LSTM.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qTNk0K82pfPp"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "Fx8_XN29wDQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyper-parameters \n",
        "# input_size = 784 # 28x28\n",
        "num_classes = 10\n",
        "num_epochs = 2\n",
        "batch_size = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "input_size = 28\n",
        "sequence_length = 28\n",
        "hidden_size = 128\n",
        "num_layers = 2"
      ],
      "metadata": {
        "id": "hd2ppdF8wGGO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MNIST dataset \n",
        "train_dataset = torchvision.datasets.MNIST(root='./data', \n",
        "                                           train=True, \n",
        "                                           transform=transforms.ToTensor(),  \n",
        "                                           download=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
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        "outputId": "cf81ea77-d062-4261-e1d0-20521ace0872"
      },
      "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 ./data/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 ./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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              "  0%|          | 0/28881 [00:00<?, ?it/s]"
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        {
          "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",
          "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 ./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": {
            "text/plain": [
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              "version_minor": 0,
              "model_id": "0a8ed40798ca4e598d89bfa0ae5281a1"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = torchvision.datasets.MNIST(root='./data', \n",
        "                                          train=False, \n",
        "                                          transform=transforms.ToTensor())"
      ],
      "metadata": {
        "id": "PR3yeaUewQLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 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)"
      ],
      "metadata": {
        "id": "vt1h-LjvwS6z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fully connected neural network with one hidden layer\n",
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n",
        "        super(RNN, self).__init__()\n",
        "        self.num_layers = num_layers\n",
        "        self.hidden_size = hidden_size\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        # -> x needs to be: (batch_size, seq, input_size)\n",
        "        self.fc = nn.Linear(hidden_size, num_classes)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        # Set initial hidden 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",
        "        out, _ = self.lstm(x, (h0,c0))  \n",
        "        out = out[:, -1, :]\n",
        "        out = self.fc(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "ux57_Q-kwURM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)"
      ],
      "metadata": {
        "id": "P8R9CBmKwW-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
      ],
      "metadata": {
        "id": "VjWQvsKrwZ55"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "n_total_steps = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):  \n",
        "        # origin shape: [N, 1, 28, 28]\n",
        "        # resized: [N, 28, 28]\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 (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXCeNfh2wdHc",
        "outputId": "7c8e842e-e03f-427d-d0b8-7e5604dac0b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/2], Step [100/600], Loss: 0.7830\n",
            "Epoch [1/2], Step [200/600], Loss: 0.3266\n",
            "Epoch [1/2], Step [300/600], Loss: 0.2001\n",
            "Epoch [1/2], Step [400/600], Loss: 0.1414\n",
            "Epoch [1/2], Step [500/600], Loss: 0.1559\n",
            "Epoch [1/2], Step [600/600], Loss: 0.1810\n",
            "Epoch [2/2], Step [100/600], Loss: 0.1410\n",
            "Epoch [2/2], Step [200/600], Loss: 0.2739\n",
            "Epoch [2/2], Step [300/600], Loss: 0.1263\n",
            "Epoch [2/2], Step [400/600], Loss: 0.0547\n",
            "Epoch [2/2], Step [500/600], Loss: 0.1721\n",
            "Epoch [2/2], Step [600/600], Loss: 0.1209\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model\n",
        "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
        "with torch.no_grad():\n",
        "    n_correct = 0\n",
        "    n_samples = 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",
        "        # max returns (value ,index)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        n_samples += labels.size(0)\n",
        "        n_correct += (predicted == labels).sum().item()\n",
        "\n",
        "    acc = 100.0 * n_correct / n_samples\n",
        "    print(f'Accuracy of the network on the 10000 test images: {acc} %')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyAK6bzawhdD",
        "outputId": "b681104e-bf2a-4272-aa2f-9818c21ffc9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 10000 test images: 97.63 %\n"
          ]
        }
      ]
    }
  ]
}