09-경쟁하며_학습하는_GAN/gan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN으로 새로운 패션아이템 생성하기\n",
    "*GAN을 이용하여 새로운 패션 아이템을 만들어봅니다*\n",
    "\n",
    "이 프로젝트는 최윤제님의 파이토치 튜토리얼 사용 허락을 받아 참고했습니다.\n",
    "\n",
    "* [yunjey/pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) - MIT License"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import transforms, datasets\n",
    "from torchvision.utils import save_image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EPOCHS 과 BATCH_SIZE 등 학습에 필요한 하이퍼 파라미터 들을 설정해 줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Device: cuda\n"
     ]
    }
   ],
   "source": [
    "# 하이퍼파라미터\n",
    "EPOCHS = 500\n",
    "BATCH_SIZE = 100\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "DEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\n",
    "print(\"Using Device:\", DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "학습에 필요한 데이터셋을 로딩합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fashion MNIST 데이터셋\n",
    "trainset = datasets.FashionMNIST(\n",
    "    './.data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transforms.Compose([\n",
    "       transforms.ToTensor(),\n",
    "       transforms.Normalize((0.5,), (0.5,))\n",
    "    ])\n",
    ")\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset     = trainset,\n",
    "    batch_size  = BATCH_SIZE,\n",
    "    shuffle     = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생성자는 64차원의 랜덤한 텐서를 입력받아 이에 행렬곱(Linear)과 활성화 함수(ReLU, Tanh) 연산을 실행합니다. 생성자의 결과값은 784차원, 즉 Fashion MNIST 속의 이미지와 같은 차원의 텐서입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 생성자 (Generator)\n",
    "G = nn.Sequential(\n",
    "        nn.Linear(64, 256),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(256, 256),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(256, 784),\n",
    "        nn.Tanh())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판별자는 784차원의 텐서를 입력받습니다. 판별자 역시 입력된 데이터에 행렬곱과 활성화 함수를 실행시키지만, 생성자와 달리 판별자의 결과값은 입력받은 텐서가 진짜인지 구분하는 예측값입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 판별자 (Discriminator)\n",
    "D = nn.Sequential(\n",
    "        nn.Linear(784, 256),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        nn.Linear(256, 256),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        nn.Linear(256, 1),\n",
    "        nn.Sigmoid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생성자와 판별자 학습에 쓰일 오차 함수와 최적화 알고리즘도 정의해 줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델의 가중치를 지정한 장치로 보내기\n",
    "D = D.to(DEVICE)\n",
    "G = G.to(DEVICE)\n",
    "\n",
    "# 이진 크로스 엔트로피 (Binary cross entropy) 오차 함수와\n",
    "# 생성자와 판별자를 최적화할 Adam 모듈\n",
    "criterion = nn.BCELoss()\n",
    "d_optimizer = optim.Adam(D.parameters(), lr=0.0002)\n",
    "g_optimizer = optim.Adam(G.parameters(), lr=0.0002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델 학습에 필요한 준비는 끝났습니다. 그럼 본격적으로 GAN을 학습시키는 loop을 만들어 보겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0/500], d_loss: 0.0353, g_loss: 4.5795, D(x): 0.99, D(G(z)): 0.02\n",
      "Epoch [1/500], d_loss: 0.0207, g_loss: 4.8400, D(x): 0.99, D(G(z)): 0.01\n",
      "Epoch [2/500], d_loss: 0.0208, g_loss: 6.7210, D(x): 0.99, D(G(z)): 0.01\n",
      "Epoch [3/500], d_loss: 0.0562, g_loss: 5.7327, D(x): 0.99, D(G(z)): 0.04\n",
      "Epoch [4/500], d_loss: 0.1537, g_loss: 7.3955, D(x): 0.96, D(G(z)): 0.01\n",
      "Epoch [5/500], d_loss: 0.2890, g_loss: 5.9182, D(x): 0.93, D(G(z)): 0.04\n",
      "Epoch [6/500], d_loss: 0.2335, g_loss: 5.8002, D(x): 0.93, D(G(z)): 0.03\n",
      "Epoch [7/500], d_loss: 0.1111, g_loss: 5.1781, D(x): 0.99, D(G(z)): 0.07\n",
      "Epoch [8/500], d_loss: 0.3939, g_loss: 6.0438, D(x): 0.89, D(G(z)): 0.01\n",
      "Epoch [9/500], d_loss: 0.1982, g_loss: 4.1193, D(x): 0.96, D(G(z)): 0.09\n",
      "Epoch [10/500], d_loss: 0.3053, g_loss: 5.0000, D(x): 0.93, D(G(z)): 0.09\n",
      "Epoch [11/500], d_loss: 0.3597, g_loss: 3.8982, D(x): 0.87, D(G(z)): 0.05\n",
      "Epoch [12/500], d_loss: 0.3082, g_loss: 4.2954, D(x): 0.93, D(G(z)): 0.10\n",
      "Epoch [13/500], d_loss: 0.2371, g_loss: 5.3084, D(x): 0.92, D(G(z)): 0.03\n",
      "Epoch [14/500], d_loss: 0.3004, g_loss: 4.0390, D(x): 0.94, D(G(z)): 0.11\n",
      "Epoch [15/500], d_loss: 0.2624, g_loss: 4.2300, D(x): 0.92, D(G(z)): 0.07\n",
      "Epoch [16/500], d_loss: 0.4186, g_loss: 3.2536, D(x): 0.88, D(G(z)): 0.09\n",
      "Epoch [17/500], d_loss: 0.2966, g_loss: 3.1124, D(x): 0.95, D(G(z)): 0.17\n",
      "Epoch [18/500], d_loss: 0.3810, g_loss: 3.8865, D(x): 0.91, D(G(z)): 0.13\n",
      "Epoch [19/500], d_loss: 0.4771, g_loss: 4.1472, D(x): 0.85, D(G(z)): 0.11\n",
      "Epoch [20/500], d_loss: 0.4038, g_loss: 2.6455, D(x): 0.92, D(G(z)): 0.18\n",
      "Epoch [21/500], d_loss: 0.3549, g_loss: 3.5024, D(x): 0.91, D(G(z)): 0.12\n",
      "Epoch [22/500], d_loss: 0.2232, g_loss: 3.2193, D(x): 0.91, D(G(z)): 0.06\n",
      "Epoch [23/500], d_loss: 0.3045, g_loss: 3.8436, D(x): 0.90, D(G(z)): 0.13\n",
      "Epoch [24/500], d_loss: 0.4334, g_loss: 3.4976, D(x): 0.84, D(G(z)): 0.09\n",
      "Epoch [25/500], d_loss: 0.6626, g_loss: 2.8920, D(x): 0.87, D(G(z)): 0.24\n",
      "Epoch [26/500], d_loss: 0.3871, g_loss: 3.3799, D(x): 0.88, D(G(z)): 0.12\n",
      "Epoch [27/500], d_loss: 0.4851, g_loss: 3.2646, D(x): 0.85, D(G(z)): 0.11\n",
      "Epoch [28/500], d_loss: 0.4783, g_loss: 3.6343, D(x): 0.85, D(G(z)): 0.11\n",
      "Epoch [29/500], d_loss: 0.4668, g_loss: 2.3135, D(x): 0.87, D(G(z)): 0.21\n",
      "Epoch [30/500], d_loss: 0.5571, g_loss: 2.8470, D(x): 0.88, D(G(z)): 0.16\n",
      "Epoch [31/500], d_loss: 0.4593, g_loss: 3.2937, D(x): 0.84, D(G(z)): 0.13\n",
      "Epoch [32/500], d_loss: 0.5466, g_loss: 3.1846, D(x): 0.81, D(G(z)): 0.13\n",
      "Epoch [33/500], d_loss: 0.4839, g_loss: 2.5507, D(x): 0.86, D(G(z)): 0.15\n",
      "Epoch [34/500], d_loss: 0.4760, g_loss: 2.6080, D(x): 0.90, D(G(z)): 0.21\n",
      "Epoch [35/500], d_loss: 0.5840, g_loss: 3.2098, D(x): 0.82, D(G(z)): 0.16\n",
      "Epoch [36/500], d_loss: 0.7232, g_loss: 2.5683, D(x): 0.77, D(G(z)): 0.12\n",
      "Epoch [37/500], d_loss: 0.4694, g_loss: 3.0153, D(x): 0.89, D(G(z)): 0.14\n",
      "Epoch [38/500], d_loss: 0.5294, g_loss: 2.4553, D(x): 0.82, D(G(z)): 0.15\n",
      "Epoch [39/500], d_loss: 0.4357, g_loss: 3.2864, D(x): 0.86, D(G(z)): 0.14\n",
      "Epoch [40/500], d_loss: 0.5010, g_loss: 2.9378, D(x): 0.84, D(G(z)): 0.12\n",
      "Epoch [41/500], d_loss: 0.6251, g_loss: 2.8454, D(x): 0.79, D(G(z)): 0.12\n",
      "Epoch [42/500], d_loss: 0.7347, g_loss: 3.2269, D(x): 0.73, D(G(z)): 0.08\n",
      "Epoch [43/500], d_loss: 0.5337, g_loss: 2.6454, D(x): 0.86, D(G(z)): 0.22\n",
      "Epoch [44/500], d_loss: 0.5710, g_loss: 2.9795, D(x): 0.78, D(G(z)): 0.13\n",
      "Epoch [45/500], d_loss: 0.5998, g_loss: 2.4841, D(x): 0.85, D(G(z)): 0.19\n",
      "Epoch [46/500], d_loss: 0.7843, g_loss: 2.1948, D(x): 0.78, D(G(z)): 0.17\n",
      "Epoch [47/500], d_loss: 0.6242, g_loss: 2.7627, D(x): 0.85, D(G(z)): 0.25\n",
      "Epoch [48/500], d_loss: 0.6231, g_loss: 2.6626, D(x): 0.79, D(G(z)): 0.13\n",
      "Epoch [49/500], d_loss: 0.7630, g_loss: 3.5203, D(x): 0.80, D(G(z)): 0.17\n",
      "Epoch [50/500], d_loss: 0.5719, g_loss: 2.4131, D(x): 0.81, D(G(z)): 0.16\n",
      "Epoch [51/500], d_loss: 0.8938, g_loss: 1.7677, D(x): 0.80, D(G(z)): 0.30\n",
      "Epoch [52/500], d_loss: 0.8256, g_loss: 2.4449, D(x): 0.79, D(G(z)): 0.24\n",
      "Epoch [53/500], d_loss: 0.5849, g_loss: 2.4096, D(x): 0.86, D(G(z)): 0.23\n",
      "Epoch [54/500], d_loss: 0.7261, g_loss: 2.5420, D(x): 0.81, D(G(z)): 0.22\n",
      "Epoch [55/500], d_loss: 0.5505, g_loss: 2.3990, D(x): 0.87, D(G(z)): 0.25\n",
      "Epoch [56/500], d_loss: 0.8936, g_loss: 3.2751, D(x): 0.75, D(G(z)): 0.24\n",
      "Epoch [57/500], d_loss: 0.5805, g_loss: 2.1544, D(x): 0.84, D(G(z)): 0.25\n",
      "Epoch [58/500], d_loss: 0.6203, g_loss: 2.3525, D(x): 0.81, D(G(z)): 0.18\n",
      "Epoch [59/500], d_loss: 0.6470, g_loss: 2.3724, D(x): 0.78, D(G(z)): 0.19\n",
      "Epoch [60/500], d_loss: 0.5552, g_loss: 2.7438, D(x): 0.77, D(G(z)): 0.12\n",
      "Epoch [61/500], d_loss: 0.9309, g_loss: 2.5658, D(x): 0.71, D(G(z)): 0.18\n",
      "Epoch [62/500], d_loss: 0.5131, g_loss: 2.5370, D(x): 0.86, D(G(z)): 0.20\n",
      "Epoch [63/500], d_loss: 0.6984, g_loss: 2.8204, D(x): 0.78, D(G(z)): 0.14\n",
      "Epoch [64/500], d_loss: 0.6754, g_loss: 1.7643, D(x): 0.79, D(G(z)): 0.21\n",
      "Epoch [65/500], d_loss: 0.5948, g_loss: 2.3132, D(x): 0.85, D(G(z)): 0.24\n",
      "Epoch [66/500], d_loss: 0.9070, g_loss: 2.1512, D(x): 0.76, D(G(z)): 0.28\n",
      "Epoch [67/500], d_loss: 0.5339, g_loss: 2.6446, D(x): 0.80, D(G(z)): 0.13\n",
      "Epoch [68/500], d_loss: 0.7459, g_loss: 2.1979, D(x): 0.80, D(G(z)): 0.25\n",
      "Epoch [69/500], d_loss: 0.6126, g_loss: 2.4961, D(x): 0.80, D(G(z)): 0.18\n",
      "Epoch [70/500], d_loss: 0.5729, g_loss: 2.8205, D(x): 0.74, D(G(z)): 0.13\n",
      "Epoch [71/500], d_loss: 0.7766, g_loss: 2.3885, D(x): 0.72, D(G(z)): 0.16\n",
      "Epoch [72/500], d_loss: 0.7822, g_loss: 2.6657, D(x): 0.77, D(G(z)): 0.20\n",
      "Epoch [73/500], d_loss: 0.8265, g_loss: 2.6101, D(x): 0.72, D(G(z)): 0.19\n",
      "Epoch [74/500], d_loss: 0.6122, g_loss: 2.6391, D(x): 0.84, D(G(z)): 0.23\n",
      "Epoch [75/500], d_loss: 0.7539, g_loss: 2.0521, D(x): 0.86, D(G(z)): 0.34\n",
      "Epoch [76/500], d_loss: 0.7667, g_loss: 1.8303, D(x): 0.80, D(G(z)): 0.30\n",
      "Epoch [77/500], d_loss: 0.6584, g_loss: 2.1489, D(x): 0.88, D(G(z)): 0.30\n",
      "Epoch [78/500], d_loss: 0.6255, g_loss: 2.6509, D(x): 0.82, D(G(z)): 0.19\n",
      "Epoch [79/500], d_loss: 0.7525, g_loss: 2.3574, D(x): 0.73, D(G(z)): 0.17\n",
      "Epoch [80/500], d_loss: 0.7160, g_loss: 1.7728, D(x): 0.81, D(G(z)): 0.29\n",
      "Epoch [81/500], d_loss: 0.5326, g_loss: 2.7399, D(x): 0.85, D(G(z)): 0.21\n",
      "Epoch [82/500], d_loss: 0.9103, g_loss: 1.6615, D(x): 0.81, D(G(z)): 0.36\n",
      "Epoch [83/500], d_loss: 0.8975, g_loss: 2.0681, D(x): 0.72, D(G(z)): 0.21\n",
      "Epoch [84/500], d_loss: 0.6805, g_loss: 1.9258, D(x): 0.76, D(G(z)): 0.21\n",
      "Epoch [85/500], d_loss: 0.8092, g_loss: 1.6428, D(x): 0.86, D(G(z)): 0.35\n",
      "Epoch [86/500], d_loss: 0.5977, g_loss: 2.6141, D(x): 0.82, D(G(z)): 0.18\n",
      "Epoch [87/500], d_loss: 0.6894, g_loss: 2.4755, D(x): 0.73, D(G(z)): 0.14\n",
      "Epoch [88/500], d_loss: 0.6143, g_loss: 1.7354, D(x): 0.80, D(G(z)): 0.23\n",
      "Epoch [89/500], d_loss: 0.7044, g_loss: 2.0786, D(x): 0.80, D(G(z)): 0.26\n",
      "Epoch [90/500], d_loss: 0.6391, g_loss: 2.3855, D(x): 0.75, D(G(z)): 0.13\n",
      "Epoch [91/500], d_loss: 0.6587, g_loss: 2.2705, D(x): 0.72, D(G(z)): 0.14\n",
      "Epoch [92/500], d_loss: 0.8436, g_loss: 2.1588, D(x): 0.77, D(G(z)): 0.26\n",
      "Epoch [93/500], d_loss: 0.5117, g_loss: 2.3292, D(x): 0.84, D(G(z)): 0.20\n",
      "Epoch [94/500], d_loss: 0.6810, g_loss: 2.4156, D(x): 0.73, D(G(z)): 0.16\n",
      "Epoch [95/500], d_loss: 0.4709, g_loss: 2.8735, D(x): 0.84, D(G(z)): 0.15\n",
      "Epoch [96/500], d_loss: 0.6960, g_loss: 2.2731, D(x): 0.75, D(G(z)): 0.20\n",
      "Epoch [97/500], d_loss: 0.7565, g_loss: 1.7992, D(x): 0.76, D(G(z)): 0.24\n",
      "Epoch [98/500], d_loss: 0.8444, g_loss: 2.2126, D(x): 0.68, D(G(z)): 0.20\n",
      "Epoch [99/500], d_loss: 0.7740, g_loss: 1.7081, D(x): 0.70, D(G(z)): 0.20\n",
      "Epoch [100/500], d_loss: 0.7537, g_loss: 2.4329, D(x): 0.75, D(G(z)): 0.20\n",
      "Epoch [101/500], d_loss: 0.7988, g_loss: 1.7567, D(x): 0.76, D(G(z)): 0.27\n",
      "Epoch [102/500], d_loss: 0.9476, g_loss: 2.2052, D(x): 0.71, D(G(z)): 0.23\n",
      "Epoch [103/500], d_loss: 1.0190, g_loss: 1.7593, D(x): 0.70, D(G(z)): 0.24\n",
      "Epoch [104/500], d_loss: 1.0124, g_loss: 1.5721, D(x): 0.77, D(G(z)): 0.37\n",
      "Epoch [105/500], d_loss: 0.6753, g_loss: 1.8733, D(x): 0.76, D(G(z)): 0.23\n",
      "Epoch [106/500], d_loss: 0.7078, g_loss: 1.9609, D(x): 0.78, D(G(z)): 0.25\n",
      "Epoch [107/500], d_loss: 0.7360, g_loss: 2.4600, D(x): 0.75, D(G(z)): 0.20\n",
      "Epoch [108/500], d_loss: 0.8563, g_loss: 1.4943, D(x): 0.74, D(G(z)): 0.30\n",
      "Epoch [109/500], d_loss: 0.6901, g_loss: 2.3865, D(x): 0.76, D(G(z)): 0.21\n",
      "Epoch [110/500], d_loss: 1.0278, g_loss: 1.9630, D(x): 0.69, D(G(z)): 0.26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [111/500], d_loss: 0.7124, g_loss: 2.2414, D(x): 0.75, D(G(z)): 0.22\n",
      "Epoch [112/500], d_loss: 0.9356, g_loss: 1.7013, D(x): 0.69, D(G(z)): 0.24\n",
      "Epoch [113/500], d_loss: 0.8027, g_loss: 1.7171, D(x): 0.72, D(G(z)): 0.27\n",
      "Epoch [114/500], d_loss: 0.8141, g_loss: 1.6392, D(x): 0.74, D(G(z)): 0.28\n",
      "Epoch [115/500], d_loss: 0.8254, g_loss: 1.6652, D(x): 0.71, D(G(z)): 0.22\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [221/500], d_loss: 1.1263, g_loss: 1.3004, D(x): 0.66, D(G(z)): 0.39\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [331/500], d_loss: 0.9890, g_loss: 1.1310, D(x): 0.71, D(G(z)): 0.40\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [441/500], d_loss: 0.9420, g_loss: 1.2448, D(x): 0.69, D(G(z)): 0.36\n",
      "Epoch [442/500], d_loss: 0.8875, g_loss: 1.5646, D(x): 0.71, D(G(z)): 0.32\n",
      "Epoch [443/500], d_loss: 0.9400, g_loss: 1.2521, D(x): 0.70, D(G(z)): 0.33\n",
      "Epoch [444/500], d_loss: 1.1099, g_loss: 1.2434, D(x): 0.68, D(G(z)): 0.37\n",
      "Epoch [445/500], d_loss: 1.2045, g_loss: 1.3568, D(x): 0.59, D(G(z)): 0.34\n",
      "Epoch [446/500], d_loss: 0.8978, g_loss: 1.3511, D(x): 0.69, D(G(z)): 0.32\n",
      "Epoch [447/500], d_loss: 0.7988, g_loss: 1.5881, D(x): 0.72, D(G(z)): 0.28\n",
      "Epoch [448/500], d_loss: 0.8578, g_loss: 1.3999, D(x): 0.76, D(G(z)): 0.34\n",
      "Epoch [449/500], d_loss: 0.9504, g_loss: 1.3742, D(x): 0.72, D(G(z)): 0.35\n",
      "Epoch [450/500], d_loss: 1.2930, g_loss: 1.1855, D(x): 0.61, D(G(z)): 0.40\n",
      "Epoch [451/500], d_loss: 1.2956, g_loss: 1.1676, D(x): 0.64, D(G(z)): 0.45\n",
      "Epoch [452/500], d_loss: 0.9261, g_loss: 1.4396, D(x): 0.67, D(G(z)): 0.31\n",
      "Epoch [453/500], d_loss: 1.2335, g_loss: 1.1853, D(x): 0.62, D(G(z)): 0.40\n",
      "Epoch [454/500], d_loss: 1.2923, g_loss: 1.2196, D(x): 0.55, D(G(z)): 0.37\n",
      "Epoch [455/500], d_loss: 0.9682, g_loss: 1.3824, D(x): 0.69, D(G(z)): 0.33\n",
      "Epoch [456/500], d_loss: 1.0812, g_loss: 1.3144, D(x): 0.68, D(G(z)): 0.38\n",
      "Epoch [457/500], d_loss: 1.0000, g_loss: 1.6382, D(x): 0.59, D(G(z)): 0.26\n",
      "Epoch [458/500], d_loss: 0.8316, g_loss: 1.6093, D(x): 0.69, D(G(z)): 0.26\n",
      "Epoch [459/500], d_loss: 0.7658, g_loss: 1.8536, D(x): 0.72, D(G(z)): 0.24\n",
      "Epoch [460/500], d_loss: 0.8877, g_loss: 1.3639, D(x): 0.70, D(G(z)): 0.32\n",
      "Epoch [461/500], d_loss: 1.2193, g_loss: 1.1515, D(x): 0.61, D(G(z)): 0.38\n",
      "Epoch [462/500], d_loss: 0.9441, g_loss: 1.6214, D(x): 0.62, D(G(z)): 0.26\n",
      "Epoch [463/500], d_loss: 0.8721, g_loss: 1.4527, D(x): 0.70, D(G(z)): 0.32\n",
      "Epoch [464/500], d_loss: 0.7644, g_loss: 1.6146, D(x): 0.72, D(G(z)): 0.28\n",
      "Epoch [465/500], d_loss: 0.8092, g_loss: 1.8323, D(x): 0.70, D(G(z)): 0.25\n",
      "Epoch [466/500], d_loss: 1.0277, g_loss: 1.5180, D(x): 0.62, D(G(z)): 0.30\n",
      "Epoch [467/500], d_loss: 1.3432, g_loss: 1.2022, D(x): 0.62, D(G(z)): 0.42\n",
      "Epoch [468/500], d_loss: 1.0822, g_loss: 1.4048, D(x): 0.65, D(G(z)): 0.37\n",
      "Epoch [469/500], d_loss: 0.8350, g_loss: 1.4815, D(x): 0.71, D(G(z)): 0.29\n",
      "Epoch [470/500], d_loss: 0.9572, g_loss: 1.4335, D(x): 0.66, D(G(z)): 0.30\n",
      "Epoch [471/500], d_loss: 1.0063, g_loss: 1.3566, D(x): 0.66, D(G(z)): 0.34\n",
      "Epoch [472/500], d_loss: 0.9418, g_loss: 1.6834, D(x): 0.67, D(G(z)): 0.29\n",
      "Epoch [473/500], d_loss: 1.0807, g_loss: 1.2172, D(x): 0.65, D(G(z)): 0.37\n",
      "Epoch [474/500], d_loss: 1.0318, g_loss: 1.3176, D(x): 0.63, D(G(z)): 0.34\n",
      "Epoch [475/500], d_loss: 1.0777, g_loss: 1.1631, D(x): 0.69, D(G(z)): 0.39\n",
      "Epoch [476/500], d_loss: 0.7117, g_loss: 1.6953, D(x): 0.75, D(G(z)): 0.27\n",
      "Epoch [477/500], d_loss: 0.8447, g_loss: 1.5539, D(x): 0.70, D(G(z)): 0.28\n",
      "Epoch [478/500], d_loss: 0.8978, g_loss: 1.4826, D(x): 0.66, D(G(z)): 0.28\n",
      "Epoch [479/500], d_loss: 0.8325, g_loss: 1.6255, D(x): 0.68, D(G(z)): 0.25\n",
      "Epoch [480/500], d_loss: 0.6100, g_loss: 1.9152, D(x): 0.80, D(G(z)): 0.24\n",
      "Epoch [481/500], d_loss: 1.0318, g_loss: 1.2833, D(x): 0.70, D(G(z)): 0.37\n",
      "Epoch [482/500], d_loss: 0.7879, g_loss: 1.5580, D(x): 0.73, D(G(z)): 0.28\n",
      "Epoch [483/500], d_loss: 0.7703, g_loss: 1.5651, D(x): 0.74, D(G(z)): 0.30\n",
      "Epoch [484/500], d_loss: 1.1391, g_loss: 1.4132, D(x): 0.63, D(G(z)): 0.34\n",
      "Epoch [485/500], d_loss: 0.9411, g_loss: 1.5307, D(x): 0.65, D(G(z)): 0.27\n",
      "Epoch [486/500], d_loss: 1.0826, g_loss: 1.3360, D(x): 0.65, D(G(z)): 0.36\n",
      "Epoch [487/500], d_loss: 0.8969, g_loss: 1.3325, D(x): 0.68, D(G(z)): 0.32\n",
      "Epoch [488/500], d_loss: 0.9951, g_loss: 1.2462, D(x): 0.72, D(G(z)): 0.38\n",
      "Epoch [489/500], d_loss: 0.9283, g_loss: 1.3946, D(x): 0.69, D(G(z)): 0.32\n",
      "Epoch [490/500], d_loss: 1.1680, g_loss: 1.1607, D(x): 0.68, D(G(z)): 0.42\n",
      "Epoch [491/500], d_loss: 1.2781, g_loss: 1.2009, D(x): 0.62, D(G(z)): 0.39\n",
      "Epoch [492/500], d_loss: 0.9245, g_loss: 1.6082, D(x): 0.66, D(G(z)): 0.28\n",
      "Epoch [493/500], d_loss: 0.8367, g_loss: 1.6735, D(x): 0.69, D(G(z)): 0.27\n",
      "Epoch [494/500], d_loss: 1.1558, g_loss: 1.1857, D(x): 0.65, D(G(z)): 0.39\n",
      "Epoch [495/500], d_loss: 1.2399, g_loss: 1.2862, D(x): 0.59, D(G(z)): 0.36\n",
      "Epoch [496/500], d_loss: 1.0204, g_loss: 1.4843, D(x): 0.68, D(G(z)): 0.33\n",
      "Epoch [497/500], d_loss: 1.0776, g_loss: 1.4313, D(x): 0.64, D(G(z)): 0.33\n",
      "Epoch [498/500], d_loss: 1.2960, g_loss: 1.1681, D(x): 0.60, D(G(z)): 0.40\n",
      "Epoch [499/500], d_loss: 0.7900, g_loss: 1.6450, D(x): 0.72, D(G(z)): 0.27\n"
     ]
    }
   ],
   "source": [
    "total_step = len(train_loader)\n",
    "for epoch in range(EPOCHS):\n",
    "    for i, (images, _) in enumerate(train_loader):\n",
    "        images = images.reshape(BATCH_SIZE, -1).to(DEVICE)\n",
    "        \n",
    "        # '진짜'와 '가짜' 레이블 생성\n",
    "        real_labels = torch.ones(BATCH_SIZE, 1).to(DEVICE)\n",
    "        fake_labels = torch.zeros(BATCH_SIZE, 1).to(DEVICE)\n",
    "        \n",
    "        # 판별자가 진짜 이미지를 진짜로 인식하는 오차를 예산\n",
    "        outputs = D(images)\n",
    "        d_loss_real = criterion(outputs, real_labels)\n",
    "        real_score = outputs\n",
    "        \n",
    "        # 무작위 텐서로 가짜 이미지 생성\n",
    "        z = torch.randn(BATCH_SIZE, 64).to(DEVICE)\n",
    "        fake_images = G(z)\n",
    "        \n",
    "        # 판별자가 가짜 이미지를 가짜로 인식하는 오차를 계산\n",
    "        outputs = D(fake_images)\n",
    "        d_loss_fake = criterion(outputs, fake_labels)\n",
    "        fake_score = outputs\n",
    "        \n",
    "        # 진짜와 가짜 이미지를 갖고 낸 오차를 더해서 판별자의 오차 계산\n",
    "        d_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "        # 역전파 알고리즘으로 판별자 모델의 학습을 진행\n",
    "        d_optimizer.zero_grad()\n",
    "        g_optimizer.zero_grad()\n",
    "        d_loss.backward()\n",
    "        d_optimizer.step()\n",
    "        \n",
    "        # 생성자가 판별자를 속였는지에 대한 오차를 계산\n",
    "        fake_images = G(z)\n",
    "        outputs = D(fake_images)\n",
    "        g_loss = criterion(outputs, real_labels)\n",
    "        \n",
    "        # 역전파 알고리즘으로 생성자 모델의 학습을 진행\n",
    "        d_optimizer.zero_grad()\n",
    "        g_optimizer.zero_grad()\n",
    "        g_loss.backward()\n",
    "        g_optimizer.step()\n",
    "        \n",
    "    # 학습 진행 알아보기\n",
    "    print('Epoch [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' \n",
    "          .format(epoch, EPOCHS, d_loss.item(), g_loss.item(), \n",
    "                  real_score.mean().item(), fake_score.mean().item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "학습이 끝난 생성자의 결과물을 한번 확인해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0940qNM+/Z88eM6+rq4vNQqfc5so6ZRcAdu+OnxGurq42t+3t7c1qTJQZ7vmJnGL5iZxi+YmcYvmJnGL5iZxi+YmcYvmJnJJiXoJYRIYAzJy8rQZwtmgD+HbSOra0jgvg2LKVz7EtV9XFmdyxqOX/xi8XaUvrtf3SOra0jgvg2LKV1Nj4tJ/IKZafyKmky9+a8O+3pHVsaR0XwLFlK5GxJfqan4iSk/Sen4gSkkj5ReRhEflMRI6LyDNJjCGOiPSISKeIHEp6ibFoGbRBETk847aFIvKOiByLPs+6TFpCY3teRPqjx+6QiDyS0NjqReR/ReSoiBwRkaej2xN97IxxJfK4Ff1pv4iUAfgcwBoAfQAOAtigqkeLOpAYItIDoFlVE58TFpEHAIwBeEVVm6Lb/gPAsKq+GP3hrFLVX6dkbM8DGEt65eZoQZklM1eWBvAYgH9Dgo+dMa71SOBxS2LPvwrAcVX9QlUvA/gLgHUJjCP1VHUfgOtX5FgHYFv09TZM/+MpupixpYKqDqhqe/T1KIBrK0sn+tgZ40pEEuVfBuDUjO/7kK4lvxXA30XkYxFpSXows6iNlk0HgC8B1CY5mFkEV24uputWlk7NY5fNitf5xjf8vul+Vb0HwI8B/Dx6eptKOv2aLU3TNRmt3Fwss6ws/U9JPnbZrnidb0mUvx9A/Yzv66LbUkFV+6PPgwDeRPpWHz5zbZHU6PNgwuP5pzSt3DzbytJIwWOXphWvkyj/QQCNIvJdEZkH4KcAdiYwjm8QkcrojRiISCWAHyF9qw/vBLAx+nojgB0JjuVr0rJyc9zK0kj4sUvditeqWvQPAI9g+h3/EwCeS2IMMeP6FwD/F30cSXpsAF7H9NPAK5h+b2QTgEUA9gI4BuBdAAtTNLZXAXQC6MB00ZYkNLb7Mf2UvgPAoejjkaQfO2NciTxuPMKPyCm+4UfkFMtP5BTLT+QUy0/kFMtP5BTLT+QUy0/kFMtP5NT/A8xInlLcqIdzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = torch.randn(BATCH_SIZE, 64).to(DEVICE)\n",
    "fake_images = G(z)\n",
    "for i in range(10):\n",
    "    fake_images_img = np.reshape(fake_images.data.cpu().numpy()[i],(28, 28))\n",
    "    plt.imshow(fake_images_img, cmap = 'gray')\n",
    "    plt.show()"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}