assignment2/ConvolutionalNetworks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764967e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 1e-8\n",
    "print 'Testing conv_forward_naive'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/lib/python2.7/site-packages/ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/lib/python2.7/site-packages/ipykernel_launcher.py:11: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
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pmdz93D1d5ly5tUG/qWgQoY0ZXf5cPXLlyskUMN3EkMk9nxp/pbZM301BW6luuTLnyp7L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DPyue0snJ6e1muKxnFcL78TRVGdTfjzP//zXL9+nXv37tWgRHRkcKbnkqQOe41HkRHI+JdQokgI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\nFAK8RCsxGo0yFgxOhkBMrYMI7IVVkUbTj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DDk0tWnef/mbRLl8OJHP8Vb796kOlfne6/9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\nvcGI/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GJ9ZOc/HA6NKynZX3Jcsk/sfjqP2bSBmm1I5L8NxJKgzP4cewwkmUlxP4k7LHk9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\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86CN4vV4uXrzIhz70IQb6+slldsjlckQiEUqlElevXmVi7BAnTpxgoK+fN944TyQS4Uc/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oHp4TIcjUikUrQ7HUYTi3qzTjwRx7ImtDsdAqEQo/EQf8A/dWk3abXbFPNFfIZJdzCg2W4TjEYo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\niCfGqICNGDFixIgRI0Y8Mf4/25SSe1dIT/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6eb0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d/2:-d/2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  2.41193603377e-08\n",
      "dw error:  2.69214440368e-10\n",
      "db error:  1.85530273787e-11\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-9'\n",
    "print 'Testing conv_backward_naive function'\n",
    "print 'dx error: ', rel_error(dx, dx_num)\n",
    "print 'dw error: ', rel_error(dw, dw_num)\n",
    "print 'db error: ', rel_error(db, db_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print 'Testing max_pool_forward_naive function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27561740897e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print 'Testing max_pool_backward_naive function:'\n",
    "print 'dx error: ', rel_error(dx, dx_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** **The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. ** If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 0.113217s\n",
      "Fast: 0.014034s\n",
      "Speedup: 8.067309x\n",
      "Difference:  1.23770984321e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 0.546447s\n",
      "Fast: 0.010856s\n",
      "Speedup: 50.335244x\n",
      "dx difference:  2.35850889955e-10\n",
      "dw difference:  2.46474029485e-12\n",
      "db difference:  5.19607061423e-14\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing conv_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'Difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting conv_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)\n",
    "print 'dw difference: ', rel_error(dw_naive, dw_fast)\n",
    "print 'db difference: ', rel_error(db_naive, db_fast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.351963s\n",
      "fast: 0.002269s\n",
      "speedup: 155.116108x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.330383s\n",
      "speedup: 23.543162x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing pool_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'fast: %fs' % (t2 - t1)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting pool_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  1.98794209848e-08\n",
      "dw error:  3.99266816104e-10\n",
      "db error:  1.87854835489e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu_pool'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.28239681556e-09\n",
      "dw error:  8.30876354811e-09\n",
      "db error:  5.37170112189e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258525545\n",
      "Initial loss (with regularization):  2.50867124412\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (no regularization): ', loss\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (with regularization): ', loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 4.258079e-04\n",
      "W2 max relative error: 2.001145e-02\n",
      "W3 max relative error: 1.524614e-05\n",
      "b1 max relative error: 2.072239e-04\n",
      "b2 max relative error: 2.300217e-07\n",
      "b3 max relative error: 1.144057e-09\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 20) loss: 2.448517\n",
      "(Epoch 0 / 10) train acc: 0.140000; val_acc: 0.077000\n",
      "(Iteration 2 / 20) loss: 2.794873\n",
      "(Epoch 1 / 10) train acc: 0.160000; val_acc: 0.119000\n",
      "(Iteration 3 / 20) loss: 3.450987\n",
      "(Iteration 4 / 20) loss: 2.301127\n",
      "(Epoch 2 / 10) train acc: 0.230000; val_acc: 0.128000\n",
      "(Iteration 5 / 20) loss: 2.874447\n",
      "(Iteration 6 / 20) loss: 2.447081\n",
      "(Epoch 3 / 10) train acc: 0.350000; val_acc: 0.105000\n",
      "(Iteration 7 / 20) loss: 2.002926\n",
      "(Iteration 8 / 20) loss: 1.935400\n",
      "(Epoch 4 / 10) train acc: 0.250000; val_acc: 0.138000\n",
      "(Iteration 9 / 20) loss: 1.889067\n",
      "(Iteration 10 / 20) loss: 1.811830\n",
      "(Epoch 5 / 10) train acc: 0.460000; val_acc: 0.165000\n",
      "(Iteration 11 / 20) loss: 1.606248\n",
      "(Iteration 12 / 20) loss: 1.872210\n",
      "(Epoch 6 / 10) train acc: 0.510000; val_acc: 0.206000\n",
      "(Iteration 13 / 20) loss: 1.563451\n",
      "(Iteration 14 / 20) loss: 1.375654\n",
      "(Epoch 7 / 10) train acc: 0.580000; val_acc: 0.214000\n",
      "(Iteration 15 / 20) loss: 1.590880\n",
      "(Iteration 16 / 20) loss: 1.227591\n",
      "(Epoch 8 / 10) train acc: 0.700000; val_acc: 0.222000\n",
      "(Iteration 17 / 20) loss: 0.876226\n",
      "(Iteration 18 / 20) loss: 1.161554\n",
      "(Epoch 9 / 10) train acc: 0.760000; val_acc: 0.225000\n",
      "(Iteration 19 / 20) loss: 0.920736\n",
      "(Iteration 20 / 20) loss: 0.549144\n",
      "(Epoch 10 / 10) train acc: 0.810000; val_acc: 0.222000\n"
     ]
    }
   ],
   "source": [
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ATAB3fxr4BPAlMzsM1AJ3uLsHVY+IiLQfqbZMk8jpCvIuy4mtHP8xkWkxREQkjTQs09Qw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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33cad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304790\n",
      "(Epoch 0 / 1) train acc: 0.102000; val_acc: 0.115000\n",
      "(Iteration 21 / 980) loss: 1.920200\n",
      "(Iteration 41 / 980) loss: 2.063788\n",
      "(Iteration 61 / 980) loss: 1.980521\n",
      "(Iteration 81 / 980) loss: 1.761237\n",
      "(Iteration 101 / 980) loss: 1.880817\n",
      "(Iteration 121 / 980) loss: 1.725506\n",
      "(Iteration 141 / 980) loss: 1.867239\n",
      "(Iteration 161 / 980) loss: 1.822754\n",
      "(Iteration 181 / 980) loss: 1.570156\n",
      "(Iteration 201 / 980) loss: 1.855376\n",
      "(Iteration 221 / 980) loss: 1.732204\n",
      "(Iteration 241 / 980) loss: 1.863651\n",
      "(Iteration 261 / 980) loss: 1.751230\n",
      "(Iteration 281 / 980) loss: 1.554058\n",
      "(Iteration 301 / 980) loss: 1.605254\n",
      "(Iteration 321 / 980) loss: 1.567495\n",
      "(Iteration 341 / 980) loss: 1.680586\n",
      "(Iteration 361 / 980) loss: 1.853178\n",
      "(Iteration 381 / 980) loss: 1.518545\n",
      "(Iteration 401 / 980) loss: 1.657378\n",
      "(Iteration 421 / 980) loss: 1.861914\n",
      "(Iteration 441 / 980) loss: 1.542648\n",
      "(Iteration 461 / 980) loss: 1.667460\n",
      "(Iteration 481 / 980) loss: 1.584779\n",
      "(Iteration 501 / 980) loss: 1.593805\n",
      "(Iteration 521 / 980) loss: 1.763550\n",
      "(Iteration 541 / 980) loss: 1.764670\n",
      "(Iteration 561 / 980) loss: 1.699263\n",
      "(Iteration 581 / 980) loss: 1.361361\n",
      "(Iteration 601 / 980) loss: 1.627209\n",
      "(Iteration 621 / 980) loss: 1.678852\n",
      "(Iteration 641 / 980) loss: 1.751790\n",
      "(Iteration 661 / 980) loss: 1.530051\n",
      "(Iteration 681 / 980) loss: 1.586572\n",
      "(Iteration 701 / 980) loss: 1.551437\n",
      "(Iteration 721 / 980) loss: 1.383898\n",
      "(Iteration 741 / 980) loss: 1.798922\n",
      "(Iteration 761 / 980) loss: 1.871173\n",
      "(Iteration 781 / 980) loss: 1.524938\n",
      "(Iteration 801 / 980) loss: 1.731295\n",
      "(Iteration 821 / 980) loss: 1.405990\n",
      "(Iteration 841 / 980) loss: 1.876562\n",
      "(Iteration 861 / 980) loss: 1.469692\n",
      "(Iteration 881 / 980) loss: 1.747887\n",
      "(Iteration 901 / 980) loss: 1.517017\n",
      "(Iteration 921 / 980) loss: 1.820280\n",
      "(Iteration 941 / 980) loss: 1.641114\n",
      "(Iteration 961 / 980) loss: 1.884698\n",
      "(Epoch 1 / 1) train acc: 0.438000; val_acc: 0.426000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5rS19JuLlDJ5bdF7Ac3GZ3vthLh/+ZRjn/67O7uQfgGvP9w7C3OrbD3NXCzejtrb3qOz5\n1W/Atc9e+T7Mb+gNe0VE5FxDv/+/aj4G16a9eL63lcCf7YBjA+a7Pb0q+9QrvwHXLj37EMzP/BjP\nm54P49d4aUGfQRq5B+9CuyuKr6HELrz+Nx75rMqsN/4dXPviNp5NvpLCfz8XfZMwPzd/TWVvbN6E\na50ZPIPbKd6pEZFRWNSIyCgsakRkFBY1IjJKVxsFjzw6DfPDR/phnl7Wm8V9/bs/hGvfvHgb5sUK\n/jH7+OOPwFxe0JGjcR0uLV1OwLwxF4L5eltvhukJjMO1D07jH5YbIbyp4HMwFamB49M2x3BTxSkl\nmOeccZxnZmE+OHhFZbti+DFELxURkdMe3My4HNSNgoWNI3BteVQ3ZkRE6lX9I7yISL4fb4i4s2i3\nBal2+wv4DZVj+BpvtPDmkW3PhMrCY4/CtYECPk7w0nW80SiS+v4bMM8P66aPiIjLqZsnIiLWVbwp\n6w44TXGugf9+3KMw7hjv1IjIKCxqRGQUFjUiMgqLGhEZhUWNiIzS1e7nE4/gcahgUx8dJyLy6utn\nVfZX3/k2XOvtxyNLc0dPwPzg0YMwR6782Qdhnp71wDx/bgrmmy3dRXtfYhWuvbTig3nfgO6K/SSv\nbuhj1ZpJ/Bhbu3DX1lXFn1W2ibuCvQU95lLdxqNJdmpBfGziulPPT63ch8fYqll8tF99NA9zt1yG\neRN1om/BpfLftnGXM17H41Br/UdhPhbT/6dA7qI+Nk9EpOHEXc5SCh+ph9zJ49G+guD/e8Aaw0cB\nOjdx135/EXzmxw7BtWHPCMw7xTs1IjIKixoRGYVFjYiMwqJGREZhUSMio3S1+znUV4X5a998Cebf\n/cYzKrt+C88tfuj+d8H8Pe/H3U+fE3cukRd6ce13nMePEQrh7tr9RyZVdj6HO5GzUTCEKiKOu/zG\nghG90ebaOO7EeV146K44grtuzdIczPMO3RnbdTyJXyDe81N2IngzzHL4mMp68vj9ZCfxZoNt5yLM\nC068qWJ/uvMuYnYFf4YDI/ixR2Zw1345rDuAS1fDcG2uhjuOldHOr/FNB57xLK7i7z4fCMDcO4hn\nQvsCepb3viieWV1Y88K8U7xTIyKjsKgRkVFY1IjIKCxqRGQUFjUiMkpXu58L53HX6QfPXYL5jVt6\nJnRiCp+d9sTf/wWYH33wXpifttnpE9kzj+cztx7E3aVsDc9t1i9dUFkiiOcnx5dwB2jgMfxa/gKm\nIoH7IirbLXiWc6EXP+doEM+y+m12bQ336m6h0+Z92tkzhXehdew0VVa9vwHX9u6kYL7jxt3M0VwF\n5kNj+jntfGIIz7haMXxNnPUvwNz9sp5b3Yrh4+piB/Hsa6t8EeZI/9oZmN8R3OHuX8ZdUUd8BeZH\nx/T7uWdgDK7dct3dtfJOvFMjIqOwqBGRUVjUiMgoLGpEZBQWNSIySle7n9/+y1MwX1jC24juOaZ3\n/5w9is+DnHkInym6uYXPybx0/SbMkcjUAZgfPofnNk9N4h1X9wZ0JzLkxDN0pUE8Q+hK4g6YncgH\n9Pzj9BqewfUM4VnJrXIb5qGaH+bBtN6Jtb8H76Bqp7qxAfN8T1JlriTulLr8+vMWEfFn8WfY48DX\nRKKFXwsyPIm7fxtpPYMrItI8lYR5rkd/5rF1fL0Nrr0F88OzuGuL3mXPKN6Ftj+Id9U9n8YznrNJ\nPOM6enC3yoI5/Le5p4676p3inRoRGYVFjYiMwqJGREZhUSMio3S1UXDjWhLmkeFBmJ84+TMqm3to\nHK511/EYysVLeFTk5krnP/4OrOPxoYU0HlmaGsbrVzx6rGo6gTfnm07hEaxIovORHRER1x79Y/5O\nDP/IOzKAL4fq+A7Ma6dxAyV28o5+jJ243UuE4s55/B/A5okHy3js6ZID/xAtvXh8yr+Dx/h8Drwe\nOTL0GMyjw3gEa+vPcZPj5uYySPHxezMn8DUxWMLfDzJxD25unfsafuwjPnwNlR7Fn7nV0H+3t1fx\new8cv7tr5Z14p0ZERmFRIyKjsKgRkVFY1IjIKCxqRGQUh2VZ3Xsyh6N7T0ZERrEsq6NZO96pEZFR\nWNSIyCgsakRkFBY1IjIKixoRGaWrs5+f/b2PwHy9gI/Karm3VHY7hY/IK3vxvKU/j4/bWnKGYP7m\nf/3nKqv+wj+Ca+8E8czh1Qv78fqqnufc4yvBtblj+Fi6X5oZgLnjN0/A/HNP6c0GS6U1uDaQz8D8\n+jA+Us/Tug3zwg09R+gs4H8/n76C536/FPpPMN9rrassUsdH5PlsRgjrbnyMXTmCN71cCw6p7BfP\n/Spc+4HPfAzmq1f06xYRGRjrg7nHpz+vchNv7pnosTlqbjsP8xc++0WVffKpX4drh+L4s0oMJ2A+\nEMF/h862/h8fttP4e8vW8Exop3inRkRGYVEjIqOwqBGRUVjUiMgoLGpEZJSudj938mdgni7gXWgT\ndTDqNYA7Jq7SMMytCO6k7E7jrtObIKvaHHlXuoZ3+SxO4SO+ItlHVdY3fRmuPXBYHykmIrJSXoC5\nneh4Wr+OJB7Bne/ZhPlc5YcwX6/YdMYiuu3Yat3d2O9eC7+W4bLu0EYs3EF2F/FReC3LZoffShjm\nzgR+fMQrWZhfXbsC81tpfPygeGoqym3r71JEJJzA10oogo8IRE6euBfm+/bYdDlH8d+Pvwfv2GxV\n9d/h0rz+vxtERFaXcae4U7xTIyKjsKgRkVFY1IjIKCxqRGSUrjYK3GfxD4OhqW2Yr4X1CI1nEzcV\n1qv4B9dEDo++3NqFj0NDvpP8U5jfjj4B87Ybj2ZVx/SP3K59+EdRK4l/nH7TiX9wtlPK6tcSKOoj\n7EREih48VrOewd+Pv4rzjEePoFmCjza046vjayUq+ljCiB83bAI2ewriYSMRy1WAebve+b/9D7/3\nJMyPv/txmA8EcANlNa/ff7GEm2QtB77GV1P4c/nqMxdUllrFTQhHFV+HC/MrMJcqPk6xnNefenod\nN1VKZfxaHv61j+PnfAfeqRGRUVjUiMgoLGpEZBQWNSIyCosaERmlq93PiQSuofkx3AWZK02rbO1w\nE651beNxKM8reJyjuoZfC+otlncfgGv3VnCXpmcQd/qqR/QGj62r74JrUw98F+blsy2Y2xnfrT/b\nDTCCIyJy/xoewaocxJ3l0DZ+LTNZvXmkq427mf8ZpiLpfpvvsxxQWc2D+5m9FdwtbPfgjmPGwuNt\nGVfn//Zvb+CuoH9PFOZ9U3jEKZ7Xf5plH+5yVu0uiYu3bP6DduM67sK/lbL5vw1SeEPRyjrufro9\n4Htz4L/l4b7Ox7sQ3qkRkVFY1IjIKCxqRGQUFjUiMgqLGhEZpavdz3wcz5EdOYs3eMz4dHdkT113\nUURErGXcAbti4a6TtaZnCO3kSkmYbwTwsXT3+fH73Hdab1r4fO07cO2d62i7SpHbS3c3+5lx6BlX\na8YH11YiuAN2yIc7l8u+eZjHDuiZy8I8/t7svDGCj0Lcruv51N0tvBmkt41nP+te3HVz2mx6udiL\nN49EvvbSazAvPYe7gkfvOYRfS11fQ8VmBa6t2LzPXBl3f5GFm3hTzmt3cPdzNWWzMacbbxIZSeh5\n4Fgc/23GJ/SRhHeDd2pEZBQWNSIyCosaERmFRY2IjMKiRkRG6Wr386Q8APPNMbxD57HQLpU9NzEC\n1763gjtaQx59XJuIyJZjFOYvv/hllU0W8E6xYSee24sVfgzz2nXdMZrZ0buQioisRtZgPrRxHuZf\ngKnIBUu//0NOPGtbG8Azoamo7lyJiIyH+2Heu6A7abG+uzsi77QDf2/bbj2HulbHjx3EjTgRmxPv\nij7c5VzNdj6L2D+M50fbm3h+9s5qCubRmH6cnSLembfZcMG8b2QM5shKsxfmjf49MPfF8LXi8eFO\nrCcOjtoL4c91Z5izn0REf41FjYiMwqJGREZhUSMio7CoEZFRutr9PGDT0fI49fmeIiItUHNPFPCM\n2psTszCvJ/AOqunruKOHvJXBu+e6B3GXZnYLd1ZrPt3RTFXwtqXDa7ij1YjbzcXhGb3bztdV1grj\nmcBo8TTMJ2xOynTNLMHcK/qs0Tkn/n7svJmYhPlGTX/mwzG8A7Gv4YW5lcDd0nI1CPOVEOiK2hwb\ne+iI3q1ZRCS2gjurGZuOpqNXdxFDAdxZrQu+VnpH9U7LdkYP4L/N3WM49zrwLG+7F+/O2xPU88al\nAn7dwQD+m+0U79SIyCgsakRkFBY1IjIKixoRGcVhWXc3vvI3ejKHo3tPRkRGsSwLz2C9A+/UiMgo\nLGpEZBQWNSIyCosaERmFRY2IjNLVMakP/7OPw9xZx0ecRYN648fj9+JxmzuX9GiOiMjLL+Jj3IZ6\nJ2H+zA/+i8o+/49/Ha4NxfGoSKmBR5/EoceTelx4lKfZwo/d8uIxlH/y6U/C/Oj3HldZxIl3SZw8\niMfBBn14g8Mk2PRSRCQxM6cyjwePMv1B5Bswv/G7/wHmy4UnVLbhx6NJWxH82T7/5l/A/NlX8TXk\nvl8/TvPpj8K1H/oTGMuJMf26RUS2vXj0aWNRjxXNrv0duPZm8iLMHVc/BPM/OvWwyp588tNw7UoK\nj8LVb2ZgPj2NN6ace/Ckyj74D34WrvWU8Lhap3inRkRGYVEjIqOwqBGRUVjUiMgoLGpEZJSudj+b\nZdxdKhbxEXnxoD5Wa3gXPpaumMObJGa38CZ8cX/nG9FNHzkO88RwFObegR78nOBIsPW1bbg2v4m7\nS1s7+P3YmTuoN+BcT+KNNmM13LXNO3EnNhjBo3hhj36fS/P4Oe1sv/YizONj+vPafEh3eEVEBlaO\nwHy//zDMt0LXYH4hvQJz5N3v/4cwX39tGOa1WAXmyZQ+fnAx/CpcO506AfO+Ad1BtTPexBuBzg6O\nw7x/P85Hx/CxiZZT/72devrrcO3qKu64/s7D/wbm78Q7NSIyCosaERmFRY2IjMKiRkRGYVEjIqN0\ntfuZr+LuZz6FO0CBo3rmbnZmBK7dWdqB+dZWCuZhN+5cIndC+Ag/K4zzRBh3gNqjen29tYyftCcL\n48Yy7gzZmWrp9QdG8Ne+nLwFc5cff299MTznl1nXM66NTOcdRBGRaAsfzRa9oLufCcc6XLuxD3fo\nLh7B3dzZ1qMw35YXVbYAV4ps3MZHLy6FajB//HsemGfX9CbR4eEn4dqtyI9gfsiFP0Pk5L24OzsV\nx8dAhtv4eLtUahXmf/rSSyp78fSbcG05gO+1fkfY/SSin0IsakRkFBY1IjIKixoRGYVFjYiM0tXu\nZ6uKj/0s1fFcoLs1pbJYPAzXer14d9adAp6ttEY7n/38yrfOwrxewh2t7RzuAEV7dVd0ZAjP5w31\n411oXYHO5/lERDYX9fufOYA7v1P78CxnXxB3nOcXcce5kdM74u4bw51ikRsw3R+/jVfX9Zyw8xye\nh21s489w/8l9MN96LAnzN7+FdxtGill8vZXKeDb5TPbnYb7zqL7f2FvEM56J+PthHnP8D5jLH+to\nqICv2UEvnglNruNr/0oSf5/1nM57B3SXXERkxDsK807xTo2IjMKiRkRGYVEjIqOwqBGRUbraKKhb\nuIZGbcY5fG59fFi9jH8ob+JJHvG18XN6HHhUBvFb+If1rQwelllexj8Wpz169GlzCR+RNrsPv8/h\nfrwBpZ22SzdnXn/pClz73vfMwNxvc9TcW5cvwdwT2auy4w/vt3mFeOPD7QxuTviyeqwovYg3GV21\n/jvMA4l3wbw2jDeV7JvCP34j7SE8stU6hRtcpcN6fEhE5APr+vO6dxqPml363tMw73fpRpudHyzj\n73J3zuaoxib+m9h05mEe2X2vyg4fwqOAuezdNcPeiXdqRGQUFjUiMgqLGhEZhUWNiIzCokZERulq\n99PdxJ0UVwB39LI53R25fnkerk2t4FGrHm8vfk5f56Mvv/8R3c0TEQlHcEcv5MeP3ezVnbudZXwU\nXrmCj6ur4OkUeeZLOI9G9EjQ/BX8nK9fxuNG9STu5soOHn164KEnVLYKNj38SdYeOQbz3LN6nOfZ\nw/hDaWfwv9nuvfiyd7rxSNCxft3p+wFcKbLyh3gcau+hLZhXNofw+kHdWX+rjI/wGx/BG01Gv/s8\nzJGrVTw2eO0O7mbGgvgaig3jDVKdft3lTwTxxpStuB6zuxu8UyMio7CoEZFRWNSIyCgsakRkFBY1\nIjJKV7uf/SE809UzijtAIaecEHvbAAAEJklEQVTuXKa2cWekaHP83uQkPiZtoLfz7mc19QrMvTXc\noYwl8NxiyNJd3na9CNdmc7i7lFvGHUo7qyndoTx2H95U0NrEGzNWXLiD/OQH3wPzWL/uCn/xmW/b\nvUQoPYw7kdmj+rUEy3j2c0PwJpYbr+Nu7s7P/BzM/+nj98AcOZXB11UmcxXmD+3Fr/FbzfepbGbz\nBbj27OobOD9oM98Mxm2XbYan/YN449ClRdxxHg7gOdnwkN6wc3oE31NVyngeulO8UyMio7CoEZFR\nWNSIyCgsakRkFBY1IjKKw7Lubibvb/RkDkf3noyIjGJZFm7FvgPv1IjIKCxqRGQUFjUiMgqLGhEZ\npatjUl/+4h/BvOTER+RlLJfKgsO74FpXMw3zUBuPfzRyeNzq1576qMo+881/jx/7Kh5xanknYO7L\ntFX2QASPQ12NHIb5k0H8W2n4oz8Pc6KfNrxTIyKjsKgRkVFY1IjIKCxqRGQUFjUiMkpXu5+v7+Cj\nvFouvOHc7bJXZcEW3lSvVsPdzBFXA+aOFt74EHGv4g0Lo4KPFbtp4a5oeL8+buxyFb++gRV9PKCI\nyGojCHMiehvv1IjIKCxqRGQUFjUiMgqLGhEZhUWNiIzS1e7ncgMfhVcr6a6giMj8akVl0YrN0XF5\n3HHc8eC3WK7hTizScuPa/8a0nk0VERlcwe+n5dBH0G2H8ZxoJbQEc/dg56+b6KcR79SIyCgsakRk\nFBY1IjIKixoRGYVFjYiM0tXuZ6mBO3eVqp7xFBHZaBRUls9E4NpqWndKRUSaTvzYrkAPzJHFRBjm\nJ5ZwJ3begTuxjWi/yh67g+dKi1HcQU3ERmBORG/jnRoRGYVFjYiMwqJGREZhUSMio7CoEZFRutr9\nnO3X3UwRkXYV7+YaCVkqC8TxGaHWNn5OpxfvqtvY0Wdwiog8B7KDMdzN3Krixx7zt2AeXtVZewLv\n5Bvawm/IkcNdUSJ6G+/UiMgoLGpEZBQWNSIyCosaERmlq42CRwd3wTwYjME849YNhJFxPCbkzOEf\n3Dd2NmHe2sHH26FGQTGJN3IM1fBjV8fwaFZPakVl7kwUro0UczBPxxdhTkRv450aERmFRY2IjMKi\nRkRGYVEjIqOwqBGRUbra/bz+ta/DvBHHm0cubOoO5eDucbg2u12Cua+IN2GMhnHHFZl4cAbm5WQK\n5sMpPOLk6ttS2biFN5qc8p6Fea8bd2KJ6G28UyMio7CoEZFRWNSIyCgsakRkFBY1IjKKw7L0RoxE\nRP+/4p0aERmFRY2IjMKiRkRGYVEjIqOwqBGRUVjUiMgoLGpEZBQWNSIyCosaERmFRY2IjMKiRkRG\nYVEjIqOwqBGRUVjUiMgoLGpEZBQWNSIyCosaERmFRY2IjMKiRkRGYVEjIqOwqBGRUVjUiMgoLGpE\nZJT/DcgIm3f05ZMAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33ca6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different images and different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`.\n",
    "\n",
    "**IMPORTANT: for each feature channel, there's only ONE mean & var. Because we expect convovled result is concistent in various image location**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [  8.95129423   8.60368726  10.6709787 ]\n",
      "  Stds:  [ 3.38617949  4.02104684  4.23519119]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ -5.93969318e-16   2.33146835e-16   2.72004641e-16]\n",
      "  Stds:  [ 0.99999956  0.99999969  0.99999972]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.  7.  8.]\n",
      "  Stds:  [ 2.99999869  3.99999876  4.99999861]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print 'Before spatial batch normalization:'\n",
    "print '  Shape: ', x.shape\n",
    "print '  Means: ', x.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', x.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization:'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization (nontrivial gamma, beta):'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.02201284  0.04871229  0.0099652   0.03349758]\n",
      "  stds:  [ 0.99371114  1.00378607  1.00861488  1.01767897]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in xrange(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "    \n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After spatial batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=(0, 2, 3))\n",
    "print '  stds: ', a_norm.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  8.61843824863e-08\n",
      "dgamma error:  1.6860336193e-10\n",
      "dbeta error:  1.19032428651e-11\n"
     ]
    }
   ],
   "source": [
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment!\n",
    "Experiment and try to get the best performance that you can on CIFAR-10 using a ConvNet. Here are some ideas to get you started:\n",
    "\n",
    "### Things you should try:\n",
    "- Filter size: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- Number of filters: Above we used 32 filters. Do more or fewer do better?\n",
    "- Batch normalization: Try adding spatial batch normalization after convolution layers and vanilla batch normalization aafter affine layers. Do your networks train faster?\n",
    "- Network architecture: The network above has two layers of trainable parameters. Can you do better with a deeper network? You can implement alternative architectures in the file `cs231n/classifiers/convnet.py`. Some good architectures to try include:\n",
    "    - [conv-relu-pool]xN - conv - relu - [affine]xM - [softmax or SVM]\n",
    "    - [conv-relu-pool]XN - [affine]XM - [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN - [affine]xM - [softmax or SVM]\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the course-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at least 65% accuracy on the validation set. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training, validation, and test set accuracies for your final trained network. In this notebook you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers.convnet import *\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train a really good model on CIFAR-10\n",
    "model = MyConvNet((3, 32, 32), [100, 100], 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258529663\n",
      "Initial loss (with regularization):  2.30377237726\n"
     ]
    }
   ],
   "source": [
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (no regularization): ', loss\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (with regularization): ', loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W0 max relative error: 2.559890e-07\n",
      "shape = (3, 3, 3, 3)\n",
      "b0 max relative error: 4.562323e-08\n",
      "shape = (3,)\n",
      "beta0 max relative error: 1.269309e-06\n",
      "shape = (3,)\n",
      "fc:W0 max relative error: 8.985514e-06\n",
      "shape = (192, 7)\n",
      "fc:W1 max relative error: 5.148159e-09\n",
      "shape = (7, 10)\n",
      "fc:b0 max relative error: 2.775558e-09\n",
      "shape = (7,)\n",
      "fc:b1 max relative error: 7.983573e-10\n",
      "shape = (10,)\n",
      "fc:beta0 max relative error: 9.901253e-07\n",
      "shape = (7,)\n",
      "fc:gamma0 max relative error: 4.911058e-06\n",
      "shape = (7,)\n",
      "gamma0 max relative error: 1.937663e-07\n",
      "shape = (3,)\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.\n",
    "num_classes = 10\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = MyConvNet(input_dim, [7], 10, num_filters=[3], filter_size=3, dtype=np.float64, \n",
    "                  reg=reg, dropout=0.4, use_batchnorm=True, seed=123) # seed is important for dropout check\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))\n",
    "    print 'shape =', grads[param_name].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 20) loss: 2.302946\n",
      "(Epoch 0 / 10) train acc: 0.250000; val_acc: 0.146000\n",
      "(Iteration 2 / 20) loss: 2.293112\n",
      "(Epoch 1 / 10) train acc: 0.370000; val_acc: 0.136000\n",
      "(Iteration 3 / 20) loss: 2.278394\n",
      "(Iteration 4 / 20) loss: 2.266723\n",
      "(Epoch 2 / 10) train acc: 0.480000; val_acc: 0.173000\n",
      "(Iteration 5 / 20) loss: 2.243297\n",
      "(Iteration 6 / 20) loss: 2.202491\n",
      "(Epoch 3 / 10) train acc: 0.520000; val_acc: 0.188000\n",
      "(Iteration 7 / 20) loss: 2.186145\n",
      "(Iteration 8 / 20) loss: 2.172898\n",
      "(Epoch 4 / 10) train acc: 0.570000; val_acc: 0.199000\n",
      "(Iteration 9 / 20) loss: 2.178867\n",
      "(Iteration 10 / 20) loss: 2.086765\n",
      "(Epoch 5 / 10) train acc: 0.610000; val_acc: 0.205000\n",
      "(Iteration 11 / 20) loss: 2.089116\n",
      "(Iteration 12 / 20) loss: 2.043282\n",
      "(Epoch 6 / 10) train acc: 0.610000; val_acc: 0.207000\n",
      "(Iteration 13 / 20) loss: 1.998335\n",
      "(Iteration 14 / 20) loss: 2.002931\n",
      "(Epoch 7 / 10) train acc: 0.630000; val_acc: 0.214000\n",
      "(Iteration 15 / 20) loss: 1.921294\n",
      "(Iteration 16 / 20) loss: 1.935523\n",
      "(Epoch 8 / 10) train acc: 0.680000; val_acc: 0.204000\n",
      "(Iteration 17 / 20) loss: 1.862527\n",
      "(Iteration 18 / 20) loss: 1.861028\n",
      "(Epoch 9 / 10) train acc: 0.680000; val_acc: 0.194000\n",
      "(Iteration 19 / 20) loss: 1.857008\n",
      "(Iteration 20 / 20) loss: 1.801703\n",
      "(Epoch 10 / 10) train acc: 0.750000; val_acc: 0.203000\n"
     ]
    }
   ],
   "source": [
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = MyConvNet((3, 32, 32), [100], 10, num_filters=[100], filter_size=3, dtype=np.float64, \n",
    "                  reg=reg, dropout=0.4, use_batchnorm=True)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cDtvyc3NYsqA0kNeTzKNJ/SIikpFSOdG+/fl0lqX0lQKZiIhkpO4m2gcRlBbNKVYAkz7T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\nyMzMzMwycyAzMzMzy+z/AW3vFjt1+kYwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7640f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.301414\n",
      "(Epoch 0 / 5) train acc: 0.135000; val_acc: 0.130000\n",
      "(Iteration 101 / 2450) loss: 1.646185\n",
      "(Iteration 201 / 2450) loss: 1.366840\n",
      "(Iteration 301 / 2450) loss: 1.358478\n",
      "(Iteration 401 / 2450) loss: 1.388845\n",
      "(Epoch 1 / 5) train acc: 0.587000; val_acc: 0.602000\n",
      "(Iteration 501 / 2450) loss: 1.189738\n",
      "(Iteration 601 / 2450) loss: 1.279704\n",
      "(Iteration 701 / 2450) loss: 1.327070\n",
      "(Iteration 801 / 2450) loss: 1.015802\n",
      "(Iteration 901 / 2450) loss: 1.241868\n",
      "(Epoch 2 / 5) train acc: 0.684000; val_acc: 0.653000\n",
      "(Iteration 1001 / 2450) loss: 1.013656\n",
      "(Iteration 1101 / 2450) loss: 0.999788\n",
      "(Iteration 1201 / 2450) loss: 1.039294\n",
      "(Iteration 1301 / 2450) loss: 0.840934\n",
      "(Iteration 1401 / 2450) loss: 0.803364\n",
      "(Epoch 3 / 5) train acc: 0.723000; val_acc: 0.669000\n",
      "(Iteration 1501 / 2450) loss: 1.074615\n",
      "(Iteration 1601 / 2450) loss: 1.049856\n",
      "(Iteration 1701 / 2450) loss: 0.951684\n",
      "(Iteration 1801 / 2450) loss: 0.770122\n",
      "(Iteration 1901 / 2450) loss: 0.939765\n",
      "(Epoch 4 / 5) train acc: 0.736000; val_acc: 0.690000\n",
      "(Iteration 2001 / 2450) loss: 0.852381\n",
      "(Iteration 2101 / 2450) loss: 0.658431\n",
      "(Iteration 2201 / 2450) loss: 0.757980\n",
      "(Iteration 2301 / 2450) loss: 0.971709\n",
      "(Iteration 2401 / 2450) loss: 0.782054\n",
      "(Epoch 5 / 5) train acc: 0.753000; val_acc: 0.711000\n",
      "1e-06 0.005 0.001 last loss, tr_acc, val_acc = \t\t1.07551043697 0.753 0.711\n",
      "best_solver: 0.711\n"
     ]
    }
   ],
   "source": [
    "image_input_dim = (3, 32, 32)\n",
    "num_classes = 10\n",
    "\n",
    "best_model = None\n",
    "best_solver = None\n",
    "\n",
    "reg = 1e-6\n",
    "fc_hidden_dims = [100, 100]\n",
    "# for learning_rate in np.logspace(-4, -2, num=4):\n",
    "#     for weight_scale in np.logspace(-4, -1.5, num=4): \n",
    "learning_rate=1e-3\n",
    "weight_scale = 5e-3\n",
    "\n",
    "model = MyConvNet(image_input_dim, fc_hidden_dims, num_classes, \n",
    "                  num_filters=[32, 64], filter_size=3, dtype=np.float64, \n",
    "                  reg=reg, dropout=0.4, use_batchnorm=True)\n",
    "solver = Solver(model, data,\n",
    "                print_every=100, num_epochs=5, batch_size=100,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                },\n",
    "                verbose=True\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "if best_solver is None or solver.val_acc_history[-1] > best_solver.val_acc_history[-1]:\n",
    "    best_model = model\n",
    "    best_solver = solver\n",
    "\n",
    "print reg, weight_scale, learning_rate, 'last loss, tr_acc, val_acc = \\t\\t', \\\n",
    "    solver.loss_history[-1], solver.train_acc_history[-1], solver.val_acc_history[-1]\n",
    "\n",
    "print 'best_solver:', best_solver.val_acc_history[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W0': array([[[[-0.03780288, -0.01855142, -0.05937975],\n",
       "          [-0.03275102, -0.01549053, -0.05340646],\n",
       "          [-0.05941286, -0.03072535, -0.06662013]],\n",
       " \n",
       "         [[ 0.006921  ,  0.04711975, -0.01658528],\n",
       "          [ 0.03714612,  0.0760592 ,  0.01842994],\n",
       "          [ 0.01590948,  0.05333224, -0.01375311]],\n",
       " \n",
       "         [[-0.03255113,  0.02999483, -0.02375484],\n",
       "          [ 0.02241033,  0.07810508,  0.03240984],\n",
       "          [-0.00602538,  0.05185921, -0.00409745]]],\n",
       " \n",
       " \n",
       "        [[[-0.06384357, -0.04890819,  0.0466886 ],\n",
       "          [-0.04220463, -0.02065175,  0.00754903],\n",
       "          [ 0.05610441,  0.04636507, -0.08825561]],\n",
       " \n",
       "         [[-0.00773697,  0.0054345 ,  0.08756981],\n",
       "          [ 0.01353398,  0.02992413,  0.04543932],\n",
       "          [ 0.07808555,  0.06793123, -0.07404149]],\n",
       " \n",
       "         [[-0.10096351, -0.08813281,  0.03925737],\n",
       "          [-0.05782637, -0.04607122,  0.00844226],\n",
       "          [ 0.05668733,  0.04363059, -0.06817207]]],\n",
       " \n",
       " \n",
       "        [[[-0.0312169 , -0.00551195, -0.10037694],\n",
       "          [-0.0533458 ,  0.0816139 , -0.0481121 ],\n",
       "          [-0.0751438 ,  0.02410939,  0.01133046]],\n",
       " \n",
       "         [[ 0.04618409,  0.08079981, -0.01387284],\n",
       "          [ 0.01111324,  0.15425138,  0.02889966],\n",
       "          [-0.04734248,  0.05752   ,  0.06375891]],\n",
       " \n",
       "         [[-0.01432637,  0.00553873, -0.05595718],\n",
       "          [-0.05192935,  0.06279916, -0.04211585],\n",
       "          [-0.07522739, -0.00412187, -0.00063968]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01676318,  0.03465846, -0.01595406],\n",
       "          [ 0.0121828 ,  0.02509782,  0.00533037],\n",
       "          [-0.01961455, -0.00466203, -0.01050467]],\n",
       " \n",
       "         [[-0.00208616,  0.04072923, -0.02598694],\n",
       "          [-0.01125838,  0.02954887,  0.00447996],\n",
       "          [-0.05750026, -0.02231389, -0.02218339]],\n",
       " \n",
       "         [[ 0.10422236,  0.20555544,  0.0168653 ],\n",
       "          [ 0.07039416,  0.17269602,  0.09025044],\n",
       "          [-0.04295275,  0.02301149,  0.00429358]]],\n",
       " \n",
       " \n",
       "        [[[-0.06488038, -0.00334957, -0.01752027],\n",
       "          [ 0.00812401,  0.05758534,  0.03615458],\n",
       "          [ 0.03623686,  0.06067243,  0.02145416]],\n",
       " \n",
       "         [[-0.07361218,  0.02634508,  0.02108452],\n",
       "          [-0.00961579,  0.06864472,  0.06676436],\n",
       "          [ 0.00876534,  0.05361772,  0.02004066]],\n",
       " \n",
       "         [[-0.04854245,  0.04180703,  0.0333612 ],\n",
       "          [-0.00296556,  0.08108299,  0.06493028],\n",
       "          [-0.02865913,  0.01478654, -0.03265403]]],\n",
       " \n",
       " \n",
       "        [[[-0.01906419, -0.04493326,  0.03955777],\n",
       "          [ 0.01854234, -0.08184051, -0.04969036],\n",
       "          [ 0.07568782,  0.04176247,  0.0142325 ]],\n",
       " \n",
       "         [[-0.00589681, -0.05329044,  0.04043534],\n",
       "          [ 0.03580131, -0.08575154, -0.05280449],\n",
       "          [ 0.08697011,  0.04612826,  0.01024703]],\n",
       " \n",
       "         [[ 0.00856396, -0.021376  ,  0.06439827],\n",
       "          [ 0.02085666, -0.0756654 , -0.03588687],\n",
       "          [ 0.04091245,  0.01414417, -0.0064124 ]]],\n",
       " \n",
       " \n",
       "        [[[ 0.02923162,  0.04020282,  0.02275292],\n",
       "          [ 0.02562776,  0.04675046,  0.0355639 ],\n",
       "          [-0.04490725, -0.07468961, -0.06214036]],\n",
       " \n",
       "         [[ 0.01811537,  0.03724965,  0.02075818],\n",
       "          [ 0.00856177,  0.03759313,  0.03748561],\n",
       "          [-0.05757215, -0.09697728, -0.06401533]],\n",
       " \n",
       "         [[-0.0102963 ,  0.00355853, -0.00938139],\n",
       "          [ 0.0177948 ,  0.04332586,  0.04345783],\n",
       "          [-0.01088533, -0.04661319, -0.02895056]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01395665,  0.02060912, -0.01559707],\n",
       "          [ 0.03487041,  0.01861299, -0.04314951],\n",
       "          [ 0.0292535 ,  0.00794674, -0.03082388]],\n",
       " \n",
       "         [[ 0.02063652,  0.0101146 , -0.04049199],\n",
       "          [ 0.04451428,  0.01077543, -0.07410482],\n",
       "          [ 0.03137775, -0.00029199, -0.05366478]],\n",
       " \n",
       "         [[ 0.02773374,  0.0069298 , -0.03983661],\n",
       "          [ 0.05146111,  0.01298299, -0.07035666],\n",
       "          [ 0.04221058,  0.00771701, -0.04768874]]],\n",
       " \n",
       " \n",
       "        [[[-0.0623307 , -0.06825612, -0.02385821],\n",
       "          [-0.05793184, -0.06697002, -0.02752229],\n",
       "          [ 0.00961258,  0.00095272,  0.02778314]],\n",
       " \n",
       "         [[-0.09219484, -0.07800604, -0.02541643],\n",
       "          [-0.09730004, -0.09658614, -0.05246236],\n",
       "          [-0.04064987, -0.05561269, -0.02495735]],\n",
       " \n",
       "         [[-0.03611913, -0.0308697 , -0.00069973],\n",
       "          [-0.01585805, -0.02267853, -0.01124705],\n",
       "          [ 0.02073007,  0.00718965,  0.01463089]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04520494,  0.04424711,  0.03257474],\n",
       "          [-0.0181524 , -0.0055834 ,  0.00258451],\n",
       "          [-0.06835773, -0.09071793, -0.06123115]],\n",
       " \n",
       "         [[ 0.04990064,  0.05221375,  0.02900029],\n",
       "          [ 0.02203509,  0.03331443,  0.03272747],\n",
       "          [-0.03950423, -0.04854155, -0.03142203]],\n",
       " \n",
       "         [[-0.01213399, -0.01112981, -0.02171644],\n",
       "          [ 0.01086862,  0.01320005,  0.02033202],\n",
       "          [-0.01193977, -0.02531971, -0.01193406]]],\n",
       " \n",
       " \n",
       "        [[[-0.02172082, -0.04010917, -0.02198786],\n",
       "          [-0.01829066, -0.02868247, -0.01383817],\n",
       "          [-0.04695231, -0.05839439, -0.03645364]],\n",
       " \n",
       "         [[-0.01150024, -0.01576431, -0.01268867],\n",
       "          [ 0.01155608,  0.01843253,  0.01262304],\n",
       "          [-0.0495271 , -0.04965229, -0.04425188]],\n",
       " \n",
       "         [[ 0.01553082,  0.02268341,  0.02219092],\n",
       "          [ 0.06384653,  0.08072787,  0.06903445],\n",
       "          [-0.00736666,  0.00213528,  0.00250602]]],\n",
       " \n",
       " \n",
       "        [[[ 0.0199924 , -0.04015193,  0.02328814],\n",
       "          [ 0.03333544, -0.06436791,  0.03789702],\n",
       "          [ 0.02706936, -0.04262544,  0.01953136]],\n",
       " \n",
       "         [[ 0.0045067 , -0.06312987,  0.04021326],\n",
       "          [ 0.02910401, -0.08659312,  0.05562997],\n",
       "          [ 0.02162129, -0.06403619,  0.02523003]],\n",
       " \n",
       "         [[ 0.01296216, -0.04483942,  0.04016732],\n",
       "          [ 0.03142647, -0.0700558 ,  0.05142756],\n",
       "          [ 0.02994367, -0.05018393,  0.01771469]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01009513,  0.05867858, -0.02505722],\n",
       "          [ 0.04553255,  0.07592887,  0.01170957],\n",
       "          [-0.0221574 ,  0.01126558, -0.03304601]],\n",
       " \n",
       "         [[-0.0142212 ,  0.07072509, -0.01000484],\n",
       "          [ 0.01941978,  0.08562982,  0.02306943],\n",
       "          [-0.04820882,  0.00650018, -0.02328415]],\n",
       " \n",
       "         [[ 0.02382548,  0.10450335,  0.01850272],\n",
       "          [ 0.04651076,  0.11337176,  0.0292433 ],\n",
       "          [-0.0366847 ,  0.01685803, -0.03112697]]],\n",
       " \n",
       " \n",
       "        [[[-0.02438848,  0.03240134, -0.01500985],\n",
       "          [ 0.02888772,  0.01246098, -0.03448788],\n",
       "          [ 0.00872482, -0.03256649, -0.00543129]],\n",
       " \n",
       "         [[ 0.0428146 ,  0.08619202, -0.00830694],\n",
       "          [ 0.07859671,  0.02877358, -0.0585126 ],\n",
       "          [ 0.00666562, -0.06891884, -0.06847392]],\n",
       " \n",
       "         [[ 0.04727714,  0.09615347, -0.03586191],\n",
       "          [ 0.1054154 ,  0.03407645, -0.09035273],\n",
       "          [ 0.00816506, -0.08475182, -0.10829243]]],\n",
       " \n",
       " \n",
       "        [[[-0.0156559 , -0.06456344, -0.03044662],\n",
       "          [-0.04581817, -0.01204222,  0.02251767],\n",
       "          [ 0.05177289,  0.09330298,  0.02798193]],\n",
       " \n",
       "         [[-0.00955832, -0.06365915, -0.02087725],\n",
       "          [-0.04364812, -0.01065491,  0.03270219],\n",
       "          [ 0.04649533,  0.085231  ,  0.019817  ]],\n",
       " \n",
       "         [[-0.01077383, -0.0587116 , -0.01883068],\n",
       "          [-0.04820926, -0.02331402,  0.01752565],\n",
       "          [ 0.03891043,  0.06893124,  0.01948397]]],\n",
       " \n",
       " \n",
       "        [[[-0.01796789, -0.00608298,  0.02741002],\n",
       "          [-0.07581804,  0.02583029,  0.05887386],\n",
       "          [-0.05578941,  0.0443919 ,  0.03343659]],\n",
       " \n",
       "         [[-0.04201516, -0.03256253,  0.02103779],\n",
       "          [-0.10064571,  0.00880617,  0.06021937],\n",
       "          [-0.07368067,  0.03762356,  0.0299147 ]],\n",
       " \n",
       "         [[-0.02121538, -0.01171786,  0.03114486],\n",
       "          [-0.07517228,  0.02170553,  0.05708561],\n",
       "          [-0.0534231 ,  0.04217015,  0.03308845]]],\n",
       " \n",
       " \n",
       "        [[[-0.0925505 , -0.09697445, -0.06696312],\n",
       "          [-0.03249687, -0.00957441,  0.00820332],\n",
       "          [ 0.01387051,  0.02319133,  0.04547325]],\n",
       " \n",
       "         [[-0.0839019 , -0.07512487, -0.0634576 ],\n",
       "          [-0.00610558,  0.02328789,  0.01832911],\n",
       "          [ 0.01322149,  0.02294779,  0.02913911]],\n",
       " \n",
       "         [[-0.03239178, -0.02228693, -0.01218995],\n",
       "          [ 0.04460687,  0.06086408,  0.05733845],\n",
       "          [ 0.01765738,  0.01534393,  0.02070395]]],\n",
       " \n",
       " \n",
       "        [[[ 0.08669951,  0.1035561 ,  0.05675381],\n",
       "          [ 0.05012647,  0.06002272,  0.01338617],\n",
       "          [-0.03994467, -0.03645954, -0.07040152]],\n",
       " \n",
       "         [[-0.03695151, -0.04234366, -0.03336875],\n",
       "          [-0.02717869, -0.03086366, -0.02037519],\n",
       "          [ 0.01462715,  0.0113989 ,  0.02160606]],\n",
       " \n",
       "         [[-0.06212853, -0.07588618, -0.0301015 ],\n",
       "          [-0.04728906, -0.05920394, -0.01040318],\n",
       "          [ 0.04748898,  0.05471186,  0.08569813]]],\n",
       " \n",
       " \n",
       "        [[[-0.04967742, -0.03904796, -0.03599533],\n",
       "          [ 0.08397338,  0.11549396,  0.12697114],\n",
       "          [ 0.05341198,  0.08654641,  0.09851776]],\n",
       " \n",
       "         [[-0.03539288, -0.00824691, -0.00555437],\n",
       "          [ 0.00255036,  0.03989382,  0.04348854],\n",
       "          [-0.0433931 , -0.00720959,  0.00027946]],\n",
       " \n",
       "         [[-0.0179023 ,  0.02353173,  0.0297748 ],\n",
       "          [-0.01040918,  0.04021799,  0.04970605],\n",
       "          [-0.08259282, -0.03672899, -0.03199842]]],\n",
       " \n",
       " \n",
       "        [[[ 0.00746831,  0.0656278 ,  0.00860586],\n",
       "          [-0.11520192, -0.03414975,  0.03513201],\n",
       "          [-0.03384074, -0.10670819, -0.02361238]],\n",
       " \n",
       "         [[ 0.03856018,  0.09788682,  0.01862226],\n",
       "          [-0.07450747,  0.01316667,  0.08043285],\n",
       "          [-0.00325365, -0.06925824,  0.01455499]],\n",
       " \n",
       "         [[ 0.00481572,  0.05400663, -0.0153268 ],\n",
       "          [-0.07275207,  0.00768739,  0.05870792],\n",
       "          [ 0.0033034 , -0.04812784,  0.02431681]]],\n",
       " \n",
       " \n",
       "        [[[-0.08129937,  0.0494114 ,  0.0354666 ],\n",
       "          [ 0.03703306,  0.04568737,  0.00610177],\n",
       "          [ 0.04559168, -0.07663049, -0.06445087]],\n",
       " \n",
       "         [[-0.07014466,  0.06436152,  0.05140628],\n",
       "          [ 0.04367292,  0.05681046,  0.01272411],\n",
       "          [ 0.03206406, -0.09297774, -0.07257307]],\n",
       " \n",
       "         [[-0.05597744,  0.04570296,  0.03792059],\n",
       "          [ 0.03562848,  0.05125963,  0.01981384],\n",
       "          [ 0.03767774, -0.07073269, -0.05689837]]],\n",
       " \n",
       " \n",
       "        [[[ 0.05764393,  0.03065278, -0.05177881],\n",
       "          [ 0.09376361, -0.12697251, -0.03099721],\n",
       "          [-0.05555037, -0.10235788,  0.0813821 ]],\n",
       " \n",
       "         [[ 0.06480756,  0.05609035, -0.02370641],\n",
       "          [ 0.10239075, -0.08457665,  0.0039258 ],\n",
       "          [-0.0191934 , -0.04255659,  0.09668094]],\n",
       " \n",
       "         [[-0.00772465, -0.00371926, -0.04303417],\n",
       "          [ 0.0335208 , -0.11093598, -0.00397608],\n",
       "          [-0.03424285, -0.03508021,  0.10164294]]],\n",
       " \n",
       " \n",
       "        [[[-0.02273309, -0.03638459, -0.02358943],\n",
       "          [-0.03837347, -0.05428246, -0.03310595],\n",
       "          [-0.04507886, -0.05331257, -0.03456842]],\n",
       " \n",
       "         [[ 0.0094399 ,  0.00931359,  0.00566807],\n",
       "          [ 0.01614539,  0.01520713,  0.01619589],\n",
       "          [-0.00404708,  0.00177889, -0.0004178 ]],\n",
       " \n",
       "         [[ 0.03976535,  0.05109268,  0.03152749],\n",
       "          [ 0.06204586,  0.06976148,  0.06359064],\n",
       "          [ 0.03375546,  0.04922148,  0.04497452]]],\n",
       " \n",
       " \n",
       "        [[[ 0.05887719,  0.01156234, -0.11264952],\n",
       "          [-0.04728893,  0.08680314,  0.05256971],\n",
       "          [-0.00127165, -0.05311643,  0.04398554]],\n",
       " \n",
       "         [[ 0.04584039,  0.0274012 , -0.08242658],\n",
       "          [-0.07467648,  0.07104476,  0.05734352],\n",
       "          [-0.02242704, -0.08333367,  0.02904372]],\n",
       " \n",
       "         [[ 0.06760698,  0.04182985, -0.05187626],\n",
       "          [-0.05582486,  0.0678395 ,  0.0577969 ],\n",
       "          [-0.04012861, -0.08751586,  0.02749611]]],\n",
       " \n",
       " \n",
       "        [[[-0.05918316, -0.09642445, -0.06500001],\n",
       "          [ 0.05672324,  0.0675999 ,  0.03702538],\n",
       "          [ 0.02308566,  0.00357937,  0.00917327]],\n",
       " \n",
       "         [[-0.068948  , -0.10772692, -0.0849742 ],\n",
       "          [ 0.04172087,  0.05906515,  0.01294231],\n",
       "          [-0.00238449, -0.01882726, -0.03029225]],\n",
       " \n",
       "         [[-0.04130782, -0.06137312, -0.03246572],\n",
       "          [ 0.08176519,  0.11658688,  0.07892321],\n",
       "          [ 0.00118092, -0.00315343, -0.00268928]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04237023,  0.05546889,  0.04172067],\n",
       "          [ 0.01835914,  0.0383107 ,  0.02096337],\n",
       "          [-0.00682339,  0.00337463, -0.01307501]],\n",
       " \n",
       "         [[-0.01934059, -0.02653299, -0.02613054],\n",
       "          [-0.06547824, -0.06791785, -0.06545371],\n",
       "          [-0.04781819, -0.05743244, -0.06460748]],\n",
       " \n",
       "         [[ 0.01534863,  0.01157624,  0.01275623],\n",
       "          [ 0.00610535,  0.00332181,  0.00452119],\n",
       "          [ 0.03876121,  0.03287905,  0.01826629]]],\n",
       " \n",
       " \n",
       "        [[[ 0.0909172 ,  0.00590748, -0.05232792],\n",
       "          [ 0.12652077,  0.0841621 , -0.00245058],\n",
       "          [ 0.03799835,  0.08246414,  0.03183487]],\n",
       " \n",
       "         [[ 0.01596169, -0.01932584, -0.07873781],\n",
       "          [ 0.0191502 ,  0.02473203, -0.05428333],\n",
       "          [-0.04079037,  0.01358203, -0.01657919]],\n",
       " \n",
       "         [[ 0.06303399,  0.02595317, -0.07331343],\n",
       "          [ 0.06591636,  0.06436717, -0.05733252],\n",
       "          [-0.00679245,  0.02263052, -0.05103152]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01993512, -0.02912706,  0.00417581],\n",
       "          [ 0.01956277,  0.02522338,  0.01358176],\n",
       "          [ 0.02855995,  0.04247742,  0.05799434]],\n",
       " \n",
       "         [[-0.04391072, -0.0925573 , -0.09399283],\n",
       "          [-0.06332322, -0.05318676, -0.08585035],\n",
       "          [-0.07659844, -0.05155351, -0.0442783 ]],\n",
       " \n",
       "         [[ 0.10237554,  0.04073066,  0.0010866 ],\n",
       "          [ 0.08970382,  0.10294398,  0.02990713],\n",
       "          [ 0.01972623,  0.06316407,  0.04806092]]],\n",
       " \n",
       " \n",
       "        [[[ 0.02845306,  0.07621729,  0.01331353],\n",
       "          [-0.01180174, -0.06977259, -0.07219521],\n",
       "          [-0.01269855, -0.00628469,  0.03436708]],\n",
       " \n",
       "         [[ 0.04483627,  0.0807687 ,  0.01256314],\n",
       "          [-0.00720438, -0.07048444, -0.07710536],\n",
       "          [-0.01557201, -0.00754476,  0.03198591]],\n",
       " \n",
       "         [[ 0.0033404 ,  0.03953545,  0.00073901],\n",
       "          [-0.01011421, -0.05623927, -0.05378392],\n",
       "          [ 0.00032109,  0.01404299,  0.05394666]]],\n",
       " \n",
       " \n",
       "        [[[-0.01334358, -0.03705181,  0.06506658],\n",
       "          [ 0.04880323, -0.06528343, -0.04791459],\n",
       "          [ 0.03733805,  0.04801272, -0.03437592]],\n",
       " \n",
       "         [[-0.02696991, -0.03690227,  0.07806347],\n",
       "          [ 0.03860221, -0.06278814, -0.05784935],\n",
       "          [ 0.01819772,  0.04668343, -0.06200498]],\n",
       " \n",
       "         [[-0.04919309, -0.0925416 ,  0.02804553],\n",
       "          [ 0.04789689, -0.09154915, -0.08908037],\n",
       "          [ 0.0498789 ,  0.05011691, -0.06510671]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04561682, -0.01812775, -0.01507723],\n",
       "          [ 0.05668921, -0.00962777, -0.03724204],\n",
       "          [ 0.00953817, -0.0002635 , -0.00861964]],\n",
       " \n",
       "         [[ 0.06599755, -0.02885111, -0.04936792],\n",
       "          [ 0.07642033, -0.01834042, -0.06433292],\n",
       "          [ 0.02386324, -0.00023389, -0.0247523 ]],\n",
       " \n",
       "         [[ 0.05446933, -0.02350525, -0.04107998],\n",
       "          [ 0.067209  , -0.00903921, -0.05597879],\n",
       "          [ 0.02072558,  0.00468681, -0.01511599]]],\n",
       " \n",
       " \n",
       "        [[[-0.04247818, -0.03326674, -0.04350928],\n",
       "          [-0.02490121, -0.01243611, -0.02206185],\n",
       "          [-0.01199773, -0.00354918, -0.00646715]],\n",
       " \n",
       "         [[-0.02612405,  0.00371216, -0.02419857],\n",
       "          [ 0.0133777 ,  0.04294258,  0.01819668],\n",
       "          [-0.0034902 ,  0.02545182,  0.00435284]],\n",
       " \n",
       "         [[ 0.02936268,  0.06210652,  0.0087808 ],\n",
       "          [ 0.07701027,  0.11931218,  0.06617242],\n",
       "          [ 0.02618115,  0.06465857,  0.02592627]]]]),\n",
       " 'W1': array([[[[ -1.86407952e-02,  -4.58063455e-02,  -5.43216766e-02],\n",
       "          [ -2.97001443e-02,  -4.07028314e-02,  -4.03982624e-02],\n",
       "          [  2.04381591e-02,  -4.74174021e-03,   1.18827727e-02]],\n",
       " \n",
       "         [[  7.19299700e-02,   5.92362820e-03,  -1.50867370e-02],\n",
       "          [ -3.17997651e-03,  -2.63837622e-02,  -3.18302441e-02],\n",
       "          [ -5.96527783e-03,  -3.02561252e-02,   1.17214387e-02]],\n",
       " \n",
       "         [[ -1.79274589e-02,  -3.61934971e-02,  -2.88041063e-02],\n",
       "          [ -6.40866073e-02,  -5.16919915e-02,  -3.89583196e-02],\n",
       "          [  2.01940788e-03,  -1.79041341e-02,   3.95520238e-03]],\n",
       " \n",
       "         ..., \n",
       "         [[  6.23878070e-02,   3.70826875e-02,   3.33195238e-02],\n",
       "          [  2.41173618e-02,   2.46077308e-02,   1.76239498e-02],\n",
       "          [  2.03921184e-02,   8.96649389e-03,   3.28868720e-03]],\n",
       " \n",
       "         [[  1.75545128e-02,  -9.89566323e-02,  -1.04872976e-01],\n",
       "          [ -1.31673505e-02,  -1.14254703e-01,  -1.52198667e-01],\n",
       "          [  7.24186945e-02,  -3.34337443e-02,   3.03366524e-03]],\n",
       " \n",
       "         [[  3.08840147e-02,   1.77820237e-02,   2.08360614e-02],\n",
       "          [  3.30221307e-03,  -9.58989302e-03,  -1.42978064e-02],\n",
       "          [  5.30708783e-03,  -6.06456429e-03,  -8.33606513e-03]]],\n",
       " \n",
       " \n",
       "        [[[  2.56624320e-03,   2.15353136e-02,   1.61640728e-02],\n",
       "          [ -1.74568621e-02,   7.37965202e-03,   1.67719574e-02],\n",
       "          [ -4.00069654e-02,  -6.60743900e-02,  -1.58522210e-02]],\n",
       " \n",
       "         [[  4.66082853e-02,  -4.14138735e-02,  -8.99919986e-02],\n",
       "          [ -8.01218186e-02,  -8.02525696e-02,  -7.53202979e-02],\n",
       "          [ -3.11212716e-02,  -8.88153791e-04,   4.81094720e-03]],\n",
       " \n",
       "         [[  6.52353084e-03,  -1.08389484e-01,  -1.46410958e-01],\n",
       "          [  4.30186328e-02,   4.33114652e-02,  -1.89686559e-02],\n",
       "          [  8.94023767e-02,   1.14678207e-01,   1.30471746e-01]],\n",
       " \n",
       "         ..., \n",
       "         [[ -2.13416070e-03,  -2.55382907e-02,  -4.51809112e-02],\n",
       "          [ -4.91376237e-02,  -6.69530811e-02,  -3.86720079e-02],\n",
       "          [ -3.63218007e-02,  -3.17267189e-02,   3.24783440e-03]],\n",
       " \n",
       "         [[  5.40166360e-02,   2.68606808e-02,  -3.24620717e-02],\n",
       "          [ -5.61915362e-02,  -2.45903698e-02,  -4.66639596e-02],\n",
       "          [ -5.51711365e-02,   8.18972071e-03,  -2.59451433e-02]],\n",
       " \n",
       "         [[ -1.88598711e-02,  -1.85603877e-02,  -4.19593389e-02],\n",
       "          [ -3.32376275e-02,  -1.60796233e-02,  -1.49212892e-02],\n",
       "          [ -4.43959956e-02,  -2.03183891e-02,  -8.01666373e-03]]],\n",
       " \n",
       " \n",
       "        [[[ -2.10991770e-02,   9.52149262e-05,  -3.53323971e-03],\n",
       "          [  5.63385984e-02,   1.68703350e-02,  -5.48687842e-03],\n",
       "          [  3.18506243e-02,  -4.60674897e-03,  -3.55226567e-02]],\n",
       " \n",
       "         [[ -7.97640584e-02,  -3.43739226e-02,   4.19026540e-02],\n",
       "          [ -1.60819783e-01,  -8.88122893e-02,   3.55980264e-03],\n",
       "          [ -9.51746133e-02,  -7.34907491e-02,  -1.43431147e-03]],\n",
       " \n",
       "         [[ -1.26024034e-01,  -4.67651843e-02,  -5.80591999e-02],\n",
       "          [ -3.54492629e-02,  -7.01597260e-02,  -5.82272658e-02],\n",
       "          [  1.69084797e-02,  -4.06981082e-03,  -8.58573194e-03]],\n",
       " \n",
       "         ..., \n",
       "         [[ -3.12390702e-02,   2.46002104e-02,   6.80580701e-02],\n",
       "          [ -5.80901601e-02,   1.85874136e-02,   4.38872170e-02],\n",
       "          [ -4.85388310e-02,   1.93341665e-02,   7.81966302e-03]],\n",
       " \n",
       "         [[  1.29688132e-01,   3.92037878e-02,   1.93137818e-02],\n",
       "          [  1.44180084e-01,   6.32059215e-02,   4.51972379e-03],\n",
       "          [  9.09613502e-02,   3.45476678e-02,   9.04489768e-05]],\n",
       " \n",
       "         [[  7.63373996e-03,  -4.12000864e-02,  -4.98886703e-02],\n",
       "          [  7.32357094e-02,   5.37879983e-03,  -4.63602459e-02],\n",
       "          [  4.31837736e-02,  -3.47727124e-04,  -4.46926919e-02]]],\n",
       " \n",
       " \n",
       "        ..., \n",
       "        [[[  1.65180480e-02,   2.97487980e-02,   7.37540003e-03],\n",
       "          [  2.40638450e-02,   3.68759059e-02,   3.63922693e-02],\n",
       "          [ -3.43242044e-03,   7.75158794e-03,  -2.59407115e-03]],\n",
       " \n",
       "         [[  8.67983984e-03,  -2.31975555e-02,  -3.59792817e-02],\n",
       "          [  1.90399909e-02,  -4.73424947e-02,  -8.08389629e-02],\n",
       "          [  3.39097396e-02,  -1.73052517e-02,  -6.71002918e-03]],\n",
       " \n",
       "         [[  2.45287638e-02,   2.72011012e-02,  -3.45074784e-02],\n",
       "          [ -6.92725870e-02,  -6.24806004e-02,  -2.33197695e-02],\n",
       "          [  1.71025440e-02,   1.16734606e-02,   5.13666265e-02]],\n",
       " \n",
       "         ..., \n",
       "         [[ -7.57773062e-02,  -1.06847872e-01,   7.37469892e-02],\n",
       "          [ -4.94415443e-02,  -4.82874889e-02,   3.21124840e-02],\n",
       "          [ -5.21480122e-04,   2.72747955e-02,   5.03534121e-02]],\n",
       " \n",
       "         [[ -4.53233618e-02,  -4.10402323e-03,   1.43077041e-01],\n",
       "          [ -1.82002330e-01,  -2.01190971e-02,   2.32012476e-02],\n",
       "          [ -1.73310964e-01,  -3.69541170e-03,   1.95216536e-02]],\n",
       " \n",
       "         [[  1.18685716e-02,   2.18599262e-02,  -7.58366425e-04],\n",
       "          [ -1.50115495e-02,  -2.17861595e-02,  -2.43873251e-02],\n",
       "          [ -2.74644381e-02,  -3.66782321e-02,  -5.04765000e-02]]],\n",
       " \n",
       " \n",
       "        [[[ -3.56524033e-02,  -8.66447780e-02,  -8.06547517e-03],\n",
       "          [ -3.43729179e-02,  -1.10706981e-01,  -7.09793817e-02],\n",
       "          [ -8.06235086e-04,  -4.57415511e-02,  -8.73934458e-03]],\n",
       " \n",
       "         [[ -2.80928426e-02,   1.06186426e-01,  -6.24915949e-02],\n",
       "          [  8.50888042e-02,  -6.64228019e-02,  -3.63200309e-03],\n",
       "          [ -5.64884836e-02,   3.54224555e-02,  -2.34432412e-02]],\n",
       " \n",
       "         [[  1.79344501e-02,  -1.09473394e-01,  -8.09659688e-02],\n",
       "          [ -8.44922825e-02,  -3.93945662e-02,  -2.23095005e-02],\n",
       "          [ -5.91034753e-02,  -7.12359041e-02,  -5.45061452e-02]],\n",
       " \n",
       "         ..., \n",
       "         [[ -7.56713659e-02,  -7.91359631e-03,  -6.80510723e-03],\n",
       "          [ -2.63708322e-02,  -1.82776276e-02,   1.82264398e-02],\n",
       "          [  2.65539647e-02,   6.52680201e-03,  -6.47957310e-02]],\n",
       " \n",
       "         [[  6.68631011e-03,  -3.19752596e-02,   5.39318716e-02],\n",
       "          [ -4.14790181e-02,   7.97336501e-02,   8.30355531e-02],\n",
       "          [  8.93048268e-02,  -3.10267769e-02,  -2.17723531e-02]],\n",
       " \n",
       "         [[ -1.03078292e-01,  -1.05199581e-01,  -8.16381219e-02],\n",
       "          [ -4.85894674e-02,  -8.34572802e-02,  -4.20663410e-02],\n",
       "          [ -6.43867383e-03,  -3.56802438e-02,  -1.83675201e-02]]],\n",
       " \n",
       " \n",
       "        [[[  1.85433600e-02,   2.80835276e-02,   3.40799635e-02],\n",
       "          [  2.21990981e-02,   2.76906136e-02,   3.73774400e-02],\n",
       "          [ -3.32954899e-02,  -5.48976272e-02,  -4.74839110e-02]],\n",
       " \n",
       "         [[ -2.93633162e-03,   2.37658879e-02,   3.36793225e-02],\n",
       "          [ -3.24668596e-02,   1.57258133e-02,   4.84749580e-02],\n",
       "          [ -1.06463110e-02,   4.76202703e-02,   2.74873531e-02]],\n",
       " \n",
       "         [[  5.04215315e-03,  -6.66648532e-03,  -1.83948305e-02],\n",
       "          [  1.56513802e-02,  -4.15053772e-03,  -6.13657302e-03],\n",
       "          [  3.68631999e-02,  -3.54277821e-02,  -3.89027560e-04]],\n",
       " \n",
       "         ..., \n",
       "         [[ -3.61788965e-02,  -3.78753230e-02,   1.59763176e-02],\n",
       "          [ -4.56757083e-02,  -2.07797298e-02,   4.47622584e-02],\n",
       "          [ -4.15424402e-03,   2.15892477e-02,   5.23571696e-02]],\n",
       " \n",
       "         [[ -9.49149741e-02,  -1.33077705e-01,  -9.12214722e-02],\n",
       "          [ -1.18347802e-01,  -1.63094050e-01,  -2.99810627e-02],\n",
       "          [ -5.45639476e-02,  -7.63719526e-02,  -2.19578369e-02]],\n",
       " \n",
       "         [[  5.39035522e-03,  -1.16509533e-02,  -1.29864647e-02],\n",
       "          [  3.47560977e-02,   2.09315868e-02,   1.32516611e-02],\n",
       "          [  1.10780512e-02,  -1.37003518e-02,  -2.72822318e-02]]]]),\n",
       " 'b0': array([  3.59698270e-12,  -7.43902470e-11,   4.99023161e-11,\n",
       "         -1.49659120e-11,  -3.45113100e-11,  -3.20746245e-10,\n",
       "          1.68185362e-10,  -4.08361925e-10,   9.44058030e-12,\n",
       "          5.23355584e-11,   2.68544220e-11,   2.45628653e-10,\n",
       "          1.08688326e-11,  -6.74019018e-11,  -9.00343780e-11,\n",
       "          1.19632015e-10,  -4.98266217e-12,  -8.53195775e-11,\n",
       "          1.01155497e-11,  -7.55847265e-12,  -1.51258387e-10,\n",
       "          9.25294165e-11,  -1.83123009e-13,  -3.29153235e-11,\n",
       "          4.38902799e-11,   6.44831728e-11,   1.08755477e-12,\n",
       "          5.14482524e-11,   1.49707835e-11,  -2.51215970e-11,\n",
       "          8.09141208e-11,  -1.14103356e-11]),\n",
       " 'b1': array([ -7.94914186e-11,  -2.98362905e-10,   7.04838272e-10,\n",
       "          2.43701940e-10,  -5.37297416e-11,   3.97394215e-10,\n",
       "          4.12008129e-11,  -2.57132255e-11,   6.13627719e-10,\n",
       "         -7.57702638e-10,   1.19702357e-10,   2.44854742e-10,\n",
       "         -1.26588595e-10,   2.61476980e-10,  -4.64996880e-10,\n",
       "          6.65917731e-11,   5.15334936e-11,  -1.58583537e-10,\n",
       "          3.18136736e-10,  -4.73477479e-10,   1.20370874e-10,\n",
       "          3.56267825e-11,   2.64937879e-12,  -1.28173077e-10,\n",
       "          8.19004138e-11,  -1.43450189e-10,  -9.88995719e-11,\n",
       "         -1.20865614e-10,  -8.49252808e-10,  -3.26530500e-12,\n",
       "          5.37132659e-10,  -6.64368457e-11,  -3.85642237e-10,\n",
       "          4.76767533e-11,   9.23593162e-11,  -1.11860409e-10,\n",
       "          2.71337022e-10,  -2.02102031e-10,  -1.54404824e-10,\n",
       "         -3.65431184e-10,  -1.05421033e-10,  -4.47789652e-10,\n",
       "          1.92077603e-10,  -2.62773260e-10,  -2.03771941e-10,\n",
       "          1.45221257e-11,   1.45850598e-10,   2.24317859e-10,\n",
       "         -1.05286218e-10,   5.93013784e-10,   1.89107745e-10,\n",
       "         -1.23596245e-10,  -1.93889237e-10,  -2.65803167e-10,\n",
       "          8.99184846e-12,   1.65248505e-10,  -6.45965997e-10,\n",
       "          1.36435241e-10,  -1.75436913e-10,   5.82344977e-11,\n",
       "          1.13505641e-10,   1.87319681e-10,   8.12459778e-10,\n",
       "          6.11631546e-10]),\n",
       " 'beta0': array([ 0.09320518, -0.03795648, -0.08732203, -0.23676322, -0.33890382,\n",
       "        -0.01009313, -0.07826558, -0.06287737, -0.28308307, -0.0021411 ,\n",
       "        -0.02549193, -0.07206649, -0.2863651 , -0.12321823,  0.02431104,\n",
       "        -0.00221942, -0.18878307, -0.20547304, -0.26248693, -0.27482364,\n",
       "        -0.28559234, -0.25926268,  0.1049994 , -0.28136991, -0.05453365,\n",
       "        -0.17545811, -0.22490686, -0.03936156, -0.03478222, -0.25672556,\n",
       "         0.02914835, -0.20759289]),\n",
       " 'beta1': array([-0.43555402, -0.30437887, -0.47353869, -0.17149783, -0.37462661,\n",
       "        -0.21675791, -0.23546613, -0.18151344, -0.32464463, -0.50194007,\n",
       "        -0.33842408, -0.37955417, -0.3210801 , -0.24190596, -0.17729062,\n",
       "        -0.44667316, -0.26910405, -0.45033552, -0.21083149, -0.21859796,\n",
       "        -0.40319054, -0.37342949, -0.45021455, -0.33994949, -0.34204719,\n",
       "        -0.30159944, -0.39089128, -0.35439435, -0.46312196, -0.42142799,\n",
       "        -0.14802091, -0.38476816, -0.20510949, -0.38356702, -0.30390213,\n",
       "        -0.23081493, -0.20577482, -0.28334736, -0.24669659, -0.2679828 ,\n",
       "        -0.45583686, -0.47468044, -0.39811648, -0.29898412, -0.49926316,\n",
       "        -0.34565269, -0.45351177, -0.37194317, -0.31221466, -0.56244929,\n",
       "        -0.35306892, -0.24127208, -0.39862044, -0.33778969, -0.41605977,\n",
       "        -0.34702995, -0.45977547, -0.35333713, -0.36867456, -0.18289609,\n",
       "        -0.40586404, -0.28275744, -0.24148048, -0.39442445]),\n",
       " 'fc:W0': array([[ 0.07857548,  0.0292612 ,  0.13575068, ..., -0.03319471,\n",
       "          0.00919441,  0.00663342],\n",
       "        [ 0.06646953,  0.01946808,  0.04044041, ..., -0.0376209 ,\n",
       "          0.02298589,  0.02115817],\n",
       "        [ 0.00912126,  0.04508648,  0.00305906, ..., -0.04107363,\n",
       "          0.01453663,  0.00947008],\n",
       "        ..., \n",
       "        [ 0.00963811, -0.06917201,  0.0066251 , ..., -0.05321382,\n",
       "         -0.00956671, -0.08071645],\n",
       "        [ 0.00952984,  0.04654211, -0.01115423, ..., -0.06604286,\n",
       "         -0.05221078,  0.07231538],\n",
       "        [-0.07253657, -0.02601997,  0.0121176 , ..., -0.00181671,\n",
       "         -0.05894005,  0.04754883]]),\n",
       " 'fc:W1': array([[ 0.00018971, -0.01239426,  0.07868496, ..., -0.04677279,\n",
       "         -0.03011606, -0.00071672],\n",
       "        [ 0.00663783, -0.03143199,  0.04768223, ..., -0.06019918,\n",
       "          0.01865977,  0.06155434],\n",
       "        [ 0.00858565, -0.03588261,  0.01568192, ...,  0.03439568,\n",
       "         -0.06037485,  0.03418546],\n",
       "        ..., \n",
       "        [-0.00095293, -0.07125251, -0.03576155, ...,  0.11262721,\n",
       "         -0.03619949,  0.02076042],\n",
       "        [-0.02096646,  0.01319681,  0.00429555, ...,  0.02210697,\n",
       "          0.01920589, -0.15487247],\n",
       "        [-0.03684361, -0.06724452, -0.0114549 , ..., -0.0479485 ,\n",
       "          0.01432299, -0.01395713]]),\n",
       " 'fc:W2': array([[ -1.22370566e-01,   5.87704382e-02,   4.69475977e-02,\n",
       "          -6.66948585e-02,   2.74819735e-02,  -1.13386099e-01,\n",
       "          -1.52864336e-01,   1.01960880e-01,  -2.07187627e-01,\n",
       "           8.53817605e-02],\n",
       "        [ -1.18133947e-01,  -1.92993235e-01,  -1.19066896e-01,\n",
       "           7.58967118e-02,   1.00024659e-01,   8.12746211e-02,\n",
       "           5.94040210e-02,   8.19986665e-02,  -1.72726167e-01,\n",
       "          -1.98171840e-01],\n",
       "        [  6.63930640e-02,   8.19226569e-02,  -3.61212262e-02,\n",
       "           2.93641349e-02,   6.43859173e-02,  -5.56898194e-02,\n",
       "          -1.23394847e-01,  -1.58539412e-01,  -6.27958534e-02,\n",
       "          -4.71946304e-02],\n",
       "        [ -2.78417954e-01,  -2.05346167e-01,   7.09044557e-02,\n",
       "           9.07393276e-03,   1.02726582e-01,   3.32963658e-02,\n",
       "           9.06268806e-02,   1.01637707e-01,  -2.44851930e-01,\n",
       "          -2.56653603e-01],\n",
       "        [  4.12449342e-02,   8.08355818e-02,  -6.00557664e-02,\n",
       "          -1.24566226e-01,  -1.02748176e-01,  -1.27163751e-01,\n",
       "           1.24559706e-01,  -2.09016907e-01,  -7.77428105e-02,\n",
       "           7.89631444e-02],\n",
       "        [  7.21415939e-02,   7.50008560e-02,  -6.64381120e-02,\n",
       "          -1.47557418e-01,  -6.76920461e-02,  -1.27903586e-01,\n",
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       "        -0.31869792, -0.24722791, -0.35298502, -0.33435119, -0.37457694,\n",
       "        -0.25952627, -0.23709282, -0.28086262, -0.32000714, -0.23438135,\n",
       "        -0.25296674, -0.29806385, -0.13490697, -0.32126387, -0.36419589,\n",
       "        -0.31788745, -0.15557757, -0.31141286, -0.33393894, -0.35830063,\n",
       "        -0.30906183, -0.22850635, -0.06087566, -0.24336189, -0.35205025,\n",
       "        -0.21271914, -0.21301699, -0.25336101, -0.25733836, -0.21643714,\n",
       "        -0.30368413, -0.34228199, -0.2809623 , -0.29175962, -0.3593822 ]),\n",
       " 'fc:beta1': array([ 0.05783552,  0.10992808,  0.14829905,  0.22055637,  0.18128731,\n",
       "         0.19164743,  0.17593566,  0.11786047,  0.14606098,  0.11429009,\n",
       "         0.13907392,  0.11582068,  0.16335365,  0.07776309,  0.21601075,\n",
       "         0.10172566,  0.12238763,  0.17745977,  0.15988623,  0.17104362,\n",
       "         0.10867815,  0.16052258,  0.1304687 ,  0.25715465,  0.09051253,\n",
       "         0.233024  ,  0.09828186,  0.20617548,  0.15938272,  0.14941353,\n",
       "         0.13594848,  0.14365754,  0.07815524,  0.12306999,  0.17546608,\n",
       "         0.05687307,  0.11794332,  0.00515947,  0.10158458,  0.15664578,\n",
       "         0.10156496,  0.09892302,  0.16772684,  0.08093909,  0.25628816,\n",
       "         0.08835593,  0.1388102 ,  0.1578673 ,  0.15120757,  0.06765657,\n",
       "         0.186184  ,  0.15883521,  0.11626266,  0.13366707,  0.14301148,\n",
       "         0.10390214, -0.00641229,  0.082679  ,  0.0419402 ,  0.08529926,\n",
       "         0.12026177,  0.12177164,  0.06072421,  0.0843727 ,  0.06938194,\n",
       "         0.15145993,  0.19309594,  0.06533861,  0.2950469 ,  0.11448307,\n",
       "         0.17773059,  0.16011342,  0.21856068,  0.20208539,  0.15727863,\n",
       "         0.00334999,  0.10140672,  0.14854373,  0.04742502,  0.14130338,\n",
       "         0.14187228,  0.09809913,  0.14173702,  0.09280391,  0.05860987,\n",
       "         0.16681259,  0.13763197,  0.19070351,  0.12552169,  0.12296764,\n",
       "         0.17333687,  0.08982382,  0.18226412,  0.15092879,  0.08825783,\n",
       "         0.16992917,  0.1201498 ,  0.15034244,  0.15234276,  0.20146669]),\n",
       " 'fc:gamma0': array([ 0.92823782,  0.95980761,  0.90767686,  0.95754523,  0.96661906,\n",
       "         0.98639159,  0.98660151,  0.94357133,  0.90284894,  0.95355938,\n",
       "         0.9669824 ,  0.98504991,  1.01151621,  0.97857642,  0.99951616,\n",
       "         0.94527403,  1.00842443,  0.95673922,  1.02233866,  0.98050354,\n",
       "         0.97419471,  0.98089497,  0.921467  ,  1.02160819,  0.99704501,\n",
       "         0.97009965,  0.99512239,  0.95654552,  0.9697865 ,  0.97226072,\n",
       "         0.99879586,  0.96874409,  0.90307622,  0.96960471,  0.93131978,\n",
       "         0.9622308 ,  0.96284497,  0.96935405,  0.99544359,  1.00375465,\n",
       "         0.95819591,  0.9354423 ,  1.00656526,  0.94217988,  1.0369051 ,\n",
       "         0.92624333,  0.92861038,  0.96452984,  0.96332056,  1.04364146,\n",
       "         0.96923697,  0.91737321,  0.95640715,  0.98115151,  1.02421039,\n",
       "         0.95685208,  0.96053337,  0.98548716,  0.99523097,  0.99684935,\n",
       "         0.96850193,  0.93333621,  0.94034072,  0.97619621,  0.91914096,\n",
       "         0.96438128,  0.94949974,  0.98355841,  0.97356174,  0.94437139,\n",
       "         0.98143055,  0.9801581 ,  0.96646658,  0.98225168,  0.94803264,\n",
       "         0.95842008,  0.97859558,  0.97475866,  0.94987943,  0.98152874,\n",
       "         0.95261978,  0.99668324,  0.91504354,  1.00237103,  0.96959515,\n",
       "         1.00369843,  0.94573935,  0.96672172,  0.98143186,  0.95601437,\n",
       "         0.93516705,  0.93275897,  0.95979112,  0.93175624,  0.97785561,\n",
       "         0.95347084,  1.00181991,  0.93398906,  0.97378701,  0.98975347]),\n",
       " 'fc:gamma1': array([ 1.25393922,  1.34329764,  1.15082166,  1.497292  ,  1.25687665,\n",
       "         1.24751485,  1.1580263 ,  1.23233362,  1.26999504,  1.22198991,\n",
       "         1.26509133,  1.26125366,  1.22956425,  1.23973516,  1.37979792,\n",
       "         1.22705841,  1.1573139 ,  1.36829361,  1.21636373,  1.28629506,\n",
       "         1.34444417,  1.26002951,  1.2445657 ,  1.32403396,  1.22638168,\n",
       "         1.44710251,  1.24204347,  1.27025629,  1.21793572,  1.36299479,\n",
       "         1.20122602,  1.26356414,  1.17911026,  1.28009155,  1.29497945,\n",
       "         1.29171129,  1.24889041,  1.2316481 ,  1.21190995,  1.28504628,\n",
       "         1.33904168,  1.23980267,  1.37737514,  1.24100514,  1.35314666,\n",
       "         1.24411702,  1.30590521,  1.24781425,  1.21650449,  1.24858766,\n",
       "         1.28422511,  1.29425026,  1.31235709,  1.24485787,  1.21259003,\n",
       "         1.2490617 ,  1.18231234,  1.30554149,  1.17014732,  1.30211544,\n",
       "         1.17969277,  1.24843978,  1.20615328,  1.1657117 ,  1.24954669,\n",
       "         1.33064095,  1.22388955,  1.23615946,  1.27579321,  1.21178664,\n",
       "         1.27983305,  1.21450938,  1.28001127,  1.3046533 ,  1.19462281,\n",
       "         1.2531129 ,  1.21406933,  1.23484431,  1.27086603,  1.25849114,\n",
       "         1.22172003,  1.20002556,  1.25651742,  1.19896643,  1.27379506,\n",
       "         1.28721878,  1.21574946,  1.33235147,  1.25394518,  1.28246132,\n",
       "         1.26572949,  1.23095746,  1.24239188,  1.25941363,  1.21677162,\n",
       "         1.25270712,  1.30243682,  1.28543696,  1.38837702,  1.25818374]),\n",
       " 'gamma0': array([ 0.86598266,  0.9038979 ,  0.9338179 ,  0.68311432,  0.6139916 ,\n",
       "         1.13192604,  0.93480759,  1.13134933,  0.83107577,  0.86124786,\n",
       "         0.84271128,  1.34896218,  0.66061673,  0.86040615,  1.12334989,\n",
       "         1.2680722 ,  0.87405707,  1.09346575,  0.74150516,  0.88242753,\n",
       "         0.87211357,  0.96098444,  0.84559845,  0.9251698 ,  0.80773208,\n",
       "         0.96556953,  0.74457346,  0.77272155,  1.11014076,  0.90069774,\n",
       "         1.10805075,  0.64127061]),\n",
       " 'gamma1': array([ 1.01615121,  0.91513244,  1.13916895,  1.05756525,  0.99564318,\n",
       "         0.88016363,  0.90185151,  0.92289813,  0.88144794,  0.87662681,\n",
       "         0.9627953 ,  0.85863639,  0.91726112,  1.08756334,  0.85112838,\n",
       "         0.94328375,  0.9720368 ,  0.97431916,  1.15403214,  0.97766906,\n",
       "         0.84244168,  0.98645261,  1.01419424,  0.86133223,  1.08622522,\n",
       "         0.95944052,  0.90798593,  0.85713347,  1.2252162 ,  0.86014723,\n",
       "         0.95885427,  0.85533263,  1.15814659,  0.83796665,  0.93708351,\n",
       "         0.93364597,  0.98112543,  1.03153613,  0.8957984 ,  0.97390983,\n",
       "         0.95313469,  0.86059392,  1.00042407,  1.03107729,  0.96255335,\n",
       "         0.91266241,  0.99508753,  0.99555561,  1.00213495,  1.00521814,\n",
       "         0.94483208,  0.82052587,  0.9608122 ,  0.88868695,  1.03146829,\n",
       "         0.77000143,  1.08549932,  0.84239575,  0.94729457,  0.92207167,\n",
       "         0.95582238,  0.92896078,  1.03781878,  0.984532  ])}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W0': array([[[[-0.03780288, -0.01855142, -0.05937975],\n",
       "          [-0.03275102, -0.01549053, -0.05340646],\n",
       "          [-0.05941286, -0.03072535, -0.06662013]],\n",
       " \n",
       "         [[ 0.006921  ,  0.04711975, -0.01658528],\n",
       "          [ 0.03714612,  0.0760592 ,  0.01842994],\n",
       "          [ 0.01590948,  0.05333224, -0.01375311]],\n",
       " \n",
       "         [[-0.03255113,  0.02999483, -0.02375484],\n",
       "          [ 0.02241033,  0.07810508,  0.03240984],\n",
       "          [-0.00602538,  0.05185921, -0.00409745]]],\n",
       " \n",
       " \n",
       "        [[[-0.06384357, -0.04890819,  0.0466886 ],\n",
       "          [-0.04220463, -0.02065175,  0.00754903],\n",
       "          [ 0.05610441,  0.04636507, -0.08825561]],\n",
       " \n",
       "         [[-0.00773697,  0.0054345 ,  0.08756981],\n",
       "          [ 0.01353398,  0.02992413,  0.04543932],\n",
       "          [ 0.07808555,  0.06793123, -0.07404149]],\n",
       " \n",
       "         [[-0.10096351, -0.08813281,  0.03925737],\n",
       "          [-0.05782637, -0.04607122,  0.00844226],\n",
       "          [ 0.05668733,  0.04363059, -0.06817207]]],\n",
       " \n",
       " \n",
       "        [[[-0.0312169 , -0.00551195, -0.10037694],\n",
       "          [-0.0533458 ,  0.0816139 , -0.0481121 ],\n",
       "          [-0.0751438 ,  0.02410939,  0.01133046]],\n",
       " \n",
       "         [[ 0.04618409,  0.08079981, -0.01387284],\n",
       "          [ 0.01111324,  0.15425138,  0.02889966],\n",
       "          [-0.04734248,  0.05752   ,  0.06375891]],\n",
       " \n",
       "         [[-0.01432637,  0.00553873, -0.05595718],\n",
       "          [-0.05192935,  0.06279916, -0.04211585],\n",
       "          [-0.07522739, -0.00412187, -0.00063968]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01676318,  0.03465846, -0.01595406],\n",
       "          [ 0.0121828 ,  0.02509782,  0.00533037],\n",
       "          [-0.01961455, -0.00466203, -0.01050467]],\n",
       " \n",
       "         [[-0.00208616,  0.04072923, -0.02598694],\n",
       "          [-0.01125838,  0.02954887,  0.00447996],\n",
       "          [-0.05750026, -0.02231389, -0.02218339]],\n",
       " \n",
       "         [[ 0.10422236,  0.20555544,  0.0168653 ],\n",
       "          [ 0.07039416,  0.17269602,  0.09025044],\n",
       "          [-0.04295275,  0.02301149,  0.00429358]]],\n",
       " \n",
       " \n",
       "        [[[-0.06488038, -0.00334957, -0.01752027],\n",
       "          [ 0.00812401,  0.05758534,  0.03615458],\n",
       "          [ 0.03623686,  0.06067243,  0.02145416]],\n",
       " \n",
       "         [[-0.07361218,  0.02634508,  0.02108452],\n",
       "          [-0.00961579,  0.06864472,  0.06676436],\n",
       "          [ 0.00876534,  0.05361772,  0.02004066]],\n",
       " \n",
       "         [[-0.04854245,  0.04180703,  0.0333612 ],\n",
       "          [-0.00296556,  0.08108299,  0.06493028],\n",
       "          [-0.02865913,  0.01478654, -0.03265403]]],\n",
       " \n",
       " \n",
       "        [[[-0.01906419, -0.04493326,  0.03955777],\n",
       "          [ 0.01854234, -0.08184051, -0.04969036],\n",
       "          [ 0.07568782,  0.04176247,  0.0142325 ]],\n",
       " \n",
       "         [[-0.00589681, -0.05329044,  0.04043534],\n",
       "          [ 0.03580131, -0.08575154, -0.05280449],\n",
       "          [ 0.08697011,  0.04612826,  0.01024703]],\n",
       " \n",
       "         [[ 0.00856396, -0.021376  ,  0.06439827],\n",
       "          [ 0.02085666, -0.0756654 , -0.03588687],\n",
       "          [ 0.04091245,  0.01414417, -0.0064124 ]]],\n",
       " \n",
       " \n",
       "        [[[ 0.02923162,  0.04020282,  0.02275292],\n",
       "          [ 0.02562776,  0.04675046,  0.0355639 ],\n",
       "          [-0.04490725, -0.07468961, -0.06214036]],\n",
       " \n",
       "         [[ 0.01811537,  0.03724965,  0.02075818],\n",
       "          [ 0.00856177,  0.03759313,  0.03748561],\n",
       "          [-0.05757215, -0.09697728, -0.06401533]],\n",
       " \n",
       "         [[-0.0102963 ,  0.00355853, -0.00938139],\n",
       "          [ 0.0177948 ,  0.04332586,  0.04345783],\n",
       "          [-0.01088533, -0.04661319, -0.02895056]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01395665,  0.02060912, -0.01559707],\n",
       "          [ 0.03487041,  0.01861299, -0.04314951],\n",
       "          [ 0.0292535 ,  0.00794674, -0.03082388]],\n",
       " \n",
       "         [[ 0.02063652,  0.0101146 , -0.04049199],\n",
       "          [ 0.04451428,  0.01077543, -0.07410482],\n",
       "          [ 0.03137775, -0.00029199, -0.05366478]],\n",
       " \n",
       "         [[ 0.02773374,  0.0069298 , -0.03983661],\n",
       "          [ 0.05146111,  0.01298299, -0.07035666],\n",
       "          [ 0.04221058,  0.00771701, -0.04768874]]],\n",
       " \n",
       " \n",
       "        [[[-0.0623307 , -0.06825612, -0.02385821],\n",
       "          [-0.05793184, -0.06697002, -0.02752229],\n",
       "          [ 0.00961258,  0.00095272,  0.02778314]],\n",
       " \n",
       "         [[-0.09219484, -0.07800604, -0.02541643],\n",
       "          [-0.09730004, -0.09658614, -0.05246236],\n",
       "          [-0.04064987, -0.05561269, -0.02495735]],\n",
       " \n",
       "         [[-0.03611913, -0.0308697 , -0.00069973],\n",
       "          [-0.01585805, -0.02267853, -0.01124705],\n",
       "          [ 0.02073007,  0.00718965,  0.01463089]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04520494,  0.04424711,  0.03257474],\n",
       "          [-0.0181524 , -0.0055834 ,  0.00258451],\n",
       "          [-0.06835773, -0.09071793, -0.06123115]],\n",
       " \n",
       "         [[ 0.04990064,  0.05221375,  0.02900029],\n",
       "          [ 0.02203509,  0.03331443,  0.03272747],\n",
       "          [-0.03950423, -0.04854155, -0.03142203]],\n",
       " \n",
       "         [[-0.01213399, -0.01112981, -0.02171644],\n",
       "          [ 0.01086862,  0.01320005,  0.02033202],\n",
       "          [-0.01193977, -0.02531971, -0.01193406]]],\n",
       " \n",
       " \n",
       "        [[[-0.02172082, -0.04010917, -0.02198786],\n",
       "          [-0.01829066, -0.02868247, -0.01383817],\n",
       "          [-0.04695231, -0.05839439, -0.03645364]],\n",
       " \n",
       "         [[-0.01150024, -0.01576431, -0.01268867],\n",
       "          [ 0.01155608,  0.01843253,  0.01262304],\n",
       "          [-0.0495271 , -0.04965229, -0.04425188]],\n",
       " \n",
       "         [[ 0.01553082,  0.02268341,  0.02219092],\n",
       "          [ 0.06384653,  0.08072787,  0.06903445],\n",
       "          [-0.00736666,  0.00213528,  0.00250602]]],\n",
       " \n",
       " \n",
       "        [[[ 0.0199924 , -0.04015193,  0.02328814],\n",
       "          [ 0.03333544, -0.06436791,  0.03789702],\n",
       "          [ 0.02706936, -0.04262544,  0.01953136]],\n",
       " \n",
       "         [[ 0.0045067 , -0.06312987,  0.04021326],\n",
       "          [ 0.02910401, -0.08659312,  0.05562997],\n",
       "          [ 0.02162129, -0.06403619,  0.02523003]],\n",
       " \n",
       "         [[ 0.01296216, -0.04483942,  0.04016732],\n",
       "          [ 0.03142647, -0.0700558 ,  0.05142756],\n",
       "          [ 0.02994367, -0.05018393,  0.01771469]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01009513,  0.05867858, -0.02505722],\n",
       "          [ 0.04553255,  0.07592887,  0.01170957],\n",
       "          [-0.0221574 ,  0.01126558, -0.03304601]],\n",
       " \n",
       "         [[-0.0142212 ,  0.07072509, -0.01000484],\n",
       "          [ 0.01941978,  0.08562982,  0.02306943],\n",
       "          [-0.04820882,  0.00650018, -0.02328415]],\n",
       " \n",
       "         [[ 0.02382548,  0.10450335,  0.01850272],\n",
       "          [ 0.04651076,  0.11337176,  0.0292433 ],\n",
       "          [-0.0366847 ,  0.01685803, -0.03112697]]],\n",
       " \n",
       " \n",
       "        [[[-0.02438848,  0.03240134, -0.01500985],\n",
       "          [ 0.02888772,  0.01246098, -0.03448788],\n",
       "          [ 0.00872482, -0.03256649, -0.00543129]],\n",
       " \n",
       "         [[ 0.0428146 ,  0.08619202, -0.00830694],\n",
       "          [ 0.07859671,  0.02877358, -0.0585126 ],\n",
       "          [ 0.00666562, -0.06891884, -0.06847392]],\n",
       " \n",
       "         [[ 0.04727714,  0.09615347, -0.03586191],\n",
       "          [ 0.1054154 ,  0.03407645, -0.09035273],\n",
       "          [ 0.00816506, -0.08475182, -0.10829243]]],\n",
       " \n",
       " \n",
       "        [[[-0.0156559 , -0.06456344, -0.03044662],\n",
       "          [-0.04581817, -0.01204222,  0.02251767],\n",
       "          [ 0.05177289,  0.09330298,  0.02798193]],\n",
       " \n",
       "         [[-0.00955832, -0.06365915, -0.02087725],\n",
       "          [-0.04364812, -0.01065491,  0.03270219],\n",
       "          [ 0.04649533,  0.085231  ,  0.019817  ]],\n",
       " \n",
       "         [[-0.01077383, -0.0587116 , -0.01883068],\n",
       "          [-0.04820926, -0.02331402,  0.01752565],\n",
       "          [ 0.03891043,  0.06893124,  0.01948397]]],\n",
       " \n",
       " \n",
       "        [[[-0.01796789, -0.00608298,  0.02741002],\n",
       "          [-0.07581804,  0.02583029,  0.05887386],\n",
       "          [-0.05578941,  0.0443919 ,  0.03343659]],\n",
       " \n",
       "         [[-0.04201516, -0.03256253,  0.02103779],\n",
       "          [-0.10064571,  0.00880617,  0.06021937],\n",
       "          [-0.07368067,  0.03762356,  0.0299147 ]],\n",
       " \n",
       "         [[-0.02121538, -0.01171786,  0.03114486],\n",
       "          [-0.07517228,  0.02170553,  0.05708561],\n",
       "          [-0.0534231 ,  0.04217015,  0.03308845]]],\n",
       " \n",
       " \n",
       "        [[[-0.0925505 , -0.09697445, -0.06696312],\n",
       "          [-0.03249687, -0.00957441,  0.00820332],\n",
       "          [ 0.01387051,  0.02319133,  0.04547325]],\n",
       " \n",
       "         [[-0.0839019 , -0.07512487, -0.0634576 ],\n",
       "          [-0.00610558,  0.02328789,  0.01832911],\n",
       "          [ 0.01322149,  0.02294779,  0.02913911]],\n",
       " \n",
       "         [[-0.03239178, -0.02228693, -0.01218995],\n",
       "          [ 0.04460687,  0.06086408,  0.05733845],\n",
       "          [ 0.01765738,  0.01534393,  0.02070395]]],\n",
       " \n",
       " \n",
       "        [[[ 0.08669951,  0.1035561 ,  0.05675381],\n",
       "          [ 0.05012647,  0.06002272,  0.01338617],\n",
       "          [-0.03994467, -0.03645954, -0.07040152]],\n",
       " \n",
       "         [[-0.03695151, -0.04234366, -0.03336875],\n",
       "          [-0.02717869, -0.03086366, -0.02037519],\n",
       "          [ 0.01462715,  0.0113989 ,  0.02160606]],\n",
       " \n",
       "         [[-0.06212853, -0.07588618, -0.0301015 ],\n",
       "          [-0.04728906, -0.05920394, -0.01040318],\n",
       "          [ 0.04748898,  0.05471186,  0.08569813]]],\n",
       " \n",
       " \n",
       "        [[[-0.04967742, -0.03904796, -0.03599533],\n",
       "          [ 0.08397338,  0.11549396,  0.12697114],\n",
       "          [ 0.05341198,  0.08654641,  0.09851776]],\n",
       " \n",
       "         [[-0.03539288, -0.00824691, -0.00555437],\n",
       "          [ 0.00255036,  0.03989382,  0.04348854],\n",
       "          [-0.0433931 , -0.00720959,  0.00027946]],\n",
       " \n",
       "         [[-0.0179023 ,  0.02353173,  0.0297748 ],\n",
       "          [-0.01040918,  0.04021799,  0.04970605],\n",
       "          [-0.08259282, -0.03672899, -0.03199842]]],\n",
       " \n",
       " \n",
       "        [[[ 0.00746831,  0.0656278 ,  0.00860586],\n",
       "          [-0.11520192, -0.03414975,  0.03513201],\n",
       "          [-0.03384074, -0.10670819, -0.02361238]],\n",
       " \n",
       "         [[ 0.03856018,  0.09788682,  0.01862226],\n",
       "          [-0.07450747,  0.01316667,  0.08043285],\n",
       "          [-0.00325365, -0.06925824,  0.01455499]],\n",
       " \n",
       "         [[ 0.00481572,  0.05400663, -0.0153268 ],\n",
       "          [-0.07275207,  0.00768739,  0.05870792],\n",
       "          [ 0.0033034 , -0.04812784,  0.02431681]]],\n",
       " \n",
       " \n",
       "        [[[-0.08129937,  0.0494114 ,  0.0354666 ],\n",
       "          [ 0.03703306,  0.04568737,  0.00610177],\n",
       "          [ 0.04559168, -0.07663049, -0.06445087]],\n",
       " \n",
       "         [[-0.07014466,  0.06436152,  0.05140628],\n",
       "          [ 0.04367292,  0.05681046,  0.01272411],\n",
       "          [ 0.03206406, -0.09297774, -0.07257307]],\n",
       " \n",
       "         [[-0.05597744,  0.04570296,  0.03792059],\n",
       "          [ 0.03562848,  0.05125963,  0.01981384],\n",
       "          [ 0.03767774, -0.07073269, -0.05689837]]],\n",
       " \n",
       " \n",
       "        [[[ 0.05764393,  0.03065278, -0.05177881],\n",
       "          [ 0.09376361, -0.12697251, -0.03099721],\n",
       "          [-0.05555037, -0.10235788,  0.0813821 ]],\n",
       " \n",
       "         [[ 0.06480756,  0.05609035, -0.02370641],\n",
       "          [ 0.10239075, -0.08457665,  0.0039258 ],\n",
       "          [-0.0191934 , -0.04255659,  0.09668094]],\n",
       " \n",
       "         [[-0.00772465, -0.00371926, -0.04303417],\n",
       "          [ 0.0335208 , -0.11093598, -0.00397608],\n",
       "          [-0.03424285, -0.03508021,  0.10164294]]],\n",
       " \n",
       " \n",
       "        [[[-0.02273309, -0.03638459, -0.02358943],\n",
       "          [-0.03837347, -0.05428246, -0.03310595],\n",
       "          [-0.04507886, -0.05331257, -0.03456842]],\n",
       " \n",
       "         [[ 0.0094399 ,  0.00931359,  0.00566807],\n",
       "          [ 0.01614539,  0.01520713,  0.01619589],\n",
       "          [-0.00404708,  0.00177889, -0.0004178 ]],\n",
       " \n",
       "         [[ 0.03976535,  0.05109268,  0.03152749],\n",
       "          [ 0.06204586,  0.06976148,  0.06359064],\n",
       "          [ 0.03375546,  0.04922148,  0.04497452]]],\n",
       " \n",
       " \n",
       "        [[[ 0.05887719,  0.01156234, -0.11264952],\n",
       "          [-0.04728893,  0.08680314,  0.05256971],\n",
       "          [-0.00127165, -0.05311643,  0.04398554]],\n",
       " \n",
       "         [[ 0.04584039,  0.0274012 , -0.08242658],\n",
       "          [-0.07467648,  0.07104476,  0.05734352],\n",
       "          [-0.02242704, -0.08333367,  0.02904372]],\n",
       " \n",
       "         [[ 0.06760698,  0.04182985, -0.05187626],\n",
       "          [-0.05582486,  0.0678395 ,  0.0577969 ],\n",
       "          [-0.04012861, -0.08751586,  0.02749611]]],\n",
       " \n",
       " \n",
       "        [[[-0.05918316, -0.09642445, -0.06500001],\n",
       "          [ 0.05672324,  0.0675999 ,  0.03702538],\n",
       "          [ 0.02308566,  0.00357937,  0.00917327]],\n",
       " \n",
       "         [[-0.068948  , -0.10772692, -0.0849742 ],\n",
       "          [ 0.04172087,  0.05906515,  0.01294231],\n",
       "          [-0.00238449, -0.01882726, -0.03029225]],\n",
       " \n",
       "         [[-0.04130782, -0.06137312, -0.03246572],\n",
       "          [ 0.08176519,  0.11658688,  0.07892321],\n",
       "          [ 0.00118092, -0.00315343, -0.00268928]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04237023,  0.05546889,  0.04172067],\n",
       "          [ 0.01835914,  0.0383107 ,  0.02096337],\n",
       "          [-0.00682339,  0.00337463, -0.01307501]],\n",
       " \n",
       "         [[-0.01934059, -0.02653299, -0.02613054],\n",
       "          [-0.06547824, -0.06791785, -0.06545371],\n",
       "          [-0.04781819, -0.05743244, -0.06460748]],\n",
       " \n",
       "         [[ 0.01534863,  0.01157624,  0.01275623],\n",
       "          [ 0.00610535,  0.00332181,  0.00452119],\n",
       "          [ 0.03876121,  0.03287905,  0.01826629]]],\n",
       " \n",
       " \n",
       "        [[[ 0.0909172 ,  0.00590748, -0.05232792],\n",
       "          [ 0.12652077,  0.0841621 , -0.00245058],\n",
       "          [ 0.03799835,  0.08246414,  0.03183487]],\n",
       " \n",
       "         [[ 0.01596169, -0.01932584, -0.07873781],\n",
       "          [ 0.0191502 ,  0.02473203, -0.05428333],\n",
       "          [-0.04079037,  0.01358203, -0.01657919]],\n",
       " \n",
       "         [[ 0.06303399,  0.02595317, -0.07331343],\n",
       "          [ 0.06591636,  0.06436717, -0.05733252],\n",
       "          [-0.00679245,  0.02263052, -0.05103152]]],\n",
       " \n",
       " \n",
       "        [[[ 0.01993512, -0.02912706,  0.00417581],\n",
       "          [ 0.01956277,  0.02522338,  0.01358176],\n",
       "          [ 0.02855995,  0.04247742,  0.05799434]],\n",
       " \n",
       "         [[-0.04391072, -0.0925573 , -0.09399283],\n",
       "          [-0.06332322, -0.05318676, -0.08585035],\n",
       "          [-0.07659844, -0.05155351, -0.0442783 ]],\n",
       " \n",
       "         [[ 0.10237554,  0.04073066,  0.0010866 ],\n",
       "          [ 0.08970382,  0.10294398,  0.02990713],\n",
       "          [ 0.01972623,  0.06316407,  0.04806092]]],\n",
       " \n",
       " \n",
       "        [[[ 0.02845306,  0.07621729,  0.01331353],\n",
       "          [-0.01180174, -0.06977259, -0.07219521],\n",
       "          [-0.01269855, -0.00628469,  0.03436708]],\n",
       " \n",
       "         [[ 0.04483627,  0.0807687 ,  0.01256314],\n",
       "          [-0.00720438, -0.07048444, -0.07710536],\n",
       "          [-0.01557201, -0.00754476,  0.03198591]],\n",
       " \n",
       "         [[ 0.0033404 ,  0.03953545,  0.00073901],\n",
       "          [-0.01011421, -0.05623927, -0.05378392],\n",
       "          [ 0.00032109,  0.01404299,  0.05394666]]],\n",
       " \n",
       " \n",
       "        [[[-0.01334358, -0.03705181,  0.06506658],\n",
       "          [ 0.04880323, -0.06528343, -0.04791459],\n",
       "          [ 0.03733805,  0.04801272, -0.03437592]],\n",
       " \n",
       "         [[-0.02696991, -0.03690227,  0.07806347],\n",
       "          [ 0.03860221, -0.06278814, -0.05784935],\n",
       "          [ 0.01819772,  0.04668343, -0.06200498]],\n",
       " \n",
       "         [[-0.04919309, -0.0925416 ,  0.02804553],\n",
       "          [ 0.04789689, -0.09154915, -0.08908037],\n",
       "          [ 0.0498789 ,  0.05011691, -0.06510671]]],\n",
       " \n",
       " \n",
       "        [[[ 0.04561682, -0.01812775, -0.01507723],\n",
       "          [ 0.05668921, -0.00962777, -0.03724204],\n",
       "          [ 0.00953817, -0.0002635 , -0.00861964]],\n",
       " \n",
       "         [[ 0.06599755, -0.02885111, -0.04936792],\n",
       "          [ 0.07642033, -0.01834042, -0.06433292],\n",
       "          [ 0.02386324, -0.00023389, -0.0247523 ]],\n",
       " \n",
       "         [[ 0.05446933, -0.02350525, -0.04107998],\n",
       "          [ 0.067209  , -0.00903921, -0.05597879],\n",
       "          [ 0.02072558,  0.00468681, -0.01511599]]],\n",
       " \n",
       " \n",
       "        [[[-0.04247818, -0.03326674, -0.04350928],\n",
       "          [-0.02490121, -0.01243611, -0.02206185],\n",
       "          [-0.01199773, -0.00354918, -0.00646715]],\n",
       " \n",
       "         [[-0.02612405,  0.00371216, -0.02419857],\n",
       "          [ 0.0133777 ,  0.04294258,  0.01819668],\n",
       "          [-0.0034902 ,  0.02545182,  0.00435284]],\n",
       " \n",
       "         [[ 0.02936268,  0.06210652,  0.0087808 ],\n",
       "          [ 0.07701027,  0.11931218,  0.06617242],\n",
       "          [ 0.02618115,  0.06465857,  0.02592627]]]]),\n",
       " 'W1': array([[[[ -1.86407952e-02,  -4.58063455e-02,  -5.43216766e-02],\n",
       "          [ -2.97001443e-02,  -4.07028314e-02,  -4.03982624e-02],\n",
       "          [  2.04381591e-02,  -4.74174021e-03,   1.18827727e-02]],\n",
       " \n",
       "         [[  7.19299700e-02,   5.92362820e-03,  -1.50867370e-02],\n",
       "          [ -3.17997651e-03,  -2.63837622e-02,  -3.18302441e-02],\n",
       "          [ -5.96527783e-03,  -3.02561252e-02,   1.17214387e-02]],\n",
       " \n",
       "         [[ -1.79274589e-02,  -3.61934971e-02,  -2.88041063e-02],\n",
       "          [ -6.40866073e-02,  -5.16919915e-02,  -3.89583196e-02],\n",
       "          [  2.01940788e-03,  -1.79041341e-02,   3.95520238e-03]],\n",
       " \n",
       "         ..., \n",
       "         [[  6.23878070e-02,   3.70826875e-02,   3.33195238e-02],\n",
       "          [  2.41173618e-02,   2.46077308e-02,   1.76239498e-02],\n",
       "          [  2.03921184e-02,   8.96649389e-03,   3.28868720e-03]],\n",
       " \n",
       "         [[  1.75545128e-02,  -9.89566323e-02,  -1.04872976e-01],\n",
       "          [ -1.31673505e-02,  -1.14254703e-01,  -1.52198667e-01],\n",
       "          [  7.24186945e-02,  -3.34337443e-02,   3.03366524e-03]],\n",
       " \n",
       "         [[  3.08840147e-02,   1.77820237e-02,   2.08360614e-02],\n",
       "          [  3.30221307e-03,  -9.58989302e-03,  -1.42978064e-02],\n",
       "          [  5.30708783e-03,  -6.06456429e-03,  -8.33606513e-03]]],\n",
       " \n",
       " \n",
       "        [[[  2.56624320e-03,   2.15353136e-02,   1.61640728e-02],\n",
       "          [ -1.74568621e-02,   7.37965202e-03,   1.67719574e-02],\n",
       "          [ -4.00069654e-02,  -6.60743900e-02,  -1.58522210e-02]],\n",
       " \n",
       "         [[  4.66082853e-02,  -4.14138735e-02,  -8.99919986e-02],\n",
       "          [ -8.01218186e-02,  -8.02525696e-02,  -7.53202979e-02],\n",
       "          [ -3.11212716e-02,  -8.88153791e-04,   4.81094720e-03]],\n",
       " \n",
       "         [[  6.52353084e-03,  -1.08389484e-01,  -1.46410958e-01],\n",
       "          [  4.30186328e-02,   4.33114652e-02,  -1.89686559e-02],\n",
       "          [  8.94023767e-02,   1.14678207e-01,   1.30471746e-01]],\n",
       " \n",
       "         ..., \n",
       "         [[ -2.13416070e-03,  -2.55382907e-02,  -4.51809112e-02],\n",
       "          [ -4.91376237e-02,  -6.69530811e-02,  -3.86720079e-02],\n",
       "          [ -3.63218007e-02,  -3.17267189e-02,   3.24783440e-03]],\n",
       " \n",
       "         [[  5.40166360e-02,   2.68606808e-02,  -3.24620717e-02],\n",
       "          [ -5.61915362e-02,  -2.45903698e-02,  -4.66639596e-02],\n",
       "          [ -5.51711365e-02,   8.18972071e-03,  -2.59451433e-02]],\n",
       " \n",
       "         [[ -1.88598711e-02,  -1.85603877e-02,  -4.19593389e-02],\n",
       "          [ -3.32376275e-02,  -1.60796233e-02,  -1.49212892e-02],\n",
       "          [ -4.43959956e-02,  -2.03183891e-02,  -8.01666373e-03]]],\n",
       " \n",
       " \n",
       "        [[[ -2.10991770e-02,   9.52149262e-05,  -3.53323971e-03],\n",
       "          [  5.63385984e-02,   1.68703350e-02,  -5.48687842e-03],\n",
       "          [  3.18506243e-02,  -4.60674897e-03,  -3.55226567e-02]],\n",
       " \n",
       "         [[ -7.97640584e-02,  -3.43739226e-02,   4.19026540e-02],\n",
       "          [ -1.60819783e-01,  -8.88122893e-02,   3.55980264e-03],\n",
       "          [ -9.51746133e-02,  -7.34907491e-02,  -1.43431147e-03]],\n",
       " \n",
       "         [[ -1.26024034e-01,  -4.67651843e-02,  -5.80591999e-02],\n",
       "          [ -3.54492629e-02,  -7.01597260e-02,  -5.82272658e-02],\n",
       "          [  1.69084797e-02,  -4.06981082e-03,  -8.58573194e-03]],\n",
       " \n",
       "         ..., \n",
       "         [[ -3.12390702e-02,   2.46002104e-02,   6.80580701e-02],\n",
       "          [ -5.80901601e-02,   1.85874136e-02,   4.38872170e-02],\n",
       "          [ -4.85388310e-02,   1.93341665e-02,   7.81966302e-03]],\n",
       " \n",
       "         [[  1.29688132e-01,   3.92037878e-02,   1.93137818e-02],\n",
       "          [  1.44180084e-01,   6.32059215e-02,   4.51972379e-03],\n",
       "          [  9.09613502e-02,   3.45476678e-02,   9.04489768e-05]],\n",
       " \n",
       "         [[  7.63373996e-03,  -4.12000864e-02,  -4.98886703e-02],\n",
       "          [  7.32357094e-02,   5.37879983e-03,  -4.63602459e-02],\n",
       "          [  4.31837736e-02,  -3.47727124e-04,  -4.46926919e-02]]],\n",
       " \n",
       " \n",
       "        ..., \n",
       "        [[[  1.65180480e-02,   2.97487980e-02,   7.37540003e-03],\n",
       "          [  2.40638450e-02,   3.68759059e-02,   3.63922693e-02],\n",
       "          [ -3.43242044e-03,   7.75158794e-03,  -2.59407115e-03]],\n",
       " \n",
       "         [[  8.67983984e-03,  -2.31975555e-02,  -3.59792817e-02],\n",
       "          [  1.90399909e-02,  -4.73424947e-02,  -8.08389629e-02],\n",
       "          [  3.39097396e-02,  -1.73052517e-02,  -6.71002918e-03]],\n",
       " \n",
       "         [[  2.45287638e-02,   2.72011012e-02,  -3.45074784e-02],\n",
       "          [ -6.92725870e-02,  -6.24806004e-02,  -2.33197695e-02],\n",
       "          [  1.71025440e-02,   1.16734606e-02,   5.13666265e-02]],\n",
       " \n",
       "         ..., \n",
       "         [[ -7.57773062e-02,  -1.06847872e-01,   7.37469892e-02],\n",
       "          [ -4.94415443e-02,  -4.82874889e-02,   3.21124840e-02],\n",
       "          [ -5.21480122e-04,   2.72747955e-02,   5.03534121e-02]],\n",
       " \n",
       "         [[ -4.53233618e-02,  -4.10402323e-03,   1.43077041e-01],\n",
       "          [ -1.82002330e-01,  -2.01190971e-02,   2.32012476e-02],\n",
       "          [ -1.73310964e-01,  -3.69541170e-03,   1.95216536e-02]],\n",
       " \n",
       "         [[  1.18685716e-02,   2.18599262e-02,  -7.58366425e-04],\n",
       "          [ -1.50115495e-02,  -2.17861595e-02,  -2.43873251e-02],\n",
       "          [ -2.74644381e-02,  -3.66782321e-02,  -5.04765000e-02]]],\n",
       " \n",
       " \n",
       "        [[[ -3.56524033e-02,  -8.66447780e-02,  -8.06547517e-03],\n",
       "          [ -3.43729179e-02,  -1.10706981e-01,  -7.09793817e-02],\n",
       "          [ -8.06235086e-04,  -4.57415511e-02,  -8.73934458e-03]],\n",
       " \n",
       "         [[ -2.80928426e-02,   1.06186426e-01,  -6.24915949e-02],\n",
       "          [  8.50888042e-02,  -6.64228019e-02,  -3.63200309e-03],\n",
       "          [ -5.64884836e-02,   3.54224555e-02,  -2.34432412e-02]],\n",
       " \n",
       "         [[  1.79344501e-02,  -1.09473394e-01,  -8.09659688e-02],\n",
       "          [ -8.44922825e-02,  -3.93945662e-02,  -2.23095005e-02],\n",
       "          [ -5.91034753e-02,  -7.12359041e-02,  -5.45061452e-02]],\n",
       " \n",
       "         ..., \n",
       "         [[ -7.56713659e-02,  -7.91359631e-03,  -6.80510723e-03],\n",
       "          [ -2.63708322e-02,  -1.82776276e-02,   1.82264398e-02],\n",
       "          [  2.65539647e-02,   6.52680201e-03,  -6.47957310e-02]],\n",
       " \n",
       "         [[  6.68631011e-03,  -3.19752596e-02,   5.39318716e-02],\n",
       "          [ -4.14790181e-02,   7.97336501e-02,   8.30355531e-02],\n",
       "          [  8.93048268e-02,  -3.10267769e-02,  -2.17723531e-02]],\n",
       " \n",
       "         [[ -1.03078292e-01,  -1.05199581e-01,  -8.16381219e-02],\n",
       "          [ -4.85894674e-02,  -8.34572802e-02,  -4.20663410e-02],\n",
       "          [ -6.43867383e-03,  -3.56802438e-02,  -1.83675201e-02]]],\n",
       " \n",
       " \n",
       "        [[[  1.85433600e-02,   2.80835276e-02,   3.40799635e-02],\n",
       "          [  2.21990981e-02,   2.76906136e-02,   3.73774400e-02],\n",
       "          [ -3.32954899e-02,  -5.48976272e-02,  -4.74839110e-02]],\n",
       " \n",
       "         [[ -2.93633162e-03,   2.37658879e-02,   3.36793225e-02],\n",
       "          [ -3.24668596e-02,   1.57258133e-02,   4.84749580e-02],\n",
       "          [ -1.06463110e-02,   4.76202703e-02,   2.74873531e-02]],\n",
       " \n",
       "         [[  5.04215315e-03,  -6.66648532e-03,  -1.83948305e-02],\n",
       "          [  1.56513802e-02,  -4.15053772e-03,  -6.13657302e-03],\n",
       "          [  3.68631999e-02,  -3.54277821e-02,  -3.89027560e-04]],\n",
       " \n",
       "         ..., \n",
       "         [[ -3.61788965e-02,  -3.78753230e-02,   1.59763176e-02],\n",
       "          [ -4.56757083e-02,  -2.07797298e-02,   4.47622584e-02],\n",
       "          [ -4.15424402e-03,   2.15892477e-02,   5.23571696e-02]],\n",
       " \n",
       "         [[ -9.49149741e-02,  -1.33077705e-01,  -9.12214722e-02],\n",
       "          [ -1.18347802e-01,  -1.63094050e-01,  -2.99810627e-02],\n",
       "          [ -5.45639476e-02,  -7.63719526e-02,  -2.19578369e-02]],\n",
       " \n",
       "         [[  5.39035522e-03,  -1.16509533e-02,  -1.29864647e-02],\n",
       "          [  3.47560977e-02,   2.09315868e-02,   1.32516611e-02],\n",
       "          [  1.10780512e-02,  -1.37003518e-02,  -2.72822318e-02]]]]),\n",
       " 'b0': array([  3.59698270e-12,  -7.43902470e-11,   4.99023161e-11,\n",
       "         -1.49659120e-11,  -3.45113100e-11,  -3.20746245e-10,\n",
       "          1.68185362e-10,  -4.08361925e-10,   9.44058030e-12,\n",
       "          5.23355584e-11,   2.68544220e-11,   2.45628653e-10,\n",
       "          1.08688326e-11,  -6.74019018e-11,  -9.00343780e-11,\n",
       "          1.19632015e-10,  -4.98266217e-12,  -8.53195775e-11,\n",
       "          1.01155497e-11,  -7.55847265e-12,  -1.51258387e-10,\n",
       "          9.25294165e-11,  -1.83123009e-13,  -3.29153235e-11,\n",
       "          4.38902799e-11,   6.44831728e-11,   1.08755477e-12,\n",
       "          5.14482524e-11,   1.49707835e-11,  -2.51215970e-11,\n",
       "          8.09141208e-11,  -1.14103356e-11]),\n",
       " 'b1': array([ -7.94914186e-11,  -2.98362905e-10,   7.04838272e-10,\n",
       "          2.43701940e-10,  -5.37297416e-11,   3.97394215e-10,\n",
       "          4.12008129e-11,  -2.57132255e-11,   6.13627719e-10,\n",
       "         -7.57702638e-10,   1.19702357e-10,   2.44854742e-10,\n",
       "         -1.26588595e-10,   2.61476980e-10,  -4.64996880e-10,\n",
       "          6.65917731e-11,   5.15334936e-11,  -1.58583537e-10,\n",
       "          3.18136736e-10,  -4.73477479e-10,   1.20370874e-10,\n",
       "          3.56267825e-11,   2.64937879e-12,  -1.28173077e-10,\n",
       "          8.19004138e-11,  -1.43450189e-10,  -9.88995719e-11,\n",
       "         -1.20865614e-10,  -8.49252808e-10,  -3.26530500e-12,\n",
       "          5.37132659e-10,  -6.64368457e-11,  -3.85642237e-10,\n",
       "          4.76767533e-11,   9.23593162e-11,  -1.11860409e-10,\n",
       "          2.71337022e-10,  -2.02102031e-10,  -1.54404824e-10,\n",
       "         -3.65431184e-10,  -1.05421033e-10,  -4.47789652e-10,\n",
       "          1.92077603e-10,  -2.62773260e-10,  -2.03771941e-10,\n",
       "          1.45221257e-11,   1.45850598e-10,   2.24317859e-10,\n",
       "         -1.05286218e-10,   5.93013784e-10,   1.89107745e-10,\n",
       "         -1.23596245e-10,  -1.93889237e-10,  -2.65803167e-10,\n",
       "          8.99184846e-12,   1.65248505e-10,  -6.45965997e-10,\n",
       "          1.36435241e-10,  -1.75436913e-10,   5.82344977e-11,\n",
       "          1.13505641e-10,   1.87319681e-10,   8.12459778e-10,\n",
       "          6.11631546e-10]),\n",
       " 'beta0': array([ 0.09320518, -0.03795648, -0.08732203, -0.23676322, -0.33890382,\n",
       "        -0.01009313, -0.07826558, -0.06287737, -0.28308307, -0.0021411 ,\n",
       "        -0.02549193, -0.07206649, -0.2863651 , -0.12321823,  0.02431104,\n",
       "        -0.00221942, -0.18878307, -0.20547304, -0.26248693, -0.27482364,\n",
       "        -0.28559234, -0.25926268,  0.1049994 , -0.28136991, -0.05453365,\n",
       "        -0.17545811, -0.22490686, -0.03936156, -0.03478222, -0.25672556,\n",
       "         0.02914835, -0.20759289]),\n",
       " 'beta1': array([-0.43555402, -0.30437887, -0.47353869, -0.17149783, -0.37462661,\n",
       "        -0.21675791, -0.23546613, -0.18151344, -0.32464463, -0.50194007,\n",
       "        -0.33842408, -0.37955417, -0.3210801 , -0.24190596, -0.17729062,\n",
       "        -0.44667316, -0.26910405, -0.45033552, -0.21083149, -0.21859796,\n",
       "        -0.40319054, -0.37342949, -0.45021455, -0.33994949, -0.34204719,\n",
       "        -0.30159944, -0.39089128, -0.35439435, -0.46312196, -0.42142799,\n",
       "        -0.14802091, -0.38476816, -0.20510949, -0.38356702, -0.30390213,\n",
       "        -0.23081493, -0.20577482, -0.28334736, -0.24669659, -0.2679828 ,\n",
       "        -0.45583686, -0.47468044, -0.39811648, -0.29898412, -0.49926316,\n",
       "        -0.34565269, -0.45351177, -0.37194317, -0.31221466, -0.56244929,\n",
       "        -0.35306892, -0.24127208, -0.39862044, -0.33778969, -0.41605977,\n",
       "        -0.34702995, -0.45977547, -0.35333713, -0.36867456, -0.18289609,\n",
       "        -0.40586404, -0.28275744, -0.24148048, -0.39442445]),\n",
       " 'fc:W0': array([[ 0.07857548,  0.0292612 ,  0.13575068, ..., -0.03319471,\n",
       "          0.00919441,  0.00663342],\n",
       "        [ 0.06646953,  0.01946808,  0.04044041, ..., -0.0376209 ,\n",
       "          0.02298589,  0.02115817],\n",
       "        [ 0.00912126,  0.04508648,  0.00305906, ..., -0.04107363,\n",
       "          0.01453663,  0.00947008],\n",
       "        ..., \n",
       "        [ 0.00963811, -0.06917201,  0.0066251 , ..., -0.05321382,\n",
       "         -0.00956671, -0.08071645],\n",
       "        [ 0.00952984,  0.04654211, -0.01115423, ..., -0.06604286,\n",
       "         -0.05221078,  0.07231538],\n",
       "        [-0.07253657, -0.02601997,  0.0121176 , ..., -0.00181671,\n",
       "         -0.05894005,  0.04754883]]),\n",
       " 'fc:W1': array([[ 0.00018971, -0.01239426,  0.07868496, ..., -0.04677279,\n",
       "         -0.03011606, -0.00071672],\n",
       "        [ 0.00663783, -0.03143199,  0.04768223, ..., -0.06019918,\n",
       "          0.01865977,  0.06155434],\n",
       "        [ 0.00858565, -0.03588261,  0.01568192, ...,  0.03439568,\n",
       "         -0.06037485,  0.03418546],\n",
       "        ..., \n",
       "        [-0.00095293, -0.07125251, -0.03576155, ...,  0.11262721,\n",
       "         -0.03619949,  0.02076042],\n",
       "        [-0.02096646,  0.01319681,  0.00429555, ...,  0.02210697,\n",
       "          0.01920589, -0.15487247],\n",
       "        [-0.03684361, -0.06724452, -0.0114549 , ..., -0.0479485 ,\n",
       "          0.01432299, -0.01395713]]),\n",
       " 'fc:W2': array([[ -1.22370566e-01,   5.87704382e-02,   4.69475977e-02,\n",
       "          -6.66948585e-02,   2.74819735e-02,  -1.13386099e-01,\n",
       "          -1.52864336e-01,   1.01960880e-01,  -2.07187627e-01,\n",
       "           8.53817605e-02],\n",
       "        [ -1.18133947e-01,  -1.92993235e-01,  -1.19066896e-01,\n",
       "           7.58967118e-02,   1.00024659e-01,   8.12746211e-02,\n",
       "           5.94040210e-02,   8.19986665e-02,  -1.72726167e-01,\n",
       "          -1.98171840e-01],\n",
       "        [  6.63930640e-02,   8.19226569e-02,  -3.61212262e-02,\n",
       "           2.93641349e-02,   6.43859173e-02,  -5.56898194e-02,\n",
       "          -1.23394847e-01,  -1.58539412e-01,  -6.27958534e-02,\n",
       "          -4.71946304e-02],\n",
       "        [ -2.78417954e-01,  -2.05346167e-01,   7.09044557e-02,\n",
       "           9.07393276e-03,   1.02726582e-01,   3.32963658e-02,\n",
       "           9.06268806e-02,   1.01637707e-01,  -2.44851930e-01,\n",
       "          -2.56653603e-01],\n",
       "        [  4.12449342e-02,   8.08355818e-02,  -6.00557664e-02,\n",
       "          -1.24566226e-01,  -1.02748176e-01,  -1.27163751e-01,\n",
       "           1.24559706e-01,  -2.09016907e-01,  -7.77428105e-02,\n",
       "           7.89631444e-02],\n",
       "        [  7.21415939e-02,   7.50008560e-02,  -6.64381120e-02,\n",
       "          -1.47557418e-01,  -6.76920461e-02,  -1.27903586e-01,\n",
       "          -1.61522317e-01,  -1.87271236e-01,   7.74636755e-02,\n",
       "          -8.37367272e-03],\n",
       "        [  4.95142512e-02,  -3.65590572e-02,   7.48311517e-03,\n",
       "          -1.26208269e-01,   1.32545601e-02,  -1.71299313e-01,\n",
       "           1.74627554e-02,  -1.28237622e-01,   8.02697827e-02,\n",
       "          -7.89925508e-02],\n",
       "        [ -1.29310461e-01,   7.55157700e-02,  -1.19697969e-01,\n",
       "           4.98312147e-02,  -1.59469638e-01,   4.98595414e-02,\n",
       "           1.02896925e-01,  -1.67488931e-01,   4.27419732e-02,\n",
       "          -4.97287332e-02],\n",
       "        [ -2.09497743e-01,  -2.40656223e-01,  -2.75811280e-02,\n",
       "           5.02948581e-02,  -2.09230071e-03,   1.27743807e-01,\n",
       "           8.78575859e-02,  -3.14567576e-02,  -8.46322147e-02,\n",
       "           5.49151531e-02],\n",
       "        [ -1.20457327e-01,  -1.09736282e-01,   8.47308850e-02,\n",
       "           5.26321201e-02,   9.72783459e-02,  -1.42421841e-02,\n",
       "           6.73303529e-02,  -1.30895615e-01,  -1.48116922e-01,\n",
       "          -1.20540088e-01],\n",
       "        [  6.45748561e-02,  -1.81549717e-01,   8.25292900e-02,\n",
       "           7.95753421e-02,  -1.15846495e-01,   1.00385897e-01,\n",
       "          -1.18434594e-01,   5.05308436e-03,  -1.56277352e-01,\n",
       "          -9.56322824e-02],\n",
       "        [ -7.78726723e-02,   7.99421184e-02,  -1.22445848e-01,\n",
       "          -5.54132757e-02,  -1.39106361e-02,  -1.62160769e-01,\n",
       "           1.07725824e-01,  -9.92992016e-02,  -1.34168442e-01,\n",
       "           9.53666446e-02],\n",
       "        [  7.18401777e-02,  -1.83198791e-01,   9.69784799e-02,\n",
       "          -5.29089771e-02,  -4.83622261e-02,  -9.14154308e-02,\n",
       "          -6.88376892e-02,  -8.38861682e-02,   6.81176153e-02,\n",
       "          -8.21105663e-02],\n",
       "        [ -6.23846272e-03,  -1.91529904e-01,  -1.43904216e-02,\n",
       "           6.86830588e-02,  -1.72060216e-01,   1.10135839e-01,\n",
       "          -7.19195024e-02,   9.04108542e-02,   7.85629929e-03,\n",
       "          -2.79507996e-02],\n",
       "        [ -2.31266489e-01,  -2.47951116e-01,   7.30666443e-02,\n",
       "           7.87487387e-02,   6.12841627e-02,   9.78330066e-02,\n",
       "           8.77112755e-02,  -1.16209752e-01,  -1.31946993e-01,\n",
       "          -1.32062208e-01],\n",
       "        [ -6.85881256e-03,  -1.27263631e-01,  -9.59644812e-02,\n",
       "           1.45612931e-02,   9.92241812e-02,  -5.23592115e-02,\n",
       "          -1.42927240e-01,   1.13855874e-01,  -1.08595061e-01,\n",
       "           6.74725494e-02],\n",
       "        [  7.39806591e-02,  -9.14678671e-02,  -3.54325029e-02,\n",
       "          -4.16333909e-02,  -2.04124823e-02,  -1.31258938e-01,\n",
       "          -7.82511905e-02,  -1.05657877e-01,   7.16540755e-02,\n",
       "           2.02790998e-02],\n",
       "        [  4.11047924e-02,   9.01863288e-02,  -1.43619096e-01,\n",
       "          -1.93507070e-01,  -1.12146425e-01,  -1.79163285e-01,\n",
       "          -1.80990822e-01,  -1.81776997e-01,   8.03083015e-02,\n",
       "           7.56019877e-02],\n",
       "        [  4.82717572e-02,  -1.64490351e-01,   7.98426420e-02,\n",
       "          -7.84936306e-02,   5.45788803e-02,  -6.71468352e-02,\n",
       "          -1.17323339e-01,  -4.61787046e-02,   6.75809848e-02,\n",
       "          -1.49041879e-01],\n",
       "        [ -1.91981546e-01,  -1.84751769e-01,   3.84574583e-02,\n",
       "           4.36155831e-02,   8.76480449e-02,  -5.65138585e-02,\n",
       "           1.16087781e-01,   4.57422476e-02,  -2.12136912e-01,\n",
       "          -1.39948548e-01],\n",
       "        [ -1.51925885e-01,   1.01469866e-01,  -1.08846945e-01,\n",
       "           7.07298401e-02,  -1.80348630e-01,   1.12622853e-01,\n",
       "          -5.75870881e-02,   7.97321102e-02,  -2.21433559e-01,\n",
       "           3.58830481e-02],\n",
       "        [ -1.66059045e-01,  -1.18575664e-01,   5.44119562e-02,\n",
       "           6.93286860e-02,   9.70369922e-02,  -1.08069177e-02,\n",
       "           9.28314934e-02,  -1.18789890e-01,  -1.82801392e-01,\n",
       "          -1.21362575e-01],\n",
       "        [ -1.82059911e-02,  -1.48887927e-01,  -2.63632131e-02,\n",
       "           1.05651453e-01,   1.42036604e-02,   9.89000551e-02,\n",
       "           8.89286677e-02,  -4.88557117e-02,  -1.87220467e-01,\n",
       "          -1.37354407e-01],\n",
       "        [  9.93803296e-02,   9.15696509e-02,  -1.49652988e-01,\n",
       "          -6.27796996e-02,  -6.71776899e-02,  -1.26209567e-01,\n",
       "          -1.29264769e-01,  -2.52717323e-01,   2.39172626e-03,\n",
       "           5.26143859e-02],\n",
       "        [  9.19207844e-02,  -1.65993264e-01,  -5.36754585e-02,\n",
       "           6.64468951e-02,   2.64681732e-02,  -7.91825772e-02,\n",
       "          -1.19676544e-01,  -1.33568107e-01,   7.14336041e-02,\n",
       "          -1.87000514e-02],\n",
       "        [ -2.30634616e-01,  -2.92878127e-01,   7.62829119e-02,\n",
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       "        [  8.01127563e-02,  -3.25486985e-02,   7.08387658e-02,\n",
       "           3.89552706e-02,  -1.65601965e-01,   9.77085735e-02,\n",
       "          -1.13191126e-01,   2.69102619e-02,  -1.36435129e-01,\n",
       "          -1.54546816e-01],\n",
       "        [  5.17557789e-02,   6.71714079e-02,  -7.91065441e-02,\n",
       "           4.94434659e-02,  -1.71909273e-01,  -9.98735284e-03,\n",
       "          -1.04041637e-01,  -4.73969075e-02,   7.61121954e-02,\n",
       "          -1.62859533e-01],\n",
       "        [ -5.18738967e-02,  -2.28711055e-01,   6.00402113e-02,\n",
       "           5.58374530e-02,   8.32640751e-03,   8.81207689e-02,\n",
       "          -2.10839095e-01,   1.21147523e-01,  -6.94503527e-02,\n",
       "          -1.61811759e-01],\n",
       "        [  4.13642338e-03,   1.18299667e-01,  -8.41554471e-03,\n",
       "          -1.61966723e-01,   1.05859453e-02,  -1.43787406e-01,\n",
       "          -1.07131479e-01,  -1.37058673e-01,   7.87667158e-02,\n",
       "          -8.40803948e-02],\n",
       "        [  9.83926624e-05,   5.16195363e-02,  -1.32244211e-01,\n",
       "          -1.06390923e-01,  -1.36102585e-01,  -9.74477536e-02,\n",
       "          -1.25855965e-01,  -1.90176282e-01,   1.07234441e-01,\n",
       "           8.00066597e-02],\n",
       "        [ -6.58096613e-02,  -2.03262781e-01,   4.40267824e-02,\n",
       "          -1.37031808e-02,   7.09022180e-02,  -6.17471690e-02,\n",
       "           5.88112075e-02,  -1.54499818e-01,   1.38575171e-01,\n",
       "          -1.41913386e-01],\n",
       "        [  1.05309231e-02,  -5.07756008e-02,  -1.41062963e-01,\n",
       "          -1.05380119e-01,   5.34021363e-03,  -1.63997222e-01,\n",
       "          -1.26711934e-01,   1.06950096e-02,   1.01986467e-01,\n",
       "           9.08289081e-02],\n",
       "        [  6.80464784e-02,  -1.45194310e-01,   6.54169481e-02,\n",
       "           4.67276033e-02,  -4.68111532e-02,  -1.33803027e-01,\n",
       "           4.65218095e-02,  -1.86408580e-01,   6.17344786e-02,\n",
       "          -1.33258012e-01],\n",
       "        [ -1.11041335e-01,   5.43448520e-02,  -7.66458740e-02,\n",
       "          -8.99726226e-02,  -2.26941314e-01,   1.05811668e-01,\n",
       "          -6.48347046e-02,  -1.08010316e-01,   9.44348001e-02,\n",
       "           6.88928204e-02],\n",
       "        [ -7.67228568e-02,   1.19052359e-01,  -6.52646892e-02,\n",
       "          -3.64082946e-02,  -4.48460871e-02,  -8.36720300e-02,\n",
       "          -1.39039287e-01,   1.21199851e-02,   9.23810174e-02,\n",
       "          -3.09546438e-02],\n",
       "        [ -1.54561410e-01,  -1.66693553e-01,   6.76810650e-02,\n",
       "           9.55178538e-03,   1.13108541e-01,   1.99986527e-02,\n",
       "           9.83052653e-02,  -7.09100171e-02,  -1.74497464e-01,\n",
       "          -1.41798388e-01],\n",
       "        [ -9.74067352e-03,  -1.22386163e-01,  -7.12494471e-02,\n",
       "          -6.08508145e-02,   8.99253599e-02,  -6.03314313e-02,\n",
       "          -2.54389015e-01,   1.01577301e-01,   8.06313677e-02,\n",
       "           6.74883827e-02],\n",
       "        [ -6.79869473e-02,   8.86881855e-02,   3.09070146e-02,\n",
       "          -1.61881266e-01,  -2.57760133e-02,  -4.94941943e-02,\n",
       "          -1.91093551e-01,  -1.82310004e-01,   5.52972657e-02,\n",
       "           8.76765020e-02],\n",
       "        [ -2.17876055e-01,  -1.69514547e-01,   1.05906592e-01,\n",
       "          -2.64894947e-02,   8.07074470e-02,   1.07881540e-01,\n",
       "          -2.92054149e-02,   1.07960159e-01,  -1.62711340e-01,\n",
       "          -2.46412298e-01],\n",
       "        [  1.13014475e-01,   5.56216411e-02,  -9.19960995e-02,\n",
       "          -1.55548140e-02,  -1.74182988e-01,  -1.08689448e-01,\n",
       "           2.39528784e-02,  -2.35046052e-01,  -7.33075035e-02,\n",
       "           7.05929453e-02]]),\n",
       " 'fc:b0': array([ -6.67943330e-13,   2.35072844e-12,   1.80604680e-12,\n",
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       "         -2.22721276e-12,   4.41014192e-13,  -1.61830428e-13,\n",
       "          9.43376757e-13,   1.09405854e-12,  -4.84627450e-13,\n",
       "          1.69593039e-14,  -5.92884443e-13,  -4.26342965e-12,\n",
       "         -1.47677252e-12,  -1.06564945e-12,   3.23248069e-12,\n",
       "         -6.51936566e-13,  -1.34782890e-12,   7.01692341e-13,\n",
       "          2.54161887e-12,  -3.36715066e-13,  -1.76344274e-12,\n",
       "         -1.59080782e-12,  -2.98756147e-12,  -4.68580284e-13,\n",
       "         -2.44481348e-12,   1.45349540e-12,  -3.46711373e-12,\n",
       "         -1.92022630e-13,   1.51203849e-12,  -1.41275457e-12,\n",
       "          4.90959201e-13,   6.99520393e-13,  -4.48597435e-12,\n",
       "          2.10795336e-12,   1.65921723e-12,   9.84467500e-15,\n",
       "         -1.84417284e-12,  -1.23909200e-13,   1.24298995e-12,\n",
       "          3.22226073e-12,  -1.24024271e-12,   9.69960891e-14,\n",
       "         -5.44849953e-13,  -2.26796610e-12,   2.46058249e-13,\n",
       "         -5.23039632e-13,   1.98472689e-12,   7.57006152e-13,\n",
       "          1.35544165e-12,   1.16779083e-12,  -5.00391563e-12,\n",
       "         -1.71208352e-12,  -3.26184730e-12,  -9.64172313e-14,\n",
       "          2.96261653e-13,  -7.62855623e-13,  -1.98875021e-12,\n",
       "          2.26923473e-12,  -1.81991397e-12,  -5.42447561e-13,\n",
       "          3.57396081e-12,   4.53856993e-13,  -1.38006653e-12,\n",
       "         -2.76167939e-13,   1.26692364e-12,  -2.66274742e-12,\n",
       "         -6.78285782e-13,   1.57869955e-12,   4.42575106e-14,\n",
       "         -1.95470072e-12,  -2.75355802e-12,  -6.67874983e-13,\n",
       "          1.51554748e-12,  -2.69417449e-14,   1.41781102e-12,\n",
       "         -1.04424993e-12,  -2.21652782e-12,   2.58803648e-13,\n",
       "         -1.29498591e-12,  -7.18357997e-13,   2.62404450e-12,\n",
       "          1.80476646e-12,  -3.22659439e-13,  -2.35421926e-12,\n",
       "         -4.27466722e-13,   2.35456135e-12,  -2.49774962e-12,\n",
       "         -5.46543236e-13,  -2.87125799e-13,  -1.81161594e-12,\n",
       "          1.27882576e-13,  -1.30060214e-12,   1.29154005e-13,\n",
       "          1.83715614e-12,  -1.90882444e-12,  -2.14395812e-12,\n",
       "         -2.20152992e-12]),\n",
       " 'fc:b1': array([ -1.08447611e-11,   4.44661040e-12,  -7.42972568e-12,\n",
       "         -3.56635635e-11,  -2.94176354e-12,  -1.07613649e-11,\n",
       "         -9.65287828e-12,  -2.67390985e-11,  -2.43416153e-11,\n",
       "         -7.08925796e-12,   2.14724421e-11,   8.39972445e-12,\n",
       "          8.53440652e-12,   6.13672345e-13,   2.19515251e-12,\n",
       "          5.81520209e-12,  -1.23887620e-12,   7.92036681e-12,\n",
       "          8.16840910e-12,   1.79974287e-11,  -3.58741296e-12,\n",
       "         -1.10681158e-11,   3.20660314e-11,   2.54533973e-11,\n",
       "          1.79710549e-12,  -2.65252810e-11,   6.62109337e-12,\n",
       "          2.80957256e-11,  -1.48278664e-11,   2.96711363e-11,\n",
       "         -1.43380912e-11,   7.75840162e-12,  -1.32591960e-11,\n",
       "         -1.77847044e-12,  -1.13747963e-11,  -1.54471376e-12,\n",
       "         -3.37550615e-11,   6.13575435e-12,  -1.03942554e-11,\n",
       "         -2.42369063e-11,  -1.37269112e-12,   2.78052152e-11,\n",
       "         -6.98355275e-12,  -1.69472370e-11,   1.03147704e-11,\n",
       "          4.18673806e-12,   3.10775028e-14,   1.57503030e-11,\n",
       "          2.34499334e-11,   1.84284733e-11,  -2.28698152e-11,\n",
       "          1.59609909e-11,  -1.04253523e-11,   8.63965974e-12,\n",
       "          1.87668666e-11,   1.66570945e-11,  -7.44837246e-12,\n",
       "          7.14788272e-12,   5.56290318e-12,   2.08423411e-11,\n",
       "          1.77781235e-11,   9.64948171e-12,   1.43411453e-11,\n",
       "          1.28752573e-11,  -1.56450199e-11,   2.78738150e-12,\n",
       "         -7.69283511e-12,  -1.28867719e-11,  -4.41363620e-12,\n",
       "         -3.91460044e-12,  -1.30038174e-11,   1.35457655e-12,\n",
       "          8.65298940e-12,  -1.42478892e-11,  -4.72797222e-12,\n",
       "         -1.48961249e-11,   3.05367556e-12,   1.59368301e-11,\n",
       "          9.04078216e-12,  -1.11437159e-11,  -1.06192637e-11,\n",
       "          1.35542675e-11,   3.15481381e-12,  -5.36283099e-12,\n",
       "          3.25342450e-11,  -2.85429120e-11,   1.64281431e-11,\n",
       "          2.36214954e-11,  -5.93068475e-12,  -3.55374983e-12,\n",
       "         -5.16916168e-12,  -2.19148324e-12,  -2.49758256e-11,\n",
       "          5.30426586e-12,  -1.20116672e-11,   1.79088236e-11,\n",
       "          2.31680225e-11,  -1.84977129e-11,  -1.41031927e-11,\n",
       "          1.27027034e-11]),\n",
       " 'fc:b2': array([-0.05050561, -0.06091904,  0.00962661,  0.0666937 ,  0.05247268,\n",
       "         0.08452519,  0.04711093,  0.0355312 , -0.09881917, -0.08104586]),\n",
       " 'fc:beta0': array([-0.15830805, -0.22843853, -0.12328765, -0.35014888, -0.18811478,\n",
       "        -0.29352123, -0.26917733, -0.31656716, -0.308165  , -0.25892821,\n",
       "        -0.29504295, -0.20935789, -0.29509846, -0.28428891, -0.26147744,\n",
       "        -0.21700464, -0.29298832, -0.17288244, -0.23899869, -0.19110207,\n",
       "        -0.29983606, -0.25710524, -0.20247438, -0.26785368, -0.24074884,\n",
       "        -0.41136115, -0.20848209, -0.41111783, -0.23258682, -0.3347307 ,\n",
       "        -0.24024283, -0.27426056, -0.1666129 , -0.25246954, -0.05799781,\n",
       "        -0.17133262, -0.30934287, -0.19496256, -0.16312321, -0.27852825,\n",
       "        -0.12190887, -0.2525107 , -0.17051029, -0.24582837, -0.25604338,\n",
       "        -0.27895921, -0.238282  , -0.23965754, -0.25554276, -0.20033329,\n",
       "        -0.26717035, -0.15315068, -0.22300112, -0.35894993, -0.30172977,\n",
       "        -0.2429172 , -0.21132153, -0.27550879, -0.15633918, -0.33356691,\n",
       "        -0.2091097 , -0.23095719, -0.38223278, -0.35836285, -0.26861189,\n",
       "        -0.31869792, -0.24722791, -0.35298502, -0.33435119, -0.37457694,\n",
       "        -0.25952627, -0.23709282, -0.28086262, -0.32000714, -0.23438135,\n",
       "        -0.25296674, -0.29806385, -0.13490697, -0.32126387, -0.36419589,\n",
       "        -0.31788745, -0.15557757, -0.31141286, -0.33393894, -0.35830063,\n",
       "        -0.30906183, -0.22850635, -0.06087566, -0.24336189, -0.35205025,\n",
       "        -0.21271914, -0.21301699, -0.25336101, -0.25733836, -0.21643714,\n",
       "        -0.30368413, -0.34228199, -0.2809623 , -0.29175962, -0.3593822 ]),\n",
       " 'fc:beta1': array([ 0.05783552,  0.10992808,  0.14829905,  0.22055637,  0.18128731,\n",
       "         0.19164743,  0.17593566,  0.11786047,  0.14606098,  0.11429009,\n",
       "         0.13907392,  0.11582068,  0.16335365,  0.07776309,  0.21601075,\n",
       "         0.10172566,  0.12238763,  0.17745977,  0.15988623,  0.17104362,\n",
       "         0.10867815,  0.16052258,  0.1304687 ,  0.25715465,  0.09051253,\n",
       "         0.233024  ,  0.09828186,  0.20617548,  0.15938272,  0.14941353,\n",
       "         0.13594848,  0.14365754,  0.07815524,  0.12306999,  0.17546608,\n",
       "         0.05687307,  0.11794332,  0.00515947,  0.10158458,  0.15664578,\n",
       "         0.10156496,  0.09892302,  0.16772684,  0.08093909,  0.25628816,\n",
       "         0.08835593,  0.1388102 ,  0.1578673 ,  0.15120757,  0.06765657,\n",
       "         0.186184  ,  0.15883521,  0.11626266,  0.13366707,  0.14301148,\n",
       "         0.10390214, -0.00641229,  0.082679  ,  0.0419402 ,  0.08529926,\n",
       "         0.12026177,  0.12177164,  0.06072421,  0.0843727 ,  0.06938194,\n",
       "         0.15145993,  0.19309594,  0.06533861,  0.2950469 ,  0.11448307,\n",
       "         0.17773059,  0.16011342,  0.21856068,  0.20208539,  0.15727863,\n",
       "         0.00334999,  0.10140672,  0.14854373,  0.04742502,  0.14130338,\n",
       "         0.14187228,  0.09809913,  0.14173702,  0.09280391,  0.05860987,\n",
       "         0.16681259,  0.13763197,  0.19070351,  0.12552169,  0.12296764,\n",
       "         0.17333687,  0.08982382,  0.18226412,  0.15092879,  0.08825783,\n",
       "         0.16992917,  0.1201498 ,  0.15034244,  0.15234276,  0.20146669]),\n",
       " 'fc:gamma0': array([ 0.92823782,  0.95980761,  0.90767686,  0.95754523,  0.96661906,\n",
       "         0.98639159,  0.98660151,  0.94357133,  0.90284894,  0.95355938,\n",
       "         0.9669824 ,  0.98504991,  1.01151621,  0.97857642,  0.99951616,\n",
       "         0.94527403,  1.00842443,  0.95673922,  1.02233866,  0.98050354,\n",
       "         0.97419471,  0.98089497,  0.921467  ,  1.02160819,  0.99704501,\n",
       "         0.97009965,  0.99512239,  0.95654552,  0.9697865 ,  0.97226072,\n",
       "         0.99879586,  0.96874409,  0.90307622,  0.96960471,  0.93131978,\n",
       "         0.9622308 ,  0.96284497,  0.96935405,  0.99544359,  1.00375465,\n",
       "         0.95819591,  0.9354423 ,  1.00656526,  0.94217988,  1.0369051 ,\n",
       "         0.92624333,  0.92861038,  0.96452984,  0.96332056,  1.04364146,\n",
       "         0.96923697,  0.91737321,  0.95640715,  0.98115151,  1.02421039,\n",
       "         0.95685208,  0.96053337,  0.98548716,  0.99523097,  0.99684935,\n",
       "         0.96850193,  0.93333621,  0.94034072,  0.97619621,  0.91914096,\n",
       "         0.96438128,  0.94949974,  0.98355841,  0.97356174,  0.94437139,\n",
       "         0.98143055,  0.9801581 ,  0.96646658,  0.98225168,  0.94803264,\n",
       "         0.95842008,  0.97859558,  0.97475866,  0.94987943,  0.98152874,\n",
       "         0.95261978,  0.99668324,  0.91504354,  1.00237103,  0.96959515,\n",
       "         1.00369843,  0.94573935,  0.96672172,  0.98143186,  0.95601437,\n",
       "         0.93516705,  0.93275897,  0.95979112,  0.93175624,  0.97785561,\n",
       "         0.95347084,  1.00181991,  0.93398906,  0.97378701,  0.98975347]),\n",
       " 'fc:gamma1': array([ 1.25393922,  1.34329764,  1.15082166,  1.497292  ,  1.25687665,\n",
       "         1.24751485,  1.1580263 ,  1.23233362,  1.26999504,  1.22198991,\n",
       "         1.26509133,  1.26125366,  1.22956425,  1.23973516,  1.37979792,\n",
       "         1.22705841,  1.1573139 ,  1.36829361,  1.21636373,  1.28629506,\n",
       "         1.34444417,  1.26002951,  1.2445657 ,  1.32403396,  1.22638168,\n",
       "         1.44710251,  1.24204347,  1.27025629,  1.21793572,  1.36299479,\n",
       "         1.20122602,  1.26356414,  1.17911026,  1.28009155,  1.29497945,\n",
       "         1.29171129,  1.24889041,  1.2316481 ,  1.21190995,  1.28504628,\n",
       "         1.33904168,  1.23980267,  1.37737514,  1.24100514,  1.35314666,\n",
       "         1.24411702,  1.30590521,  1.24781425,  1.21650449,  1.24858766,\n",
       "         1.28422511,  1.29425026,  1.31235709,  1.24485787,  1.21259003,\n",
       "         1.2490617 ,  1.18231234,  1.30554149,  1.17014732,  1.30211544,\n",
       "         1.17969277,  1.24843978,  1.20615328,  1.1657117 ,  1.24954669,\n",
       "         1.33064095,  1.22388955,  1.23615946,  1.27579321,  1.21178664,\n",
       "         1.27983305,  1.21450938,  1.28001127,  1.3046533 ,  1.19462281,\n",
       "         1.2531129 ,  1.21406933,  1.23484431,  1.27086603,  1.25849114,\n",
       "         1.22172003,  1.20002556,  1.25651742,  1.19896643,  1.27379506,\n",
       "         1.28721878,  1.21574946,  1.33235147,  1.25394518,  1.28246132,\n",
       "         1.26572949,  1.23095746,  1.24239188,  1.25941363,  1.21677162,\n",
       "         1.25270712,  1.30243682,  1.28543696,  1.38837702,  1.25818374]),\n",
       " 'gamma0': array([ 0.86598266,  0.9038979 ,  0.9338179 ,  0.68311432,  0.6139916 ,\n",
       "         1.13192604,  0.93480759,  1.13134933,  0.83107577,  0.86124786,\n",
       "         0.84271128,  1.34896218,  0.66061673,  0.86040615,  1.12334989,\n",
       "         1.2680722 ,  0.87405707,  1.09346575,  0.74150516,  0.88242753,\n",
       "         0.87211357,  0.96098444,  0.84559845,  0.9251698 ,  0.80773208,\n",
       "         0.96556953,  0.74457346,  0.77272155,  1.11014076,  0.90069774,\n",
       "         1.10805075,  0.64127061]),\n",
       " 'gamma1': array([ 1.01615121,  0.91513244,  1.13916895,  1.05756525,  0.99564318,\n",
       "         0.88016363,  0.90185151,  0.92289813,  0.88144794,  0.87662681,\n",
       "         0.9627953 ,  0.85863639,  0.91726112,  1.08756334,  0.85112838,\n",
       "         0.94328375,  0.9720368 ,  0.97431916,  1.15403214,  0.97766906,\n",
       "         0.84244168,  0.98645261,  1.01419424,  0.86133223,  1.08622522,\n",
       "         0.95944052,  0.90798593,  0.85713347,  1.2252162 ,  0.86014723,\n",
       "         0.95885427,  0.85533263,  1.15814659,  0.83796665,  0.93708351,\n",
       "         0.93364597,  0.98112543,  1.03153613,  0.8957984 ,  0.97390983,\n",
       "         0.95313469,  0.86059392,  1.00042407,  1.03107729,  0.96255335,\n",
       "         0.91266241,  0.99508753,  0.99555561,  1.00213495,  1.00521814,\n",
       "         0.94483208,  0.82052587,  0.9608122 ,  0.88868695,  1.03146829,\n",
       "         0.77000143,  1.08549932,  0.84239575,  0.94729457,  0.92207167,\n",
       "         0.95582238,  0.92896078,  1.03781878,  0.984532  ])}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.713\n",
      "Test set accuracy:  0.741\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == data['y_val']).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == data['y_test']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEyCAYAAACbGke8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBpQiakAp/wtGSRR8w3YLggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcfc5c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(best_model.params['W0'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Watch how your model judges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p47gESok3Gp1cMG23OwRBoNfAltpP7duzG1ef0+p1SVU6dtpdDu63HqXvf//7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\n04SSGhmfQ2mamjyCC502VHH+2raxsJw9fQbjMU1QvVY118e5c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lhYaPLwRSYgZRQC775L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jBo6LPF3+3PiSFdAlfpUC+i8bFhrTTyxUsy4Ke25vtHHEdTXSsYCalVZq46FVVBVmTEXXCkiZ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CetL9VZvSLXOZ3gm/oZlsI9LiaLKfY6XoV4KhRCJaMiSrOVap26p7np5foSB6Ju3jAtYhFO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rsvdfBH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\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IaaHSJfYCIGcG7/zsCW5ep+v/7H9/F+d/Rq7v1Z0NOA8pYzXUEt8ZE1s4g4b4Oo0XVSTo\ncZbqnqvQYvdp89od3L62fIkOrwmIslrTS7ia+s9oH4oTQ2YJoCNmoTdy5A8z7p/Camb5Tgu+2+TP\nL7C9QwzUay99D00QQy7jAl7FUIwKrHo0Rz+9W+C9Dt3/XmHgMpPZb7cs23l+NkTKf4+GZyjy5fXQ\nlKDEBgAwZQrF69l6M0SViDbJckzZ/aSzGD7rpynlodWidcX1XOte7nXbWF+ntVZ5GlMuP+OHPtY2\nac7t7VyDz7VPh6OBdfP1kgghM7GmLJEvLD6Vu/N5man11TV0OLu3BAXUA8BkNMHTA0rGgV9gyJps\n1/obcLifE2icMxs81BJxlUyWl2h1aO2UaYHLGbVlogCH69o6no+4KkkHBY9dh74SNgPbcQOEXKbK\n8zybrKa1nrv3l8AnbExhnnYI2LpZQswNKImrUgRWHw/aGl+LMDCWjjdiscaOsS68yuNb/acaA1ez\n9vCxf/81X+AzINIU4I1eQaPFVGUr8pGw0qQjHGh2WSWpRlbVGjIawp1Hj9mBW5S24GKRxPh3XycD\n5tXbO9hYp4V6pdXA+gotFt1OgDa7oCJfg9cHGOj5hqscZJVBBzFv7zOgsxD379PimecGXpsy+G5u\nvYynT6p4HwWfhQJf//SrKFAp2aYQHE/25OIETyc0iZqTAlt9mmi3mjexWVBbX75+G5cuDfKHDz7C\nv/kOGZFaedh4/QYAYDyd2YwmNcnw0QFtEK/c2Yb0qX33nxxi5E+Xah8ASM9Fyn7Vt+49QsJxW7e3\nm1hjl2maSwhDWWZNH1Bc8wl+Cz6n/g+yM3z7h7QBfXQcozHmd5uWmJxTNlw3bODObaq12GoEqILX\nDCREtYErZVN9Fw8S0KU9AECQW3xZpHFiY6Za7TY8lkYodQGn8idMU2h2O0MKPD6i7KaL0QweT8zX\nXrqJkBel6zubOBpwdmlxiYLrwEnPhccuvI3eGl66Sdl/oefi4OSIm5JjOGBF8LQAeKOMel2EES2A\nTREgcpen3Y+fjjFjeZHpZYKYDRxZJMh549AmsQV/80yT2x1Uh0xUSsumBCo5laKwRhOgbRZfmqYo\nbK0yDVfNY8raIc3LhlvgkhWezy6nSFr0W9v9AI01FkZUGVwWYXwWsiJFj4UcVeDCYbHPqBHiCccU\nTtJTK2rstwOU7G6dFilKDoNQbQerbTLmCjHF8QUr1N/6DFJeW999/69wwIZYLwjhcfteVw5u+rSZ\nT3WCCYv/jmWIlMdvQ3pIH9F7uPzm12ECmh/9z37pmW2UyoHmjF6jNVwea1oZaL/KulrD8Qk928nQ\nsTXpZqczzKZVfF6CZoOe0wkcrPTJcO83ZxAxXZ8WGm5VV7WIkWu6ZiXK8fpNeubvfqiRZFy4vRfh\neJ/WGyUjlILdtnqEgw9mz2xbhbww0LxLybKw9SHXOx2kBbv6J2O0mrRGBhseAnY1Osq3mc3Kc+08\nbrZbHJMIuA5QsFGpfAdhg4se+wEiW7g4QM6uxlQLOKpyU8r5IWFB8dw8Z6HjMAixe53kKIJmw8oA\nGQ34XhXvk+NXfvXXAADJyQDvcoxpCoMRH67iEkg5mw9laZXjfTfAdMaioy5gOAOxmE7gcGZfI2rO\nszI9BbcS/3R8Gx/pOM4VV6bvLW8U126+GjVq1KhRo0aNF8AnykyRwTdnf6wAGOaHbWMWLDxBGQQA\nM1TWFTi/z2IGkxFzJYxF55xZYJaMmTNiiyzljzrz5kzN80h2AuUshuITqu8obHCGwfbmCEXKgaCz\nAnlJJw4lXESyquU2FyIsi9JWx9YG1kKOAgebG3Sy2NrsoRWRRb3abaHXpdNTI1JoNaqTi4CoopSF\ntJpZRkgUfH9IaWnmZ+GjBz/AE85E233pDq7vvgwAuLG3hy/8PIs0qgY0n/AuJueIE2IcXm73MWJt\nqf2mj0ySq+AgO0J5Qaein7jxOm67HDR6KRCDmL3zSYr1FgVOup6Hm7dvAADiPMb7LIo4KCdQKQck\nvvMUfc5iLM8TNKPeUu2jzgEMT42zQYbLH1DgbZG04b9Kz3ZwPMAJuwK3Nn4Ld7YpUFcGaxjNqF/P\nL97BwwfEvpVxgTEHlrpwbELB0dGZ1Tf7tS++gR5nYX733Yd48PScn0cuBJfPM/sg1Zy61eZ5kmus\nQB8AXJyc9t8DQwAAIABJREFUos1lScajAbKq1M2TQ6QX9LcygOLMllGcosfMI4yxGWXNqAO/S6zK\n4XBqtdGa7SbanKiwubWLMbMjP/jhO7g4ozmx0utC8InTj5po8PP0t7YRNuieUaNlg1iXwdMPnkJV\ngp+zBFPWz1KFRsqnbeEUUJrakiSZDZrXZQZZlfgoEhhmoIo4RcIZRFqXyPMqs2/O+nq+QszBsEWp\noDizazg6wmjMLiil4PXoXeeOQci1JgNfwfOXi0Df2ozQbLCuV2BgEprbjYYDn/spiCQ2ezT2fd8g\n5mzXcpyjxcHNO+sRHI/aOjmd4jN7XwYAvHrnb2E0oTFbQOCcA9NVXECxO1pKAcNs0dTvwuX+3igy\nhOzqF+MYj79DGchfvfhv4YT0bn/3D//wmW3UmUaVGqN8D6qqh5XnEBVDqArsH9F4/v47Ghmv5ePx\nOZotanuWFBgZWm9MGOPtew8BAJ1ggHWuBVvmCTSLcwqp7b4RT1MYzUKzWmDA+m+hkpilxEB5nRY6\nHerPsAWEwfI8RZoDTOJCmtJmGu5sbsHrcOZ0nkHzOq60gstaSNJxID1OjvA8KL8S9vQgq8ysokTD\n5/qZroJgXTuhXEhmJ8NGgCYzgKlJIZmpKQpj2Z9F7wUFoy/dRPzkl74EzWEubuDb8Wm0sKE8nq/Q\n8qnt43KC1VeIsb9/OcblJWvWaSDg8aY8FzGz63Eaz8WMC0CxIK7yHETsNQh8HyGvW1HoQ1XP43g2\nGF0qBcUhNVJJq4m3DD5hY2pRtFN87OeYbxVXzBiDH3VjfIwxZT4+OfyKEWeubjrLuLeeJ2Yq07n9\nhpYGHU7x3ru+iZKNrOPTAeK4eh5nTvGqeYuNMXDcKuZkLhDZ6YRYYwmCfr+JkKnuThSgzUrnzciH\nlDwx0hQOZ7U5mNcykkJa919pMpglRTsn8RDNLYp/CLoCXsALTjrG4ykZWZ1+FxlTwsNZimbY5UYV\nOJ9SLMdJGaN/m7JuhkcPcXZGmT/vj+5BrXJMTaOHg6dkyDx87wN85ZdpkX/t9U/D4dpaUkpMjsio\nOXjrMda4Btjw5AyzY1o8heMiKZ/jLRbFPEsODnLe5I9Phhju0O/OkjHef482iGvbPWxsUJ+srvfQ\nZLn9N25fxzq7W88fHQCV0ez4ELxwnZ6d4v59EkD8jV/+Bdz67Z+na4TEo8dkiBnjXDXoq9KSi5+S\nwu3STdQAJmw07a6vIWdKffRwH5MB9dv+06eYsNGVuyEirlsHoxAUVa0sF7LKTC01mDnHzb3rmN6n\n+pCTvMTwjDblJ+fv44LVx0ejoXVfX2qBmzv07tJSoNMgo3Vr9yUbt1AUGbJyuaxTANBxhpKV2tM4\nRsaxjKF04LARWmhtF8widJFyu0qdQvNvFWmMgo2pLM0Qxxx7ZUiwlf4W9jmNozGa0TiPQg8OL/In\nl+eYsuyH3/fRWKON/tZLd7DWv0Gf+xJRczk3X9gM0eL6o75UuMbrgnJ8tDhOMloL4Lm8CbsGwyFt\n/ht7a1hvc+p5VoBLY+Lmzpfxxuf+NrXblDB8+ElMibsse/HpOIWuahUag3CXY3D+7u8jvqC5mFwM\nkHz9XwMA3DJDwtIPw/dmNp39d5doY1nMC+z6BjY0QUrASHqfjudirUufd5pjvP+A1pIHT0poVvNf\n133ELl3fXjPQTRqox+MSHktmJNPMFjhXkYchG2tJGeDgnPowhw9pWJbFVbj5CoU5HJ2NMJ1UISw+\nPH95KZa0KFDyeqAACDa+w1YHXZ/Gadd1rTwACgFVxQlL2DhYIwWMNVg8uFzHskgzgPcD6SiAi5EL\nIWyWX6klhCRDw/UkVFVrdGG/1HqxaPCP2Wx/DE4Gh3Z++IlvYxAXU+eFFNh/8hAAMJtNcRLT+nQc\nJ5jx3qmUsrVJ8yyzFkxelvb+ruvaOqKO48Bn48tVEi6vwa7j29hHBVj3seu69j6O4yDgcINlULv5\natSoUaNGjRo1XgCfKDPF/NJf/3SRHbqi+zQ/ewsBW0dvngP41+/+Y3950Y/4cT/2o8+x9J2vYiIK\nSBYAc6WwJV62dtbAsZkwEhix5lSeGRTMCglhrICgo+ZWse9JuIqu6fYi9Ffo5NLtz+nShh+h1aqY\nA4Msr7ImfVtNu5QGUlVtKSzTZ0QKVy1HZ0btPbzy2s/Q87oRkLMIYRLYjMtpnCIuiOlYXbuJZEq/\n/933v42SWZtRGqPg7JSm30fp08n16fAEgjVsXr12GzKgIRqEAabgiuIRMObSPHff+iHee5PcfF6s\nUXKQ7CzPkZzSPSMRIH6ec8PswrKFgjIl6NmSEvcaVQBjgYLZrr/49nvorRDL9pu/vgK/SUzcrds3\n8Af/6e8DAL76tT/BB+9/AAD46MFjO6KKtMC//uqfUlvuP0Gbs1Pu3X8MnTBdILyFmSAh+EQlSB0S\n1YOK56jiboREm5mJIPCRM4OjXUBxNuVO62U0JlROZLCYdZPGNjg7EEDOTIMMPRbmBbwwxPoO6fTM\nMoPphN61MRrFkNrVWd/A2uqa/bx/nfpQGAW3QWM5aret9lNe5LYm4zKQZY6Yn83owmYQxUWGZp/a\n3u024bJAoSgNJlzKI80mdnHMi8xm+WVZPhfnnNOXcB3HvotJlqLSI4t8gdmUgpSn8SU8Tka889lX\nsHGThCzXd6+h3SEmLmq0ELIb7FnwPYOoxTpXpYDLQcOFKRG2uE5cMkCY03jc2GzjxiaVcwqbPZQz\ndoeNDDa3yV1/7dbLcJgZ0/EYM65lFgYeNrnsVVxqfIOTSiZ5iZdYFPElqbF9g9o07Zzhz/6EmIVu\nCaxyXTk9c3CK5VmbpCys7mBpNHTCDIvnQLaYYXEd7F6jvz+nQ9x7TGN5mgElu6gGkzFKztps6z4M\nJ4+8de8S+R67KU8kRuccuCwjfMQJQ6dTgaOzeX231U1iAy/Oh2gYzg6Dh0vOED3PJmg0lt9aizKf\n+2OEQMYMkQwa8FkXSTnenJkSzrw+nZmXl5JK2j1MCAnF3gypXBgWOxVyHvahFCBRiVJqCGapAs+3\n7E+RF7YPqZbm3MOD5yizdnxyMP/HwneVEjA8V5RUNqFjOBrimOu+DoeXC+x0gYLHZFmWxLSBGKvq\nmsUMvHIhaF4pZcNZ0jSdh04oCU9UigALtf+EuaoW/gx88jFTc7XNOb0nFrLJhPg4ewuAmNdx/VGb\nbPHzZz3DlS8s98zPEzMlnAhV9T9XOvB99uNGBoYz00rj4ZCzzqaT1I5J5Qj4nNIZRSFcNsRcR8Jz\n6J6dTgNdVjFuNj1rfHleAC3nAw7s83aVsumyUkmaQQCMlFY6Qkrnqsvob8D67kvoblBWxiQtMOS0\n9dWNBqImuRmS/BKuR5vh+fAS771Lqcfvv/8dOGwg9ld30PVpITKpA+FzhsyNEJqtzoezY7zxuZ8C\nAOjNDg5ZTuC9D5+g16RN+O2338chxxatBy2kvBlurfaRctZNNimQZ8sv4CaP7eJmoAGOhUlK4K23\nKDvPcxUqP+nF4BDH5/+KrtEN/Mqv/AoAoNmI8HM/+wUAwN/6hS/hzb96CwDwX/3X/x3usiioUE1w\n+S2cf/8+hCDXmICErDZrU8DoubQHOJ3ZQFj9DCEd+/6XQZEXUOxiGyUzXHBsjG4H2N6mDVFpiQf3\nyJXpugFaHJtx+ugjSCv/EcPv07uOywJjHkfn8QwFuxya3QiraxQ72Ouv4o2CYiGkVGhy5qOBgcNj\n89q1a9ZghBQouB+cMEDKcTvLQBtti5wWZYG8iuGJHPgbNPYa3QYES2iIuETMauVJMkYrpHmmdWEL\nCidJOnfLS4F5DKhAwe6o8WCEgkVNUc4wGNG41QGw+wa5hXo3emivcsWAdgedLo1n3+8iCJZTQPdF\nB05O1/pBjFLSGE+TEoFkt3NnBf0uGcRhs4+NtRv0WBrob5ObUboB/CqmxlNIeSwYneLkkgzfx9MJ\nmlyFIdMGtx0Wx/Ulcs7C/MN/+j+gy1m5Wpf4IUtgaAW8wTFkoTBWdHEZpKEDlxfILM8h+HCF0ENZ\n1btVEoo3zcgvAXb/TbMYmaBnaHhtNDimFKKDp/vU9vf3m/jWu1x/beIjHtGa+/LLr+JRSYb4YXwB\nT3GGthRIOSMzTQwefUjZqNASUrGx1nWw2ls+RrMo52uTFgKa1xvpeVaiAELZguhQHgo9rw+pVGUc\nKWvoF0VhjQUhxUJopUGV3651iYzdZ4UGjKmKtc9rtRZSXDGm5plucymhZfDWmx8g5SoLdCBhA0oJ\nex5USqLIq8LwsTWsdCnmtXWLAiU/m1TSttfxPPicySiEsO53x3GsO08IMQ8vMgvZ+6ZExhn4oSPh\neY79rvsctUBrN1+NGjVq1KhRo8YL4BNlpsQCG8WVfv76RQbWrXIlSF38CB1V0VTzwyHH4C5m8fHn\nxtisQIO5LP9VmtIsEFxX8v+ei5qaxIDkH0ihofjLiS5RMlsUdVewLumUFMcZn3DJMveYjfJ831rj\nUghIPlU7kWfrw8VG2KzAOAXKirEwsKViHDO33qm2EmuGGG37SqDEeLScXf3k5AEuv0kuvM1r1zDm\n2lH3Hr2Lmzeo5pbnaaQZXUOUMp0kms09xJz90vI9rHFcukoS5AO63hMBBLMw3c46Olwe4dMv3UFx\nn04bpZB4ekCaR0JK9FeoRE3UjtC/RX6Un/ypz+LtPyddqh98730M4ufQmWqsLNC72jJ4wggUXMsl\nzwCTVq6rGAMuIfQ//eN/ho8+Ig2pn/vyF9DrVoKpEvtHFKCfKx+IyJUjpY/qTCMg7aAlQch5UoAd\n1uZHPNXzj5fWCgOA9fU1pPwuRuMBpszgJGWO0YhO6pEXYXdnFwAwaUyQTomBavmAYqZhfXUNU87g\nGicJoi65KV9q7mHALM/m+gYCzgSME40hj5l2p40Wu5TCKIRisUo/9KCZUTi7uLBiiOnwEkG4/JI1\nmU0w48xBI4HOFrEFrfUmXBa1LZWAw78bZmbeJ6NLRMz6FkWOlE/Js1kMyW7HMApQLATEV+xVOomh\n+IQ9mlzikt186zc30du9AQBwwh6iiBijRriNMCCXaBj2EIXLsRo7e3toMZOtZNPqZW2shYg4gL8s\nUxjWyPJEiCxn11tnHTkzRybLIKucuUTa4GZdpBhd0vXvTIaIqiXCKRFyOmoAAYfnhD+bYnJGY9wV\nwOvsdsy0sSW/vNxgyyx/hlftELJa44ZDRJzNlyGcsy1ZhqxgodlcIeX1bpIN8egRMatZOoJq0Tox\nOp3i8JIY5v3RDGstWoiEzjHjOf3F3VfgapoHww/PsbJOQfbDcYonRw+pjb6Ey+EUk8EEgjPFwpZn\nQzeWAeWgz0NYbBC571H9TQBZriGqoHAxd+cpV0Hz+y2yzO6puixtdumi4LUxxt5Ta40ir0RqyY0K\n8H9tBp+02e9GG7sna6NRlssLTX344YeWjRJyrhhpjFkQAjVz1klKNLkUlOO4H5u8JoWEZDa72+3a\nsBhjzDy4XCmEzDB7voeSXYRSSriceOJKgyjkfdfzbLiE77twnOWzhz9hYwpYMHHm/8OCL01gTkmK\nRfXxK/NPzDP9FuwzITCPG1nM2FtUQF8wlK4qowNVheUrysbiqlH3LExTiYizMSZZDlkNAteHy4rf\nq34HazuVkqacD1CtrWF1xfVpxELGg4bmlO3cZMjZqNSlgK7SZYVAFZYgCkCayr9urL9ciPmrl7q0\n2XHPwq1be1i/QQtLq9/F6TEtnvfuPsagze4JkyOLub5Ut4EuK/Ruvv46Ev5cySGmF7SgnZ0cYHjB\nMTu5i0DSs5yqc7gFDeYvfOHn8Oo6uRdPxpe4GJE77PLyCFu7tBF99mc/j3CLNuc3v/MdfHCXYpRm\nRWHVu5eB8Zu4IjluxVyF9acLMzdwF8Vfj2OD//VrfwEA+OPvvoeIpQ7yIsVgSAZjnBqIDj2zLnJK\nWQLI91IdJGBgMJ/IlXtfCAUrdC4FKulqAwFplm9k6PlWSV36DrortIEPTo4x4bpfl8UEQVXrq9HA\nlFW1cy9Af5s2ppXtbZxwMe9UuehwrFMhAIcLIydpgrUqPT8QWN/e5GcGcq6hNUsSdNgNM0tmeHr8\nAABwenGBvT2Kb/G0h0A9hzE1jeFy/zdWGuht0DOIUNiVz+QFiqreW+SiZC/ibDRF1mShwCRFzJuO\nURKKM8TGsxJPWR7DUdLW45vNLqG5+O/J2SkcdoOu39qDEBwL5m+g1aTsxcBbg6/ICG16K2h6yxlT\noZsj4nnbaASIIjIKup1dNJr0uZEC1enRcQz8SvRUKExmNB6TJMZ0wnFjC8rWUgocslCrKTUGvOlJ\nAVTV9UqUdi1WrgF7uuBCwGUDLdISbqXaLwwKs7yhEYZdTId0/TSbwZfU92enJbTmft1ReHpITzSZ\ntdBgV6DQEgf8/GhNETjktjt5cA+Hh2TgNjZ97OzRWB6bUwS8OX90+D66ferPTtSC5Gy7TsfF7h65\nrI8enSDguMMsB7RD1zguZ9AtibwsrhgLVUxQXhR2P9AASs40zcrcVtDwXMeKapZlaY2IUpcoqzqv\nCxmrrutaI7rIc8BKpEhkbBxlRW5FamWZ21jAxW1Qa3NFxPNZ2N5ZWciCNdZ1WCxIipRlaY0dbTQc\nFrAOfN8ablX9S6CSLiADttFoIAxpzC/GT4VhaMVLFxXNfd+zfSWhEbIxRVmMHPfpBXgeJqV289Wo\nUaNGjRo1arwAxPO4BmrUqFGjRo0aNWpcRc1M1ahRo0aNGjVqvABqY6pGjRo1atSoUeMFUBtTNWrU\nqFGjRo0aL4DamKpRo0aNGjVq1HgB1MZUjRo1atSoUaPGC6A2pmrUqFGjRo0aNV4AtTFVo0aNGjVq\n1KjxAqiNqRo1atSoUaNGjRdAbUzVqFGjRo0aNWq8AGpjqkaNGjVq1KhR4wVQG1M1atSoUaNGjRov\ngNqYqlGjRo0aNWrUeAHUxlSNGjVq1KhRo8YLoDamatSoUaNGjRo1XgC1MVWjRo0aNWrUqPECcD7J\nH/t3BzBK8A/LEkoYAIA0DgxfowVQ8jVGAJL/h+q//29gjPkx/xYojbC/VfJTFNAoNX2elxKFpqt/\new9imZ9b/B3NvzWaxPjq1/4YADAexdjavAEAiMIenh48BQAkaYYkjun60QhpktCNtLG3DUMfP/+V\nnwUAfOr1m+j1IvuzUoqqWS+Cv/Hbf/Qv/okxTgAAcL0AZVnQ366DIkvpSQoNw/2qTWm/q7WG0TkA\nYL2tEAYeAKAwCrGhv/vrmwj8EAAgtYYw1PlFWaIoNd8nh8MDyXFcCH7k6XSCJJ4CAMqyRJrS88RJ\ngjTLAAB/9/f+4Jm985M/+3OmGiPGGAhBZw6JHLvrHXqG0uDoZMA95sPApeulgOvRT7z22k3sbK3S\ns41GOD+7BACcXkxwNhjRPV0X1XB0hYvIo35Y6bag0xkAII+nkFIBAGZJAe359LkAPJ+msOdJtDtN\nAMA//yf/yzPb+B/9gzdMOqJ3l+cFVnbond759B6US+Oud32GRtGlay4CmOt3+ds+pKTf1SiRn9E1\n5wcpurt0H9kcQGfUbyKYwGj6raxUKAt6L+vudSCjfivcBMdPeFy1NAQ1F06msN6g5sgLiYLf++/+\nB199Zht//5dfMsOYfrexfg23Xv8MAGC138T08gQAcHJ0iBkP0VJGKBIaY2/94B3cf7hPbRQChseA\nki4k/x2EAQKfnjkrMghJ382zAihpnF+/1kOnRX01HUzs5IqiCGlG/XxxNkDGD7G1s4XRjMbqm/ee\n/o1t/Jnf+kXz+s/cot/fiTCKaN6UXgMx33s0OYfv0Bqx0d9FCZ5PEmhMaDx+aTJDekHPuLm1jfEp\nzaFJnqK30gYAPD4+Q2N3CwDwIHuIpEnvXHVWEJf0PhM9QZnQuP69z/wyfuvWLwAA7g3vY6tzHQDg\nOx4Erxkbbv+Z7/C/+YPfMb0mv3+viWD1NgDAba1ZKsD3PDvXnzx5gumUnr8oCkhFA8lRClLSFxzH\nsRtK5Hto+jRvtnd24bbo79QYKEN9IgwgBD2DEAJlSe9KQcBo6k+pDIbDCwC0DlXP9vf+/j96Zhv/\n2bcuTbW+SylRLeNSCjvvpZRw+J5CCCj+PM1STMZDAMDm1qZtoxAC1V4rJCC5vVLAjl8hDAR/riQg\nhbbftWueMJA8ZqQUC/0ASEXXvBI+e8f5R19708iqrwxs/0gJSP66NHN2R2D+W/Rvvl4I+7ex/+/q\nHm+MgeF9A1ra/UEbA83PYIyB4S9rY+z3tdYoy2rt1/bz//7v/dwz21gzUzVq1KhRo0aNGi+AT5SZ\najiAMHQqUaKwljYxOPQPLQDFFqkWAqqyiv/GOz/DaBTAAmE0t2ZhoCsGRQAlM2WOMSjZSndgoK2F\nvKztOf8twd95cniKwxM6Ca70N3B+SayDVD1M+TRstIucTz1QETSfGtI4xunpKQBgMLjE/YeHAICv\nfOWL+PVf+2kAwNpqH3O+7f9fSP4lISQgaAgpIewpsNCANlWbxF9jBum7C6ccGHuKklJC8X1KY6A1\nf1cArke/ZYyC4HclpITiU1Sr1UIjIqZgNpvZU0iapggDf/n2yfl71lpb5igMfHzx8z8BAOh3e/jq\nH/85AODp8RBGufxlB4bHTp5kULpiX0uEPrWrEfm4GNDnjSCgYx6I0fBCev7hZIJkTMyXMhqeS4xV\nkmsU3C63EaLBJ+ksizGezpZu4+qqxKDk06cbwuVnm2TncA21RYwFgoCY0uywi9IhliLaEIhjYl7O\nHuUQPEcdt4NiwmPg0EXp0jX+uobLk92RGaoOTUfCrgFwS/gtWhviREKkGwAAr3MJJYhliR3gdJgs\n3UYIF35I/XZ9ZwM31lsAAFMkUA7NM9kJMEnp71gDBxfErKTxGFsbKwAA5fmWYQ4bwjLAvh9Aa/7u\nzOCS53SjESCeELskihyTATElo/MRru+uAwAuzk8QNehdr3UbiB1iVtb6EffmsxElPn4i6wMAOnmE\nQUx98wNxig+SMT17LuGHNPZFEKHj07sN0hwo6N2ud1bQzulFbIQdHOfM2h3u4/xdatNe4OLBB28B\nAG61Q0QRjc3GRobLHs3LbzUBBMRYpaVBkxnslgoQahoXvnGgKtpxCeTCRcHstgtYJlyVJTQz1VII\nZMw8G2Msc1T9F6C9pFpvjNH2u1mWI9bUxtFoBIffZ6I1fEX9poS8wvhUvyUMICyjkWPGrPhsNkVW\n5Eu30XoU+G+H/60WmCAp6f/oGQykqtZgjSxP5p9XjI+Y31dKflj+3LG/tci4acjqGgm7vkoBCDln\n5WS1FwpALf8a4bsSoqyeTcBuqYIYPoB2VzFf7rG4k1V/VWs9ULFRFYskrvxdbRtSCohqj9cGprIt\nIKBReTpg9xl5ZZx8/N714/CJGlNNZz74HAn4HnVMnuUo2Ldn5JxSN0JBVCaCxkLf/qi5YLm+53oe\nA9hO10LYRawwBrqiAGUJY6oXuIwxtfiCJeKYJt5773+ERqsHAOitrEOKBgDAUQE83uiTpIBiN48o\nUvuy0yLFNOEJP53YxeX7P3gLK2tE7X/5Zz+PTpvuabS5MkH/v4QxhkYfN9VOdiHsQJVSgNckur56\nLwu0LYyBYppYa6BiZWHM3JhxHGs4GGNgcnYnjIcYnROl3u71sL5N7gftKLA3CVEUzSeCMfMXvQSE\nECiKYvFRAZArc32VNq87t2/j6MkBAOD05JtI2NXheA4cXhF0GiPiNvY21vCEN4LjsxFCn965AxAP\nD0CFAUJ+h7MZMBtSJ/pKQLpM6+clSn7/Ok1xdHiE6hauu/x0DqSLZkQbq2w4SHJa/LO0QKtLzzMb\nZZh6bOBMU5iPrlE/BBNMc3KBnXw/wMoWuZHEpotsn4y7lhti1P6AnjNWMAFvpqFBs0FGTZ76KBUb\ngCJD1KKxf3w0Qb9N12SFg8EltTe6SBGIZQ80QK4U1tdobHR9D8fv/wAAUGYxipQ2Pl0WUOzmiYI2\nyoyMKc8r4bNh7gchbt4id9osHgA8L4+PT9Bgg6jfjaDZza0AZPyOBqcD9Ho0RzutFqSkNrpuiPOz\nCQBgfaWD7Q1yB89mMZ4eHS3Vvr2NNfQ62wCAHdfB50J6n+2zfUxLat84dJEbeq7rRmB7QmMkLAps\n+WTYfd6PsK8fAQDuv/0AIqJnafdfxvYNWrMaqwFW3n4HALD90i0MjuhAt9PpI+iS0fnWu99H/sYN\nAMBUl5jkZKw13MiuRwYG+jne4TjO4Usag56WmBqa9yI2EDy3XM+zIRHj8RjDIbm9siyD6/LBQAi7\nriilUGhal0PPw0qb5vTp6Qn23yND0m+3cG2Lxo4SAkpRv0klkWU0Vxy4cCTdX5scl5d0WI7jGfLy\nOYwpNV9jpDRQC8aUvUbMjSlqR2VMASEfwBaNICHF3PhaMMSkEFCLhoacGxHSGlbSGlBX7zlfwsnN\nt3QT4bkKpjowY27oCTF38yksGlMLe4WYfw6zaAZ8vGEF0CGe/hBz80DP3YEGmIf4SG23NG0MFO/z\ni27BZVC7+WrUqFGjRo0aNV4AnygzFSoNw5SFIwpEDgeflhlSDkrVhYR0ydKm0Dc2f+XcLK7uAeBK\nkBqek41ZJCy0ALR188EyU0YuMCtLdlcV/CYgcffufQDAo0cH2Lm2CwAYjkbodqiNk9kQymPLP9dI\nZ3Say8sEo8kZAGA8GUC6TG+7JZRL9x+MLnB0Sie1737/HXz5Sz9J10jAdThwUX28vUyB1c/PXhlj\nbNClwcI9xCJlvPhehD1KCFwN5LTvk24MgNiVNKVT5tnBPtzq5CQV9u8T0yHSKcYDOgU6boToK78E\nAGitrdpTjuM4CENiBLqdLookfe52VhBizkxqzcHHnodb14kVWO34OBzQe3OgELAbY6PdwK3tTQCA\nH3k4P6f3mSUxWg1ic3ShMZ4RO2McheSUTrSFLlHyeI7LAim78DSU7Te94MYQWiBfmBfPgnIDbOzQ\nfWbYK0+AAAAgAElEQVRlAt8lJqPdaSGb0TgvZYaLU3rXAVKYAbEU2XSMksd4pBS6UwrKNwOBZkos\nT9jxcRpT/09jjd4O9X/YCO07cv0SOfdzWTq4PK2Yag/SpzFg0nXMJnR9ObiAWp0zhs/CZRzDnVC7\nHg0HEGOaK7ubq0jZhToaDRA0yDVV+GMoZizaLQeKmfNG04Pr0HNOhgmiiNoYuB2cHdM7NabE9Wtr\n1N5RDJPRNZ1WACabMRrOcHBIzxAGoWXj7z86Qzui8TDLM6Rmufe4ubqC7SZ9bzt0EfK66Z4IdA9o\nHIn1AuPr1KbYfIhJSOziW5MTXHPo93tPY0xPiQ07Oh8CDer7KOoj99kVrJoYtonJ2lrp4/6HlDRz\n8vAIx3eJ1XpYjrHjEuM6yaaYZjRm214TpaQ2pSZHdYZvI3xmG5M8hxLUgQ0vwjRnF57KEbapjz0l\nINhN3e11AF77lFRo8BpAzMs8NKAK7B5cjvCUE0m6PYmcugphuci8GBjQuNMlLPMsBVDw55BAwXtG\no9VEFFWJQc+GmhNNkFJAWRbJWCaIgtGrtdOg4Ll/cnyIKKB9VAk9d8lJA8HhBmqRdZLkbanu49hA\nc3ll/caCy08ueB/sWr7AlC2DwHOtx0lCQNroeANl5m4+ix9xJFg3H+ZME8TCdcZc+UrFKAlIu31T\nyMbcj2gD0PWcgdLGoJp9pTEw5fLM1CdqTAUOwO8XSgCeoZGrMIMpifLe3z9As0O08SwukLCvvbu6\niW6XM0hcd24ELUTcC2NgUHXiYlrdfEDYDwAYIVDa+CmByptXYsHI0qV1Xy0Hg+rVF0WJp/tEG88m\nKTyHFgUtMzQizvCZDNDguI7paIZzjo2KkxEuzo8BAEeH+/A5g8t1FJzKXw6DDz96CABoRKH9+87L\nNzH3cL1AGuTHQAhhsy2B+STVMAtcbOXDpuvN4ouAXR1gMJ+k+ZTcK+9+/zt4cJ8W5/jiCJtdWpSU\nG2I0GvNtcvRa7FpyCjy4/xAA8OmVDahqRC+0Wyll4/CeF8YYKJ74SZLhkuNfpHDg8WK7vdGB4Y3J\nCIVeg1x1t7bX0XDmuSdZWrkiRvBczgiSjs1YdNwAOfdhEecwvHEIre0YFFLagS0WjFmj50buMvDC\nHN0VNmTGI2xf50OOnOL+PXrO3esdbBZ0AIjaq3jCrjEzCbG9SXOx8VIbep8MSXPiIlilzetwcoaP\nHtHY39tdgeE4kyRNYTS7mkoPMcfqDIdtxFMa42HUQDym5/F0Ccn9cO67aLaXN4r3jy9xcUpjJigK\nrLHbbm1FA3xIK7ISqSa3UHJ5hrBJz98PHIx5oDebHRwd0rxstzs2ZkaXBdbXycDwfIVGRPeMZwUU\nu3Hhe8jYzea4CscnZEyljRS6cn25Do45Fsxz5Y+sVT8e2/0+rvOhbD0IMcqon2ZFCV0lmp4O0S+o\nv/dfMziQdD06HnJ2C54MU+wqave1vocGr7MmGeH8iNr9YN/D23c/AgDkT1fxb/hvsdtH46U71Dd7\ne0iSBwCAu08O8MEKuUa/vPsTuChpfk8Qo2Sjf8NZeWYbNQyKnI0C4+D1O58CAPgrO0jYp3/vvTcx\nmdA7LBwPCS/qTc+x8ZfQ5YJBITBlI/u733sLb71/DwBw6/bLeONVyhZMJxMcHdH6q42Gw1u9EsJu\nDsZIlGxRFNJgOqGx1lAu0nj5caqA+bIorrrVKgNKKWEP6VIqlCkZy48fPcC1TRqD8tr2vL3CoFp6\nhJjzDEaX/w97b9IsWXJeiR33O8f4IuLNU2ZWTpVZ8wAUQIAgyAa7m6A1ZWyxRUls00ILmcy4kOkH\n6BdoLTMtZFppoTZra7LZAEESRJMAAQMBFGpCZeVQObx88xDzdGd3Lb7verwCu5mRDVmtni+qoqLi\nRVy/14fPz/nO+WDxAdWxilnAP2/ujzYMmvxMwHUuh0k+H3YRJJOCHYcQM17TsmbBkS0lcv6NXEpY\nvPdrPeP5HKFhn0u7yYuMkGyWB5RJIOdIWAHmAGxbEpm5iBSSDy25FgCvSUkYQgmm670yRDr/AfWC\n5rtoF+2iXbSLdtEu2kX7FdrnikxZOkUaET0z6B3B0nQac+IBkNFJIYhHOL5LqpGHD4/x8JhONHZt\nEVuXrwIA1jauYGWNTszVZgt+iU6BrswhQBG7EoDQ7DGiLROFCj2LSDWAtMAqtYLmE02mBRSr1DIi\ns56zp/Q9w8EEoxGd2qGESTp+6eXbcD1Co9Jkijjh300VPCfgy0lh8bVNRgOkrOaqVuvQnNyotUKn\nS0fQo+MTWAxQNusVrK2t/MOr0nqW2PdfmJ8uhDiXlKeNWpHu0rkswdlf/NJ/f/a7AGDY7+Ldv/8A\nAPD48S4yPg1stCrQJfqtKIxQZWTBtQOTnOmXPZwcUyL4druDpXU6pak8n0G3Shk11jxNnTvFnk+a\nT9MMx8dE68RxYhRqL918AVd5rEVxjhLDY42yC4f7PppO0e31+Y5oeJwsrs4hSnmeQRS+MlmOwuDM\nFhK2wdRndxlKmXt4zopsvj5GgObfaqyUsbhIYzOJY3iMmjWSDQwm7AWWhxDMXOh+DS4zNFZJIdRE\n51hZHVrShzo7Q+zvEzLl2gKax7un2rCZCkIWYMDU3niUw7JornTTIaqM3C5U64gGtDZMZIRSbf7E\nXtspIWIPnngaweEk+/5gAIfR3UwpgIUNllZwWHgQaRcnfUIansZ7KHNCvJQSIXvBeb7zGUXqeEz3\nodcfwGIUehpF0Io+v1SrQQiiAm3PRcxIUqc/RI6Y++tgMplPsbjVbJnx0o0SPGrT/W73+1AZoWeL\nTg3N4h6vlHBQp3vQG/ZQikideacWoM7q0pKjUGpyAvq0hPqIvvPfvP8xnu4TqrZeLuG1ZaKvq0t1\nnPAcvd+9Byvi5O/KIr63/y4AYK3ZMnRYmidYYMXfPC1XAkP2CitVJTTPrf5ghINjurZOe2RSA3Jb\nY8yojZqEyHlvyEFCJ/C/3ULQkU3BOeSYRCNMokKAUDLedFJIJCwuyOIE1TLRi92zU0z5Myvbm8hT\nuofTOMF4GM7dx8LfCQAsoc/RaueS0JUytJprWxDMZtRrFdi8DgmtDAIvLGFQp/MU4QfvfoQSf+aN\nt18pABnOxjiv5qO3bTlT3ArzD/rXc+Sfw9n5xUyxryQS5lMrlcCkK2gp4PLztaUDK2eBjHQQsQtA\n6gEOzy2hJbKi70KZ5U8KCZv3b+Q5pM1ir2oLaYFY6dykC+UQsHj9Pto5hrDpt2rbNYwHo7n7+LkG\nU0rkmIS04JycniJweAIjQhYSzRfnCfrcgWkYQ8X0/unOMaIe8fqnTx5geYXyVVbXN7CyRhO7Xq8j\nYTPByKog44z+QGjUOLfAtW1AFWpBjSLpP09TCN7ULMuCUgWUL8/lzMzTZtRLu93FgM0ZS37ZbIg6\nz1Cr0OL8+uu38PgRbdCPH5xCpcUFSSy3KCDqLy6h3aaFTGoLeUb3rX3axtIG3YcPP/oY9+7Q4JgM\nOvijP/pDvhppqL5C2QL8l9N/QgqIIm3lHGsnzv3zl+/HZ/+LJ6xtodejPv3N3/4Ih/t0Dzx7plwr\n1WtY3CAqoN8fwfZoUjQbS4gnNEZsneMaB1BOMgGKPCIx66PATMkzVx+1MnC5xizYsSUwGlJA1Ol3\njQPtrVsvGioiyZUxmNVhDJvzArudHk45mLIlUGaKZTCeIuV8QUSAw3SuOG88JyUk64oFMkhexoSw\njUIm18q8nqfFkUb/mAOWFzQS0OKfiTEqPTJYXFAvYvUaHVruPX0XTkQP/vBgaGhcKYHLS/T5k0Fs\nlK/brU0cx6T4Ojw9Q6lOuTrri1dQLtGzS6Y9rK7Shh6XLYwGdB/Opl1ECZuRWgvIBQXRuYoQRfM/\nx+XmIiTnk+SDIdSUNvrReGgsNIQljeWG1Ao64RwxpeHyeB7GEbIR2zlEMZaXKCCK4xjd7syo8erV\nSwCAcqmETofmfa0eYMg2CU/a+1heJuVYFCVIMqZ6LWmoHdtShi58Vqv6DmIe72GS4fEpzaG90wFi\n7kej7KCwL0nv9LCV8Ca8aOEwo2DkfmsLWKH18VYvgjOiax8mOcIurb8vtRpocmDiyRSfcq5YsnuA\n6BKtU4ObZfzu1tsAgEq7jR/d+3v6Lc/CjaUr9L7lGTPMOVKmICwPMc/ds3GCJ3/ztwCAmy+/jVJA\nQU2vO4TP88nzSkhB99tJJ6hWaM3IHRcnnF/a7fVx4zpdz83btxDqwgAzxWhKgXtjoYKFBXpW0ECb\nA7d2u4cqq1H7Zx30hjSmLl15ARnnxo0HE/Q6/Wd3jpuFWfAiBWY2MQAEP19pSaN+FjqDb9M9fOX2\ni0gjmrt5msBjI2QpLDjnbBIUByMP7t0FO5bgtddvwXKKgOW8AlwhZ3NladnGukCcS+MQAJznOJH/\n7o1VFOlHAhJJcVB0bYADf6lSs10oIZFhtsZEnMsWO0DAOV+WSk1qTiwSFAugpSxYRWijNQY+fejE\nnSLSNM61chBxYCVUDo9/d3x2gIB/S9t1dJ/e5R58/Zl9vKD5LtpFu2gX7aJdtIt20X6F9rkiUx/c\n/QUUR9HTYYzVRUIdXrj6EsBJmuPJFO4ioVd2s436KSUjj3oHWGrSiVDnCrYkWLcpB0iPOgCAp081\nTiYUH4bBGtpMD8g8xLXr1wEA9UYLRZbydDqBygoaLkUW02sHKXK+zizWiDksvvKv/5s5eimRM5LR\n6ffQZwXReJrhpz/9KQDg44/fx+UrdFJfWGjAL5EayvU1qnU6/WW5QrlM729trRqL+8Avw7bo9GE7\nLiw24YujyJQVULKKn71HSZWuB9h8RPE8B80mQeyLrSZkoTqcCevwLPCWINRzBmfFX2mcM0TDuQTw\n87b9s/cP9w5xtrdDfxtFePEyebr4rkDIJqbXXnoZZS/jb7ERF4mEQQmtJUIjVZqgxsno9bIDME2j\nHGeGKArxXB5ktj2bFmmamZvjODZCNuY7PDrGjauE2rTqFQz5JJpBG9OsRMOobvqDMYZM+VqWZdQs\nGjMlSRrGyPnElieJMYR0pDanLirvwN3Sv5Tc/xwZoROZQk+41Eo6xmGXkIbOz8twf0Gn0tGrbSyf\nEnxwqbSJXYsScsPoFOMhzdEXGy+gtUzIRCI6eO9DouidioeNdbo/08dTWDmNWSvaxLS3AwAIbECw\nKahbDuAwUlNREnwwhm1LuDajLJYHkSzO3ccSFK5cp4Ti5WoZu4+oHE7JP2cWihyC6fEszpAWFJHt\nGlTRd31YTFMuLy4iPqcMLbPYYGFhARF7HdmObdDpPFcGUQjKgfnMJEphMXVfKZUhOdk2DVMkyXxJ\nr75jwWEEtRNNcMqGo4NpgpSP7GGeI+Nk6DRTiD+ghHK9ZKP5Dq0vkZthb4mQuu04w3KHPt+4dAtX\nrt8EAMgPP8TP//bH9JmtJlZWSdm5c9RGrUzoYku7uNqnubM99vCdCSmZ7yx9irdX6XsWSk1TCmWe\nVqu3sLFB4+hn7/4MT3doP+jHCtsbhIg+enwPCwuEFjU3ttDnselNx8ZI02610BvS6zDKcf/+DgCg\n3KxiOKL1/fj0DC6vrZWgAq9Mc3o6nuDogFIJwtEYMZf7wTQCC6vROT7DlBH7/d1jxOwvOE+T4peR\nqZk/XqHGtq1ZmRkSnsxEVwHvAdC5MdBVWQ7wOiYdywi/tra34VksQrAtQyNOJyOkjBA1Gg1jzGwJ\nGBXkZ64ZgP0cyFTVt2YeUhpIVOERZqMoM+NAGu+nVNgkqwcAYSGOOWXHBoKiRJDWpr86s6HTwkNK\nIi28+5TCCSuFepaAYvhQQEIwgimgYPF+Pzjax5DXgGEaobNzZ+4+fq7B1Le/9e+geLMYdoe4ee0G\nAODKlauoVmmTr/gafoMl0s4JPN4o83gFi6wyycLQ1DArVR1MJ7QRVDMF2aYJ04+PcXxG8t12Z4At\nrqkWWQIffUKLaprEsDKaeLlWmAwpnwtxD4iJigijFGNWfvzruYIpgSwvggeJl199hfo7nBr33mqt\nYhbYMEywukELwTu/9iZizg/wfKeYC4ijEElC70tho1Ri08M0nVFBkFD8u3Gcol8o31QCyy4UIRFO\nT6gvK8tTXLtKFCH9TjFhnhFMSWmcb8V5mSo+k83zmaYKUzYJdHd2AAAPHz5A2acOrjVLEAUd1hsi\n4A2qVK0imdKCVqo04LMjca25jBZToI4XwLWLQCOBdIociZkCBBDPE0vB931T6+t8vpX0PVOncWd3\nHy/dovGrYaPTp0A2KAcImHLQyDHmAKrbHSAMc/5+CzE/zzTLzbVZljSUolK5WapIhjwjUg0NILSx\nKICe5a/N03QGgHOglPIwOqX5kew2UPMpgDo5eopxlwIoS0oEHNwvOC5KE6bhah46TNdG0wmuXmYl\nblZGP6NDjuVn6LCKrVxxIKtsa1HW6Hap874M4dTonrTcOqZ9uofT4SmU4AODEFCD+V3eb20sI2AL\nitXNLUimNMLBESZDujbImSFjmsbI2Q6htbEGldFY2u8MjVXKdBIa+nhzc8OYu1YqJcSckvDw010T\nkI8nE5OfRZsgUzUAUs4NtB2FcomfXWIhms6XMxWmMbpjGqd7p6c44fQIS1rwON2h5JfQZ5VZFOXw\neR/t7kVY4s2q+ZUKMs5ZXaw0YR3s0K2J+nBbFLxqK0PMas5ffHSGd77xNQBAY2kRHzyldbZVXsRC\nnTao+/ee4Ppv/iYAYOvG66iWiQK9HJTgOdW5+gcAq2vbyJimfHrYRmdE13Dy4x/h09YnAIAvvPEy\nbt8mReFxb4CXXrkNAOg/3sFH9+8BAE6PDnFyyvUwY8u4jIc6w/4JjwVIdDp0rxxxgOYi9X08GpsA\n2vcD3L1L37ngOiYdIHv8FAGr0I9PO0ii+S08hMzO2RLM6H3bsoySWIrc1NcDBBzeP+JoiohTHoSw\ncHRAz2JxsYHFZpGb5hofA2lZcCocUNgCDn///V/cx94eGfH+yz/4XeQ8roVtQdqzGoWmbiA0ZhV1\nnx1U9aQHyQdLz5kBDvA8JDEHblIgmtD48ZRGjWucaikAl/YEKXMotr6Ic42MDx6ZAuocUEMo5Jz3\nZGVAmfM4/ViaCiwONDx2EBj0utjiZ/17v/VlfPiL9wAAv3jwEfKT3Wf2rWgXNN9Fu2gX7aJdtIt2\n0S7ar9A+V2RqdLYPj2VATx/eQ/eYImFXTLGxTrTXpcs3scbw7dLiAiRnyx0d52hskpqvVi7D4RPe\nj376fWQZRZjbrQpcSVH6VtXFsETvi4nCzXWK0oOqj58ePwQA+J4LFIoNN4DFviXts2PIhE7PmU6e\niyICgJDVOCqXWOJyFtvbgUkA11DIWZEnpIDkk3cah3AY+vd9Dz4b5rUaVfyCSzn0+yM0WW2zurJq\nakztPn2KUQFjhxFUxnBpOqty7roOWi06PR3sPsAaQ/X1+vwGc1IIOimgOKWcP53Ic69n96xQFp08\nuo+jTymhLxYStTIjAkmKTreo9K7RXCY6V1oStQYlgeo0RWmB0KjVzUsGwbEs2yAO2iobk1d5zjdM\nn6sKPk87/3mttTmluZ6Hja1t7qLAlE+faRTj7j2iVS9d2cTGBiXET6MpnjylMb67d4CMn0mcKvRH\nNDajODUYv2UJpIxYUe2DWdmeotK7LYQRMkghgXMVzmc1eZ7dolABrGpthja8nFAn3y3DYxrm4PAU\njSqd9gLXQcoIx5XlBqoNGju5lGgP6cS/c7oPz2PjSLmEXpfRPUvCY8VXMunDZ44tthRESifO9ngK\nndL3BHoV0FzeJlHQNiFE2o+Moe887daVTTw6INHKSbsHv0LX7FsZNI/J4XAEwSiOljaal2kdevs3\nvoEhU5N3H+4Y876gVDJI1vr6OiKuh7e7uwOXUa233noTjs2GpdMBjo/2AQDZeIIhU/E5RrBksfym\ncLlW4PJqHSur8yE3YZbijKm9R8cdZAkLXLRGmdeONE0x4FJUV1brePsKUWZ/8f4dHN0hRGYxSvAH\nv0E1PjcrDUxcMsetZKd4/D75SfXb+/jqLUKy752GmLJQ5u2Xb+H+LqEh9X6Cq7eI8vv+aICM6afa\n2nV8wkrHFZ+EE9Serer74U/fR7tD1zkJU1y/SUj/4f5jNJo0dlbW16AZIfIcG7fZK0pf2cKll+n1\nzz78Bf6uQ4jDKE0wSGmeDeKJEZIIlUGwqtKWCQr2zLY1RhPaD1aWlpEJGjt7ozFSNjoqJxov+NT3\ncqMBPZg/AV3KHDN0R51DnmHmtICc1YEMQzg8J3r9Lj54j8skpcoknV/aXsE6K7pbSw2UqjS/8ySB\nZJWcBQWbx7LrBqgE9BkbGuGUFa6+Y0x2JQCrQOkx87GaB5k66IyRsTlxyXVwncdhNB3jyR6h38Lx\noXg/rp0e48EPf0jXE3i4/ge/DwDwNzdw0KM5NIk1cmZTcx1jm+shNh3A47hBSgs+p/IstXtIi7Vc\naGSs7mwmKVYDmscv1ABngeZ9sN5EWn+O9JC5P/n/Q1P9E0z4YVTtEC4HL5/8/HsYHRMMHHbPMO3R\nBKg364gLtVKW4PCEFsaouWyUHNJbxOryBgCiCF1Bi5jvexhm9JmNFyq4tEWfsS0Lv/fbXwUAONIG\nQIPyYJTiEX+/VfJx/Jik+oi6ZoDO07QGTpnS2N8/glL0OlcJfFNsV6HwWa1UawAHUyqHMSYNfAcW\na3Z9z8atFynnazgcYTigBdQSKTbWKLCa9Lu4/wkFXJ12B6MxO6mnGmN2z84yhbffegsA8M3f+Zqh\noGq18mfK5j27k/QvW36G5TMKL5xzPbdtGw8f0YJ8970PoViGvr29As1GgUeDLpp1elbLK2toseza\nc2xUy0Fxy7C4ToGMsGzkXJzUkhKq8KzVM4qSqLHCYT97LrveMJxROeed2svlMja3aZwuVCvwefEZ\n9c6wtMI14BqLRhYfJwnGEwqahqPRjP5NFTKu5ae1mFF1mTJBojgXHGn1WQq1oAQsaZl3FbQJHudp\nyVga+mky0Oh3GPpvT7DgFfA6MOB8m3LJRZnpgWq9hpwX4XZviIM+5eEcJUOErLh1nSNMmNasrlvY\n5hpvMkuQpvRM45FEr0t97E1HWCxxnhFSFJ3RogzJjvKZjI1dxDzNcl3YrNp7+PA+tpf5YLPYgL9W\nqLwamEwG3C+NK698EQDQ2roFa0IB/gtaweHxvNpag2ZKWgkqZg0AWRhBcG7GpUuXjcmuhkbClPFo\nMMLOUzrIHTz9FPmA6dHRCLvHFDB0TkdY3Vyfq39ZnqPNtFdnOEUcF5YvGikHaoFt4ctXKbj/8lsv\nYesFUrEtXVrF//5//SkAQLYzVCO63k9+9GPUIroulVjY52fbO+kg5jqAb/yTL+KjH/0EAHB3fAoW\nZaOeZvjef/w7AMChtDAe0SZ59+73cG9Ka9bi5Tfx2iUy83y2ZSfQ7hyYlIiNjRaWlijQrJauYalB\n8y8o1fHuu1yE+epVDHpsnlkuo7VIAcU7b3ugUsnAu+9+gkd7lAOV5BmKLETXFqjzOtRs1HB0QAeh\nJE2Nlclg2MPyMq25YZxA8+HNthyomMZRo1pCPajN0TtqKkswHBTrmY0FPtw6jgubKS3HcRCx/cOd\nj+/h7t0H/L4HcJH1aq2JCisuj9tdxKz67g16KPHeI+MQMmbZYZrjZMSqTKFRXaY5qu1Z4CaV+kwA\ndV5pKJ+D2PrCF74EldD1H3/4Hg7fp8D28ltv4c23aE0VwsXRd/8CALD7p3+Gs4+pj6Wyj/usin/5\nf/1jXLpK8cFwnGLY5/X1dA+ffPe7AIDKe++jwhUONCwDLKg0BdgoPPdspFNWSGc5Tllhmjsuam+S\nMewf/qt/idian669oPku2kW7aBftol20i3bRfoX2uSJTC66AxR5CrUoTOXs5+XYONaYT7e79GIgp\nqnzh+hUsbxHsfn37MrRkn5NSHXe4BMDR4z20rpFSZDTVSDSdGh51pnh4QDBenhyhtkynJKkygxzk\nSmASUtTdT2x4SxTxLpUaiFld0/v0fSTh/MZdALC3R7D+g/uPUSrR+SvJxnC470rlRs3lOC6kNTNc\nWeXSAKWyB5cT8CI7QaVKn6lUfPM6HE+MgWcpcPAX3/4zAMBkEqJap5P3+uYyHj6iCD+OMty4Qafz\nai0wUO7zOXhqKC6noPLcoCq243ym0neBvB0ct/HkPiX8265EwBmwJVdirzvi1zYWW1zLrBbAYbrE\nkdJ4fwXluqFJJ+HEJNVDCIMiKaVMza1fbqbMwjw9PEfzSSkNxeZ6HlKmTJNMwfXplLy04qLZoJOo\nH7iAKmhBH81Wk6+/A93jenOQyM/RiKZ2lFbAOU+rQqnlOjPVjdDKqP8yaGTF30I8lx+aY9kY9lnN\nYgMqZNPRaQjJ1IXnesj5xKaEjYB9d1LLQXtCc7Q97aHL1Hqw6CDirPbKooeWk/DnNTLupCsqGI+L\nAmgpggYnAisbkzbTf0EGRuzhWNIsUiJRqJTnN0PMz5UdShs11NlPahQmSF0ahzdeewdpSt9ZrXho\nrNEpOY4TTLj+o4pDeOxXlE4nEExl2uUSLB7Ply9fhuTvD0olTKcFGpyhXiO02XVKqBbIRz3A7seE\nfvfTBKtrNC97kxCPmfZ4VhtPIqN+8l0HJ0yTQmh4TEV99a1buHmN+tQ+7WDnhD7zxq99FX+4T2hY\nkPSgB4RAJZnApduvAwDuffQePn1I6+yb//SbGHNC8907n+DXfuufAwCePLyLzTLdpzfe+Qp+8lek\n+DsadtFiNGQ47CDiNf2Hux/h9TVK15hn97l2ZXlmTKsUMkbNykEZa6u0N/S6I5RZEe05JRwdUl+a\nDYUxC1hSneDyZUK2d54e4KRH1zzNcjOnHduCz89TCoExe0hBCHi8dud5DsFrj2e5M8pdWrAkjSOR\nxc+VVjAeDfEf/v13+F5NsM5sw/rGGhrMVDRbLWRMNT95vIce07v9wQirG8S6CO3g7JT20SeP7pHh\nO5IAACAASURBVOHaC4QAbm804fM1V70aQkbuDnd20eMyXsNRAofrG8ZxCInCMFgbM2j7nNJQaG3o\nv3kwGa9Sh2LvuM6HH+DgLwhFKu13AZdTCeIE3T/9twCAzMqx8N/99wCA/fffw+Tv3wcAvD5OUeP5\nNOoe4t77ZAy7tbqGlSbdh/jBv4fDRs6wS8jkTPRRoJxJyYFgQVhJKYPuTbXE+MkOACBSY/jlxjP7\nVrTPNZjKtYTNFw2VGLl/lGpYhSlyNsbOQ1JpHB88huAcotbSKpaZSllaWTOLlZ+NkbLyrh/lRv6a\npClsllr3xxP8zfeJfw18Fx4rAcejIfb3yVhwaf0FvP5FUqgMkiqqL1AOwcCvY2/n/bn7KATgB2y8\nKISRQkPCKPIAZYo7KhUbN15pCZyd0CAolzw42zQ4XNsGCmQ2TZExr2xbAkOGMx3HQokpsSzL4DIs\n7fku6nXOwahZcF0OSMYThGygmiY5HKYons2GCVP/UANGlSHyFAXpJ6WFASuL7nzyCSp8Pxr1BQiG\nj7vTFBscQNVcYaS4UsIsUK604LFRp1euYsyqlTxPISTXOcwVLLuo4YRZkWkxG9pCCOMmP0+zLOuz\nOVO8iGW5wuMnJM1WaY7bN0g1tLRQR1hAxkLA5jFbKlfhBbT4C6lR6JMVbIji+zGreWUJYWwkHNtC\nmTejSuCaxTyNQ7CRN1KljfGjxsw5eZ6WS2VkwskkR7lWFMYNEXJQ47qzueKXSgDTW73hCMOEi3Dr\nKQYWzblmKUBlgQ02xcRYXPTDBIKjvqWmD5tNONOshPISX78YIB5yHUZoJGy0F8Z9VNgeA3kA8Z8p\n3P2falInqHAMvbC1AYvHzHc//Ah9Pkd889J1rG1RsFGvBUbFuf/gE5wckpLH9T00eJM9PHoMi4vY\nXrn1EixWj3q2bzZZy5mNn9F4ZIJf27IBTkN44dqLAKv2PKeEF9l8dxgl+JNvf3eu/k3i1ATQeZaZ\n8VW1bXzxJh0wX7z2EiZ8ePx//+pPzIJfbzTxpZeo3+/+9R3cv0dU/Ne/+fvwUy78u7SMxQn1Y+ut\nr+Iv//rPAQBZv4eWpk14z5fYvkTfo5QLLdk9em0RVc7TKZeXYXl0z55O+3jUpmBna/vqM/voYmbk\nCAFzj+Nogt09mot5niOJ2Rh15zFaTTrAWlJihQOTerNmisL7/izv7bzJcpZlMxVv7qPM61az1TQW\nGNPpFKenZOA56veNSbDnWgCKPFg8V1pBEoY42KUDeKczxBOusSolzuXQ+vBLbMgpbZQCDkD8Cg52\n6fOT6gBrK9TfRjXA4JT2knv9A/h8/yulBjZWiPp0d4SxWAiHCWprlKtqZRkidihPk2hW488S59xX\ncszcm+fIuc2BmK1Yntx9guHHRHf/wP0TNI5pvFVOekiYxm3d+iIe8j3/yd4evplxzihdCQDg9Kxr\n1pjwJEJ4TOvQ5ZfegH/1MgBAuT5sptylJSH4gJo7EooTrlQ4QpEp0kgFupxLGoQpgtYczrLcLmi+\ni3bRLtpFu2gX7aJdtF+hfb5qvmEfo1FByaTGr8jSAllIUb1lWab0x6AHUxfv8Oljc4IoBSUsLVEE\n3lxcQTyhyPjylRfhn9BpLx1PcKlBUa6ym+YaSqUSNNNU3X4H64tsKS8lJoekihj1IwxHRdJoF2k+\nfwVwAEYxNxr3Ua8xJeA4huYjD0muC5TnKE40vW4PtRrRRQd7e6hx2QJHVuBzvbQ4TlFhn6nRcIA+\n1w+r1xfw+utvAgB2nuwYZCYLA2ytUUKd75Vgafr+v/neuxhP6ISYZTHe+fKbc/WNTtx0GnA9CxmX\nrdAC0ExvxXGKjOuXXVmsIhtz0nCSQDG6sbpSwVKVjRyzDOUFOsVWSgFcTjguB1VUW5SMPk4iRBEb\n8Nk2rMKPSWtYxhtLm8RJy3KMN5PW6lxy/LOb47omETxLc9h8NFO5NvWcAs8zkLESQMx0q2+5kIy+\nlqt12IWxj2VBnLueWZX4GVgOIQy65Dg2ghKdijzPRb1Kp7TRANA8XnQGONz3XCvMTywAbuAhY3pL\nqDKkJEpjLEIELMrIsxiKl4hYRUjYd2mchRgLVttVLSDmUk2+j6BEc2U4SDA4I+TTdSuQnHQ+GYWw\nR4yyIceArz9RAcbsN7NR9RHY7DfjBNhYpesMowALlfk9ihxpIQgK0YeFQy798uB0DxErmnrjMZYl\nKYtyx0fnhJDq/Z1PoZjK9BstTDr0t8POCdyU5pDQOTyutwnHQiHOs2wLgV8IYXyorDBAdGC5hMY6\njgBusJePtrC0RvR+bXkJh/35lGCB5aM7IrqqPxii4bHB6tYq3vnmvwAA6GSM//v/+D8BAI+eHGN5\nldCHn//t3+ILrxEa1tk9hjemRN7R3j385CNS3G68/ia+8OXfAgCc9DvY+4QYg9albYT3ycywf3YK\nVH8dAHC2ewftPiEspatfwGvXXgMAHHY7mI5pXba8Gn5wsgMA+Pr2l57Zx+ScGOR8+apJmOHgiBCi\narVqPpNkJbx4ixCv6ze3sdCkdSXNFe5+n9IdHtx/ik6bVZUqN/UNHduBy8/z9u1buHWTvmdra9Os\ny5ZlYYe98n7+s/cwYj+/s/YxBgOiTYVQz2WgC5XOCDNhn3dQRs7J0+NRiPF0zO8KWDxfHceBw0nS\nwzzHxgpRYJvrq/B4bmmRYsyq02k0wcd33uP7IOHzejydJPjyl9+hvqwtQLJJrdLaiIaEmJUDE+fF\n23MgU64U6DMT5Swv46RFc85963VE3/prAEArVki/RoxQTS8hZrPeqZ1BFTVLhcaAlZIff3wPmtHm\nsOTgSZ/o8fSDD7DCJsSxZaFcoJBCIGUkztECstiv0gmsnO6zHQPdRepP4w/+BQZVQmBnEcR/vn2u\nwVSz6psgIoxS2Jw3ZEMaGsNxXRM0JUlsFE22lMYgTyYp+sc0sDpHD/H4UxocG1duYsjut8NOGz5v\ndpVSBRWWePueb1R1w8kI/Zg+P54mGE/o2sZhhjRjBZG2MZ4cP1c/O1xw9M///M/wu7/7XwMAlpc3\njDJDa2UCRsexcfcuBXHf+c638fLLrwIAvvbrv41dlhyfHAGuxyZ8Jd+4cI9GI/g8+RdbEW7fftX8\nli4qWCofxQqUZamBsdtnXZyckVrl/fcDvPOlN/jqn70I5Dy5smiKCdebC0cDjFj23Wl34HA+XElm\nALvLWo4NsMplbWkBAVOLWTiBzxuBF9RQZguE8vIaFNOVeTijS7TWZr0Rn6G2zuU6iVk9vqJc37xN\n2jZyls0qrWFxcJRnOeqcO/PqS6/A5jE7iabg0nyoeh4EB+t2EKDGNhbLa2vYOaExm8TxLAdKCJjK\nhsIy+VOuY5tgMM0y2Ey3BZUqhGSH5yyHZAn+eDJFkj6P8kQDOT2vk3ZozAHjiWPo34rlI+PnOBiO\nEbCsdeLn0E1aXirVGs4e0Thqn52h7NLY6Lc1Eq6jt7zpoMzXX5F19E9pAxr1umgGdPAouxoRX0MU\n+fBYldQMXDRT2ggOpxofPGDTzt9/dh8DqwzBEvIpgH2miVXFQtSj7zk+3sfVq5RbAmtWgNXRKZp1\nDoR1bsY5kgiNKgX45cCDdApDQxfSYrsO2zo39nLkBW3glmBxrUAtNEo1zqdMLew+JErxiudge3v5\n2Z0DUHH9mXO60rjUorn1xTfewMo2XWP/6BNErFZcqFdxpcZKsWyKj39MOVs3rt7AMtcDfPT4Pt6/\n9wQAsPTGO8g4f/Hw3h6+9CbV3bv5O7+N7mMKTK6129C8nn7/+/fx/iEFFNWv2KjxYWkSKXTOWL0q\nfDzhYshzNeLuAdD8KxzBy76DEgesaZ6RYheA0DEqZbrH9WbVONfv757ghz+mfeLwpIeEN89cCyg+\nLNcrZXzlNyjA+8pX30GJ11bHcY2jtpISzVW6t2986R2U+LC08+QRPvqQvj/LInPQmqcl4QQqYzsd\nlZgcTSFgKh+ImUAa0rJmNexyhcLeN4xi7HGu0NrKCoTm+1CroL5AY3Ch6mHSpfv26OEheqwGlZbA\nBz8nheb+k4fYvEGqT+f1awh4jJdrMHun5wXG/d12nx1qnO2f4HhA++Lu0ROc2LS+Xjnqo8KH5yTT\n6J3S4WDRt1Gr0/tVpIhZTpsLjb2nND4llLEmeTw8Qo8rK2x09lHlYNkCUOZrUIApBG1rIOeb6IuZ\n+5EE4Pv0mSf7h+hybuXtZ/bwgua7aBftol20i3bRLtpF+5Xa54pM2SqF4lNaxbNQhNcqzeDw6d8W\nuUGgHDkrneFYGimjVHmeImcoIM1TxIzUvH/SgWDPJkcrnDEtBClR4qTRLMuMKgxSIORk7kxJCElR\naJYLeOWiXl7Z1CGbtw3HA/6eGBqMplnS1F2DUDMfUJFh/4Ai7U7vGB9/TJ+5cvkm2JYDtaqPBnuq\nNJuL8H3qS6u1bEqdpEmGRmuZr7mKbpsrpLe76PXpdb/fMa89z5+hIK5/Tn3yjyNTAho5Z0B3Tk/Q\nZ1pkPOwateViYxk1Lo+RTzrIYlbIuA5GfBLK4xjSI7TQK9fQ2iB0YGHlEnwuy6CljYiVlBr4DDJV\nnAPkOQWb1ucQcik5E5RH2XOg7nmukDOdp3VucknD6dCUQ3rxxdsoF2V9stiU+HEthZBLlWgIlGv0\n3NZWV+G5VKtMpQKuVXhFCThcrsG2bRTatZJvG7WrgCSDWRCSmcZF4rKFep3UJmmaIormp6O7gyFE\nRtc86Q+wFxVzzsFI0Pc7wwmqPlPTtQAjNn8cTHsIM3qmk+MTdPdYFVZTmFqMDPcy1D0uP+NbqDKq\n1d7vISvmhCtRWaHfaqwkcI9p/ASeB8mfd8I+4hF7MEV1jPPh3H103ApSHhCDeIzTlK6tulyHYOi/\nP2gjLOgT1USDETf31m1Y7MHTbQ8Qct9tkWGRFZquFxjbK1kUPwMAATOfsnxW21FIy4xJIQXABqRO\nqYyjp6Sai9Ih+vl86uGKH5j1cX2xivVtuq5ur4c2eySVhMBlNkR+cHiA33mZXr//wU/Q2yfE/Rv/\n7Bs4YoWXv3gJr/860X/e6hIeMwrw+MOP8LXf/qcAgOWFGg4ZnXaDKmotGoOLFR+FKZ9EhMNDUvF2\n+l3YXDrK9soYJPMrMr/y1a8Y9Nn3fYQ8xlWWwy5EJlIalEFBYWOThErScRAl9P7uwSlO2oR6JHkO\nXSh0Hc+IhJqtGt78AiH0MnDRZg8jIQRKnAztuR4SLgO0d3wMzbLTS5sb+M3FbwAgI8pCDTdPe3h/\nF9mU7n826cFm809hOZ9JAVDsoycwSwfI0xyiSHMQAmdnbGg5juHxWrK8tIBKmVMVsgpqZaICq40Y\nNS7dVq4EKHNCedzv4OB9qiP7yeAR6qt0P6+/+ArsRUK1dD4x99CewzBsFA6w/7O/p/vzgx9ho0V/\ne/P+R3B4vGtfoXSXhBD1ZBcLH9BvfWOgsMRpIKnKTOJ4POph7z77tj34BG6PkK8vexKlEqcnuDYE\nl1yypECJVddqMIVIitqnQMTMTyiAMvsTHn96gjuK5ugfPLuLn28wlcQxMuYpLUub2mlSSKN+UNBG\nNm5JWaCcSPIMI8MZw7h655mGsItAyTIO0rXAg8+DZpxGSDhYy5RAzNytK51ZPoN0jEoKsJAwfDjo\nniHNny+Y8lk9UC4FiJgyiaIpSgEDjkKa3BshLIMxLrYW8fJtcvh9eP8BJgMaBNVKFS9cJdh1c0Oh\n2aJ+7R/s4vSUNpowDFFlXv/Pv/1t3LtHBp7TyQAhB5VKpUh4I3777S/j8jYpftbX1zBvtKGFgmR6\nyy+XyTQOwLK8YigVy/KRs4N1lm9DMP1kiwwe13oLbAceByCl+hKW16l/mU6h2VhNQsLmxDql9eyZ\n5+cXKjGj+jTOFTeWxowPQpg8ubn6qIBcFWq+DNxdBB5QqdAEbyw04PsVvp5ZwJVGA5MzlWQ5Uh5U\no0EfAe98bjlAwHRPteTDL+6nY5nfch2JCY/BMM7gM90SRhnR3wAsz4PHsLtlz4onz9Mm/T6ELq7f\nRq9Dv7W0YKPMFNuwncGz2c7BraPPtaxyxPDrtLhhqrH5AtNzsQ+k1K9LGwrLK/SZ9dUYbspWI06M\nlKsYr98QuETlDXHWKaHRoIXLLwUYsOHj1HfwhGOLg84+FoL51TXwfIw5GN9pH2Ck2RjRt2EzFXQ6\nPsOYnd1FAiytUi5Hc6GG3i4Fv26+C69LC3UcZyiSo5Lcgc6KQqtD5Ky8srVrDoSAMHk4OFeTLE8z\nJJxnBxeQXEtMhlOSas/RdjonGPDc3mhV8f4Dot4uXS/j6K9+AAC4sljH5SsUHNUWPOgyXctoEOK/\n+m3KT9l58gjf/iHlQP23/8sf48UXaPP81ne+D5/p2ZVmDROmlO+/+1Os71Be1Z89OsLWa2SCvLUY\nINil+3o5lFjjjd2zJBLeoDxHYTBn/wBgeXnV2BWsrq7h6S4p+OIwRsTFhJM0MZYrXimAw9TMaDRF\nj80wd3aeot2hHFHHtWHxOpHmibFfuf7CNaxyAfWz02OMuUpBqVRGwsVv80yZ4KvfG2DAdSlr5Qqc\nQmkYp6YG3Dztzi/um7qw08EpHN6fytUFsyprLQztjzw9p2DWppoGhI2itmo4zaA55+jktI0BO+J3\n2n1UuF6l0HqWcwkLg5T6a01GWOPKE1c3L2PCwcv+o2OscABSrkrkPB5KcwRTG1fXcf//ocD8rbMh\najfZoicB2uzcX5c1NBfp/rc//AAVprCXrqyjzbnHmc5g8d55vPMp7nLQlw4naBZ7quUaw+tgbQ3T\nA9ojVZZBMhBhW2WkMT0j3e9D1OhA0HzjBqZ8cK2kFhafzGKOZ7ULmu+iXbSLdtEu2kW7aBftV2if\nr5ovjE3Sbg5tktEhFATDpWmaGqTGcVwkTMMppUwdJCmlOR0KR0MXCesSGDH8nCPHAhtXJiqDZs7M\n93xzEk3SDIq/E7k232l7EjHX7sqSENVKkcI2X9vcoNPt1cvXYHMJg4f372FtnXyjNje34bkFpZhA\naD79tTawskinyKODYwhdeH0k6LNJ25NHu3jMyZ/vvvsTOIzKbWxsGvrnow9/gpMzSl4X0jKny6Wl\nZdQ5EX9rcxPrm0QLbm9vzI9q6JkvkluqAQxJW0Ij4dIjmc4NKiTsAJqr08cqR7DE0HPNJ/8sAFAa\noy6dGgNbImOaVLo+gjqhcInrIJoWNeyscwjUuSR0KWf2LufeF1IYWnieprRGAWRprVFnk9TL26vY\n3qbkeGnnRlUnpEDOiGuuMkimkOIkxZhVUuNBD80aQ+RKmPIUjVoZ9QqhBbWSC88plIMpphEr4FIF\nLen6O5ORUSzmeXYO3YXxEJun+Y6NlA1rc6XRatEYt/IUMXc+FRopn9oPun3kLlNdrg2/Q/1qLJcR\nZjQGx9MJVhuEdrVawNImjfEkAaZjuj/ra3WkIDSlvOkhTanvYfcQbink7xki8NiMtiqgFY2xM3uA\n+w+fI3lZShx0CFHaaR8icum+Ka2NKmln7ynuPyI66tLmZVQW6LecchOVZa7v5ZdhHdH9Od17BAZE\nMB6nQCGKyWPYFaa2tTaIZBAEkIwiKGTIitIy3Q56J4S+6VEfi1W6b1XHxTiajwZ7dHKKMdOzJ48G\nWODTtSNsDM8oYf5nT/awukDX9eJmE8ecoGzHY8MSTDDFq69eputdquPgjNYaP9Wwh4S8LN+8hjv3\nCY3a1ENsXiZIcTsp4/CTewCAg/0DpCk98+ZJiss7LHbZP0WzSf1757UqHp3Of9rf3z8y9UfDaYJO\nl1AGaEEoIYAoiSC5rE85Vyg3+N5bChkjh67rGmXncDSFZnWYJTWuM+r/pS98ERNO6Tg9OjPzeKHu\nQSual91B3yj4Op2e8Qscj6cIvFlZMJ3Pv97EaWjUanapDCkZFc/jmUJXCGhG+7WyINkYSUrbIJxK\n21BcT1JZNgQb7kJrkw4hITDRNL4CV8Bi7GswHOHwiJDYbNJBdP1lAMD6XhfTMaFmg/5drK0QDHX9\nlduorJIJ6uIcfUxVBMUoXiVNcOUF+qvhbhf+KT27Iy/AHjNLrVoNAe/lUfcA6ngWqmQjekYyzApr\nNdRaDTg8MbtHCXq8V0wHCvnbJJzYPTzEoErPaL28joUqjVX1wfukwARQDaqY8Hwd7T5EOZkf7f9c\ng6lppiH5wVs2kHMAlasM44hpoSwzShjbdpAy5Gm5HgJWgiVpipSpIyFiQPL3OD4cvllJmiDOOGcq\nncKyWZmTKTiycKh2EbGqI8kzhKzsS9M+PFaXlUvBOen9fK3CMvZLl7ZRY+O6o+MDPHlIi0ivfYa1\nNQqsGs0FY0CqlMbZKQcVvo9wytcPz2zKg+EQCTsdLy8vo9Om167rGhVWvV7H9mWCUV97/U1cvUKL\nxdraGqolurZvf/s7ZlM+V0rvmU1CIOONIk2UMXK0hDLmo3mujEt6rjRUkT+gNXIectoCwLXhMiHR\n5UtZdl1kbQoWRRKjfIPcmOvlBYRcY9C2XeNoLqU8p+ibvRZaQ/DvSmjjLD5PS5OsqB8MW9pYXSQ5\n+ZWtTaystrhfMRRD3nGUmJyacDw0OXz94QgRHwYcS6DFFKHWFvJzqrGaT89kqVmFLYuNY4JmgwLJ\nVDgYhFyYdTw2yrs8z1DkHTqOBd935+9jbmGBx4L0AK0pEJehhRZTiqflCaYTVueNYzQX2RldeDhs\n04ZilXJ4bqHK9VCts6WEFhhzbozSgOLAeWvBhmJbhWHuYDTgAGo4RYuDnX6YoNVgQ9/2GfYfU78W\nnCr2xvNvxGf9Hh6d7PNvxUg4wNe5njld22P88Kc/AgCsra3jKwtcDNn3ITwKAOwFiTpTeML2Idix\neTgcQ7Fpar3kmY1pMh7D5v5aUpqAN9cpxpxPt3fvY6S8uTQ9B2B3eZErEzw8qw2GIUKux2cJF9ub\nFOh/8uBjbLZos+r2uuh1aay9vOCixkrQ1TdvIF0is82XrryMv/8JOUl/79t/ZZ7Vq2++BoepLtSa\nUIcUNN149RZq7/wafefSPfT/8i8BAN9/tI81Vka+uLEOwdRue7cNe0r3oL6cYXi3PVf/AOCD9z9C\nj5WU1WoFlQo9E8tyINmMNCiXsLxG9ND69iXU+AA2neYY9GdU3euvk1XDuz9/zwRKX/nKr+H1Vym1\nYrnVwCcfU8B4eHBglJp+UDa1FvOMaDwA6HYHRt07ncTmMHn/3h0c7tO4+6M//t+e2cf+sAPN6RJu\ndRFWXlCK8WdyWTVv+FJYRv3ner7JF8tywUWTAVs5UKzmU8o+V7VCgz8CH9r0q1b2sX2LgiPbXoey\nad86PO6ixuvKxkodS2wAPRpkGPjU31vP7CEwPBvBLdFzkatbSFjFG5YEHpZoLfm7tI/wmPr4pcom\n3uF7u1C24HHgM3IkTvmwUV9eQYWrUIiqZ+pmB7euoR5wpQ2rhLsc5fx4cIC8Re8/mIT4xiYdCDZu\nbAEFeAILAedPDcQOHpzNr+S/oPku2kW7aBftol20i3bRfoX2uSJTnQgIOLlLJgppkU1vOdCiMBiT\nJvKXWW78e6LxBAXbRp4iRVmBCIzKYXFxARGrItIohWRDSdu2MY2KkiCRUfJkEFD8A7nSEKyWSJMU\nmpVUuZCw5PyoBgAT7ZfLJUO9qDw21FGnk6Dfo9NZpVpBt0en1aXWIl56ieL848MTlEuExHl+DWVO\nGnRs2xiKlstl1Kp0oo3jGIMBUSCbm1v4n/7n/xEA8PYX3jJeVALA4T7Rf0kS4eyMaAbLwtzQlFAw\nNaIUzKGbyqgwWqSVhjb/Ayhun4ac0XMaQFFHT3jG16l/9gj6kBQdzlmCqEan59LtdQQ+1zsT1kwh\n9Q/q0bExpsqhZWHGoiCew9JSKW1UWra0ETE6M+wOYbNi0bZcQyEkaYKkMMAUpJQEgGZr0SjFVBqj\nzuUgslQjKlRgKjdmp57nIs9YJeV6WFwiRCyXDqope6KUSuaeHx6dGEpcQyNltGue1jkYonGZJpRj\n2dg5oHu7VgpQYWRC2DbCYi4uulhiH6OlWgUlRo8rFQcWJ6lnykKjWcwzx5yka6UKRoz6yqqGq+hv\nz9pjSIf66ziW8cnKsxK6p3QN424P4FI9p2cZri3NPxeftI/Qjuj+Z+45EYLSxkB1eX0Jx1wL7+9+\n8n1cvUz1OVdX1s06MR3nGDEyiFINU77ONGobH6AkcSHYnHY6naC1SCfver1uPIEsYWE8ICT5cG8X\nywGNh3ppZkSa5wrZnChqrqWpeVgLHLSZKj/tT5Aw7ZVlGa4u0ZG9bmsUWpqVN9/BaJHSEf7td9/F\n939ECehvri5jY5VQrePdI3zxzbcAAJ8etXGlKM3iAtkR0YVxu4NoTH1yggpeYMT98nILh4y8+Z6H\nzSX2JfN8tAfzq/l2d/dg8XgMwxDdbo/7Dly6QqaaS7UabEbIO+0ujo/pPqQJsHdAamNtSWxt0bUl\naYL9fVI7WkJDMSrx+NEjdM7ob4+PjjDgfvmeh5s3XwRA680Jf/9oNEbAKRRxHCPj9WY8HhvV8jwt\nSzXMgpOFjDgDWqVm7YS2jRhLy8yU7UnzDDavN0muoXhcZ1qZdUXnFrKM7mGUWQgzuldRCqQu/UDd\nT43CuOo1AWZsfGmjWrjRJjFOYn7frUNk8zM2uw+fIl2ntJLN/+FfIed0D2shxnsfEfqzv3uGtQbN\nmw9KGktb9Pqd9RWMfUZgJzmWuPxT9/YU2buEqAqlUeOadDpTmA5YLCMy3GvT93cHfWzx3rwbDnDn\nI/rMQlBGdcol2rSNfo3m4+v/7GtYLLzm5mifazDVHozgsy2BZ0uUeDFxXBuqyDnJckTMhXu+Bc3y\nV2nBqKTSOMVkwptUnqHO+QaTUWQy9C3hwLIYmrc0Yt6sYbkYMufquTAOsEppWHxtFc9GzPRMpFJT\neHfe5nDB05WVJezsUPASTgYG+pfCQlYswvEYASuytre38MorxFUP+kOkPMlFGiJO6NratQSowwAA\nIABJREFU7TMMxwR7N5tbeOMNUuQ8/PQxdvd2AADXrl3D9hZBtkma4IgXvqP9Q9y7SzB2GE5x+Sot\nRpub63P3TUhplCRU2HhmkqlNgCNM0WNxzu2bNOOz79G8aGRJiJzrtcXdY0hWKLreEixWjeXagSqk\nuJY4526ujWVC8d8A5dgV16CfI5Ci7yCVKAAI10H7lILUiuPCsWiseW7N5LRVKw4qlUIho0y/RpMh\nnu6QdNf3HFQ5Z6PXHhhDWUtoxKwOyvIcFo9H1/NhMbXkuj6CalEjr2zGcn8woOLIoCLJBc07Txsn\n5PQPAE7JxaBH86mqNFTIeVuBhBfQ66DkYpE3fScIsLJEz0WIHKMu56iUElRKdP1xqDDmy/EWJOKA\ngqZYpZjErIbTGg2mCI+EhQc7tMlmOkTIhZdH/Qw3N+i3VCVEJuZXST3tnyEyVZJhTAYh7EL0BDuQ\nWN2iRf7pwS4ePCKX72ajZlR48WiEnU8pwD/pnkKxqtS1XIx4LUmz3NDKg8EAr75GBrqk+qSx4dsl\nWKBxPh6H8JjO2Vxdprp9AFzbggjmo2u1EEgS+v3ECrC/x5S/8BHxuChZDlb4ALt86xqkQ9d1d+8I\n3/3WvwEA/PkPfo71FQp2lpeX8dYXvggAyOottG5R9QS12sXffevfAQDuZTHeuUyBSalzhENOTWi0\nWri+SXSbFBoDTl8ol2y8ukYbo0oS9AbzU7XCsk0VDAhhCnvHcYwRFyK+e2eAtFjfpTA0qWv7GHPN\nPqtchizUllBwOMXh/scfYNieFZYu5uJ0OgFYdfr43ie4vEmBZ1CuoM9Fkl3HRs5Kt1ylpv6nlNoY\nUs/TSl4Azdc5HvYhUexhuVk6hXahOKhRUprfUplAwJ8vSRsJ03+WcmcHP9uGxco4aBthQTvnGpOY\ngr7uWOOU11HP7iPge+V43uxQihStazQXr7s1uPn8OZqT0wNIdi5//KOfIuC1vBPnaJ8QsPB6qY5l\nBkDez3v49CmNk2vvfYQJz+O8chlVziteXVnFl75GSlLHsjF5zPUN73yIKo8xIUs4YyVuJcvhcr3N\nacPFpxO657e3Ggg5BSNrlFBZoUOsf2kd2405pIrcLmi+i3bRLtpFu2gX7aJdtF+hfb6mnbZlkmc1\ncmR8MkszoMqJhUmSGOhaSAsuR8hpGGPCJ53zSepCWEg4kb0UBIYaiaYhMqZMLMVV7wGiLZhe0paF\nMf+tUBqSk0ltKeGIwksmN74i87YiufX6jasY9Om0uL//xPRlYaGBgD2nlBIG6VlbWzP12DQUTk4I\nntTCQsBJyp7nQsiCSglxxrC04wRY4Yg6Tsb4D9/6MwBAp9fG0yc7AIDJeIIpJ/Cur6/j937vm3Tf\nyiVj4CmecaCyHBuux3yrJT9jmlnATvQds9PkDBiaKS+VVgCrKoM0hmBVZXTSxgJD5wuvvADBiEyS\npOaEan2mnEJu7h+9Oasdo3VxbdbMc2qepmffYVkVRKzYinIbDhstCghTed52XEAE3K8MeUH5DbvI\nmWZaXVlEccAb9vpw5Aw1K5QkEhplHqeW4xh6ww8CiKKkjQI2uL5a59IGxmFhjiZ+yX/rH2+bG1X0\nT2h+rFccLNW4zInOkeuiQrtArUnvCwmELgsoVIaIDoFwbELdAKBVqmJ4SqfMkZ7AYsPSQTiFLzlR\nN4kRMXI30EPEId23RCs87bAn22gC3+L5OhboMjpWr7fwk5+dzt3HbjpGKmZGr4W3kJTy3HiXcAsk\nyM6xu08+Ru+8/UVCVQGoJMWI6+XdefgJJpxK4DuzxN4oSQ29nyUp+j8idHVtdQm3blKCsxI5VtdJ\nGHL55k0cPCXUspenWOaTcZImmLfKYhLFKPG9n8YxHFYOW8gLUAWuZ8Npcu3E7ev48CeETHfOhvjn\nX/86AKDpLWLrFaKx+r0z/OVHlGheh4bn0d9e+/KX8NoXKOn8x3/9HVxhVaAKbByljF7W60YhlaUx\nohGtmxXXQ4mTenfPBhhM5qf5cjUrveW5LpaXCUW8d/8+dtlzynXdzxj6WqaOmwXNc2hzbRkhU5+9\nfh8Vt/hMjsEpDWbHccxv+VJDcNpC9+wEp9zf6zdvGQPdaqVi2BVgtnZKy5rlP8zRbn/xG1BM0R/s\n3sPTT6isy2RwDBYRwkJmVjApBYTZ/wTynMZdqVKdpV1kMHUjXSlMKbPA8eHzWuU4FrRgVaAlYbHC\n3PY9iKJsTKMBN6AxUA081LlWapI4UNn86NvTB3fh9mltaB8dIOa0lbM0QVhhNfviMha47NDCyIX/\n60Qx39lto3vC4+0X70F3iboVvQniI9ojoywxYoNqWaDLxq3doz4+2ick8dr6Mi5tksBk/+gQMZuv\ntt+8js03afzXaiU0ecx3hY0knz914nMNplzPgWszvQFlAisFIGP51HA8MYosz/eQFYtVFBnncs/z\nzGtpCaQsoXQ812zWYRyZWpMCFibTIq8DhkqJsxySr0frHC4K6FQhYDWAXy1hMJrPkbhotl3kk5Qx\n5oW3P+iaICvJEjgcWNXrDVNrz3FstNhdudlYwM5johZyaNNHN3ZppgDo9BITVLSaywg56Ov2zjCe\n0uI/Gg9nKjjLwnRCv/Wbv/l13L5NA4g29DknhhQQhS3FucK8gJrlSUED/8kNYfZ5HU5g5UXOnIuA\nc2oG0LBcLlp6ugtvhegEub36mXpXmjc6IT7L2xfvU2gyU8L8w9yqf6zNxqa0S8b2IJO2GS9CzPJu\nhBRQunBbt4ydR799DDCMvrbSwojHoOMADlNCQgrUqjR5S4GPgBV2QXXBqJW0sEyeGrSGw9e2sbaC\nB0wjR0lqrD3maSXLwlHISqeoglvXKB9m0kshOI8iThTqvFknZRdnbIRnJ4DrsQy53QcY7nfWJFSN\nx0PJRiJpzIaxgM+b6VSHGAwLdZvCkO08yqUSylw3btKJcPkGbZqP753hdECfGeUOJsn8hRZjKzYF\nqyHELMzOYVR4ju0Yra7vuRgO6XlpbSHjnSwDsLRGi3Nr8BRZJzJfVGPbjJrwDQVl5Q5OnxJ19B+/\n+5dAQn1fu3QZoxEdrkbaRVdRf9/bOcPLV/gykwTunDGxVBpVDr7bUR+KA8ccuhDKQuU5Qh6bgzBG\nbZEC3JXr6xgM6Vrq66u4wyq2u3s72OBC7be2LyE+pU2s9/gOrt4g9dPJg4/x8Oc/p98KylC8rq01\nFlBjM9osSzHlZyX/P/beLNay7Lzv+6219nSGO1fVrbGr54ndYosURVqWKGuwZBmIHcSODXhAkhfF\nDw4QIE7iBAliwE5gOYj9YAMGgiCIAQuxEycOEg1WHFmSNVGkxUlNstnsZnd1Vd+qurfudMY9rpWH\n9e11zi12d53rMvpp/wGCp07vu89ea6/hW///N0SKnkRT3x/fZ36OYplpkoQqD0mShILDly5eDBHO\ncRwHY6ppmiV3CgViNO1e3OHwQIpCOxeMoMg0Z9wBWjcB51Tw47XAm294+ffK1avcFN+rvT0Y9CUq\nVDlvROHXxUUU3qOxdeOVUO907dI1tta9j9tX/9VvMB55A8SYhFRGcGQ0sfjiRlGCk/Q4a5vbxBKF\nHsUp60P/HvuDPgPJ4N7vD0l74qrQy1Bxmwk+IRGfI53EaJn3Jk4DsRApHXwEHVCz+nv8xX/6y+TW\nz6386JCTu7JnW00ihuFk23AqdTjvHJ6w99u+1uH0+IRc0jmY++8Tyxq5VmucHFYjrUD8wvYGsDeR\nyhDz0vvzANM0Zv9Jv7/GUU0s7+i9gwOSe/77p/pPUEiNzcSBOodbQSfzdejQoUOHDh06PAY+VmYq\njjWF5IgwyjGUPDoKw0RKZygThRpjcRIyFJ1hTuI4DuUalFFEyluex6MRfaHaoyQO+ZBGkylWLOre\ncI1ETtinR0chyaNBk0i0lUNRChOQPvTb54Jy/P7vfwmAaT5lJnl39HhMKQ6HcZKyue1ZAWu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iyfLqYSBur+YUkT+3Gh0jVyuU+dQDRsmbtHY+1SSiaRMLubazzzoshbJ3A8bsd+SSLsQlRZZsKa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\ngAQxBQCQIKYAABLEFABAwi+vnR9/Y1G8agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e3fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.data_utils import get_CIFAR10_original_data\n",
    "_, _, _, _, test_images, _ = get_CIFAR10_original_data() # has not preprocessed or transposed\n",
    "\n",
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "\n",
    "\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_test_pred == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(test_images[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}