week5a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Seed:  6170\n",
      "Generator(\n",
      "  (main): Sequential(\n",
      "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (13): Tanh()\n",
      "  )\n",
      ")\n",
      "Discriminator(\n",
      "  (main): Sequential(\n",
      "    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
      "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (12): Sigmoid()\n",
      "  )\n",
      ")\n",
      "[0/5][0/938] Loss_D: 1.3481 Loss_G: 3.6149 D(x): 0.5024 D(G(z)): 0.4024 / 0.0349\n",
      "[0/5][1/938] Loss_D: 1.7869 Loss_G: 5.3687 D(x): 0.9987 D(G(z)): 0.7943 / 0.0065\n",
      "[0/5][2/938] Loss_D: 0.5759 Loss_G: 6.7588 D(x): 0.9975 D(G(z)): 0.3973 / 0.0014\n",
      "[0/5][3/938] Loss_D: 0.2471 Loss_G: 6.8529 D(x): 0.9936 D(G(z)): 0.1880 / 0.0017\n",
      "[0/5][4/938] Loss_D: 0.2070 Loss_G: 6.6945 D(x): 0.9609 D(G(z)): 0.1321 / 0.0017\n",
      "[0/5][5/938] Loss_D: 0.2891 Loss_G: 7.3373 D(x): 0.9547 D(G(z)): 0.1702 / 0.0010\n",
      "[0/5][6/938] Loss_D: 0.1665 Loss_G: 6.9995 D(x): 0.9294 D(G(z)): 0.0626 / 0.0015\n",
      "[0/5][7/938] Loss_D: 0.1871 Loss_G: 6.6667 D(x): 0.9463 D(G(z)): 0.0937 / 0.0017\n",
      "[0/5][8/938] Loss_D: 0.1938 Loss_G: 8.4905 D(x): 0.9800 D(G(z)): 0.1498 / 0.0003\n",
      "[0/5][9/938] Loss_D: 0.0854 Loss_G: 7.5341 D(x): 0.9581 D(G(z)): 0.0360 / 0.0009\n",
      "[0/5][10/938] Loss_D: 0.2018 Loss_G: 8.3258 D(x): 0.9541 D(G(z)): 0.1282 / 0.0003\n",
      "[0/5][11/938] Loss_D: 0.1083 Loss_G: 8.7990 D(x): 0.9817 D(G(z)): 0.0795 / 0.0003\n",
      "[0/5][12/938] Loss_D: 0.0703 Loss_G: 7.6975 D(x): 0.9685 D(G(z)): 0.0340 / 0.0007\n",
      "[0/5][13/938] Loss_D: 0.1686 Loss_G: 9.3408 D(x): 0.9562 D(G(z)): 0.1059 / 0.0001\n",
      "[0/5][14/938] Loss_D: 0.1260 Loss_G: 7.0780 D(x): 0.9153 D(G(z)): 0.0239 / 0.0015\n",
      "[0/5][15/938] Loss_D: 0.3397 Loss_G: 14.9536 D(x): 0.9864 D(G(z)): 0.2536 / 0.0000\n",
      "[0/5][16/938] Loss_D: 0.1510 Loss_G: 13.5563 D(x): 0.9004 D(G(z)): 0.0002 / 0.0000\n",
      "[0/5][17/938] Loss_D: 0.0370 Loss_G: 8.1651 D(x): 0.9676 D(G(z)): 0.0014 / 0.0005\n",
      "[0/5][18/938] Loss_D: 0.2835 Loss_G: 14.0262 D(x): 0.9920 D(G(z)): 0.2257 / 0.0000\n",
      "[0/5][19/938] Loss_D: 0.1241 Loss_G: 12.9388 D(x): 0.9314 D(G(z)): 0.0004 / 0.0000\n",
      "[0/5][20/938] Loss_D: 0.0419 Loss_G: 7.8000 D(x): 0.9638 D(G(z)): 0.0027 / 0.0007\n",
      "[0/5][21/938] Loss_D: 0.4211 Loss_G: 18.7489 D(x): 0.9950 D(G(z)): 0.3065 / 0.0000\n",
      "[0/5][22/938] Loss_D: 0.6245 Loss_G: 14.0934 D(x): 0.6201 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][23/938] Loss_D: 0.0165 Loss_G: 4.7800 D(x): 0.9999 D(G(z)): 0.0160 / 0.0181\n",
      "[0/5][24/938] Loss_D: 6.0636 Loss_G: 23.4241 D(x): 1.0000 D(G(z)): 0.9962 / 0.0000\n",
      "[0/5][25/938] Loss_D: 0.4763 Loss_G: 25.8177 D(x): 0.7632 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][26/938] Loss_D: 0.2339 Loss_G: 21.2853 D(x): 0.8284 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][27/938] Loss_D: 0.0258 Loss_G: 13.1776 D(x): 0.9790 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][28/938] Loss_D: 0.0181 Loss_G: 5.0716 D(x): 0.9941 D(G(z)): 0.0119 / 0.0090\n",
      "[0/5][29/938] Loss_D: 2.4498 Loss_G: 23.9822 D(x): 0.9920 D(G(z)): 0.8724 / 0.0000\n",
      "[0/5][30/938] Loss_D: 1.2660 Loss_G: 23.6015 D(x): 0.4983 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][31/938] Loss_D: 0.0279 Loss_G: 18.3994 D(x): 0.9791 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][32/938] Loss_D: 0.0338 Loss_G: 8.4173 D(x): 0.9762 D(G(z)): 0.0003 / 0.0004\n",
      "[0/5][33/938] Loss_D: 1.1377 Loss_G: 20.9122 D(x): 0.9700 D(G(z)): 0.6051 / 0.0000\n",
      "[0/5][34/938] Loss_D: 0.2095 Loss_G: 22.4815 D(x): 0.9090 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][35/938] Loss_D: 0.3092 Loss_G: 16.2711 D(x): 0.8135 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][36/938] Loss_D: 0.0216 Loss_G: 7.6769 D(x): 0.9832 D(G(z)): 0.0010 / 0.0008\n",
      "[0/5][37/938] Loss_D: 0.6190 Loss_G: 18.4604 D(x): 0.9992 D(G(z)): 0.4108 / 0.0000\n",
      "[0/5][38/938] Loss_D: 0.0919 Loss_G: 20.2208 D(x): 0.9389 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][39/938] Loss_D: 0.1094 Loss_G: 16.2287 D(x): 0.9368 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][40/938] Loss_D: 0.1233 Loss_G: 9.5773 D(x): 0.9378 D(G(z)): 0.0001 / 0.0001\n",
      "[0/5][41/938] Loss_D: 0.0462 Loss_G: 5.6776 D(x): 0.9942 D(G(z)): 0.0386 / 0.0052\n",
      "[0/5][42/938] Loss_D: 0.3958 Loss_G: 17.0535 D(x): 0.9884 D(G(z)): 0.2848 / 0.0000\n",
      "[0/5][43/938] Loss_D: 0.1136 Loss_G: 18.9109 D(x): 0.9260 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][44/938] Loss_D: 0.1198 Loss_G: 16.3362 D(x): 0.9235 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][45/938] Loss_D: 0.0631 Loss_G: 11.9387 D(x): 0.9768 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][46/938] Loss_D: 0.0057 Loss_G: 6.4064 D(x): 0.9970 D(G(z)): 0.0027 / 0.0024\n",
      "[0/5][47/938] Loss_D: 0.3191 Loss_G: 16.5712 D(x): 0.9980 D(G(z)): 0.2523 / 0.0000\n",
      "[0/5][48/938] Loss_D: 0.0247 Loss_G: 18.6597 D(x): 0.9768 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][49/938] Loss_D: 0.2171 Loss_G: 14.8728 D(x): 0.8690 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][50/938] Loss_D: 0.0344 Loss_G: 9.2764 D(x): 0.9716 D(G(z)): 0.0001 / 0.0001\n",
      "[0/5][51/938] Loss_D: 0.0327 Loss_G: 5.3833 D(x): 0.9923 D(G(z)): 0.0233 / 0.0065\n",
      "[0/5][52/938] Loss_D: 0.4079 Loss_G: 21.0100 D(x): 0.9940 D(G(z)): 0.2991 / 0.0000\n",
      "[0/5][53/938] Loss_D: 0.1718 Loss_G: 23.5055 D(x): 0.8884 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][54/938] Loss_D: 0.3060 Loss_G: 19.4324 D(x): 0.8211 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][55/938] Loss_D: 0.0151 Loss_G: 14.8648 D(x): 0.9864 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][56/938] Loss_D: 0.0264 Loss_G: 9.5626 D(x): 0.9844 D(G(z)): 0.0000 / 0.0001\n",
      "[0/5][57/938] Loss_D: 0.0151 Loss_G: 5.2162 D(x): 0.9953 D(G(z)): 0.0101 / 0.0083\n",
      "[0/5][58/938] Loss_D: 0.5459 Loss_G: 23.6102 D(x): 0.9926 D(G(z)): 0.3565 / 0.0000\n",
      "[0/5][59/938] Loss_D: 0.0560 Loss_G: 28.0751 D(x): 0.9527 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][60/938] Loss_D: 0.7923 Loss_G: 20.3690 D(x): 0.6557 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][61/938] Loss_D: 0.0631 Loss_G: 12.0950 D(x): 0.9630 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][62/938] Loss_D: 0.0405 Loss_G: 3.4735 D(x): 0.9874 D(G(z)): 0.0161 / 0.0450\n",
      "[0/5][63/938] Loss_D: 4.5077 Loss_G: 33.9841 D(x): 0.9797 D(G(z)): 0.9806 / 0.0000\n",
      "[0/5][64/938] Loss_D: 5.3112 Loss_G: 32.7594 D(x): 0.0420 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][65/938] Loss_D: 0.0148 Loss_G: 31.6616 D(x): 0.9861 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][66/938] Loss_D: 0.0453 Loss_G: 30.6597 D(x): 0.9646 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][67/938] Loss_D: 0.0465 Loss_G: 29.2515 D(x): 0.9790 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][68/938] Loss_D: 0.0045 Loss_G: 26.1919 D(x): 0.9957 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][69/938] Loss_D: 0.0061 Loss_G: 18.6484 D(x): 0.9944 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][70/938] Loss_D: 0.0047 Loss_G: 7.1419 D(x): 0.9968 D(G(z)): 0.0013 / 0.0016\n",
      "[0/5][71/938] Loss_D: 2.3534 Loss_G: 31.8939 D(x): 0.9812 D(G(z)): 0.8045 / 0.0000\n",
      "[0/5][72/938] Loss_D: 12.2224 Loss_G: 22.0341 D(x): 0.0002 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][73/938] Loss_D: 0.5672 Loss_G: 12.3827 D(x): 0.7900 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][74/938] Loss_D: 0.0284 Loss_G: 3.7147 D(x): 0.9912 D(G(z)): 0.0159 / 0.0827\n",
      "[0/5][75/938] Loss_D: 3.1782 Loss_G: 17.5125 D(x): 0.9958 D(G(z)): 0.8549 / 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0/5][76/938] Loss_D: 0.4790 Loss_G: 18.6008 D(x): 0.8068 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][77/938] Loss_D: 1.4232 Loss_G: 9.0903 D(x): 0.5040 D(G(z)): 0.0000 / 0.0007\n",
      "[0/5][78/938] Loss_D: 0.6501 Loss_G: 9.7983 D(x): 0.9527 D(G(z)): 0.2758 / 0.0001\n",
      "[0/5][79/938] Loss_D: 0.3863 Loss_G: 10.5486 D(x): 0.9094 D(G(z)): 0.1208 / 0.0001\n",
      "[0/5][80/938] Loss_D: 0.1318 Loss_G: 8.9059 D(x): 0.9461 D(G(z)): 0.0572 / 0.0004\n",
      "[0/5][81/938] Loss_D: 0.7230 Loss_G: 11.8884 D(x): 0.8380 D(G(z)): 0.2683 / 0.0000\n",
      "[0/5][82/938] Loss_D: 0.3837 Loss_G: 6.8235 D(x): 0.7727 D(G(z)): 0.0107 / 0.0018\n",
      "[0/5][83/938] Loss_D: 1.3205 Loss_G: 22.9077 D(x): 0.9623 D(G(z)): 0.6374 / 0.0000\n",
      "[0/5][84/938] Loss_D: 3.4657 Loss_G: 12.1966 D(x): 0.1214 D(G(z)): 0.0000 / 0.0000\n",
      "[0/5][85/938] Loss_D: 0.2830 Loss_G: 5.4809 D(x): 0.9993 D(G(z)): 0.1654 / 0.0072\n",
      "[0/5][86/938] Loss_D: 1.8919 Loss_G: 18.5427 D(x): 0.9991 D(G(z)): 0.7594 / 0.0000\n",
      "[0/5][87/938] Loss_D: 0.3184 Loss_G: 17.2890 D(x): 0.7979 D(G(z)): 0.0001 / 0.0000\n",
      "[0/5][88/938] Loss_D: 0.6245 Loss_G: 8.1914 D(x): 0.7151 D(G(z)): 0.0005 / 0.0023\n",
      "[0/5][89/938] Loss_D: 0.5709 Loss_G: 7.5280 D(x): 0.9303 D(G(z)): 0.3306 / 0.0016\n",
      "[0/5][90/938] Loss_D: 0.2849 Loss_G: 6.9466 D(x): 0.8795 D(G(z)): 0.0901 / 0.0017\n",
      "[0/5][91/938] Loss_D: 0.4012 Loss_G: 5.3411 D(x): 0.8199 D(G(z)): 0.0852 / 0.0070\n",
      "[0/5][92/938] Loss_D: 0.2441 Loss_G: 8.1095 D(x): 0.9448 D(G(z)): 0.1519 / 0.0005\n",
      "[0/5][93/938] Loss_D: 0.3500 Loss_G: 4.4902 D(x): 0.7765 D(G(z)): 0.0068 / 0.0181\n",
      "[0/5][94/938] Loss_D: 0.1764 Loss_G: 6.5446 D(x): 0.9835 D(G(z)): 0.1414 / 0.0031\n",
      "[0/5][95/938] Loss_D: 0.0765 Loss_G: 6.3983 D(x): 0.9652 D(G(z)): 0.0389 / 0.0029\n",
      "[0/5][96/938] Loss_D: 0.1077 Loss_G: 5.0871 D(x): 0.9230 D(G(z)): 0.0165 / 0.0113\n",
      "[0/5][97/938] Loss_D: 0.1401 Loss_G: 5.0161 D(x): 0.9553 D(G(z)): 0.0820 / 0.0106\n",
      "[0/5][98/938] Loss_D: 0.1573 Loss_G: 5.7470 D(x): 0.9551 D(G(z)): 0.0882 / 0.0076\n",
      "[0/5][99/938] Loss_D: 0.2110 Loss_G: 4.3618 D(x): 0.8890 D(G(z)): 0.0505 / 0.0263\n",
      "[0/5][100/938] Loss_D: 0.2732 Loss_G: 7.1086 D(x): 0.9381 D(G(z)): 0.1689 / 0.0028\n",
      "[0/5][101/938] Loss_D: 0.4384 Loss_G: 2.8965 D(x): 0.7513 D(G(z)): 0.0064 / 0.0912\n",
      "[0/5][102/938] Loss_D: 0.3480 Loss_G: 8.0076 D(x): 0.9925 D(G(z)): 0.2676 / 0.0012\n",
      "[0/5][103/938] Loss_D: 0.1456 Loss_G: 7.5892 D(x): 0.8854 D(G(z)): 0.0048 / 0.0023\n",
      "[0/5][104/938] Loss_D: 0.0916 Loss_G: 4.8679 D(x): 0.9361 D(G(z)): 0.0193 / 0.0221\n",
      "[0/5][105/938] Loss_D: 0.0799 Loss_G: 4.1986 D(x): 0.9807 D(G(z)): 0.0562 / 0.0228\n",
      "[0/5][106/938] Loss_D: 0.1204 Loss_G: 5.4956 D(x): 0.9819 D(G(z)): 0.0918 / 0.0071\n",
      "[0/5][107/938] Loss_D: 0.1829 Loss_G: 4.6548 D(x): 0.9170 D(G(z)): 0.0482 / 0.0179\n",
      "[0/5][108/938] Loss_D: 0.1287 Loss_G: 4.5893 D(x): 0.9478 D(G(z)): 0.0639 / 0.0174\n",
      "[0/5][109/938] Loss_D: 0.1808 Loss_G: 4.6483 D(x): 0.9243 D(G(z)): 0.0785 / 0.0148\n",
      "[0/5][110/938] Loss_D: 0.1568 Loss_G: 4.4974 D(x): 0.9318 D(G(z)): 0.0544 / 0.0158\n",
      "[0/5][111/938] Loss_D: 0.0738 Loss_G: 4.9512 D(x): 0.9706 D(G(z)): 0.0405 / 0.0096\n",
      "[0/5][112/938] Loss_D: 0.1154 Loss_G: 3.8668 D(x): 0.9337 D(G(z)): 0.0401 / 0.0271\n",
      "[0/5][113/938] Loss_D: 0.1143 Loss_G: 4.6722 D(x): 0.9638 D(G(z)): 0.0693 / 0.0121\n",
      "[0/5][114/938] Loss_D: 0.0796 Loss_G: 4.6369 D(x): 0.9563 D(G(z)): 0.0311 / 0.0138\n",
      "[0/5][115/938] Loss_D: 0.0756 Loss_G: 4.6147 D(x): 0.9693 D(G(z)): 0.0385 / 0.0139\n",
      "[0/5][116/938] Loss_D: 0.1059 Loss_G: 4.1289 D(x): 0.9417 D(G(z)): 0.0255 / 0.0232\n",
      "[0/5][117/938] Loss_D: 0.1160 Loss_G: 5.0837 D(x): 0.9713 D(G(z)): 0.0760 / 0.0101\n",
      "[0/5][118/938] Loss_D: 0.0483 Loss_G: 5.4255 D(x): 0.9766 D(G(z)): 0.0229 / 0.0068\n",
      "[0/5][119/938] Loss_D: 0.1392 Loss_G: 3.1363 D(x): 0.9013 D(G(z)): 0.0186 / 0.0609\n",
      "[0/5][120/938] Loss_D: 0.1094 Loss_G: 4.9754 D(x): 0.9889 D(G(z)): 0.0909 / 0.0097\n",
      "[0/5][121/938] Loss_D: 0.1032 Loss_G: 4.9002 D(x): 0.9487 D(G(z)): 0.0257 / 0.0111\n",
      "[0/5][122/938] Loss_D: 0.0826 Loss_G: 4.6679 D(x): 0.9603 D(G(z)): 0.0358 / 0.0160\n",
      "[0/5][123/938] Loss_D: 0.0704 Loss_G: 5.4127 D(x): 0.9830 D(G(z)): 0.0491 / 0.0078\n",
      "[0/5][124/938] Loss_D: 0.0642 Loss_G: 5.0329 D(x): 0.9642 D(G(z)): 0.0241 / 0.0110\n",
      "[0/5][125/938] Loss_D: 0.0721 Loss_G: 4.5372 D(x): 0.9631 D(G(z)): 0.0314 / 0.0188\n",
      "[0/5][126/938] Loss_D: 0.0615 Loss_G: 5.0841 D(x): 0.9796 D(G(z)): 0.0383 / 0.0110\n",
      "[0/5][127/938] Loss_D: 0.1030 Loss_G: 4.8472 D(x): 0.9518 D(G(z)): 0.0425 / 0.0128\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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     ]
    },
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      "[2/5][1/938] Loss_D: 0.8914 Loss_G: 2.0951 D(x): 0.8387 D(G(z)): 0.4622 / 0.1409\n",
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2/5][204/938] Loss_D: 0.1063 Loss_G: 3.5601 D(x): 0.9457 D(G(z)): 0.0455 / 0.0372\n",
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     ]
    },
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
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     ]
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "[4/5][890/938] Loss_D: 0.1208 Loss_G: 3.5643 D(x): 0.9497 D(G(z)): 0.0624 / 0.0401\n",
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      "[4/5][892/938] Loss_D: 0.0972 Loss_G: 3.6358 D(x): 0.9430 D(G(z)): 0.0355 / 0.0388\n",
      "[4/5][893/938] Loss_D: 0.0723 Loss_G: 3.6320 D(x): 0.9543 D(G(z)): 0.0238 / 0.0399\n",
      "[4/5][894/938] Loss_D: 0.1117 Loss_G: 4.1786 D(x): 0.9734 D(G(z)): 0.0785 / 0.0202\n",
      "[4/5][895/938] Loss_D: 0.0467 Loss_G: 4.7504 D(x): 0.9733 D(G(z)): 0.0191 / 0.0120\n",
      "[4/5][896/938] Loss_D: 0.1002 Loss_G: 3.5179 D(x): 0.9443 D(G(z)): 0.0382 / 0.0411\n",
      "[4/5][897/938] Loss_D: 0.0770 Loss_G: 4.3724 D(x): 0.9775 D(G(z)): 0.0495 / 0.0198\n",
      "[4/5][898/938] Loss_D: 0.0619 Loss_G: 4.4691 D(x): 0.9471 D(G(z)): 0.0066 / 0.0159\n",
      "[4/5][899/938] Loss_D: 0.0753 Loss_G: 3.7242 D(x): 0.9700 D(G(z)): 0.0424 / 0.0338\n",
      "[4/5][900/938] Loss_D: 0.1132 Loss_G: 5.0930 D(x): 0.9811 D(G(z)): 0.0839 / 0.0085\n",
      "[4/5][901/938] Loss_D: 0.0968 Loss_G: 4.0778 D(x): 0.9198 D(G(z)): 0.0083 / 0.0296\n",
      "[4/5][902/938] Loss_D: 0.0893 Loss_G: 4.3884 D(x): 0.9808 D(G(z)): 0.0644 / 0.0188\n",
      "[4/5][903/938] Loss_D: 0.1071 Loss_G: 3.1541 D(x): 0.9170 D(G(z)): 0.0167 / 0.0606\n",
      "[4/5][904/938] Loss_D: 0.0704 Loss_G: 4.4232 D(x): 0.9934 D(G(z)): 0.0606 / 0.0161\n",
      "[4/5][905/938] Loss_D: 0.0413 Loss_G: 5.0777 D(x): 0.9860 D(G(z)): 0.0259 / 0.0098\n",
      "[4/5][906/938] Loss_D: 0.0875 Loss_G: 4.0231 D(x): 0.9561 D(G(z)): 0.0399 / 0.0259\n",
      "[4/5][907/938] Loss_D: 0.0834 Loss_G: 3.7357 D(x): 0.9580 D(G(z)): 0.0376 / 0.0331\n",
      "[4/5][908/938] Loss_D: 0.0797 Loss_G: 3.8179 D(x): 0.9603 D(G(z)): 0.0364 / 0.0316\n",
      "[4/5][909/938] Loss_D: 0.0480 Loss_G: 4.3073 D(x): 0.9736 D(G(z)): 0.0202 / 0.0200\n",
      "[4/5][910/938] Loss_D: 0.0337 Loss_G: 4.6356 D(x): 0.9907 D(G(z)): 0.0237 / 0.0144\n",
      "[4/5][911/938] Loss_D: 0.0694 Loss_G: 3.7664 D(x): 0.9516 D(G(z)): 0.0184 / 0.0349\n",
      "[4/5][912/938] Loss_D: 0.0836 Loss_G: 4.8158 D(x): 0.9860 D(G(z)): 0.0637 / 0.0132\n",
      "[4/5][913/938] Loss_D: 0.0734 Loss_G: 4.0535 D(x): 0.9434 D(G(z)): 0.0123 / 0.0244\n",
      "[4/5][914/938] Loss_D: 0.0850 Loss_G: 4.6256 D(x): 0.9824 D(G(z)): 0.0622 / 0.0145\n",
      "[4/5][915/938] Loss_D: 0.0755 Loss_G: 4.6580 D(x): 0.9690 D(G(z)): 0.0417 / 0.0135\n",
      "[4/5][916/938] Loss_D: 0.1076 Loss_G: 3.4549 D(x): 0.9106 D(G(z)): 0.0063 / 0.0490\n",
      "[4/5][917/938] Loss_D: 0.1309 Loss_G: 5.4755 D(x): 0.9893 D(G(z)): 0.1079 / 0.0063\n",
      "[4/5][918/938] Loss_D: 0.0745 Loss_G: 4.9576 D(x): 0.9348 D(G(z)): 0.0055 / 0.0107\n",
      "[4/5][919/938] Loss_D: 0.0403 Loss_G: 4.0907 D(x): 0.9811 D(G(z)): 0.0206 / 0.0238\n",
      "[4/5][920/938] Loss_D: 0.0530 Loss_G: 4.5259 D(x): 0.9820 D(G(z)): 0.0332 / 0.0169\n",
      "[4/5][921/938] Loss_D: 0.0501 Loss_G: 4.6729 D(x): 0.9775 D(G(z)): 0.0260 / 0.0151\n",
      "[4/5][922/938] Loss_D: 0.0619 Loss_G: 4.0954 D(x): 0.9561 D(G(z)): 0.0151 / 0.0281\n",
      "[4/5][923/938] Loss_D: 0.0706 Loss_G: 4.6250 D(x): 0.9883 D(G(z)): 0.0540 / 0.0141\n",
      "[4/5][924/938] Loss_D: 0.0636 Loss_G: 4.5568 D(x): 0.9479 D(G(z)): 0.0081 / 0.0174\n",
      "[4/5][925/938] Loss_D: 0.0431 Loss_G: 4.0893 D(x): 0.9818 D(G(z)): 0.0238 / 0.0236\n",
      "[4/5][926/938] Loss_D: 0.0675 Loss_G: 4.8088 D(x): 0.9900 D(G(z)): 0.0541 / 0.0106\n",
      "[4/5][927/938] Loss_D: 0.0404 Loss_G: 5.2640 D(x): 0.9716 D(G(z)): 0.0111 / 0.0088\n",
      "[4/5][928/938] Loss_D: 0.0617 Loss_G: 4.0190 D(x): 0.9659 D(G(z)): 0.0258 / 0.0274\n",
      "[4/5][929/938] Loss_D: 0.0507 Loss_G: 4.2115 D(x): 0.9758 D(G(z)): 0.0250 / 0.0224\n",
      "[4/5][930/938] Loss_D: 0.0682 Loss_G: 5.1125 D(x): 0.9873 D(G(z)): 0.0521 / 0.0091\n",
      "[4/5][931/938] Loss_D: 0.0501 Loss_G: 4.8718 D(x): 0.9585 D(G(z)): 0.0058 / 0.0108\n",
      "[4/5][932/938] Loss_D: 0.0561 Loss_G: 3.5241 D(x): 0.9665 D(G(z)): 0.0208 / 0.0392\n",
      "[4/5][933/938] Loss_D: 0.0625 Loss_G: 4.7715 D(x): 0.9920 D(G(z)): 0.0520 / 0.0116\n",
      "[4/5][934/938] Loss_D: 0.0463 Loss_G: 5.1008 D(x): 0.9618 D(G(z)): 0.0053 / 0.0094\n",
      "[4/5][935/938] Loss_D: 0.0543 Loss_G: 4.2638 D(x): 0.9755 D(G(z)): 0.0282 / 0.0216\n",
      "[4/5][936/938] Loss_D: 0.0505 Loss_G: 4.9056 D(x): 0.9870 D(G(z)): 0.0356 / 0.0121\n",
      "[4/5][937/938] Loss_D: 0.0439 Loss_G: 4.7233 D(x): 0.9770 D(G(z)): 0.0200 / 0.0121\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--dataset', default = 'mnist', help='cifar10 | lsun | mnist |imagenet | folder | lfw | fake')\n",
    "parser.add_argument('--dataroot', default = './data/mnist_png', help='path to dataset')\n",
    "parser.add_argument('--workers', type=int, help='number of data loading workers', default=2)\n",
    "parser.add_argument('--batchSize', type=int, default=64, help='input batch size')\n",
    "parser.add_argument('--imageSize', type=int, default=64, help='the height / width of the input image to network')\n",
    "parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector')\n",
    "parser.add_argument('--ngf', type=int, default=64)\n",
    "parser.add_argument('--ndf', type=int, default=64)\n",
    "parser.add_argument('--niter', type=int, default=5, help='number of epochs to train for')\n",
    "parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default=0.0002')\n",
    "parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')\n",
    "parser.add_argument('--dry-run', action='store_true', help='check a single training cycle works')\n",
    "parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')\n",
    "parser.add_argument('--netG', default='', help=\"path to netG (to continue training)\")\n",
    "parser.add_argument('--netD', default='', help=\"path to netD (to continue training)\")\n",
    "parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')\n",
    "\n",
    "opt, unknown = parser.parse_known_args()\n",
    "\n",
    "\n",
    "opt.manualSeed = random.randint(1, 10000)\n",
    "print(\"Random Seed: \", opt.manualSeed)\n",
    "random.seed(opt.manualSeed)\n",
    "torch.manual_seed(opt.manualSeed)\n",
    "\n",
    "cudnn.benchmark = True\n",
    "\n",
    "\n",
    "dataset = dset.MNIST(root=opt.dataroot, download=True,\n",
    "                     transform=transforms.Compose([\n",
    "                     transforms.Resize(opt.imageSize),\n",
    "                     transforms.ToTensor(),\n",
    "                     transforms.Normalize((0.5,), (0.5,)),]))\n",
    "nc=1\n",
    "\n",
    "\n",
    "\n",
    "assert dataset\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,\n",
    "                                         shuffle=True, num_workers=int(opt.workers))\n",
    "\n",
    "device = torch.device(\"cuda:0\")\n",
    "ngpu = int(opt.ngpu)\n",
    "nz = int(opt.nz)\n",
    "ngf = int(opt.ngf)\n",
    "ndf = int(opt.ndf)\n",
    "\n",
    "\n",
    "# custom weights initialization called on netG and netD\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        torch.nn.init.normal_(m.weight, 1.0, 0.02)\n",
    "        torch.nn.init.zeros_(m.bias)\n",
    "\n",
    "\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self, ngpu):\n",
    "        super(Generator, self).__init__()\n",
    "        self.ngpu = ngpu\n",
    "        self.main = nn.Sequential(\n",
    "            # input is Z, going into a convolution\n",
    "            nn.ConvTranspose2d(     nz, ngf * 8, 4, 1, 0, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 8),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*8) x 4 x 4\n",
    "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 4),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*4) x 8 x 8\n",
    "            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 2),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*2) x 16 x 16\n",
    "            nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf) x 32 x 32\n",
    "            nn.ConvTranspose2d(    ngf,      nc, 4, 2, 1, bias=False),\n",
    "            nn.Tanh()\n",
    "            # state size. (nc) x 64 x 64\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        if input.is_cuda and self.ngpu > 1:\n",
    "            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))\n",
    "        else:\n",
    "            output = self.main(input)\n",
    "        return output\n",
    "\n",
    "\n",
    "netG = Generator(ngpu).to(device)\n",
    "netG.apply(weights_init)\n",
    "if opt.netG != '':\n",
    "    netG.load_state_dict(torch.load(opt.netG))\n",
    "print(netG)\n",
    "\n",
    "\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self, ngpu):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.ngpu = ngpu\n",
    "        self.main = nn.Sequential(\n",
    "            # input is (nc) x 64 x 64\n",
    "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf) x 32 x 32\n",
    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 2),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*2) x 16 x 16\n",
    "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 4),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*4) x 8 x 8\n",
    "            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 8),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*8) x 4 x 4\n",
    "            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        if input.is_cuda and self.ngpu > 1:\n",
    "            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))\n",
    "        else:\n",
    "            output = self.main(input)\n",
    "\n",
    "        return output.view(-1, 1).squeeze(1)\n",
    "\n",
    "\n",
    "netD = Discriminator(ngpu).to(device)\n",
    "netD.apply(weights_init)\n",
    "if opt.netD != '':\n",
    "    netD.load_state_dict(torch.load(opt.netD))\n",
    "print(netD)\n",
    "\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "fixed_noise = torch.randn(opt.batchSize, nz, 1, 1, device=device)\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "# setup optimizer\n",
    "optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n",
    "optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n",
    "\n",
    "if opt.dry_run:\n",
    "    opt.niter = 1\n",
    "\n",
    "for epoch in range(opt.niter):\n",
    "    for i, data in enumerate(dataloader, 0):\n",
    "        ############################\n",
    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
    "        ###########################\n",
    "        # train with real\n",
    "        netD.zero_grad()\n",
    "        real_cpu = data[0].to(device)\n",
    "        batch_size = real_cpu.size(0)\n",
    "        label = torch.full((batch_size,), real_label,\n",
    "                           dtype=real_cpu.dtype, device=device)\n",
    "\n",
    "        output = netD(real_cpu)\n",
    "        errD_real = criterion(output, label)\n",
    "        errD_real.backward()\n",
    "        D_x = output.mean().item()\n",
    "\n",
    "        # train with fake\n",
    "        noise = torch.randn(batch_size, nz, 1, 1, device=device)\n",
    "        fake = netG(noise)\n",
    "        label.fill_(fake_label)\n",
    "        output = netD(fake.detach())\n",
    "        errD_fake = criterion(output, label)\n",
    "        errD_fake.backward()\n",
    "        D_G_z1 = output.mean().item()\n",
    "        errD = errD_real + errD_fake\n",
    "        optimizerD.step()\n",
    "\n",
    "        ############################\n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        ###########################\n",
    "        netG.zero_grad()\n",
    "        label.fill_(real_label)  # fake labels are real for generator cost\n",
    "        output = netD(fake)\n",
    "        errG = criterion(output, label)\n",
    "        errG.backward()\n",
    "        D_G_z2 = output.mean().item()\n",
    "        optimizerG.step()\n",
    "\n",
    "        print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n",
    "              % (epoch, opt.niter, i, len(dataloader),\n",
    "                 errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n",
    "        if i % 100 == 0:\n",
    "            vutils.save_image(real_cpu,\n",
    "                    '%s/real_samples.png' % opt.outf,\n",
    "                    normalize=True)\n",
    "            fake = netG(fixed_noise)\n",
    "            vutils.save_image(fake.detach(),\n",
    "                    '%s/fake_samples_epoch_%03d.png' % (opt.outf, epoch),\n",
    "                    normalize=True)\n",
    "\n",
    "        if opt.dry_run:\n",
    "            break\n",
    "    # do checkpointing\n",
    "    torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.outf, epoch))\n",
    "    torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (opt.outf, epoch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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