{
 "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 3.7.3\n",
      "IPython 7.6.1\n",
      "\n",
      "torch 1.2.0\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 = 75\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: 20.94 min\n",
      "Total Training Time: 20.94 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 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.plot(range(len(gener_costs)), gener_costs, label='Generator loss')\n",
    "ax1.plot(range(len(discr_costs)), discr_costs, label='Discriminator loss')\n",
    "ax1.set_xlabel('Iterations')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.legend()\n",
    "\n",
    "###################\n",
    "# Set scond x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(NUM_EPOCHS+1))\n",
    "iter_per_epoch = len(train_loader)\n",
    "newpos = [e*iter_per_epoch for e in newlabel]\n",
    "\n",
    "ax2.set_xticklabels(newlabel[::10])\n",
    "ax2.set_xticks(newpos[::10])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "###################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": "3.7.3"
  },
  "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": 4
}