{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 SmoothGrad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from models.models_3_5 import MNIST_CNN\n",
    "from utils import pixel_range\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "images = mnist.train.images\n",
    "labels = mnist.train.labels\n",
    "\n",
    "logdir = './tf_logs/3_5_SG/'\n",
    "ckptdir = logdir + 'model'\n",
    "\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Building Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Classifier'):\n",
    "\n",
    "    # Initialize neural network\n",
    "    DNN = MNIST_CNN('CNN')\n",
    "\n",
    "    # Setup training process\n",
    "    X = tf.placeholder(tf.float32, [None, 784], name='X')\n",
    "    Y = tf.placeholder(tf.float32, [None, 10], name='Y')\n",
    "    training = tf.placeholder_with_default(False, shape=[], name='training')\n",
    "    activations = DNN(X, training)\n",
    "    \n",
    "    tf.add_to_collection('placeholders', training)\n",
    "    tf.add_to_collection('placeholders', X)\n",
    "\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=activations[-1], labels=Y))\n",
    "\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost, var_list=DNN.vars)\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(activations[-1], 1), tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    ind = tf.placeholder(tf.int32, shape=[], name='index')\n",
    "    \n",
    "    tf.add_to_collection('placeholders', ind)\n",
    "    \n",
    "    # Generate Vanilla Grad\n",
    "    grad = tf.gradients(activations[-1][:, ind], X)[0]\n",
    "    \n",
    "    # Generate SmoothGrad\n",
    "    sample_size = tf.placeholder(tf.int32, shape=[], name='sample_size')\n",
    "    sigma = tf.placeholder(tf.float32, shape=[], name='sigma')\n",
    "    \n",
    "    X_input1 = tf.tile(X, multiples=[sample_size, 1]) + tf.random_normal([sample_size, 784], stddev=sigma)\n",
    "    X_output1 = DNN(X_input1, training, reuse=True)\n",
    "    \n",
    "    smoothgrad = tf.reduce_mean(tf.gradients(X_output1[-1][:, ind], X_input1)[0], axis=0)\n",
    "    \n",
    "    tf.add_to_collection('placeholders', sample_size)\n",
    "    tf.add_to_collection('placeholders', sigma)\n",
    "    \n",
    "    # Generate Integrated Gradients\n",
    "    steps = tf.placeholder(tf.int32, shape=[], name='steps')\n",
    "        \n",
    "    X_input2 = tf.tile(X, multiples=[steps, 1]) * tf.reshape(tf.linspace(0., 1., num=steps + 1)[1:], [steps, 1])\n",
    "    X_output2 = DNN(X_input2, training, reuse=True)\n",
    "    \n",
    "    intgrad = X * tf.reduce_mean(tf.gradients(X_output2[-1][:, ind], X_input2)[0], axis=0, keep_dims=True)\n",
    "    \n",
    "    tf.add_to_collection('placeholders', steps)\n",
    "    \n",
    "    tf.add_to_collection('grads', grad)\n",
    "    tf.add_to_collection('grads', smoothgrad)\n",
    "    tf.add_to_collection('grads', intgrad)\n",
    "    \n",
    "cost_summary = tf.summary.scalar('Cost', cost)\n",
    "accuray_summary = tf.summary.scalar('Accuracy', accuracy)\n",
    "summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.311481388 accuracy = 0.906363638\n",
      "Epoch: 0002 cost = 0.105582403 accuracy = 0.968581827\n",
      "Epoch: 0003 cost = 0.079347551 accuracy = 0.975690918\n",
      "Epoch: 0004 cost = 0.065745663 accuracy = 0.980454556\n",
      "Epoch: 0005 cost = 0.055103825 accuracy = 0.982563647\n",
      "Epoch: 0006 cost = 0.048535820 accuracy = 0.984890919\n",
      "Epoch: 0007 cost = 0.041984907 accuracy = 0.986527282\n",
      "Epoch: 0008 cost = 0.039468893 accuracy = 0.987745464\n",
      "Epoch: 0009 cost = 0.035014743 accuracy = 0.988545463\n",
      "Epoch: 0010 cost = 0.031578903 accuracy = 0.990200009\n",
      "Epoch: 0011 cost = 0.029123417 accuracy = 0.990618190\n",
      "Epoch: 0012 cost = 0.027011044 accuracy = 0.991254553\n",
      "Epoch: 0013 cost = 0.026021425 accuracy = 0.991727280\n",
      "Epoch: 0014 cost = 0.024496022 accuracy = 0.992218189\n",
      "Epoch: 0015 cost = 0.021346025 accuracy = 0.992581825\n",
      "Accuracy: 0.9835\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "\n",
    "# Hyper parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "    avg_cost = 0\n",
    "    avg_acc = 0\n",
    "    \n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        mean = np.mean(batch_xs, axis=0, keepdims=True)\n",
    "        batch_xs = batch_xs - mean\n",
    "        \n",
    "        _, c, a, summary_str = sess.run([optimizer, cost, accuracy, summary], feed_dict={X: batch_xs, Y: batch_ys, training: True})\n",
    "        avg_cost += c / total_batch\n",
    "        avg_acc += a / total_batch\n",
    "        \n",
    "        file_writer.add_summary(summary_str, epoch * total_batch + i)\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'accuracy =', '{:.9f}'.format(avg_acc))\n",
    "    \n",
    "    saver.save(sess, ckptdir)\n",
    "\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Restoring Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./tf_logs/3_5_SG/model\n"
     ]
    }
   ],
   "source": [
    "batch, _ = mnist.train.next_batch(100)\n",
    "mean = np.mean(batch, axis=0, keepdims=True)\n",
    "sample_imgs = [images[np.argmax(labels, axis=1) == i][1] - mean for i in range(10)]\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "new_saver = tf.train.import_meta_graph(ckptdir + '.meta')\n",
    "new_saver.restore(sess, tf.train.latest_checkpoint(logdir))\n",
    "\n",
    "placeholders = tf.get_collection('placeholders')\n",
    "training = placeholders[0]\n",
    "X = placeholders[1]\n",
    "\n",
    "ind = placeholders[2]\n",
    "\n",
    "sample_size = placeholders[3]\n",
    "sigma = placeholders[4]\n",
    "steps = placeholders[5]\n",
    "\n",
    "grads = tf.get_collection('grads')\n",
    "\n",
    "grad = grads[0]\n",
    "smoothgrad = grads[1]\n",
    "intgrad = grads[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Displaying Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8NjAAAAAAAAAhff/ADZR0Zwtg64BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221372c5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22135df61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+U3XWd3/HXZ+78TG4mk8nvMAlJCDGmCNkQIGIWqSCi\nRygrrk11a91dW8+pboutbqn116rrdmlPay3HHtf6Y5G6dk+X7foDsV1/LigrSFkNIgghQAwh5Mdk\nMskk8+vbP+4gAfJ+zeQ7cyefufN8nDOHkNd8f9x7v5/v93s/uTOvVBSFAAAAAAAActZ0pncAAAAA\nAABgPExgAAAAAACA7DGBAQAAAAAAsscEBgAAAAAAyB4TGAAAAAAAIHtMYAAAAAAAgOwxgTGDpJSK\nlNK6SSz/vpTSf5/q7wVmMsYVMPUYV8DUY1wBU49xNfMwgSEppbQrpXTlBL/3uymlt7/g71JK6V0p\npZ+klI6llPaOfd/2+uxxuF/HU0pHUkp9KaUfp5RuTCm1Pfs9RVF8vCiKt7v1nOp7U0qrxwZ38zj7\n8O6xx96XUvrcydvG7MO4erHTHVcppfNSSt9MKe1PKRWTf0SY6RhXL1ZiXP2TsW32pZR2p5RuGu/6\nhsbGuHqxEuNqe0rpoZTS4ZTSvpTSn6aUOif/yDBTMa5erMz7q5P25Vun8/2NjAmMqfFJSTdI+teS\nFko6S9L7JV19qm8eG5D1eO7fVRTFPEnLx/Zlu6TbU0qpDtt6npTSayTdKOkKSWdLWivpD+q9XTS0\nWT+uJA1J+nNJvzsN28LswLiS5qj2HCySdIlq1633TMN20bgYV9Jdkl5RFMV81e4BmyV9bBq2i8bF\nuBqTUnqLpJbp2l72iqKY9V+Sdkm6cuzPb5N0p6T/KOmQpMckvXYs+0NJI5KOS+qXdLOk9WN/t2Wc\nbXx3bPm7JA1IWifptyU9KOmIpJ2S3vGCZd4r6SlJeyT9jqRC0jqz/re/4O9WSTom6fVj//9hSbee\nlL9V0uOSDkj6wAueh199r6QnxrbdP/b18lNs/0uSPn7S/18hae+Zfm35OnNfjKvJj6uT1rlOUnGm\nX1O+zvwX42rqxtVJ6/5Xkr56pl9bvs7cF+NqaseVpKqkWyTdfqZfW77O3BfjamrGlaT5kh6WtHXs\n+5vP9Gt7pr/4BMapXSLpIdX+deYmSZ9NKaWiKP6dpL9RbSauWhTFuyS9StKTRVHcO4H1/mNJ/0zS\nPNUO7H2SXi+pU7XB9p9TSpslKaV0tWr/IvRqSedKmtBHsE5WFMUTku6V9OsvzFJKGyV9StJbVJtR\nnK/azOapXDb2366xx/3DU3zP35P0dyf9/99JWppSWni6+42Gxbh6vomMK2A8jKvnKzOuLpP0wOnu\nMxoa4+r5JjSuUkrbUkqHVXvjeL2kT5zuPqOhMa6eb6LXq49L+m+S9p7uvjYqJjBO7fGiKD5TFMWI\npD9V7QBcGnzvIr3ggBr7mdresZ+ZOvuk6AtFUTxQFMVwURRDRVF8vSiKR4ua70n6P3puMLxJ0ueL\nothRFMVR1WbsytgjqfsUf/9G1f7F6c6iKAYlfVC1Wb2yqpIOn/T/z/553iTWicbCuAKmHuNqElJK\nvyNpi2r/Kgg8i3FVwti65kvqkfQfVPuXZ+BZjKvTlFLaIukVkv5r2XU0IiYwTu1XA6YoimNjf6wG\n33tAtQH4K0VR9Kg28NoknfzzUU+e/H0ppdemlO5OKR1MKfVKet3YcpK04gXf//jpPogxZ0k6eIq/\nf976xx7ngZLbkGoffTr5lzU9++cjk1gnGgvjCph6jKuSUkrXSfoj1T7GvH+y60NDYVxNQlEUv5R0\nh6QvT8X60DAYV6dh7Pd5fErSvyyKYrjMOhoVExin74WzaN+W1DM2QzbhZcd+e+1fqPavPkuLouiS\ndLueG5BPSVp50rKrTndHU0orJV2o2seyXugp1WbIn/3eDtV+QY7db+MBSRec9P8XSHq6KArevGEi\nGFfA1GNcxdu7WtJnJF1TFMVPT29vMcsxriamWdI5JZbD7MS4erFO1T4h+D9TSnsl3TP297tTSi/6\n8ZXZhAmM0/e0ar9dWZJUFMVDkj4t6csppVenlDpSShVJl46znlbVZhCfkTScUnqtpKtOyv9c0ttS\nShtTSnMkfWiiO5hSmpNSeqWkv5L0I9UG7gv9L0nXpJQuTSm1qvYRqui36T4jaVQnPe5TuEXS747t\nb5dqvyX4CxPdZ8x6jKtTbzOllNrHHpdSSu2JemJMHOPq1Nt8laT/Ien6oih+NNF9BcYwrk69zbek\nlFaN/fls1X6x4rcmus+Y9RhXL3ZYtU90bBr7et3Y318o6W8nut+NiAmM0/dfJL0xpXQopfTJsb97\np2pVP/9JtY8T7Zb0UUn/ULXfMPsiRVEckfQvVBtIhyS9WdJXTsq/odovP/q2pEfG/juem1NKR1Q7\nCXxCtRnIq4uiGD3F9h+Q9HuqfbzvKdV+BGSfpBOn+N5jGvsNv2M/e7b1FN9zh2q/kOc7Y4/5cZ3G\nSQGzHuPqFONKtUriAT33CwYHVPsFWMBEMK5OPa4+oNovVrs9pdQ/9vWNCewzIDGuonG1UdIPUkpH\nVWuEeEjSP53APgMS4+pF42rsd3jsffZLtQkPqfYJ98EJ7HfDSkXBp5ghpZSqknolnVsUxWNnen+A\nRsC4AqYe4wqYeowrYOoxruqDT2DMYimla8Y+DjVXtZ8V+6n4jdHApDCugKnHuAKmHuMKmHqMq/pj\nAmN2+weq1QDtUa0LeXvBR3KAyWJcAVOPcQVMPcYVMPUYV3XGj5AAAAAAAIDs8QkMAAAAAACQvebT\n+eZFixYVq1evrtOuADPLrl27tH///qgaacIYV8BzpmJcMaaA53CtAqYe4wqYehMdV6c1gbF69Wrd\ne8895fcKaCBbLrpoStbDuAKeMxXjijEFPIdrFTD1GFfA1JvouOJHSAAAAAAAQPaYwAAAAAAAANlj\nAgMAAAAAAGSPCQwAAAAAAJA9JjAAAAAAAED2mMAAAAAAAADZYwIDAAAAAABkjwkMAAAAAACQPSYw\nAAAAAABA9pjAAAAAAAAA2WMCAwAAAAAAZK/5TO8ATq1QmvZtJhXTvk2gEZQdr4w5NLLJXMcYG8Cp\njTeu3Nipx70lYxXAdOMTGAAAAAAAIHtMYAAAAAAAgOwxgQEAAAAAALLHBAYAAAAAAMgeExgAAAAA\nACB7TGAAAAAAAIDsUaM6Bc5E5akzPFx2yfhxNJmpLpeV3xNquWaDrOpHR0fLL6pKydXGj7+luVwV\nHmNndqvHmHLrdMd3pWmcY3FwMIyGmtrCrNncuXD8ox5yOueOt72y54CyYzmn5waYUcejG3T1eHPV\nQHh2AAAAAABA9pjAAAAAAAAA2WMCAwAAAAAAZI8JDAAAAAAAkD0mMAAAAAAAQPaYwAAAAAAAANmb\ndTWq012vY2upRofCbEgtYeYq5CSptzfO+vrizK134cI46+jw+xMZt2KvhBlVn4Tyr0nZytPjx+Ns\nnIH12J641nHNsoEwG22OB8h4Yzky3ccy42oWMGMqmXHTPzw3zOZX/Th1VaktD+2IF5w3L85WrQqj\nrGqbkZ3S5znTXX+oP76XW9A5EmYjppq7sv/peF8kpZ/9LA7XrYvX29MTL2frHuN9dbiuzCyf/0L8\nei1aFC93zXmPhdnIqjVhVlE8PlwFd3L1oyYrmuOxOs4mdexYnFWrbnfisTPdnzCYaWOOT2AAAAAA\nAIDsMYEBAAAAAACyxwQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsteQNar1qGay\n6xyNq3727YsrcnbsiCt7XJvVkiVxJkk/+UmcbdwYZ8uXmefGdbNW2uPM1RmZDsnCVAu513Cm1QBN\nxnRXkNVle2XrUPv748x1kx48GGednXaTixfHlY+6+eY4u+G9YZT2mTo810vmxlUdzKZxNSO4ylN3\nbOzfH2clx8b8pngsnmhfGq9T49QIn3tunJnOupHR+Dw1zcMGDcRe/3bvDrMFR4/GK/36/WFUcd31\nK1fGmSStWBFnhw/HmalLHlkbj8f+vniVra3x89baGi/X1ETF6nim+x7wvvvizJ1br9n1tTCrXHxx\nvOCWLXG2d2+cuXs5814muZ5USW1dXWE2NG95mO3cGa9z1644W78+zrq746zdvCVzY26m1YxzOQcA\nAAAAANljAgMAAAAAAGSPCQwAAAAAAJA9JjAAAAAAAED2mMAAAAAAAADZYwIDAAAAAABkL+sa1emu\ndHH1a05/f1z5effd8XKbN8eZawFyrVzjLevqdbRvX5yZ2so7nzonzLZdOBCv01QWJd+vFWezSF0q\ngctW1JatQzW1bXadrvKxWi23nOsullS9///F4apVdtmQq4M1lV22C8s9b4ydmcO9jiY70Btfjxaa\n+lF7/M+ZE0b9R+PzyYgZ3pI0v9VcH1zHqskq9/84zIrNF4ZZOm72xV44kZt61Ev2marQ+W7BoaE4\nu+CCOHM3cq5iWyp/vJo62MpgPD6amjpMFm/OZVSljm+6n6OPfCTOXFWobjb9q5s2xZmrNXXvV9x9\n3vBwnLmxKtnq1urF8Xh9SWd88tjRH9evulsAN8RtPXkD4W4WAAAAAABkjwkMAAAAAACQPSYwAAAA\nAABA9pjAAAAAAAAA2WMCAwAAAAAAZI8JDAAAAAAAkL0zXrZStiq17Dpd7VBFI2HWPxBX0+3ZE+/L\nb1x+KA4ffjjOVl0cRguG98fLSerZ3B2Hrpdnl+kJe/rpMNr2irVh9uk/ieu13vH6X8bbc1WYpl6s\nHvVpM1HZcVX6+Stbo1q278ltzxyrdrneXr/Nu+6KM1N52iJTzeX2x1Sl2tfJddNRsVpa2bFRdiyO\nKr7mDI+aqtTRZ+KVmu61X+yZG2auYfWQucS5lmRJGu6Orw+muVXDagmzeWYclz6HuTo/dw5jTJ0R\nZa/1brn2djOOlyyJM3NteHpwQZh1m9u4lsGjcSjpcH98fnBNystNHfjQcPz4q6bScTbddzW6BV3x\na3nh+aae9Jxz4uzw4TB6dE98fTjn05+O17lsWZy56uLVq+NMkg4cCKMjo/H1c95wXOt6fc/fxttb\nG78P1BNPxNmKFXFmPrdQNMXnjRxxdQUAAAAAANljAgMAAAAAAGSPCQwAAAAAAJA9JjAAAAAAAED2\nmMAAAAAAAADZYwIDAAAAAABkb1pqVOtRlVqPaiZXITM3bsjRS5t/EYe7TY/cli1xtmNHnLlKN8lW\n5dmqONeV993vxtmnPhVG77jooni5OW+Ls2FTyYS6KT2uytahutfZdTC65ZYujTNXa7hrV5xJ0he/\nGGc33hhnbl9d5aur9GqOaySnuyq1kaqL61E/XJZ7GW1rsaltdON0nWmCTKNxxfiCBfF1szJOK1vH\naFwHOdIaX3TtJXBgIM7Knm9cdbe5b5hpx3+91KOCuB7LOW2tZrlW0/lrxtzSg4+aLZarkpd8Vao7\nPbiq1P7+cussfS/nrlUlr2ONdK3Kjquh/+xn4+yDHwyj7leY7V1zTZxt2BBn+/fHmXsPJEkbN4bR\nvGZz3Xn44TAqXnVFmKXBE/E6D8bVrLZG1oydmTYG+AQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA\n7DGBAQAAAAAAsscEBgAAAAAAyB4TGAAAAAAAIHvTUqPqlK3Cqkdt3dG40U3VZx6Lw29+M86uvz7O\nXBfcEtNpVzX1WpKvg9uzxy8b2b49zm69Nc6++tUwOvHP3x1mbYNH4nWaWq5kKssarUKrHhVz015b\n5yrWXFdkd3cYHWhaHGYLq6aW6o474kzytV3nnhtnrtbYLWcefxoeCrORprhitVLyNZwtpvs84J7z\nss3EhanYdVWI8/qfCrOBruVh1tERr9M1mkqS2lrDqNIUvxbt7eZYddXEtn/WqEP98GwyU66vA8fj\n46qjKb52nFBbmLW52kZ3XLXGY8P2pEpafnxvHD4SnwROrH9ZmC2oxtcc7dodRoe61oSZrV+tg5ly\nHNabvV8zldnOkfb4vmveli3xgrfcEkbt2387Xu7qq+PM3XPddlucjWfbtji74YZSq0yXXhqHu+Nx\n5e6B3c1DI9V+c1UGAAAAAADZYwIDAAAAAABkjwkMAAAAAACQPSYwAAAAAABA9pjAAAAAAAAA2WMC\nAwAAAAAAZG9aalTLVrOUXa5sRVBbW1wvo76+OLvggnhflsX1c6n3ULxOV5XqarkkX6Pqaitdx56r\nX/3a1+Js3bowslWpTskqvJlWETSeeoyrelQXnxiMl4vL5+Rr5H72szBa2GXGR/eGONu0ye2NtNdU\n073kJWFkz0duPJZUkatBm/o560YbV9PJPnemZrvFnQOb4wq5ecf2ldpeR5+pJjXHcHW8+tGnTR3k\nnDlh1Lwkvq6qszPO3P64WvM6aLRa75zY58+MnfZ2dw8Y31e1Vc2t9CFznzdkqkl7e+PMjI1x17sh\nvga27XwozI6siK9x81atCrMFvQfifRmNe1Rd3SNqcjqHHD4cZ/OuvTYOv/3tMLK3R4Pm2uHu1cx7\nEp1/vtmg/Hsdd7968cVhNKC4h7zDdam7c4A5x6UGqgRvnEcCAAAAAAAaFhMYAAAAAAAge0xgAAAA\nAACA7DGBAQAAAAAAsscEBgAAAAAAyB4TGAAAAAAAIHvTUqNatuqnbB2qLXs0VaHHm+aHWcv998fr\n3Lo13pefPxgv1xVXSMnV57iKVcnXqLbHFXu2Ys7tj1tu7do4e+tb4+y22+KsDtWTGF/ZserapY70\nxxVSbjlbv3osrtf645vi/fw3q01VsiQ98UQYfe8HLWH2Sn0vXqcbO26cuyfHjXFXv0ltXemqYKf8\nNS5+rYr2eNyk3U9ObMdeyF033HHjzsdunZK/ruwzla+uRnXz5jAqmuNxmty+GFSejq8e46rs9lx1\ndXLVjD//eZxt3BhGJ849L8zcsOoYjHspdx+J71Ulqecsc0x+4hNxtnhxGM37zdXxcoPuuhJf44ZG\nzTXHNUVPyzuXWcrdk5gDtmf+kXg5d09y1VVhlNxp4wc/iLMVK8LoxHkXhlmzOeYkqbJ+fRyuWRNn\nCxaEkWvv7nCV4O4ecJbgExgAAAAAACB7TGAAAAAAAIDsMYEBAAAAAACyxwQGAAAAAADIHhMYAAAA\nAAAge0xgAAAAAACA7E1LGVFWNWOmzseW0ixZEmeu7tBUOrpKV1tl5CqJJMlV77hlDx6MM9NbNdI+\nN8wql10Wr/OOO+LMVegtWhRnqJuyVXiuKs41QbnltGxZGA31xHVWN8SNdtLNu00oW5W3bZtZ7tZd\ncebOHa6e0o1jtxxVkVbZytN6bM+9jqn3ULycGzhls7I12uNdq1zNqqkZ37EjXuz8888JM/t8u8dR\n0nTXh+Zq2sfV8FAcuvPjzp1xtmtXnJljdeisl4RZtWMkXuf+uNa758t/Ei8n+euKuXbqZS8LowP9\ncXn5wj0/jde5dm0YtTSZx28UiutXZ8u1Spr+x1qYqvXkzuU//GGczY3fP1SvG4iXc3XZK84Ks3//\nkXiVW7fGmSS9Zu4zcXhWvM0Ti+Js/mBcP1t0L/Q7NMVKV76foTHHJzAAAAAAAED2mMAAAAAAAADZ\nYwIDAAAAAABkjwkMAAAAAACQPSYwAAAAAABA9pjAAAAAAAAA2ZuWGtWyylazjJiKpUprPGfT9qbf\njFe621Qsfu5zcfbEE3Hm6kAnU4XoqlvnzIkzV6M3ENcZuV2tvPnNcbh+fRjtHloaZj3DJ+J1ul5O\nSCpfW+fG48hovM7BwXidHa1xjdpT++JxfKAlrkrV03HU86Sp83rPe+JMkr7whTCq7PxFvNxCU4W1\nND7ObcWkG6um0s8uZyrScqzQmm6lH6ftA67Dcm7A7dkTZz09cWZO8iOr4rFY6TN1r5KvdDTXsQva\nzTVg1Fwf3XPjKl/tuInvKdKoqYmsQ23rTFSP88eQWsKs73icVTdcEGZ37Iqzzea2aqWrSr311jjr\n7Y2z7u44k6Tt28PoW3fHtZVXzH8yzBZ+/y/j7V15ZZyVvSdz42qWXHPqpezzZ89nnZ1xZup5ddNN\ncTZitvfOd4bRX/91vNj73x9n9j5Okvo64qy/P4za7vxWmD244oowW7063px7GzjeW8TITLvP4woK\nAAAAAACyxwQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsscEBgAAAAAAyF7WNaqW\nqXWrjMZVac/0xzU4i13l5/e/H2cbNsTZvn1x5ipWXRWiW6fkq+lcdZuru1oTV+XZlixXrbRxYxi5\ndknm3cZXtiq17DpLtgzasFqNF5u379E4dN1TX/xunF18cZxJ0tVXx9k998SZG8sdppbLDSxX+eie\n8JLVjTlWaDWEsq+Vq9jt64uzVavizB2npn7bXY66uxfEoaQ2DYVZ0Tk/zFLf4Xil7ppjntMDx+Kx\n2GmGoruJmvqzMH7FXHRaFGfDw3GNqmvZdYeVa6539Yo6//w4278/zjZtMhuUvbe8YoMbH+ZodtWt\nbl8XLYozc15B/ZSuyyz7/sHV7Lr3QR/+cJx95zth9Gp38/idi+LMVd5L/nG4e7Lxao8D5m1uaWVv\nOXKsWOWdIAAAAAAAyB4TGAAAAAAAIHtMYAAAAAAAgOwxgQEAAAAAALLHBAYAAAAAAMgeExgAAAAA\nACB7TGAAAAAAAIDsuQrzvJkOcFeeu3NnvNhi11d9OO6dt/24vb3xOt32enribLzubNfHvGdPnK1e\nHWc/+UkYpa1b4+VcsfAtt4RRi3sMmzfHGcblOpvLdj23mDPJicF4nerrC6Pm1vnxcseOhdGIKmFW\nmcRxNbJoabze+WZfK/H+6NFH42xpvD0NDsaZ6yM3khurKK1oil//NDpSbqVdXaUWG+iMj6mOdtPl\nvm9fGC0fPB4v11/1O2QeRxo8EWaHFY+3TrM5d4wv7I4f/9BwufMi6sjdA5rXeengk/Fyx+J7x+Hh\nNWG2YkW8yqJqrg2bfi2M0vBQvM7mlnidktLevXF4001xdvXV5bLdu+PMXHOHRuNzo7salb1UMVYn\noOS4knuvY3zj4g+F2cZdcXb2zu/EK20x42PlyjjrdFcP6cn+BWHmbsnOWXckzJrMWzLztlPulrPZ\n3I+XfXlzNMN2FwAAAAAAzEZMYAAAAAAAgOwxgQEAAAAAALLHBAYAAAAAAMgeExgAAAAAACB7TGAA\nAAAAAIDsTUuNatlqRruc6XsZap8XZpesPxRm+vhX4uyii8LI1dJUNmyIQ1MFqeOmmm68rhuXL1tW\nbn9M5at9nVxV7JIlcTYwEEYjo/H23EOnQmty3OvsuEond5x3uLpgc1yZQ0dV13W1alWcSTp4MM4W\nn3deHJpaZzsGXFVm2b6rmdaTNc3KXqscW5Xqjg0zcAZG28Ksw1TBddz+F/H23PHmar3dOd5dU8Zj\nqltNg5x00NQIm9pmVePK15a1a+PlSr6G9oTibirc852putwDlt0Zdw40x8Cru+4JsxOt8f1h2vPL\nCe3Wi5gq1DTOtUruvnP79jj76EfjzNWhXnZFmLUMxxfkllbuySajHtcrNz7s9ty9VX9/GL229Vvx\ncj2Xh9GR7r8fZmYY2+flcJ8/q7hb0kceibOurvg9qRvKZsiV5i4tMw13swAAAAAAIHtMYAAAAAAA\ngOwxgQEAAAAAALLHBAYAAAAAAMgeExgAAAAAACB7TGAAAAAAAIDsTUuNal3qK03Vj2vJGqouCLOW\nr5ga1SvimqhKk3l87abSze2oq1hzFauS5CrfenvjbMWKODNVcbaWaHhumFXe+wdhVp0br9PNulGV\nWlO68rF0pV283PBwvFzFjI8Toy1h1rxkeZhVf/A3YVbc+G/D7K67wkiStK3pQBz2x3WRRc/KMHN1\n0APD8eN39VrufFS2CtdppDFXl8fiesvMC+leq44Pfyhe57p1pbanTZvifeleGGbp4YfidY5X92hq\nTYfWvdQvG2h54tE4vP/+OLvttjjbsyfOvvSlOHPX6rvvjjP3Gl5+eZxlqh7XI3v/5Kpty1ZJm/uq\ntuOH4+X2748zt5+O7SaXra1Ud3ecfeYzcXbffWHU0myuOc0dYdZI145GUfY+b2TR0jAb7Y6z5g3x\ned5VkM8bPhRmheL3ea4q1b3tkqQ1q+PH39wcr/fBB+N1bjv7yTg0Y3WoNX5v5bhTR9n7/zOFT2AA\nAAAAAIDsMYEBAAAAAACyxwQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsjctNapl\na3nKcjWCleHBeMGNG0tlQ6YmssVV6M2ZE2ddXXE2aB7DeLnr0DHLnWifH2Ztj/wizHpbzw2znp54\nVzA50155ZI7ztiYzBkytb1t7XEG4eyiu5epZvz7M3HBcHjez1pgaOSeZKqwT1bie0s0u2/Y9U+dV\ntkFwtqjLtWq8ysNoe//7L8ut09Vsmqrs/oFKmFX3PxOv0x1UrrZV0qHWeBzv3xUvd+6Ko2FWrD0n\nzEZXx1nl2mvjDb7znXFmzjf6yEfibPv2OHM1qjNQ2XFllyt7MnPLVatxdiyuyrbj0dXTu3tAU8F7\npDmuiZSkeQcfj8MdO8Jo5Lrrw6xy7M54nQ8/HEbJPX73WrjnBnVjq1JHy91XuvsVN3RGFF+TKuY9\nkmuvLrsvkqQHHgijX++MK18frV4Qr7O9Pc7MOafFXVtL3nM4OVYeczsLAAAAAACyxwQGAAAAAADI\nHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsscEBgAAAAAAyN601KiWrV8pXdviuhJddt11cdbX\nF0amCVKLXS2XMdS1uNT2JGnx6NNxaDqE+itxVWp1dCDMnumKq1LvM81brgVo2bI61K5lWAM0GfWo\nSi39HLluqrK9VeYAWWxahnXlG8Oo8rGPhdmKi19pVippznlx1t8fZ+YxtvXF9ZQD1fgc4LhTHDWq\nXl2uOeb4f+pgW5gtf/3r43VedVUYnWieG2b33xuv8pKuh+LQXHSObLwkzOb1H47XKampKb7muCZx\nd25wLeJ+Cz9LAAAJ3klEQVRtw3H9qr0gffGLYfT4H30pzFxVuBuLjXatcupxHbvznnhctbTE1b2X\nrHoqXqm7jrmKVXdc7d4dZ+aaMm+Rud6Mt803vCGMKv1H4uX27ImzrVvjzB3oXJAmZbrfW1Wayt17\nu6ZQty9NTebcYMZHh7nn6jh+MMwWVn1178Ci+B6w48fxm51zdsaV6P2v/o0wa2mJ96WtKa5ttRro\nBnFm7S0AAAAAAJiVmMAAAAAAAADZYwIDAAAAAABkjwkMAAAAAACQPSYwAAAAAABA9pjAAAAAAAAA\n2ZuWGtWyfCWm4apgXL3UqlVx9qW4Km3xnM/Gy73udXG2f38YtZharsVuPyXpYPyyFmvPCbOqqTM6\nMdgRZu7p3rAhzpYvK1fJ5Mym+rmydbJO6efd1aG6zFQ6FV0Lwqxtx0/jdV56aZxt3hxGHU+YGknJ\n10+5GtU5ppqrszOMyrbPuaebCuI6KfmCuGZGV93teunamuN6tbPOqsTrXL4uzszjm/fAjni5Eyfi\nTNL89evDrFg0L8z6j8b9ctURX90aci+GGd9nr+iOl2vK+hZr2tTjWuWqS7d9Pa7L1u//fpw9sS/O\nzLFaukZ89eo4c/eq4xgajp/Tlj4zPsz16MGr3x1m61bEq2zpO1Bqe5hZ0vBQHdZpxo4bHyXP5dq7\n1+7Pz5suDLNfu+iieME744rV6g//b7zclVeavYmvyY/ujMf/qlXxPcAjD8dbW7kyzjrit4e2eney\n+AQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsscEBgAAAAAAyB4TGAAAAAAAIHuN\n2fE1OBhnrmPQVCzq6NE4a2uLs66uOOvtjTNXoXfvvXEmSVu2hFEa9LV2kT174se4ZnVck7PQNMxN\ne9XnLKqCLFtb55pCXVPk0GhczeSWq5jx6Gq5Pn/vy8LsDe/74zA7GDcX6+FdL4lDSVddFWfpa1+N\nQ1e/Z6rA2lrzOV6pWPXsOakpHhsrq4fi5fpM3ZupHzzcH2/vrLPiVWrYDP7jx+NsQVx3bAe/JB08\nGEbJ1GRWXTWx21e3nNvXbnMhc/cUBmOqpvRjdTWKl11WLtthKoE/+ckwGnj774VZx+4H43W6e8Ce\nnjgbR4sbA+4xLlsWRis2vjze3qC5P65W46zk2EH9lL2/HlZcbV32vrK5PV5n6jXXTneedzszzvHY\nYlrI/+y2+D3Stm1XhNnKzrjW+LFd8Wtx663xvrzvfXFWOR6P1Zfu/VG84DFTebxpU5zV8XMSfAID\nAAAAAABkjwkMAAAAAACQPSYwAAAAAABA9pjAAAAAAAAA2WMCAwAAAAAAZI8JDAAAAAAAkL2Z22E0\nXj1bxFXomGo6vTyukNKiRXH2pjfF2Y03xpmrpRkejjPJVrAOzZkfZi3DA2G2pstUFvXHh9FQ+7x4\ne83l6tOon6sfN6zc0CmbNS1bHmauufi3fivOTPuibVh0y0m+1tVWMLu6P1fbVbZ7rA4YV+W5l1Gd\ncQVpU1ecuddjftOReHvD7XHmKh1dNflkjtN2sz+uftHtj1vO7Y+r3sOMMnLVa8OssmRJvOAHPhBn\nX/lKGHUcjqsQ9ZrXxJm7l9tvOr9XrIgzyd/LXnttGA0Mxj2R7W4oN5lrnBlzZSs7y+I6VlP2ebfX\nspLL2br4spWnbly5a844NarnLYrfB61cGV+vTVu4ve6u6YwfxwduiMfc0Gj8vqvintPLLw+jHN93\n8QkMAAAAAACQPSYwAAAAAABA9pjAAAAAAAAA2WMCAwAAAAAAZI8JDAAAAAAAkD0mMAAAAAAAQPay\nrlG11SyuDs1lJWurBqqLw6zjjjvidX7ve3F2331x5qpu1q6NM0nauzeMHlFco9rU1BFm69fHmXud\nWupQr0MV1uS4589VJZVt7nSNh888E2dDprV09eo4m3/7n8XZ1q1htuG6NfFKJR042BJmXcvOCjP3\nvKXRkTikKnXGcM9dU1O5yrqy4zS5mjjHHW+uCri7u9w6J6GYMzfM7JhyGG8zi6k8rLhj4Pzzw2jo\nr24PM1sF2WQuVmbBorUtzNzx0X/Un1OqbeX2p8N2pcYKxdfGemDsTE7Z569iDg+XNTfHx+uJwTgb\nHY1rfTtKXuf6F8f3edW5/nlx111TXGxbjXWwXO33yJy4KrWlydw7VOPl3HGR45jjExgAAAAAACB7\nTGAAAAAAAIDsMYEBAAAAAACyxwQGAAAAAADIHhMYAAAAAAAge0xgAAAAAACA7GVdo1qWrZgz1Vuu\nDq6j90C83LZtcfaqV8XZ7t1x5upex7N+fRhtcJWOw656q2RtLWaUslVJra3xmGtrjdfZ3m7qtU7E\n2/vlL+Ps6OZ/FGarlsXLdYzz2Ds7432tmNoqqw5jx53/nBxrshpBPZ7XurxWVVPnZq6bI4qr7s7I\nlaHkmLL3DWUrbRlTZ4Y7BkzWYu+BzD1Za1zpODJqaiLNKluGj4dZde44FZLufs0uZ7piS46rsmMH\njcNVW7e5d6HueBwcjLPjZuzMmWO2Z+rCJXu0uuugU3E15EbZa2sjXZN45wkAAAAAALLHBAYAAAAA\nAMgeExgAAAAAACB7TGAAAAAAAIDsMYEBAAAAAACyxwQGAAAAAADIXkPWqJauiXE1USWrbqzVq0st\nNl71VOnHb6rycqq7aqQaoEZR9jVpaY6XazFnp7lzTTXd1DfBSbLDoy5yGnOYQab5PF6ZQedjxhQs\nd5IveQGoKK6QrDS7uldf6WhlVG3PmIM9HsvesLWbKuFWM3ZKVixL4xzL5mGUXiesfM5yAAAAAAAA\nASYwAAAAAABA9pjAAAAAAAAA2WMCAwAAAAAAZI8JDAAAAAAAkD0mMAAAAAAAQPYaska1LFdn42oi\np3u5eilb50OtKXLimrAmc6xO93HOuEKk7LWj7PWorLpVfpdUj+0xThtHXe7lylaa1qkKtVHuV9Eg\n6nGcm3XWq7a07MNg7JTHJzAAAAAAAED2mMAAAAAAAADZYwIDAAAAAABkjwkMAAAAAACQPSYwAAAA\nAABA9pjAAAAAAAAA2aNG9SRl62xyW64eNUFU/SAnHI+YzWZKHSjXKswk030vVy/1qDzN7TECZXAc\nNw4+gQEAAAAAALLHBAYAAAAAAMgeExgAAAAAACB7TGAAAAAAAIDsMYEBAAAAAACyxwQGAAAAAADI\nXiqKiVfKpJSekfR4/XYHmFHOLopi8WRXwrgCnmfS44oxBTwP1ypg6jGugKk3oXF1WhMYAAAAAAAA\nZwI/QgIAAAAAALLHBAYAAAAAAMgeExgAAAAAACB7TGAAAAAAAIDsMYEBAAAAAACyxwQGAAAAAADI\nHhMYAAAAAAAge0xgAAAAAACA7DGBAQAAAAAAsvf/AUvTcOZch++BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221372c5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213af83e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213b5d7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213b7f7630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res11 = [sess.run(grad, feed_dict={X: sample_imgs[i].reshape(1, 784), ind: i})[0] for i in range(10)]\n",
    "res12 = [sess.run(intgrad, feed_dict={X: sample_imgs[i].reshape(1, 784), steps: 50, ind: i})[0] for i in range(10)]\n",
    "res13 = [sess.run(smoothgrad, feed_dict={X: sample_imgs[i].reshape(1, 784), sample_size: 100, sigma: 0.2, ind: i}) for i in range(10)]\n",
    "\n",
    "for i in range(2):\n",
    "    plt.figure(figsize=(15,15))\n",
    "    for j in range(5):\n",
    "        plt.subplot(2, 5, i * 5 + j + 1)\n",
    "        vmin, vmax = pixel_range(res11[i * 5 + j])\n",
    "        plt.imshow(res11[i * 5 + j].reshape(28,28), vmin=vmin, vmax=vmax, cmap='bwr')\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title('Grad Digit {}'.format(i * 5 + j))\n",
    "    plt.tight_layout()\n",
    "\n",
    "for i in range(2):\n",
    "    plt.figure(figsize=(15,15))\n",
    "    for j in range(5):\n",
    "        plt.subplot(2, 5, i * 5 + j + 1)\n",
    "        vmin, vmax = pixel_range(res12[i * 5 + j])\n",
    "        plt.imshow(res12[i * 5 + j].reshape(28,28), vmin=vmin, vmax=vmax, cmap='bwr')\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title('IntGrad Digit {}'.format(i * 5 + j))\n",
    "    plt.tight_layout()\n",
    "\n",
    "for i in range(2):\n",
    "    plt.figure(figsize=(15,15))\n",
    "    for j in range(5):\n",
    "        plt.subplot(2, 5, i * 5 + j + 1)\n",
    "        vmin, vmax = pixel_range(res13[i * 5 + j])\n",
    "        plt.imshow(res13[i * 5 + j].reshape(28,28), vmin=vmin, vmax=vmax, cmap='bwr')\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title('SmoothGrad Digit {}'.format(i * 5 + j))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADDCAYAAAB03eGyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0uj5AkOSJEmSJI3e/w9aGVAcKSk99QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22144d3bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas = np.linspace(0., 0.5, num=6)\n",
    "res2 = [sess.run(smoothgrad, feed_dict={X: sample_imgs[0].reshape(1, 784), sample_size: 50, sigma: std, ind: 0}) for std in sigmas]\n",
    "\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "for i in range(6):\n",
    "    plt.subplot(1, 6, i + 1)\n",
    "    plt.imshow(res2[i].reshape(28,28), cmap='gray')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.title(r'SmoothGrad $\\sigma$ {:.1f}'.format(sigmas[i]))\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyNuA+wx6wlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221450d2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADlCAYAAAClKAeZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221445b5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_sizes = np.linspace(10, 100, num=10, dtype=np.int32)\n",
    "res3 = [sess.run(smoothgrad, feed_dict={X: sample_imgs[0].reshape(1, 784), sample_size: size, sigma: 0.1, ind: 0}) for size in sample_sizes]\n",
    "\n",
    "for i in range(2):\n",
    "    fig = plt.figure(figsize=(15,15))\n",
    "    for j in range(5):\n",
    "        plt.subplot(2, 5, i * 5 + j + 1)\n",
    "        plt.imshow(res3[i * 5 + j].reshape(28,28), cmap='gray')\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.title(r'SmoothGrad N {}'.format(sample_sizes[i * 5 + j]))\n",
    "    plt.tight_layout()\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}