recurrent_neural_network.ipynb

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      "cell_type": "code",
      "metadata": {
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        "id": "jkCe-gnuwI5O",
        "outputId": "90917f76-fd97-4d98-c72c-3979cf437cac"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Hyper-parameters\n",
        "sequence_length = 28\n",
        "input_size = 28\n",
        "hidden_size = 128\n",
        "num_layers = 2\n",
        "num_classes = 10\n",
        "batch_size = 100\n",
        "num_epochs = 2\n",
        "learning_rate = 0.001\n",
        "\n",
        "# MNIST dataset\n",
        "train_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                           train=True,\n",
        "                                           transform=transforms.ToTensor(),\n",
        "                                           download=True)\n",
        "test_dataset = torchvision.datasets.MNIST(root='../../data/',\n",
        "                                          train=False,\n",
        "                                          transform=transforms.ToTensor())\n",
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=batch_size,\n",
        "                                           shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          shuffle=False)\n",
        "# Recurrent neural network (many-to-one)\n",
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
        "        super(RNN, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Set initial hidden and cell states\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n",
        "\n",
        "        # Forward propagate LSTM\n",
        "        out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)\n",
        "\n",
        "        # Decode the hidden state of the last time step\n",
        "        out = self.fc(out[:, -1, :])\n",
        "        return out\n",
        "\n",
        "model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        \n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if (i+1) % 100 == 0:\n",
        "            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "            .format(epoch+1, num_epochs,i+1, total_step, loss.item()))\n",
        "\n",
        "# Test the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.reshape(-1, sequence_length, input_size).to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print('Test accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))\n",
        "\n",
        "# Save the model checkpoint\n",
        "torch.save(model.state_dict(), 'model.ckpt')"
      ],
      "execution_count": 1,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2
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            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "d2c31577d57442df87c028127822643a",
              "version_minor": 0,
              "version_major": 2
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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"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
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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",
            "Epoch [1/2], Step [100/600], Loss: 0.5055\n",
            "Epoch [1/2], Step [200/600], Loss: 0.4538\n",
            "Epoch [1/2], Step [300/600], Loss: 0.2044\n",
            "Epoch [1/2], Step [400/600], Loss: 0.2905\n",
            "Epoch [1/2], Step [500/600], Loss: 0.1261\n",
            "Epoch [1/2], Step [600/600], Loss: 0.1264\n",
            "Epoch [2/2], Step [100/600], Loss: 0.1377\n",
            "Epoch [2/2], Step [200/600], Loss: 0.0383\n",
            "Epoch [2/2], Step [300/600], Loss: 0.1365\n",
            "Epoch [2/2], Step [400/600], Loss: 0.0521\n",
            "Epoch [2/2], Step [500/600], Loss: 0.0824\n",
            "Epoch [2/2], Step [600/600], Loss: 0.2972\n",
            "Test accuracy of the model on the 10000 test images: 97.09 %\n"
          ]
        }
      ]
    }
  ]
}