GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 라이브러리 및 데이터 불러오기\n",
    "# 필요한 라이브러리를 불러온다.\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import pickle\n",
    "\n",
    "# 데이터 전처리 방식을 지정한다.\n",
    "transform = transforms.Compose([\n",
    "        transforms.ToTensor(), # 데이터를 PyTorch의 Tensor 형식으로 바꾼다.\n",
    "        transforms.Normalize(mean=(0.5,), std=(0.5,)) # 픽셀값 0 ~ 1 -> -1 ~ 1\n",
    "])\n",
    "\n",
    "# MNIST 데이터셋을 불러온다. 지정한 폴더에 없을 경우 자동으로 다운로드한다.\n",
    "mnist = datasets.MNIST(root='data', download=True, transform=transform)\n",
    "\n",
    "# 데이터를 한번에 batch_size만큼만 가져오는 dataloader를 만든다.\n",
    "dataloader = DataLoader(mnist, batch_size=60, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import imageio\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    use_gpu = True\n",
    "leave_log = True\n",
    "if leave_log:\n",
    "    result_dir = 'GAN_generated_images'\n",
    "    if not os.path.isdir(result_dir):\n",
    "        os.mkdir(result_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### GAN의 생성자(Generator)\n",
    "# 생성자는 랜덤 벡터 z를 입력으로 받아 가짜 이미지를 출력한다.\n",
    "class Generator(nn.Module):\n",
    "\n",
    "    # 네트워크 구조\n",
    "    def __init__(self):\n",
    "        super(Generator, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            nn.Linear(in_features=100, out_features=256),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(in_features=256, out_features=512),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(in_features=512, out_features=1024),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Linear(in_features=1024, out_features=28*28),\n",
    "            nn.Tanh())\n",
    "    \n",
    "    # (batch_size x 100) 크기의 랜덤 벡터를 받아 \n",
    "    # 이미지를 (batch_size x 1 x 28 x 28) 크기로 출력한다.\n",
    "    def forward(self, inputs):\n",
    "        return self.main(inputs).view(-1, 1, 28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### GAN의 구분자(Discriminator)\n",
    "# 구분자는 이미지를 입력으로 받아 이미지가 진짜인지 가짜인지 출력한다.\n",
    "class Discriminator(nn.Module):\n",
    "    \n",
    "    # 네트워크 구조\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.main = nn.Sequential(\n",
    "            nn.Linear(in_features=28*28, out_features=1024),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Dropout(inplace=True),\n",
    "            nn.Linear(in_features=1024, out_features=512),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Dropout(inplace=True),\n",
    "            nn.Linear(in_features=512, out_features=256),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            nn.Dropout(inplace=True),\n",
    "            nn.Linear(in_features=256, out_features=1),\n",
    "            nn.Sigmoid())\n",
    "    \n",
    "    # (batch_size x 1 x 28 x 28) 크기의 이미지를 받아\n",
    "    # 이미지가 진짜일 확률을 0~1 사이로 출력한다.\n",
    "    def forward(self, inputs):\n",
    "        inputs = inputs.view(-1, 28*28)\n",
    "        return self.main(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### 생성자와 구분자 객체 만들기\n",
    "G = Generator()\n",
    "D = Discriminator()\n",
    "\n",
    "if use_gpu:\n",
    "    G.cuda()\n",
    "    D.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 손실 함수와 최적화 기법 지정하기\n",
    "# Binary Cross Entropy loss\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "# 생성자의 매개 변수를 최적화하는 Adam optimizer\n",
    "G_optimizer = Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
    "# 구분자의 매개 변수를 최적화하는 Adam optimizer\n",
    "D_optimizer = Adam(D.parameters(), lr=0.0002, betas=(0.5, 0.999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습 결과 시각화하기\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def square_plot(data, path):\n",
    "    \"\"\"Take an array of shape (n, height, width) or (n, height, width , 3)\n",
    "       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
    "\n",
    "    if type(data) == list:\n",
    "\t    data = np.concatenate(data)\n",
    "    # normalize data for display\n",
    "    data = (data - data.min()) / (data.max() - data.min())\n",
    "\n",
    "    # force the number of filters to be square\n",
    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
    "\n",
    "    padding = (((0, n ** 2 - data.shape[0]) ,\n",
    "                (0, 1), (0, 1))  # add some space between filters\n",
    "               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\n",
    "    data = np.pad(data , padding, mode='constant' , constant_values=1)  # pad with ones (white)\n",
    "\n",
    "    # tilethe filters into an image\n",
    "    data = data.reshape((n , n) + data.shape[1:]).transpose((0 , 2 , 1 , 3) + tuple(range(4 , data.ndim + 1)))\n",
    "\n",
    "    data = data.reshape((n * data.shape[1] , n * data.shape[3]) + data.shape[4:])\n",
    "\n",
    "    plt.imsave(path, data, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if leave_log:\n",
    "    train_hist = {}\n",
    "    train_hist['D_losses'] = []\n",
    "    train_hist['G_losses'] = []\n",
    "    generated_images = []\n",
    "    \n",
    "z_fixed = Variable(torch.randn(5 * 5, 100), volatile=True)\n",
    "if use_gpu:\n",
    "    z_fixed = z_fixed.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0   D Loss: 0.863213 G Loss: 2.02531  True Positive Rate: 80.0% True Negative Rate: 96.7%\n",
      "Epoch: 1   D Loss: 0.588318 G Loss: 2.8339   True Positive Rate: 90.0% True Negative Rate: 98.3%\n",
      "Epoch: 2   D Loss: 0.641012 G Loss: 2.36517  True Positive Rate: 83.3% True Negative Rate: 100.0%\n",
      "Epoch: 3   D Loss: 0.654862 G Loss: 2.14756  True Positive Rate: 85.0% True Negative Rate: 95.0%\n",
      "Epoch: 4   D Loss: 0.800644 G Loss: 1.77717  True Positive Rate: 68.3% True Negative Rate: 88.3%\n",
      "Epoch: 5   D Loss: 0.898469 G Loss: 1.55838  True Positive Rate: 88.3% True Negative Rate: 98.3%\n",
      "Epoch: 6   D Loss: 1.01998  G Loss: 1.29777  True Positive Rate: 66.7% True Negative Rate: 75.0%\n",
      "Epoch: 7   D Loss: 1.08073  G Loss: 1.17069  True Positive Rate: 76.7% True Negative Rate: 91.7%\n",
      "Epoch: 8   D Loss: 1.15293  G Loss: 1.06377  True Positive Rate: 66.7% True Negative Rate: 76.7%\n",
      "Epoch: 9   D Loss: 1.17763  G Loss: 1.0239   True Positive Rate: 58.3% True Negative Rate: 90.0%\n",
      "Epoch: 10  D Loss: 1.18642  G Loss: 1.00436  True Positive Rate: 51.7% True Negative Rate: 78.3%\n",
      "Epoch: 11  D Loss: 1.19234  G Loss: 0.998511 True Positive Rate: 55.0% True Negative Rate: 73.3%\n",
      "Epoch: 12  D Loss: 1.20263  G Loss: 0.979631 True Positive Rate: 61.7% True Negative Rate: 70.0%\n",
      "Epoch: 13  D Loss: 1.20182  G Loss: 0.977905 True Positive Rate: 43.3% True Negative Rate: 71.7%\n",
      "Epoch: 14  D Loss: 1.22249  G Loss: 0.950691 True Positive Rate: 70.0% True Negative Rate: 66.7%\n",
      "Epoch: 15  D Loss: 1.24068  G Loss: 0.923008 True Positive Rate: 60.0% True Negative Rate: 76.7%\n",
      "Epoch: 16  D Loss: 1.2506   G Loss: 0.905336 True Positive Rate: 56.7% True Negative Rate: 66.7%\n",
      "Epoch: 17  D Loss: 1.25427  G Loss: 0.907931 True Positive Rate: 65.0% True Negative Rate: 63.3%\n",
      "Epoch: 18  D Loss: 1.26299  G Loss: 0.89341  True Positive Rate: 50.0% True Negative Rate: 71.7%\n",
      "Epoch: 19  D Loss: 1.26346  G Loss: 0.887841 True Positive Rate: 51.7% True Negative Rate: 70.0%\n",
      "Epoch: 20  D Loss: 1.26776  G Loss: 0.884488 True Positive Rate: 58.3% True Negative Rate: 78.3%\n",
      "Epoch: 21  D Loss: 1.26183  G Loss: 0.890224 True Positive Rate: 51.7% True Negative Rate: 60.0%\n",
      "Epoch: 22  D Loss: 1.26986  G Loss: 0.87816  True Positive Rate: 68.3% True Negative Rate: 55.0%\n",
      "Epoch: 23  D Loss: 1.27067  G Loss: 0.877424 True Positive Rate: 55.0% True Negative Rate: 70.0%\n",
      "Epoch: 24  D Loss: 1.27121  G Loss: 0.878668 True Positive Rate: 73.3% True Negative Rate: 70.0%\n",
      "Epoch: 25  D Loss: 1.27081  G Loss: 0.877486 True Positive Rate: 53.3% True Negative Rate: 80.0%\n",
      "Epoch: 26  D Loss: 1.26598  G Loss: 0.884247 True Positive Rate: 60.0% True Negative Rate: 73.3%\n",
      "Epoch: 27  D Loss: 1.27128  G Loss: 0.878305 True Positive Rate: 53.3% True Negative Rate: 71.7%\n",
      "Epoch: 28  D Loss: 1.27847  G Loss: 0.865866 True Positive Rate: 55.0% True Negative Rate: 71.7%\n",
      "Epoch: 29  D Loss: 1.27558  G Loss: 0.871415 True Positive Rate: 65.0% True Negative Rate: 76.7%\n",
      "Epoch: 30  D Loss: 1.26968  G Loss: 0.881089 True Positive Rate: 60.0% True Negative Rate: 55.0%\n",
      "Epoch: 31  D Loss: 1.26832  G Loss: 0.885176 True Positive Rate: 66.7% True Negative Rate: 73.3%\n",
      "Epoch: 32  D Loss: 1.27113  G Loss: 0.879169 True Positive Rate: 50.0% True Negative Rate: 60.0%\n",
      "Epoch: 33  D Loss: 1.27026  G Loss: 0.885199 True Positive Rate: 58.3% True Negative Rate: 80.0%\n",
      "Epoch: 34  D Loss: 1.27038  G Loss: 0.87875  True Positive Rate: 66.7% True Negative Rate: 68.3%\n",
      "Epoch: 35  D Loss: 1.27052  G Loss: 0.879328 True Positive Rate: 46.7% True Negative Rate: 60.0%\n",
      "Epoch: 36  D Loss: 1.26955  G Loss: 0.885118 True Positive Rate: 58.3% True Negative Rate: 71.7%\n",
      "Epoch: 37  D Loss: 1.26334  G Loss: 0.895759 True Positive Rate: 51.7% True Negative Rate: 66.7%\n",
      "Epoch: 38  D Loss: 1.26675  G Loss: 0.88641  True Positive Rate: 61.7% True Negative Rate: 71.7%\n",
      "Epoch: 39  D Loss: 1.2734   G Loss: 0.877009 True Positive Rate: 55.0% True Negative Rate: 70.0%\n",
      "Epoch: 40  D Loss: 1.26453  G Loss: 0.887025 True Positive Rate: 60.0% True Negative Rate: 55.0%\n",
      "Epoch: 41  D Loss: 1.26503  G Loss: 0.886018 True Positive Rate: 53.3% True Negative Rate: 66.7%\n",
      "Epoch: 42  D Loss: 1.26548  G Loss: 0.889935 True Positive Rate: 70.0% True Negative Rate: 73.3%\n",
      "Epoch: 43  D Loss: 1.26924  G Loss: 0.882719 True Positive Rate: 66.7% True Negative Rate: 68.3%\n",
      "Epoch: 44  D Loss: 1.27167  G Loss: 0.88028  True Positive Rate: 63.3% True Negative Rate: 71.7%\n",
      "Epoch: 45  D Loss: 1.26691  G Loss: 0.887079 True Positive Rate: 56.7% True Negative Rate: 80.0%\n",
      "Epoch: 46  D Loss: 1.26559  G Loss: 0.891199 True Positive Rate: 61.7% True Negative Rate: 73.3%\n",
      "Epoch: 47  D Loss: 1.26742  G Loss: 0.886114 True Positive Rate: 56.7% True Negative Rate: 68.3%\n",
      "Epoch: 48  D Loss: 1.26816  G Loss: 0.890576 True Positive Rate: 61.7% True Negative Rate: 78.3%\n",
      "Epoch: 49  D Loss: 1.26476  G Loss: 0.890051 True Positive Rate: 45.0% True Negative Rate: 70.0%\n",
      "Epoch: 50  D Loss: 1.27259  G Loss: 0.878683 True Positive Rate: 65.0% True Negative Rate: 73.3%\n",
      "Epoch: 51  D Loss: 1.2676   G Loss: 0.887603 True Positive Rate: 55.0% True Negative Rate: 80.0%\n",
      "Epoch: 52  D Loss: 1.26999  G Loss: 0.884101 True Positive Rate: 66.7% True Negative Rate: 65.0%\n",
      "Epoch: 53  D Loss: 1.26571  G Loss: 0.885341 True Positive Rate: 51.7% True Negative Rate: 61.7%\n",
      "Epoch: 54  D Loss: 1.26892  G Loss: 0.883143 True Positive Rate: 60.0% True Negative Rate: 65.0%\n",
      "Epoch: 55  D Loss: 1.26653  G Loss: 0.885291 True Positive Rate: 58.3% True Negative Rate: 73.3%\n",
      "Epoch: 56  D Loss: 1.27076  G Loss: 0.879968 True Positive Rate: 55.0% True Negative Rate: 75.0%\n",
      "Epoch: 57  D Loss: 1.2662   G Loss: 0.888148 True Positive Rate: 61.7% True Negative Rate: 66.7%\n",
      "Epoch: 58  D Loss: 1.26939  G Loss: 0.885148 True Positive Rate: 68.3% True Negative Rate: 75.0%\n",
      "Epoch: 59  D Loss: 1.27361  G Loss: 0.878795 True Positive Rate: 55.0% True Negative Rate: 58.3%\n",
      "Epoch: 60  D Loss: 1.26921  G Loss: 0.882433 True Positive Rate: 65.0% True Negative Rate: 70.0%\n",
      "Epoch: 61  D Loss: 1.27305  G Loss: 0.879444 True Positive Rate: 61.7% True Negative Rate: 58.3%\n",
      "Epoch: 62  D Loss: 1.27762  G Loss: 0.87216  True Positive Rate: 66.7% True Negative Rate: 56.7%\n",
      "Epoch: 63  D Loss: 1.27427  G Loss: 0.878386 True Positive Rate: 68.3% True Negative Rate: 80.0%\n",
      "Epoch: 64  D Loss: 1.26874  G Loss: 0.880613 True Positive Rate: 61.7% True Negative Rate: 66.7%\n",
      "Epoch: 65  D Loss: 1.27218  G Loss: 0.878255 True Positive Rate: 61.7% True Negative Rate: 66.7%\n",
      "Epoch: 66  D Loss: 1.27247  G Loss: 0.879643 True Positive Rate: 55.0% True Negative Rate: 78.3%\n",
      "Epoch: 67  D Loss: 1.2731   G Loss: 0.877306 True Positive Rate: 60.0% True Negative Rate: 65.0%\n",
      "Epoch: 68  D Loss: 1.27055  G Loss: 0.882303 True Positive Rate: 50.0% True Negative Rate: 65.0%\n",
      "Epoch: 69  D Loss: 1.27522  G Loss: 0.874905 True Positive Rate: 75.0% True Negative Rate: 76.7%\n",
      "Epoch: 70  D Loss: 1.27238  G Loss: 0.880492 True Positive Rate: 50.0% True Negative Rate: 66.7%\n",
      "Epoch: 71  D Loss: 1.27514  G Loss: 0.874179 True Positive Rate: 60.0% True Negative Rate: 71.7%\n",
      "Epoch: 72  D Loss: 1.27622  G Loss: 0.871139 True Positive Rate: 58.3% True Negative Rate: 73.3%\n",
      "Epoch: 73  D Loss: 1.2775   G Loss: 0.874441 True Positive Rate: 56.7% True Negative Rate: 58.3%\n",
      "Epoch: 74  D Loss: 1.27772  G Loss: 0.873282 True Positive Rate: 58.3% True Negative Rate: 76.7%\n",
      "Epoch: 75  D Loss: 1.28074  G Loss: 0.865126 True Positive Rate: 60.0% True Negative Rate: 80.0%\n",
      "Epoch: 76  D Loss: 1.27559  G Loss: 0.877896 True Positive Rate: 60.0% True Negative Rate: 75.0%\n",
      "Epoch: 77  D Loss: 1.2708   G Loss: 0.878238 True Positive Rate: 50.0% True Negative Rate: 61.7%\n",
      "Epoch: 78  D Loss: 1.27641  G Loss: 0.87174  True Positive Rate: 63.3% True Negative Rate: 63.3%\n",
      "Epoch: 79  D Loss: 1.27915  G Loss: 0.868104 True Positive Rate: 63.3% True Negative Rate: 73.3%\n",
      "Epoch: 80  D Loss: 1.28017  G Loss: 0.867159 True Positive Rate: 58.3% True Negative Rate: 66.7%\n",
      "Epoch: 81  D Loss: 1.2784   G Loss: 0.86979  True Positive Rate: 66.7% True Negative Rate: 60.0%\n",
      "Epoch: 82  D Loss: 1.27558  G Loss: 0.870775 True Positive Rate: 61.7% True Negative Rate: 68.3%\n",
      "Epoch: 83  D Loss: 1.2754   G Loss: 0.873937 True Positive Rate: 63.3% True Negative Rate: 71.7%\n",
      "Epoch: 84  D Loss: 1.2752   G Loss: 0.873828 True Positive Rate: 38.3% True Negative Rate: 63.3%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 85  D Loss: 1.27848  G Loss: 0.86645  True Positive Rate: 56.7% True Negative Rate: 58.3%\n",
      "Epoch: 86  D Loss: 1.27695  G Loss: 0.870793 True Positive Rate: 60.0% True Negative Rate: 78.3%\n",
      "Epoch: 87  D Loss: 1.28143  G Loss: 0.865612 True Positive Rate: 46.7% True Negative Rate: 71.7%\n",
      "Epoch: 88  D Loss: 1.27984  G Loss: 0.868262 True Positive Rate: 38.3% True Negative Rate: 53.3%\n",
      "Epoch: 89  D Loss: 1.27558  G Loss: 0.870339 True Positive Rate: 76.7% True Negative Rate: 71.7%\n",
      "Epoch: 90  D Loss: 1.28778  G Loss: 0.856272 True Positive Rate: 51.7% True Negative Rate: 68.3%\n",
      "Epoch: 91  D Loss: 1.27999  G Loss: 0.867708 True Positive Rate: 43.3% True Negative Rate: 80.0%\n",
      "Epoch: 92  D Loss: 1.27956  G Loss: 0.866868 True Positive Rate: 61.7% True Negative Rate: 78.3%\n",
      "Epoch: 93  D Loss: 1.27606  G Loss: 0.867709 True Positive Rate: 60.0% True Negative Rate: 71.7%\n",
      "Epoch: 94  D Loss: 1.27712  G Loss: 0.872978 True Positive Rate: 60.0% True Negative Rate: 70.0%\n",
      "Epoch: 95  D Loss: 1.28076  G Loss: 0.865007 True Positive Rate: 51.7% True Negative Rate: 68.3%\n",
      "Epoch: 96  D Loss: 1.28044  G Loss: 0.863879 True Positive Rate: 58.3% True Negative Rate: 68.3%\n",
      "Epoch: 97  D Loss: 1.28307  G Loss: 0.860898 True Positive Rate: 61.7% True Negative Rate: 68.3%\n",
      "Epoch: 98  D Loss: 1.28338  G Loss: 0.864145 True Positive Rate: 61.7% True Negative Rate: 58.3%\n",
      "Epoch: 99  D Loss: 1.28162  G Loss: 0.870137 True Positive Rate: 45.0% True Negative Rate: 68.3%\n"
     ]
    }
   ],
   "source": [
    "### 모델 학습을 위한 반복문\n",
    "# 데이터셋을 100번 돌며 학습한다.\n",
    "for epoch in range(100):\n",
    "    \n",
    "    if leave_log:\n",
    "        D_losses = []\n",
    "        G_losses = []\n",
    "    \n",
    "    # 한번에 batch_size만큼 데이터를 가져온다.\n",
    "    for real_data, _ in dataloader:\n",
    "        batch_size = real_data.size(0)\n",
    "        \n",
    "        # 데이터를 pytorch의 변수로 변환한다.\n",
    "        real_data = Variable(real_data)\n",
    "\n",
    "        ### 구분자 학습시키기\n",
    "\n",
    "        # 이미지가 진짜일 때 정답 값은 1이고 가짜일 때는 0이다.\n",
    "        # 정답지에 해당하는 변수를 만든다.\n",
    "        target_real = Variable(torch.ones(batch_size, 1))\n",
    "        target_fake = Variable(torch.zeros(batch_size, 1))\n",
    "         \n",
    "        if use_gpu:\n",
    "            real_data, target_real, target_fake = real_data.cuda(), target_real.cuda(), target_fake.cuda()\n",
    "            \n",
    "        # 진짜 이미지를 구분자에 넣는다.\n",
    "        D_result_from_real = D(real_data)\n",
    "        # 구분자의 출력값이 정답지인 1에서 멀수록 loss가 높아진다.\n",
    "        D_loss_real = criterion(D_result_from_real, target_real)\n",
    "\n",
    "        # 생성자에 입력으로 줄 랜덤 벡터 z를 만든다.\n",
    "        z = Variable(torch.randn((batch_size, 100)))\n",
    "        \n",
    "        if use_gpu:\n",
    "            z = z.cuda()\n",
    "            \n",
    "        # 생성자로 가짜 이미지를 생성한다.\n",
    "        fake_data = G(z)\n",
    "        \n",
    "        # 생성자가 만든 가짜 이미지를 구분자에 넣는다.\n",
    "        D_result_from_fake = D(fake_data)\n",
    "        # 구분자의 출력값이 정답지인 0에서 멀수록 loss가 높아진다.\n",
    "        D_loss_fake = criterion(D_result_from_fake, target_fake)\n",
    "        \n",
    "        # 구분자의 loss는 두 문제에서 계산된 loss의 합이다.\n",
    "        D_loss = D_loss_real + D_loss_fake\n",
    "        \n",
    "        # 구분자의 매개 변수의 미분값을 0으로 초기화한다.\n",
    "        D.zero_grad()\n",
    "        # 역전파를 통해 매개 변수의 loss에 대한 미분값을 계산한다.\n",
    "        D_loss.backward()\n",
    "        # 최적화 기법을 이용해 구분자의 매개 변수를 업데이트한다.\n",
    "        D_optimizer.step()\n",
    "        \n",
    "        if leave_log:\n",
    "            D_losses.append(D_loss.data[0])\n",
    "\n",
    "        # train generator G\n",
    "\n",
    "        ### 생성자 학습시키기\n",
    "        \n",
    "        # 생성자에 입력으로 줄 랜덤 벡터 z를 만든다.\n",
    "        z = Variable(torch.randn((batch_size, 100)))\n",
    "        \n",
    "        if use_gpu:\n",
    "            z = z.cuda()\n",
    "        \n",
    "        # 생성자로 가짜 이미지를 생성한다.\n",
    "        fake_data = G(z)\n",
    "        # 생성자가 만든 가짜 이미지를 구분자에 넣는다.\n",
    "        D_result_from_fake = D(fake_data)\n",
    "        # 생성자의 입장에서 구분자의 출력값이 1에서 멀수록 loss가 높아진다.\n",
    "        G_loss = criterion(D_result_from_fake, target_real)\n",
    "        \n",
    "        # 생성자의 매개 변수의 미분값을 0으로 초기화한다.\n",
    "        G.zero_grad()\n",
    "        # 역전파를 통해 매개 변수의 loss에 대한 미분값을 계산한다.\n",
    "        G_loss.backward()\n",
    "        # 최적화 기법을 이용해 생성자의 매개 변수를 업데이트한다.\n",
    "        G_optimizer.step()\n",
    "        \n",
    "        if leave_log:\n",
    "            G_losses.append(G_loss.data[0])\n",
    "    if leave_log:\n",
    "        true_positive_rate = (D_result_from_real > 0.5).float().mean().data[0]\n",
    "        true_negative_rate = (D_result_from_fake < 0.5).float().mean().data[0]\n",
    "        base_message = (\"Epoch: {epoch:<3d} D Loss: {d_loss:<8.6} G Loss: {g_loss:<8.6} \"\n",
    "                        \"True Positive Rate: {tpr:<5.1%} True Negative Rate: {tnr:<5.1%}\"\n",
    "                       )\n",
    "        message = base_message.format(\n",
    "                    epoch=epoch,\n",
    "                    d_loss=sum(D_losses)/len(D_losses),\n",
    "                    g_loss=sum(G_losses)/len(G_losses),\n",
    "                    tpr=true_positive_rate,\n",
    "                    tnr=true_negative_rate\n",
    "        )\n",
    "        print(message)\n",
    "    \n",
    "    if leave_log:\n",
    "        fake_data_fixed = G(z_fixed)\n",
    "        image_path = result_dir + '/epoch{}.png'.format(epoch)\n",
    "        square_plot(fake_data_fixed.view(25, 28, 28).cpu().data.numpy(), path=image_path)\n",
    "        generated_images.append(image_path)\n",
    "    \n",
    "    if leave_log:\n",
    "        train_hist['D_losses'].append(torch.mean(torch.FloatTensor(D_losses)))\n",
    "        train_hist['G_losses'].append(torch.mean(torch.FloatTensor(G_losses)))\n",
    "\n",
    "torch.save(G.state_dict(), \"gan_generator.pkl\")\n",
    "torch.save(D.state_dict(), \"gan_discriminator.pkl\")\n",
    "with open('gan_train_history.pkl', 'wb') as f:\n",
    "    pickle.dump(train_hist, f)\n",
    "\n",
    "generated_image_array = [imageio.imread(generated_image) for generated_image in generated_images]\n",
    "imageio.mimsave(result_dir + '/GAN_generation.gif', generated_image_array, fps=5)"
   ]
  }
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