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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/selimunal/Pytorch/blob/main/88_GAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ddY-VrQBm0MB"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms, models\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from torchvision.utils import save_image\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# min to max values\n",
        "transform=transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=(0.5,),\n",
        "                         std=(0.5,))\n",
        "])"
      ],
      "metadata": {
        "id": "sumF9cvCm9dV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset =torchvision.datasets.MNIST(\n",
        "    root=\".\",\n",
        "    train=True,\n",
        "    transform=transform,\n",
        "    download=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
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        "id": "88nPVptrm_12",
        "outputId": "2ab9a884-def1-4806-d416-1975fd2a9647"
      },
      "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/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./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/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./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/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./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/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "4098e1f692a54f888518d6a34fd08ffd"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqYcwjF1nYlU",
        "outputId": "52c9ec42-5810-4af7-f104-a0440a06a5be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DISCRIMINATOR \n",
        "\n",
        "D=nn.Sequential(\n",
        "    nn.Linear(784,512),\n",
        "    nn.LeakyReLU(0.2),\n",
        "    nn.Linear(512,256),\n",
        "    nn.LeakyReLU(0.2),\n",
        "    nn.Linear(256,1),\n",
        "    #nn.Sigmoid()\n",
        ")"
      ],
      "metadata": {
        "id": "WEvSU8zLnZIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#GENERATOR \n",
        "\n",
        "latent_dim=100\n",
        "G=nn.Sequential(\n",
        "    nn.Linear(latent_dim,256),\n",
        "    nn.LeakyReLU(0.2),\n",
        "    nn.BatchNorm1d(256,momentum=0.7),\n",
        "    nn.Linear(256,512),\n",
        "    nn.LeakyReLU(0.2),\n",
        "    nn.BatchNorm1d(512,momentum=0.7),\n",
        "    nn.Linear(512,1024),\n",
        "    nn.LeakyReLU(0.2),\n",
        "    nn.BatchNorm1d(1024,momentum=0.7),\n",
        "    nn.Linear(1024,784),\n",
        "    nn.Tanh()\n",
        "\n",
        ")\n"
      ],
      "metadata": {
        "id": "c8qYpn1inbXn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device =torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device\n",
        "D=D.to(device)\n",
        "G=G.to(device)"
      ],
      "metadata": {
        "id": "uk1bFfUHndQU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion=nn.BCEWithLogitsLoss()\n",
        "d_optimizer=torch.optim.Adam(D.parameters(), lr=0.0002, betas=(0.5,0.999))\n",
        "g_optimizer=torch.optim.Adam(G.parameters(), lr=0.0002, betas=(0.5,0.999))"
      ],
      "metadata": {
        "id": "_ftvr6SDnfix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scale image back to 0,1\n",
        "def scale_image(img):\n",
        "    out=(img+1) / 2\n",
        "    return out"
      ],
      "metadata": {
        "id": "fgzHDDaqn1n5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if not os.path.exists(\"gan_images\"):\n",
        "    os.makedirs(\"gan_images\")"
      ],
      "metadata": {
        "id": "ab5LiZb7n5CB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size=128\n",
        "data_loader=torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")"
      ],
      "metadata": {
        "id": "IecZtWVUobTa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training loop\n",
        "\n",
        "#labels to  use in the loop\n",
        "ones_=torch.ones(batch_size,1).to(device)\n",
        "zeros_=torch.zeros(batch_size,1).to(device)\n",
        "\n",
        "#save loosses\n",
        "d_losses=[]\n",
        "g_losses=[]\n",
        "\n",
        "for epoch in range(200):\n",
        "    for inputs, _ in data_loader: # NO TARGET IS NEEDED SINCE GAN\n",
        "        #reshape\n",
        "        n=inputs.size(0)\n",
        "        inputs=inputs.reshape(n,784).to(device)\n",
        "\n",
        "        ones=ones_[:n]\n",
        "        zeros=zeros_[:n]\n",
        "\n",
        "        ###### train discriminator ###\n",
        "        \n",
        "        #real images\n",
        "        real_outputs=D(inputs)\n",
        "        d_loss_real=criterion(real_outputs, ones)\n",
        "\n",
        "        #fake images\n",
        "        noise=torch.randn(n, latent_dim).to(device)\n",
        "        fake_images=G(noise)\n",
        "        fake_outputs=D(fake_images)\n",
        "        d_loss_fake=criterion(fake_outputs,zeros)\n",
        "\n",
        "        #gradient descent\n",
        "\n",
        "        d_loss=0.5*(d_loss_real+d_loss_fake)\n",
        "        d_optimizer.zero_grad()\n",
        "        g_optimizer.zero_grad()\n",
        "        d_loss.backward()\n",
        "        d_optimizer.step()\n",
        "\n",
        "        ###### train generator ######\n",
        "        #do it twice\n",
        "\n",
        "        for _ in range(2):\n",
        "            #fake images\n",
        "            noise=torch.randn(n,latent_dim).to(device)\n",
        "            fake_images=G(noise)\n",
        "            fake_outputs=D(fake_images)\n",
        "\n",
        "            #reverse labels\n",
        "            g_loss=criterion(fake_outputs,ones)\n",
        "\n",
        "            #gradient descent\n",
        "            d_optimizer.zero_grad()\n",
        "            g_optimizer.zero_grad()\n",
        "            g_loss.backward()\n",
        "            g_optimizer.step()\n",
        "\n",
        "        #save losses\n",
        "        d_losses.append(d_loss.item())\n",
        "        g_losses.append(g_loss.item())\n",
        "\n",
        "    #print and save\n",
        "    print(f\"Epoch: {epoch}, d_loss: {d_loss.item()}, g_loss: {g_loss.item()}\")\n",
        "\n",
        "    #save images to file\n",
        "    fake_images=fake_images.reshape(-1,1,28,28)\n",
        "    save_image(scale_image(fake_images),f\"gan_images/{epoch+1}.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUv_YgcTn7Es",
        "outputId": "4b118797-0edb-41a7-921d-26b299d4c5ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epich: 199, d_loss: 0.5451967120170593, g_loss: 1.1327400207519531\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(g_losses, label=\"g_losses\")\n",
        "plt.plot(d_losses,label=\"d_losses\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "W0QVIo4goL_V",
        "outputId": "0e023d2b-b3e2-4378-cdf8-baaa295e6a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc4d01e33a0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "izNNvH1M4hoY",
        "outputId": "12c5c486-77b1-415b-a560-ec3fb4cffc32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4owRoMY94j5s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}