{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DCGAN for MNIST (PyTorch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arXiv: https://arxiv.org/abs/1511.06434\n",
    "\n",
    "<img src=\"https://lh3.googleusercontent.com/M0D8jzjxyELjUFLVyZYF0UQVuYyLywwIorNRhyOwSeOsATqK_tOZS3cHAnHZSP5I-fmjSCFGB5Ztrxmd7SJpY2amIXOox7H_OVZhGZMqdGk5yUcrDK4Qh5941rMLfW_N67eNAyetS0lB6LlAShsdtqGJCfBrW5a7EBXJnmpM0v640_5-xFDgsxIaC5IliQqSxoz5cYhK14d3tG2V_Qa39CZ4yKlQNnZb0OhWRTezWoFQ3uZdK2RQHQ22PA3CrEW27kTVl95688fhA1EKvZloEZ7YZolxErdsx9Lc3zcE8kiI0gmqErKSsOOImwnL9ESC6rPosze4CSvs8-bSUOLXMyO8fQIDvObuRFfYT6TAflAYSp8PXCevA2LhvUCFat5YV5N6XpFmKoZam6w-lX8koEEeZI6djv8z5eETO3ab1Fhn7_UB1hper6kScrtj_4CRdeP_mvuALhRQvEB81_jV6N5KIFTJ2b7oCoo8to32XZvmk42w6IR8WasMtOQJB3yeKWY5mGAtoLOpuJ4H7r0KAXxbaHpDvltlIR227Ria2-7FSfSJTnaA3bv1MjHpLALaNnelZAwySJynzP0lof8kz9EiQCsjSL2TN-pudLC7jWHnyy-3FAKuj6sK=w807-h208-no\">\n",
    "\n",
    "<img src=\"https://lh3.googleusercontent.com/CqWKmMExGj3Ws9_vYROf2hnnV-HH5hlzvga0Dv_NrHXZKTwoSFWNZ4GO3ee9dVar-kCZmNkpLxkTZ9dLQj9uKeY__ys608i7jfWQqoIRrF2NU-ESZBiWbSxqeEwOICy-W-oWYDAK9Cxse4JPOoWOxFrfaMaNg-cC7UsJCa-PTtt8G11wENorShWIaa2_2dAwn92kaPj8bABqKLRuDEcOxwLdZpnGQOWZPRNgVprefoStGMnfpyRXb9fx-yoBhgSExMv-ybPZLbbymZ8PdMY8bCiNZZ0MRvjvRe-hYGJ_4fWwjmNRaB2ixKLjaaM0Ha2xLA3G_zbpfEo9ygLGIufntEWX6M3mXz-ctK3QrninW3Bfia1IYNQFKJUVnaGvZf6-bFswghn-ody073j89m6fYYysYrVzxxE9HQJeKGuwQKBkG1e7mG1PPwnr-hovxLiHRduMmi0BjNYxOtSYkffBPYzR9n8h66qC-AepoKAGO-Z7LBAuBKpwflhih1Ho9ttBRxBYTwkVeYOMIV8aQkNNY9CKbC-VazHnN7x-eWV0us2tDrJCOcYU3wjBMi-76oalxldFxQQ9MQy1x5SWkiI6vEW_hCbaAlWGJB3bud91PbciIuTk-LIH9Nh4=w805-h350-no\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Convolution GANに以下の改善を行う。\n",
    "- すべてのプーリングレイヤを strided convolutions(discriminator)と fractional-stirided convolutions(generator)に変更する。\n",
    "- generator と discriminator に batchnormを使う。\n",
    "- 全結合隠れ層を取り除く。\n",
    "- ReLU 活性関数を generatorで使う。ただし、output層は tanhを使う。\n",
    "- LeakyReLU活性関数をdiscriminatorのすべての層で使う。\n",
    "\n",
    "もとい！\n",
    "\n",
    "公式チュートリアルにサンプルコードが公開されているので、それを参考に実装する。\n",
    "- [examples/dcgan at master · pytorch/examples](https://github.com/pytorch/examples/tree/master/dcgan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "import torchvision\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": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bs = 100\n",
    "sz = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision.transforms import ToTensor\n",
    "imagenet_data = ImageFolder('/home/ubuntu/cutting-edge-dl-for-coders-part2/data/default/', \n",
    "                            transform=transforms.Compose([\n",
    "                            transforms.Scale(sz),\n",
    "                            transforms.ToTensor()]))\n",
    "dataloader = torch.utils.data.DataLoader(imagenet_data,\n",
    "                                         batch_size=bs,\n",
    "                                         shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nz = 100\n",
    "ngf = 32\n",
    "ndf = 32\n",
    "nc = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 2),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\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",
    "            nn.Conv2d(ndf * 4, 1 , 4, 1, 0, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        #x = x.view(100, -1)\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.ConvTranspose2d(nz, ngf * 4, 4, 1, 0, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 4),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 2),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d( ngf,nc, 4, 2, 1, bias=False),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x = x.view(bs,100)\n",
    "        x = self.main(x)\n",
    "        #x = x.view(-1, 1, sz, sz)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netD (\n",
      "  (main): Sequential (\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): LeakyReLU (0.2, inplace)\n",
      "    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (4): LeakyReLU (0.2, inplace)\n",
      "    (5): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (7): LeakyReLU (0.2, inplace)\n",
      "    (8): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (9): Sigmoid ()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(net_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netG (\n",
      "  (main): Sequential (\n",
      "    (0): ConvTranspose2d(100, 128, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (2): ReLU (inplace)\n",
      "    (3): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (5): ReLU (inplace)\n",
      "    (6): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)\n",
      "    (8): ReLU (inplace)\n",
      "    (9): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (10): Tanh ()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(net_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "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": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input = t.FloatTensor(bs, nc, sz, sz)\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": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "niter = 4000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0/4000][0/100] Loss_D: 0.4049 Loss_G: 1.6940 D(x): 0.8760 D(G(z)): 0.2310 / 0.1891\n",
      "[1/4000][0/100] Loss_D: 0.2647 Loss_G: 2.2955 D(x): 0.8981 D(G(z)): 0.1360 / 0.1052\n",
      "[2/4000][0/100] Loss_D: 0.3044 Loss_G: 2.3675 D(x): 0.8820 D(G(z)): 0.1496 / 0.1069\n",
      "[3/4000][0/100] Loss_D: 0.3290 Loss_G: 2.3378 D(x): 0.8591 D(G(z)): 0.1450 / 0.1067\n",
      "[4/4000][0/100] Loss_D: 0.4345 Loss_G: 2.4491 D(x): 0.7695 D(G(z)): 0.1272 / 0.0943\n",
      "[5/4000][0/100] Loss_D: 0.4679 Loss_G: 2.1508 D(x): 0.8217 D(G(z)): 0.2107 / 0.1351\n",
      "[6/4000][0/100] Loss_D: 0.2948 Loss_G: 2.7199 D(x): 0.8445 D(G(z)): 0.1039 / 0.0771\n",
      "[7/4000][0/100] Loss_D: 0.4960 Loss_G: 2.2441 D(x): 0.7508 D(G(z)): 0.1524 / 0.1188\n",
      "[8/4000][0/100] Loss_D: 0.3113 Loss_G: 2.7032 D(x): 0.8493 D(G(z)): 0.1237 / 0.0773\n",
      "[9/4000][0/100] Loss_D: 0.1932 Loss_G: 3.0354 D(x): 0.9071 D(G(z)): 0.0833 / 0.0543\n",
      "[10/4000][0/100] Loss_D: 0.3096 Loss_G: 2.9119 D(x): 0.8532 D(G(z)): 0.1135 / 0.0691\n",
      "[11/4000][0/100] Loss_D: 0.2173 Loss_G: 3.0430 D(x): 0.9015 D(G(z)): 0.0969 / 0.0599\n",
      "[12/4000][0/100] Loss_D: 0.4308 Loss_G: 2.5355 D(x): 0.8060 D(G(z)): 0.1523 / 0.1078\n",
      "[13/4000][0/100] Loss_D: 0.4249 Loss_G: 2.5583 D(x): 0.8081 D(G(z)): 0.1475 / 0.1103\n",
      "[14/4000][0/100] Loss_D: 0.3359 Loss_G: 3.0752 D(x): 0.8209 D(G(z)): 0.0874 / 0.0569\n",
      "[15/4000][0/100] Loss_D: 0.2054 Loss_G: 3.1428 D(x): 0.9062 D(G(z)): 0.0878 / 0.0644\n",
      "[16/4000][0/100] Loss_D: 0.3651 Loss_G: 2.8941 D(x): 0.8339 D(G(z)): 0.1315 / 0.0719\n",
      "[17/4000][0/100] Loss_D: 0.1666 Loss_G: 3.8871 D(x): 0.9055 D(G(z)): 0.0497 / 0.0282\n",
      "[18/4000][0/100] Loss_D: 0.2157 Loss_G: 3.4911 D(x): 0.8861 D(G(z)): 0.0682 / 0.0473\n",
      "[19/4000][0/100] Loss_D: 0.2218 Loss_G: 3.2230 D(x): 0.9060 D(G(z)): 0.1007 / 0.0564\n",
      "[20/4000][0/100] Loss_D: 0.3289 Loss_G: 2.7762 D(x): 0.8723 D(G(z)): 0.1463 / 0.0834\n",
      "[21/4000][0/100] Loss_D: 0.2807 Loss_G: 3.0610 D(x): 0.8665 D(G(z)): 0.1021 / 0.0681\n",
      "[22/4000][0/100] Loss_D: 0.3187 Loss_G: 2.5577 D(x): 0.8789 D(G(z)): 0.1419 / 0.1080\n",
      "[23/4000][0/100] Loss_D: 0.1368 Loss_G: 4.1411 D(x): 0.9399 D(G(z)): 0.0628 / 0.0330\n",
      "[24/4000][0/100] Loss_D: 0.3456 Loss_G: 2.9579 D(x): 0.8306 D(G(z)): 0.0955 / 0.0757\n",
      "[25/4000][0/100] Loss_D: 0.1948 Loss_G: 3.0858 D(x): 0.9399 D(G(z)): 0.1073 / 0.0733\n",
      "[26/4000][0/100] Loss_D: 0.3855 Loss_G: 3.2845 D(x): 0.8243 D(G(z)): 0.1190 / 0.0609\n",
      "[27/4000][0/100] Loss_D: 0.4120 Loss_G: 2.7199 D(x): 0.8096 D(G(z)): 0.1150 / 0.0957\n",
      "[28/4000][0/100] Loss_D: 0.4894 Loss_G: 3.0436 D(x): 0.7800 D(G(z)): 0.1048 / 0.0833\n",
      "[29/4000][0/100] Loss_D: 0.8739 Loss_G: 2.1338 D(x): 0.8151 D(G(z)): 0.4243 / 0.1596\n",
      "[30/4000][0/100] Loss_D: 0.2988 Loss_G: 2.4274 D(x): 0.9150 D(G(z)): 0.1666 / 0.1281\n",
      "[31/4000][0/100] Loss_D: 0.3170 Loss_G: 2.5486 D(x): 0.8588 D(G(z)): 0.1319 / 0.1058\n",
      "[32/4000][0/100] Loss_D: 0.4307 Loss_G: 2.4441 D(x): 0.8024 D(G(z)): 0.1473 / 0.1141\n",
      "[33/4000][0/100] Loss_D: 0.4028 Loss_G: 2.2585 D(x): 0.8488 D(G(z)): 0.1820 / 0.1474\n",
      "[34/4000][0/100] Loss_D: 0.4683 Loss_G: 2.7041 D(x): 0.8207 D(G(z)): 0.1702 / 0.1119\n",
      "[35/4000][0/100] Loss_D: 0.3071 Loss_G: 2.7606 D(x): 0.8926 D(G(z)): 0.1446 / 0.0969\n",
      "[36/4000][0/100] Loss_D: 0.3540 Loss_G: 2.9385 D(x): 0.8197 D(G(z)): 0.1111 / 0.0799\n",
      "[37/4000][0/100] Loss_D: 0.3580 Loss_G: 2.5705 D(x): 0.8475 D(G(z)): 0.1458 / 0.1012\n",
      "[38/4000][0/100] Loss_D: 0.2469 Loss_G: 2.9230 D(x): 0.8971 D(G(z)): 0.1123 / 0.0787\n",
      "[39/4000][0/100] Loss_D: 0.4518 Loss_G: 2.5479 D(x): 0.8344 D(G(z)): 0.1818 / 0.1190\n",
      "[40/4000][0/100] Loss_D: 0.2763 Loss_G: 2.9390 D(x): 0.8634 D(G(z)): 0.0959 / 0.0760\n",
      "[41/4000][0/100] Loss_D: 0.2932 Loss_G: 2.8342 D(x): 0.8652 D(G(z)): 0.1097 / 0.0836\n",
      "[42/4000][0/100] Loss_D: 0.4925 Loss_G: 2.1592 D(x): 0.8041 D(G(z)): 0.1803 / 0.1609\n",
      "[43/4000][0/100] Loss_D: 0.3032 Loss_G: 2.8758 D(x): 0.8718 D(G(z)): 0.1301 / 0.0931\n",
      "[44/4000][0/100] Loss_D: 0.7978 Loss_G: 2.3334 D(x): 0.7165 D(G(z)): 0.2316 / 0.1551\n",
      "[45/4000][0/100] Loss_D: 0.4061 Loss_G: 2.6933 D(x): 0.8499 D(G(z)): 0.1708 / 0.1179\n",
      "[46/4000][0/100] Loss_D: 0.3570 Loss_G: 2.9281 D(x): 0.8629 D(G(z)): 0.1557 / 0.0922\n",
      "[47/4000][0/100] Loss_D: 0.4717 Loss_G: 2.5250 D(x): 0.7754 D(G(z)): 0.1376 / 0.1167\n",
      "[48/4000][0/100] Loss_D: 0.3429 Loss_G: 2.7918 D(x): 0.8810 D(G(z)): 0.1408 / 0.0927\n",
      "[49/4000][0/100] Loss_D: 0.4571 Loss_G: 2.8704 D(x): 0.7889 D(G(z)): 0.1170 / 0.0915\n",
      "[50/4000][0/100] Loss_D: 0.3740 Loss_G: 2.5480 D(x): 0.9179 D(G(z)): 0.2183 / 0.1302\n",
      "[51/4000][0/100] Loss_D: 0.3307 Loss_G: 2.8535 D(x): 0.8890 D(G(z)): 0.1598 / 0.0947\n",
      "[52/4000][0/100] Loss_D: 0.2358 Loss_G: 3.5288 D(x): 0.9238 D(G(z)): 0.1252 / 0.0533\n",
      "[53/4000][0/100] Loss_D: 0.4603 Loss_G: 2.8176 D(x): 0.8426 D(G(z)): 0.1693 / 0.1123\n",
      "[54/4000][0/100] Loss_D: 0.3713 Loss_G: 3.0801 D(x): 0.8447 D(G(z)): 0.1322 / 0.0873\n",
      "[55/4000][0/100] Loss_D: 0.4645 Loss_G: 2.4591 D(x): 0.8610 D(G(z)): 0.2110 / 0.1420\n",
      "[56/4000][0/100] Loss_D: 0.3985 Loss_G: 2.6912 D(x): 0.8415 D(G(z)): 0.1548 / 0.1063\n",
      "[57/4000][0/100] Loss_D: 0.5195 Loss_G: 2.4593 D(x): 0.8152 D(G(z)): 0.2108 / 0.1278\n",
      "[58/4000][0/100] Loss_D: 0.2022 Loss_G: 3.4499 D(x): 0.9186 D(G(z)): 0.0958 / 0.0580\n",
      "[59/4000][0/100] Loss_D: 0.2689 Loss_G: 3.1476 D(x): 0.8824 D(G(z)): 0.1072 / 0.0763\n",
      "[60/4000][0/100] Loss_D: 0.3676 Loss_G: 3.0577 D(x): 0.8585 D(G(z)): 0.1328 / 0.0908\n",
      "[61/4000][0/100] Loss_D: 0.2325 Loss_G: 3.1628 D(x): 0.9025 D(G(z)): 0.1061 / 0.0633\n",
      "[62/4000][0/100] Loss_D: 0.2838 Loss_G: 2.9311 D(x): 0.8964 D(G(z)): 0.1196 / 0.0921\n",
      "[63/4000][0/100] Loss_D: 0.4589 Loss_G: 3.0041 D(x): 0.8375 D(G(z)): 0.1925 / 0.1018\n",
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      "[74/4000][0/100] Loss_D: 0.6967 Loss_G: 2.4096 D(x): 0.7328 D(G(z)): 0.2127 / 0.1509\n",
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      "[76/4000][0/100] Loss_D: 0.4404 Loss_G: 2.5271 D(x): 0.8434 D(G(z)): 0.1726 / 0.1274\n",
      "[77/4000][0/100] Loss_D: 0.2834 Loss_G: 3.5429 D(x): 0.8667 D(G(z)): 0.0751 / 0.0526\n",
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      "[82/4000][0/100] Loss_D: 0.4737 Loss_G: 2.6769 D(x): 0.7827 D(G(z)): 0.1383 / 0.1206\n",
      "[83/4000][0/100] Loss_D: 0.4637 Loss_G: 2.3420 D(x): 0.8602 D(G(z)): 0.2162 / 0.1571\n",
      "[84/4000][0/100] Loss_D: 0.6439 Loss_G: 2.0996 D(x): 0.7643 D(G(z)): 0.2317 / 0.1812\n",
      "[85/4000][0/100] Loss_D: 0.6072 Loss_G: 2.5143 D(x): 0.7432 D(G(z)): 0.1842 / 0.1387\n",
      "[86/4000][0/100] Loss_D: 0.6275 Loss_G: 2.1913 D(x): 0.7697 D(G(z)): 0.2308 / 0.1710\n",
      "[87/4000][0/100] Loss_D: 0.6701 Loss_G: 2.0496 D(x): 0.7347 D(G(z)): 0.2274 / 0.1828\n",
      "[88/4000][0/100] Loss_D: 0.4150 Loss_G: 2.7037 D(x): 0.8449 D(G(z)): 0.1834 / 0.1143\n",
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      "[90/4000][0/100] Loss_D: 0.6238 Loss_G: 2.3890 D(x): 0.7296 D(G(z)): 0.1812 / 0.1513\n",
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      "[93/4000][0/100] Loss_D: 0.5013 Loss_G: 2.4595 D(x): 0.7766 D(G(z)): 0.1526 / 0.1258\n",
      "[94/4000][0/100] Loss_D: 0.5064 Loss_G: 2.2673 D(x): 0.8044 D(G(z)): 0.1989 / 0.1460\n",
      "[95/4000][0/100] Loss_D: 0.3882 Loss_G: 2.4698 D(x): 0.8672 D(G(z)): 0.1720 / 0.1450\n",
      "[96/4000][0/100] Loss_D: 0.6628 Loss_G: 2.1897 D(x): 0.7633 D(G(z)): 0.2371 / 0.1767\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[97/4000][0/100] Loss_D: 0.4624 Loss_G: 2.5559 D(x): 0.8429 D(G(z)): 0.1850 / 0.1312\n",
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      "[172/4000][0/100] Loss_D: 1.0011 Loss_G: 1.6177 D(x): 0.7124 D(G(z)): 0.3774 / 0.2759\n",
      "[173/4000][0/100] Loss_D: 0.4390 Loss_G: 2.4449 D(x): 0.8120 D(G(z)): 0.1613 / 0.1328\n",
      "[174/4000][0/100] Loss_D: 0.3941 Loss_G: 2.9966 D(x): 0.7948 D(G(z)): 0.1035 / 0.0820\n",
      "[175/4000][0/100] Loss_D: 0.8711 Loss_G: 1.6038 D(x): 0.7221 D(G(z)): 0.3213 / 0.2682\n",
      "[176/4000][0/100] Loss_D: 0.6338 Loss_G: 1.9471 D(x): 0.7781 D(G(z)): 0.2568 / 0.2140\n",
      "[177/4000][0/100] Loss_D: 0.7366 Loss_G: 1.8599 D(x): 0.6996 D(G(z)): 0.2395 / 0.2204\n",
      "[178/4000][0/100] Loss_D: 0.8713 Loss_G: 1.7078 D(x): 0.6542 D(G(z)): 0.2599 / 0.2363\n",
      "[179/4000][0/100] Loss_D: 0.7214 Loss_G: 1.7861 D(x): 0.7193 D(G(z)): 0.2706 / 0.2286\n",
      "[180/4000][0/100] Loss_D: 0.5402 Loss_G: 1.8803 D(x): 0.8278 D(G(z)): 0.2449 / 0.2172\n"
     ]
    }
   ],
   "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",
    "        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",
    "        \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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fake = net_G(fixed_noise)\n",
    "vutils.save_image(fake.data[:64], '%s/fake_samples4.png' % 'results' ,normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa12fcd8a90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+j90g4GR+wnFV4p2n7Rq6p5cP3uHrBa9evk77ecmj6YLzly+iZcDlLCdgwPb5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yuqBwEikN/UQzKS6TXdMWtGZG1FzDhRLtBbHSFLYlVAIUqMByY6vPxo0b3Or28KJCdCHLBphy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa17c072d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "im = Image.open(\"results/fake_samples4.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
}