3.5 SmoothGrad.ipynb

{
 "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": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221372c5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22135df61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x221372c5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213af83e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213b5d7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "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": 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DVvGcRN9de9ZxLHOyP4EhSZIkSZJGzxcYkiRJkiRp9HyBIUmSJEmSRs8XGJIk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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": 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VK9IaRSjScdIYIL3WKvpMWnPoeKiXaQ6oXasoJvOuu+5Ka3R/qa9qnrXGuq/o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\nyNuA+wx6wlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x221450d2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pjc7XRRddVFWj+Yp6jvqK5qte94DUV3RsKO6W7qtr7wFf/OIXpzXqjzvuuCOt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\nV19R71CsHcWS0fbQdZOix6i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      "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
}