DCGAN_(MNIST).ipynb

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  "metadata": {
    "colab": {
      "name": "DCGAN (MNIST).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "T8134NSeyqDE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "from torchvision import transforms, datasets, models\n",
        "from torchvision.utils import save_image\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KQ-MyM1JzCUq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available else 'cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EB_VlfB5zCWx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learning_rate= 2e-4\n",
        "batch_size = 100\n",
        "num_epochs = 20\n",
        "n_noise = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PGFIBFE9zCZJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sample_dir = 'samples'\n",
        "\n",
        "if not os.path.exists(sample_dir):\n",
        "  os.makedirs(sample_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wWY-chZjzCbj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "7eca0026-bb74-421f-e8a6-2404ae339666"
      },
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([transforms.ToTensor()])\n",
        "\n",
        "dataset = datasets.MNIST(root='../../data/', train=True, transform=transform, download=True)\n",
        "data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "9920512it [00:01, 8036246.50it/s]                            \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../../data/MNIST/raw/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "1654784it [00:00, 2134546.92it/s]                            \n",
            "0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "8192it [00:00, 47872.32it/s]            \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z90tJiRBzCd5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Discriminator, self).__init__()\n",
        "    self.conv = nn.Sequential(nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1, bias=False), \n",
        "                                nn.BatchNorm2d(64), # (n, 64, 14, 14)\n",
        "                                nn.ReLU(True),\n",
        "                                nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),\n",
        "                                nn.BatchNorm2d(128), # (n, 128, 7, 7)\n",
        "                                nn.ReLU(True),\n",
        "                                nn.Conv2d(128, 256, kernel_size =3, stride=2, padding=1, bias=False),\n",
        "                                nn.BatchNorm2d(256), # (n, 256, 4, 4)\n",
        "                                nn.ReLU(True),\n",
        "                                nn.AvgPool2d(4))\n",
        "    \n",
        "    self.fc = nn.Sequential(nn.Linear(256,1),\n",
        "                            nn.Sigmoid())\n",
        "    \n",
        "  def forward(self, x):\n",
        "    out = self.conv(x)\n",
        "    out = out.view(out.size(0), -1)\n",
        "    out = self.fc(out)\n",
        "    \n",
        "    return out\n",
        "  \n",
        "  \n",
        "class Generator(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Generator, self).__init__()\n",
        "    \n",
        "    self.layer1 = nn.Linear(n_noise, 256*4*4)\n",
        "    \n",
        "    self.convT = nn.Sequential(nn.ConvTranspose2d(256, 128, 4, 1, 0), \n",
        "                              nn.BatchNorm2d(128),  # (n, 128, 7, 7)\n",
        "                              nn.LeakyReLU(0.2, True),\n",
        "                              nn.ConvTranspose2d(128, 64, 4, 2, 1), \n",
        "                              nn.BatchNorm2d(64),   # (n, 64, 14, 14)\n",
        "                              nn.LeakyReLU(0.2, True),\n",
        "                              nn.ConvTranspose2d(64, 1, 4, 2, 1),   \n",
        "                              nn.Tanh())            # (n, 1, 28, 28) \n",
        "    \n",
        "  def forward(self, x):\n",
        "    out = self.layer1(x)\n",
        "    \n",
        "    out = out.view(out.size(0), 256, 4, 4)\n",
        "    out = self.convT(out)\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cZDOIj-6zCgY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "D = Discriminator().to(device)\n",
        "G = Generator().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nkusPNH0zClM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "criterion = nn.BCELoss()\n",
        "d_optimizer = torch.optim.Adam(D.parameters(), lr=learning_rate)\n",
        "g_optimizer = torch.optim.Adam(G.parameters(), lr=learning_rate)\n",
        "\n",
        "def zero_grad():\n",
        "  d_optimizer.zero_grad()\n",
        "  g_optimizer.zero_grad()\n",
        "  \n",
        "def norm(images):\n",
        "  return images*2 - 1\n",
        "  \n",
        "def denorm(images):\n",
        "  images = (images+1) / 2\n",
        "  return images.clamp(0,1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VJvf7T8QzCni",
        "colab_type": "code",
        "outputId": "9a319bb2-71c9-4112-b10a-5b30e30b1649",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1097
        }
      },
      "cell_type": "code",
      "source": [
        "for epoch in range(num_epochs):\n",
        "  for i, (images, _) in enumerate(data_loader):\n",
        "    images = images.to(device)\n",
        "    images = norm(images)\n",
        "    \n",
        "    real_labels = torch.ones(images.size(0), 1).to(device)\n",
        "    fake_labels = torch.zeros(images.size(0), 1).to(device)\n",
        "    #===================================================\n",
        "    #                 Train Discriminator\n",
        "    # ==================================================\n",
        "    outputs = D(images)\n",
        "    d_loss_real = criterion(outputs, real_labels)\n",
        "    \n",
        "    z = torch.randn(images.size(0), n_noise).to(device)\n",
        "    fake_image = G(z)\n",
        "    outputs = D(fake_image)\n",
        "    d_loss_fake = criterion(outputs, fake_labels)\n",
        "    \n",
        "    \n",
        "    d_loss = d_loss_real + d_loss_fake\n",
        "    \n",
        "    zero_grad()\n",
        "    d_loss.backward()\n",
        "    d_optimizer.step()\n",
        "    \n",
        "    #===================================================\n",
        "    #                 Train Generator\n",
        "    # ==================================================\n",
        "    z = torch.randn(images.size(0), n_noise).to(device)\n",
        "    fake_image = G(z)\n",
        "    outputs_fake = D(fake_image)\n",
        "    g_loss = criterion(outputs_fake, real_labels)\n",
        "    \n",
        "    zero_grad()\n",
        "    g_loss.backward()\n",
        "    g_optimizer.step()\n",
        "    \n",
        "    \n",
        "    if (i+1) % 200 == 0:\n",
        "      print('Epoch [{}/{}], Step [{}/{}], d_loss {:.4f}, g_loss : {:.4f}'\n",
        "            .format(epoch, num_epochs, i+1, len(data_loader), d_loss.item(), g_loss.item()))\n",
        "\n",
        "    \n",
        "  fake_image = fake_image.view(fake_image.size(0), 1, 28, 28)\n",
        "  save_image(denorm(fake_image), os.path.join(sample_dir, 'fake_images={}.png'.format(str(epoch).zfill(2))))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0/20], Step [200/600], d_loss 0.6713, g_loss : 1.2155\n",
            "Epoch [0/20], Step [400/600], d_loss 0.3163, g_loss : 1.9528\n",
            "Epoch [0/20], Step [600/600], d_loss 0.3374, g_loss : 1.8241\n",
            "Epoch [1/20], Step [200/600], d_loss 0.1965, g_loss : 2.3952\n",
            "Epoch [1/20], Step [400/600], d_loss 0.2188, g_loss : 2.3181\n",
            "Epoch [1/20], Step [600/600], d_loss 0.1961, g_loss : 2.2199\n",
            "Epoch [2/20], Step [200/600], d_loss 0.1722, g_loss : 2.8748\n",
            "Epoch [2/20], Step [400/600], d_loss 0.1308, g_loss : 2.9564\n",
            "Epoch [2/20], Step [600/600], d_loss 0.0818, g_loss : 2.5200\n",
            "Epoch [3/20], Step [200/600], d_loss 0.2403, g_loss : 2.2032\n",
            "Epoch [3/20], Step [400/600], d_loss 0.2487, g_loss : 2.0210\n",
            "Epoch [3/20], Step [600/600], d_loss 0.5632, g_loss : 2.4171\n",
            "Epoch [4/20], Step [200/600], d_loss 0.2828, g_loss : 2.3414\n",
            "Epoch [4/20], Step [400/600], d_loss 0.3549, g_loss : 2.4552\n",
            "Epoch [4/20], Step [600/600], d_loss 0.2746, g_loss : 1.6154\n",
            "Epoch [5/20], Step [200/600], d_loss 0.3723, g_loss : 2.4689\n",
            "Epoch [5/20], Step [400/600], d_loss 0.4078, g_loss : 1.3406\n",
            "Epoch [5/20], Step [600/600], d_loss 0.2033, g_loss : 2.8393\n",
            "Epoch [6/20], Step [200/600], d_loss 0.1722, g_loss : 3.0192\n",
            "Epoch [6/20], Step [400/600], d_loss 0.3183, g_loss : 2.0450\n",
            "Epoch [6/20], Step [600/600], d_loss 0.2852, g_loss : 1.2666\n",
            "Epoch [7/20], Step [200/600], d_loss 0.2316, g_loss : 2.4960\n",
            "Epoch [7/20], Step [400/600], d_loss 0.4994, g_loss : 1.5484\n",
            "Epoch [7/20], Step [600/600], d_loss 0.3604, g_loss : 1.4420\n",
            "Epoch [8/20], Step [200/600], d_loss 0.6436, g_loss : 2.3688\n",
            "Epoch [8/20], Step [400/600], d_loss 0.3521, g_loss : 3.1592\n",
            "Epoch [8/20], Step [600/600], d_loss 0.2162, g_loss : 2.5891\n",
            "Epoch [9/20], Step [200/600], d_loss 0.2033, g_loss : 2.5090\n",
            "Epoch [9/20], Step [400/600], d_loss 0.1598, g_loss : 2.8218\n",
            "Epoch [9/20], Step [600/600], d_loss 0.3811, g_loss : 2.2751\n",
            "Epoch [10/20], Step [200/600], d_loss 0.2854, g_loss : 2.4288\n",
            "Epoch [10/20], Step [400/600], d_loss 0.3247, g_loss : 2.0661\n",
            "Epoch [10/20], Step [600/600], d_loss 0.2035, g_loss : 2.3710\n",
            "Epoch [11/20], Step [200/600], d_loss 0.1492, g_loss : 2.2074\n",
            "Epoch [11/20], Step [400/600], d_loss 0.2616, g_loss : 2.0524\n",
            "Epoch [11/20], Step [600/600], d_loss 0.1287, g_loss : 2.9337\n",
            "Epoch [12/20], Step [200/600], d_loss 0.1026, g_loss : 2.0107\n",
            "Epoch [12/20], Step [400/600], d_loss 0.4621, g_loss : 3.1345\n",
            "Epoch [12/20], Step [600/600], d_loss 0.2008, g_loss : 1.8999\n",
            "Epoch [13/20], Step [200/600], d_loss 0.2029, g_loss : 3.5226\n",
            "Epoch [13/20], Step [400/600], d_loss 0.1756, g_loss : 3.7825\n",
            "Epoch [13/20], Step [600/600], d_loss 0.1805, g_loss : 3.4846\n",
            "Epoch [14/20], Step [200/600], d_loss 0.1429, g_loss : 2.8796\n",
            "Epoch [14/20], Step [400/600], d_loss 0.1608, g_loss : 3.1518\n",
            "Epoch [14/20], Step [600/600], d_loss 0.1667, g_loss : 3.6403\n",
            "Epoch [15/20], Step [200/600], d_loss 0.1832, g_loss : 2.5675\n",
            "Epoch [15/20], Step [400/600], d_loss 0.1118, g_loss : 3.4086\n",
            "Epoch [15/20], Step [600/600], d_loss 0.1679, g_loss : 3.2509\n",
            "Epoch [16/20], Step [200/600], d_loss 0.4043, g_loss : 2.6225\n",
            "Epoch [16/20], Step [400/600], d_loss 0.1045, g_loss : 2.6184\n",
            "Epoch [16/20], Step [600/600], d_loss 0.1125, g_loss : 3.6207\n",
            "Epoch [17/20], Step [200/600], d_loss 0.1121, g_loss : 4.1621\n",
            "Epoch [17/20], Step [400/600], d_loss 0.2863, g_loss : 2.7515\n",
            "Epoch [17/20], Step [600/600], d_loss 0.1452, g_loss : 3.2212\n",
            "Epoch [18/20], Step [200/600], d_loss 0.1533, g_loss : 3.8077\n",
            "Epoch [18/20], Step [400/600], d_loss 0.1536, g_loss : 2.7288\n",
            "Epoch [18/20], Step [600/600], d_loss 0.1455, g_loss : 3.2480\n",
            "Epoch [19/20], Step [200/600], d_loss 0.2132, g_loss : 2.8939\n",
            "Epoch [19/20], Step [400/600], d_loss 0.1141, g_loss : 2.3386\n",
            "Epoch [19/20], Step [600/600], d_loss 0.1108, g_loss : 3.2719\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vPnJBpxGzCqL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from matplotlib.pyplot import imshow, imsave\n",
        "import numpy as np\n",
        "def get_sample_image(G, n_noise=100):\n",
        "    \"\"\"\n",
        "        save sample 100 images\n",
        "    \"\"\"\n",
        "    img = np.zeros([280, 280])\n",
        "    for j in range(10):\n",
        "        z = torch.randn(10, n_noise).to(device)\n",
        "        y_hat = G(z).view(10, 28, 28)\n",
        "        result = y_hat.cpu().data.numpy()\n",
        "        img[j*28:(j+1)*28] = np.concatenate([x for x in result], axis=-1)\n",
        "    return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n4lz8IN4zCsa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "e896f306-5b1d-461d-a2cf-f8722f48e96a"
      },
      "cell_type": "code",
      "source": [
        "imshow(get_sample_image(G, n_noise), cmap='gray')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd52a9c7c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QCBAUFIRRo0bh9OnTAIDS0lKkpKQ8koDsHj16YNasWfjll19w69Yt7Ny5EwkJ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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}