9.2 Saliency Maps.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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "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 [==============================] - 139s 2ms/step - loss: 0.2495 - acc: 0.9229 - val_loss: 0.0486 - val_acc: 0.9843\n",
      "Test loss: 0.04858853696002625\n",
      "Test accuracy: 0.9843\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": [
    "### Saliency Visualizations\n",
    "\n",
    "\n",
    "\n",
    "To visualize activation over final dense layer outputs, we need to switch the softmax activation out for linear since gradient of output node will depend on all the other node activations. Doing this in keras is tricky, so we provide 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 dont do this at the end.\n",
    "\n",
    "Lets pick an input over which we want to show the attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb2a9fb4438>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFPlJREFUeJzt3X+M1HV6B/D3I2CpQIuIIgeURRd/EDw53XJepJVGNGrs4fUSKjkaSFXsKbb2uB8UL3Fz0chdEWrkTrMCAaunNkERjUdVclS9nIaVICDgwdGlgsCKHspqqcA+/WMGWWS/z3vY7+zMmM/7lZDdnWc+3+9nvjsPMzvP54e5O0QkPadUuwMiUh1KfpFEKflFEqXkF0mUkl8kUUp+kUQp+UUSpeQXSZSSXyRRPSt5MrM+DvTPcYRoNKKxs+c4NgD0CmKfkbbsMrNz5xmFmfe6sHiPk+jLyTpI4n9M4ofL1ZFOHMnZvr0svTjRfrh/yn5pAHImv5ldA+ABFJ4BC919TtyiP4DbcpzxUBCLkhPgD5U9Uc4KYrtI2wE5zx09bibvdWHt+51EX07WNhK/iMRby9WRTnycs/3/lqUXJ2oq+Z5dfttvZj0A/BzAtQBGAZhsZqO6ejwRqaw8f/OPBbDN3be7+2cAngQwsTzdEpHulif5hwB4t8PPO4u3HcfMpptZs5k1A5/kOJ2IlFO3f9rv7k3u3uDuDUCf7j6diJQoT/LvAjCsw89DwT/5EpEakSf51wAYaWYjzOxUADcCWFGebolId+tyqc/dD5vZDAD/iUKpb7G7v01aIV+5LornKYcBvBwXYfVm9rg+JPE6Em8JYuxxsTdrg0iclSmj87NyGbuueUp57Nzsd8b69ickvjeIdVcZ8Hi56vzu/gKAF8rUFxGpIA3vFUmUkl8kUUp+kUQp+UUSpeQXSZSSXyRRFZ3PX5gbzuqnkaiWz+rZeequQFzPPmFKwxewOj6bmsrU5Tj310mcXdfNJB7JOz5iAomPDGJsPBqbTsyeT5Wp1eehV36RRCn5RRKl5BdJlJJfJFFKfpFEKflFElXhUh+b0stEZSdWkmJTU1m/oqmvV5C2I0iceYPEtwaxvyZtyePuT9ZkrSOlwqhCupGVV1mpj/3O7g1i9aTthST+KYmzvkWPrTtXcz5Gr/wiiVLyiyRKyS+SKCW/SKKU/CKJUvKLJErJL5KoCtf5T0E8jZNNk4x2ymVTV39D4nmml75M2taROJsWy+rdUU36adKW2E9+J+OHxvHXoyAbv8AeN1t2PPqdsrYtJJ53nMCGIMbGrLSQeGn0yi+SKCW/SKKU/CKJUvKLJErJL5IoJb9IopT8IonKVec3sxYABwAcAXDY3RvydYfN747mMR8gbVkdnxkexOpI26+S+FVx+AbSvG8Qe6yFNJ4Uh/ufFscfJoc/GP1O2dgMVu9mS6a3BLG8y4L/isQvIXE27qT7lWOQz1+5+74yHEdEKkhv+0USlTf5HcCLZvammU0vR4dEpDLyvu0f5+67zOwsAC+Z2RZ3f6XjHYr/KRT/Y+if83QiUi65XvndfVfxayuAZwCM7eQ+Te7eUPgwsE+e04lIGXU5+c2sj5n1O/o9gKsBbCxXx0Ske+V52z8IwDNmdvQ4v3T3lWXplYh0uy4nv7tvB3DxyZ8uqt2yum9Uy4/m+gP554ZHWy6vzdEWQD2p8y9fQo5/OYlHFsbh/ex3cnMcviCY77/lb8ixybiP3tEW3AAOPhcE/4Gcmz1uso4B3aI7ej7mHZNSGpX6RBKl5BdJlJJfJFFKfpFEKflFEqXkF0lUhZfuPgSglcQj0XLJ20hbVupjlyIqUZJjjyalvJUH4/jN00j7TUGQtMUHJP54HB4Xl7wmv7o4M/bEv/x9fOw58+I4Gkk8KkOy8mye6cIAfy5HpUBWZiwPvfKLJErJL5IoJb9IopT8IolS8oskSskvkiglv0iizN0rdzL7in++olenWC0+wqZBsjo+m4IZbVXNzt1C4t8l8YdI/IdBjG3RfQaJk+vWEI9hOOX5TzJj/QfuD9t+eDOptS/5aRwf+KPsGBlagbYl5A5sHECeLcDzHLsJ7u8ZOQAAvfKLJEvJL5IoJb9IopT8IolS8oskSskvkiglv0iiKlznH+LAbcE9WC0+qrWzOj2rnbI51FOD2FLSlm3XzLZ7voPEHwxig0hbNu+cjb3oR+LfzA7NjX6fwOiZa8L4RovHCcRbo28lbeO+8efbyyR+URDLs4z8fLi/qzq/iGRT8oskSskvkiglv0iilPwiiVLyiyRKyS+SKFrnN7PFAK4H0Oruo4u3DQDwFIA6FCYmT3L3P9CT0fn8TLRuP6uNHibx4Tnat5C2rGYc7QkAxI8biPcsYHX+zXF4yffiOLusjUFs54qwad+2b4TxO/pE4xuA+37wk+zgXLYnwHdInK37z6wPYnU5jjsb7r8vW51/CYBrvnDbLACr3H0kgFXFn0XkS4Qmv7u/ghOHv03EsWFtSwHcUOZ+iUg36+rf/IPcfXfx+z3g7y1FpMbk/sDPCx8aZH5wYGbTzazZzJqBT/OeTkTKpKvJv9fMBgNA8Wvm7pvu3uTuDe7eAJzWxdOJSLl1NflX4Ng0t6kAni1Pd0SkUmjym9kTAH4L4Hwz22lmNwGYA+AqM9sKYELxZxH5EmET6OHukzNCV5786Qzx/HC2/n1Uz2bzzlktnc3PDubk950WN20jh6br8m8g8Y/ZCbKNIXX83qT9ljjcd8v7mbG25mCuP4C2vqvD+ASP58wP/Nd9mbGZc8eGbdFCni91bOzFcyQejf1gaXkgiLWTtsdohJ9IopT8IolS8oskSskvkiglv0iilPwiiaKlvvKybjwlKxOeReJkbmrPS7Nj08ihH2ZxskU3WwX6ySi4LG677r/j+IIRcbx/HL60z5uZsXlXxGXGH/jPwviVRpZMXzAyO8ZGpjSQ+JTg2ADw2CRygIVBjJURo+nrR0jbY/TKL5IoJb9IopT8IolS8oskSskvkiglv0iilPwiiaqxLbrZtNto6urXSVtSOx06NI7vjIIfxG1nnRGGh98Xz4vdMfmC+PhnB7HVcVOs2xSGT9kTL2nefmef+PjjskM33b4gbLrov2bExx7/UhyPtl2f+7dhyxkz4zEGv9h7exhvP/uXYRz1t2THtrHl7uYHsV/AfZe26BaRbEp+kUQp+UUSpeQXSZSSXyRRSn6RRCn5RRJV4fn8f4R4K+wdpH2wfHae5asBvtV0VHJeEC8b7uvjsmsjqcretT+On/rjYKzGuuz59AXRMtBAe/2ouDlblvz57NCi50kdfyM5ds+r4vjhYK2C7y8Jmy6Y8MP42GTJcoCMGwlr+fHvJB4PU3pK65VfJFFKfpFEKflFEqXkF0mUkl8kUUp+kUQp+UUSRYuCZrYYwPUAWt19dPG2RgC3ADi6//Jsd3+Bn64d8VbYrDtrgxhbt/+iOHyQNF9wKDv242i7ZWDrPfGh7+gRx09dEq+50Pue7Hnr9z64KGw708h+BkPHx/Et98bxtqBevo5sq87GEBxmW5t/J4iRdfWXk0NHu8UDQP9r4/j+eUGQjVmpC2Klv56Xcs8lAK7p5Pb57j6m+K+ExBeRWkKT391fQbgkioh8GeX5m3+Gma03s8VmdnrZeiQiFdHV5H8IwLkAxgDYDeD+rDua2XQzazazZj5mWUQqpUvJ7+573f2Iu7cDeATA2OC+Te7e4O4NQL+u9lNEyqxLyW9mgzv8+C3w+VciUmNKKfU9AWA8gIFmthPA3QDGm9kYAA6gBcCt3dhHEekGFV63/1wHovXQWVEhmiPN1jon+6mHe54DQFC3JdPS9yyIJ+zXt7WG8ba+Z4bxc/ztzNj2HeeFbVEXjbsAyIYFQGM83//Fu/8iM3b1G6/Gx77sjTjeQPZqiPYzeC1uiv3xfgbAb0icPd+i68o+GwvGnGAu3P9H6/aLSDYlv0iilPwiiVLyiyRKyS+SKCW/SKIqvHQ3w0p90RRQVqoj00fHfzOOjwliwTbUADDoe3GclfLY8bfbwCAalYUA4Fdx+Pp4K+ur7342jNehJTu4MD41vk9KedHDBoBZURk73oIb+CqJs1LeFSQebbPNpvRGU8hLL93rlV8kUUp+kUQp+UUSpeQXSZSSXyRRSn6RRCn5RRJV4Tp/O+LpitHWwwBweRBjYwTIsVcvI/FgO+h/i1co2uXk3DvJuuENveP4lEHZscf2xm1vjOv4uD4Ov/iNiWH8vNejvazJ9uH1l8ZxclniWjoZQ4DNJP7dONyXNG+LlkxnK15Fz3XV+UWEUPKLJErJL5IoJb9IopT8IolS8oskSskvkqgKL909xIHbgnuwOj+pWYfIfH4MJ/FoDvUlYctv++owfh5+F8bv6/WTMN5rT/b870MD2TVjW5uzejf7nQW1+kbS9DCJ3/MUucOFQYwsCx5u7w2g7rQ43p8cfl20NPhK0jhao+FBuO/U0t0ikk3JL5IoJb9IopT8IolS8oskSskvkiglv0ii6Hx+MxsG4FEAg1CYLNzk7g+Y2QAATwGoA9ACYJK7/yE+miOuUbLuRHVbVs9m9ehtcbhvY3asLd4efNmZU+Jjz4rDqIvDh5ZHYxDIVtJnB1uPA8AethU1EW2TPYWMMakn5erlZC2C5UHsebIu/744jJbHyB12kHg0NoRtmx7lUDtpe0wpr/yHAcx091EALgNwu5mNQuEpu8rdRwJYBf4UFpEaQpPf3Xe7+9ri9wdQGPI1BMBEAEuLd1sK4Ibu6qSIlN9J/c1vZnUAvobC2MhB7r67GNqDwp8FIvIlUXLym1lfAMsA3Onuxw0m98IEgU7/gDOz6WbWbGbNQPy3sYhUTknJb2a9UEj8x9396eLNe81scDE+GEBrZ23dvcndG9y9ASCTIUSkYmjym5kBWARgs7vP6xBaAWBq8fupAOLtWkWkppSydPflAP4OwAYzW1e8bTaAOQD+w8xuQqGuMYkfyhBPrWXLb0fY1FRWeiEfWbQ1BsEoBmDfzjjef2gY/vbWuKy0zIJSYj0p5W27N47jrjg8gzRfEMTqn4vbjo63Te89Pn6+9JxwJDPW1pNsi76QTfk9g8TZFPKtJN79aPK7+2soZG1nrixvd0SkUjTCTyRRSn6RRCn5RRKl5BdJlJJfJFFKfpFEVXjp7qEO3BHcg027jab0vkzaRtNeATqNcmBQ724ghyY7cF/869fD+CO4JYxvwqjM2Hz8c9j2rXcuC+Ojz18TxjfaOWEcD2fXw/902p6w6UfTovnAoLOw0RwFf0oaR881ANhA4qyKHo0ryV6KvSAa39AE9/e0dLeIZFPyiyRKyS+SKCW/SKKU/CKJUvKLJErJL5KoUubzl/l0US1/F2kf1erZfH72UONttrEvmPe+km3vHS8T/ZbFe1GPJXV+LP/HzNDIiW/FbafF4Y2v/1l8hynxvPbRt2aPE3jng/PjY8fDH4AWVquPttlmzxe2fPYFJL6FxKNaPtubvDz0yi+SKCW/SKKU/CKJUvKLJErJL5IoJb9IopT8IomqcJ2/B+JaPVvrPNqGm9Vth5B4C4lH4xMuj5uePSKON8cT/mcOWR3Gf/5R9vzurfdfHLa98Ldrw/h7//eVMP7RyjCMjZP/PDv45EtxYzqnnuxJEP7O2foO7PnE5txH22gD8TgClgfloVd+kUQp+UUSpeQXSZSSXyRRSn6RRCn5RRKl5BdJFF2338yGAXgUhYXGHUCTuz9gZo0AbgHwfvGus939hfhYQxy4LUd3o2EJbP41qxmzPQOitdLrSVvWt5FxeEG0xjuAuiBGlr7HThIndXw8/AG5Q7C+fc/xcdPDm8ixW0m8XxBj6+6zOn/0fGDnZu0PkLbRGILS1+0vZZDPYQAz3X2tmfUD8KaZHR2dMd/d55ZyIhGpLTT53X03gN3F7w+Y2Wbw4XIiUuNO6m9+M6sD8DUAbxRvmmFm681ssZmdntFmupk1m1kz8EmuzopI+ZSc/GbWF8AyAHe6+8cAHgJwLoAxKLwzuL+zdu7e5O4N7t4A9ClDl0WkHEpKfjPrhULiP+7uTwOAu+919yPu3g7gEQBju6+bIlJuNPnNzAAsArDZ3ed1uH1wh7t9C8DG8ndPRLpLKaW+cQBeRaE20l68eTaAySi85XcU5sPeWvxwMDjWMAfZMjrGplHmcRGJR9Ms827XzEqBbIpntHT4DtKWTT2dQOJ1JB5NnY2nE/M6JNujOyrXsWued2lvUp4N2+d5npex1OfurwHo7GBhTV9EaptG+IkkSskvkiglv0iilPwiiVLyiyRKyS+SqAov3X0E3Vurz4NtqRxdKlYrZ3FWU2bTjaOaMds+nNWrXyZxhvU9Dzb+gT227moLxMvM1wa98oskSskvkiglv0iilPwiiVLyiyRKyS+SKCW/SKLofP6ynszsfRw/wXwggH0V68DJqdW+1Wq/APWtq8rZt+HufmYpd6xo8p9wcrPmwtp+tadW+1ar/QLUt66qVt/0tl8kUUp+kURVO/mbqnz+SK32rVb7BahvXVWVvlX1b34RqZ5qv/KLSJVUJfnN7Boze8fMtpnZrGr0IYuZtZjZBjNbV9hirKp9WWxmrWa2scNtA8zsJTPbWvza6TZpVepbo5ntKl67dWZ2XZX6NszMfm1mm8zsbTP7p+LtVb12Qb+qct0q/rbfzHoA+B2Aq1BYmH0NgMnuzvZjrggzawHQ4O5Vrwmb2V8CaAPwqLuPLt72MwAfuvuc4n+cp7v7j2qkb40A2qq9c3NxQ5nBHXeWBnADgGmo4rUL+jUJVbhu1XjlHwtgm7tvd/fPADwJYGIV+lHz3P0VnLiR+0QAS4vfL0XhyVNxGX2rCe6+293XFr8/AODoztJVvXZBv6qiGsk/BMC7HX7eidra8tsBvGhmb5rZ9Gp3phODOuyMtAd8a5hKozs3V9IXdpaumWvXlR2vy00f+J1onLtfAuBaALcX397WJC/8zVZL5ZqSdm6ulE52lv5cNa9dV3e8LrdqJP8uAMM6/Dy0eFtNcPddxa+tAJ5B7e0+vPfoJqnFr61V7s/namnn5s52lkYNXLta2vG6Gsm/BsBIMxthZqcCuBHAiir04wRm1qf4QQzMrA+Aq1F7uw+vADC1+P1UAM9WsS/HqZWdm7N2lkaVr13N7Xjt7hX/B+A6FD7x/z2Au6rRh4x+nQPgreK/t6vdNwBPoPA28BAKn43cBOAMAKsAbEVhad0BNdS3f0dhy+L1KCTa4Cr1bRwKb+nXA1hX/Hddta9d0K+qXDeN8BNJlD7wE0mUkl8kUUp+kUQp+UUSpeQXSZSSXyRRSn6RRCn5RRL1/wd8puqBSZD4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_saliency\n",
    "from vis.utils import utils\n",
    "from keras import activations\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "class_idx = 0\n",
    "indices = np.where(y_test[:, class_idx] == 1.)[0]\n",
    "\n",
    "# pick some random input from here.\n",
    "idx = indices[0]\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",
    "grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, seed_input=x_test[idx])\n",
    "# Plot with 'jet' colormap to visualize as a heatmap.\n",
    "plt.imshow(grads, cmap='jet')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To used guided saliency, we need to set backprop_modifier='guided'. For rectified saliency or deconv saliency, use backprop_modifier='relu'. Lets try these options quickly and see how they compare to vanilla saliency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for modifier in ['guided', 'relu']:\n",
    "    grads = visualize_saliency(model, layer_idx, filter_indices=class_idx,\n",
    "                               seed_input=x_test[idx], backprop_modifier=modifier)\n",
    "    plt.figure()\n",
    "    plt.title(modifier)\n",
    "    plt.imshow(grads, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Both of them look a lot better than vanilla saliency! This in inline with observation in the paper.\n",
    "\n",
    "We can also visualize negative gradients to see the parts of the image that contribute negatively to the output by using grad_modifier='negate'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb2a62a42e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, seed_input=x_test[idx], \n",
    "                           backprop_modifier='guided', grad_modifier='negate')\n",
    "plt.imshow(grads, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Lets try all the classes and show original inputs and their heatmaps side by side. We cannot overlay the heatmap on original image since its grayscale.\n",
    "\n",
    "We will also compare the outputs of guided and rectified or deconv saliency.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This corresponds to the Dense linear layer.\n",
    "for class_idx in np.arange(10):    \n",
    "    indices = np.where(y_test[:, class_idx] == 1.)[0]\n",
    "    idx = indices[0]\n",
    "\n",
    "    f, ax = plt.subplots(1, 4)\n",
    "    ax[0].imshow(x_test[idx][..., 0])\n",
    "    \n",
    "    for i, modifier in enumerate([None, 'guided', 'relu']):\n",
    "        grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, \n",
    "                                   seed_input=x_test[idx], backprop_modifier=modifier)\n",
    "        if modifier is None:\n",
    "            modifier = 'vanilla'\n",
    "        ax[i+1].set_title(modifier)    \n",
    "        ax[i+1].imshow(grads, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guided saliency seems to give the best results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## grad-CAM - vanilla, guided, rectified\n",
    "\n",
    "These should contain more detail since they use Conv or Pooling features that contain more spatial detail which is lost in Dense layers. The only additional detail compared to saliency is the penultimate_layer_idx. This specifies the pre-layer whose gradients should be used. See this paper for technical details: https://arxiv.org/pdf/1610.02391v1.pdf\n",
    "\n",
    "By default, if penultimate_layer_idx is not defined, it searches for the nearest pre layer. For our architecture, that would be the MaxPooling2D layer after all the Conv layers. Lets look at all the visualizations like before.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vis.visualization import visualize_cam\n",
    "\n",
    "# This corresponds to the Dense linear layer.\n",
    "for class_idx in np.arange(10):    \n",
    "    indices = np.where(y_test[:, class_idx] == 1.)[0]\n",
    "    idx = indices[0]\n",
    "\n",
    "    f, ax = plt.subplots(1, 4)\n",
    "    ax[0].imshow(x_test[idx][..., 0])\n",
    "    \n",
    "    for i, modifier in enumerate([None, 'guided', 'relu']):\n",
    "        grads = visualize_cam(model, layer_idx, filter_indices=class_idx, \n",
    "                              seed_input=x_test[idx], backprop_modifier=modifier)        \n",
    "        if modifier is None:\n",
    "            modifier = 'vanilla'\n",
    "        ax[i+1].set_title(modifier)    \n",
    "        ax[i+1].imshow(grads, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "In this case it appears that saliency is better than grad-CAM as penultimate MaxPooling2D layer has (12, 12) spatial resolution which is relatively large as compared to input of (28, 28). Is is likely that the conv layer hasnt captured enough high level information and most of that is likely within dense_4 layer.\n",
    "\n",
    "Here is the model summary for reference.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "preds (Dense)                (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Visualization without swapping softmax\n",
    "\n",
    "As alluded at the beginning of the tutorial, we want to compare and see what happens if we didnt swap out softmax for linear activation. Lets try this with guided saliency which gave us the best results so far.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vyCOPl0fEuQe4/S9ba6ePfnxicdcCAPgXLg/nEYCJ6pbH1trnImLbEuwCAHBAziMAk7eQz3l8VWZ+bfQ0kmMXbSMAgOGcRwCWyHzL46UR8ciIOD0ibo+IvzhYMDMvyMwNmblhLu6d55sDANjPvM4jEfcs1X4AU2Ve5bG1trW1tru1tici3hERZz5A9rLW2vrW2vrZOHy+ewIA/AvzPY+M9VspAEyxeZXHzDxxn1++ICJuPlgWAGAcnEcAllb3O2Rm5vsi4uyIOC4zN0fE6yPi7Mw8PSJa7P0urq8Y444AwArnPAIwed3y2Fo77wA3v3MMuwAAHJDzCMDkLeSrrQIAALBCdB95ZPz27NhRyn9qy6MHZz9/+ntLs2//+ENK+c+//Sml/HJx12mtlH/wuh+V8k/+mU2Ds3tiT2l2VdYuFYAltbqYf2ghu6s2+oRa/AuvPGNw9v/Eo0qzZ39Quc6IuROOHh7etb00O+LWWnzV44dnX1wbXb2PYvMzhmf/ujb6jg0/W/sNG6oHksqfmdpZOqL6Z6Dy93RncfahxSOPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdM1MegHqjv3DVYOzv3TReaXZVz3u8lL+z17/5VJ+udhw72Gl/O7i/7Osf9B9hXSWZled8pabSvk9Y9oDgANZU8xXjm731EZvvqgU/8VnXD88/IbS6HjME28o5Td+5Izh4R8/ojT7pF+9pZT/f1v/aXB2z11HlmbHf6vFY3Mhu6s4+/3FfPx1Mf/yQvaK4uztxXzl7+mm4uxDi0ceAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6JqZ9ALMw1duGhx9yLNro19y9m+W8nedenjtDSwTD33Hl8c6f8uVjx2cvf5Jl49vkYjYs2PHWOcDsBA7i/ktw6MzZ9VG77qolr/ma8Oz668rjd4YR9d2iX8sZNeVJm+ObaV8xJWF7FOLs6vvlxuGR1/80trou2vxiMcU85uqb2CMtk56gWXDI48AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0zUx6AZaXw669oZR/6LXj2eNQt3PTUcPDTxrfHhER7amnl/L5xRvHtAkA+9tWzO8cHt11fXH2ulr8hMcPz84UshERm2vxiHsK2a3F2Y+pxVcVPrCvqo2ORxfz/3Dd8Ow5xdnfLOZjYzG/aUzZiIjZYl5lup9HHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOiamfQCMJVyePSnxvx/OPnFG8c6H4CltLOQ/WRx9upa/I61xfkVc2OcfWcxf1Qt/uPZQvbf12afUJgdERFrBief+7IPliZ//O5fq63ymqNr+VhXyG4pzq78PWJf3VNrZp6cmZ/JzG9k5tcz89Wj29dk5tWZecvo52PHvy4AsBI5jwBM3pCHPHZFxIWttdMi4skR8crMPC0iXhcRn26tnRoRnx79GgBgHJxHACasWx5ba7e31m4YvbwjIjZGxNqIeF5EXDGKXRERzx/XkgDAyuY8AjB5pU+2ysx1EfGEiLguIo5vrd0+etUdEXH8om4GAHAAziMAkzG4PGbmgyPiwxHxmtba9n1f11prEdEO8vsuyMwNmblhLu5d0LIAwMq2GOeRiHuWYFOA6TOoPGbmbOz9h/o9rbUrRzdvzcwTR68/MQ7ypataa5e11ta31tbPxuGLsTMAsAIt1nkk4oilWRhgygz5aqsZEe+MiI2ttUv2edXHIuL80cvnR8RHF389AADnEYDlYMj3eXxqRLwkIm7KzPu/YdzvRcQbIuIDmfmyiLgtIl44nhUBAJxHACatWx5ba1+Ig3/L86cv7joAAPtzHgGYvNJXWwUAAGBlGvK0VaDqgF/r78D2xJ7x7QHACjY35vz2fuSfzRZnLyeV64wofbeYVcX3y7paPNb96uDoK4oP4H/8mF8rLrO1mF9XyK4uzq7aOeb5hw6PPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANA1M+kFYBrtWbVnbLO/v/vesc0GgPGYm/QCC7C6mC9c6/ri6HXF/DnDoz8f367NPqEWj5gt5q8rZLcVZzNfHnkEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACga2bSC8A0eve5/31wduN9e0qzz7v8d0r5U+JLpTwAsK9d48vfXBy9oZi/Znj0L+O3arOvrcUjzinmby1kbynO3lbMcz+PPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANA1M+kFYBr90Xf/zeDsP71tbWn2KR/+UnUdAGDeqsflQv6u4ujYXotffPTg6KXXvrY2+w21eMQ1xfxRheyu4mzmyyOPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdCmPAAAAdM1MegGYSk/fPDh6ZAzPAgDL3Y5C9mPF2dtr8Y88tJBdXZsdXyvm1xbzRxeytxVnzxbzc8X89PLIIwAAAF3d8piZJ2fmZzLzG5n59cx89ej2izJzS2beOPrx7PGvCwCsRM4jAJM35GmruyLiwtbaDZl5VERcn5lXj173l621i8e3HgBARDiPAExctzy21m6PiNtHL+/IzI1Rf9IyAMC8OY8ATF7pcx4zc11EPCEirhvd9KrM/Fpmviszj13k3QAA9uM8AjAZg8tjZj44Ij4cEa9prW2PiEsj4pERcXrs/Z/AvzjI77sgMzdk5oa5uHcRVgYAVqrFOI9E3LNk+wJMk0HlMTNnY+8/1O9prV0ZEdFa29pa291a2xMR74iIMw/0e1trl7XW1rfW1s/G4Yu1NwCwwizWeSTiiKVbGmCKDPlqqxkR74yIja21S/a5/cR9Yi+IiJsXfz0AAOcRgOVgyFdbfWpEvCQibsrMG0e3/V5EnJeZp0dEi4hNEfGKsWwIAOA8AjBxQ77a6hciIg/wqk8s/joAAPtzHgGYvNJXWwUAAGBlGvK0VQAAYNFtKea3F/N3FrKri7PXFfPbivkdhexccTbz5ZFHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAumYmvQAAACxfO5fR7DXF/EmF7Nbi7G1jzm8v5lkKHnkEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgK1trS/fGMr8fEbcd4FXHRcQPlmyRyXGd02elXKvrHJ+Ht9YetsRvE1Y05xHXOWVWynVGrJxrXbbnkSUtjwddInNDa239pPcYN9c5fVbKtbpOYCVYKf8GuM7pslKuM2LlXOtyvk5PWwUAAKBLeQQAAKBruZTHyya9wBJxndNnpVyr6wRWgpXyb4DrnC4r5TojVs61LtvrXBaf8wgAAMDytlweeQQAAGAZm2h5zMxzM/NbmXlrZr5ukruMW2ZuysybMvPGzNww6X0WS2a+KzPvzMyb97ltTWZenZm3jH4+dpI7LoaDXOdFmblldJ/emJnPnuSOiyEzT87Mz2TmNzLz65n56tHtU3WfPsB1Tt19CvStlPPItJ5FIpxHpu1jl/PI8r1PJ/a01cw8LCK+HRHPiIjNEfHViDivtfaNiSw0Zpm5KSLWt9am6nvTZOa/joi7I+JvWmuPG9325xGxrbX2htEH4WNba787yT0X6iDXeVFE3N1au3iSuy2mzDwxIk5srd2QmUdFxPUR8fyIeGlM0X36ANf5wpiy+xR4YCvpPDKtZ5EI55GYso9dziPL9zwyyUcez4yIW1tr32mt3RcR74+I501wH+ahtfa5iNj2Ezc/LyKuGL18Rez9S3BIO8h1Tp3W2u2ttRtGL++IiI0RsTam7D59gOsEVh7nkSngPDJdnEeWr0mWx7UR8b19fr05lvk7a4FaRHwqM6/PzAsmvcyYHd9au3308h0RcfwklxmzV2Xm10ZPIzmknzrxkzJzXUQ8ISKuiym+T3/iOiOm+D4FDmglnUdW0lkkYoo/dh3A1H7sch5ZXvepL5izdM5qrZ0REc+KiFeOnnYw9dre50VP65f0vTQiHhkRp0fE7RHxF5NdZ/Fk5oMj4sMR8ZrW2vZ9XzdN9+kBrnNq71OAWKFnkYjp+th1AFP7sct5ZPndp5Msj1si4uR9fn3S6Lap1FrbMvr5zoi4KvY+TWZabR09h/v+53LfOeF9xqK1trW1tru1tici3hFTcp9m5mzs/QfsPa21K0c3T919eqDrnNb7FHhAK+Y8ssLOIhFT+LHrQKb1Y5fzyPK8TydZHr8aEadm5iMy80ER8eKI+NgE9xmbzDxy9EmwkZlHRsQzI+LmB/5dh7SPRcT5o5fPj4iPTnCXsbn/H6+RF8QU3KeZmRHxzojY2Fq7ZJ9XTdV9erDrnMb7FOhaEeeRFXgWiZiyj10HM40fu5xHlu99OrGvthoRMfqys2+KiMMi4l2ttT+Z2DJjlJk/G3v/hy8iYiYi3jst15qZ74uIsyPiuIjYGhGvj4iPRMQHIuKUiLgtIl7YWjukP7n7INd5dux9OkGLiE0R8Yp9nod/SMrMsyLi8xFxU0TsGd38e7H3+fdTc58+wHWeF1N2nwJ9K+E8Ms1nkQjnkZiyj13OI8v3PDLR8ggAAMChwRfMAQAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoOv/A26A4OPNprMwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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74ivJ/MpENvN33u3/v3seAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaBrq9wLkHTj9d52zO2W/PlBqcpvubj9n11T+0OHhVP7In57aOXvwq/81NXtTKj11DO/xUCr/h5Hcv4FNDzyQygMMvj27R/fOzr42F39N9+iiM3Oj/+6SO3JvcMVxifDZudnzN+byZyfOL/udk5udde+iRPik5PCbc/EHpuXyFz+3e/aSQ1Kj18Ss3C4rftk9O29ObvbyXDwn82+3WwdwzyMAAABNzfJYSrm0lLK2lHLLFpctKqWsLqXcOPrrJb1dEwCYypxHAPqvyz2Pl0XEwq1c/oFa6xGjv74+vmsBAPwHl4XzCEBfNctjrfX7EbFuAnYBANgq5xGA/hvL9zy+oZRy0+jDSB4/bhsBAHTnPAIwQba3PH40Ig6OiCMi4p6IeP+2gqWUs0opS0spSzfGg9v57gAAHmW7ziMRf5yo/QAGynaVx1rrmlrrw7XWTRHxsYg46jGyi2ut82ut84cj9zT9AADbsr3nkYjpE7ckwADZrvJYStl/iz+eGBG3bCsLANALziMAE2uoFSilfDoiFkTE3qWUVRHxzohYUEo5Ijb/NMmVEfG6Hu4IAExxziMA/dcsj7XWU7Zy8cd7sAsAwFY5jwD031iebRUAAIAponnPI5PPptq982+KTbnhtSS36W7/Wb9L5bO7H7bPms7Zf0tNnlp2njunc/bWYy9NzT72ppNS+Rnxi1QeYMezMZlf1j16yZHJ2Tfn4v+YyI7kRsclB+Tyqfn/KTX6wCfdkcqvWnBI9/Bnf5maHdH9c/RmL09kv5ecvWcy/6pkflHn5HmvzT0p55rYN7nL8kT0wOTs1cl8f7nnEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgCblEQAAgKahfi/A1LHXax9I5Zf8YDiVv+iJX+mcfc573pya/WcfuiuVH1l9dyo/mRx6ZffruubhDanZu31wZnKbXyTzAIMu8XF3r+zso1Lpaw9/Zufshpie3CX3+SV3ol2UGr3qNbl8XJg47zx7Tm527vgSEd9LZJ+XnD07mf9GLn7xos7RkXhvavRb3vrh3C7xiUR2SXL2mmR+RjI/vtzzCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQNNQvxcgYue5c1L5Yx/3Lz3apLdGVt+dyr/nhSek8k//3J2ds7ec+qHU7L963vNT+XteOrNz9uHfrkvN/t1pz0nljzlvSSr/jlnXdc4eecWbU7MP/uaPU3kAHml19+jSHs6OiFfGP3fOnhMfTu7y3Fw8dV1fmJt92Sdy+Ti1ezS5Srwimf9sJrwhOfy7ufjef5GK7/O6X3XOXvTKv8nt8tkLcvlYmMjm/h9FTEvm+1vf3PMIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABAk/IIAABA01C/FyDi4RW/TOWvuPeoztkTD/5mavaTjvlVKr/zjBmdsw+vX5+aPXLnylT++md0/1rIsaedk5o986bfpfJl742ds7+86KDU7FuPvSiVX/PwhlT+yCve3Dl78Jt/nJoNwCMN93D2Hcl898/pERH3Hvrkztkly7qfXSIi4to5ufwxH+qenZs7A8S8Y3L5yzLZK3Ozk7dRxFO7R8/OXc+dFv0hld+UO0rFb8oTE+ncWTri5cl893NdxMrk7Kx1PZ7/2NzzCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQJPyCAAAQNNQvxcg74EzZ3TOXvC5eanZX533pVT+3Kuf2zn7k4ufk5q9x90jqXzGb561KZV/1jl3pvLvf8K1nbM7Jb+Gs/jfZ6fyl73vZan8wZf+KJUHYCw2JvPdzwARK5Kz983Fl7+rc/RLr3x7bvb5uXicd0737IUX5GaveGMunzldjyRvo4W5v8fDv/HTztnT4q9Ts9/573+Xyj8wb3kqH7EukZ2bnL06mV+VyM5Kzs7uMpzIZj++tDVPraWUg0op15RSbiul3FpKOXf08pmllKtKKXeM/v74cd8OACCcRwAmgy53eYxExJtqrYdFxLMj4vWllMNi89eErq61HhIRV0f+a0QAAF05jwD0WbM81lrvqbXeMPry/RGxLCIOiIjjI+Ly0djlEXFCr5YEAKY25xGA/kt9s1UpZXZEPCMilkTErFrrPaOvujfyD/AFAEhzHgHoj87lsZSyR0R8LiLOq7Wu3/J1tdYaEXUbb3dWKWVpKWXpxnhwTMsCAFPbeJxHIv44AZsCDJ5O5bGUMhybP1B/stb6+dGL15RS9h99/f4RsXZrb1trXVxrnV9rnT8cu47HzgDAFDRe55GI6ROzMMCA6fJsqyUiPh4Ry2qtWz638Zcj4vTRl0+PiNzPeAAA6Mh5BKD/uvwkmudGxGkRcXMp5cbRy94WEe+OiM+UUl4bEXdFxEm9WREAwHkEoN+a5bHWem1ElG28+gXjuw4AwKM5jwD0X+rZVgEAAJiayuYnJpsYM8rMenTxxcGJNPTk2an88V/7SSp/+oy7Uvle2inxtZBNsamHm+Q87drXpvJz33hfKj+y+u5Unom1pF4d6+u6bd2bAvRAKU+oEWf1e40JMCOZPzSZ/20iuy43+oXnpOL7XPWrztnD4rbcLknfu3Vh9/BIcvhXk/nZieyp2U5wQy6+35G5/L3XJ8LLcrPj/mQ+8++3y3cFbmlDMt8ri6PWu5vnEfc8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0KQ8AgAA0DTU7wXorZE7V6byX1xweCr/odec0Dn7hzkbU7O/tfDCVP4/f+u87uGaGp32lEse6Jyd/dObUrNHsssAMECmJbIbkrNXJvMHJLKzc6O/865U/DflxM7Z7/3twtTs/f7+zlQ+rk1kz86NjrgyFx96VffswpKbverIXP6W9bl8rEhk1yZnZ3fJyJ13dzTueQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKCp1Fon7J3NKDPr0eUFE/b+ACa7JfXqWF/XlX7vAVNJKU+oEWf1e40JMJzMDyXzhyayM3Kjhxbk8mcksnvlRsfyZP7eRHYkObuXblyVfIMbkvllyfy0RHZdcjaPtjhqvbt5HnHPIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE3KIwAAAE1D/V4AAIDJYEMyf0MiO5wbPbIxl79kQffsvOQu83LxuC+RXVmTw7+TzK9OZFcmZ09L5keS+XXJPBPBPY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0KY8AAAA0DfV7AQAAemFjj+cPJ7LTkrPXJPM3dI8un5kbvTz79/iTRHZdcvb6ZL6XNvR7AfrAPY8AAAA0NctjKeWgUso1pZTbSim3llLOHb18USlldSnlxtFfL+n9ugDAVOQ8AtB/XR62OhIRb6q13lBK2TMiri+lXDX6ug/UWt/Xu/UAACLCeQSg75rlsdZ6T0TcM/ry/aWUZRFxQK8XAwD4E+cRgP5Lfc9jKWV2RDwjIpaMXvSGUspNpZRLSymPH+fdAAAexXkEoD86l8dSyh4R8bmIOK/Wuj4iPhoRB0fEEbH5K4Hv38bbnVVKWVpKWboxHhyHlQGAqWo8ziMRf5ywfQEGSafyWEoZjs0fqD9Za/18REStdU2t9eFa66aI+FhEHLW1t621Lq61zq+1zh+OXcdrbwBgihmv80jE9IlbGmCAdHm21RIRH4+IZbXWC7a4fP8tYidGxC3jvx4AgPMIwGTQ5dlWnxsRp0XEzaWUG0cve1tEnFJKOSIiakSsjIjX9WRDAADnEYC+6/Jsq9dGRNnKq74+/usAADya8whA/6WebRUAAICpqcvDVgEA4BE29igbEbE+mb8/kd03OXskmc/snr2e0F/ueQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBJeQQAAKBpqN8LAADA2KzpURbYknseAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaFIeAQAAaCq11ol7Z6X8JiLu2sqr9o6I+yZskf5xPQfPVLmurmfvPKnWus8Ev0+Y0pxHXM8BM1WuZ8TUua6T9jwyoeVxm0uUsrTWOr/fe/Sa6zl4psp1dT2BqWCqfAxwPQfLVLmeEVPnuk7m6+lhqwAAADQpjwAAADRNlvK4uN8LTBDXc/BMlevqegJTwVT5GOB6Dpapcj0jps51nbTXc1J8zyMAAACT22S55xEAAIBJrK/lsZSysJTy81LKilLK+f3cpddKKStLKTeXUm4spSzt9z7jpZRyaSllbSnlli0um1lKuaqUcsfo74/v547jYRvXc1EpZfXobXpjKeUl/dxxPJRSDiqlXFNKua2Ucmsp5dzRywfqNn2M6zlwtynQNlXOI4N6FolwHhm0z13OI5P3Nu3bw1ZLKTtHxO0R8aKIWBURP42IU2qtt/VloR4rpayMiPm11oH62TSllGMj4vcR8X9rrYePXvbeiFhXa3336Cfhx9da39LPPcdqG9dzUUT8vtb6vn7uNp5KKftHxP611htKKXtGxPURcUJEnBEDdJs+xvU8KQbsNgUe21Q6jwzqWSTCeSQG7HOX88jkPY/0857HoyJiRa31zlrrQxFxRUQc38d92A611u9HxLpHXHx8RFw++vLlsfk/wQ5tG9dz4NRa76m13jD68v0RsSwiDogBu00f43oCU4/zyABwHhksziOTVz/L4wER8est/rwqJvlf1hjViPh2KeX6UspZ/V6mx2bVWu8ZffneiJjVz2V67A2llJtGH0ayQz904pFKKbMj4hkRsSQG+DZ9xPWMGODbFNiqqXQemUpnkYgB/ty1FQP7uct5ZHLdpp4wZ+IcU2t9ZkQcFxGvH33YwcCrmx8XPahP6fvRiDg4Io6IiHsi4v39XWf8lFL2iIjPRcR5tdb1W75ukG7TrVzPgb1NAWKKnkUiButz11YM7Ocu55HJd5v2szyujoiDtvjzgaOXDaRa6+rR39dGxBdi88NkBtWa0cdw/+mx3Gv7vE9P1FrX1FofrrVuioiPxYDcpqWU4dj8AeyTtdbPj148cLfp1q7noN6mwGOaMueRKXYWiRjAz11bM6ifu5xHJudt2s/y+NOIOKSUMqeUsktEnBwRX+7jPj1TStl99Jtgo5Sye0S8OCJueey32qF9OSJOH3359Ij4Uh936Zk/ffAadWIMwG1aSikR8fGIWFZrvWCLVw3Ubbqt6zmItynQNCXOI1PwLBIxYJ+7tmUQP3c5j0ze27Rvz7YaETH6tLMXRsTOEXFprfVdfVumh0opT47NX+GLiBiKiE8NynUtpXw6IhZExN4RsSYi3hkRX4yIz0TEEyPirog4qda6Q39z9zau54LY/HCCGhErI+J1WzwOf4dUSjkmIn4QETdHxKbRi98Wmx9/PzC36WNcz1NiwG5ToG0qnEcG+SwS4TwSA/a5y3lk8p5H+loeAQAA2DF4whwAAACalEcAAACalEcAAACalEcAAACalEcAAACalEcAAACalEcAAACalEcAAACa/h+ie5jK/OyxoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swap linear back with softmax\n",
    "model.layers[layer_idx].activation = activations.softmax\n",
    "model = utils.apply_modifications(model)\n",
    "\n",
    "for class_idx in np.arange(10):    \n",
    "    indices = np.where(y_test[:, class_idx] == 1.)[0]\n",
    "    idx = indices[0]\n",
    "    \n",
    "    grads = visualize_saliency(model, layer_idx, filter_indices=class_idx, \n",
    "                               seed_input=x_test[idx], backprop_modifier='guided')\n",
    "\n",
    "    f, ax = plt.subplots(1, 2)\n",
    "    ax[0].imshow(x_test[idx][..., 0])\n",
    "    ax[1].imshow(grads, cmap='jet')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "It does not work as well!\n",
    "\n",
    "The reason is that maximizing an output node can be done by minimizing other outputs. Softmax is weird that way. It is the only activation that depends on other node output(s) in the layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}