VAE_FASHION_MNIST.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0RPK3dEz4J_K"
      },
      "outputs": [],
      "source": [
        "from __future__ import print_function\n",
        "import argparse\n",
        "import torch\n",
        "import torch.utils.data\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from torchvision.utils import save_image\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(3)\n",
        "BATCH_SIZE = 64\n",
        "NUMBER_OF_EPOCH = 100\n",
        "\n",
        "device = torch.device(\"cuda\")\n",
        "\n",
        "kwargs = {'num_workers': 1, 'pin_memory': True} \n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.FashionMNIST('/./FashionMNIST', train=True, download=True,\n",
        "                   transform=transforms.ToTensor()),\n",
        "    batch_size=BATCH_SIZE, shuffle=True, **kwargs)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.FashionMNIST('/./FashionMNIST', train=False, transform=transforms.ToTensor()),\n",
        "    batch_size=BATCH_SIZE, shuffle=True, **kwargs)"
      ],
      "metadata": {
        "id": "I7HvlFuY4SQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class VAE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VAE, self).__init__()\n",
        "\n",
        "        self.fc1 = nn.Linear(784, 400)\n",
        "        self.fc2 = nn.Linear(400, 400)\n",
        "        self.fc31 = nn.Linear(400, 20)\n",
        "        self.fc32 = nn.Linear(400, 20)\n",
        "        self.fc4 = nn.Linear(20, 400)\n",
        "        self.fc5 = nn.Linear(400, 400)\n",
        "        self.fc6 = nn.Linear(400, 784)\n",
        "\n",
        "    def encode(self, x):\n",
        "        h1 = F.relu(self.fc1(x))\n",
        "        h1 = F.relu(self.fc2(h1))\n",
        "        return self.fc31(h1), self.fc32(h1)\n",
        "\n",
        "    def reparameterize(self, mu, logvar):\n",
        "        std = torch.exp(0.5*logvar)\n",
        "        eps = torch.randn_like(std)\n",
        "        return mu + eps*std\n",
        "\n",
        "    def decode(self, z):\n",
        "        h3 = F.relu(self.fc4(z))\n",
        "        h3 = F.relu(self.fc5(h3))\n",
        "        return torch.sigmoid(self.fc6(h3))\n",
        "\n",
        "    def forward(self, x):\n",
        "        mu, logvar = self.encode(x.view(-1, 784))\n",
        "        z = self.reparameterize(mu, logvar)\n",
        "        return self.decode(z), mu, logvar"
      ],
      "metadata": {
        "id": "LAyxhS5w4X-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reconstruction + KL divergence losses summed over all elements and batch\n",
        "# https://arxiv.org/abs/1312.6114\n",
        "def loss_function(recon_x, x, mu, logvar):\n",
        "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')\n",
        "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
        "\n",
        "    return BCE + KLD"
      ],
      "metadata": {
        "id": "TzxiDREz4bSY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(epoch):\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "    for batch_idx, (data, _) in enumerate(train_loader):\n",
        "        data = data.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        recon_batch, mu, logvar = model(data)\n",
        "        loss = loss_function(recon_batch, data, mu, logvar)\n",
        "        loss.backward()\n",
        "        train_loss += loss.item()\n",
        "        optimizer.step()\n",
        "        LOG_INTERVALS = 300\n",
        "        # if batch_idx % LOG_INTERVALS == 0:\n",
        "        #     print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "        #         epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "        #         100. * batch_idx / len(train_loader),\n",
        "        #         loss.item() / len(data)))\n",
        "\n",
        "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))"
      ],
      "metadata": {
        "id": "3mmRrEe04dFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "losses = []\n",
        "def test(epoch):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    with torch.no_grad():\n",
        "        for i, (data, _) in enumerate(test_loader):\n",
        "            data = data.to(device)\n",
        "            recon_batch, mu, logvar = model(data)\n",
        "            test_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
        "            if i == 0:\n",
        "                n = min(data.size(0), 8)\n",
        "                comparison = torch.cat([data[:n], recon_batch.view(BATCH_SIZE, 1, 28, 28)[:n]])\n",
        "                save_image(comparison.cpu(), f'./reconstruction_{epoch}.png', nrow=n)\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    losses.append(test_loss)\n",
        "    print('====> Test set loss: {:.4f}'.format(test_loss))"
      ],
      "metadata": {
        "id": "g76-aK7J4ewQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(device)\n",
        "model = VAE().to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5y75ib04wZ-",
        "outputId": "40511a99-53d8-42d9-cc30-3d577b9882c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(45)\n",
        "for epoch in range(1, NUMBER_OF_EPOCH+1):\n",
        "    train(epoch)\n",
        "    test(epoch)\n",
        "    with torch.no_grad():\n",
        "        sample = torch.randn(64, 20).to(device)\n",
        "        sample = model.decode(sample).cpu()\n",
        "        save_image(sample.view(64, 1, 28, 28), f'./sample_{epoch}.png')\n",
        "        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97LuHqQf4yA1",
        "outputId": "0414b2c8-86ab-435d-a2bb-b340b1fc859e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "====> Epoch: 1 Average loss: 276.0783\n",
            "====> Test set loss: 255.7206\n",
            "====> Epoch: 2 Average loss: 250.9982\n",
            "====> Test set loss: 250.2790\n",
            "====> Epoch: 3 Average loss: 247.1881\n",
            "====> Test set loss: 247.6669\n",
            "====> Epoch: 4 Average loss: 245.1567\n",
            "====> Test set loss: 246.2467\n",
            "====> Epoch: 5 Average loss: 243.6794\n",
            "====> Test set loss: 244.5136\n",
            "====> Epoch: 6 Average loss: 242.5068\n",
            "====> Test set loss: 244.8574\n",
            "====> Epoch: 7 Average loss: 241.6922\n",
            "====> Test set loss: 243.7591\n",
            "====> Epoch: 8 Average loss: 241.0743\n",
            "====> Test set loss: 242.9837\n",
            "====> Epoch: 9 Average loss: 240.5762\n",
            "====> Test set loss: 242.2145\n",
            "====> Epoch: 10 Average loss: 240.1740\n",
            "====> Test set loss: 241.8867\n",
            "====> Epoch: 11 Average loss: 239.8287\n",
            "====> Test set loss: 241.5618\n",
            "====> Epoch: 12 Average loss: 239.5613\n",
            "====> Test set loss: 241.5181\n",
            "====> Epoch: 13 Average loss: 239.3318\n",
            "====> Test set loss: 241.6806\n",
            "====> Epoch: 14 Average loss: 239.1182\n",
            "====> Test set loss: 240.7976\n",
            "====> Epoch: 15 Average loss: 238.9132\n",
            "====> Test set loss: 241.1846\n",
            "====> Epoch: 16 Average loss: 238.7292\n",
            "====> Test set loss: 240.8814\n",
            "====> Epoch: 17 Average loss: 238.6239\n",
            "====> Test set loss: 240.6880\n",
            "====> Epoch: 18 Average loss: 238.4296\n",
            "====> Test set loss: 241.1676\n",
            "====> Epoch: 19 Average loss: 238.3592\n",
            "====> Test set loss: 240.4303\n",
            "====> Epoch: 20 Average loss: 238.2461\n",
            "====> Test set loss: 241.0473\n",
            "====> Epoch: 21 Average loss: 238.0943\n",
            "====> Test set loss: 240.2995\n",
            "====> Epoch: 22 Average loss: 238.0528\n",
            "====> Test set loss: 240.3887\n",
            "====> Epoch: 23 Average loss: 237.9233\n",
            "====> Test set loss: 240.1995\n",
            "====> Epoch: 24 Average loss: 237.8443\n",
            "====> Test set loss: 240.9101\n",
            "====> Epoch: 25 Average loss: 237.7451\n",
            "====> Test set loss: 240.2284\n",
            "====> Epoch: 26 Average loss: 237.6571\n",
            "====> Test set loss: 240.2159\n",
            "====> Epoch: 27 Average loss: 237.5646\n",
            "====> Test set loss: 239.9736\n",
            "====> Epoch: 28 Average loss: 237.5079\n",
            "====> Test set loss: 240.0283\n",
            "====> Epoch: 29 Average loss: 237.4759\n",
            "====> Test set loss: 239.7996\n",
            "====> Epoch: 30 Average loss: 237.3761\n",
            "====> Test set loss: 239.8495\n",
            "====> Epoch: 31 Average loss: 237.4129\n",
            "====> Test set loss: 239.6802\n",
            "====> Epoch: 32 Average loss: 237.2744\n",
            "====> Test set loss: 240.0487\n",
            "====> Epoch: 33 Average loss: 237.3036\n",
            "====> Test set loss: 240.1178\n",
            "====> Epoch: 34 Average loss: 237.2172\n",
            "====> Test set loss: 239.8257\n",
            "====> Epoch: 35 Average loss: 237.1431\n",
            "====> Test set loss: 239.8079\n",
            "====> Epoch: 36 Average loss: 237.0861\n",
            "====> Test set loss: 239.6968\n",
            "====> Epoch: 37 Average loss: 237.0243\n",
            "====> Test set loss: 239.9257\n",
            "====> Epoch: 38 Average loss: 237.0516\n",
            "====> Test set loss: 239.6709\n",
            "====> Epoch: 39 Average loss: 236.9391\n",
            "====> Test set loss: 239.4450\n",
            "====> Epoch: 40 Average loss: 236.9681\n",
            "====> Test set loss: 239.6053\n",
            "====> Epoch: 41 Average loss: 236.8695\n",
            "====> Test set loss: 239.4994\n",
            "====> Epoch: 42 Average loss: 236.8375\n",
            "====> Test set loss: 239.3399\n",
            "====> Epoch: 43 Average loss: 236.8374\n",
            "====> Test set loss: 239.8638\n",
            "====> Epoch: 44 Average loss: 236.7714\n",
            "====> Test set loss: 239.5230\n",
            "====> Epoch: 45 Average loss: 236.7683\n",
            "====> Test set loss: 239.4934\n",
            "====> Epoch: 46 Average loss: 236.7773\n",
            "====> Test set loss: 239.5970\n",
            "====> Epoch: 47 Average loss: 236.6552\n",
            "====> Test set loss: 239.5783\n",
            "====> Epoch: 48 Average loss: 236.6855\n",
            "====> Test set loss: 239.6145\n",
            "====> Epoch: 49 Average loss: 236.6474\n",
            "====> Test set loss: 239.4322\n",
            "====> Epoch: 50 Average loss: 236.5815\n",
            "====> Test set loss: 239.3661\n",
            "====> Epoch: 51 Average loss: 236.6076\n",
            "====> Test set loss: 239.2961\n",
            "====> Epoch: 52 Average loss: 236.5579\n",
            "====> Test set loss: 239.1364\n",
            "====> Epoch: 53 Average loss: 236.4981\n",
            "====> Test set loss: 239.5737\n",
            "====> Epoch: 54 Average loss: 236.5797\n",
            "====> Test set loss: 239.3198\n",
            "====> Epoch: 55 Average loss: 236.4363\n",
            "====> Test set loss: 239.6278\n",
            "====> Epoch: 56 Average loss: 236.4700\n",
            "====> Test set loss: 239.2276\n",
            "====> Epoch: 57 Average loss: 236.4031\n",
            "====> Test set loss: 239.6415\n",
            "====> Epoch: 58 Average loss: 236.4501\n",
            "====> Test set loss: 239.2695\n",
            "====> Epoch: 59 Average loss: 236.4243\n",
            "====> Test set loss: 239.6721\n",
            "====> Epoch: 60 Average loss: 236.3479\n",
            "====> Test set loss: 239.5296\n",
            "====> Epoch: 61 Average loss: 236.3408\n",
            "====> Test set loss: 239.4605\n",
            "====> Epoch: 62 Average loss: 236.3635\n",
            "====> Test set loss: 239.2093\n",
            "====> Epoch: 63 Average loss: 236.3097\n",
            "====> Test set loss: 239.4760\n",
            "====> Epoch: 64 Average loss: 236.2954\n",
            "====> Test set loss: 239.2228\n",
            "====> Epoch: 65 Average loss: 236.2777\n",
            "====> Test set loss: 239.1820\n",
            "====> Epoch: 66 Average loss: 236.2815\n",
            "====> Test set loss: 239.1301\n",
            "====> Epoch: 67 Average loss: 236.2928\n",
            "====> Test set loss: 239.2307\n",
            "====> Epoch: 68 Average loss: 236.1907\n",
            "====> Test set loss: 239.3358\n",
            "====> Epoch: 69 Average loss: 236.2011\n",
            "====> Test set loss: 239.2167\n",
            "====> Epoch: 70 Average loss: 236.2295\n",
            "====> Test set loss: 239.3919\n",
            "====> Epoch: 71 Average loss: 236.1633\n",
            "====> Test set loss: 239.1577\n",
            "====> Epoch: 72 Average loss: 236.1683\n",
            "====> Test set loss: 238.9999\n",
            "====> Epoch: 73 Average loss: 236.1492\n",
            "====> Test set loss: 239.0862\n",
            "====> Epoch: 74 Average loss: 236.1352\n",
            "====> Test set loss: 239.2406\n",
            "====> Epoch: 75 Average loss: 236.1139\n",
            "====> Test set loss: 239.1287\n",
            "====> Epoch: 76 Average loss: 236.1051\n",
            "====> Test set loss: 239.1608\n",
            "====> Epoch: 77 Average loss: 236.0559\n",
            "====> Test set loss: 239.2630\n",
            "====> Epoch: 78 Average loss: 236.0711\n",
            "====> Test set loss: 239.1744\n",
            "====> Epoch: 79 Average loss: 236.0502\n",
            "====> Test set loss: 239.1475\n",
            "====> Epoch: 80 Average loss: 236.0633\n",
            "====> Test set loss: 239.2053\n",
            "====> Epoch: 81 Average loss: 236.0262\n",
            "====> Test set loss: 239.1103\n",
            "====> Epoch: 82 Average loss: 236.0840\n",
            "====> Test set loss: 239.0205\n",
            "====> Epoch: 83 Average loss: 236.0136\n",
            "====> Test set loss: 239.3679\n",
            "====> Epoch: 84 Average loss: 235.9742\n",
            "====> Test set loss: 238.9952\n",
            "====> Epoch: 85 Average loss: 235.9905\n",
            "====> Test set loss: 239.1509\n",
            "====> Epoch: 86 Average loss: 235.9512\n",
            "====> Test set loss: 239.0946\n",
            "====> Epoch: 87 Average loss: 235.9261\n",
            "====> Test set loss: 239.2043\n",
            "====> Epoch: 88 Average loss: 235.9646\n",
            "====> Test set loss: 239.3298\n",
            "====> Epoch: 89 Average loss: 235.9254\n",
            "====> Test set loss: 239.1135\n",
            "====> Epoch: 90 Average loss: 235.9931\n",
            "====> Test set loss: 239.2330\n",
            "====> Epoch: 91 Average loss: 235.9166\n",
            "====> Test set loss: 239.5166\n",
            "====> Epoch: 92 Average loss: 235.8711\n",
            "====> Test set loss: 239.3409\n",
            "====> Epoch: 93 Average loss: 235.8858\n",
            "====> Test set loss: 239.2527\n",
            "====> Epoch: 94 Average loss: 235.8724\n",
            "====> Test set loss: 239.2015\n",
            "====> Epoch: 95 Average loss: 235.9174\n",
            "====> Test set loss: 239.4304\n",
            "====> Epoch: 96 Average loss: 235.8543\n",
            "====> Test set loss: 239.1664\n",
            "====> Epoch: 97 Average loss: 235.9600\n",
            "====> Test set loss: 239.0922\n",
            "====> Epoch: 98 Average loss: 235.8159\n",
            "====> Test set loss: 239.3529\n",
            "====> Epoch: 99 Average loss: 235.8771\n",
            "====> Test set loss: 239.2262\n",
            "====> Epoch: 100 Average loss: 235.7901\n",
            "====> Test set loss: 239.0568\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r \"/content/img.zip\" \"/content/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWiBErFp48fK",
        "outputId": "fa4a9fcc-1b9b-42ba-ea64-e02be074f200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/ (stored 0%)\n",
            "  adding: content/.config/ (stored 0%)\n",
            "  adding: content/.config/logs/ (stored 0%)\n",
            "  adding: content/.config/logs/2023.04.14/ (stored 0%)\n",
            "  adding: content/.config/logs/2023.04.14/13.33.49.775521.log (deflated 57%)\n",
            "  adding: content/.config/logs/2023.04.14/13.33.23.944257.log (deflated 91%)\n",
            "  adding: content/.config/logs/2023.04.14/13.34.46.037681.log (deflated 56%)\n",
            "  adding: content/.config/logs/2023.04.14/13.34.12.944578.log (deflated 86%)\n",
            "  adding: content/.config/logs/2023.04.14/13.34.45.328952.log (deflated 57%)\n",
            "  adding: content/.config/logs/2023.04.14/13.34.21.160489.log (deflated 58%)\n",
            "  adding: content/.config/.last_survey_prompt.yaml (stored 0%)\n",
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            "  adding: content/.config/configurations/ (stored 0%)\n",
            "  adding: content/.config/configurations/config_default (deflated 15%)\n",
            "  adding: content/.config/active_config (stored 0%)\n",
            "  adding: content/sample_26.png (deflated 1%)\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nnXBaSBBCR2j"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}