pytorch_ipynb/gan/gan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.8\n",
      "IPython 7.2.0\n",
      "\n",
      "torch 1.0.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Runs on CPU or GPU (if available)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Generative Adversarial Networks (GAN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a standard GAN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": {},
   "source": [
    "## Settings and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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:2\" 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": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": {},
   "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": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Time elapsed: 32.89 min\n",
      "Total Training Time: 32.89 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)\n",
    "        gener_costs.append(gener_loss)\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": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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"
    }
   ],
   "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################\n",
    "### VISUALIZATION\n",
    "##########################\n",
    "\n",
    "\n",
    "model.eval()\n",
    "# Make new images\n",
    "z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "generated_features = model.generator_forward(z)\n",
    "imgs = generated_features.view(-1, 28, 28)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))\n",
    "\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
   ]
  }
 ],
 "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": "3.7.1"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}