09-Generative-Adversarial-Networks/01-gan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.1 GAN으로 새로운 패션아이템 생성하기\n",
    "*GAN을 이용하여 새로운 패션 아이템을 만들어봅니다*\n",
    "\n",
    "GAN을 구현하기 위해 그 구조를 더 자세히 알아보겠습니다.\n",
    "\n",
    "GAN은 생성자(Generator)와 판별자(Discriminator) 2개의 신경망으로\n",
    "이루어져 있습니다.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GAN 구현하기"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f0a581ba1f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(1)    # reproducible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Hyper Parameters\n",
    "EPOCHS = 100\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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fashion MNIST digits dataset\n",
    "trainset = datasets.FashionMNIST('./.data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transforms.Compose([\n",
    "       transforms.ToTensor(),\n",
    "       transforms.Normalize((0.5,), (0.5,))\n",
    "    ]))\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset     = trainset,\n",
    "    batch_size  = BATCH_SIZE,\n",
    "    shuffle     = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f76404aea14a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m G = nn.Sequential(\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReLU\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      5\u001b[0m         \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'nn' is not defined"
     ]
    }
   ],
   "source": [
    "# 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": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Device setting\n",
    "D = D.to(DEVICE)\n",
    "G = G.to(DEVICE)\n",
    "\n",
    "# Binary cross entropy loss and optimizer\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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_loader' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f0c67b163a59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtotal_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_loader' is not defined"
     ]
    }
   ],
   "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",
    "        # Create the labels which are later used as input for the BCE loss\n",
    "        real_labels = torch.ones(BATCH_SIZE, 1).to(DEVICE)\n",
    "        fake_labels = torch.zeros(BATCH_SIZE, 1).to(DEVICE)\n",
    "\n",
    "        # Train Discriminator\n",
    "\n",
    "        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))\n",
    "        # Second term of the loss is always zero since real_labels == 1\n",
    "        outputs = D(images)\n",
    "        d_loss_real = criterion(outputs, real_labels)\n",
    "        real_score = outputs\n",
    "        \n",
    "        # Compute BCELoss using fake images\n",
    "        # First term of the loss is always zero since fake_labels == 0\n",
    "        z = torch.randn(BATCH_SIZE, 64).to(DEVICE)\n",
    "        fake_images = G(z)\n",
    "        outputs = D(fake_images)\n",
    "        d_loss_fake = criterion(outputs, fake_labels)\n",
    "        fake_score = outputs\n",
    "        \n",
    "        # Backprop and optimize\n",
    "        d_loss = d_loss_real + d_loss_fake\n",
    "        d_optimizer.zero_grad()\n",
    "        d_loss.backward()\n",
    "        d_optimizer.step()\n",
    "        \n",
    "        # Train Generator\n",
    "\n",
    "        # Compute loss with fake images\n",
    "        z = torch.randn(BATCH_SIZE, 64).to(DEVICE)\n",
    "        fake_images = G(z)\n",
    "        outputs = D(fake_images)\n",
    "        \n",
    "        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))\n",
    "        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf\n",
    "        g_loss = criterion(outputs, real_labels)\n",
    "        \n",
    "        # Backprop and optimize\n",
    "        d_optimizer.zero_grad()\n",
    "        g_optimizer.zero_grad()\n",
    "        g_loss.backward()\n",
    "        g_optimizer.step()\n",
    "        \n",
    "        if (i+1) % 200 == 0:\n",
    "            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' \n",
    "                  .format(epoch, EPOCHS, i+1, total_step, d_loss.item(), g_loss.item(), \n",
    "                          real_score.mean().item(), fake_score.mean().item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 참고\n",
    "본 튜토리얼은 다음 자료를 참고하여 만들어졌습니다.\n",
    "\n",
    "* [yunjey/pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) - MIT License"
   ]
  }
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