[4][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[5][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[6][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[7][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[8][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 30, 1) 257 concatenate_1[0][0] \n", + " concatenate_1[1][0] \n", + " concatenate_1[2][0] \n", + " concatenate_1[3][0] \n", + " concatenate_1[4][0] \n", + " concatenate_1[5][0] \n", + " concatenate_1[6][0] \n", + " concatenate_1[7][0] \n", + " concatenate_1[8][0] \n", + " concatenate_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "attention_weights (Activation) (None, 30, 1) 0 dense_1[0][0] \n", + " dense_1[1][0] \n", + " dense_1[2][0] \n", + " dense_1[3][0] \n", + " dense_1[4][0] \n", + " dense_1[5][0] \n", + " dense_1[6][0] \n", + " dense_1[7][0] \n", + " dense_1[8][0] \n", + " dense_1[9][0] \n", + "____________________________________________________________________________________________________\n", + "dot_1 (Dot) (None, 1, 128) 0 attention_weights[0][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[1][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[2][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[3][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[4][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[5][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[6][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[7][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[8][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[9][0] \n", + " bidirectional_1[0][0] \n", + "____________________________________________________________________________________________________\n", + "c0 (InputLayer) (None, 128) 0 \n", + "____________________________________________________________________________________________________\n", + "lstm_1 (LSTM) [(None, 128), (None, 131584 dot_1[0][0] \n", + " s0[0][0] \n", + " c0[0][0] \n", + " dot_1[1][0] \n", + " lstm_1[0][0] \n", + " lstm_1[0][2] \n", + " dot_1[2][0] \n", + " lstm_1[1][0] \n", + " lstm_1[1][2] \n", + " dot_1[3][0] \n", + " lstm_1[2][0] \n", + " lstm_1[2][2] \n", + " dot_1[4][0] \n", + " lstm_1[3][0] \n", + " lstm_1[3][2] \n", + " dot_1[5][0] \n", + " lstm_1[4][0] \n", + " lstm_1[4][2] \n", + " dot_1[6][0] \n", + " lstm_1[5][0] \n", + " lstm_1[5][2] \n", + " dot_1[7][0] \n", + " lstm_1[6][0] \n", + " lstm_1[6][2] \n", + " dot_1[8][0] \n", + " lstm_1[7][0] \n", + " lstm_1[7][2] \n", + " dot_1[9][0] \n", + " lstm_1[8][0] \n", + " lstm_1[8][2] \n", + "____________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 11) 1419 lstm_1[0][0] \n", + " lstm_1[1][0] \n", + " lstm_1[2][0] \n", + " lstm_1[3][0] \n", + " lstm_1[4][0] \n", + " lstm_1[5][0] \n", + " lstm_1[6][0] \n", + " lstm_1[7][0] \n", + " lstm_1[8][0] \n", + " lstm_1[9][0] \n", + "====================================================================================================\n", + "Total params: 185,484\n", + "Trainable params: 185,484\n", + "Non-trainable params: 0\n", + "____________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Navigate through the output of `model.summary()` above. You can see that the layer named `attention_weights` outputs the `alphas` of shape (m, 30, 1) before `dot_2` computes the context vector for every time step $t = 0, \\ldots, T_y-1$. Lets get the activations from this layer.\n", + "\n", + "The function `attention_map()` pulls out the attention values from your model and plots them." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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Ses5iLUlSz1msJUnqOYu1JEk9Z7GWJKnnLNaSJPWcxVqSpJ6zWEuS1HMWa0mS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\nuaM4EXh7d0LTxJ6M9wM/qar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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "attention_map = plot_attention_map(model, human_vocab, inv_machine_vocab, \"Tuesday April 08 1993\", num = 6, n_s = 128)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the generated plot you can observe the values of the attention weights for each character of the predicted output. Examine this plot and check that where the network is paying attention makes sense to you.\n", + "\n", + "In the date translation application, you will observe that most of the time attention helps predict the year, and hasn't much impact on predicting the day/month." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations!\n", + "\n", + "\n", + "You have come to the end of this assignment \n", + "\n", + " **Here's what you should remember from this notebook**:\n", + "\n", + "- Machine translation models can be used to map from one sequence to another. They are useful not just for translating human languages (like French->English) but also for tasks like date format translation. \n", + "- An attention mechanism allows a network to focus on the most relevant parts of the input when producing a specific part of the output. \n", + "- A network using an attention mechanism can translate from inputs of length $T_x$ to outputs of length $T_y$, where $T_x$ and $T_y$ can be different. \n", + "- You can visualize attention weights $\\alpha^{\\langle t,t' \\rangle}$ to see what the network is paying attention to while generating each output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations on finishing this assignment! You are now able to implement an attention model and use it to learn complex mappings from one sequence to another. " + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "n16CQ", + "launcher_item_id": "npjGi" + }, + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_mechanism.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_mechanism.png new file mode 100644 index 0000000..32fcfd7 Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_mechanism.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_model.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_model.png new file mode 100644 index 0000000..2af9b7a Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/attn_model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention.png new file mode 100644 index 0000000..a74071b Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention2.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention2.png new file mode 100644 index 0000000..139c84d Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/date_attention2.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/poorly_trained_model.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/poorly_trained_model.png new file mode 100644 index 0000000..160e6db Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/poorly_trained_model.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/table.png b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/table.png new file mode 100644 index 0000000..479038e Binary files /dev/null and b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Machine Translation/images/table.png differ diff --git a/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Trigger word detection/Trigger word detection - v1.ipynb b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Trigger word detection/Trigger word detection - v1.ipynb new file mode 100644 index 0000000..97dfa86 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Sequence Models/Week3/Trigger word detection/Trigger word detection - v1.ipynb @@ -0,0 +1,1816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Trigger Word Detection\n", + "\n", + "Welcome to the final programming assignment of this specialization! \n", + "\n", + "In this week's videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. \n", + "\n", + "For this exercise, our trigger word will be \"Activate.\" Every time it hears you say \"activate,\" it will make a \"chiming\" sound. By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying \"activate.\" \n", + "\n", + "After completing this assignment, perhaps you can also extend it to run on your laptop so that every time you say \"activate\" it starts up your favorite app, or turns on a network connected lamp in your house, or triggers some other event? \n", + "\n", + "\n", + "\n", + "In this assignment you will learn to: \n", + "- Structure a speech recognition project\n", + "- Synthesize and process audio recordings to create train/dev datasets\n", + "- Train a trigger word detection model and make predictions\n", + "\n", + "Lets get started! Run the following cell to load the package you are going to use. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pydub import AudioSegment\n", + "import random\n", + "import sys\n", + "import io\n", + "import os\n", + "import glob\n", + "import IPython\n", + "from td_utils import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Data synthesis: Creating a speech dataset \n", + "\n", + "Let's start by building a dataset for your trigger word detection algorithm. A speech dataset should ideally be as close as possible to the application you will want to run it on. In this case, you'd like to detect the word \"activate\" in working environments (library, home, offices, open-spaces ...). You thus need to create recordings with a mix of positive words (\"activate\") and negative words (random words other than activate) on different background sounds. Let's see how you can create such a dataset. \n", + "\n", + "## 1.1 - Listening to the data \n", + "\n", + "One of your friends is helping you out on this project, and they've gone to libraries, cafes, restaurants, homes and offices all around the region to record background noises, as well as snippets of audio of people saying positive/negative words. This dataset includes people speaking in a variety of accents. \n", + "\n", + "In the raw_data directory, you can find a subset of the raw audio files of the positive words, negative words, and background noise. You will use these audio files to synthesize a dataset to train the model. The \"activate\" directory contains positive examples of people saying the word \"activate\". The \"negatives\" directory contains negative examples of people saying random words other than \"activate\". There is one word per audio recording. The \"backgrounds\" directory contains 10 second clips of background noise in different environments.\n", + "\n", + "Run the cells below to listen to some examples." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"./raw_data/activates/1.wav\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"./raw_data/negatives/4.wav\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"./raw_data/backgrounds/1.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will use these three type of recordings (positives/negatives/backgrounds) to create a labelled dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 - From audio recordings to spectrograms\n", + "\n", + "What really is an audio recording? A microphone records little variations in air pressure over time, and it is these little variations in air pressure that your ear also perceives as sound. You can think of an audio recording is a long list of numbers measuring the little air pressure changes detected by the microphone. We will use audio sampled at 44100 Hz (or 44100 Hertz). This means the microphone gives us 44100 numbers per second. Thus, a 10 second audio clip is represented by 441000 numbers (= $10 \\times 44100$). \n", + "\n", + "It is quite difficult to figure out from this \"raw\" representation of audio whether the word \"activate\" was said. In order to help your sequence model more easily learn to detect triggerwords, we will compute a *spectrogram* of the audio. The spectrogram tells us how much different frequencies are present in an audio clip at a moment in time. \n", + "\n", + "(If you've ever taken an advanced class on signal processing or on Fourier transforms, a spectrogram is computed by sliding a window over the raw audio signal, and calculates the most active frequencies in each window using a Fourier transform. If you don't understand the previous sentence, don't worry about it.) \n", + "\n", + "Lets see an example. 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Ijx29/oMi8vv2t1+whvNf//g6yREIvMcnyo4rGYg7QX3Nf2LSyOPaaOvLSb7B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yAZvZ+xCjO6JlJALSTMVgXlBhWHXxiHxSbQRVQJbGdoDYAY8flcyXAtZiJmn5lbQ6UYvh5CM4zS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\nqZdVymBIvSFuCrTOzEqZe/ukRZVxOWPwljFIcMrYO+LOidohyvwalZnNe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = graph_spectrogram(\"audio_examples/example_train.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The graph above represents how active each frequency is (y axis) over a number of time-steps (x axis). \n", + "\n", + "\n", + "
**Figure 1**: Spectrogram of an audio recording, where the color shows the degree to which different frequencies are present (loud) in the audio at different points in time. Green squares means a certain frequency is more active or more present in the audio clip (louder); blue squares denote less active frequencies.
\n", + "\n", + "The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input. In this notebook, we will be working with 10 second audio clips as the \"standard length\" for our training examples. The number of timesteps of the spectrogram will be 5511. You'll see later that the spectrogram will be the input $x$ into the network, and so $T_x = 5511$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time steps in audio recording before spectrogram (441000,)\n", + "Time steps in input after spectrogram (101, 5511)\n" + ] + } + ], + "source": [ + "_, data = wavfile.read(\"audio_examples/example_train.wav\")\n", + "print(\"Time steps in audio recording before spectrogram\", data[:,0].shape)\n", + "print(\"Time steps in input after spectrogram\", x.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you can define:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Tx = 5511 # The number of time steps input to the model from the spectrogram\n", + "n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that even with 10 seconds being our default training example length, 10 seconds of time can be discretized to different numbers of value. You've seen 441000 (raw audio) and 5511 (spectrogram). In the former case, each step represents $10/441000 \\approx 0.000023$ seconds. In the second case, each step represents $10/5511 \\approx 0.0018$ seconds. \n", + "\n", + "For the 10sec of audio, the key values you will see in this assignment are:\n", + "\n", + "- $441000$ (raw audio)\n", + "- $5511 = T_x$ (spectrogram output, and dimension of input to the neural network). \n", + "- $10000$ (used by the `pydub` module to synthesize audio) \n", + "- $1375 = T_y$ (the number of steps in the output of the GRU you'll build). \n", + "\n", + "Note that each of these representations correspond to exactly 10 seconds of time. It's just that they are discretizing them to different degrees. All of these are hyperparameters and can be changed (except the 441000, which is a function of the microphone). We have chosen values that are within the standard ranges uses for speech systems. \n", + "\n", + "Consider the $T_y = 1375$ number above. This means that for the output of the model, we discretize the 10s into 1375 time-intervals (each one of length $10/1375 \\approx 0.0072$s) and try to predict for each of these intervals whether someone recently finished saying \"activate.\" \n", + "\n", + "Consider also the 10000 number above. This corresponds to discretizing the 10sec clip into 10/10000 = 0.001 second itervals. 0.001 seconds is also called 1 millisecond, or 1ms. So when we say we are discretizing according to 1ms intervals, it means we are using 10,000 steps. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Ty = 1375 # The number of time steps in the output of our model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 - Generating a single training example\n", + "\n", + "Because speech data is hard to acquire and label, you will synthesize your training data using the audio clips of activates, negatives, and backgrounds. It is quite slow to record lots of 10 second audio clips with random \"activates\" in it. Instead, it is easier to record lots of positives and negative words, and record background noise separately (or download background noise from free online sources). \n", + "\n", + "To synthesize a single training example, you will:\n", + "\n", + "- Pick a random 10 second background audio clip\n", + "- Randomly insert 0-4 audio clips of \"activate\" into this 10sec clip\n", + "- Randomly insert 0-2 audio clips of negative words into this 10sec clip\n", + "\n", + "Because you had synthesized the word \"activate\" into the background clip, you know exactly when in the 10sec clip the \"activate\" makes its appearance. You'll see later that this makes it easier to generate the labels $y^{\\langle t \\rangle}$ as well. \n", + "\n", + "You will use the pydub package to manipulate audio. Pydub converts raw audio files into lists of Pydub data structures (it is not important to know the details here). Pydub uses 1ms as the discretization interval (1ms is 1 millisecond = 1/1000 seconds) which is why a 10sec clip is always represented using 10,000 steps. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "background len: 10000\n", + "activate[0] len: 916\n", + "activate[1] len: 1579\n" + ] + } + ], + "source": [ + "# Load audio segments using pydub \n", + "activates, negatives, backgrounds = load_raw_audio()\n", + "\n", + "print(\"background len: \" + str(len(backgrounds[0]))) # Should be 10,000, since it is a 10 sec clip\n", + "print(\"activate[0] len: \" + str(len(activates[0]))) # Maybe around 1000, since an \"activate\" audio clip is usually around 1 sec (but varies a lot)\n", + "print(\"activate[1] len: \" + str(len(activates[1]))) # Different \"activate\" clips can have different lengths " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Overlaying positive/negative words on the background**:\n", + "\n", + "Given a 10sec background clip and a short audio clip (positive or negative word), you need to be able to \"add\" or \"insert\" the word's short audio clip onto the background. To ensure audio segments inserted onto the background do not overlap, you will keep track of the times of previously inserted audio clips. You will be inserting multiple clips of positive/negative words onto the background, and you don't want to insert an \"activate\" or a random word somewhere that overlaps with another clip you had previously added. \n", + "\n", + "For clarity, when you insert a 1sec \"activate\" onto a 10sec clip of cafe noise, you end up with a 10sec clip that sounds like someone sayng \"activate\" in a cafe, with \"activate\" superimposed on the background cafe noise. You do *not* end up with an 11 sec clip. You'll see later how pydub allows you to do this. \n", + "\n", + "**Creating the labels at the same time you overlay**:\n", + "\n", + "Recall also that the labels $y^{\\langle t \\rangle}$ represent whether or not someone has just finished saying \"activate.\" Given a background clip, we can initialize $y^{\\langle t \\rangle}=0$ for all $t$, since the clip doesn't contain any \"activates.\" \n", + "\n", + "When you insert or overlay an \"activate\" clip, you will also update labels for $y^{\\langle t \\rangle}$, so that 50 steps of the output now have target label 1. You will train a GRU to detect when someone has *finished* saying \"activate\". For example, suppose the synthesized \"activate\" clip ends at the 5sec mark in the 10sec audio---exactly halfway into the clip. Recall that $T_y = 1375$, so timestep $687 = $ `int(1375*0.5)` corresponds to the moment at 5sec into the audio. So, you will set $y^{\\langle 688 \\rangle} = 1$. Further, you would quite satisfied if the GRU detects \"activate\" anywhere within a short time-internal after this moment, so we actually set 50 consecutive values of the label $y^{\\langle t \\rangle}$ to 1. Specifically, we have $y^{\\langle 688 \\rangle} = y^{\\langle 689 \\rangle} = \\cdots = y^{\\langle 737 \\rangle} = 1$. \n", + "\n", + "This is another reason for synthesizing the training data: It's relatively straightforward to generate these labels $y^{\\langle t \\rangle}$ as described above. In contrast, if you have 10sec of audio recorded on a microphone, it's quite time consuming for a person to listen to it and mark manually exactly when \"activate\" finished. \n", + "\n", + "Here's a figure illustrating the labels $y^{\\langle t \\rangle}$, for a clip which we have inserted \"activate\", \"innocent\", activate\", \"baby.\" Note that the positive labels \"1\" are associated only with the positive words. \n", + "\n", + "\n", + "
**Figure 2**
\n", + "\n", + "To implement the training set synthesis process, you will use the following helper functions. All of these function will use a 1ms discretization interval, so the 10sec of audio is alwsys discretized into 10,000 steps. \n", + "\n", + "1. `get_random_time_segment(segment_ms)` gets a random time segment in our background audio\n", + "2. `is_overlapping(segment_time, existing_segments)` checks if a time segment overlaps with existing segments\n", + "3. `insert_audio_clip(background, audio_clip, existing_times)` inserts an audio segment at a random time in our background audio using `get_random_time_segment` and `is_overlapping`\n", + "4. `insert_ones(y, segment_end_ms)` inserts 1's into our label vector y after the word \"activate\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `get_random_time_segment(segment_ms)` returns a random time segment onto which we can insert an audio clip of duration `segment_ms`. Read through the code to make sure you understand what it is doing. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_random_time_segment(segment_ms):\n", + " \"\"\"\n", + " Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.\n", + " \n", + " Arguments:\n", + " segment_ms -- the duration of the audio clip in ms (\"ms\" stands for \"milliseconds\")\n", + " \n", + " Returns:\n", + " segment_time -- a tuple of (segment_start, segment_end) in ms\n", + " \"\"\"\n", + " \n", + " segment_start = np.random.randint(low=0, high=10000-segment_ms) # Make sure segment doesn't run past the 10sec background \n", + " segment_end = segment_start + segment_ms - 1\n", + " \n", + " return (segment_start, segment_end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, suppose you have inserted audio clips at segments (1000,1800) and (3400,4500). I.e., the first segment starts at step 1000, and ends at step 1800. Now, if we are considering inserting a new audio clip at (3000,3600) does this overlap with one of the previously inserted segments? In this case, (3000,3600) and (3400,4500) overlap, so we should decide against inserting a clip here. \n", + "\n", + "For the purpose of this function, define (100,200) and (200,250) to be overlapping, since they overlap at timestep 200. However, (100,199) and (200,250) are non-overlapping. \n", + "\n", + "**Exercise**: Implement `is_overlapping(segment_time, existing_segments)` to check if a new time segment overlaps with any of the previous segments. You will need to carry out 2 steps:\n", + "\n", + "1. Create a \"False\" flag, that you will later set to \"True\" if you find that there is an overlap.\n", + "2. Loop over the previous_segments' start and end times. Compare these times to the segment's start and end times. If there is an overlap, set the flag defined in (1) as True. You can use:\n", + "```python\n", + "for ....:\n", + " if ... <= ... and ... >= ...:\n", + " ...\n", + "```\n", + "Hint: There is overlap if the segment starts before the previous segment ends, and the segment ends after the previous segment starts." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: is_overlapping\n", + "\n", + "def is_overlapping(segment_time, previous_segments):\n", + " \"\"\"\n", + " Checks if the time of a segment overlaps with the times of existing segments.\n", + " \n", + " Arguments:\n", + " segment_time -- a tuple of (segment_start, segment_end) for the new segment\n", + " previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments\n", + " \n", + " Returns:\n", + " True if the time segment overlaps with any of the existing segments, False otherwise\n", + " \"\"\"\n", + " \n", + " segment_start, segment_end = segment_time\n", + " \n", + " ### START CODE HERE ### (≈ 4 line)\n", + " # Step 1: Initialize overlap as a \"False\" flag. (≈ 1 line)\n", + " overlap = False\n", + " \n", + " # Step 2: loop over the previous_segments start and end times.\n", + " # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)\n", + " for previous_start, previous_end in previous_segments:\n", + " if segment_start <= previous_end and segment_end >= previous_start:\n", + " overlap = True\n", + " ### END CODE HERE ###\n", + "\n", + " return overlap" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overlap 1 = False\n", + "Overlap 2 = True\n" + ] + } + ], + "source": [ + "overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])\n", + "overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])\n", + "print(\"Overlap 1 = \", overlap1)\n", + "print(\"Overlap 2 = \", overlap2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Overlap 1**\n", + " \n", + " False\n", + "
\n", + " **Overlap 2**\n", + " \n", + " True\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, lets use the previous helper functions to insert a new audio clip onto the 10sec background at a random time, but making sure that any newly inserted segment doesn't overlap with the previous segments. \n", + "\n", + "**Exercise**: Implement `insert_audio_clip()` to overlay an audio clip onto the background 10sec clip. You will need to carry out 4 steps:\n", + "\n", + "1. Get a random time segment of the right duration in ms.\n", + "2. Make sure that the time segment does not overlap with any of the previous time segments. If it is overlapping, then go back to step 1 and pick a new time segment.\n", + "3. Add the new time segment to the list of existing time segments, so as to keep track of all the segments you've inserted. \n", + "4. Overlay the audio clip over the background using pydub. We have implemented this for you." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: insert_audio_clip\n", + "\n", + "def insert_audio_clip(background, audio_clip, previous_segments):\n", + " \"\"\"\n", + " Insert a new audio segment over the background noise at a random time step, ensuring that the \n", + " audio segment does not overlap with existing segments.\n", + " \n", + " Arguments:\n", + " background -- a 10 second background audio recording. \n", + " audio_clip -- the audio clip to be inserted/overlaid. \n", + " previous_segments -- times where audio segments have already been placed\n", + " \n", + " Returns:\n", + " new_background -- the updated background audio\n", + " \"\"\"\n", + " \n", + " # Get the duration of the audio clip in ms\n", + " segment_ms = len(audio_clip)\n", + " \n", + " ### START CODE HERE ### \n", + " # Step 1: Use one of the helper functions to pick a random time segment onto which to insert \n", + " # the new audio clip. (≈ 1 line)\n", + " segment_time = get_random_time_segment(segment_ms)\n", + " \n", + " # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep \n", + " # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)\n", + " while is_overlapping(segment_time, previous_segments):\n", + " segment_time = get_random_time_segment(segment_ms)\n", + "\n", + " # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)\n", + " previous_segments.append(segment_time)\n", + " ### END CODE HERE ###\n", + " \n", + " # Step 4: Superpose audio segment and background\n", + " new_background = background.overlay(audio_clip, position = segment_time[0])\n", + " \n", + " return new_background, segment_time" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Segment Time: (2254, 3169)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(5)\n", + "audio_clip, segment_time = insert_audio_clip(backgrounds[0], activates[0], [(3790, 4400)])\n", + "audio_clip.export(\"insert_test.wav\", format=\"wav\")\n", + "print(\"Segment Time: \", segment_time)\n", + "IPython.display.Audio(\"insert_test.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Segment Time**\n", + " \n", + " (2254, 3169)\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Expected audio\n", + "IPython.display.Audio(\"audio_examples/insert_reference.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, implement code to update the labels $y^{\\langle t \\rangle}$, assuming you just inserted an \"activate.\" In the code below, `y` is a `(1,1375)` dimensional vector, since $T_y = 1375$. \n", + "\n", + "If the \"activate\" ended at time step $t$, then set $y^{\\langle t+1 \\rangle} = 1$ as well as for up to 49 additional consecutive values. However, make sure you don't run off the end of the array and try to update `y[0][1375]`, since the valid indices are `y[0][0]` through `y[0][1374]` because $T_y = 1375$. So if \"activate\" ends at step 1370, you would get only `y[0][1371] = y[0][1372] = y[0][1373] = y[0][1374] = 1`\n", + "\n", + "**Exercise**: Implement `insert_ones()`. You can use a for loop. (If you are an expert in python's slice operations, feel free also to use slicing to vectorize this.) If a segment ends at `segment_end_ms` (using a 10000 step discretization), to convert it to the indexing for the outputs $y$ (using a $1375$ step discretization), we will use this formula: \n", + "```\n", + " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: insert_ones\n", + "\n", + "def insert_ones(y, segment_end_ms):\n", + " \"\"\"\n", + " Update the label vector y. The labels of the 50 output steps strictly after the end of the segment \n", + " should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the\n", + " 50 followinf labels should be ones.\n", + " \n", + " \n", + " Arguments:\n", + " y -- numpy array of shape (1, Ty), the labels of the training example\n", + " segment_end_ms -- the end time of the segment in ms\n", + " \n", + " Returns:\n", + " y -- updated labels\n", + " \"\"\"\n", + " \n", + " # duration of the background (in terms of spectrogram time-steps)\n", + " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", + " \n", + " # Add 1 to the correct index in the background label (y)\n", + " ### START CODE HERE ### (≈ 3 lines)\n", + " for i in range(segment_end_y+1, segment_end_y+51):\n", + " if i < Ty:\n", + " y[0, i] = 1.0\n", + " ### END CODE HERE ###\n", + " \n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sanity checks: 0.0 1.0 0.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arr1 = insert_ones(np.zeros((1, Ty)), 9700)\n", + "plt.plot(insert_ones(arr1, 4251)[0,:])\n", + "print(\"sanity checks:\", arr1[0][1333], arr1[0][634], arr1[0][635])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **sanity checks**:\n", + " \n", + " 0.0 1.0 0.0\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, you can use `insert_audio_clip` and `insert_ones` to create a new training example.\n", + "\n", + "**Exercise**: Implement `create_training_example()`. You will need to carry out the following steps:\n", + "\n", + "1. Initialize the label vector $y$ as a numpy array of zeros and shape $(1, T_y)$.\n", + "2. Initialize the set of existing segments to an empty list.\n", + "3. Randomly select 0 to 4 \"activate\" audio clips, and insert them onto the 10sec clip. Also insert labels at the correct position in the label vector $y$.\n", + "4. Randomly select 0 to 2 negative audio clips, and insert them into the 10sec clip. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: create_training_example\n", + "\n", + "def create_training_example(background, activates, negatives):\n", + " \"\"\"\n", + " Creates a training example with a given background, activates, and negatives.\n", + " \n", + " Arguments:\n", + " background -- a 10 second background audio recording\n", + " activates -- a list of audio segments of the word \"activate\"\n", + " negatives -- a list of audio segments of random words that are not \"activate\"\n", + " \n", + " Returns:\n", + " x -- the spectrogram of the training example\n", + " y -- the label at each time step of the spectrogram\n", + " \"\"\"\n", + " \n", + " # Set the random seed\n", + " np.random.seed(18)\n", + " \n", + " # Make background quieter\n", + " background = background - 20\n", + "\n", + " ### START CODE HERE ###\n", + " # Step 1: Initialize y (label vector) of zeros (≈ 1 line)\n", + " y = np.zeros((1, Ty))\n", + "\n", + " # Step 2: Initialize segment times as empty list (≈ 1 line)\n", + " previous_segments = []\n", + " ### END CODE HERE ###\n", + " \n", + " # Select 0-4 random \"activate\" audio clips from the entire list of \"activates\" recordings\n", + " number_of_activates = np.random.randint(0, 5)\n", + " random_indices = np.random.randint(len(activates), size=number_of_activates)\n", + " random_activates = [activates[i] for i in random_indices]\n", + " \n", + " ### START CODE HERE ### (≈ 3 lines)\n", + " # Step 3: Loop over randomly selected \"activate\" clips and insert in background\n", + " for random_activate in random_activates:\n", + " # Insert the audio clip on the background\n", + " background, segment_time = insert_audio_clip(background, random_activate, previous_segments)\n", + " # Retrieve segment_start and segment_end from segment_time\n", + " segment_start, segment_end = segment_time\n", + " # Insert labels in \"y\"\n", + " y = insert_ones(y, segment_end)\n", + " ### END CODE HERE ###\n", + "\n", + " # Select 0-2 random negatives audio recordings from the entire list of \"negatives\" recordings\n", + " number_of_negatives = np.random.randint(0, 3)\n", + " random_indices = np.random.randint(len(negatives), size=number_of_negatives)\n", + " random_negatives = [negatives[i] for i in random_indices]\n", + "\n", + " ### START CODE HERE ### (≈ 2 lines)\n", + " # Step 4: Loop over randomly selected negative clips and insert in background\n", + " for random_negative in random_negatives:\n", + " # Insert the audio clip on the background \n", + " background, _ = insert_audio_clip(background, random_negative, previous_segments)\n", + " ### END CODE HERE ###\n", + " \n", + " # Standardize the volume of the audio clip \n", + " background = match_target_amplitude(background, -20.0)\n", + "\n", + " # Export new training example \n", + " file_handle = background.export(\"train\" + \".wav\", format=\"wav\")\n", + " print(\"File (train.wav) was saved in your directory.\")\n", + " \n", + " # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)\n", + " x = graph_spectrogram(\"train.wav\")\n", + " \n", + " return x, y" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File (train.wav) was saved in your directory.\n" + ] + }, + { + "data": { + "image/png": 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rn6L0BG6uZ4gnMKeHDveYspLEVExzI89BD71o6FBFgPuKB4dfauksxnlAOGlq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\nKWhJD3uEwDXhaCqzZSQfrHRbzPC0udtSTSCVCXM2ovcGdzbK6beMmCJiW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gk/WYZgCUmz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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = create_training_example(backgrounds[0], activates, negatives)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can listen to the training example you created and compare it to the spectrogram generated above." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"train.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"audio_examples/train_reference.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, you can plot the associated labels for the generated training example." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(y[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 - Full training set\n", + "\n", + "You've now implemented the code needed to generate a single training example. We used this process to generate a large training set. To save time, we've already generated a set of training examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load preprocessed training examples\n", + "X = np.load(\"./XY_train/X.npy\")\n", + "Y = np.load(\"./XY_train/Y.npy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.5 - Development set\n", + "\n", + "To test our model, we recorded a development set of 25 examples. While our training data is synthesized, we want to create a development set using the same distribution as the real inputs. Thus, we recorded 25 10-second audio clips of people saying \"activate\" and other random words, and labeled them by hand. This follows the principle described in Course 3 that we should create the dev set to be as similar as possible to the test set distribution; that's why our dev set uses real rather than synthesized audio. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load preprocessed dev set examples\n", + "X_dev = np.load(\"./XY_dev/X_dev.npy\")\n", + "Y_dev = np.load(\"./XY_dev/Y_dev.npy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Model\n", + "\n", + "Now that you've built a dataset, lets write and train a trigger word detection model! \n", + "\n", + "The model will use 1-D convolutional layers, GRU layers, and dense layers. Let's load the packages that will allow you to use these layers in Keras. This might take a minute to load. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.callbacks import ModelCheckpoint\n", + "from keras.models import Model, load_model, Sequential\n", + "from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D\n", + "from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape\n", + "from keras.optimizers import Adam" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 - Build the model\n", + "\n", + "Here is the architecture we will use. Take some time to look over the model and see if it makes sense. \n", + "\n", + "\n", + "
**Figure 3**
\n", + "\n", + "One key step of this model is the 1D convolutional step (near the bottom of Figure 3). It inputs the 5511 step spectrogram, and outputs a 1375 step output, which is then further processed by multiple layers to get the final $T_y = 1375$ step output. This layer plays a role similar to the 2D convolutions you saw in Course 4, of extracting low-level features and then possibly generating an output of a smaller dimension. \n", + "\n", + "Computationally, the 1-D conv layer also helps speed up the model because now the GRU has to process only 1375 timesteps rather than 5511 timesteps. The two GRU layers read the sequence of inputs from left to right, then ultimately uses a dense+sigmoid layer to make a prediction for $y^{\\langle t \\rangle}$. Because $y$ is binary valued (0 or 1), we use a sigmoid output at the last layer to estimate the chance of the output being 1, corresponding to the user having just said \"activate.\"\n", + "\n", + "Note that we use a uni-directional RNN rather than a bi-directional RNN. This is really important for trigger word detection, since we want to be able to detect the trigger word almost immediately after it is said. If we used a bi-directional RNN, we would have to wait for the whole 10sec of audio to be recorded before we could tell if \"activate\" was said in the first second of the audio clip. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implementing the model can be done in four steps:\n", + " \n", + "**Step 1**: CONV layer. Use `Conv1D()` to implement this, with 196 filters, \n", + "a filter size of 15 (`kernel_size=15`), and stride of 4. [[See documentation.](https://keras.io/layers/convolutional/#conv1d)]\n", + "\n", + "**Step 2**: First GRU layer. To generate the GRU layer, use:\n", + "```\n", + "X = GRU(units = 128, return_sequences = True)(X)\n", + "```\n", + "Setting `return_sequences=True` ensures that all the GRU's hidden states are fed to the next layer. Remember to follow this with Dropout and BatchNorm layers. \n", + "\n", + "**Step 3**: Second GRU layer. This is similar to the previous GRU layer (remember to use `return_sequences=True`), but has an extra dropout layer. \n", + "\n", + "**Step 4**: Create a time-distributed dense layer as follows: \n", + "```\n", + "X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X)\n", + "```\n", + "This creates a dense layer followed by a sigmoid, so that the parameters used for the dense layer are the same for every time step. [[See documentation](https://keras.io/layers/wrappers/).]\n", + "\n", + "**Exercise**: Implement `model()`, the architecture is presented in Figure 3." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(input_shape):\n", + " \"\"\"\n", + " Function creating the model's graph in Keras.\n", + " \n", + " Argument:\n", + " input_shape -- shape of the model's input data (using Keras conventions)\n", + "\n", + " Returns:\n", + " model -- Keras model instance\n", + " \"\"\"\n", + " \n", + " X_input = Input(shape = input_shape)\n", + " \n", + " ### START CODE HERE ###\n", + " \n", + " # Step 1: CONV layer (≈4 lines)\n", + " X = Conv1D(196, 15, strides=4)(X_input) # CONV1D\n", + " X = BatchNormalization()(X) # Batch normalization\n", + " X = Activation('relu')(X) # ReLu activation\n", + " X = Dropout(0.8)(X) # dropout (use 0.8)\n", + "\n", + " # Step 2: First GRU Layer (≈4 lines)\n", + " X = GRU(units = 128, return_sequences=True)(X) # GRU (use 128 units and return the sequences)\n", + " X = Dropout(0.8)(X) # dropout (use 0.8)\n", + " X = BatchNormalization()(X) # Batch normalization\n", + " \n", + " # Step 3: Second GRU Layer (≈4 lines)\n", + " X = GRU(units = 128, return_sequences=True)(X) # GRU (use 128 units and return the sequences)\n", + " X = Dropout(0.8)(X) # dropout (use 0.8)\n", + " X = BatchNormalization()(X) # Batch normalization\n", + " X = Dropout(0.8)(X) # dropout (use 0.8)\n", + " \n", + " # Step 4: Time-distributed dense layer (≈1 line)\n", + " X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X) # time distributed (sigmoid)\n", + "\n", + " ### END CODE HERE ###\n", + "\n", + " model = Model(inputs = X_input, outputs = X)\n", + " \n", + " return model " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = model(input_shape = (Tx, n_freq))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print the model summary to keep track of the shapes." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_3 (InputLayer) (None, 5511, 101) 0 \n", + "_________________________________________________________________\n", + "conv1d_1 (Conv1D) (None, 1375, 196) 297136 \n", + "_________________________________________________________________\n", + "batch_normalization_1 (Batch (None, 1375, 196) 784 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 1375, 196) 0 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 1375, 196) 0 \n", + "_________________________________________________________________\n", + "gru_1 (GRU) (None, 1375, 128) 124800 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 1375, 128) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_2 (Batch (None, 1375, 128) 512 \n", + "_________________________________________________________________\n", + "gru_2 (GRU) (None, 1375, 128) 98688 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 1375, 128) 0 \n", + "_________________________________________________________________\n", + "batch_normalization_3 (Batch (None, 1375, 128) 512 \n", + "_________________________________________________________________\n", + "dropout_4 (Dropout) (None, 1375, 128) 0 \n", + "_________________________________________________________________\n", + "time_distributed_1 (TimeDist (None, 1375, 1) 129 \n", + "=================================================================\n", + "Total params: 522,561\n", + "Trainable params: 521,657\n", + "Non-trainable params: 904\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **Total params**\n", + " \n", + " 522,561\n", + "
\n", + " **Trainable params**\n", + " \n", + " 521,657\n", + "
\n", + " **Non-trainable params**\n", + " \n", + " 904\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the network is of shape (None, 1375, 1) while the input is (None, 5511, 101). The Conv1D has reduced the number of steps from 5511 at spectrogram to 1375. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 - Fit the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trigger word detection takes a long time to train. To save time, we've already trained a model for about 3 hours on a GPU using the architecture you built above, and a large training set of about 4000 examples. Let's load the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = load_model('./models/tr_model.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can train the model further, using the Adam optimizer and binary cross entropy loss, as follows. This will run quickly because we are training just for one epoch and with a small training set of 26 examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)\n", + "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1\n", + "26/26 [==============================] - 23s - loss: 0.0727 - acc: 0.9806 \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X, Y, batch_size = 5, epochs=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 - Test the model\n", + "\n", + "Finally, let's see how your model performs on the dev set." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25/25 [==============================] - 4s\n", + "Dev set accuracy = 0.94600725174\n" + ] + } + ], + "source": [ + "loss, acc = model.evaluate(X_dev, Y_dev)\n", + "print(\"Dev set accuracy = \", acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This looks pretty good! However, accuracy isn't a great metric for this task, since the labels are heavily skewed to 0's, so a neural network that just outputs 0's would get slightly over 90% accuracy. We could define more useful metrics such as F1 score or Precision/Recall. But let's not bother with that here, and instead just empirically see how the model does. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 - Making Predictions\n", + "\n", + "Now that you have built a working model for trigger word detection, let's use it to make predictions. This code snippet runs audio (saved in a wav file) through the network. \n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def detect_triggerword(filename):\n", + " plt.subplot(2, 1, 1)\n", + "\n", + " x = graph_spectrogram(filename)\n", + " # the spectogram outputs (freqs, Tx) and we want (Tx, freqs) to input into the model\n", + " x = x.swapaxes(0,1)\n", + " x = np.expand_dims(x, axis=0)\n", + " predictions = model.predict(x)\n", + " \n", + " plt.subplot(2, 1, 2)\n", + " plt.plot(predictions[0,:,0])\n", + " plt.ylabel('probability')\n", + " plt.show()\n", + " return predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you've estimated the probability of having detected the word \"activate\" at each output step, you can trigger a \"chiming\" sound to play when the probability is above a certain threshold. Further, $y^{\\langle t \\rangle}$ might be near 1 for many values in a row after \"activate\" is said, yet we want to chime only once. So we will insert a chime sound at most once every 75 output steps. This will help prevent us from inserting two chimes for a single instance of \"activate\". (This plays a role similar to non-max suppression from computer vision.) \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "chime_file = \"audio_examples/chime.wav\"\n", + "def chime_on_activate(filename, predictions, threshold):\n", + " audio_clip = AudioSegment.from_wav(filename)\n", + " chime = AudioSegment.from_wav(chime_file)\n", + " Ty = predictions.shape[1]\n", + " # Step 1: Initialize the number of consecutive output steps to 0\n", + " consecutive_timesteps = 0\n", + " # Step 2: Loop over the output steps in the y\n", + " for i in range(Ty):\n", + " # Step 3: Increment consecutive output steps\n", + " consecutive_timesteps += 1\n", + " # Step 4: If prediction is higher than the threshold and more than 75 consecutive output steps have passed\n", + " if predictions[0,i,0] > threshold and consecutive_timesteps > 75:\n", + " # Step 5: Superpose audio and background using pydub\n", + " audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio_clip.duration_seconds)*1000)\n", + " # Step 6: Reset consecutive output steps to 0\n", + " consecutive_timesteps = 0\n", + " \n", + " audio_clip.export(\"chime_output.wav\", format='wav')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 - Test on dev examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's explore how our model performs on two unseen audio clips from the development set. Lets first listen to the two dev set clips. " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"./raw_data/dev/1.wav\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IPython.display.Audio(\"./raw_data/dev/2.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets run the model on these audio clips and see if it adds a chime after \"activate\"!" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZWYHbamAyeWDyR5rVON49p2CbzPnj4xpv0LO8UOwyF4FLAsHucXip3iN0d7wj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\nHmrk8tNnsGB6LumpKYT/KFUnTudej/+9qx63PdSG0DpUj2ujovT0KbuPt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Z1ES4bzqj8XcjEN86tC8D8qZSsDE68loHWWDmmXfjpPBfXdMOQEG0wGpPhqd5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filename = \"./raw_data/dev/2.wav\"\n", + "prediction = detect_triggerword(filename)\n", + "chime_on_activate(filename, prediction, 0.5)\n", + "IPython.display.Audio(\"./chime_output.wav\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Congratulations \n", + "\n", + "You've come to the end of this assignment! \n", + "\n", + "Here's what you should remember:\n", + "- Data synthesis is an effective way to create a large training set for speech problems, specifically trigger word detection. \n", + "- Using a spectrogram and optionally a 1D conv layer is a common pre-processing step prior to passing audio data to an RNN, GRU or LSTM.\n", + "- An end-to-end deep learning approach can be used to built a very effective trigger word detection system. \n", + "\n", + "*Congratulations* on finishing the fimal assignment! \n", + "\n", + "Thank you for sticking with us through the end and for all the hard work you've put into learning deep learning. We hope you have enjoyed the course! \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4 - Try your own example! (OPTIONAL/UNGRADED)\n", + "\n", + "In this optional and ungraded portion of this notebook, you can try your model on your own audio clips! \n", + "\n", + "Record a 10 second audio clip of you saying the word \"activate\" and other random words, and upload it to the Coursera hub as `myaudio.wav`. Be sure to upload the audio as a wav file. If your audio is recorded in a different format (such as mp3) there is free software that you can find online for converting it to wav. If your audio recording is not 10 seconds, the code below will either trim or pad it as needed to make it 10 seconds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Preprocess the audio to the correct format\n", + "def preprocess_audio(filename):\n", + " # Trim or pad audio segment to 10000ms\n", + " padding = AudioSegment.silent(duration=10000)\n", + " segment = AudioSegment.from_wav(filename)[:10000]\n", + " segment = padding.overlay(segment)\n", + " # Set frame rate to 44100\n", + " segment = segment.set_frame_rate(44100)\n", + " # Export as wav\n", + " segment.export(filename, format='wav')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you've uploaded your audio file to Coursera, put the path to your file in the variable below." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "your_filename = \"audio_examples/my_audio.wav\"" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocess_audio(your_filename)\n", + "IPython.display.Audio(your_filename) # listen to the audio you uploaded " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, use the model to predict when you say activate in the 10 second audio clip, and trigger a chime. If beeps are not being added appropriately, try to adjust the chime_threshold." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Bm/GvJPjD1xmYhTOw++5KpKvwmf4gvMJXzynYxPKTNBw5GFYIATavSPj6yTTC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\n0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RkHU8CkeDDjTZhguMgwslJO+WLEyWqG6SKUk+vgDX0rzJ7aiuO3tJ5fff2OqhhQRwOD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recognition in the city of Peacetopia (case study).md b/Deep Learning Notebooks by Andrew NG/Structuring Machine Learning Projects/Week 1 Quiz - Bird recognition in the city of Peacetopia (case study).md new file mode 100644 index 0000000..7661e12 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Structuring Machine Learning Projects/Week 1 Quiz - Bird recognition in the city of Peacetopia (case study).md @@ -0,0 +1,109 @@ +## Week 1 Quiz - Bird recognition in the city of Peacetopia (case study) + +1. Having three evaluation metrics makes it harder for you to quickly choose between two different algorithms, and will slow down the speed with which your team can iterate. True/False? + + - [x] True + - [ ] False + +2. If you had the three following models, which one would you choose? + + - Test Accuracy 98% + - Runtime 9 sec + - Memory size 9MB + +3. Based on the city’s requests, which of the following would you say is true? + + - [x] Accuracy is an optimizing metric; running time and memory size are a satisficing metrics. + - [ ] Accuracy is a satisficing metric; running time and memory size are an optimizing metric. + - [ ] Accuracy, running time and memory size are all optimizing metrics because you want to do well on all three. + - [ ] Accuracy, running time and memory size are all satisficing metrics because you have to do sufficiently well on all three for your system to be acceptable. + +4. Before implementing your algorithm, you need to split your data into train/dev/test sets. Which of these do you think is the best choice? + + - Train 9,500,000 + - Dev 250,000 + - Test 250,000 + +5. After setting up your train/dev/test sets, the City Council comes across another 1,000,000 images, called the “citizens’ data”. Apparently the citizens of Peacetopia are so scared of birds that they volunteered to take pictures of the sky and label them, thus contributing these additional 1,000,000 images. These images are different from the distribution of images the City Council had originally given you, but you think it could help your algorithm. + + You should not add the citizens’ data to the training set, because this will cause the training and dev/test set distributions to become different, thus hurting dev and test set performance. True/False? + + - [ ] True + - [x] False + +6. One member of the City Council knows a little about machine learning, and thinks you should add the 1,000,000 citizens’ data images to the test set. You object because: + + - The test set no longer reflects the distribution of data (security cameras) you most care about. + - This would cause the dev and test set distributions to become different. This is a bad idea because you’re not aiming where you want to hit. + +7. You train a system, and its errors are as follows (error = 100%-Accuracy): + + - Training set error 4.0% + - Dev set error 4.5% + + This suggests that one good avenue for improving performance is to train a bigger network so as to drive down the 4.0% training error. Do you agree? + + - No, because there is insufficient information to tell. + +8. You ask a few people to label the dataset so as to find out what is human-level performance. You find the following levels of accuracy: + + - Bird watching expert #1 0.3% error + - Bird watching expert #2 0.5% error + - Normal person #1 (not a bird watching expert) 1.0% error + - Normal person #2 (not a bird watching expert) 1.2% error + + If your goal is to have “human-level performance” be a proxy (or estimate) for Bayes error, how would you define “human-level performance”? + + - 0.3% (accuracy of expert #1) + +9. Which of the following statements do you agree with? + + - A learning algorithm’s performance can be better human-level performance but it can never be better than Bayes error. + +10. You find that a team of ornithologists debating and discussing an image gets an even better 0.1% performance, so you define that as “human-level performance.” After working further on your algorithm, you end up with the following: + + - Human-level performance 0.1% + - Training set error 2.0% + - Dev set error 2.1% + + Based on the evidence you have, which two of the following four options seem the most promising to try? (Check two options.) + + - Try decreasing regularization. + - Train a bigger model to try to do better on the training set. + +11. You also evaluate your model on the test set, and find the following: + + - Human-level performance 0.1% + - Training set error 2.0% + - Dev set error 2.1% + - Test set error 7.0% + + What does this mean? (Check the two best options.) + + - You should try to get a bigger dev set. + - You have overfit to the dev set. + +12. After working on this project for a year, you finally achieve: + + - Human-level performance 0.10% + - Training set error 0.05% + - Dev set error 0.05% + + What can you conclude? (Check all that apply.) + + - It is now harder to measure avoidable bias, thus progress will be slower going forward. + - If the test set is big enough for the 0,05% error estimate to be accurate, this implies Bayes error is ≤0.05 + +13. It turns out Peacetopia has hired one of your competitors to build a system as well. Your system and your competitor both deliver systems with about the same running time and memory size. However, your system has higher accuracy! However, when Peacetopia tries out your and your competitor’s systems, they conclude they actually like your competitor’s system better, because even though you have higher overall accuracy, you have more false negatives (failing to raise an alarm when a bird is in the air). What should you do? + + - Rethink the appropriate metric for this task, and ask your team to tune to the new metric. + +14. You’ve handily beaten your competitor, and your system is now deployed in Peacetopia and is protecting the citizens from birds! But over the last few months, a new species of bird has been slowly migrating into the area, so the performance of your system slowly degrades because your data is being tested on a new type of data. + + - Use the data you have to define a new evaluation metric (using a new dev/test set) taking into account the new species, and use that to drive further progress for your team. + +15. The City Council thinks that having more Cats in the city would help scare off birds. They are so happy with your work on the Bird detector that they also hire you to build a Cat detector. (Wow Cat detectors are just incredibly useful aren’t they.) Because of years of working on Cat detectors, you have such a huge dataset of 100,000,000 cat images that training on this data takes about two weeks. Which of the statements do you agree with? (Check all that agree.) + + - If 100,000,000 examples is enough to build a good enough Cat detector, you might be better of training with just 10,000,000 examples to gain a ≈10x improvement in how quickly you can run experiments, even if each model performs a bit worse because it’s trained on less data. + - Buying faster computers could speed up your teams’ iteration speed and thus your team’s productivity. + - Needing two weeks to train will limit the speed at which you can iterate. diff --git a/Deep Learning Notebooks by Andrew NG/Structuring Machine Learning Projects/Week 2 Quiz - Autonomous driving (case study).md b/Deep Learning Notebooks by Andrew NG/Structuring Machine Learning Projects/Week 2 Quiz - Autonomous driving (case study).md new file mode 100644 index 0000000..4ed1807 --- /dev/null +++ b/Deep Learning Notebooks by Andrew NG/Structuring Machine Learning Projects/Week 2 Quiz - Autonomous driving (case study).md @@ -0,0 +1,141 @@ +## Week 2 Quiz - Autonomous driving (case study) + +1. You are just getting started on this project. What is the first thing you do? Assume each of the steps below would take about an equal amount of time (a few days). + + - Spend a few days training a basic model and see what mistakes it makes. + + > As discussed in lecture, applied ML is a highly iterative process. If you train a basic model and carry out error analysis (see what mistakes it makes) it will help point you in more promising directions. + +2. Your goal is to detect road signs (stop sign, pedestrian crossing sign, construction ahead sign) and traffic signals (red and green lights) in images. The goal is to recognize which of these objects appear in each image. You plan to use a deep neural network with ReLU units in the hidden layers. + + For the output layer, a softmax activation would be a good choice for the output layer because this is a multi-task learning problem. True/False? + + - [ ] True + - [x] False + + > Softmax would be a good choice if one and only one of the possibilities (stop sign, speed bump, pedestrian crossing, green light and red light) was present in each image. + +3. You are carrying out error analysis and counting up what errors the algorithm makes. Which of these datasets do you think you should manually go through and carefully examine, one image at a time? + + - [ ] 10,000 randomly chosen images + - [ ] 500 randomly chosen images + - [x] 500 images on which the algorithm made a mistake + - [ ] 10,000 images on which the algorithm made a mistake + +4. After working on the data for several weeks, your team ends up with the following data: + + - 100,000 labeled images taken using the front-facing camera of your car. + - 900,000 labeled images of roads downloaded from the internet. + + Each image’s labels precisely indicate the presence of any specific road signs and traffic signals or combinations of them. For example, y(i) = [1 0 0 1 0] means the image contains a stop sign and a red traffic light. + Because this is a multi-task learning problem, you need to have all your y(i) vectors fully labeled. If one example is equal to [0 ? 1 1 ?] then the learning algorithm will not be able to use that example. True/False? + + - [ ] True + - [x] False + + > As seen in the lecture on multi-task learning, you can compute the cost such that it is not influenced by the fact that some entries haven’t been labeled. + +5. The distribution of data you care about contains images from your car’s front-facing camera; which comes from a different distribution than the images you were able to find and download off the internet. How should you split the dataset into train/dev/test sets? + + - [ ] Mix all the 100,000 images with the 900,000 images you found online. Shuffle everything. Split the 1,000,000 images dataset into 600,000 for the training set, 200,000 for the dev set and 200,000 for the test set. + - [ ] Mix all the 100,000 images with the 900,000 images you found online. Shuffle everything. Split the 1,000,000 images dataset into 980,000 for the training set, 10,000 for the dev set and 10,000 for the test set. + - [x] Choose the training set to be the 900,000 images from the internet along with 80,000 images from your car’s front-facing camera. The 20,000 remaining images will be split equally in dev and test sets. + - [ ] Choose the training set to be the 900,000 images from the internet along with 20,000 images from your car’s front-facing camera. The 80,000 remaining images will be split equally in dev and test sets. + > As seen in lecture, it is important that your dev and test set have the closest possible distribution to “real”-data. It is also important for the training set to contain enough “real”-data to avoid having a data-mismatch problem. + +6. Assume you’ve finally chosen the following split between of the data: + + - Training 940,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 8.8% + - Training-Dev 20,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 9.1% + - Dev 20,000 images from your car’s front-facing camera 14.3% + - Test 20,000 images from the car’s front-facing camera 14.8% + + You also know that human-level error on the road sign and traffic signals classification task is around 0.5%. Which of the following are True? (Check all that apply). + + - You have a large avoidable-bias problem because your training error is quite a bit higher than the human-level error. + - You have a large data-mismatch problem because your model does a lot better on the training-dev set than on the dev set. + +7. Based on table from the previous question, a friend thinks that the training data distribution is much easier than the dev/test distribution. What do you think? + + - There’s insufficient information to tell if your friend is right or wrong. + + > The algorithm does better on the distribution of data it trained on. But you don’t know if it’s because it trained on that no distribution or if it really is easier. To get a better sense, measure human-level error separately on both distributions. + +8. You decide to focus on the dev set and check by hand what are the errors due to. Here is a table summarizing your discoveries: + + - Overall dev set error 14.3% + - Errors due to incorrectly labeled data 4.1% + - Errors due to foggy pictures 8.0% + - Errors due to rain drops stuck on your car’s front-facing camera 2.2% + - Errors due to other causes 1.0% + + In this table, 4.1%, 8.0%, etc.are a fraction of the total dev set (not just examples your algorithm mislabeled). I.e. about 8.0/14.3 = 56% of your errors are due to foggy pictures. + + The results from this analysis implies that the team’s highest priority should be to bring more foggy pictures into the training set so as to address the 8.0% of errors in that category. True/False? + + - [x] False because this would depend on how easy it is to add this data and how much you think your team thinks it’ll help. + - [ ] True because it is the largest category of errors. As discussed in lecture, we should prioritize the largest category of error to avoid wasting the team’s time. + - [ ] True because it is greater than the other error categories added together (8.0 > 4.1+2.2+1.0). + - [ ] False because data augmentation (synthesizing foggy images by clean/non-foggy images) is more efficient. + +9. You can buy a specially designed windshield wiper that help wipe off some of the raindrops on the front-facing camera. Based on the table from the previous question, which of the following statements do you agree with? + + - 2.2% would be a reasonable estimate of the maximum amount this windshield wiper could improve performance. + + > You will probably not improve performance by more than 2.2% by solving the raindrops problem. If your dataset was infinitely big, 2.2% would be a perfect estimate of the improvement you can achieve by purchasing a specially designed windshield wiper that removes the raindrops. + +10. You decide to use data augmentation to address foggy images. You find 1,000 pictures of fog off the internet, and “add” them to clean images to synthesize foggy days, like this: + + Which of the following statements do you agree with? (Check all that apply.) + + - So long as the synthesized fog looks realistic to the human eye, you can be confident that the synthesized data is accurately capturing the distribution of real foggy images, since human vision is very accurate for the problem you’re solving. + + > If the synthesized images look realistic, then the model will just see them as if you had added useful data to identify road signs and traffic signals in a foggy weather. I will very likely help. + +11. After working further on the problem, you’ve decided to correct the incorrectly labeled data on the dev set. Which of these statements do you agree with? (Check all that apply). + + - You should not correct incorrectly labeled data in the training set as well so as to avoid your training set now being even more different from your dev set. + + > Deep learning algorithms are quite robust to having slightly different train and dev distributions. + + - You should also correct the incorrectly labeled data in the test set, so that the dev and test sets continue to come from the same distribution + + > Because you want to make sure that your dev and test data come from the same distribution for your algorithm to make your team’s iterative development process is efficient. + +12. So far your algorithm only recognizes red and green traffic lights. One of your colleagues in the startup is starting to work on recognizing a yellow traffic light. (Some countries call it an orange light rather than a yellow light; we’ll use the US convention of calling it yellow.) Images containing yellow lights are quite rare, and she doesn’t have enough data to build a good model. She hopes you can help her out using transfer learning. + + What do you tell your colleague? + + - She should try using weights pre-trained on your dataset, and fine-tuning further with the yellow-light dataset. + + > You have trained your model on a huge dataset, and she has a small dataset. Although your labels are different, the parameters of your model have been trained to recognize many characteristics of road and traffic images which will be useful for her problem. This is a perfect case for transfer learning, she can start with a model with the same architecture as yours, change what is after the last hidden layer and initialize it with your trained parameters. + +13. Another colleague wants to use microphones placed outside the car to better hear if there’re other vehicles around you. For example, if there is a police vehicle behind you, you would be able to hear their siren. However, they don’t have much to train this audio system. How can you help? + + - Neither transfer learning nor multi-task learning seems promising. + + > The problem he is trying to solve is quite different from yours. The different dataset structures make it probably impossible to use transfer learning or multi-task learning. + +14. To recognize red and green lights, you have been using this approach: + + - (A) Input an image (x) to a neural network and have it directly learn a mapping to make a prediction as to whether there’s a red light and/or green light (y). + + A teammate proposes a different, two-step approach: + + - (B) In this two-step approach, you would first (i) detect the traffic light in the image (if any), then (ii) determine the color of the illuminated lamp in the traffic light. + Between these two, Approach B is more of an end-to-end approach because it has distinct steps for the input end and the output end. True/False? + + + - [ ] True + - [x] False + + > (A) is an end-to-end approach as it maps directly the input (x) to the output (y). + +15. Approach A (in the question above) tends to be more promising than approach B if you have a ________ (fill in the blank). + + - [x] Large training set + - [ ] Multi-task learning problem. + - [ ] Large bias problem. + - [ ] Problem with a high Bayes error. + + > In many fields, it has been observed that end-to-end learning works better in practice, but requires a large amount of data.