{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vanilla GAN for MNIST (PyTorch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vannila_gan.ipynb で うまく行かなかった(Dが0に収束してしまう)ので、以下の改善を行う。\n",
    "- ReLU の 代わりにLeakyReLUを使う。\n",
    "- BatchNormalizationを使う。\n",
    "- Adam の 学習率を小さくする。\n",
    "- ノイズは正規分布からサンプリングする。\n",
    "- ネットワークのニューロン数を変更する。\n",
    "\n",
    "参考:\n",
    "- [soumith/ganhacks: starter from \"How to Train a GAN?\" at NIPS2016](https://github.com/soumith/ganhacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    import torch.cuda as t\n",
    "else:\n",
    "    import torch as t\n",
    "\n",
    "from torchvision import datasets, models, transforms, utils\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "import numpy as np\n",
    "from numpy.random import normal\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mnist datasetの準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('data/mnist', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor()\n",
    "                   ])),\n",
    "    batch_size=bs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''Discriminater'''\n",
    "class netD(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(netD, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            nn.Linear(784, 512),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(256, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.size(0), 784)\n",
    "        x = self.main(x)\n",
    "        return x\n",
    "\n",
    "'''Generator'''\n",
    "class netG(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(netG, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            nn.Linear(100, 256),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(256, 512),\n",
    "            nn.BatchNorm1d(512),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(512, 1024),\n",
    "            nn.BatchNorm1d(1024),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(1024, 1*28*28),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(bs,100)\n",
    "        x = self.main(x)\n",
    "        x = x.view(-1, 1, 28, 28)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criteion = nn.BCELoss()\n",
    "net_D = netD()\n",
    "net_G = netG()\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    D = net_D.cuda()\n",
    "    G = net_G.cuda()\n",
    "    criteion = criteion.cuda()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizerD = optim.Adam(net_D.parameters(), lr = 0.00005)\n",
    "optimizerG = optim.Adam(net_G.parameters(), lr = 0.00005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = t.FloatTensor(bs, 1, 28, 28)\n",
    "noise = t.FloatTensor(normal(0, 1,(bs, 100, 1, 1)))\n",
    "fixed_noise = t.FloatTensor(bs, 100, 1, 1).normal_(0, 1)\n",
    "label = t.FloatTensor(bs)\n",
    "\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "input = Variable(input)\n",
    "label = Variable(label)\n",
    "noise = Variable(noise)\n",
    "fixed_noise = Variable(fixed_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "niter = 4000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0/4000][0/600] Loss_D: 1.3885 Loss_G: 0.7193 D(x): 0.5018 D(G(z)): 0.5029 / 0.4871\n",
      "[0/4000][100/600] Loss_D: 0.2039 Loss_G: 4.1782 D(x): 0.8943 D(G(z)): 0.0672 / 0.0608\n",
      "[0/4000][200/600] Loss_D: 0.3222 Loss_G: 3.9727 D(x): 0.8210 D(G(z)): 0.0836 / 0.0762\n",
      "[0/4000][300/600] Loss_D: 0.2737 Loss_G: 2.9381 D(x): 0.8235 D(G(z)): 0.0632 / 0.0626\n",
      "[0/4000][400/600] Loss_D: 0.1356 Loss_G: 3.3121 D(x): 0.9232 D(G(z)): 0.0521 / 0.0406\n",
      "[0/4000][500/600] Loss_D: 0.1118 Loss_G: 3.3953 D(x): 0.9374 D(G(z)): 0.0428 / 0.0367\n",
      "[1/4000][0/600] Loss_D: 0.0891 Loss_G: 3.9906 D(x): 0.9615 D(G(z)): 0.0463 / 0.0256\n",
      "[1/4000][100/600] Loss_D: 0.1392 Loss_G: 3.1369 D(x): 0.9465 D(G(z)): 0.0652 / 0.0549\n",
      "[1/4000][200/600] Loss_D: 0.1490 Loss_G: 3.5705 D(x): 0.9557 D(G(z)): 0.0707 / 0.0501\n",
      "[1/4000][300/600] Loss_D: 0.1450 Loss_G: 3.3625 D(x): 0.9671 D(G(z)): 0.0888 / 0.0581\n",
      "[1/4000][400/600] Loss_D: 0.1139 Loss_G: 3.3370 D(x): 0.9723 D(G(z)): 0.0782 / 0.0482\n",
      "[1/4000][500/600] Loss_D: 0.2371 Loss_G: 2.9955 D(x): 0.9235 D(G(z)): 0.0951 / 0.0676\n",
      "[2/4000][0/600] Loss_D: 0.2712 Loss_G: 3.1270 D(x): 0.9171 D(G(z)): 0.1225 / 0.0849\n",
      "[2/4000][100/600] Loss_D: 0.2722 Loss_G: 3.7160 D(x): 0.9201 D(G(z)): 0.1158 / 0.0948\n",
      "[2/4000][200/600] Loss_D: 0.1986 Loss_G: 4.1082 D(x): 0.9617 D(G(z)): 0.1039 / 0.0720\n",
      "[2/4000][300/600] Loss_D: 0.1760 Loss_G: 4.1830 D(x): 0.9602 D(G(z)): 0.0742 / 0.0570\n",
      "[2/4000][400/600] Loss_D: 0.3505 Loss_G: 3.7002 D(x): 0.9014 D(G(z)): 0.0894 / 0.0646\n",
      "[2/4000][500/600] Loss_D: 0.3939 Loss_G: 2.2979 D(x): 0.8788 D(G(z)): 0.1603 / 0.1508\n",
      "[3/4000][0/600] Loss_D: 0.3538 Loss_G: 2.5244 D(x): 0.8994 D(G(z)): 0.1405 / 0.1289\n",
      "[3/4000][100/600] Loss_D: 0.1664 Loss_G: 2.9389 D(x): 0.9714 D(G(z)): 0.1004 / 0.0886\n",
      "[3/4000][200/600] Loss_D: 0.2222 Loss_G: 2.9919 D(x): 0.9638 D(G(z)): 0.1059 / 0.0916\n",
      "[3/4000][300/600] Loss_D: 0.2514 Loss_G: 2.7258 D(x): 0.9506 D(G(z)): 0.1109 / 0.0952\n",
      "[3/4000][400/600] Loss_D: 0.2591 Loss_G: 2.7858 D(x): 0.9091 D(G(z)): 0.0914 / 0.0815\n",
      "[3/4000][500/600] Loss_D: 0.2596 Loss_G: 2.9744 D(x): 0.9055 D(G(z)): 0.0939 / 0.0962\n",
      "[4/4000][0/600] Loss_D: 0.2063 Loss_G: 3.2386 D(x): 0.9363 D(G(z)): 0.0815 / 0.0656\n",
      "[4/4000][100/600] Loss_D: 0.1462 Loss_G: 3.1894 D(x): 0.9608 D(G(z)): 0.0836 / 0.0641\n",
      "[4/4000][200/600] Loss_D: 0.1372 Loss_G: 3.2696 D(x): 0.9537 D(G(z)): 0.0696 / 0.0625\n",
      "[4/4000][300/600] Loss_D: 0.2280 Loss_G: 3.5655 D(x): 0.9383 D(G(z)): 0.0727 / 0.0615\n",
      "[4/4000][400/600] Loss_D: 0.2120 Loss_G: 3.4613 D(x): 0.9136 D(G(z)): 0.0694 / 0.0596\n",
      "[4/4000][500/600] Loss_D: 0.2006 Loss_G: 3.5587 D(x): 0.9294 D(G(z)): 0.0685 / 0.0668\n",
      "[5/4000][0/600] Loss_D: 0.1872 Loss_G: 4.1383 D(x): 0.9201 D(G(z)): 0.0448 / 0.0333\n",
      "[5/4000][100/600] Loss_D: 0.0926 Loss_G: 3.4749 D(x): 0.9769 D(G(z)): 0.0594 / 0.0451\n",
      "[5/4000][200/600] Loss_D: 0.1370 Loss_G: 4.0923 D(x): 0.9674 D(G(z)): 0.0683 / 0.0593\n",
      "[5/4000][300/600] Loss_D: 0.1363 Loss_G: 3.6710 D(x): 0.9584 D(G(z)): 0.0581 / 0.0499\n",
      "[5/4000][400/600] Loss_D: 0.2072 Loss_G: 4.0631 D(x): 0.9341 D(G(z)): 0.0665 / 0.0549\n",
      "[5/4000][500/600] Loss_D: 0.1829 Loss_G: 3.3004 D(x): 0.9182 D(G(z)): 0.0659 / 0.0635\n",
      "[6/4000][0/600] Loss_D: 0.1689 Loss_G: 4.0778 D(x): 0.9482 D(G(z)): 0.0543 / 0.0391\n",
      "[6/4000][100/600] Loss_D: 0.1177 Loss_G: 4.1545 D(x): 0.9681 D(G(z)): 0.0489 / 0.0396\n",
      "[6/4000][200/600] Loss_D: 0.1109 Loss_G: 4.1085 D(x): 0.9611 D(G(z)): 0.0460 / 0.0387\n",
      "[6/4000][300/600] Loss_D: 0.1258 Loss_G: 4.1783 D(x): 0.9515 D(G(z)): 0.0444 / 0.0341\n",
      "[6/4000][400/600] Loss_D: 0.1103 Loss_G: 3.8319 D(x): 0.9586 D(G(z)): 0.0526 / 0.0406\n",
      "[6/4000][500/600] Loss_D: 0.2525 Loss_G: 3.5775 D(x): 0.9066 D(G(z)): 0.0756 / 0.0687\n",
      "[7/4000][0/600] Loss_D: 0.2865 Loss_G: 4.0141 D(x): 0.8872 D(G(z)): 0.0520 / 0.0465\n",
      "[7/4000][100/600] Loss_D: 0.1952 Loss_G: 4.2610 D(x): 0.9499 D(G(z)): 0.0471 / 0.0412\n",
      "[7/4000][200/600] Loss_D: 0.1716 Loss_G: 3.5190 D(x): 0.9519 D(G(z)): 0.0786 / 0.0525\n",
      "[7/4000][300/600] Loss_D: 0.1748 Loss_G: 3.7782 D(x): 0.9414 D(G(z)): 0.0426 / 0.0356\n",
      "[7/4000][400/600] Loss_D: 0.1249 Loss_G: 3.8327 D(x): 0.9622 D(G(z)): 0.0571 / 0.0494\n",
      "[7/4000][500/600] Loss_D: 0.1273 Loss_G: 3.3972 D(x): 0.9455 D(G(z)): 0.0481 / 0.0446\n",
      "[8/4000][0/600] Loss_D: 0.1458 Loss_G: 3.9861 D(x): 0.9501 D(G(z)): 0.0469 / 0.0419\n",
      "[8/4000][100/600] Loss_D: 0.1197 Loss_G: 4.4349 D(x): 0.9507 D(G(z)): 0.0397 / 0.0351\n",
      "[8/4000][200/600] Loss_D: 0.1258 Loss_G: 4.1831 D(x): 0.9646 D(G(z)): 0.0519 / 0.0398\n",
      "[8/4000][300/600] Loss_D: 0.1578 Loss_G: 4.1769 D(x): 0.9509 D(G(z)): 0.0567 / 0.0403\n",
      "[8/4000][400/600] Loss_D: 0.1303 Loss_G: 4.0567 D(x): 0.9570 D(G(z)): 0.0530 / 0.0427\n",
      "[8/4000][500/600] Loss_D: 0.2715 Loss_G: 3.5448 D(x): 0.9246 D(G(z)): 0.0762 / 0.0803\n",
      "[9/4000][0/600] Loss_D: 0.2412 Loss_G: 4.7126 D(x): 0.9081 D(G(z)): 0.0354 / 0.0330\n",
      "[9/4000][100/600] Loss_D: 0.2192 Loss_G: 4.2275 D(x): 0.9219 D(G(z)): 0.0290 / 0.0309\n",
      "[9/4000][200/600] Loss_D: 0.1545 Loss_G: 4.0230 D(x): 0.9366 D(G(z)): 0.0461 / 0.0353\n",
      "[9/4000][300/600] Loss_D: 0.1920 Loss_G: 3.9555 D(x): 0.9337 D(G(z)): 0.0651 / 0.0471\n",
      "[9/4000][400/600] Loss_D: 0.1427 Loss_G: 4.2804 D(x): 0.9538 D(G(z)): 0.0376 / 0.0327\n",
      "[9/4000][500/600] Loss_D: 0.1630 Loss_G: 3.5189 D(x): 0.9534 D(G(z)): 0.0701 / 0.0590\n",
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      "[10/4000][200/600] Loss_D: 0.1640 Loss_G: 4.1148 D(x): 0.9443 D(G(z)): 0.0454 / 0.0344\n",
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      "[10/4000][400/600] Loss_D: 0.1110 Loss_G: 3.8258 D(x): 0.9714 D(G(z)): 0.0523 / 0.0432\n",
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      "[11/4000][0/600] Loss_D: 0.2576 Loss_G: 4.1064 D(x): 0.9148 D(G(z)): 0.0321 / 0.0376\n",
      "[11/4000][100/600] Loss_D: 0.1720 Loss_G: 4.6965 D(x): 0.9319 D(G(z)): 0.0453 / 0.0370\n",
      "[11/4000][200/600] Loss_D: 0.1852 Loss_G: 4.8085 D(x): 0.9413 D(G(z)): 0.0477 / 0.0342\n",
      "[11/4000][300/600] Loss_D: 0.2178 Loss_G: 4.7535 D(x): 0.9301 D(G(z)): 0.0419 / 0.0332\n",
      "[11/4000][400/600] Loss_D: 0.1047 Loss_G: 4.3306 D(x): 0.9569 D(G(z)): 0.0314 / 0.0267\n",
      "[11/4000][500/600] Loss_D: 0.3046 Loss_G: 3.4021 D(x): 0.9160 D(G(z)): 0.0936 / 0.0899\n",
      "[12/4000][0/600] Loss_D: 0.2531 Loss_G: 4.7488 D(x): 0.9221 D(G(z)): 0.0402 / 0.0378\n",
      "[12/4000][100/600] Loss_D: 0.1239 Loss_G: 4.2564 D(x): 0.9577 D(G(z)): 0.0452 / 0.0361\n",
      "[12/4000][200/600] Loss_D: 0.1882 Loss_G: 4.2550 D(x): 0.9200 D(G(z)): 0.0373 / 0.0286\n",
      "[12/4000][300/600] Loss_D: 0.1465 Loss_G: 4.4029 D(x): 0.9436 D(G(z)): 0.0445 / 0.0345\n",
      "[12/4000][400/600] Loss_D: 0.1528 Loss_G: 5.1419 D(x): 0.9562 D(G(z)): 0.0408 / 0.0257\n",
      "[12/4000][500/600] Loss_D: 0.1873 Loss_G: 3.8282 D(x): 0.9376 D(G(z)): 0.0502 / 0.0480\n",
      "[13/4000][0/600] Loss_D: 0.2298 Loss_G: 4.9067 D(x): 0.9253 D(G(z)): 0.0280 / 0.0282\n",
      "[13/4000][100/600] Loss_D: 0.0710 Loss_G: 5.0316 D(x): 0.9749 D(G(z)): 0.0233 / 0.0190\n",
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      "[13/4000][300/600] Loss_D: 0.1968 Loss_G: 4.8715 D(x): 0.9360 D(G(z)): 0.0498 / 0.0328\n",
      "[13/4000][400/600] Loss_D: 0.1804 Loss_G: 4.4136 D(x): 0.9358 D(G(z)): 0.0386 / 0.0271\n",
      "[13/4000][500/600] Loss_D: 0.3379 Loss_G: 3.5644 D(x): 0.9285 D(G(z)): 0.0920 / 0.0958\n",
      "[14/4000][0/600] Loss_D: 0.1777 Loss_G: 4.7153 D(x): 0.9436 D(G(z)): 0.0356 / 0.0227\n",
      "[14/4000][100/600] Loss_D: 0.1163 Loss_G: 4.1540 D(x): 0.9521 D(G(z)): 0.0390 / 0.0339\n",
      "[14/4000][200/600] Loss_D: 0.2591 Loss_G: 5.5598 D(x): 0.9160 D(G(z)): 0.0317 / 0.0255\n",
      "[14/4000][300/600] Loss_D: 0.3559 Loss_G: 4.0681 D(x): 0.9081 D(G(z)): 0.0763 / 0.0606\n",
      "[14/4000][400/600] Loss_D: 0.3546 Loss_G: 3.6916 D(x): 0.8991 D(G(z)): 0.0591 / 0.0592\n",
      "[14/4000][500/600] Loss_D: 0.2818 Loss_G: 3.2939 D(x): 0.9179 D(G(z)): 0.0762 / 0.0752\n",
      "[15/4000][0/600] Loss_D: 0.2752 Loss_G: 4.3120 D(x): 0.9116 D(G(z)): 0.0562 / 0.0409\n",
      "[15/4000][100/600] Loss_D: 0.2013 Loss_G: 4.3576 D(x): 0.9291 D(G(z)): 0.0483 / 0.0391\n",
      "[15/4000][200/600] Loss_D: 0.2562 Loss_G: 4.7551 D(x): 0.9246 D(G(z)): 0.0572 / 0.0337\n",
      "[15/4000][300/600] Loss_D: 0.2374 Loss_G: 4.1621 D(x): 0.9384 D(G(z)): 0.0629 / 0.0382\n",
      "[15/4000][400/600] Loss_D: 0.2246 Loss_G: 4.2673 D(x): 0.9579 D(G(z)): 0.0739 / 0.0595\n",
      "[15/4000][500/600] Loss_D: 0.4626 Loss_G: 3.5991 D(x): 0.9029 D(G(z)): 0.0950 / 0.0830\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16/4000][0/600] Loss_D: 0.3111 Loss_G: 4.6631 D(x): 0.9173 D(G(z)): 0.0490 / 0.0298\n",
      "[16/4000][100/600] Loss_D: 0.2168 Loss_G: 3.9748 D(x): 0.9185 D(G(z)): 0.0514 / 0.0434\n",
      "[16/4000][200/600] Loss_D: 0.2139 Loss_G: 3.4214 D(x): 0.9595 D(G(z)): 0.0956 / 0.0674\n",
      "[16/4000][300/600] Loss_D: 0.2179 Loss_G: 3.9391 D(x): 0.9377 D(G(z)): 0.0506 / 0.0394\n",
      "[16/4000][400/600] Loss_D: 0.1874 Loss_G: 4.2472 D(x): 0.9392 D(G(z)): 0.0703 / 0.0507\n",
      "[16/4000][500/600] Loss_D: 0.3276 Loss_G: 3.8257 D(x): 0.8834 D(G(z)): 0.0516 / 0.0435\n",
      "[17/4000][0/600] Loss_D: 0.3193 Loss_G: 3.6067 D(x): 0.8978 D(G(z)): 0.0738 / 0.0794\n",
      "[17/4000][100/600] Loss_D: 0.1693 Loss_G: 4.2673 D(x): 0.9341 D(G(z)): 0.0389 / 0.0303\n",
      "[17/4000][200/600] Loss_D: 0.1585 Loss_G: 3.8715 D(x): 0.9612 D(G(z)): 0.0853 / 0.0533\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31/4000][500/600] Loss_D: 0.4482 Loss_G: 3.0086 D(x): 0.8355 D(G(z)): 0.1195 / 0.1058\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-b08d64d40419>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_label\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet_D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m         \u001b[0merrD_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriteion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0merrD_real\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-0001a6f9f072>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m784\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_for_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0;31m# cuBLAS doesn't support 0 strides in sger, so we can't use expand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(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 (data)\n",
    "        net_D.zero_grad()\n",
    "        real, _ = data\n",
    "        input.data.resize_(real.size()).copy_(real)\n",
    "        label.data.resize_(bs).fill_(real_label)\n",
    "\n",
    "        output = net_D(input)\n",
    "        errD_real = criteion(output, label)\n",
    "        errD_real.backward()\n",
    "        D_x = output.data.mean()\n",
    "\n",
    "        #train with fake (generated)\n",
    "        noise.data.resize_(bs, 100, 1, 1)\n",
    "        noise.data.normal_(0, 1)\n",
    "        fake = net_G(noise)\n",
    "        label.data.fill_(fake_label)\n",
    "        output = net_D(fake.detach())\n",
    "        errD_fake = criteion(output, label)\n",
    "        errD_fake.backward()\n",
    "        D_G_z1 = output.data.mean()\n",
    "\n",
    "        errD = errD_real + errD_fake\n",
    "        optimizerD.step()\n",
    "\n",
    "        ############################\n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        ###########################\n",
    "        net_G.zero_grad()\n",
    "        label.data.fill_(real_label)\n",
    "        output = net_D(fake)\n",
    "        errG = criteion(output, label)\n",
    "        errG.backward()\n",
    "        D_G_z2 = output.data.mean()\n",
    "        optimizerG.step()\n",
    "        if i % 100 == 0:\n",
    "            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n",
    "                 % (epoch, niter, i, len(dataloader),\n",
    "                   errD.data[0], errG.data[0],  D_x, D_G_z1, D_G_z2))\n",
    "    if epoch % 10 == 0:\n",
    "        fake = net_G(fixed_noise)\n",
    "        vutils.save_image(fake.data, '%s/fake_samples_epoch_%03d.png'\n",
    "                              % ('results', epoch),normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fake = net_G(fixed_noise)\n",
    "vutils.save_image(fake.data[:64], '%s/fake_samples2.png' % 'results' ,normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f123d862f60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pnz5cyhUEAZ06dbqhXocV38edgDKUUnTq1Ak+Pj44fvw4Jk2ahPz8fHTs2JHr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\nO3uhl/BzCEafuOT9MyMjIxSy1y7ls3btWlF3FRUVgq+zi2n1fKPAJSo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f123d925ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "im = Image.open(\"results/fake_samples2.png\", \"r\")\n",
    "plt.imshow(np.array(im))"
   ]
  },
  {
   "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"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "83px",
    "width": "254px"
   },
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}