FastGradientWhiteBox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast gradient algorithm\n",
    "\n",
    "This is a simple whitebox proof of concept implementation of Ian Goodfellows Fast Gradient algorithm.\n",
    "\n",
    "White box because I have access to the learned model hence the true gradients. For MNIST it's not too hard to train a substitute model and get it working. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data   \n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import ImageClassifier\n",
    "from jack import Jack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "Tensor(\"gradients_1/MatMul_grad/MatMul:0\", shape=(?, 784), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# Import data as tensors                                                                                                                               \n",
    "mnist = input_data.read_data_sets(\"./MNIST_data/\",                                                                                                     \n",
    "                                  one_hot=True)                                                                                                        \n",
    "\n",
    "# Train a classifier                                                                                                                                   \n",
    "data = mnist.train.images                                                                                                                              \n",
    "labels = mnist.train.labels                                                                                                                            \n",
    "im = ImageClassifier( data,                                                                                                                            \n",
    "                      labels,                                                                                                                          \n",
    "                      len(data[0]),                                                                                                                    \n",
    "                      10)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 training_accuracy: 0.44685454545454545\n",
      "\n",
      "500 training_accuracy: 0.8934909090909091\n",
      "\n",
      "1000 training_accuracy: 0.9081818181818182\n",
      "\n",
      "1500 training_accuracy: 0.9158\n",
      "\n",
      "2000 training_accuracy: 0.9186363636363636\n",
      "\n",
      "2500 training_accuracy: 0.9199818181818182\n",
      "\n",
      "3000 training_accuracy: 0.9233636363636364\n",
      "\n",
      "3500 training_accuracy: 0.9248909090909091\n",
      "\n",
      "4000 training_accuracy: 0.925\n",
      "\n",
      "4500 training_accuracy: 0.9275454545454546\n",
      "\n",
      "Training finished\n"
     ]
    }
   ],
   "source": [
    "im.train(epochs=5000)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation error\n",
    "\n",
    "Note the model can be improved to get better accuracy but that's not the point of this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on validation data: 0.9248\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_truth =  [np.argmax(i) for i in mnist.test.labels]                                                                                                   \n",
    "y_pred =  [np.argmax(i) for i in im.oracle(mnist.test.images[:])]                                                                                      \n",
    "print('Accuracy on validation data: {}\\n'.format(accuracy_score(y_truth, y_pred)))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "j  = Jack()                                                                                                                                            \n",
    "fake_labels = np.zeros((mnist.test.labels.shape))                                                                                                      \n",
    "for i,p in enumerate(y_pred):                                                                                                                          \n",
    "    fake_labels[i, p] = 1                                                                                                                              \n",
    "\n",
    "# these our the bad ones                                                                                                                               \n",
    "pirates = j.turn_em_into_a_pirate(mnist.test.images,                                                                                                   \n",
    "                                  fake_labels,                                                                                                         \n",
    "                                  im,                                                                                                                  \n",
    "                                  eps=0.04,                                                                                                            \n",
    "                                  num_test_images = 'all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noiseless accuracy: 0.9248 Jacked Accuracy: 0.235\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_bad   = [np.argmax(i) for i in im.oracle(pirates)]                                                                                                   \n",
    "\n",
    "s = 'Noiseless accuracy: {} Jacked Accuracy: {}\\n'\n",
    "print(s.format(accuracy_score(y_truth, y_pred),                                                                                  \n",
    "               accuracy_score(y_truth, y_bad)))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing 10 random validation examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, axarr = plt.subplots(10, 2,figsize=(20,20))\n",
    "for i in range(10):\n",
    "    index = np.random.randint(len(pirates))\n",
    "    pixels = np.array(mnist.test.images[index])\n",
    "    pixels = pixels.reshape((28, 28))\n",
    "    axarr[i,0].imshow(pixels, cmap='gray')\n",
    "    axarr[i,0].set_title('Predicted Label {}'.format(y_pred[index]))\n",
    "    \n",
    "    pixels = np.array(pirates[index])\n",
    "    pixels = pixels.reshape((28, 28)) \n",
    "    axarr[i,1].set_title('Predicted Label {}'.format(y_bad[index]))\n",
    "    axarr[i,1].imshow(pixels, cmap='gray')\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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