pytorch_ipynb/gan/dc-wgan-1.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 3.7.3\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": [
    "# Deep Convolutional Wasserstein GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a deep convolutional Wasserstein GAN based on the paper \n",
    "\n",
    "- Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:1701.07875. (https://arxiv.org/abs/1701.07875)\n",
    "\n",
    "The main differences to a conventional deep convolutional GAN are annotated in the code. In short, the main differences are \n",
    "\n",
    "1. Not using a sigmoid activation function and just using a linear output layer for the critic (i.e., discriminator).\n",
    "2. Using label -1 instead of 1 for the real images; using label 1 instead of 0 for fake images.\n",
    "3. Using Wasserstein distance (loss) for training both the critic and the generator.\n",
    "4. After each weight update, clip the weights to be in range [-0.1, 0.1].\n",
    "5. Train the critic 5 times for each generator training update.\n"
   ]
  },
  {
   "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:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 0\n",
    "generator_learning_rate = 0.00005\n",
    "discriminator_learning_rate = 0.00005\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",
    "## WGAN-specific settings\n",
    "num_iter_critic = 5\n",
    "weight_clip_value = 0.01\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",
    "                          num_workers=4,\n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=BATCH_SIZE,\n",
    "                         num_workers=4,\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",
    "class Flatten(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), -1)\n",
    "    \n",
    "class Reshape1(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), 64, 7, 7)\n",
    "\n",
    "\n",
    "def wasserstein_loss(y_true, y_pred):\n",
    "    return torch.mean(y_true * y_pred)\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",
    "              \n",
    "            nn.Linear(LATENT_DIM, 3136, bias=False),\n",
    "            nn.BatchNorm1d(num_features=3136),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
    "            Reshape1(),\n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
    "            nn.BatchNorm2d(num_features=32),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
    "            nn.BatchNorm2d(num_features=16),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=16, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=0, bias=False),\n",
    "            nn.BatchNorm2d(num_features=8),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            nn.ConvTranspose2d(in_channels=8, out_channels=1, kernel_size=(2, 2), stride=(1, 1), padding=0, bias=False),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "        self.discriminator = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=8, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
    "            nn.BatchNorm2d(num_features=8),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            nn.Conv2d(in_channels=8, out_channels=16, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
    "            nn.BatchNorm2d(num_features=16),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            nn.Conv2d(in_channels=16, out_channels=32, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
    "            nn.BatchNorm2d(num_features=32),\n",
    "            nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
    "            #nn.Dropout2d(p=0.2),\n",
    "            \n",
    "            Flatten(),\n",
    "\n",
    "            nn.Linear(512, 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)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAN(\n",
      "  (generator): Sequential(\n",
      "    (0): Linear(in_features=100, out_features=3136, bias=False)\n",
      "    (1): BatchNorm1d(3136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (3): Reshape1()\n",
      "    (4): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (6): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (7): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (8): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (9): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (10): ConvTranspose2d(16, 8, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "    (11): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (12): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (13): ConvTranspose2d(8, 1, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
      "    (14): Tanh()\n",
      "  )\n",
      "  (discriminator): Sequential(\n",
      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (3): Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (6): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): LeakyReLU(negative_slope=0.0001, inplace=True)\n",
      "    (9): Flatten()\n",
      "    (10): Linear(in_features=512, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(random_seed)\n",
    "\n",
    "#del model\n",
    "model = GAN()\n",
    "model = model.to(device)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\noutputs = []\\ndef hook(module, input, output):\\n    outputs.append(output)\\n\\nfor i, layer in enumerate(model.discriminator):\\n    if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n        model.discriminator[i].register_forward_hook(hook)\\n\\n#for i, layer in enumerate(model.generator):\\n#    if isinstance(layer, torch.nn.modules.ConvTranspose2d):\\n#        model.generator[i].register_forward_hook(hook)\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ## FOR DEBUGGING\n",
    "\n",
    "\"\"\"\n",
    "outputs = []\n",
    "def hook(module, input, output):\n",
    "    outputs.append(output)\n",
    "\n",
    "for i, layer in enumerate(model.discriminator):\n",
    "    if isinstance(layer, torch.nn.modules.conv.Conv2d):\n",
    "        model.discriminator[i].register_forward_hook(hook)\n",
    "\n",
    "#for i, layer in enumerate(model.generator):\n",
    "#    if isinstance(layer, torch.nn.modules.ConvTranspose2d):\n",
    "#        model.generator[i].register_forward_hook(hook)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "optim_gener = torch.optim.RMSprop(model.generator.parameters(), lr=generator_learning_rate)\n",
    "optim_discr = torch.optim.RMSprop(model.discriminator.parameters(), lr=discriminator_learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Total Training Time: 39.57 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",
    "        # Normalize images to [-1, 1] range\n",
    "        features = (features - 0.5)*2.\n",
    "        features = features.view(-1, IMG_SIZE).to(device) \n",
    "\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        # Regular GAN:\n",
    "        # valid = torch.ones(targets.size(0)).float().to(device)\n",
    "        # fake = torch.zeros(targets.size(0)).float().to(device)\n",
    "        \n",
    "        # WGAN:\n",
    "        valid = -(torch.ones(targets.size(0)).float()).to(device)\n",
    "        fake = torch.ones(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_(0.0, 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.view(targets.size(0), 1, 28, 28))\n",
    "        \n",
    "        # Regular GAN:\n",
    "        # gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid)\n",
    "        \n",
    "        # WGAN:\n",
    "        gener_loss = wasserstein_loss(valid, discr_pred)\n",
    "        \n",
    "        optim_gener.zero_grad()\n",
    "        gener_loss.backward()\n",
    "        optim_gener.step()\n",
    "        \n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Discriminator\n",
    "        # --------------------------        \n",
    "\n",
    "        # WGAN: Multiple loops for the discriminator\n",
    "        for _ in range(num_iter_critic):\n",
    "        \n",
    "            discr_pred_real = model.discriminator_forward(features.view(targets.size(0), 1, 28, 28))\n",
    "            # Regular GAN:\n",
    "            # real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid)\n",
    "            # WGAN:\n",
    "            real_loss = wasserstein_loss(valid, discr_pred_real)\n",
    "\n",
    "            discr_pred_fake = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28).detach())\n",
    "\n",
    "            # Regular GAN:\n",
    "            # fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)\n",
    "            # WGAN:\n",
    "            fake_loss = wasserstein_loss(fake, discr_pred_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",
    "            # WGAN:\n",
    "            for p in model.discriminator.parameters():\n",
    "                p.data.clamp_(-weight_clip_value, weight_clip_value)\n",
    "\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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor i in outputs:\\n    print(i.size())\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### For Debugging\n",
    "\n",
    "\"\"\"\n",
    "for i in outputs:\n",
    "    print(i.size())\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 10 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((10, LATENT_DIM)).uniform_(0.0, 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=10, 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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                 [-1, 3136]         313,600\n",
      "       BatchNorm1d-2                 [-1, 3136]           6,272\n",
      "         LeakyReLU-3                 [-1, 3136]               0\n",
      "          Reshape1-4             [-1, 64, 7, 7]               0\n",
      "   ConvTranspose2d-5           [-1, 32, 13, 13]          18,432\n",
      "       BatchNorm2d-6           [-1, 32, 13, 13]              64\n",
      "         LeakyReLU-7           [-1, 32, 13, 13]               0\n",
      "   ConvTranspose2d-8           [-1, 16, 25, 25]           4,608\n",
      "       BatchNorm2d-9           [-1, 16, 25, 25]              32\n",
      "        LeakyReLU-10           [-1, 16, 25, 25]               0\n",
      "  ConvTranspose2d-11            [-1, 8, 27, 27]           1,152\n",
      "      BatchNorm2d-12            [-1, 8, 27, 27]              16\n",
      "        LeakyReLU-13            [-1, 8, 27, 27]               0\n",
      "  ConvTranspose2d-14            [-1, 1, 28, 28]              32\n",
      "             Tanh-15            [-1, 1, 28, 28]               0\n",
      "================================================================\n",
      "Total params: 344,208\n",
      "Trainable params: 344,208\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.59\n",
      "Params size (MB): 1.31\n",
      "Estimated Total Size (MB): 1.91\n",
      "----------------------------------------------------------------\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [-1, 8, 14, 14]              72\n",
      "       BatchNorm2d-2            [-1, 8, 14, 14]              16\n",
      "         LeakyReLU-3            [-1, 8, 14, 14]               0\n",
      "            Conv2d-4             [-1, 16, 7, 7]           1,152\n",
      "       BatchNorm2d-5             [-1, 16, 7, 7]              32\n",
      "         LeakyReLU-6             [-1, 16, 7, 7]               0\n",
      "            Conv2d-7             [-1, 32, 4, 4]           4,608\n",
      "       BatchNorm2d-8             [-1, 32, 4, 4]              64\n",
      "         LeakyReLU-9             [-1, 32, 4, 4]               0\n",
      "          Flatten-10                  [-1, 512]               0\n",
      "           Linear-11                    [-1, 1]             513\n",
      "================================================================\n",
      "Total params: 6,457\n",
      "Trainable params: 6,457\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.07\n",
      "Params size (MB): 0.02\n",
      "Estimated Total Size (MB): 0.10\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "model = model.to('cuda:0')\n",
    "summary(model.generator, input_size=(100,))\n",
    "summary(model.discriminator, input_size=(1, 28, 28))"
   ]
  }
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