premade_mnist.ipynb

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          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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        {
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          "name": "stdout",
          "text": [
            "Extracting ../data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "text": [
            "Extracting ../data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting ../data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/FashionMNIST/raw\n",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torch.nn.utils.prune as prune\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torchvision import datasets, transforms\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "\n",
        "\n",
        "# INITIALIZE MODEL\n",
        "class FashionModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(FashionModel, self).__init__()\n",
        "\n",
        "        self.stack = nn.Sequential(\n",
        "            nn.Conv2d(1, 32, 3, 1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(32, 64, 3, 1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Dropout(0.25),\n",
        "            nn.Flatten(1),\n",
        "            nn.Linear(9216, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(128, 10),\n",
        "            nn.LogSoftmax(dim=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.stack(x)\n",
        "\n",
        "train_performance = []\n",
        "val_performance = []\n",
        "\n",
        "def train(model, device, train_loader, val_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % 10 == 0:\n",
        "          val_X, val_y = next(iter(val_loader))\n",
        "          val_X, val_y = val_X.cuda(), val_y.cuda()\n",
        "        \n",
        "          val_pred = model(val_X)\n",
        "          val_loss = F.nll_loss(val_pred, val_y)\n",
        "\n",
        "          train_performance.append(loss.item())\n",
        "          val_performance.append(val_loss.item())\n",
        "          # print(list(model.conv1.named_parameters()))\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), loss.item()))\n",
        "            # if args.dry_run:\n",
        "            #     break\n",
        "\n",
        "\n",
        "# def test(model, device, test_loader):\n",
        "#     model.eval()\n",
        "#     test_loss = 0\n",
        "#     correct = 0\n",
        "#     with torch.no_grad():\n",
        "#         for data, target in test_loader:\n",
        "#             data, target = data.to(device), target.to(device)\n",
        "#             output = model(data)\n",
        "#             test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "#             pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "#             correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "#     test_loss /= len(test_loader.dataset)\n",
        "\n",
        "#     print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "#         test_loss, correct, len(test_loader.dataset),\n",
        "#         100. * correct / len(test_loader.dataset)))\n",
        "\n",
        "\n",
        "def main():\n",
        "\n",
        "    start = torch.cuda.Event(enable_timing=True)\n",
        "    end = torch.cuda.Event(enable_timing=True)\n",
        " \n",
        "    use_cuda = torch.cuda.is_available()\n",
        "\n",
        "    torch.manual_seed(1)\n",
        "\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    train_kwargs = {'batch_size': 64}\n",
        "    test_kwargs = {'batch_size': 1000}\n",
        "    if use_cuda:\n",
        "        cuda_kwargs = {'num_workers': 1, 'pin_memory': True, 'shuffle': True}\n",
        "        train_kwargs.update(cuda_kwargs)\n",
        "        test_kwargs.update(cuda_kwargs)\n",
        "\n",
        "    transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081,))\n",
        "        ])\n",
        "\n",
        "    dataset1 = datasets.FashionMNIST('../data', train=True, download=True,\n",
        "                       transform=transform)\n",
        "    dataset2 = datasets.FashionMNIST('../data', train=False,\n",
        "                       transform=transform)\n",
        "\n",
        "    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n",
        "    val_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n",
        "\n",
        "    model = FashionModel().to(device)\n",
        "    optimizer = optim.Adadelta(model.parameters(), lr=1.0)\n",
        "\n",
        "    scheduler = StepLR(optimizer, step_size=1, gamma=0.7)\n",
        "    for epoch in range(1, 1 + 1): #epochs = 14\n",
        "        start.record()\n",
        "        train(model, device, train_loader, val_loader, optimizer, epoch)\n",
        "        end.record()\n",
        "        torch.cuda.synchronize()\n",
        "        print(start.elapsed_time(end))\n",
        "        scheduler.step()\n",
        "    \n",
        "    plt.plot(train_performance, label=\"Training Loss\")\n",
        "    plt.plot(val_performance, label=\"Validation Loss\")\n",
        "    plt.legend()\n",
        "    plt.title(\"Model Training (Unpruned)\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()"
      ]
    }
  ]
}