MNIST_FGSM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p style=\"font-size:18px; font-family:open sans; line-height:1.2\"> This notebook implements Fast Gradient Sign Method for a model trained on MNIST dataset. The training code used in this notebook is stolen from <a href=\"https://github.com/pytorch/examples/blob/master/mnist/main.py\">here</a>. This notebook doesn't contain explanation of theory behind FGSM, if you are interested in learning more check out <a href=\"https://github.com/savan77/Generating-Adversarial-Examples-with-PyTorch/blob/master/imagenet_adv_examples.ipynb\">this notebook</a>. It explains theory behind methods and also implements more method (for InceptionV3 model) than this notebook does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "import time\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.376790\n",
      "Train Epoch: 1 [640/60000 (1%)]\tLoss: 2.332813\n",
      "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 2.291239\n",
      "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 2.253216\n",
      "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 2.234785\n",
      "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 2.259442\n",
      "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 2.200626\n",
      "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 2.116516\n",
      "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 2.170818\n",
      "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 2.011620\n",
      "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 2.004478\n",
      "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 1.710951\n",
      "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 1.747870\n",
      "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 1.662681\n",
      "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 1.776260\n",
      "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 1.577364\n",
      "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 1.334061\n",
      "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 1.284160\n",
      "Train Epoch: 1 [11520/60000 (19%)]\tLoss: 1.349092\n",
      "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 1.152377\n",
      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.205587\n",
      "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 1.018645\n",
      "Train Epoch: 1 [14080/60000 (23%)]\tLoss: 1.231543\n",
      "Train Epoch: 1 [14720/60000 (25%)]\tLoss: 1.185257\n",
      "Train Epoch: 1 [15360/60000 (26%)]\tLoss: 1.147123\n",
      "Train Epoch: 1 [16000/60000 (27%)]\tLoss: 0.926886\n",
      "Train Epoch: 1 [16640/60000 (28%)]\tLoss: 0.952069\n",
      "Train Epoch: 1 [17280/60000 (29%)]\tLoss: 0.909334\n",
      "Train Epoch: 1 [17920/60000 (30%)]\tLoss: 1.069009\n",
      "Train Epoch: 1 [18560/60000 (31%)]\tLoss: 0.989111\n",
      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.931227\n",
      "Train Epoch: 1 [19840/60000 (33%)]\tLoss: 0.822332\n",
      "Train Epoch: 1 [20480/60000 (34%)]\tLoss: 0.857684\n",
      "Train Epoch: 1 [21120/60000 (35%)]\tLoss: 1.113984\n",
      "Train Epoch: 1 [21760/60000 (36%)]\tLoss: 0.863950\n",
      "Train Epoch: 1 [22400/60000 (37%)]\tLoss: 0.704552\n",
      "Train Epoch: 1 [23040/60000 (38%)]\tLoss: 0.946398\n",
      "Train Epoch: 1 [23680/60000 (39%)]\tLoss: 0.874025\n",
      "Train Epoch: 1 [24320/60000 (41%)]\tLoss: 0.735640\n",
      "Train Epoch: 1 [24960/60000 (42%)]\tLoss: 0.806181\n",
      "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.563367\n",
      "Train Epoch: 1 [26240/60000 (44%)]\tLoss: 0.848015\n",
      "Train Epoch: 1 [26880/60000 (45%)]\tLoss: 0.606730\n",
      "Train Epoch: 1 [27520/60000 (46%)]\tLoss: 0.382378\n",
      "Train Epoch: 1 [28160/60000 (47%)]\tLoss: 0.514897\n",
      "Train Epoch: 1 [28800/60000 (48%)]\tLoss: 0.495568\n",
      "Train Epoch: 1 [29440/60000 (49%)]\tLoss: 0.577093\n",
      "Train Epoch: 1 [30080/60000 (50%)]\tLoss: 0.722613\n",
      "Train Epoch: 1 [30720/60000 (51%)]\tLoss: 0.712091\n",
      "Train Epoch: 1 [31360/60000 (52%)]\tLoss: 0.692558\n",
      "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.660168\n",
      "Train Epoch: 1 [32640/60000 (54%)]\tLoss: 0.825409\n",
      "Train Epoch: 1 [33280/60000 (55%)]\tLoss: 0.384114\n",
      "Train Epoch: 1 [33920/60000 (57%)]\tLoss: 0.451732\n",
      "Train Epoch: 1 [34560/60000 (58%)]\tLoss: 0.559828\n",
      "Train Epoch: 1 [35200/60000 (59%)]\tLoss: 0.611323\n",
      "Train Epoch: 1 [35840/60000 (60%)]\tLoss: 0.532419\n",
      "Train Epoch: 1 [36480/60000 (61%)]\tLoss: 0.625243\n",
      "Train Epoch: 1 [37120/60000 (62%)]\tLoss: 0.550626\n",
      "Train Epoch: 1 [37760/60000 (63%)]\tLoss: 0.805513\n",
      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.491314\n",
      "Train Epoch: 1 [39040/60000 (65%)]\tLoss: 0.612490\n",
      "Train Epoch: 1 [39680/60000 (66%)]\tLoss: 0.760379\n",
      "Train Epoch: 1 [40320/60000 (67%)]\tLoss: 0.813759\n",
      "Train Epoch: 1 [40960/60000 (68%)]\tLoss: 0.705234\n",
      "Train Epoch: 1 [41600/60000 (69%)]\tLoss: 0.656801\n",
      "Train Epoch: 1 [42240/60000 (70%)]\tLoss: 0.512482\n",
      "Train Epoch: 1 [42880/60000 (71%)]\tLoss: 0.563536\n",
      "Train Epoch: 1 [43520/60000 (72%)]\tLoss: 0.496124\n",
      "Train Epoch: 1 [44160/60000 (74%)]\tLoss: 0.499271\n",
      "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.367745\n",
      "Train Epoch: 1 [45440/60000 (76%)]\tLoss: 0.577974\n",
      "Train Epoch: 1 [46080/60000 (77%)]\tLoss: 0.488236\n",
      "Train Epoch: 1 [46720/60000 (78%)]\tLoss: 0.398504\n",
      "Train Epoch: 1 [47360/60000 (79%)]\tLoss: 0.460203\n",
      "Train Epoch: 1 [48000/60000 (80%)]\tLoss: 0.500309\n",
      "Train Epoch: 1 [48640/60000 (81%)]\tLoss: 0.423499\n",
      "Train Epoch: 1 [49280/60000 (82%)]\tLoss: 0.514392\n",
      "Train Epoch: 1 [49920/60000 (83%)]\tLoss: 0.477168\n",
      "Train Epoch: 1 [50560/60000 (84%)]\tLoss: 0.464684\n",
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.529450\n",
      "Train Epoch: 1 [51840/60000 (86%)]\tLoss: 0.560002\n",
      "Train Epoch: 1 [52480/60000 (87%)]\tLoss: 0.332195\n",
      "Train Epoch: 1 [53120/60000 (88%)]\tLoss: 0.442446\n",
      "Train Epoch: 1 [53760/60000 (90%)]\tLoss: 0.482279\n",
      "Train Epoch: 1 [54400/60000 (91%)]\tLoss: 0.396614\n",
      "Train Epoch: 1 [55040/60000 (92%)]\tLoss: 0.397818\n",
      "Train Epoch: 1 [55680/60000 (93%)]\tLoss: 0.664012\n",
      "Train Epoch: 1 [56320/60000 (94%)]\tLoss: 0.451413\n",
      "Train Epoch: 1 [56960/60000 (95%)]\tLoss: 0.351381\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.554864\n",
      "Train Epoch: 1 [58240/60000 (97%)]\tLoss: 0.326841\n",
      "Train Epoch: 1 [58880/60000 (98%)]\tLoss: 0.338434\n",
      "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 0.523333\n",
      "\n",
      "Test set: Average loss: 0.2073, Accuracy: 9415/10000 (94%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.352124\n",
      "Train Epoch: 2 [640/60000 (1%)]\tLoss: 0.380363\n",
      "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 0.330867\n",
      "Train Epoch: 2 [1920/60000 (3%)]\tLoss: 0.537239\n",
      "Train Epoch: 2 [2560/60000 (4%)]\tLoss: 0.546924\n",
      "Train Epoch: 2 [3200/60000 (5%)]\tLoss: 0.459155\n",
      "Train Epoch: 2 [3840/60000 (6%)]\tLoss: 0.458669\n",
      "Train Epoch: 2 [4480/60000 (7%)]\tLoss: 0.350018\n",
      "Train Epoch: 2 [5120/60000 (9%)]\tLoss: 0.459143\n",
      "Train Epoch: 2 [5760/60000 (10%)]\tLoss: 0.420208\n",
      "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.588757\n",
      "Train Epoch: 2 [7040/60000 (12%)]\tLoss: 0.542246\n",
      "Train Epoch: 2 [7680/60000 (13%)]\tLoss: 0.264597\n",
      "Train Epoch: 2 [8320/60000 (14%)]\tLoss: 0.315211\n",
      "Train Epoch: 2 [8960/60000 (15%)]\tLoss: 0.384492\n",
      "Train Epoch: 2 [9600/60000 (16%)]\tLoss: 0.220960\n",
      "Train Epoch: 2 [10240/60000 (17%)]\tLoss: 0.568242\n",
      "Train Epoch: 2 [10880/60000 (18%)]\tLoss: 0.371072\n",
      "Train Epoch: 2 [11520/60000 (19%)]\tLoss: 0.353560\n",
      "Train Epoch: 2 [12160/60000 (20%)]\tLoss: 0.390051\n",
      "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.330369\n",
      "Train Epoch: 2 [13440/60000 (22%)]\tLoss: 0.501616\n",
      "Train Epoch: 2 [14080/60000 (23%)]\tLoss: 0.553508\n",
      "Train Epoch: 2 [14720/60000 (25%)]\tLoss: 0.459438\n",
      "Train Epoch: 2 [15360/60000 (26%)]\tLoss: 0.313298\n",
      "Train Epoch: 2 [16000/60000 (27%)]\tLoss: 0.679654\n",
      "Train Epoch: 2 [16640/60000 (28%)]\tLoss: 0.375587\n",
      "Train Epoch: 2 [17280/60000 (29%)]\tLoss: 0.216638\n",
      "Train Epoch: 2 [17920/60000 (30%)]\tLoss: 0.495008\n",
      "Train Epoch: 2 [18560/60000 (31%)]\tLoss: 0.231883\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.406362\n",
      "Train Epoch: 2 [19840/60000 (33%)]\tLoss: 0.453344\n",
      "Train Epoch: 2 [20480/60000 (34%)]\tLoss: 0.579164\n",
      "Train Epoch: 2 [21120/60000 (35%)]\tLoss: 0.671943\n",
      "Train Epoch: 2 [21760/60000 (36%)]\tLoss: 0.333227\n",
      "Train Epoch: 2 [22400/60000 (37%)]\tLoss: 0.402928\n",
      "Train Epoch: 2 [23040/60000 (38%)]\tLoss: 0.369218\n",
      "Train Epoch: 2 [23680/60000 (39%)]\tLoss: 0.456606\n",
      "Train Epoch: 2 [24320/60000 (41%)]\tLoss: 0.308453\n",
      "Train Epoch: 2 [24960/60000 (42%)]\tLoss: 0.403220\n",
      "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.426515\n",
      "Train Epoch: 2 [26240/60000 (44%)]\tLoss: 0.368748\n",
      "Train Epoch: 2 [26880/60000 (45%)]\tLoss: 0.434428\n",
      "Train Epoch: 2 [27520/60000 (46%)]\tLoss: 0.532540\n",
      "Train Epoch: 2 [28160/60000 (47%)]\tLoss: 0.343038\n",
      "Train Epoch: 2 [28800/60000 (48%)]\tLoss: 0.267682\n",
      "Train Epoch: 2 [29440/60000 (49%)]\tLoss: 0.295215\n",
      "Train Epoch: 2 [30080/60000 (50%)]\tLoss: 0.157972\n",
      "Train Epoch: 2 [30720/60000 (51%)]\tLoss: 0.454924\n",
      "Train Epoch: 2 [31360/60000 (52%)]\tLoss: 0.646204\n",
      "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.360033\n",
      "Train Epoch: 2 [32640/60000 (54%)]\tLoss: 0.438065\n",
      "Train Epoch: 2 [33280/60000 (55%)]\tLoss: 0.529820\n",
      "Train Epoch: 2 [33920/60000 (57%)]\tLoss: 0.261689\n",
      "Train Epoch: 2 [34560/60000 (58%)]\tLoss: 0.343070\n",
      "Train Epoch: 2 [35200/60000 (59%)]\tLoss: 0.380157\n",
      "Train Epoch: 2 [35840/60000 (60%)]\tLoss: 0.397844\n",
      "Train Epoch: 2 [36480/60000 (61%)]\tLoss: 0.150455\n",
      "Train Epoch: 2 [37120/60000 (62%)]\tLoss: 0.312010\n",
      "Train Epoch: 2 [37760/60000 (63%)]\tLoss: 0.381476\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.328816\n",
      "Train Epoch: 2 [39040/60000 (65%)]\tLoss: 0.243471\n",
      "Train Epoch: 2 [39680/60000 (66%)]\tLoss: 0.636076\n",
      "Train Epoch: 2 [40320/60000 (67%)]\tLoss: 0.487139\n",
      "Train Epoch: 2 [40960/60000 (68%)]\tLoss: 0.396382\n",
      "Train Epoch: 2 [41600/60000 (69%)]\tLoss: 0.317635\n",
      "Train Epoch: 2 [42240/60000 (70%)]\tLoss: 0.180155\n",
      "Train Epoch: 2 [42880/60000 (71%)]\tLoss: 0.390760\n",
      "Train Epoch: 2 [43520/60000 (72%)]\tLoss: 0.293068\n",
      "Train Epoch: 2 [44160/60000 (74%)]\tLoss: 0.227615\n",
      "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.248996\n",
      "Train Epoch: 2 [45440/60000 (76%)]\tLoss: 0.479650\n",
      "Train Epoch: 2 [46080/60000 (77%)]\tLoss: 0.650463\n",
      "Train Epoch: 2 [46720/60000 (78%)]\tLoss: 0.431784\n",
      "Train Epoch: 2 [47360/60000 (79%)]\tLoss: 0.401087\n",
      "Train Epoch: 2 [48000/60000 (80%)]\tLoss: 0.334853\n",
      "Train Epoch: 2 [48640/60000 (81%)]\tLoss: 0.228124\n",
      "Train Epoch: 2 [49280/60000 (82%)]\tLoss: 0.593514\n",
      "Train Epoch: 2 [49920/60000 (83%)]\tLoss: 0.326461\n",
      "Train Epoch: 2 [50560/60000 (84%)]\tLoss: 0.360971\n",
      "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.350738\n",
      "Train Epoch: 2 [51840/60000 (86%)]\tLoss: 0.492064\n",
      "Train Epoch: 2 [52480/60000 (87%)]\tLoss: 0.634274\n",
      "Train Epoch: 2 [53120/60000 (88%)]\tLoss: 0.403459\n",
      "Train Epoch: 2 [53760/60000 (90%)]\tLoss: 0.280616\n",
      "Train Epoch: 2 [54400/60000 (91%)]\tLoss: 0.369416\n",
      "Train Epoch: 2 [55040/60000 (92%)]\tLoss: 0.182426\n",
      "Train Epoch: 2 [55680/60000 (93%)]\tLoss: 0.237569\n",
      "Train Epoch: 2 [56320/60000 (94%)]\tLoss: 0.308702\n",
      "Train Epoch: 2 [56960/60000 (95%)]\tLoss: 0.208908\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.286444\n",
      "Train Epoch: 2 [58240/60000 (97%)]\tLoss: 0.359211\n",
      "Train Epoch: 2 [58880/60000 (98%)]\tLoss: 0.224668\n",
      "Train Epoch: 2 [59520/60000 (99%)]\tLoss: 0.343041\n",
      "\n",
      "Test set: Average loss: 0.1250, Accuracy: 9630/10000 (96%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.382087\n",
      "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.319850\n",
      "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.312397\n",
      "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.216360\n",
      "Train Epoch: 3 [2560/60000 (4%)]\tLoss: 0.305858\n",
      "Train Epoch: 3 [3200/60000 (5%)]\tLoss: 0.357177\n",
      "Train Epoch: 3 [3840/60000 (6%)]\tLoss: 0.222775\n",
      "Train Epoch: 3 [4480/60000 (7%)]\tLoss: 0.468773\n",
      "Train Epoch: 3 [5120/60000 (9%)]\tLoss: 0.271451\n",
      "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 0.201192\n",
      "Train Epoch: 3 [6400/60000 (11%)]\tLoss: 0.358536\n",
      "Train Epoch: 3 [7040/60000 (12%)]\tLoss: 0.320114\n",
      "Train Epoch: 3 [7680/60000 (13%)]\tLoss: 0.399320\n",
      "Train Epoch: 3 [8320/60000 (14%)]\tLoss: 0.325702\n",
      "Train Epoch: 3 [8960/60000 (15%)]\tLoss: 0.423290\n",
      "Train Epoch: 3 [9600/60000 (16%)]\tLoss: 0.273978\n",
      "Train Epoch: 3 [10240/60000 (17%)]\tLoss: 0.411916\n",
      "Train Epoch: 3 [10880/60000 (18%)]\tLoss: 0.245840\n",
      "Train Epoch: 3 [11520/60000 (19%)]\tLoss: 0.296069\n",
      "Train Epoch: 3 [12160/60000 (20%)]\tLoss: 0.282632\n",
      "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.331531\n",
      "Train Epoch: 3 [13440/60000 (22%)]\tLoss: 0.230537\n",
      "Train Epoch: 3 [14080/60000 (23%)]\tLoss: 0.451917\n",
      "Train Epoch: 3 [14720/60000 (25%)]\tLoss: 0.327143\n",
      "Train Epoch: 3 [15360/60000 (26%)]\tLoss: 0.212665\n",
      "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 0.349504\n",
      "Train Epoch: 3 [16640/60000 (28%)]\tLoss: 0.284467\n",
      "Train Epoch: 3 [17280/60000 (29%)]\tLoss: 0.310345\n",
      "Train Epoch: 3 [17920/60000 (30%)]\tLoss: 0.259339\n",
      "Train Epoch: 3 [18560/60000 (31%)]\tLoss: 0.334626\n",
      "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 0.163811\n",
      "Train Epoch: 3 [19840/60000 (33%)]\tLoss: 0.541000\n",
      "Train Epoch: 3 [20480/60000 (34%)]\tLoss: 0.164488\n",
      "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 0.215362\n",
      "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 0.186992\n",
      "Train Epoch: 3 [22400/60000 (37%)]\tLoss: 0.199298\n",
      "Train Epoch: 3 [23040/60000 (38%)]\tLoss: 0.417437\n",
      "Train Epoch: 3 [23680/60000 (39%)]\tLoss: 0.294832\n",
      "Train Epoch: 3 [24320/60000 (41%)]\tLoss: 0.303149\n",
      "Train Epoch: 3 [24960/60000 (42%)]\tLoss: 0.226240\n",
      "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.351958\n",
      "Train Epoch: 3 [26240/60000 (44%)]\tLoss: 0.449810\n",
      "Train Epoch: 3 [26880/60000 (45%)]\tLoss: 0.209623\n",
      "Train Epoch: 3 [27520/60000 (46%)]\tLoss: 0.292020\n",
      "Train Epoch: 3 [28160/60000 (47%)]\tLoss: 0.248416\n",
      "Train Epoch: 3 [28800/60000 (48%)]\tLoss: 0.316189\n",
      "Train Epoch: 3 [29440/60000 (49%)]\tLoss: 0.209072\n",
      "Train Epoch: 3 [30080/60000 (50%)]\tLoss: 0.346147\n",
      "Train Epoch: 3 [30720/60000 (51%)]\tLoss: 0.466185\n",
      "Train Epoch: 3 [31360/60000 (52%)]\tLoss: 0.394810\n",
      "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.293007\n",
      "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 0.583324\n",
      "Train Epoch: 3 [33280/60000 (55%)]\tLoss: 0.192600\n",
      "Train Epoch: 3 [33920/60000 (57%)]\tLoss: 0.295758\n",
      "Train Epoch: 3 [34560/60000 (58%)]\tLoss: 0.180289\n",
      "Train Epoch: 3 [35200/60000 (59%)]\tLoss: 0.397054\n",
      "Train Epoch: 3 [35840/60000 (60%)]\tLoss: 0.198664\n",
      "Train Epoch: 3 [36480/60000 (61%)]\tLoss: 0.330254\n",
      "Train Epoch: 3 [37120/60000 (62%)]\tLoss: 0.252834\n",
      "Train Epoch: 3 [37760/60000 (63%)]\tLoss: 0.270154\n",
      "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.184230\n",
      "Train Epoch: 3 [39040/60000 (65%)]\tLoss: 0.257958\n",
      "Train Epoch: 3 [39680/60000 (66%)]\tLoss: 0.460053\n",
      "Train Epoch: 3 [40320/60000 (67%)]\tLoss: 0.358473\n",
      "Train Epoch: 3 [40960/60000 (68%)]\tLoss: 0.281817\n",
      "Train Epoch: 3 [41600/60000 (69%)]\tLoss: 0.316654\n",
      "Train Epoch: 3 [42240/60000 (70%)]\tLoss: 0.127859\n",
      "Train Epoch: 3 [42880/60000 (71%)]\tLoss: 0.180179\n",
      "Train Epoch: 3 [43520/60000 (72%)]\tLoss: 0.505143\n",
      "Train Epoch: 3 [44160/60000 (74%)]\tLoss: 0.157226\n",
      "Train Epoch: 3 [44800/60000 (75%)]\tLoss: 0.176454\n",
      "Train Epoch: 3 [45440/60000 (76%)]\tLoss: 0.220890\n",
      "Train Epoch: 3 [46080/60000 (77%)]\tLoss: 0.348368\n",
      "Train Epoch: 3 [46720/60000 (78%)]\tLoss: 0.217436\n",
      "Train Epoch: 3 [47360/60000 (79%)]\tLoss: 0.497661\n",
      "Train Epoch: 3 [48000/60000 (80%)]\tLoss: 0.498749\n",
      "Train Epoch: 3 [48640/60000 (81%)]\tLoss: 0.337711\n",
      "Train Epoch: 3 [49280/60000 (82%)]\tLoss: 0.220375\n",
      "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.285104\n",
      "Train Epoch: 3 [50560/60000 (84%)]\tLoss: 0.412290\n",
      "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.233588\n",
      "Train Epoch: 3 [51840/60000 (86%)]\tLoss: 0.204738\n",
      "Train Epoch: 3 [52480/60000 (87%)]\tLoss: 0.506157\n",
      "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.200474\n",
      "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.260876\n",
      "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.371105\n",
      "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.290646\n",
      "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.398131\n",
      "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.201903\n",
      "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.197734\n",
      "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.364096\n",
      "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.390480\n",
      "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.319921\n",
      "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.329393\n",
      "\n",
      "Test set: Average loss: 0.0985, Accuracy: 9701/10000 (97%)\n",
      "\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.071231\n",
      "Train Epoch: 4 [640/60000 (1%)]\tLoss: 0.304365\n",
      "Train Epoch: 4 [1280/60000 (2%)]\tLoss: 0.287382\n",
      "Train Epoch: 4 [1920/60000 (3%)]\tLoss: 0.345392\n",
      "Train Epoch: 4 [2560/60000 (4%)]\tLoss: 0.279897\n",
      "Train Epoch: 4 [3200/60000 (5%)]\tLoss: 0.411393\n",
      "Train Epoch: 4 [3840/60000 (6%)]\tLoss: 0.228896\n",
      "Train Epoch: 4 [4480/60000 (7%)]\tLoss: 0.193187\n",
      "Train Epoch: 4 [5120/60000 (9%)]\tLoss: 0.126894\n",
      "Train Epoch: 4 [5760/60000 (10%)]\tLoss: 0.200111\n",
      "Train Epoch: 4 [6400/60000 (11%)]\tLoss: 0.308842\n",
      "Train Epoch: 4 [7040/60000 (12%)]\tLoss: 0.267422\n",
      "Train Epoch: 4 [7680/60000 (13%)]\tLoss: 0.405211\n",
      "Train Epoch: 4 [8320/60000 (14%)]\tLoss: 0.314685\n",
      "Train Epoch: 4 [8960/60000 (15%)]\tLoss: 0.265230\n",
      "Train Epoch: 4 [9600/60000 (16%)]\tLoss: 0.230458\n",
      "Train Epoch: 4 [10240/60000 (17%)]\tLoss: 0.266624\n",
      "Train Epoch: 4 [10880/60000 (18%)]\tLoss: 0.190997\n",
      "Train Epoch: 4 [11520/60000 (19%)]\tLoss: 0.200474\n",
      "Train Epoch: 4 [12160/60000 (20%)]\tLoss: 0.402210\n",
      "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.151036\n",
      "Train Epoch: 4 [13440/60000 (22%)]\tLoss: 0.149493\n",
      "Train Epoch: 4 [14080/60000 (23%)]\tLoss: 0.147434\n",
      "Train Epoch: 4 [14720/60000 (25%)]\tLoss: 0.339145\n",
      "Train Epoch: 4 [15360/60000 (26%)]\tLoss: 0.156505\n",
      "Train Epoch: 4 [16000/60000 (27%)]\tLoss: 0.252404\n",
      "Train Epoch: 4 [16640/60000 (28%)]\tLoss: 0.232405\n",
      "Train Epoch: 4 [17280/60000 (29%)]\tLoss: 0.113536\n",
      "Train Epoch: 4 [17920/60000 (30%)]\tLoss: 0.305994\n",
      "Train Epoch: 4 [18560/60000 (31%)]\tLoss: 0.377043\n",
      "Train Epoch: 4 [19200/60000 (32%)]\tLoss: 0.383456\n",
      "Train Epoch: 4 [19840/60000 (33%)]\tLoss: 0.256402\n",
      "Train Epoch: 4 [20480/60000 (34%)]\tLoss: 0.206277\n",
      "Train Epoch: 4 [21120/60000 (35%)]\tLoss: 0.154497\n",
      "Train Epoch: 4 [21760/60000 (36%)]\tLoss: 0.090108\n",
      "Train Epoch: 4 [22400/60000 (37%)]\tLoss: 0.152727\n",
      "Train Epoch: 4 [23040/60000 (38%)]\tLoss: 0.179576\n",
      "Train Epoch: 4 [23680/60000 (39%)]\tLoss: 0.302838\n",
      "Train Epoch: 4 [24320/60000 (41%)]\tLoss: 0.266172\n",
      "Train Epoch: 4 [24960/60000 (42%)]\tLoss: 0.247808\n",
      "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.287556\n",
      "Train Epoch: 4 [26240/60000 (44%)]\tLoss: 0.256802\n",
      "Train Epoch: 4 [26880/60000 (45%)]\tLoss: 0.218412\n",
      "Train Epoch: 4 [27520/60000 (46%)]\tLoss: 0.239004\n",
      "Train Epoch: 4 [28160/60000 (47%)]\tLoss: 0.303064\n",
      "Train Epoch: 4 [28800/60000 (48%)]\tLoss: 0.193754\n",
      "Train Epoch: 4 [29440/60000 (49%)]\tLoss: 0.125653\n",
      "Train Epoch: 4 [30080/60000 (50%)]\tLoss: 0.349183\n",
      "Train Epoch: 4 [30720/60000 (51%)]\tLoss: 0.268168\n",
      "Train Epoch: 4 [31360/60000 (52%)]\tLoss: 0.115433\n",
      "Train Epoch: 4 [32000/60000 (53%)]\tLoss: 0.224523\n",
      "Train Epoch: 4 [32640/60000 (54%)]\tLoss: 0.435151\n",
      "Train Epoch: 4 [33280/60000 (55%)]\tLoss: 0.384405\n",
      "Train Epoch: 4 [33920/60000 (57%)]\tLoss: 0.281086\n",
      "Train Epoch: 4 [34560/60000 (58%)]\tLoss: 0.246727\n",
      "Train Epoch: 4 [35200/60000 (59%)]\tLoss: 0.216816\n",
      "Train Epoch: 4 [35840/60000 (60%)]\tLoss: 0.097739\n",
      "Train Epoch: 4 [36480/60000 (61%)]\tLoss: 0.231621\n",
      "Train Epoch: 4 [37120/60000 (62%)]\tLoss: 0.156893\n",
      "Train Epoch: 4 [37760/60000 (63%)]\tLoss: 0.321192\n",
      "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.214810\n",
      "Train Epoch: 4 [39040/60000 (65%)]\tLoss: 0.222767\n",
      "Train Epoch: 4 [39680/60000 (66%)]\tLoss: 0.243232\n",
      "Train Epoch: 4 [40320/60000 (67%)]\tLoss: 0.111788\n",
      "Train Epoch: 4 [40960/60000 (68%)]\tLoss: 0.199731\n",
      "Train Epoch: 4 [41600/60000 (69%)]\tLoss: 0.227379\n",
      "Train Epoch: 4 [42240/60000 (70%)]\tLoss: 0.184612\n",
      "Train Epoch: 4 [42880/60000 (71%)]\tLoss: 0.359678\n",
      "Train Epoch: 4 [43520/60000 (72%)]\tLoss: 0.240873\n",
      "Train Epoch: 4 [44160/60000 (74%)]\tLoss: 0.201495\n",
      "Train Epoch: 4 [44800/60000 (75%)]\tLoss: 0.168709\n",
      "Train Epoch: 4 [45440/60000 (76%)]\tLoss: 0.252187\n",
      "Train Epoch: 4 [46080/60000 (77%)]\tLoss: 0.275957\n",
      "Train Epoch: 4 [46720/60000 (78%)]\tLoss: 0.096061\n",
      "Train Epoch: 4 [47360/60000 (79%)]\tLoss: 0.125045\n",
      "Train Epoch: 4 [48000/60000 (80%)]\tLoss: 0.634329\n",
      "Train Epoch: 4 [48640/60000 (81%)]\tLoss: 0.348356\n",
      "Train Epoch: 4 [49280/60000 (82%)]\tLoss: 0.252744\n",
      "Train Epoch: 4 [49920/60000 (83%)]\tLoss: 0.119039\n",
      "Train Epoch: 4 [50560/60000 (84%)]\tLoss: 0.346220\n",
      "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.270486\n",
      "Train Epoch: 4 [51840/60000 (86%)]\tLoss: 0.276223\n",
      "Train Epoch: 4 [52480/60000 (87%)]\tLoss: 0.199197\n",
      "Train Epoch: 4 [53120/60000 (88%)]\tLoss: 0.503150\n",
      "Train Epoch: 4 [53760/60000 (90%)]\tLoss: 0.086169\n",
      "Train Epoch: 4 [54400/60000 (91%)]\tLoss: 0.343518\n",
      "Train Epoch: 4 [55040/60000 (92%)]\tLoss: 0.281669\n",
      "Train Epoch: 4 [55680/60000 (93%)]\tLoss: 0.257638\n",
      "Train Epoch: 4 [56320/60000 (94%)]\tLoss: 0.212004\n",
      "Train Epoch: 4 [56960/60000 (95%)]\tLoss: 0.241934\n",
      "Train Epoch: 4 [57600/60000 (96%)]\tLoss: 0.192715\n",
      "Train Epoch: 4 [58240/60000 (97%)]\tLoss: 0.242436\n",
      "Train Epoch: 4 [58880/60000 (98%)]\tLoss: 0.239866\n",
      "Train Epoch: 4 [59520/60000 (99%)]\tLoss: 0.193490\n",
      "\n",
      "Test set: Average loss: 0.0832, Accuracy: 9747/10000 (97%)\n",
      "\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.323097\n",
      "Train Epoch: 5 [640/60000 (1%)]\tLoss: 0.153528\n",
      "Train Epoch: 5 [1280/60000 (2%)]\tLoss: 0.260388\n",
      "Train Epoch: 5 [1920/60000 (3%)]\tLoss: 0.180069\n",
      "Train Epoch: 5 [2560/60000 (4%)]\tLoss: 0.183377\n",
      "Train Epoch: 5 [3200/60000 (5%)]\tLoss: 0.118160\n",
      "Train Epoch: 5 [3840/60000 (6%)]\tLoss: 0.150747\n",
      "Train Epoch: 5 [4480/60000 (7%)]\tLoss: 0.232922\n",
      "Train Epoch: 5 [5120/60000 (9%)]\tLoss: 0.273261\n",
      "Train Epoch: 5 [5760/60000 (10%)]\tLoss: 0.322003\n",
      "Train Epoch: 5 [6400/60000 (11%)]\tLoss: 0.293311\n",
      "Train Epoch: 5 [7040/60000 (12%)]\tLoss: 0.362437\n",
      "Train Epoch: 5 [7680/60000 (13%)]\tLoss: 0.189084\n",
      "Train Epoch: 5 [8320/60000 (14%)]\tLoss: 0.183444\n",
      "Train Epoch: 5 [8960/60000 (15%)]\tLoss: 0.225659\n",
      "Train Epoch: 5 [9600/60000 (16%)]\tLoss: 0.166991\n",
      "Train Epoch: 5 [10240/60000 (17%)]\tLoss: 0.236412\n",
      "Train Epoch: 5 [10880/60000 (18%)]\tLoss: 0.305170\n",
      "Train Epoch: 5 [11520/60000 (19%)]\tLoss: 0.198014\n",
      "Train Epoch: 5 [12160/60000 (20%)]\tLoss: 0.409515\n",
      "Train Epoch: 5 [12800/60000 (21%)]\tLoss: 0.161604\n",
      "Train Epoch: 5 [13440/60000 (22%)]\tLoss: 0.273221\n",
      "Train Epoch: 5 [14080/60000 (23%)]\tLoss: 0.167118\n",
      "Train Epoch: 5 [14720/60000 (25%)]\tLoss: 0.160167\n",
      "Train Epoch: 5 [15360/60000 (26%)]\tLoss: 0.144133\n",
      "Train Epoch: 5 [16000/60000 (27%)]\tLoss: 0.225687\n",
      "Train Epoch: 5 [16640/60000 (28%)]\tLoss: 0.257729\n",
      "Train Epoch: 5 [17280/60000 (29%)]\tLoss: 0.248871\n",
      "Train Epoch: 5 [17920/60000 (30%)]\tLoss: 0.225489\n",
      "Train Epoch: 5 [18560/60000 (31%)]\tLoss: 0.134729\n",
      "Train Epoch: 5 [19200/60000 (32%)]\tLoss: 0.184726\n",
      "Train Epoch: 5 [19840/60000 (33%)]\tLoss: 0.352246\n",
      "Train Epoch: 5 [20480/60000 (34%)]\tLoss: 0.389648\n",
      "Train Epoch: 5 [21120/60000 (35%)]\tLoss: 0.159548\n",
      "Train Epoch: 5 [21760/60000 (36%)]\tLoss: 0.463786\n",
      "Train Epoch: 5 [22400/60000 (37%)]\tLoss: 0.131508\n",
      "Train Epoch: 5 [23040/60000 (38%)]\tLoss: 0.193462\n",
      "Train Epoch: 5 [23680/60000 (39%)]\tLoss: 0.148691\n",
      "Train Epoch: 5 [24320/60000 (41%)]\tLoss: 0.197089\n",
      "Train Epoch: 5 [24960/60000 (42%)]\tLoss: 0.222044\n",
      "Train Epoch: 5 [25600/60000 (43%)]\tLoss: 0.167495\n",
      "Train Epoch: 5 [26240/60000 (44%)]\tLoss: 0.146979\n",
      "Train Epoch: 5 [26880/60000 (45%)]\tLoss: 0.159638\n",
      "Train Epoch: 5 [27520/60000 (46%)]\tLoss: 0.172962\n",
      "Train Epoch: 5 [28160/60000 (47%)]\tLoss: 0.262659\n",
      "Train Epoch: 5 [28800/60000 (48%)]\tLoss: 0.191008\n",
      "Train Epoch: 5 [29440/60000 (49%)]\tLoss: 0.188408\n",
      "Train Epoch: 5 [30080/60000 (50%)]\tLoss: 0.147297\n",
      "Train Epoch: 5 [30720/60000 (51%)]\tLoss: 0.106788\n",
      "Train Epoch: 5 [31360/60000 (52%)]\tLoss: 0.168487\n",
      "Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.415704\n",
      "Train Epoch: 5 [32640/60000 (54%)]\tLoss: 0.248715\n",
      "Train Epoch: 5 [33280/60000 (55%)]\tLoss: 0.323733\n",
      "Train Epoch: 5 [33920/60000 (57%)]\tLoss: 0.249728\n",
      "Train Epoch: 5 [34560/60000 (58%)]\tLoss: 0.231316\n",
      "Train Epoch: 5 [35200/60000 (59%)]\tLoss: 0.245180\n",
      "Train Epoch: 5 [35840/60000 (60%)]\tLoss: 0.166864\n",
      "Train Epoch: 5 [36480/60000 (61%)]\tLoss: 0.193982\n",
      "Train Epoch: 5 [37120/60000 (62%)]\tLoss: 0.268843\n",
      "Train Epoch: 5 [37760/60000 (63%)]\tLoss: 0.147551\n",
      "Train Epoch: 5 [38400/60000 (64%)]\tLoss: 0.157386\n",
      "Train Epoch: 5 [39040/60000 (65%)]\tLoss: 0.329879\n",
      "Train Epoch: 5 [39680/60000 (66%)]\tLoss: 0.404460\n",
      "Train Epoch: 5 [40320/60000 (67%)]\tLoss: 0.174316\n",
      "Train Epoch: 5 [40960/60000 (68%)]\tLoss: 0.161162\n",
      "Train Epoch: 5 [41600/60000 (69%)]\tLoss: 0.276823\n",
      "Train Epoch: 5 [42240/60000 (70%)]\tLoss: 0.257084\n",
      "Train Epoch: 5 [42880/60000 (71%)]\tLoss: 0.196958\n",
      "Train Epoch: 5 [43520/60000 (72%)]\tLoss: 0.122015\n",
      "Train Epoch: 5 [44160/60000 (74%)]\tLoss: 0.188653\n",
      "Train Epoch: 5 [44800/60000 (75%)]\tLoss: 0.166569\n",
      "Train Epoch: 5 [45440/60000 (76%)]\tLoss: 0.136930\n",
      "Train Epoch: 5 [46080/60000 (77%)]\tLoss: 0.215529\n",
      "Train Epoch: 5 [46720/60000 (78%)]\tLoss: 0.084510\n",
      "Train Epoch: 5 [47360/60000 (79%)]\tLoss: 0.120768\n",
      "Train Epoch: 5 [48000/60000 (80%)]\tLoss: 0.233512\n",
      "Train Epoch: 5 [48640/60000 (81%)]\tLoss: 0.106954\n",
      "Train Epoch: 5 [49280/60000 (82%)]\tLoss: 0.213883\n",
      "Train Epoch: 5 [49920/60000 (83%)]\tLoss: 0.417396\n",
      "Train Epoch: 5 [50560/60000 (84%)]\tLoss: 0.143040\n",
      "Train Epoch: 5 [51200/60000 (85%)]\tLoss: 0.256086\n",
      "Train Epoch: 5 [51840/60000 (86%)]\tLoss: 0.113968\n",
      "Train Epoch: 5 [52480/60000 (87%)]\tLoss: 0.175152\n",
      "Train Epoch: 5 [53120/60000 (88%)]\tLoss: 0.180455\n",
      "Train Epoch: 5 [53760/60000 (90%)]\tLoss: 0.512370\n",
      "Train Epoch: 5 [54400/60000 (91%)]\tLoss: 0.238426\n",
      "Train Epoch: 5 [55040/60000 (92%)]\tLoss: 0.179841\n",
      "Train Epoch: 5 [55680/60000 (93%)]\tLoss: 0.132503\n",
      "Train Epoch: 5 [56320/60000 (94%)]\tLoss: 0.122262\n",
      "Train Epoch: 5 [56960/60000 (95%)]\tLoss: 0.151063\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss: 0.268425\n",
      "Train Epoch: 5 [58240/60000 (97%)]\tLoss: 0.142108\n",
      "Train Epoch: 5 [58880/60000 (98%)]\tLoss: 0.174616\n",
      "Train Epoch: 5 [59520/60000 (99%)]\tLoss: 0.185637\n",
      "\n",
      "Test set: Average loss: 0.0775, Accuracy: 9766/10000 (98%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "batch_size = 64\n",
    "test_batch_size = 1000\n",
    "epochs = 5\n",
    "lr = 0.01\n",
    "momentum = 0.5\n",
    "seed = 1\n",
    "log_interval = 10\n",
    "cuda = False\n",
    "\n",
    "torch.manual_seed(seed)\n",
    "\n",
    "\n",
    "kwargs = {}\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=batch_size, shuffle=True, **kwargs)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=test_batch_size, shuffle=True, **kwargs)\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(-1, 320)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "model = Net()\n",
    "if cuda:\n",
    "    model.cuda()\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n",
    "\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.data[0]))\n",
    "\n",
    "def test():\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        if cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data, volatile=True), Variable(target)\n",
    "        output = model(data)\n",
    "        test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "\n",
    "\n",
    "for epoch in range(1, epochs + 1):\n",
    "    train(epoch)\n",
    "    test()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def visualize(x, x_adv, x_grad, epsilon, clean_pred, adv_pred, clean_prob, adv_prob):\n",
    "    \n",
    "    x = x.view(28,28)\n",
    "    x = x.mul(torch.FloatTensor([0.3081,])).add(torch.FloatTensor([0.1307,])).numpy()#reverse of normalization op- \"unnormalize\"\n",
    "    \n",
    "    x_adv = x_adv.view(28,28)\n",
    "    x_adv = x_adv.mul(torch.FloatTensor([0.3081,])).add(torch.FloatTensor([0.1307,])).numpy()#reverse of normalization op\n",
    "    \n",
    "    x_grad = x_grad.view(28,28).numpy()\n",
    "    \n",
    "    figure, ax = plt.subplots(1,3, figsize=(18,8))\n",
    "    ax[0].imshow(x)\n",
    "    ax[0].set_title('Clean Example', fontsize=20)\n",
    "    \n",
    "    \n",
    "    ax[1].imshow(x_grad)\n",
    "    ax[1].set_title('Perturbation', fontsize=20)\n",
    "    ax[1].set_yticklabels([])\n",
    "    ax[1].set_xticklabels([])\n",
    "    ax[1].set_xticks([])\n",
    "    ax[1].set_yticks([])\n",
    "\n",
    "    \n",
    "    ax[2].imshow(x_adv)\n",
    "    ax[2].set_title('Adversarial Example', fontsize=20)\n",
    "    \n",
    "    ax[0].axis('off')\n",
    "    ax[2].axis('off')\n",
    "\n",
    "    ax[0].text(1.1,0.5, \"+{}*\".format(round(epsilon,3)), size=15, ha=\"center\", \n",
    "             transform=ax[0].transAxes)\n",
    "    \n",
    "    ax[0].text(0.5,-0.13, \"Prediction: {}\\n Probability: {}\".format(clean_pred, clean_prob), size=15, ha=\"center\", \n",
    "         transform=ax[0].transAxes)\n",
    "    \n",
    "    ax[1].text(1.1,0.5, \" = \", size=15, ha=\"center\", transform=ax[1].transAxes)\n",
    "\n",
    "    ax[2].text(0.5,-0.13, \"Prediction: {}\\n Probability: {}\".format(adv_pred, adv_prob), size=15, ha=\"center\", \n",
    "         transform=ax[2].transAxes)\n",
    "    \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZ4AAAIECAYAAACZsDs1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrL7gRUoGbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07bc21c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "x = images[7]  #take one image\n",
    "t = labels[7]  #take corresponding label\n",
    "x = torch.Tensor(x)\n",
    "x.unsqueeze_(0)\n",
    "x = Variable(x, requires_grad=True)\n",
    "\n",
    "target = Variable(torch.LongTensor([t]), requires_grad=False)\n",
    "\n",
    "output = model(x)\n",
    "x_pred = torch.max(output.data, 1)[1][0]\n",
    "#output is log_softmax so in order to get probs take 'exp' of it.\n",
    "output_probs = np.exp(output.data)\n",
    "prob= output_probs[:,x_pred]\n",
    "\n",
    "loss = F.nll_loss(output, target)\n",
    "loss.backward()\n",
    "#generate the adversarial example using formula, also clip it to make it a valid image\n",
    "epsilon = 0.02\n",
    "x_grad = torch.sign(x.grad.data)\n",
    "x_adversarial = torch.clamp(x.data + epsilon * x_grad, 0, 1)\n",
    "\n",
    "out2 = model(Variable(x_adversarial))\n",
    "adv_pred = torch.max(out2.data,1)[1][0]\n",
    "\n",
    "out_adv_probs = np.exp(out2.data)\n",
    "prob_adv = out_adv_probs[:,adv_pred]\n",
    "\n",
    "visualize(x.data, x_adversarial, x_grad, epsilon, x_pred, adv_pred,np.around(prob.numpy(),4), np.around(prob_adv.numpy(),4))\n"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}