gan_mlp_mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH3X5_QJb2Go"
      },
      "source": [
        "Adaptado por Bruno Menezes (https://github.com/brunoleomenezes) de Sebastian Raschka e será utilizado apenas para fins acadêmicos.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KUEoFkIb2Gs"
      },
      "source": [
        "# Generative Adversarial Networks (GAN)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_KLlvxZb2Gt"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "2HT9R5CCb2Gt"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    torch.backends.cudnn.deterministic = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9getcPmb2Gu"
      },
      "source": [
        "## Settings and Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
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        "id": "10xmd8yTb2Gu",
        "outputId": "4a5aff60-2965-4316-e19b-27bfab470483"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "dc1175dbf9bc4df0abd4f45e58f62905"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
              "model_id": "e6decc8dccbc43c3bb3419f15eec1974"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Image batch dimensions: torch.Size([128, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([128])\n"
          ]
        }
      ],
      "source": [
        "##########################\n",
        "### SETTINGS\n",
        "##########################\n",
        "\n",
        "# Device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Hyperparameters\n",
        "random_seed = 123\n",
        "generator_learning_rate = 0.001\n",
        "discriminator_learning_rate = 0.001\n",
        "num_epochs = 100\n",
        "batch_size = 128\n",
        "LATENT_DIM = 100\n",
        "IMG_SHAPE = (1, 28, 28)\n",
        "IMG_SIZE = 1\n",
        "for x in IMG_SHAPE:\n",
        "    IMG_SIZE *= x\n",
        "\n",
        "\n",
        "\n",
        "##########################\n",
        "### MNIST DATASET\n",
        "##########################\n",
        "\n",
        "# Note transforms.ToTensor() scales input images\n",
        "# to 0-1 range\n",
        "train_dataset = datasets.MNIST(root='data', \n",
        "                               train=True, \n",
        "                               transform=transforms.ToTensor(),\n",
        "                               download=True)\n",
        "\n",
        "test_dataset = datasets.MNIST(root='data', \n",
        "                              train=False, \n",
        "                              transform=transforms.ToTensor())\n",
        "\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, \n",
        "                          batch_size=batch_size, \n",
        "                          shuffle=True)\n",
        "\n",
        "test_loader = DataLoader(dataset=test_dataset, \n",
        "                         batch_size=batch_size, \n",
        "                         shuffle=False)\n",
        "\n",
        "# Checking the dataset\n",
        "for images, labels in train_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zg0fzlfhb2Gw"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "GCOJbUhgb2Gw"
      },
      "outputs": [],
      "source": [
        "##########################\n",
        "### MODEL\n",
        "##########################\n",
        "\n",
        "\n",
        "class GAN(torch.nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(GAN, self).__init__()\n",
        "        \n",
        "        \n",
        "        self.generator = nn.Sequential(\n",
        "            nn.Linear(LATENT_DIM, 128),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.Linear(128, IMG_SIZE),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "        \n",
        "        self.discriminator = nn.Sequential(\n",
        "            nn.Linear(IMG_SIZE, 128),\n",
        "            nn.LeakyReLU(inplace=True),\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.Linear(128, 1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "            \n",
        "    def generator_forward(self, z):\n",
        "        img = self.generator(z)\n",
        "        return img\n",
        "    \n",
        "    def discriminator_forward(self, img):\n",
        "        pred = model.discriminator(img)\n",
        "        return pred.view(-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "IsskIQ1Pb2Gx"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(random_seed)\n",
        "\n",
        "model = GAN()\n",
        "model = model.to(device)\n",
        "\n",
        "optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
        "optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_qqyM4kb2Gx"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Ni5lZjob2Gy",
        "outputId": "d91a5ee5-4f03-4bfd-dd7a-6bb91dfbce10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Time elapsed: 22.55 min\n",
            "Total Training Time: 22.55 min\n"
          ]
        }
      ],
      "source": [
        "start_time = time.time()    \n",
        "\n",
        "discr_costs = []\n",
        "gener_costs = []\n",
        "for epoch in range(num_epochs):\n",
        "    model = model.train()\n",
        "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
        "\n",
        "        \n",
        "        \n",
        "        features = (features - 0.5)*2.\n",
        "        features = features.view(-1, IMG_SIZE).to(device) \n",
        "        targets = targets.to(device)\n",
        "\n",
        "        valid = torch.ones(targets.size(0)).float().to(device)\n",
        "        fake = torch.zeros(targets.size(0)).float().to(device)\n",
        "        \n",
        "\n",
        "        ### FORWARD AND BACK PROP\n",
        "        \n",
        "        \n",
        "        # --------------------------\n",
        "        # Train Generator\n",
        "        # --------------------------\n",
        "        \n",
        "        # Make new images\n",
        "        z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
        "        generated_features = model.generator_forward(z)\n",
        "        \n",
        "        # Loss for fooling the discriminator\n",
        "        discr_pred = model.discriminator_forward(generated_features)\n",
        "        \n",
        "        gener_loss = F.binary_cross_entropy(discr_pred, valid)\n",
        "        \n",
        "        optim_gener.zero_grad()\n",
        "        gener_loss.backward()\n",
        "        optim_gener.step()\n",
        "        \n",
        "        # --------------------------\n",
        "        # Train Discriminator\n",
        "        # --------------------------        \n",
        "        \n",
        "        discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))\n",
        "        real_loss = F.binary_cross_entropy(discr_pred_real, valid)\n",
        "        \n",
        "        discr_pred_fake = model.discriminator_forward(generated_features.detach())\n",
        "        fake_loss = F.binary_cross_entropy(discr_pred_fake, fake)\n",
        "        \n",
        "        discr_loss = 0.5*(real_loss + fake_loss)\n",
        "\n",
        "        optim_discr.zero_grad()\n",
        "        discr_loss.backward()\n",
        "        optim_discr.step()        \n",
        "        \n",
        "        discr_costs.append(discr_loss.item())\n",
        "        gener_costs.append(gener_loss.item())\n",
        "        \n",
        "        \n",
        "        ### LOGGING\n",
        "        if not batch_idx % 100:\n",
        "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
        "                   %(epoch+1, num_epochs, batch_idx, \n",
        "                     len(train_loader), gener_loss, discr_loss))\n",
        "\n",
        "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
        "    \n",
        "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joALi4Hjb2Gy"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "kXzvgAgvb2Gy"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "4xPriTSOb2Gz",
        "outputId": "0721694a-ce4b-41e8-a4a1-6eb1d94bbd96"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(range(len(gener_costs)), gener_costs, label='generator loss')\n",
        "plt.plot(range(len(discr_costs)), discr_costs, label='discriminator loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "id": "KRdx_6eQb2Gz",
        "outputId": "cc6c8125-fb78-493c-9dcb-28c842648fc0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x180 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "##########################\n",
        "### VISUALIZATION\n",
        "##########################\n",
        "\n",
        "\n",
        "model.eval()\n",
        "# Make new images\n",
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        "\n",
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        "\n",
        "\n",
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