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        "outputId": "e8f13534-a208-4dcd-b681-d31217058490"
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      "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 data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
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              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/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 data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/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 data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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        },
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          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/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 data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor, Lambda\n",
        "\n",
        "training_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "train_dataloader = DataLoader(training_data, batch_size=64)\n",
        "test_dataloader = DataLoader(test_data, batch_size=64)\n",
        "\n",
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(NeuralNetwork, self).__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            nn.Linear(28*28, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 10),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.flatten(x)\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        return logits\n",
        "\n",
        "model = NeuralNetwork()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hyperparameters\n",
        "learning_rate = 1e-3\n",
        "batch_size = 64\n",
        "epochs = 5"
      ],
      "metadata": {
        "id": "kZHumZDQcZq0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimization Loop\n",
        "Once we set our hyperparameters, we can then train and optimize our model with an optimization loop. Each iteration of the optimization loop is called an epoch.\n",
        "\n",
        "Each epoch consists of two main parts:\n",
        "The Train Loop - iterate over the training dataset and try to converge to optimal parameters.\n",
        "The Validation/Test Loop - iterate over the test dataset to check if model performance is improving."
      ],
      "metadata": {
        "id": "Ycfd7gNIcrK6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the loss function\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "xBHItt1ockLS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "pzWu27SvcuZx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_loop(dataloader, model, loss_fn, optimizer):\n",
        "    size = len(dataloader.dataset)\n",
        "    for batch, (X, y) in enumerate(dataloader):\n",
        "        # Compute prediction and loss\n",
        "        pred = model(X)\n",
        "        loss = loss_fn(pred, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch % 100 == 0:\n",
        "            loss, current = loss.item(), batch * len(X)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
        "\n",
        "\n",
        "def test_loop(dataloader, model, loss_fn):\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    test_loss, correct = 0, 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for X, y in dataloader:\n",
        "            pred = model(X)\n",
        "            test_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
        "\n",
        "    test_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
      ],
      "metadata": {
        "id": "XWko7Dq6cwBh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "\n",
        "epochs = 5\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train_loop(train_dataloader, model, loss_fn, optimizer)\n",
        "    test_loop(test_dataloader, model, loss_fn)\n",
        "print(\"Done!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80h2QO1tc0HC",
        "outputId": "200055fd-f1f4-4281-916d-8f7e7fae6490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 2.298131  [    0/60000]\n",
            "loss: 2.294064  [ 6400/60000]\n",
            "loss: 2.288591  [12800/60000]\n",
            "loss: 2.282434  [19200/60000]\n",
            "loss: 2.284228  [25600/60000]\n",
            "loss: 2.287089  [32000/60000]\n",
            "loss: 2.266753  [38400/60000]\n",
            "loss: 2.277899  [44800/60000]\n",
            "loss: 2.264287  [51200/60000]\n",
            "loss: 2.252632  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 46.6%, Avg loss: 2.254016 \n",
            "\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 2.249784  [    0/60000]\n",
            "loss: 2.246179  [ 6400/60000]\n",
            "loss: 2.252587  [12800/60000]\n",
            "loss: 2.220010  [19200/60000]\n",
            "loss: 2.232287  [25600/60000]\n",
            "loss: 2.236027  [32000/60000]\n",
            "loss: 2.208308  [38400/60000]\n",
            "loss: 2.232460  [44800/60000]\n",
            "loss: 2.201841  [51200/60000]\n",
            "loss: 2.188701  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 64.8%, Avg loss: 2.188221 \n",
            "\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 2.183509  [    0/60000]\n",
            "loss: 2.175959  [ 6400/60000]\n",
            "loss: 2.194509  [12800/60000]\n",
            "loss: 2.128399  [19200/60000]\n",
            "loss: 2.151098  [25600/60000]\n",
            "loss: 2.153031  [32000/60000]\n",
            "loss: 2.111710  [38400/60000]\n",
            "loss: 2.154588  [44800/60000]\n",
            "loss: 2.098380  [51200/60000]\n",
            "loss: 2.079449  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 70.5%, Avg loss: 2.076007 \n",
            "\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 2.071514  [    0/60000]\n",
            "loss: 2.052994  [ 6400/60000]\n",
            "loss: 2.088719  [12800/60000]\n",
            "loss: 1.976114  [19200/60000]\n",
            "loss: 2.010123  [25600/60000]\n",
            "loss: 2.006638  [32000/60000]\n",
            "loss: 1.941803  [38400/60000]\n",
            "loss: 2.012309  [44800/60000]\n",
            "loss: 1.920430  [51200/60000]\n",
            "loss: 1.887459  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 73.0%, Avg loss: 1.879571 \n",
            "\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 1.878804  [    0/60000]\n",
            "loss: 1.836809  [ 6400/60000]\n",
            "loss: 1.897868  [12800/60000]\n",
            "loss: 1.727172  [19200/60000]\n",
            "loss: 1.769993  [25600/60000]\n",
            "loss: 1.758034  [32000/60000]\n",
            "loss: 1.667397  [38400/60000]\n",
            "loss: 1.776011  [44800/60000]\n",
            "loss: 1.644379  [51200/60000]\n",
            "loss: 1.594988  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 74.3%, Avg loss: 1.580749 \n",
            "\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QURpk5Flc1sS"
      },
      "execution_count": null,
      "outputs": []
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}