9.1 Activation Maximization using Keras Visualization Toolkit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation Maximization on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/raghakot/keras-vis/blob/master/examples/mnist/activation_maximization.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.2348 - acc: 0.9277 - val_loss: 0.0523 - val_acc: 0.9830\n",
      "Test loss: 0.05226928142679389\n",
      "Test accuracy: 0.983\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation, Input\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 1\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax', name='preds'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing our Dense Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "To visualize activation over final dense layer outputs, we need to **switch the softmax activation out for linear** since gradient of our output node will depend on all the other node activations. \n",
    "\n",
    "Doing this in keras is tricky, so they have provided **utils.apply_modifications** to modify network parameters and rebuild the graph.\n",
    "\n",
    "If this swapping is not done, the results might be suboptimal. We will start by swapping out 'softmax' for 'linear' and compare what happens if we don't do this at the end.\n",
    "\n",
    "Lets start by visualizing an input that maximizes the output of node 0. This should look like a 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc23f6ac630>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_activation\n",
    "from vis.utils import utils\n",
    "from keras import activations\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (18, 6)\n",
    "\n",
    "# Utility to search for layer index by name. \n",
    "# Alternatively we can specify this as -1 since it corresponds to the last layer.\n",
    "layer_idx = utils.find_layer_idx(model, 'preds')\n",
    "\n",
    "# Swap softmax with linear\n",
    "model.layers[layer_idx].activation = activations.linear\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "# This is the output node we want to maximize.\n",
    "filter_idx = 0\n",
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx)\n",
    "plt.imshow(img[..., 0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this sort of looks like a 0, it's not as clear as we hoped for. \n",
    "\n",
    "The reason is because because regularization parameters needs to be tuned depending on the problem. This can be improved by:\n",
    "\n",
    "- The input to network is preprocessed to range (0, 1). We should specify input_range = (0., 1.) to constrain the input to this range.\n",
    "- The regularization parameter default weights might be dominating activation maximization loss weight. One way to debug this is to use verbose=True and examine individual loss values.\n",
    " \n",
    "Lets do these step by step and see if we can improve it.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying the Input Range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First lets explore what the individual losses look like "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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      "Iteration: 195, named_losses: [('ActivationMax Loss', -544.72797),\n",
      " ('L-6.0 Norm Loss', 0.91818506),\n",
      " ('TV(2.0) Loss', 285.4459)], overall loss: -258.3638610839844\n",
      "Iteration: 196, named_losses: [('ActivationMax Loss', -545.2471),\n",
      " ('L-6.0 Norm Loss', 0.91876316),\n",
      " ('TV(2.0) Loss', 284.6686)], overall loss: -259.6596984863281\n",
      "Iteration: 197, named_losses: [('ActivationMax Loss', -545.1437),\n",
      " ('L-6.0 Norm Loss', 0.9196681),\n",
      " ('TV(2.0) Loss', 285.6504)], overall loss: -258.5736083984375\n",
      "Iteration: 198, named_losses: [('ActivationMax Loss', -545.27814),\n",
      " ('L-6.0 Norm Loss', 0.9203814),\n",
      " ('TV(2.0) Loss', 284.94098)], overall loss: -259.416748046875\n",
      "Iteration: 199, named_losses: [('ActivationMax Loss', -546.4222),\n",
      " ('L-6.0 Norm Loss', 0.92173225),\n",
      " ('TV(2.0) Loss', 287.2866)], overall loss: -258.2138366699219\n",
      "Iteration: 200, named_losses: [('ActivationMax Loss', -545.3181),\n",
      " ('L-6.0 Norm Loss', 0.9220441),\n",
      " ('TV(2.0) Loss', 285.03433)], overall loss: -259.3617248535156\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f975867e2e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = visualize_activation(model, layer_idx, filter_indices=filter_idx, input_range=(0., 1.), verbose=True)\n",
    "plt.imshow(img[..., 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's do it for all classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eXT6uSmx5uiuw3Di2MjlcH9mHtVWx7Txh3f5QvVn6+DtW9YTG1tPdsbkEjoGe/cGeAMHnqPpIYPzg2KXx9KXskhR4iiRJXkp/gJdjP3gjG2JzH12ffrA3YoeLxo8IZkAkuw4GluE3WW4OAMsGYQ0AGSCsASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4vbG8RjfQq8nN40oRnckmYl1pAhMn6jKzh2NVSuRk9g/FKsB8a6noOh+uP69iXXPrVvVWjsZjXW7KE8nl4b6fMgSeVarL7Rnf4Nqgdi/TXCvT6CvUfqvelzHzkqtiP3nRab/Omveiy59s3rHwyN/eDBo0P1254+Lrl2757B9IETe7FwZg0AGSCsASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGRgcXuDSFKoN0jnpqFgvwQvpT8gOu9Gd2wyob4WwV/HR/cOh+pP6d2TXPvAug2hsR/rGQjVR3qsdA3HelREnyMLtGQZXR/7MayMBefeFSpXqZ5eG+nfI0nde2P1jz23Lrm23jwtNHZfZSJUX07s4SFJrzh5V3Ltvp60xjOcWQNABghrAMjArGFtZteY2R4zu6/ttnVmdouZPVT8u7az0wSAlS3lzPpaSWcfcttlkm5191Mk3Vp8DQDokFnD2t3vkDR0yM3nSrqu+Pw6Se9e4HkBANrM9dUgG9x9d/H5U5Km/TO/mW2RtEWSunrXzPHbAcDKNu8/MLq7a4YX5Ln7Ve6+2d03V7tjL8UCALTMNayfNrONklT8m/5iWwBA2FzD+iZJFxSfXyDpGwszHQDAVFJeuvclST+QdKqZPWFmvyXp05LeZmYPSfqV4msAQIfM+gdGdz9/mrveGv5uJjW60peblicCy2qDy8dL9diS3cjy4ehkvBybS20guLEBDY+NfWr3k8m121dtDI394PpYvT+S/vfy6oHQE6rBHWOheqs10mtHxkNjh1nweKmk90tY04ztx2ZPbO17/fbu5Nq9J5wQGvvJ9bH9MnJM+rY+t34wuXZsPK1PAisYASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADMz1zQfmrNRI74NR70lfu18Zi/XXmL4D9+KL9EuRpFLaO9dLivcdGW2k9SmYNObp9a8bfDg09o+OOzZU/+zQUcm1XcPp/S8kqefZULlKzz6fXOsHDoTGbjy/PzaZKE8/Zqwa6/Xh9cDBK6k6kN4D/4jtvaGxGydO+54pUxpf35NebOnnwc/tTavjzBoAMkBYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAwQ1gCQAcIaADJAWANABghrAMjAovYGcZPq3el9MEr19LEjfUQkyYO/prycPn490EJAkizYp6Se3i4h3AOl3oz1zBhupvdjOLaa2ASh8JGTfhCq/7vSLyXXPlk6OjR2ozuy06X+1ek9M3p2xXp9VNauCdX7yGio3roC/T5qsV4fPtAXm0ujmT52NRZnlWeHQ/Wl8fRAKj2X/pyWRtL2IWfWAJABwhoAMkBYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAwQ1gCQAcIaADKwqMvNzWNLyBuhpenBddWx1enyQL01YmM3Aqt7w+MHt/Px4bWh+u39xyTXDlVjS7YbwclvOuKJ5NpdR8eWbB8YSl9WL0mVsWpyrVdic6kciC3xlq0LlZdq6QeYl4Pne83Yz2mkzUN5NLhfStGeE+lzr52wPn3YobQY5swaADJAWANABghrAMgAYQ0AGSCsASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIwKL2BvGS1OgK9Puopa/Ft/R3rJcklceDPQpKgb4AfbHfgT1DsblM1CP9EmJP8Z7nN4Tqr380vQeCemNNU1atHQnVr+4dS64tV2IHjAdPa2p9gedoPNYDpVmNNZMpTcS2tdmV3tckqlmNbWt5LH3u9f7YsV4ZiR2PzWr6QVBqBPZ54i7hzBoAMkBYA0AGZg1rM7vGzPaY2X1tt11hZrvM7J7i45zOThMAVraUM+trJZ09xe1/7u6bio+bF3ZaAIB2s4a1u98haWgR5gIAmMZ8rll/zMx+XFwmmfbtRcxsi5ltNbOt9bGD8/h2ALByzTWsPyfpZZI2Sdot6bPTFbr7Ve6+2d03V3r65/jtAGBlm1NYu/vT7t5w96akz0s6Y2GnBQBoN6ewNrONbV++R9J909UCAOZv1iU/ZvYlSWdJWm9mT0j6lKSzzGyTJJe0Q9JHOzhHAFjxZg1rdz9/ipuv7sBcAADTWNTeIJLUDH3HQB+RRqy/huqx8kjvkb5nooPHyvt3p2+rl6N9J2L1E4PpV9Ka5XJo7JGju0P1O19aSy9uxLazErxgWKp37jmy4LFeG4jt98gxMDEYPHiDPXws0JSlazg2eL0vtl8ifUpqXeljNytp+5Dl5gCQAcIaADJAWANABghrAMgAYQ0AGSCsASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYWtTeImxRY6q96b3ptZTTYoyDWdkKV0UA/huBUKgcbsQcElMZjY3tX7Pd397702kZ3sBdDLXZ4WqOaXDuxOtZfozISKk/u9yBJ1oz1tIj2b4n2zRlbk34MjK+O9p4Jlat6IL3Wy8Fj9/nYfmkGGsSUQj929AYBgGWDsAaADBDWAJABwhoAMkBYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAws6nJzlaR6X/ry1PJE+nLQyNJ0SaqMxZbJjq5Lr4+OrTWxZdjVwNJ3a8ae4vJobOmzlwPLqoPLnitjsfqu/en1pVrsObLYVGSB5cb1ntg5U6kem0wjsExaii0Jn1gb3DHBcln681SqxYau98SOgUjLicjYqS04OLMGgAwQ1gCQAcIaADJAWANABghrAMgAYQ0AGSCsASADhDUAZICwBoAMENYAkAHCGgAysLi9QRTrsVDvDfTjGIk1HZhYFesL0OhOrx1eGxpaFmvHIWuk/47t2hcbu3ow2L8j0qck2hciWF8NHAOR3h2SVJ6I1ZtH+pTENjS1l8TP5hI8vhrdgb4W5WCfku5YfTnQZ6fZFez3ciC2YyI9XErBPjhJYy74iACABUdYA0AGCGsAyABhDQAZIKwBIAOENQBkgLAGgAwQ1gCQAcIaADJAWANABghrAMjAovYGcZOa1fT6Ui29tlmN9QWI9mOoDaSPX6rH5jKxOtajwAJ9B2qDoaFV3R/7/V2eSN/W7qHYPi/VQ+Uqj6eP3xXsgdIsx+ZSDfRMqQd6cUhSZSzYBydw7EZFe31EeSChIn1qJMlLncuMRqRPSWIpZ9YAkIFZw9rMjjez28zsJ2Z2v5ldVNy+zsxuMbOHin+DveYAAKlSzqzrki5x99MlvU7S75rZ6ZIuk3Sru58i6dbiawBAB8wa1u6+293vLj4flrRd0rGSzpV0XVF2naR3d2qSALDSha5Zm9lJkl4t6S5JG9x9d3HXU5I2TPOYLWa21cy2NkYOzmOqALByJYe1mQ1IukHSxe6+v/0+d3dN874e7n6Vu292983lvv55TRYAVqqksDazqlpBfb27f624+Wkz21jcv1HSns5MEQCQ8moQk3S1pO3ufmXbXTdJuqD4/AJJ31j46QEApLRFMW+Q9CFJ95rZPcVtl0v6tKSvmNlvSXpc0nmdmSIAYNawdvfvafo1Nm9d2OkAAKayqMvNrSmVA0tCI0vIo0tNo0uZB/81fUl4rS+2jLV/V6w+svS90RUaWs1g/dR/Vp5aoye2ndWh2DL8cqA9QVQl2J5gYiD9hVaR9gGSNLa2swuPa6vSa0u12HPq5di2lkdD5SHRlhORPCrVA2MnlrLcHAAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADBDWAJABwhoAMkBYA0AGCGsAyMCi9gaRlPy265JUnoj0EYlNI9pHwgPz7numERu7HOuv0DOUXhvtI1EbjM1lYjC91mKtPsJz79uT/g08eJrS6OvceU2jO9hfIziViVWx8S3QN6c0HptLeTw2+UgPn8i8JanRFdsvkX4fzUpg7MRSzqwBIAOENQBkgLAGgAwQ1gCQAcIaADJAWANABghrAMgAYQ0AGSCsASADhDUAZICwBoAMLG5vEJdKE4HycnptOTCuJDW6Y/XlsfS1/rVgH4nqaKxpRrOaPpeu4fR+BpJU7432S0ivHTsiNpfyeLBPyer0/d69NzS0ml2x+vJobFsjPNJ3QlIp2gcncPj2DMXm0gz8TEtSOdB7JNwfKNjXJNJLJPJzkTzmwg8JAFhohDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGRgcZebS6FfD5XR9NrI0nQpvhy4WY4tq42o98R+Z5bH0+ceXVbfsze29D3yhFozuHx8TXDJdmD4kY2xoavDweff0+srI7Ht9OhK9uDUKyPB8QOqY8FtLaVP3hqxuUSW1UtSeSJ97t6BvODMGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADBDWAJABwhoAMkBYA0AGCGsAyMCi9gYxl0q19PrIW8tH+mW0xo6t3Q+NH2wLUKoF+yUE+qBURmO9PqL7ZdXOenLtRH/s3KD5VGwutb5AH4lgg41o75lIv49GV2xsa8TmXqp1rq9N+Ocu2sMnMn7w1DOSRZLUrKTvx0gfESWWcmYNABmYNazN7Hgzu83MfmJm95vZRcXtV5jZLjO7p/g4p/PTBYCVKeUySF3SJe5+t5kNStpmZrcU9/25u3+mc9MDAEgJYe3uuyXtLj4fNrPtko7t9MQAAC8IXbM2s5MkvVrSXcVNHzOzH5vZNWa2doHnBgAoJIe1mQ1IukHSxe6+X9LnJL1M0ia1zrw/O83jtpjZVjPbWh87uABTBoCVJymszayqVlBf7+5fkyR3f9rdG+7elPR5SWdM9Vh3v8rdN7v75kpP/0LNGwBWlJRXg5ikqyVtd/cr225vfxe790i6b+GnBwCQ0l4N8gZJH5J0r5ndU9x2uaTzzWyTWi/p3iHpox2ZIQAg6dUg39PUa/JuXvjpAACmwgpGAMjAovYGcYv1+yhNpNfWe2L9DyrRngaRvgDBXh/NcrBPSaDvQLTXR7iXSGC/9OxtxMbuis2997n02omB2HmKlzp3vFTGQkOr3hurt3q0l0h6bTPY16Q8HquP5IXFDl01umP1kf1S609//j3xUOTMGgAyQFgDQAYIawDIAGENABkgrAEgA4Q1AGSAsAaADBDWAJABwhoAMkBYA0AGFnW5ublk9fT6yFLWyNJ0SWoElzJHlnjXuzs3tiQ1AuNXxmJrcGt9sd/fof3SGxu7FFwmHVlaXxmLjV3r69xzGlmaLMWXVUd1qiWEJDWDiWOBDgXRsSPLx6Pjf/IT1yXXfvwHaX0SOLMGgAwQ1gCQAcIaADJAWANABghrAMgAYQ0AGSCsASADhDUAZICwBoAMENYAkAHCGgAyYO6xHgnz+mZmz0h6fIq71kt6dtEmsnTYzuVnpWwr29k5J7r7kbMVLWpYTzsJs63uvnmp59FpbOfys1K2le1celwGAYAMENYAkIHDJayvWuoJLBK2c/lZKdvKdi6xw+KaNQBgZofLmTUAYAZLGtZmdraZPWBmD5vZZUs5l04zsx1mdq+Z3WNmW5d6PgvFzK4xsz1mdl/bbevM7BYze6j4d+1SznEhTLOdV5jZruI5vcfMzlnKOS4EMzvezG4zs5+Y2f1mdlFx+7J6TmfYzsP2OV2yyyBmVpb0oKS3SXpC0g8lne/uP1mSCXWYme2QtNndl9VrVc3sTZIOSPpbd39lcdufSRpy908Xv4TXuvsnlnKe8zXNdl4h6YC7f2Yp57aQzGyjpI3ufreZDUraJundkj6sZfSczrCd5+kwfU6X8sz6DEkPu/uj7j4h6cuSzl3C+WAO3P0OSUOH3HyupMl3DL1OrR+CrE2zncuOu+9297uLz4clbZd0rJbZczrDdh62ljKsj5W0s+3rJ3SY76x5cknfMbNtZrZlqSfTYRvcfXfx+VOSNizlZDrsY2b24+IySdYn7gPKAAABj0lEQVSXBg5lZidJerWku7SMn9NDtlM6TJ9T/sC4eM5091+U9GuSfrf4b/Wy563rbMv1JUefk/QySZsk7Zb02aWdzsIxswFJN0i62N33t9+3nJ7TKbbzsH1OlzKsd0k6vu3r44rbliV331X8u0fSjWpdBlquni6uCU5eG9yzxPPpCHd/2t0b7t6U9Hktk+fUzKpqBdj17v614uZl95xOtZ2H83O6lGH9Q0mnmNlLzKxL0vsl3bSE8+kYM+sv/oghM+uX9HZJ9838qKzdJOmC4vMLJH1jCefSMZPhVXiPlsFzamYm6WpJ2939yra7ltVzOt12Hs7P6ZIuiileFvPfJJUlXePuf7Jkk+kgM3upWmfTklSR9MXlsq1m9iVJZ6nVrexpSZ+S9HVJX5F0glpdFs9z96z/ODfNdp6l1n+XXdIOSR9tu66bJTM7U9Kdku6V1Cxuvlyt67nL5jmdYTvP12H6nLKCEQAywB8YASADhDUAZICwBoAMENYAkAHCGgAyQFgDQAYIawDIAGENABn4/2grVUHNiLTbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating visualizations for all classes (0-9)\n",
    "for output_idx in np.arange(10):\n",
    "    img = visualize_activation(model, layer_idx, filter_indices=output_idx, input_range=(0., 1.))\n",
    "    plt.figure()\n",
    "    plt.title('Networks perception of {}'.format(output_idx))\n",
    "    plt.imshow(img[..., 0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}