GAN_MNIST.ipynb

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            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oneoftwo/generative_model_tutorial/blob/main/GAN_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzFMM2jBkw60"
      },
      "source": [
        "# **Generative Adverserial Network (GAN)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRTIvZJZdMF0"
      },
      "source": [
        "## **Import and Download Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcJJLD8jdLnP"
      },
      "source": [
        "import numpy as np\n",
        "import random as random\n",
        "import time as time\n",
        "from tqdm import tqdm\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import cm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467,
          "referenced_widgets": [
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            "0dd8771b1c524df7883d919d68868667",
            "71bc03b204914a2cbdf5a86b13315dbc",
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            "50fdec3e6bf24ef9a2f57f48607ebb3a",
            "5632c8444fc846d18a7b30c96d10701b",
            "76f871e7bda1419595cc5b31229efc93",
            "87a2c0e3e9e54961952324433de58880",
            "cbc4edcc27bc43c6a27fb716876b6017",
            "06141d4a46ca48ddbcf92d90d66f3118",
            "b13f8c9d4231461a98a21e9859706f97",
            "068b9c79a0bd400193063512505280c6",
            "27531b14e07b44cabd0275447ff3fc22",
            "826ac84fdffd427dadc2c8bf9b1ff41c",
            "5427866248e14ea7be9f1034235cfb24",
            "aa695ed68ded4177b1bfb76258740617",
            "0057cdf660c74ecfa71252819bf43cda",
            "f96e07a0b649436b89898dfb98f2b6cb",
            "ebe1211cc0234e2bb0638cd2e82bc28e",
            "6e3dbde4bc6a4bb58d647970b6f2195b",
            "c66a70bc52de4b54af66f466a67be584",
            "0fb5522e01024ff18f2eef0a92fc226b",
            "3d447b723eda45f08120e78981b7594d",
            "99186d92b03c4287a32861b4a8751189",
            "7cc40ae71f614ccda78458e8188443bc",
            "54250d4ade8a44308ab703f79c36e306",
            "61188cc5598d4332ad3c1358a0ea6bd2",
            "a132b4aa5ed04c4cbc3d623d5cd7bc7f",
            "6b9705b79bb94a178641a7b4554375ae",
            "bf04fac830f04a6fb7fd147dd3ced16b",
            "87abe394f9ee4356b1e3aa5d021601bc",
            "dccbf789ac42427b8e422b9934530a94",
            "e1645a97d924481eb6e4032da406afef",
            "faa8815aad6e4088a7cddf85b980d255"
          ]
        },
        "id": "l0BTQsa8MEVd",
        "outputId": "f4bce2bc-3f9d-4cda-add0-e911ca7d507d"
      },
      "source": [
        "#==============================================================================#\n",
        "# download dataset ============================================================#\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import MNIST\n",
        "\n",
        "from torchvision import datasets, transforms\n",
        "batch_size = 128\n",
        "download_root = './MNIST_DATASET'\n",
        "train_dataset = MNIST(download_root, transform=transforms.ToTensor(), \\\n",
        "        train=True, download=True)\n",
        "test_dataset = MNIST(download_root, transform=transforms.ToTensor(), \\\n",
        "        train=False, download=True)\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, \\\n",
        "        shuffle=True, drop_last=True)\n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, \\\n",
        "        shuffle=True, drop_last=True)\n",
        "#==============================================================================#"
      ],
      "execution_count": null,
      "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 ./MNIST_DATASET/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2471a59fe78e4bc4a6bb30279c2b99f3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST_DATASET/MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST_DATASET/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 ./MNIST_DATASET/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0dd8771b1c524df7883d919d68868667",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST_DATASET/MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST_DATASET/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 ./MNIST_DATASET/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27531b14e07b44cabd0275447ff3fc22",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST_DATASET/MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST_DATASET/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 ./MNIST_DATASET/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99186d92b03c4287a32861b4a8751189",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST_DATASET/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST_DATASET/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqwHjI824b-S"
      },
      "source": [
        "## **Utils**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "0oNzYoY_4bL6",
        "outputId": "6184dfb2-d855-4808-ce0b-601de317ee42"
      },
      "source": [
        "################################################################################\n",
        "# utils ########################################################################\n",
        "\n",
        "def plot_sample(x):\n",
        "    fig = plt.figure(figsize=(5, 25))\n",
        "    for idx in range(5):\n",
        "        img = x[idx].cpu().squeeze().detach().numpy()\n",
        "        ax = fig.add_subplot(1, 5, idx + 1)\n",
        "        ax.imshow(img, cmap=cm.gray)\n",
        "        ax.axis('off')\n",
        "        ax.axes.get_xaxis().set_visible(False)\n",
        "        ax.axes.get_yaxis().set_visible(False)\n",
        "    plt.show()\n",
        "\n",
        "################################################################################\n",
        "\n",
        "\n",
        "#==============================================================================#\n",
        "# sanity check ================================================================#\n",
        "plot_sample(torch.rand(20, 1, 28, 28))\n",
        "#==============================================================================#"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zfaSoLvzLAy5"
      },
      "source": [
        "## **Model**\n",
        "Generative adverserial network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMoa9X-2d-QT",
        "outputId": "9e9a5f71-1d50-4df9-aa39-05e5ecc14206"
      },
      "source": [
        "################################################################################\n",
        "# GAN ##########################################################################\n",
        "\n",
        "def sampler(batch_size, z_dim):\n",
        "    z = torch.randn(batch_size, z_dim)\n",
        "    return z\n",
        "\n",
        "\n",
        "# GAN generator\n",
        "class Generator(nn.Module):\n",
        "    \n",
        "    def __init__(self, z_dim=2):\n",
        "        super().__init__()\n",
        "        self.fc = nn.Sequential(\\\n",
        "                nn.Linear(z_dim, 128), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(128, 256), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(256, 512), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(512, 28 * 28))\n",
        "        \n",
        "    def forward(self, z):\n",
        "        b = z.size(0)\n",
        "        x = self.fc(z)\n",
        "        x = x.view(b, 1, 28, 28)\n",
        "        x = torch.sigmoid(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "# GAN discriminaotr, CNN based classifier\n",
        "class Discriminator(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.fc = nn.Sequential(\\\n",
        "                nn.Linear(28 * 28, 512), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(512, 256), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(256, 128), \\\n",
        "                nn.ReLU(), \\\n",
        "                nn.Dropout(0.3), \\\n",
        "                nn.Linear(128, 1))\n",
        "        \n",
        "    def forward(self, x):\n",
        "        b = x.size(0)\n",
        "        x = x.reshape(b, -1)\n",
        "        x = self.fc(x)\n",
        "        x = torch.sigmoid(x).squeeze()\n",
        "        return x\n",
        "\n",
        "\n",
        "################################################################################\n",
        "\n",
        "\n",
        "#==============================================================================#\n",
        "# sanity check ================================================================#\n",
        "g = Generator()\n",
        "d = Discriminator()\n",
        "z = sampler(32, 2)\n",
        "print(z.size())\n",
        "print(g(z).size())\n",
        "print(d(g(z)).size())\n",
        "#==============================================================================#"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([32, 2])\n",
            "torch.Size([32, 1, 28, 28])\n",
            "torch.Size([32])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CG3jM7btlc4D"
      },
      "source": [
        "## **Train**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nNWCRkHoluUL",
        "outputId": "32e024e2-dffb-40ba-c8f0-59005f8e9a65"
      },
      "source": [
        "################################################################################\n",
        "# train fxn ####################################################################\n",
        "\n",
        "def train_gan_single_epoch(generator, discriminator, train_loader, z_dim=64):\n",
        "    generator.cuda().train()\n",
        "    discriminator.cuda().train()\n",
        "    g_optimizer = optim.Adam(generator.parameters(), lr=1e-4)\n",
        "    d_optimizer = optim.Adam(discriminator.parameters(), lr=1e-4)\n",
        "    criterion = nn.BCELoss(reduction='sum')\n",
        "    epoch_loss_d, epoch_loss_g = 0, 0\n",
        "    n_data = 0\n",
        "    # update parameters\n",
        "    for img, label in (train_loader):\n",
        "        img, label = img.cuda(), label.cuda()\n",
        "        b = img.size(0)\n",
        "        n_data += img.size(0)\n",
        "        # update discriminator / to not be faked by generator\n",
        "        d_optimizer.zero_grad()\n",
        "        prob_real = discriminator(img)\n",
        "        prob_fake = discriminator(generator(sampler(b, z_dim).cuda()))\n",
        "        loss_real = criterion(prob_real, torch.ones(prob_real.size()).cuda())\n",
        "        loss_fake = criterion(prob_fake, torch.zeros(prob_fake.size()).cuda())\n",
        "        loss_d = (loss_real + loss_fake)\n",
        "        loss_d.backward()\n",
        "        d_optimizer.step()\n",
        "        epoch_loss_d += loss_d.item()\n",
        "        # update generator / to fake the discriminaotr\n",
        "        g_optimizer.zero_grad()\n",
        "        prob_fake = discriminator(generator(sampler(b, z_dim).cuda()))\n",
        "        loss_g = criterion(prob_fake, torch.ones(prob_fake.size()).cuda())\n",
        "        loss_g.backward()\n",
        "        g_optimizer.step()\n",
        "        epoch_loss_g += loss_g.item()\n",
        "    epoch_loss_g = epoch_loss_g / n_data\n",
        "    epoch_loss_d = epoch_loss_d / n_data\n",
        "    return generator, discriminator, epoch_loss_g, epoch_loss_d\n",
        "\n",
        "################################################################################\n",
        "\n",
        "\n",
        "#==============================================================================#\n",
        "# train model =================================================================#\n",
        "g = Generator(z_dim=128)\n",
        "d = Discriminator()\n",
        "loss_g_list, loss_d_list = [], []\n",
        "for epoch_idx in range(1, 401):\n",
        "    g, d, epoch_loss_g, epoch_loss_d = \\\n",
        "            train_gan_single_epoch(g, d, train_loader, z_dim=128)\n",
        "    loss_g_list.append(epoch_loss_g)\n",
        "    loss_d_list.append(epoch_loss_d)\n",
        "    if epoch_idx % 20 == 0:\n",
        "        g.eval()\n",
        "        plot_sample(g(sampler(10, 128).cuda()))\n",
        "        print('epoch {}'.format(epoch_idx))\n",
        "        print('generator loss: {:.3f}'.format(epoch_loss_g))\n",
        "        print('discriminator loss: {:.3f}'.format(epoch_loss_d))\n",
        "#==============================================================================#"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 20\n",
            "generator loss: 58.803\n",
            "discriminator loss: 0.027\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 40\n",
            "generator loss: 8.857\n",
            "discriminator loss: 0.085\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 60\n",
            "generator loss: 5.599\n",
            "discriminator loss: 0.255\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 80\n",
            "generator loss: 4.065\n",
            "discriminator loss: 0.307\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 100\n",
            "generator loss: 3.270\n",
            "discriminator loss: 0.407\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 120\n",
            "generator loss: 2.312\n",
            "discriminator loss: 0.574\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 140\n",
            "generator loss: 2.056\n",
            "discriminator loss: 0.635\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 160\n",
            "generator loss: 1.928\n",
            "discriminator loss: 0.669\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 180\n",
            "generator loss: 1.888\n",
            "discriminator loss: 0.685\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 200\n",
            "generator loss: 1.879\n",
            "discriminator loss: 0.693\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VapV//vOfhEIhvve970nlq1arlUV89y5IEaIlk0nC4bCsiRPhW62L2UghTi1UKhXHjx/n9ddfZ3BwEKPRSDQaZWVlhU8++URyL/UOcfK63W56e3txu92YzWZKpRLJZJKVlRUqlYqsA9Pr9TKUm52dJRgMEgqFpKK5XuaydtOJjSyyoyMjI3g8Hkwm079wm8LDOnz4MEeOHOFHP/oRTqcTu91OuVwmn88zNTXF+Pg4v/nNb2QB78MYt9D6nTp1iq6uLrq6uhgYGJBCTpHhjsViJBIJ/vrXv3L79m1GR0fZ2tr6t+r7SqUinYdsNkskEsHj8WA2m6U3lM/npYc0MjKC3W7ngw8+eOBwdVc9pUwmIzNp8DmX4nA4iEaj0v2F7V6S0+nE6/XKiRfxeaVSQavVotfrG6ZMoRZi4bpcrm3V88lkUv40UldGIQh0uVwYjUZ5SuZyOTKZDGazWW4Ak8mE0+kkHo9jtVoJBoMEg0GWl5fZ2traRhDvJcTJHwgEaGtrI5lMynS20WjEarXi8/kkjSA8CI/Hg9Pp5OTJkwwPD+Pz+dDr9ej1elmYev36dSYmJggGg9vW/lcNcYDodDpcLhd9fX1YLJZthbcAuVxO8oCrq6tS8Hi/51QURZL6gDTmarVaOiAajQaTyURLSwvAA497V42SWJxCyWmxWOjs7GRoaIibN29SLBYpl8uSFBcvaGRkhNOnT6PT6aTrLBa7MGpCcNlI0Ol0slBxcHAQvV5PqVRibW2NYDBILpdrGGGoWORut5u+vj5sNhs6nY5YLCa5w/b2dgYHB+nq6pJeVD6fJ5VKycryt99+m5mZGdbW1sjlcpIQ3ysIwef58+dJp9M899xz9PT0YLFY0Ov1WK1WvvGNbzA3N8fY2BipVIpsNivFlW+99RZ2u10qvsvlMhsbG/j9fn73u9/JYuSHCREyFQoF2traOHbsGE6nU+qoBDUSCoWYmZlhZmaGhYUF0un0fQ8JEeoK2YDgjkTEY7PZtmmWBN+4p0Ypn8+TzWbJ5XIUi0V0Oh2PPvoor732GhaLBb/fTywWw+FwcPz4cVpaWjCZTBw5cgS32y1l65VKhWKxSC6Xw+1209nZydraWsNsYAGhEBZjg7uG+/r160xPT9dVGPNFEEXUVqtV6loEkVqpVOju7t6WhSmXy+RyOeCumFCv1+NyuXjllVcIBAJcvXqV5eVlPv30013rWLhTVKtVisUiy8vLvPPOO5w8eZITJ07Q1taGXq+XBujIkSPSg+/t7ZXhi6iMX1xcZHFxkfHxcZaXl6WE4GFCURQMBgNut1vWtAnDIQxGsVgklUoRCARYWFjYVhJyP6jVapmUcjqduN1uXC6XLJ0SfKkgxNPp9N6XmYiWp6K5l6IoHDp0iIGBARKJBHa7Hb/fT19fH2+++aZs+WA2m9HpdJJPEsRpPp/H5XLh8/kaUq9kMpno7e3F5XKh0Whk7dT4+DjT09N7vhkfBMK7tVqttLa2ysxK7UkpQh6RqcpkMuh0Omw2Gw6HQwoUk8kkFouFa9euMTo6WhcesAjh/vznP1MoFHA4HFgsFtxuNydPnpRpflGYW6vHE57gtWvXuHDhApcuXZK9lB42FEVBr9fj8Xg4duwYnZ2d6PV6yfuIAz8WixEMBllcXCSRSHwpekQYJVGZIGQUtfWcwlvM5XLSKD0odtUoVSoVUqkUZ8+e5fDhw7z88suyWO/VV1/lhRdeIJvNYjKZ6OjoAJDlJtlsVlZc6/V6qRnRarUyfdlITcKE9uUb3/gG3d3dADJ+n5iYYHl5uWHGItx2Uaza0dEh5yWZTEqXPxKJSCmIKDex2+2MjIzIuYS7XpfdbsdsNtfVOygWiyQSCT799FPi8bjccCaTSep5arsBCI9genqa3/72t8zNzeH3+4lEInvGlYl3KzKJBoNBekmVSkWm8t955x1u377N1NQUkUjkSxkkuOvpx+NxKpUK8Xgct9sthZmCYxTdQG7cuEGhUNhbTwnuxrFTU1OS3TeZTNJjgM/b4FarVSm5F8JKERIIqy6qsButW4CQ81utVrq6umSbC5Eyj0QibG1t7fFTfnnUNgIzmUzbquZFmlulUpFKpUgkEoTDYVKplOSNcrmcVH4LCO6wnjoyCiOzsbEBQDwelwWo/05rI5rnb25ucu3aNTY2NmSh7V6NSdS2CU5MtCIWRimVSrG5ucmNGzfw+/2sr69/KRpBOAUCtWJR0RRP/Hu5XCYajcqmdg+KXTVKYpIuXbrE8vIyqVSKb33rW3zzm9/8l0GVy2UuX77Mxx9/zPr6Onq9np/97Gd4vV5JeJdKJW7cuMHly5cbKvum0Wjo7u5mYGCAw4cPy/awExMT3Lp1i2Qy2VC1fCqVipaWFnp6eujs7KStrU3OESD5A9EO9qOPPmJ9fR2j0ciBAwcYGBgAkO8hk8lw+fJlJicn6yJ0uxfiIFxbW2NlZUWS+vA5aSu0PpOTk9y5c4elpaUdt+rYTRgMBp555hkOHjwoe20LSU4ul2NycpLr168zNjZGNpv90p6MOGjdbve2MhUhnxCfyefzJJNJgsEgGxsbOyqh2nVPSbiIGxsb3Lp1S4Zk/f39mEwmWXUcDocZHR3l9u3bRCIRLBYLqVQKu90uBygGuZNK472CmLz+/n56enrk1TTFYpHV1VUWFhZ25NLuNVQqldSd1ZYSCY4ik8kQiURk7Vs8HpeHVK0CWqSTg8Eg4XB4D0f076FWq2ltbWX//v04nU7Z90rMVzqdJpfLoSiKrHOrVCr/km7fSwjeS3BfAsITFD9fxosREUtraysOh4OBgQHcbresFbTZbPL/EFm5RCIhZRE7SebsulGqVqukUinS6TSrq6uMjo7y9ttv89Zbb9Hf38+FCxdYWFjgwoULsmpapVLR2tpKKBTCarXi8Xgk6S1+Z6NAaHlefPFFDh06hMFgoFgsks1muXz5MhcvXmw4Iys6ENaWDIg/hVAwGo0yPz/PtWvXCIfDUs5RrVa3FWqXSiVSqRRTU1OsrKzU1XsQvNmpU6c4c+YMw8PDOJ1OaUwrlQobGxusrKzIcEXQDi6XS/KjewlxEJRKJeklidDt3nct5vZ+3p2o/n/qqafo7e3liSeekAanv79fSg2E5xiJRPD7/czPz7O4uLgjsv8r69EttBCidubs2bM4HA6WlpaIRqPEYrFttUSZTIbz58+TSCTo7++XoV6j9e1uaWnB4XDQ09ODx+MBYGtrSxY4iruxGgViHguFguwems/nZfZNhNmZTAZFUTCbzXg8HsrlMkNDQwwMDEgOqlgsMj4+zp07d4hGo1IyUC/o6Ojg29/+NqdPn2bfvn2yL5IwutevXycQCLC+vi49KZF1cjqdddGoT9TstbW1bePsatuWZDIZTp06JUtDVldXSafT0usrlUp4vV5aW1vZt28fXq+Xo0eP4nA46OrqkmO1WCwYjUYpidjY2OD69evcuHGDQCCwY8HoV3rFUrVaJZ1Ok06nef/99+/7uWw2y8WLF6lWq7z66qvbjFKjGCS4e92Q6MBZ22dIlFzsVS+d/waijjEej5NKpcjlcphMpm3V8blcDrVajdVqldm6kZERfD4fVqsVrVZLPp9nfHycsbExYrFYXRklRVHw+Xy88cYbkjuDz9fm2toaH374IZubm4TDYdnyQ8hZHA5HXYSj5XKZ9fV1Ojs7t9WuibrF/v5+yuUyTz75pCSqR0dH2djYkB5WLpdjYGCAQ4cO8cwzz9DV1SVv3wFkJ07B8wrn486dO4yOjvKPf/yDzc3N+jRKDwKhhL33NoTHHntMhj6N0Fxfo9Gg0+kwmUxSRLi4uMjFixd31PCqXiC6EK6urjI3N8fQ0BA2m02GLocOHaKjo4OhoSFyuRwqlYqBgQEpBRC9ia5cucLVq1frKnGh0+k4fPgwJ06c4JFHHpHtS0Q3hz/84Q8sLCwwNjYmi08XFxdluJJMJmltbSUQCOy5bKVYLDIzM0NLSwvz8/N0dXXJrLY4LLq6ujhz5ozUA77wwgsUi8VtZV4tLS20tLTgdrvlLUTwOZWiKIrsLeX3+1ldXeXixYvMzc1JA7fT91A3RklUzteenqKwsL29vSHEk+I0MhgMslFYpVIhGo3i9/sbTtogIDyiXC5HKpUiHo/L4lLRQF80+rLb7RQKBamXEY3QNjc3WV9fl83S9rrmrRbCg+jp6cHhcEiFt0jYCIV2MBiUtV3pdFreSlIoFLaJKfcS1WqVeDxOJBIhEAhgs9lobW2V1RIikzowMCCJerFWBU9WK9sR3KGo/RPcmgjZE4mE7CywsLBAMBj8rzuO1rVRgrslCuLqnnqHoijs27dPEtxwN1vj9/u5evVqw3QE+HcQi1C0SRUb0e12S72Sw+GQCzefz0sh4czMDJ999pnst15Pcghxb92bb77JwMAApVKJUCjE5uYm58+fZ3Z2lvPnz0stXVdXFz6fD4/HI2+NFXMcj8f3ejjSoK6srPCnP/2Jl156ifb2dumx1t7jVqstuleyU5tkEvNbK/1YW1tjaWmJUCjE3//+d2KxmBTP1u0NuQ8KEZveO5hyuSxLVuoZ4hTyeDxS8VwsFmVP50Qi0XC1e/eiXC6TSqXY2NiQfaqF8a1VOotbLxYWFlhfX2dycpKFhQU2NjbqqiuCoigMDg7S398vN24ikcDv9zM7O8vk5KTUH4kLKEUzOHGbSSgUolqtEolE9jzzJlCpVCQPNjc3x61bt+jp6cFqtcoERW1V/70QhkhwiaIrpRBe+v1+lpaWCAaDRKNRNjc3Jde4G8386sYoweeZtlqIk1mQdfUa/gg9x9DQEI899hh6vZ6trS2mpqZkxrGePIQHhVioKysrjI6O0tfXJ5t5abVa6e5XKhXC4TAbGxu89957+P1+pqamZD1kPUGlUvHGG29w6tQpfD6f5Io+/vhjzp07JwXA4tKH5557jieeeILHH39cZuUuXrxIIBBgdna2bvRnQpYzOztLuVxmZmaG73znO7K7g8FgwGKxyJq1e78rHAHRd11IOILBIOPj48zPzzM/Py/ndLc7eNSdUbp3UgcHB8nn85w/f166kPUI0XtHFCoqikI8HufatWsEAoGGuhzgfkgkEiwvL3PlyhU2NzdlO9hqtSobzPv9fkKhEJOTk7JUo544JPhcDGq322V/KFFw6vV66evrI5VKodPpGBwcpLe3l9OnT9Pb24vFYpHlNevr6/Iut3qC8HKEJ3fhwgWmpqawWCzY7XZ6e3vx+Xy0t7dv+14ulyMajUrpg2iVu7S0RDweZ2VlRZZJ1V5QuZuoG6MkTtl7B7h//37grv6nnpuiiT7NLpdLkqXCKK2trdXdptwJqtWq7Ez46aefsra2RkdHh2zfce3aNa5du8bs7Ky8GaNexy3CF6vVit1u32aUxPVRm5ubmEwmTpw4wb59+3jyySflvWYicxUMBolEInVHLwgeSIRdmUxGtpbxer2MjIyQyWS2cbXlcplkMsni4iITExNMTEzIsFR0DRWV/19lFrlujFJt4zcxweIWTyHqqtcFDsiaH9Gywe/3y03aSMW3XwRBZC4uLspsmkajkUY4kUjISyjreb6EIFR4TCIUbWtr48SJEwwODnLy5EkymYwktU0mE4VCgXA4zN/+9jdmZma4dOmS3Lj1KPcQ8yAyh2azmbW1Ne7cuUNrayutra1SW7a8vEw2m5WF1eI6efGuHpaQuW6MEnxOltYOWjQgB+py0gVEl8VIJML6+jorKyv4/X55r9v/CmrFcqLrQS2f1Cghqthg0WiUYDAoyzLsdjterxe73Y7FYpEXhWq1WjKZjCyxuH37NpOTkwQCgS/s2LiXEF6N8IpqCexwOMzq6ip2ux1FUZibm5OHzl62Kq4boyRSmfdyL7V3aNXzgs/lchQKBX7961/LGyPESVPPz71T1BqiRkWpVOLnP/85JpNJNnP7xS9+IVt+WCwWdDoda2trhMNhpqamuHnzJrdu3ZIkeD2JQO8HYYxE++FqtSozwysrKwAyEbHX46kbowR3X1wkEuHq1av4fD5aW1u5desW09PTX6pd516itu+xKDKul2xME/8ZYmOm02nMZjMfffSRvIlH8Eabm5vEYjEWFxeZm5tjZS2i+ZQAAADlSURBVGWFra2th3pl0m6hdj3W6118yv02jaIoD31Hud1uHnvsMc6cOcMrr7zCj3/8Yy5evMjq6uqOwqBqtfqFDORejHO30RznXfw3Y6ztiFBbWV/biO5hHDJf97msK08J7iqgZ2ZmOHv2LNPT04yPjxOLxeo2Zm/ifwe1Al5hlJp4+Kg7T2m38XU/dWrxdRjn12GM8L89zvovKGuiiSa+VmgapSaaaKKucN/wrYkmmmjiYaPpKTXRRBN1haZRaqKJJuoKTaPURBNN1BWaRqmJJpqoKzSNUhNNNFFXaBqlJppooq7w/119eNt6mQfrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 220\n",
            "generator loss: 1.894\n",
            "discriminator loss: 0.692\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 240\n",
            "generator loss: 1.926\n",
            "discriminator loss: 0.688\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 260\n",
            "generator loss: 1.951\n",
            "discriminator loss: 0.684\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 280\n",
            "generator loss: 1.993\n",
            "discriminator loss: 0.668\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 300\n",
            "generator loss: 2.048\n",
            "discriminator loss: 0.651\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 320\n",
            "generator loss: 2.072\n",
            "discriminator loss: 0.646\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 340\n",
            "generator loss: 2.129\n",
            "discriminator loss: 0.635\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 360\n",
            "generator loss: 2.191\n",
            "discriminator loss: 0.629\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 380\n",
            "generator loss: 2.242\n",
            "discriminator loss: 0.614\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 400\n",
            "generator loss: 2.302\n",
            "discriminator loss: 0.600\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7zCBtdOe4J0"
      },
      "source": [
        "## **Plot Results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "BrDvuDmV4YD-",
        "outputId": "7e291699-e45e-4539-e46c-66801731bc2a"
      },
      "source": [
        "#==============================================================================#\n",
        "# Plot ========================================================================#\n",
        "for _ in range(5):\n",
        "    plot_sample(g(sampler(10, 128).cuda()))\n",
        "x = 50\n",
        "plt.plot(range(len(loss_g_list))[x:], loss_g_list[x:])\n",
        "plt.plot(range(len(loss_d_list))[x:], loss_d_list[x:])\n",
        "plt.show()\n",
        "#==============================================================================#"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x1800 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}