generative_adversarial_networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUpgaUIdtNCP"
      },
      "source": [
        "## General Adversarial Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Tc1HJ0HEkbV"
      },
      "source": [
        "Generative Adversarial Networks (GANs) were originally proposed in the 2014 [paper of the same name](https://arxiv.org/abs/1406.2661).  The idea is to have two distinct networks, the generator and the discriminator.  The generator network takes random noise as input and produces an image.  The discriminator network is trained on these sythetically generate images as well as real images, it is then given the task of distinguishing between fake and real images.  GANs give impressive results, but training them can be tricky.  Training GANs is sometimes more of an art than a science, with instable training and mode collapse both happening often.  See [here](https://github.com/soumith/ganhacks) for a good list of tips on how to effective train GANs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "riJhtZ6x3Pl1"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import cv2\n",
        "import numpy as np\n",
        "import random\n",
        "from skimage import transform\n",
        "from torch.utils.data import Dataset\n",
        "import os\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn\n",
        "from __future__ import print_function\n",
        "import argparse\n",
        "import os\n",
        "import random\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.parallel\n",
        "import torch.backends.cudnn as cudnn\n",
        "import torch.optim as optim\n",
        "import torch.utils.data\n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.utils as vutils\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "from IPython.display import HTML\n",
        "from torchvision.datasets import MNIST"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_R2D6Tus9gZ"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xo9CpSZCEkbX"
      },
      "source": [
        "For the architecture we will use DCGAN.  The code is taken from the [PyTorch documentation](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) and is a good place to start with GANs.  The [DCGAN paper](https://arxiv.org/abs/1511.06434) also gives some good recomendations on how GAN architectures should be designed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "kYBGKQg73BIE"
      },
      "outputs": [],
      "source": [
        "# Number of workers for dataloader\n",
        "workers = 2\n",
        "\n",
        "# Batch size during training\n",
        "batch_size = 128\n",
        "\n",
        "# Spatial size of training images. All images will be resized to this\n",
        "#   size using a transformer.\n",
        "image_size = 64\n",
        "\n",
        "# Number of channels in the training images. For color images this is 3\n",
        "nc = 1\n",
        "\n",
        "# Size of z latent vector (i.e. size of generator input)\n",
        "nz = 100\n",
        "\n",
        "# Size of feature maps in generator\n",
        "ngf = 64\n",
        "\n",
        "# Size of feature maps in discriminator\n",
        "ndf = 64\n",
        "\n",
        "# Number of training epochs\n",
        "num_epochs = 10\n",
        "\n",
        "# Learning rate for optimizers\n",
        "lr = 0.0002\n",
        "\n",
        "# Beta1 hyperparam for Adam optimizers\n",
        "beta1 = 0.5\n",
        "\n",
        "# Number of GPUs available. Use 0 for CPU mode.\n",
        "ngpu = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "VxMUeoOFpuo0"
      },
      "outputs": [],
      "source": [
        "# custom weights initialization called on netG and netD\n",
        "def weights_init(m):\n",
        "    classname = m.__class__.__name__\n",
        "    if classname.find('Conv') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "        nn.init.constant_(m.bias.data, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQWzeVIPCAd3",
        "outputId": "05f454fd-71cd-486a-ef0b-a3982ea06b40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num params (generator):  3444480\n"
          ]
        }
      ],
      "source": [
        "# Generator Code\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self, ngpu):\n",
        "        super(Generator, self).__init__()\n",
        "        self.ngpu = ngpu\n",
        "        self.main1 = nn.Sequential(\n",
        "            # input is Z, going into a convolution\n",
        "            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),\n",
        "            nn.BatchNorm2d(ngf * 8),\n",
        "            nn.ReLU(True)\n",
        "        )\n",
        "\n",
        "        self.main2 = nn.Sequential(\n",
        "            # state size. (ngf*8) x 4 x 4\n",
        "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
        "            nn.BatchNorm2d(ngf * 4),\n",
        "            nn.ReLU(True)\n",
        "        )\n",
        "\n",
        "        self.main3 = nn.Sequential(\n",
        "            # state size. (ngf*4) x 8 x 8\n",
        "            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 2, bias=False),\n",
        "            nn.BatchNorm2d(ngf * 2),\n",
        "            nn.ReLU(True)\n",
        "        )\n",
        "        self.main4 = nn.Sequential(\n",
        "            nn.ConvTranspose2d( ngf * 2, nc, 4, 2, 1, bias=False),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, input):\n",
        "        x = self.main1(input)\n",
        "        x = self.main2(x)\n",
        "        x = self.main3(x)\n",
        "        x = self.main4(x)\n",
        "        return x\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Create the generator\n",
        "netG = Generator(ngpu).to(device)\n",
        "\n",
        "# Handle multi-gpu if desired\n",
        "if (device.type == 'cuda') and (ngpu > 1):\n",
        "    netG = nn.DataParallel(netG, list(range(ngpu)))\n",
        "\n",
        "# Apply the weights_init function to randomly initialize all weights\n",
        "netG.apply(weights_init)\n",
        "\n",
        "# Print the number of parameters for the generator\n",
        "print(\"Num params (generator): \", sum(p.numel() for p in netG.parameters()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBCRnf1qCaHC",
        "outputId": "52a25740-27c5-4b16-815a-2bd18f185f6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num params (discriminator):  659456\n"
          ]
        }
      ],
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, ngpu):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.ngpu = ngpu\n",
        "        self.main1 = nn.Sequential(\n",
        "            # input is (nc) x 64 x 64\n",
        "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "\n",
        "        self.main2 = nn.Sequential(\n",
        "            # state size. (ndf) x 32 x 32\n",
        "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
        "            nn.BatchNorm2d(ndf * 2),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        \n",
        "        self.main3 = nn.Sequential(\n",
        "            # state size. (ndf*2) x 16 x 16\n",
        "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
        "            nn.BatchNorm2d(ndf * 4),\n",
        "            nn.LeakyReLU(0.2, inplace=True)\n",
        "        )\n",
        "        \n",
        "        self.main4 = nn.Sequential(\n",
        "            # state size. (ndf*8) x 4 x 4\n",
        "            nn.Conv2d(ndf * 4, 1, 3, 1, 0, bias=False),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, input):\n",
        "        x = self.main1(input)\n",
        "        x = self.main2(x)\n",
        "        x = self.main3(x)\n",
        "        x = self.main4(x)\n",
        "        return x\n",
        "\n",
        "  \n",
        "# Create the Discriminator\n",
        "netD = Discriminator(ngpu).to(device)\n",
        "\n",
        "# Apply the weights_init function to randomly initialize all weights\n",
        "#  to mean=0, stdev=0.2.\n",
        "netD.apply(weights_init)\n",
        "\n",
        "# Print the number of parameters for the discriminator\n",
        "print(\"Num params (discriminator): \", sum(p.numel() for p in netD.parameters()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RU92t1HXHJxP"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECO0Yq0eHJxP"
      },
      "source": [
        "Download and plot some examples from the training data. We will use the MNIST dataset for the sake of simplicity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "1gF3n4PpHJxP",
        "outputId": "dc5a9b28-750b-48a0-adb5-52c3948979a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f588eb83fa0>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x180 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Are we using the CPU or the GPU?\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# Transforms to be applied to all images in the dataset\n",
        "tf = transforms.Compose(\n",
        "    [transforms.ToTensor()]\n",
        ")\n",
        "\n",
        "# Download/setup the MNIST dataset\n",
        "dataset = MNIST(\n",
        "    \"./data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=tf,\n",
        ")\n",
        "\n",
        "# Plot examples\n",
        "fig, ax= plt.subplots(1, 5, figsize=(12, 2.5))\n",
        "fig.suptitle('MNIST Examples')\n",
        "ax[0].imshow(dataset[0][0][0,:,:].numpy())\n",
        "ax[1].imshow(dataset[1][0][0,:,:].numpy())\n",
        "ax[2].imshow(dataset[2][0][0,:,:].numpy())\n",
        "ax[3].imshow(dataset[3][0][0,:,:].numpy())\n",
        "ax[4].imshow(dataset[4][0][0,:,:].numpy())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZRWWqfv1Qu1"
      },
      "source": [
        "## Loss Function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44pj5xr2Ekbd"
      },
      "source": [
        "GANs are optimised by playing a minimax game with the value function $V$\n",
        "\n",
        "$$ \\underset{G}{\\min} ~ \\underset{D}{\\max} ~ V(D,G) = \\mathbb{E}_{x \\sim p_{data}(x)}[\\text{log}(D(x))] + {\\mathbb{E}_{z \\sim p_{z}(z)}}[\\text{log}(1-D(G(z)))]$$\n",
        "\n",
        "where\n",
        "\n",
        "$ ~~~~~~~~~~~~ D=$ discriminator network\n",
        "\n",
        "$ ~~~~~~~~~~~~ G = $ generator network\n",
        "\n",
        "$ ~~~~~~~~~~~~ z = $ random vector\n",
        "\n",
        "$ ~~~~~~~~~~~~ x=$ real image\n",
        "\n",
        "The first term represents the discriminators predictions on real data and the second term represents the discriminators predictions on the fake data.  We want our discriminator network $D$ to predict a $1$ when it is given a real image and $0$ when given a fake image.  If $D$ acheives both of these aims, then the loss function will have been maximised. The generator on the other hand wants to fool the discriminator. The generator wants $D$ to incorrectly predict $1$ when it is given fake data.  This sets up the adversarial minimax game.\n",
        "\n",
        "We could implement this loss function directly, but lots of people use binary cross entropy loss because it is equivalent\n",
        "\n",
        "$$L_{BCE} = -\\sum_{i=1}^N{y_i \\text{log}(D(x)) + (1-y_i) \\text{log}(1-D(G(z)))} $$\n",
        "\n",
        "where $y$ is the ground truth and we have $N$ data points. So when $y=0$ the first term is ignored, and when $y=1$ the second term is ignored.  This makes intuative sense because the first term optimises on real data and the seond term on fake data.  We can then add loss for fake value (when $y=0$) and loss for real values (when $y=1$) to obtain our original loss function above (more or less).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "QIW1qQfEmCq1"
      },
      "outputs": [],
      "source": [
        "# Initialize BCELoss function\n",
        "criterion = nn.BCELoss()\n",
        "\n",
        "# Create batch of latent vectors that we will use to visualize\n",
        "#  the progression of the generator\n",
        "fixed_noise = torch.randn(64, nz, 1, 1, device=device)\n",
        "\n",
        "# Establish convention for real and fake labels during training\n",
        "real_label = 1.\n",
        "fake_label = 0.\n",
        "\n",
        "# Setup Adam optimizers for both G and D\n",
        "optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))\n",
        "optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDBypXcrEkbe"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-5O-yV0Ekbe"
      },
      "source": [
        "Training GANs happens in a two stage process which is repeated\n",
        "1. Update the weights of the discriminator.  This is done by ascending its stocastic gradient:\n",
        "$$\\nabla_{\\theta_d} \\frac{1}{m}\\sum_{i=1}^{m}\\text{log}(D(x))+\\text{log}(1-D(G(z)))$$\n",
        "\n",
        "2. Update the weights of the generator.  We can ignore the first term of the loss because the generator is not included.  This is done by descending its stocastic gradient:\n",
        "$$\\nabla_{\\theta_g} \\frac{1}{m}\\sum_{i=1}^{m}\\text{log}(1-D(G(z)))$$\n",
        "\n",
        "Step 1 can be done repeatedly, however, in this implementation it is just run once."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CbbLXRw8mWWY",
        "outputId": "3fbca2ec-5f0e-4f58-ca30-95a4eab9c095"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0/100][0/469]\tLoss_D: 1.1623\tLoss_G: 1.5797\tD(x): 0.6155\tD(G(z)): 0.4644 / 0.2281\n",
            "[0/100][200/469]\tLoss_D: 0.0828\tLoss_G: 3.2073\tD(x): 0.9428\tD(G(z)): 0.0214 / 0.0510\n",
            "[0/100][400/469]\tLoss_D: 0.1790\tLoss_G: 3.0430\tD(x): 0.9355\tD(G(z)): 0.1018 / 0.0576\n",
            "[1/100][0/469]\tLoss_D: 0.0617\tLoss_G: 4.1210\tD(x): 0.9669\tD(G(z)): 0.0265 / 0.0220\n",
            "[1/100][200/469]\tLoss_D: 0.0509\tLoss_G: 4.5549\tD(x): 0.9808\tD(G(z)): 0.0302 / 0.0158\n",
            "[1/100][400/469]\tLoss_D: 0.0347\tLoss_G: 4.6918\tD(x): 0.9853\tD(G(z)): 0.0192 / 0.0133\n",
            "[2/100][0/469]\tLoss_D: 0.0320\tLoss_G: 4.8247\tD(x): 0.9822\tD(G(z)): 0.0135 / 0.0131\n",
            "[2/100][200/469]\tLoss_D: 7.1545\tLoss_G: 0.1388\tD(x): 0.0059\tD(G(z)): 0.0034 / 0.9114\n",
            "[2/100][400/469]\tLoss_D: 0.1938\tLoss_G: 3.2588\tD(x): 0.9593\tD(G(z)): 0.1348 / 0.0482\n",
            "[3/100][0/469]\tLoss_D: 0.0567\tLoss_G: 4.1523\tD(x): 0.9684\tD(G(z)): 0.0230 / 0.0211\n",
            "[3/100][200/469]\tLoss_D: 0.0487\tLoss_G: 4.7200\tD(x): 0.9830\tD(G(z)): 0.0303 / 0.0124\n",
            "[3/100][400/469]\tLoss_D: 0.3070\tLoss_G: 2.4662\tD(x): 0.8593\tD(G(z)): 0.1318 / 0.1052\n",
            "[4/100][0/469]\tLoss_D: 0.0452\tLoss_G: 4.1168\tD(x): 0.9796\tD(G(z)): 0.0236 / 0.0249\n",
            "[4/100][200/469]\tLoss_D: 0.0772\tLoss_G: 3.8127\tD(x): 0.9618\tD(G(z)): 0.0361 / 0.0314\n",
            "[4/100][400/469]\tLoss_D: 0.2383\tLoss_G: 3.2984\tD(x): 0.8559\tD(G(z)): 0.0612 / 0.0531\n",
            "[5/100][0/469]\tLoss_D: 0.0961\tLoss_G: 3.2945\tD(x): 0.9278\tD(G(z)): 0.0147 / 0.0585\n",
            "[5/100][200/469]\tLoss_D: 0.2303\tLoss_G: 3.4001\tD(x): 0.9477\tD(G(z)): 0.1511 / 0.0456\n",
            "[5/100][400/469]\tLoss_D: 0.0505\tLoss_G: 4.2941\tD(x): 0.9716\tD(G(z)): 0.0201 / 0.0211\n",
            "[6/100][0/469]\tLoss_D: 0.5879\tLoss_G: 0.8607\tD(x): 0.8336\tD(G(z)): 0.2635 / 0.4836\n",
            "[6/100][200/469]\tLoss_D: 0.3783\tLoss_G: 1.4830\tD(x): 0.7249\tD(G(z)): 0.0127 / 0.2939\n",
            "[6/100][400/469]\tLoss_D: 0.1892\tLoss_G: 3.8420\tD(x): 0.9531\tD(G(z)): 0.1172 / 0.0311\n",
            "[7/100][0/469]\tLoss_D: 0.2384\tLoss_G: 2.3244\tD(x): 0.8275\tD(G(z)): 0.0249 / 0.1537\n",
            "[7/100][200/469]\tLoss_D: 0.1030\tLoss_G: 3.3912\tD(x): 0.9434\tD(G(z)): 0.0385 / 0.0520\n",
            "[7/100][400/469]\tLoss_D: 0.2555\tLoss_G: 3.2613\tD(x): 0.9402\tD(G(z)): 0.1578 / 0.0550\n",
            "[8/100][0/469]\tLoss_D: 0.3242\tLoss_G: 2.0863\tD(x): 0.8041\tD(G(z)): 0.0687 / 0.1758\n",
            "[8/100][200/469]\tLoss_D: 0.3201\tLoss_G: 2.4138\tD(x): 0.8041\tD(G(z)): 0.0704 / 0.1203\n",
            "[8/100][400/469]\tLoss_D: 0.1898\tLoss_G: 3.5347\tD(x): 0.9576\tD(G(z)): 0.1271 / 0.0393\n",
            "[9/100][0/469]\tLoss_D: 0.3485\tLoss_G: 2.7353\tD(x): 0.7543\tD(G(z)): 0.0098 / 0.1132\n",
            "[9/100][200/469]\tLoss_D: 0.5530\tLoss_G: 6.5547\tD(x): 0.9947\tD(G(z)): 0.3768 / 0.0023\n",
            "[9/100][400/469]\tLoss_D: 0.2396\tLoss_G: 3.9096\tD(x): 0.9280\tD(G(z)): 0.1284 / 0.0360\n",
            "[10/100][0/469]\tLoss_D: 0.3678\tLoss_G: 2.4133\tD(x): 0.7831\tD(G(z)): 0.0927 / 0.1235\n",
            "[10/100][200/469]\tLoss_D: 0.1401\tLoss_G: 3.4543\tD(x): 0.9378\tD(G(z)): 0.0668 / 0.0515\n",
            "[10/100][400/469]\tLoss_D: 0.2735\tLoss_G: 5.5179\tD(x): 0.9878\tD(G(z)): 0.2064 / 0.0062\n",
            "[11/100][0/469]\tLoss_D: 0.4318\tLoss_G: 3.4389\tD(x): 0.9696\tD(G(z)): 0.2766 / 0.0498\n",
            "[11/100][200/469]\tLoss_D: 0.1939\tLoss_G: 3.1732\tD(x): 0.9270\tD(G(z)): 0.1011 / 0.0610\n",
            "[11/100][400/469]\tLoss_D: 0.1770\tLoss_G: 3.6896\tD(x): 0.9465\tD(G(z)): 0.1057 / 0.0387\n",
            "[12/100][0/469]\tLoss_D: 0.1746\tLoss_G: 3.1779\tD(x): 0.8925\tD(G(z)): 0.0506 / 0.0659\n",
            "[12/100][200/469]\tLoss_D: 0.1062\tLoss_G: 3.8182\tD(x): 0.9237\tD(G(z)): 0.0222 / 0.0402\n",
            "[12/100][400/469]\tLoss_D: 0.1664\tLoss_G: 3.2980\tD(x): 0.8764\tD(G(z)): 0.0216 / 0.0592\n",
            "[13/100][0/469]\tLoss_D: 0.3187\tLoss_G: 4.4974\tD(x): 0.9740\tD(G(z)): 0.2270 / 0.0160\n",
            "[13/100][200/469]\tLoss_D: 0.7872\tLoss_G: 1.2161\tD(x): 0.5587\tD(G(z)): 0.0343 / 0.3993\n",
            "[13/100][400/469]\tLoss_D: 0.3193\tLoss_G: 3.1659\tD(x): 0.8323\tD(G(z)): 0.0962 / 0.0606\n",
            "[14/100][0/469]\tLoss_D: 0.4158\tLoss_G: 1.9846\tD(x): 0.7257\tD(G(z)): 0.0396 / 0.1954\n",
            "[14/100][200/469]\tLoss_D: 0.1743\tLoss_G: 3.7057\tD(x): 0.9259\tD(G(z)): 0.0831 / 0.0396\n",
            "[14/100][400/469]\tLoss_D: 0.2564\tLoss_G: 2.5760\tD(x): 0.8823\tD(G(z)): 0.1006 / 0.1153\n",
            "[15/100][0/469]\tLoss_D: 0.6722\tLoss_G: 6.0338\tD(x): 0.9838\tD(G(z)): 0.4107 / 0.0042\n",
            "[15/100][200/469]\tLoss_D: 0.4066\tLoss_G: 3.0506\tD(x): 0.8757\tD(G(z)): 0.1921 / 0.0846\n",
            "[15/100][400/469]\tLoss_D: 0.2358\tLoss_G: 3.1738\tD(x): 0.8330\tD(G(z)): 0.0249 / 0.0703\n",
            "[16/100][0/469]\tLoss_D: 0.3693\tLoss_G: 4.2118\tD(x): 0.9181\tD(G(z)): 0.2169 / 0.0230\n",
            "[16/100][200/469]\tLoss_D: 0.5804\tLoss_G: 1.4620\tD(x): 0.6271\tD(G(z)): 0.0324 / 0.3285\n",
            "[16/100][400/469]\tLoss_D: 0.2777\tLoss_G: 2.8736\tD(x): 0.8593\tD(G(z)): 0.0886 / 0.0906\n",
            "[17/100][0/469]\tLoss_D: 0.3940\tLoss_G: 3.2359\tD(x): 0.9248\tD(G(z)): 0.2342 / 0.0646\n",
            "[17/100][200/469]\tLoss_D: 0.5895\tLoss_G: 1.9624\tD(x): 0.6577\tD(G(z)): 0.0407 / 0.2386\n",
            "[17/100][400/469]\tLoss_D: 1.4287\tLoss_G: 0.6639\tD(x): 0.3541\tD(G(z)): 0.0355 / 0.5988\n",
            "[18/100][0/469]\tLoss_D: 0.1681\tLoss_G: 2.8502\tD(x): 0.9241\tD(G(z)): 0.0751 / 0.0893\n",
            "[18/100][200/469]\tLoss_D: 0.1865\tLoss_G: 3.5530\tD(x): 0.8850\tD(G(z)): 0.0507 / 0.0493\n",
            "[18/100][400/469]\tLoss_D: 0.2306\tLoss_G: 3.5014\tD(x): 0.9225\tD(G(z)): 0.1201 / 0.0461\n",
            "[19/100][0/469]\tLoss_D: 0.2052\tLoss_G: 3.4053\tD(x): 0.9205\tD(G(z)): 0.1013 / 0.0507\n",
            "[19/100][200/469]\tLoss_D: 0.2704\tLoss_G: 3.6592\tD(x): 0.9592\tD(G(z)): 0.1814 / 0.0392\n",
            "[19/100][400/469]\tLoss_D: 0.3796\tLoss_G: 2.6426\tD(x): 0.7358\tD(G(z)): 0.0135 / 0.1126\n",
            "[20/100][0/469]\tLoss_D: 0.2006\tLoss_G: 3.5020\tD(x): 0.9218\tD(G(z)): 0.1008 / 0.0491\n",
            "[20/100][200/469]\tLoss_D: 0.2778\tLoss_G: 2.7430\tD(x): 0.8302\tD(G(z)): 0.0645 / 0.0944\n",
            "[20/100][400/469]\tLoss_D: 0.3174\tLoss_G: 3.0895\tD(x): 0.9320\tD(G(z)): 0.1953 / 0.0653\n",
            "[21/100][0/469]\tLoss_D: 0.4268\tLoss_G: 4.4246\tD(x): 0.9676\tD(G(z)): 0.2773 / 0.0262\n",
            "[21/100][200/469]\tLoss_D: 0.8436\tLoss_G: 3.7549\tD(x): 0.9577\tD(G(z)): 0.4761 / 0.0409\n",
            "[21/100][400/469]\tLoss_D: 0.4021\tLoss_G: 2.2175\tD(x): 0.7396\tD(G(z)): 0.0484 / 0.1722\n",
            "[22/100][0/469]\tLoss_D: 0.2651\tLoss_G: 2.2491\tD(x): 0.8244\tD(G(z)): 0.0507 / 0.1684\n",
            "[22/100][200/469]\tLoss_D: 0.3038\tLoss_G: 2.3631\tD(x): 0.8988\tD(G(z)): 0.1549 / 0.1344\n",
            "[22/100][400/469]\tLoss_D: 0.1655\tLoss_G: 3.2083\tD(x): 0.9157\tD(G(z)): 0.0635 / 0.0574\n",
            "[23/100][0/469]\tLoss_D: 0.3296\tLoss_G: 2.2528\tD(x): 0.7821\tD(G(z)): 0.0536 / 0.1617\n",
            "[23/100][200/469]\tLoss_D: 0.2183\tLoss_G: 3.1528\tD(x): 0.8973\tD(G(z)): 0.0905 / 0.0734\n",
            "[23/100][400/469]\tLoss_D: 0.4181\tLoss_G: 1.6160\tD(x): 0.7082\tD(G(z)): 0.0353 / 0.2693\n",
            "[24/100][0/469]\tLoss_D: 0.1862\tLoss_G: 4.4660\tD(x): 0.9389\tD(G(z)): 0.1041 / 0.0202\n",
            "[24/100][200/469]\tLoss_D: 0.2895\tLoss_G: 2.8026\tD(x): 0.8418\tD(G(z)): 0.0934 / 0.1020\n",
            "[24/100][400/469]\tLoss_D: 0.5286\tLoss_G: 4.1987\tD(x): 0.9806\tD(G(z)): 0.3513 / 0.0261\n",
            "[25/100][0/469]\tLoss_D: 0.3574\tLoss_G: 4.3604\tD(x): 0.9524\tD(G(z)): 0.2320 / 0.0198\n",
            "[25/100][200/469]\tLoss_D: 0.3216\tLoss_G: 4.0274\tD(x): 0.9119\tD(G(z)): 0.1699 / 0.0328\n",
            "[25/100][400/469]\tLoss_D: 0.2783\tLoss_G: 2.5398\tD(x): 0.8234\tD(G(z)): 0.0608 / 0.1201\n",
            "[26/100][0/469]\tLoss_D: 0.2848\tLoss_G: 2.3769\tD(x): 0.8125\tD(G(z)): 0.0510 / 0.1355\n",
            "[26/100][200/469]\tLoss_D: 0.4722\tLoss_G: 2.0329\tD(x): 0.7419\tD(G(z)): 0.0904 / 0.2069\n",
            "[26/100][400/469]\tLoss_D: 0.2355\tLoss_G: 2.8841\tD(x): 0.9263\tD(G(z)): 0.1341 / 0.0848\n",
            "[27/100][0/469]\tLoss_D: 0.4118\tLoss_G: 2.1998\tD(x): 0.7205\tD(G(z)): 0.0290 / 0.1703\n",
            "[27/100][200/469]\tLoss_D: 0.4022\tLoss_G: 1.6684\tD(x): 0.7436\tD(G(z)): 0.0688 / 0.2571\n",
            "[27/100][400/469]\tLoss_D: 0.2436\tLoss_G: 3.0377\tD(x): 0.9219\tD(G(z)): 0.1319 / 0.0731\n",
            "[28/100][0/469]\tLoss_D: 0.4247\tLoss_G: 2.5597\tD(x): 0.7395\tD(G(z)): 0.0588 / 0.1271\n",
            "[28/100][200/469]\tLoss_D: 0.2705\tLoss_G: 2.7362\tD(x): 0.8962\tD(G(z)): 0.1268 / 0.1036\n",
            "[28/100][400/469]\tLoss_D: 0.2837\tLoss_G: 2.9972\tD(x): 0.8553\tD(G(z)): 0.0921 / 0.0843\n",
            "[29/100][0/469]\tLoss_D: 0.3977\tLoss_G: 3.8039\tD(x): 0.9531\tD(G(z)): 0.2493 / 0.0403\n",
            "[29/100][200/469]\tLoss_D: 0.7016\tLoss_G: 1.3587\tD(x): 0.6207\tD(G(z)): 0.1019 / 0.3280\n",
            "[29/100][400/469]\tLoss_D: 0.4064\tLoss_G: 3.1297\tD(x): 0.9073\tD(G(z)): 0.2232 / 0.0712\n",
            "[30/100][0/469]\tLoss_D: 2.0398\tLoss_G: 5.0860\tD(x): 0.9964\tD(G(z)): 0.7790 / 0.0162\n",
            "[30/100][200/469]\tLoss_D: 0.2207\tLoss_G: 3.4784\tD(x): 0.9048\tD(G(z)): 0.0981 / 0.0503\n",
            "[30/100][400/469]\tLoss_D: 0.3119\tLoss_G: 3.0091\tD(x): 0.9042\tD(G(z)): 0.1705 / 0.0714\n",
            "[31/100][0/469]\tLoss_D: 0.2292\tLoss_G: 3.8121\tD(x): 0.9083\tD(G(z)): 0.1095 / 0.0384\n",
            "[31/100][200/469]\tLoss_D: 0.2835\tLoss_G: 3.9771\tD(x): 0.8871\tD(G(z)): 0.1261 / 0.0341\n",
            "[31/100][400/469]\tLoss_D: 0.3477\tLoss_G: 2.8696\tD(x): 0.8624\tD(G(z)): 0.1505 / 0.0960\n",
            "[32/100][0/469]\tLoss_D: 0.4837\tLoss_G: 1.9203\tD(x): 0.6868\tD(G(z)): 0.0419 / 0.2132\n",
            "[32/100][200/469]\tLoss_D: 0.4893\tLoss_G: 1.5466\tD(x): 0.7141\tD(G(z)): 0.0717 / 0.2851\n",
            "[32/100][400/469]\tLoss_D: 0.5104\tLoss_G: 4.3255\tD(x): 0.9666\tD(G(z)): 0.3351 / 0.0199\n",
            "[33/100][0/469]\tLoss_D: 0.2099\tLoss_G: 2.5442\tD(x): 0.9050\tD(G(z)): 0.0930 / 0.1134\n",
            "[33/100][200/469]\tLoss_D: 0.2254\tLoss_G: 2.9571\tD(x): 0.8572\tD(G(z)): 0.0529 / 0.0957\n",
            "[33/100][400/469]\tLoss_D: 0.2239\tLoss_G: 3.7488\tD(x): 0.9193\tD(G(z)): 0.1187 / 0.0378\n",
            "[34/100][0/469]\tLoss_D: 0.6693\tLoss_G: 4.6850\tD(x): 0.9828\tD(G(z)): 0.4262 / 0.0152\n",
            "[34/100][200/469]\tLoss_D: 0.6615\tLoss_G: 2.8748\tD(x): 0.8612\tD(G(z)): 0.3327 / 0.0865\n",
            "[34/100][400/469]\tLoss_D: 0.2282\tLoss_G: 3.0706\tD(x): 0.8720\tD(G(z)): 0.0729 / 0.0732\n",
            "[35/100][0/469]\tLoss_D: 0.3829\tLoss_G: 2.7188\tD(x): 0.8006\tD(G(z)): 0.1142 / 0.1077\n",
            "[35/100][200/469]\tLoss_D: 0.7403\tLoss_G: 4.1928\tD(x): 0.9290\tD(G(z)): 0.4079 / 0.0264\n",
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            "[79/100][0/469]\tLoss_D: 0.5200\tLoss_G: 4.7909\tD(x): 0.8648\tD(G(z)): 0.2618 / 0.0181\n",
            "[79/100][200/469]\tLoss_D: 0.2709\tLoss_G: 3.4242\tD(x): 0.9176\tD(G(z)): 0.1459 / 0.0656\n",
            "[79/100][400/469]\tLoss_D: 0.3824\tLoss_G: 2.9994\tD(x): 0.8246\tD(G(z)): 0.1329 / 0.0861\n",
            "[80/100][0/469]\tLoss_D: 0.2760\tLoss_G: 3.8748\tD(x): 0.8800\tD(G(z)): 0.1131 / 0.0397\n",
            "[80/100][200/469]\tLoss_D: 0.4728\tLoss_G: 3.8461\tD(x): 0.9621\tD(G(z)): 0.3046 / 0.0360\n",
            "[80/100][400/469]\tLoss_D: 0.3788\tLoss_G: 2.9687\tD(x): 0.8001\tD(G(z)): 0.1023 / 0.0993\n",
            "[81/100][0/469]\tLoss_D: 0.3247\tLoss_G: 2.4571\tD(x): 0.8272\tD(G(z)): 0.0967 / 0.1490\n",
            "[81/100][200/469]\tLoss_D: 0.1650\tLoss_G: 3.8431\tD(x): 0.9395\tD(G(z)): 0.0866 / 0.0420\n",
            "[81/100][400/469]\tLoss_D: 1.0438\tLoss_G: 0.8703\tD(x): 0.4926\tD(G(z)): 0.1280 / 0.5466\n",
            "[82/100][0/469]\tLoss_D: 0.4492\tLoss_G: 2.9571\tD(x): 0.8700\tD(G(z)): 0.2077 / 0.0941\n",
            "[82/100][200/469]\tLoss_D: 0.3336\tLoss_G: 3.3406\tD(x): 0.7779\tD(G(z)): 0.0373 / 0.0915\n",
            "[82/100][400/469]\tLoss_D: 0.4188\tLoss_G: 2.4204\tD(x): 0.7273\tD(G(z)): 0.0593 / 0.1387\n",
            "[83/100][0/469]\tLoss_D: 1.6421\tLoss_G: 6.9136\tD(x): 0.9901\tD(G(z)): 0.6914 / 0.0037\n",
            "[83/100][200/469]\tLoss_D: 0.3340\tLoss_G: 3.2429\tD(x): 0.8686\tD(G(z)): 0.1366 / 0.0717\n",
            "[83/100][400/469]\tLoss_D: 0.4755\tLoss_G: 2.1239\tD(x): 0.6878\tD(G(z)): 0.0327 / 0.2003\n",
            "[84/100][0/469]\tLoss_D: 0.6012\tLoss_G: 3.8289\tD(x): 0.9812\tD(G(z)): 0.3758 / 0.0365\n",
            "[84/100][200/469]\tLoss_D: 0.2284\tLoss_G: 3.8080\tD(x): 0.9377\tD(G(z)): 0.1337 / 0.0394\n",
            "[84/100][400/469]\tLoss_D: 0.4214\tLoss_G: 2.8706\tD(x): 0.8506\tD(G(z)): 0.1918 / 0.0981\n",
            "[85/100][0/469]\tLoss_D: 0.2426\tLoss_G: 3.9576\tD(x): 0.9327\tD(G(z)): 0.1420 / 0.0314\n",
            "[85/100][200/469]\tLoss_D: 0.3492\tLoss_G: 2.9481\tD(x): 0.8574\tD(G(z)): 0.1417 / 0.0901\n",
            "[85/100][400/469]\tLoss_D: 0.2943\tLoss_G: 2.7932\tD(x): 0.9021\tD(G(z)): 0.1388 / 0.1022\n",
            "[86/100][0/469]\tLoss_D: 0.8703\tLoss_G: 4.0559\tD(x): 0.9660\tD(G(z)): 0.4631 / 0.0364\n",
            "[86/100][200/469]\tLoss_D: 0.2957\tLoss_G: 3.6042\tD(x): 0.8890\tD(G(z)): 0.1393 / 0.0462\n",
            "[86/100][400/469]\tLoss_D: 0.2178\tLoss_G: 3.4267\tD(x): 0.8849\tD(G(z)): 0.0782 / 0.0536\n",
            "[87/100][0/469]\tLoss_D: 0.2786\tLoss_G: 3.4430\tD(x): 0.8693\tD(G(z)): 0.1105 / 0.0614\n",
            "[87/100][200/469]\tLoss_D: 0.2665\tLoss_G: 3.5409\tD(x): 0.9372\tD(G(z)): 0.1569 / 0.0515\n",
            "[87/100][400/469]\tLoss_D: 0.6919\tLoss_G: 1.9029\tD(x): 0.5741\tD(G(z)): 0.0162 / 0.2430\n",
            "[88/100][0/469]\tLoss_D: 0.3337\tLoss_G: 3.0254\tD(x): 0.7900\tD(G(z)): 0.0589 / 0.1077\n",
            "[88/100][200/469]\tLoss_D: 0.4752\tLoss_G: 4.1485\tD(x): 0.7831\tD(G(z)): 0.1391 / 0.0331\n",
            "[88/100][400/469]\tLoss_D: 0.5665\tLoss_G: 4.4915\tD(x): 0.9600\tD(G(z)): 0.3229 / 0.0215\n",
            "[89/100][0/469]\tLoss_D: 0.4236\tLoss_G: 2.8298\tD(x): 0.7743\tD(G(z)): 0.1192 / 0.1069\n",
            "[89/100][200/469]\tLoss_D: 0.2566\tLoss_G: 3.4688\tD(x): 0.8929\tD(G(z)): 0.1144 / 0.0520\n",
            "[89/100][400/469]\tLoss_D: 0.5112\tLoss_G: 2.9237\tD(x): 0.8371\tD(G(z)): 0.2313 / 0.0887\n",
            "[90/100][0/469]\tLoss_D: 0.3684\tLoss_G: 2.9646\tD(x): 0.8483\tD(G(z)): 0.1528 / 0.1034\n",
            "[90/100][200/469]\tLoss_D: 0.4712\tLoss_G: 2.7813\tD(x): 0.7553\tD(G(z)): 0.0855 / 0.1129\n",
            "[90/100][400/469]\tLoss_D: 0.2938\tLoss_G: 2.9119\tD(x): 0.8442\tD(G(z)): 0.0934 / 0.0970\n",
            "[91/100][0/469]\tLoss_D: 0.7627\tLoss_G: 2.0092\tD(x): 0.5540\tD(G(z)): 0.0379 / 0.2310\n",
            "[91/100][200/469]\tLoss_D: 0.5092\tLoss_G: 3.3666\tD(x): 0.8923\tD(G(z)): 0.2707 / 0.0575\n",
            "[91/100][400/469]\tLoss_D: 0.7758\tLoss_G: 2.4002\tD(x): 0.5768\tD(G(z)): 0.0585 / 0.1740\n",
            "[92/100][0/469]\tLoss_D: 0.4398\tLoss_G: 4.4931\tD(x): 0.9560\tD(G(z)): 0.2644 / 0.0199\n",
            "[92/100][200/469]\tLoss_D: 0.6157\tLoss_G: 3.6115\tD(x): 0.8663\tD(G(z)): 0.3120 / 0.0451\n",
            "[92/100][400/469]\tLoss_D: 0.4744\tLoss_G: 3.7823\tD(x): 0.8802\tD(G(z)): 0.2551 / 0.0359\n",
            "[93/100][0/469]\tLoss_D: 0.4617\tLoss_G: 3.7677\tD(x): 0.9482\tD(G(z)): 0.2797 / 0.0446\n",
            "[93/100][200/469]\tLoss_D: 0.3722\tLoss_G: 4.1143\tD(x): 0.9447\tD(G(z)): 0.2203 / 0.0349\n",
            "[93/100][400/469]\tLoss_D: 0.1960\tLoss_G: 3.3124\tD(x): 0.9043\tD(G(z)): 0.0775 / 0.0693\n",
            "[94/100][0/469]\tLoss_D: 0.2578\tLoss_G: 3.1050\tD(x): 0.8840\tD(G(z)): 0.1011 / 0.0778\n",
            "[94/100][200/469]\tLoss_D: 0.6122\tLoss_G: 4.2493\tD(x): 0.9075\tD(G(z)): 0.3462 / 0.0223\n",
            "[94/100][400/469]\tLoss_D: 0.7043\tLoss_G: 5.8596\tD(x): 0.9647\tD(G(z)): 0.3904 / 0.0053\n",
            "[95/100][0/469]\tLoss_D: 0.2928\tLoss_G: 2.9601\tD(x): 0.8221\tD(G(z)): 0.0700 / 0.0902\n",
            "[95/100][200/469]\tLoss_D: 0.5354\tLoss_G: 4.1816\tD(x): 0.9913\tD(G(z)): 0.3508 / 0.0259\n",
            "[95/100][400/469]\tLoss_D: 1.0467\tLoss_G: 1.5146\tD(x): 0.4932\tD(G(z)): 0.0463 / 0.3586\n",
            "[96/100][0/469]\tLoss_D: 0.2535\tLoss_G: 3.5555\tD(x): 0.8538\tD(G(z)): 0.0683 / 0.0600\n",
            "[96/100][200/469]\tLoss_D: 0.2694\tLoss_G: 3.1327\tD(x): 0.8443\tD(G(z)): 0.0742 / 0.0688\n",
            "[96/100][400/469]\tLoss_D: 0.4656\tLoss_G: 1.5887\tD(x): 0.6994\tD(G(z)): 0.0489 / 0.3118\n",
            "[97/100][0/469]\tLoss_D: 0.3081\tLoss_G: 4.6461\tD(x): 0.8867\tD(G(z)): 0.1426 / 0.0180\n",
            "[97/100][200/469]\tLoss_D: 1.1092\tLoss_G: 6.2696\tD(x): 0.9889\tD(G(z)): 0.5230 / 0.0051\n",
            "[97/100][400/469]\tLoss_D: 0.3167\tLoss_G: 3.3764\tD(x): 0.9527\tD(G(z)): 0.2017 / 0.0543\n",
            "[98/100][0/469]\tLoss_D: 0.2850\tLoss_G: 3.3830\tD(x): 0.8996\tD(G(z)): 0.1407 / 0.0642\n",
            "[98/100][200/469]\tLoss_D: 0.5403\tLoss_G: 4.1492\tD(x): 0.9679\tD(G(z)): 0.3387 / 0.0304\n",
            "[98/100][400/469]\tLoss_D: 2.9241\tLoss_G: 0.1560\tD(x): 0.1184\tD(G(z)): 0.0112 / 0.9071\n",
            "[99/100][0/469]\tLoss_D: 0.3311\tLoss_G: 2.8508\tD(x): 0.7892\tD(G(z)): 0.0541 / 0.1028\n",
            "[99/100][200/469]\tLoss_D: 0.1426\tLoss_G: 4.5090\tD(x): 0.9330\tD(G(z)): 0.0599 / 0.0199\n",
            "[99/100][400/469]\tLoss_D: 0.3068\tLoss_G: 4.1911\tD(x): 0.8214\tD(G(z)): 0.0698 / 0.0358\n"
          ]
        }
      ],
      "source": [
        "# Hyperparameters\n",
        "num_epochs = 100\n",
        "\n",
        "# Set up dataloader\n",
        "dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=workers)\n",
        "\n",
        "# Training Loop\n",
        "for epoch in range(num_epochs):\n",
        "    # For each batch in the dataloader\n",
        "    for i, data in enumerate(dataloader, 0):\n",
        "\n",
        "        ############################\n",
        "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
        "        ###########################\n",
        "        ## Train with all-real batch\n",
        "        netD.zero_grad()\n",
        "        # Format batch\n",
        "        real_cpu = data[0].to(device)\n",
        "        b_size = real_cpu.size(0)\n",
        "        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)\n",
        "        # Forward pass real batch through D\n",
        "        output = netD(real_cpu.float()).view(-1)\n",
        "        # Calculate loss on all-real batch\n",
        "        errD_real = criterion(output, label)\n",
        "        # Calculate gradients for D in backward pass\n",
        "        errD_real.backward()\n",
        "        D_x = output.mean().item()\n",
        "\n",
        "        ## Train with all-fake batch\n",
        "        # Generate batch of latent vectors\n",
        "        noise = torch.randn(b_size, nz, 1, 1, device=device)\n",
        "        # Generate fake image batch with G\n",
        "        fake = netG(noise)\n",
        "        label.fill_(fake_label)\n",
        "        # Classify all fake batch with D\n",
        "        output = netD(fake.clone().detach()).view(-1)\n",
        "        # Calculate D's loss on the all-fake batch\n",
        "        errD_fake = criterion(output, label)\n",
        "        # Calculate the gradients for this batch, accumulated (summed) with previous gradients\n",
        "        errD_fake.backward()\n",
        "        D_G_z1 = output.mean().item()\n",
        "        # Compute error of D as sum over the fake and the real batches\n",
        "        errD = errD_real + errD_fake\n",
        "        # Update D\n",
        "        optimizerD.step()\n",
        "\n",
        "        ############################\n",
        "        # (2) Update G network: maximize log(D(G(z)))\n",
        "        ###########################\n",
        "        netG.zero_grad()\n",
        "        label.fill_(real_label)  # fake labels are real for generator cost\n",
        "        # Since we just updated D, perform another forward pass of all-fake batch through D\n",
        "        output = netD(fake).view(-1)\n",
        "        # Calculate G's loss based on this output\n",
        "        errG = criterion(output, label)\n",
        "        # Calculate gradients for G\n",
        "        errG.backward()\n",
        "        D_G_z2 = output.mean().item()\n",
        "        # Update G\n",
        "        optimizerG.step()\n",
        "\n",
        "        # Output training stats\n",
        "        if i % 200 == 0:\n",
        "            print('[%d/%d][%d/%d]\\tLoss_D: %.4f\\tLoss_G: %.4f\\tD(x): %.4f\\tD(G(z)): %.4f / %.4f'\n",
        "                  % (epoch, num_epochs, i, len(dataloader),\n",
        "                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7rO5izHEkbf"
      },
      "source": [
        "## Sampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We do not need the discriminator to generator samples, just the generator.  Here we generate and display 64 MNIST images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HgpAUnVOmZkO",
        "outputId": "ad50c4d3-0dca-48c6-cab6-87a030d6c7c1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x1440 with 64 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Generate batch of latent vectors\n",
        "batch_size = 64\n",
        "noise = torch.randn(batch_size, nz, 1, 1, device=device)\n",
        "# Generate fake image batch with G\n",
        "fake = netG(noise)\n",
        "\n",
        "# Initalize plots \n",
        "fig, axs = plt.subplots(8, 8, figsize=(15,20))\n",
        "fig.suptitle(\"Samples\", fontsize=\"x-large\")\n",
        "\n",
        "for i in range(batch_size):\n",
        "  fake_img = fake[i,0,:,:]\n",
        "  fake_img = fake_img.cpu().detach().numpy()\n",
        "\n",
        "  axs[int(i / 8), int(i % 8)].imshow(fake_img)\n",
        "  axs[int(i / 8), int(i % 8)].axis('off')\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.8 ('base')",
      "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": "3.8.8"
    },
    "vscode": {
      "interpreter": {
        "hash": "670f56a09e544e4bbfbd6b5287e0e16c8302d350aabab9be44abb05d0d81374c"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}