alzheimers_classification.ipynb

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      "source": [
        "# Detection of Alzheimer's Disease with Neuroimaging Data Using a Shallow 2D-CNN Architecture\n",
        "\n",
        "Man Ho Wong  \n",
        "School of Computer Science  \n",
        "Georgia Institute of Technology. Atlanta, GA.\n",
        "\n",
        "--\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This project aims to build a deep learning model to detect Alzheimer's disease with cross-sectional MRI data (more than 86,000 MRI slice images) using 2D-convoluted neural networks (CNN). Normalization and regularization techniques were used to create a stable model with good generalization performance. Despite its shallow architecture, the preliminary model developed here (SCNN4) was able to achieve an accuracy of 99.9%, a significant improvement from the current deep learning models (Zhao et al., 2023).  The training time at the moment is 18 minutes using a T4 GPU provided by Google Colab.\n",
        "\n",
        "## Future work\n",
        "\n",
        "Currently, the model is trained without considering class imbalance. In addition, images were split for training, validation and testing randomly without considering subject identity. The former issue may lead to a biased model (see the section \"Exploring the dataset\") and the latter may impact the model's generalization performance due to data leakage (Yagis et al., 2021). These issues will need to be addressed in the future. In terms of performance metrics, other metrics besides accuracy will be needed to assess the model more comprehensively, such as the model's prediction precision and generalization performance.\n",
        "\n",
        "\n",
        "## Data sourcing\n",
        "\n",
        "The training dataset was compiled by the [Open Access Series of Imaging Studies (OASIS)](https://www.oasis-brains.org/) (Marcus et al., 2007). The derived dataset was partially preprocessed by Ninad Aithal (Centre for Brain Research IISc - Bangalore, IN), including .nii to .jpg file conversion and patient classification (see [this page](https://www.kaggle.com/datasets/ninadaithal/imagesoasis/data) for more information).\n",
        "\n",
        "## Acknowledgments\n",
        "\n",
        "Data were provided by OASIS-1: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382.\n",
        "\n",
        "## References\n",
        "\n",
        "Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L. (2007). Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience, 19, 1498-1507.  https://doi.org/10.1162/jocn.2007.19.9.1498\n",
        "\n",
        "Yagis, E., Atnafu, S.W., García Seco de Herrera, A., Marzi C., Scheda R., Giannelli M., Tessa C., Citi L. & Diciotti S. (2021). Effect of data leakage in brain MRI classification using 2D convolutional neural networks. Nature Science Report, 11, 22544. https://doi.org/10.1038/s41598-021-0168\n",
        "\n",
        "Zhao, Z., Chuah, J. H., Lai, K. W., Chow, C. O., Gochoo, M., Dhanalakshmi, S., Wang, N., Bao, W., & Wu, X. (2023). Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review. Frontiers in computational neuroscience, 17, 1038636. https://doi.org/10.3389/fncom.2023.1038636\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQ269myET2Gf"
      },
      "source": [
        "## Accessing the dataset in Google Colab\n",
        "\n",
        "The dataset is publically available and can be accessed via Kaggle API as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1-KDN9gk1F6I"
      },
      "outputs": [],
      "source": [
        "# Upload kaggle.json to Google Colab\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "# Kaggle API setup\n",
        "! pip install -q kaggle\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "# Download dataset\n",
        "! kaggle datasets download -d ninadaithal/imagesoasis\n",
        "\n",
        "# Unzip dataset\n",
        "! unzip -q \"imagesoasis.zip\" -d mri-data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjjpLTFlboDy"
      },
      "source": [
        "# Exploring the dataset\n",
        "\n",
        "The dataset contains MRI slice images (248px by 496px; 3 channels) of four classes according to the subject's Alzheimer's Disease diagnosis and the stage of dementia:\n",
        "- Non Demented\n",
        "- Very mild Dementia\n",
        "- Mild Dementia\n",
        "- Moderate Dementia"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oEoDdIH93P-2",
        "outputId": "4db6a863-17c6-42e3-b523-46cd1c4e6236"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image example: Non Demented\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image dimensions: (248, 496)\n",
            "Number of channels: 3\n",
            "Number of samples by class:\n",
            "- Mild Dementia: 5002\n",
            "- Moderate Dementia: 488\n",
            "- Non Demented: 67222\n",
            "- Very mild Dementia: 13725\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "# Pick a random image to display\n",
        "data_dir = '/content/mri-data/Data'\n",
        "labels = os.listdir(data_dir)\n",
        "random_label = np.random.choice(labels)\n",
        "random_img = np.random.choice((os.listdir(os.path.join(data_dir, random_label))))\n",
        "img_path = os.path.join(data_dir, random_label, random_img)\n",
        "img = plt.imread(img_path)\n",
        "\n",
        "# Display the above image\n",
        "print(\"Image example:\", random_label)\n",
        "plt.imshow(img)\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "\n",
        "# Image info\n",
        "print(\"Image dimensions:\", img.shape[0:2])\n",
        "print(\"Number of channels:\", img.shape[2])\n",
        "\n",
        "# Number of samples\n",
        "print(\"Number of samples by class:\")\n",
        "for label in sorted(labels):\n",
        "  print(f\"- {label}: {len(os.listdir(os.path.join(data_dir, label)))}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Jt_yLxcQe9T"
      },
      "source": [
        "## Imbalanced dataset\n",
        "\n",
        "One of the major challenges here is that the four classes have imbalanced sample sizes: The `Non Demented` class has more than 67,000 samples while the `Moderate Dementia` class has fewer than 500 samples.\n",
        "\n",
        "Such an imbalanced sample distribution is commonly found in datasets where the minority class (in this case the subjects with dementia) occurs less frequently (or is detected less frequently) than the majority class. While an imbalanced dataset often leads to a biased model and poor predictions, this is not always the case when the minority class is well represented in the dataset, or when different classes have distinctive features that can be learned efficiently.\n",
        "\n",
        "In this dataset, the minority class with the least number of samples, `Moderate Dementia`, is also the most severe case of dementia. Therefore, it is reasonable to expect that the difference between this class and the majority class, `Non Demented`, is the most significant among all pairs of classes. Given that Alzheimer's Disease has been shown to be detectable through MRI structural features (e.g. relative sizes of different brain regions), it is likely that the minority class here has the most distinctive features. Assuming that these features are learnable through deep learning and that every class is well represented in the dataset, I will first build the model without balancing the minority and majority classes. To prevent overfitting due to imbalanced dataset, common regularization techniques (e.g. dropout) will be used.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_MVesj9gb21"
      },
      "source": [
        "# Data preprocessing\n",
        "\n",
        "The dataset used in this project contains jpg images with three color channels. Since MRI data records only one channel, we will convert the images to grayscale (one channel) to save computing resources and speed up the training process. We will then transform the images to PyTorch tensors.\n",
        "\n",
        "The dataset will be split into three separate datasets for training (70%), validation (20%) and testing (10%). Each dataset should cover all four classes with this split ratio.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqNfQ-0NhPGJ"
      },
      "source": [
        "## Transform images and load them into tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xW_vDk1vz2_o"
      },
      "outputs": [],
      "source": [
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision.transforms import ToTensor\n",
        "import torchvision.transforms as tt\n",
        "\n",
        "# List of transforms\n",
        "tf_list = tt.Compose([tt.Grayscale(), # Source files (jpg) has 3 channels. Convert them to grayscale.\n",
        "                      tt.ToTensor()])\n",
        "\n",
        "dataset = ImageFolder(data_dir, tf_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0UFrR1mhEH-"
      },
      "source": [
        "## Split the dataset for training, validation, and testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "vsVwm5LA064I"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import random_split\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "\n",
        "# Set the random seed for reproducible datasets\n",
        "random_seed = 42\n",
        "torch.manual_seed(random_seed)\n",
        "\n",
        "# Split dataset\n",
        "train_size = 0.7\n",
        "val_size = 0.2\n",
        "test_size = 0.1\n",
        "train_ds, val_ds, test_ds = random_split(dataset, [train_size, val_size, test_size])\n",
        "\n",
        "# Create data loaders\n",
        "batch_size=100\n",
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=2, pin_memory=True)\n",
        "val_dl = DataLoader(val_ds, batch_size*4, num_workers=2, pin_memory=True)\n",
        "test_dl = DataLoader(test_ds, batch_size*4, num_workers=2, pin_memory=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYp9IYvBL0AY"
      },
      "source": [
        "## Move data to GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YR4_zT2wLzIc"
      },
      "outputs": [],
      "source": [
        "# Wrap data loader with this to move it to a new device\n",
        "class relocated_dl():\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "    def __iter__(self):\n",
        "        \"\"\"Move data by yielding data batch by batch\"\"\"\n",
        "        for batch in self.dl:\n",
        "            yield move_data(batch, self.device)\n",
        "    def __len__(self):\n",
        "        return len(self.dl)\n",
        "\n",
        "# Helper function\n",
        "def move_data(data, device):\n",
        "    if isinstance(data, (list, tuple)):\n",
        "        return [move_data(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "device = torch.device('cuda')\n",
        "train_dl = relocated_dl(train_dl, device)\n",
        "val_dl = relocated_dl(val_dl, device)\n",
        "test_dl = relocated_dl(test_dl, device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JopuhcfXh3oi"
      },
      "source": [
        "# The model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U75czafcoLsH"
      },
      "source": [
        "## Define the model base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "YGjrBcPc3RGa"
      },
      "outputs": [],
      "source": [
        "# Extend nn.Module to create a model base\n",
        "# All models will use the same model base regardless of architecture.\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class ModelBase(nn.Module):\n",
        "    def train_phase(self, batch):\n",
        "        imgs, labels = batch\n",
        "        out = self(imgs)\n",
        "        loss = F.cross_entropy(out, labels)\n",
        "        return loss\n",
        "\n",
        "    def eval_phase(self, batch):\n",
        "        imgs, labels = batch\n",
        "        out = self(imgs)\n",
        "        loss = F.cross_entropy(out, labels)\n",
        "        acc = accuracy(out, labels) # see below\n",
        "        return {'val_loss': loss.detach(), 'val_acc': acc}\n",
        "\n",
        "    def epoch_stat(self, epoch_out):\n",
        "        epoch_loss = torch.stack([x['val_loss'] for x in epoch_out]).mean()\n",
        "        epoch_acc = torch.stack([x['val_acc'] for x in epoch_out]).mean()\n",
        "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "\n",
        "    def print_stat(self, epoch, stat):\n",
        "        print(\"Epoch {} -- last_lr: {:.6f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "            epoch, stat['lrs'][-1], stat['train_loss'], stat['val_loss'], stat['val_acc']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWoP-CJARYS4"
      },
      "source": [
        "## Define the metric for model performance\n",
        "\n",
        "For more intuitive interpretation, we will use accuracy (percentage of correctly predicted labels) to measure model performance for now. Other metrics may be used later depending on model performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "8O7JN4GwwiiV"
      },
      "outputs": [],
      "source": [
        "# Helper function\n",
        "def accuracy(out, labels):\n",
        "    _, preds = torch.max(out, dim=1)\n",
        "    return torch.tensor(torch.sum(preds==labels).item() / len(preds))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHKMlMQ1odQX"
      },
      "source": [
        "## Create a new model class with `ModelBase`\n",
        "\n",
        "### Neural network layers\n",
        "\n",
        "The following model (`SCNN4`) contains four CNN layers of increasing channel numbers. The intermediate feature maps generated by the last CNN layer is flattened and forwarded to a linear network as the final layer.\n",
        "\n",
        "### Activation layer\n",
        "\n",
        "ReLU is used as the activation layer between the neural network layers.\n",
        "\n",
        "### Normalization and regularization\n",
        "\n",
        "To create a more stable model, a batch normalization layer is added after each CNN layer. In addition, a dropout layer is added before the final linear layer to prevent overfitting by dropping 20% of nodes randomly.\n",
        "\n",
        "### Max pooling\n",
        "\n",
        "To speed up training, a max pooling layer is added to reduce the dimensions of input feature maps to each neural network layer (except the input to the first two CNN layers).\n",
        "\n",
        "Visualization of the model:\n",
        "\n",
        "![model](./img/alzheimers-model.png)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nWXYkvAE_TsL"
      },
      "outputs": [],
      "source": [
        "# Create a new model class by extending the class ModelBase\n",
        "# Spefify the model architecture here.\n",
        "\n",
        "class SCNN4(ModelBase):\n",
        "\n",
        "    def __init__(self, in_ch, n_classes):\n",
        "        super().__init__()                          # Size of each input tensor (channels, rows, columns):\n",
        "        self.cnn1 = cnn_block(in_ch, 8, pool=False) # 1,248,496\n",
        "        self.cnn2 = cnn_block(8, 16, pool=True)     # 8,248,496\n",
        "        self.cnn3 = cnn_block(16, 32, pool=True)    # 16,124,248\n",
        "        self.cnn4 = cnn_block(32, 64, pool=True)    # 32,61,124\n",
        "        self.last = nn.Sequential(nn.Flatten(),     # 64,31,62\n",
        "                                  nn.Dropout(0.2),\n",
        "                                  nn.Linear(64*31*62, n_classes))\n",
        "\n",
        "    def forward(self, xb):\n",
        "        out = self.cnn1(xb)\n",
        "        out = self.cnn2(out)\n",
        "        out = self.cnn3(out)\n",
        "        out = self.cnn4(out)\n",
        "        out = self.last(out)\n",
        "        return out\n",
        "\n",
        "# Helper function\n",
        "def cnn_block(in_ch, out_ch, pool=False):\n",
        "    layers = [nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n",
        "              nn.BatchNorm2d(out_ch),\n",
        "              nn.ReLU(inplace=True)]\n",
        "    if pool:\n",
        "        layers.append(nn.MaxPool2d(2))\n",
        "    return nn.Sequential(*layers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaLUSmnYmjLU"
      },
      "source": [
        "# Model training\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8561MfMm1jz"
      },
      "source": [
        "## Define the training steps\n",
        "\n",
        "Note that weight decay and learning rate scheduling are employed in the following training process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "O_qLmO0T_f7q"
      },
      "outputs": [],
      "source": [
        "# Model fitting involves a training phase and a validation phase\n",
        "# (Code adapted from https://jovian.ai/aakashns/05b-cifar10-resnet)\n",
        "\n",
        "def fit(model, train_dl, val_dl, n_epochs, max_lr,\n",
        "        weight_decay=0, opt_func=torch.optim.SGD\n",
        "        ):\n",
        "\n",
        "    torch.cuda.empty_cache()\n",
        "\n",
        "    # Custom optimizer\n",
        "    opt = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)\n",
        "    # One cycle learning rate scheduler\n",
        "    scheduler = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr, epochs=n_epochs,\n",
        "                                                    steps_per_epoch=len(train_dl))\n",
        "\n",
        "    history = []\n",
        "    for epoch in range(n_epochs):\n",
        "\n",
        "        # Training phase\n",
        "        model.train() # Declare training phase\n",
        "        train_losses = []\n",
        "        lrs = []\n",
        "        for batch in train_dl:\n",
        "            loss = model.train_phase(batch)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "            opt.step()\n",
        "            opt.zero_grad()\n",
        "            lrs.append(get_lr(opt))\n",
        "            scheduler.step()\n",
        "\n",
        "        # Validation phase\n",
        "        stat = evaluate(model, val_dl)\n",
        "\n",
        "        # Summarize epoch statistics\n",
        "        stat['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        stat['lrs'] = lrs\n",
        "        model.print_stat(epoch+1, stat)\n",
        "\n",
        "        # Save training history\n",
        "        history.append(stat)\n",
        "\n",
        "    return history\n",
        "\n",
        "# Helper functions\n",
        "\n",
        "@torch.no_grad() # tell PyTorch NOT to compute gradidents for evaluate()\n",
        "def evaluate(model, dataloader):\n",
        "    model.eval() # declare model evaluation (no randomization needed)\n",
        "    epoch_out = [model.eval_phase(batch) for batch in dataloader]\n",
        "    return model.epoch_stat(epoch_out)\n",
        "\n",
        "def get_lr(optimizer):\n",
        "    for param_group in optimizer.param_groups:\n",
        "        return param_group['lr']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KpPIYHxm77m"
      },
      "source": [
        "## Training\n",
        "\n",
        "Adjust the hyperparameters here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9WO11EyfelA",
        "outputId": "93df1c4e-723d-4d95-e35c-8905eafc985c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 0 -- last_lr: 0.002807, train_loss: 0.9020, val_loss: 0.4312, val_acc: 0.8686\n",
            "Epoch 1 -- last_lr: 0.002435, train_loss: 0.1077, val_loss: 0.0327, val_acc: 0.9892\n",
            "Epoch 2 -- last_lr: 0.000849, train_loss: 0.0128, val_loss: 0.0061, val_acc: 0.9984\n",
            "Epoch 3 -- last_lr: 0.000000, train_loss: 0.0025, val_loss: 0.0033, val_acc: 0.9993\n"
          ]
        }
      ],
      "source": [
        "# Instantiate a model and move it to GPU\n",
        "model = move_data(SCNN4(1, 4), device)\n",
        "\n",
        "# Hyperparameters\n",
        "n_epochs = 4\n",
        "max_lr = 0.003\n",
        "weight_decay = 1e-4\n",
        "opt_func = torch.optim.Adam\n",
        "\n",
        "history = fit(model, train_dl, val_dl,\n",
        "              n_epochs,\n",
        "              max_lr,\n",
        "              weight_decay,\n",
        "              opt_func\n",
        "              )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhptTG4AVyjJ"
      },
      "source": [
        "# Model evaluation with test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niuaJTQp5VNW",
        "outputId": "531da5eb-158d-4153-c84d-8ac064840d2d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'val_loss': 0.004480310250073671, 'val_acc': 0.9988636374473572}"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stat = evaluate(model, test_dl)\n",
        "stat"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUdlR-4jXo5j"
      },
      "source": [
        "## Predict image labels using the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EyV8DI8a53Uc"
      },
      "outputs": [],
      "source": [
        "def predict_label(img, model):\n",
        "    xb = move_data(img.unsqueeze(0), # unsqueeze img to add a dimension (model only reads a batch of images)\n",
        "                   device)           # move img to GPU\n",
        "    yb = model(xb)\n",
        "    _, preds  = torch.max(yb, dim=1)\n",
        "    return dataset.classes[preds[0].item()]\n",
        "\n",
        "for batch in test_dl:\n",
        "  for i in range(10): # Predict first 10 images in the first batch of test_dl\n",
        "    img = batch[0][i]\n",
        "    label = batch[1][i]\n",
        "    print('| Predicted:', predict_label(img, model),\n",
        "          'Label:', dataset.classes[label])\n",
        "  break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFjs2gljVVGy"
      },
      "source": [
        "# Saving the trained parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ixTTthepACqV"
      },
      "outputs": [],
      "source": [
        "torch.save(model.state_dict(), 'scnn4.pth')\n",
        "\n",
        "# To load the saved model:\n",
        "# model2 = move_data(SCNN4(1,4), device)\n",
        "# model2.load_state_dict(torch.load('scnn4.pth'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MHBvkiVVo2A6"
      },
      "outputs": [],
      "source": [
        "# torch.cuda.empty_cache()\n",
        "# total = torch.cuda.get_device_properties(0).total_memory\n",
        "# reserved = torch.cuda.memory_reserved(0)\n",
        "# allocated = torch.cuda.memory_allocated(0)\n",
        "# free = reserved - allocated\n",
        "# print(total, reserved, allocated, free"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}