{"cells":[{"cell_type":"markdown","metadata":{"id":"t4yuhF1aQZpz"},"source":["# Introduction to PyTorch"]},{"cell_type":"markdown","metadata":{"id":"viZAQus4Qesj"},"source":["Pytorch is a popular neural net framework with the following features:\n","*   Automatic differentation\n","*   Compiling computation graphs\n","*   Libraries of algorithms and network primitives. Provides a high-level abstractions for working with neural networks.\n","*   Support for graphics processing units (GPU)\n","\n","\n","\n","\n","\n","\n","In this lesson, we will learn the basics of PyTorch. We will cover the following topics:\n","\n","1. Tensors\n","2. Automatic differentation\n","3. Building a simple neural network\n","4. PyTorch Datasets and DataLoaders\n","5. Visualizing examples from the FashionMNIST Dataset\n","6. Training on CPU\n","7. Training on GPU\n","8. Using pre-trained weights"]},{"cell_type":"markdown","metadata":{"id":"_1xDXq0IPsRL"},"source":["## 1. Tensors\n","Tensors are a specialized data structure very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model's parameters.\n","\n","Tensors are similar to NumPy's ndarrays, except that tensors can run on GPUs or other hardware accelerators."]},{"cell_type":"markdown","metadata":{"id":"x3kqHyo0ES4V"},"source":["### Initializing a Tensor"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UZpcjhjuQeiV"},"outputs":[],"source":["import torch\n","import numpy as np\n","import math\n","\n","# Create a tensor directly from data\n","x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)\n","print(\"x:\", x)\n","\n","# Create a tensor of zeros\n","y = torch.zeros(2, 2)\n","print(\"y:\", y)\n","\n","# Create a tensor of ones\n","z = torch.ones(2, 2)\n","print(\"z:\", z)\n","\n","# Create a random tensor\n","w = torch.rand(2, 2)\n","print(\"w:\", w)\n","\n","# Create a tensor from a NumPy array\n","np_array = np.array([1,2,3])\n","x_np = torch.from_numpy(np_array)\n","print(\"x_np:\", x_np)"]},{"cell_type":"markdown","metadata":{"id":"Gqh-WteaE7v_"},"source":["### Attributes of a tensor\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oBTmCc7OFVmq"},"outputs":[],"source":["tensor = torch.tensor([[1, 2, 3], [3, 4, 5]])\n","\n","print(f\"Shape of tensor: {tensor.shape}\")\n","print(f\"Datatype of tensor: {tensor.dtype}\")\n","print(f\"Device tensor is stored on: {tensor.device}\")"]},{"cell_type":"markdown","metadata":{"id":"6vAVORr4HLq6"},"source":["### Operations on Tensors\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8D0P0NX3HeG7"},"outputs":[],"source":["# Move the tensor to GPU if available\n","if torch.cuda.is_available():\n","  tensor = tensor.to(\"cuda\")\n","\n","# Standard numpy-like indexing and slicing\n","tensor = torch.tensor([[1,2,3], [3,4,5]])\n","print(\"First row: \", tensor[0])\n","print(\"First column: \", tensor[:,0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ppjWALNsICsj"},"outputs":[],"source":["# Matrix multiplication\n","tensor = torch.ones(3, 3)\n","y1 = tensor @ tensor.T\n","y2 = tensor.matmul(tensor.T)\n","print(\"y1: \", y1)\n","print(\"y2: \", y2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"55qJyFAUL4Q9"},"outputs":[],"source":["# Element wise product\n","z1 = tensor * tensor\n","z2 = tensor.mul(tensor)\n","print(\"z1: \", z1)\n","print(\"z2: \", z2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LQHzq5n0Oi4Q"},"outputs":[],"source":["# common functions\n","a = torch.rand(2, 4) * 2 - 1\n","print('Common functions:')\n","print(torch.abs(a))\n","print(torch.ceil(a))\n","print(torch.floor(a))\n","print(torch.clamp(a, -0.5, 0.5))\n","\n","# Reshape\n","a = torch.arange(4.)\n","a_reshaped = torch.reshape(a, (2, 2))\n","b = torch.tensor([[0, 1], [2, 3]])\n","b_reshaped =torch.reshape(b, (-1,))\n","print(\"a_reshaped\", a_reshaped)\n","print(\"b_reshaped\", b_reshaped)\n"]},{"cell_type":"markdown","metadata":{"id":"viMaouQJNnwD"},"source":["### Tensor Broadcasting"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FuI1rTYzNnhe"},"outputs":[],"source":["x1 = torch.tensor([[1, 2, 3], [3, 4, 5]])\n","x2 = torch.tensor([2,2,2])\n","doubled = x1 * x2\n","\n","print(doubled)"]},{"cell_type":"markdown","metadata":{"id":"iwMuEYXxQuam"},"source":["## 2. Automatic Differentation"]},{"cell_type":"markdown","metadata":{"id":"MbGKFLWVUt7u"},"source":["\n","\n","-   Instead of computing backpropagation manually, an autodiff system performs backprop in a completely mechanical way.\n","-   An autodiff system will convert the program into a sequence of primitive operations which have specified routines for computing derivatives.\n","\n","### Distinction of the concepts\n","\n","-  **Backpropagation**: the mathematical algorithm we use to compute the gradient.\n","-  **Automatic differentiation (AutoDiff)**: any software that implements backpropagation.\n","  -   Examples: Autograd, TensorFlow, PyTorch, Jax, etc.\n","  -  **Reverse Mode AD**: A method to get exact derivatives efficiently, by storing information as you go forward that you can reuse as you go backwards\n","\n"]},{"cell_type":"markdown","metadata":{"id":"o7lsF4NLZWMr"},"source":["### 2.1 Autograd\n","\n","* [Autograd](https://github.com/HIPS/autograd) is a Python package for automatic differentiation.\n","\n","From the Autograd Github repository:\n","\n","* Autograd can automatically differentiate native Python and Numpy code.\n","* It can handle a large subset of Python's features, including loops, conditional statements (if/else), recursion and closures.\n","* It can also compute higher-order derivatives.\n","* It uses reverse-mode differentiation (a.k.a. backpropagation) so it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gMXDxNFfVPTT"},"outputs":[],"source":["import autograd.numpy as jnp  # Import thinly-wrapped numpy\n","from autograd import grad  # Basicallly the only autograd function you need"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GYNkJ8DbZjuD"},"outputs":[],"source":["# Define a function like normal, using Python and (autograd's) NumPy\n","def tanh(x):\n","    y = jnp.exp(-x)\n","    return (1.0 - y) / (1.0 + y)\n","\n","\n","# Create a *function* that computes the gradient of tanh\n","grad_tanh = grad(tanh)\n","\n","# Evaluate the gradient at x = 1.0\n","print(grad_tanh(1.0))\n","\n","# Compare to numeric gradient computed using finite differences\n","print((tanh(1.0001) - tanh(0.9999)) / 0.0002)"]},{"cell_type":"markdown","metadata":{"id":"SAwzoyXFZnCE"},"source":["### 2.2 PyTorch Automatic Differentiation"]},{"cell_type":"markdown","metadata":{"id":"tOCpyJkRuJ19"},"source":["To compute the gradients of the loss function, PyTorch has a built-in differentiation engine called `torch.autograd` that traces the computation dynamically at runtime. It supports automatic computation of gradient for any computational graph.\n","\n","- In the forward pass, `torch.autograd` runs the requested operation to compute the resulting tensor. For each primitive operation, data and the operation's gradient function are stored in the computation graph.\n","- In the backward pass, `torch.autograd` computes the gradients using the gradient function of each primitive operation and accumulates gradients using chain rule.\n","\n","\n","\n","Consider the one-layer neural network below:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"A4cKx_m9uu5C"},"outputs":[],"source":["import torch\n","\n","x = torch.ones(5)  # input tensor\n","y = torch.zeros(3)  # expected output\n","w = torch.randn(5, 3, requires_grad=True)\n","b = torch.randn(3, requires_grad=True)\n","z = torch.matmul(x, w)+b\n","loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y)"]},{"cell_type":"markdown","metadata":{"id":"N3uudNFUu0qJ"},"source":["The code above defines the following computation graph:\n","\n","![Screenshot 2024-04-24 at 8.31.20 AM.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"c7lT5RhAvheS"},"source":["Image source: https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html\n","\n","We need to be able to compute the gradients of the loss function with respect to the variables `w `and `b`, so we've set the `requires_grad` property of those tensors.\n","\n","**Computing Gradients**: To compute `dw`, `db`, we call `loss.backward()` and retrieve the values from `w.grad` and `b.grad`.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3qdirD4av8O4"},"outputs":[],"source":["loss.backward()\n","print(w.grad)\n","print(b.grad)"]},{"cell_type":"markdown","metadata":{"id":"CQqQgZEdw6IY"},"source":["**Disabling gradient tracking** : When we have trained the model and just want to run inference on the test data, we only want to do forward computations through the network. We can stop tracking computations by surrounding our computation code with `torch.no_grad()` block. This will help reducing memory consumption for computations. Another case where you might want to disable gradient tracking is to mark some parameters in the neural network as frozen parameters."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s9KfQGEax5la"},"outputs":[],"source":["z = torch.matmul(x, w)+b\n","print(z.requires_grad)\n","\n","with torch.no_grad():\n","    z = torch.matmul(x, w)+b\n","print(z.requires_grad)"]},{"cell_type":"markdown","metadata":{"id":"yzqB8CssUXDn"},"source":["## 3. Building a simple neural network"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"22JJJ3d9rBrv"},"outputs":[],"source":["# Create an instance of the neural network\n","import torch.nn as nn\n","import torch.optim as optim\n","\n","# Define the neural network\n","class SimpleNet(nn.Module):\n","    def __init__(self):\n","        super(SimpleNet, self).__init__()\n","        self.fc1 = nn.Linear(2, 8, bias=False)\n","        self.fc2 = nn.Linear(8, 1)\n","\n","    def forward(self, x):\n","        x = torch.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return x\n","\n","net = SimpleNet()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yh-P4VNDQx0h"},"outputs":[],"source":["print(net)\n","\n","# Define the loss function and the optimizer\n","criterion = nn.MSELoss()\n","optimizer = optim.SGD(net.parameters(), lr=0.01)\n","\n","# Prepare some dummy data and labels\n","data = torch.tensor([[1., 2.], [3., 4.]], dtype=torch.float32)\n","labels = torch.tensor([[0.], [1.]], dtype=torch.float32)\n","\n","# Train the neural network\n","for epoch in range(500):\n","    # Forward pass\n","    outputs = net(data)\n","    loss = criterion(outputs, labels)\n","\n","    # IMPORTANT: Zero the gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","    optimizer.step()\n","\n","    # Print the loss for this epoch\n","    if (epoch + 1) % 100 == 0:\n","        print(f\"Epoch [{epoch + 1}/{500}], Loss: {loss.item():.4f}\")\n"]},{"cell_type":"markdown","metadata":{"id":"CL495WoAR_Kw"},"source":["## 4. PyTorch Datasets and DataLoaders"]},{"cell_type":"markdown","metadata":{"id":"Kw0liepaSDDT"},"source":["Datasets and DataLoaders are essential components for handling data in PyTorch. A Dataset is a collection of data, and a DataLoader helps to efficiently load the data in batches during training.\n","\n","In this example, we'll use the FashionMNIST dataset, which contains 60,000 training images and 10,000 testing images of 10 different clothing items."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"urI46YyRSL0I"},"outputs":[],"source":["import torchvision\n","import torchvision.transforms as transforms\n","\n","# Define data transformations\n","transform = transforms.Compose([\n","    transforms.Resize(28),\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.5,), (0.5,))\n","])\n","\n","# Load the FashionMNIST dataset\n","train_dataset = torchvision.datasets.FashionMNIST(\n","    root='./data', train=True, download=True, transform=transform\n",")\n","\n","test_dataset = torchvision.datasets.FashionMNIST(\n","    root='./data', train=False, download=True, transform=transform\n",")\n","\n","# Create DataLoaders for train and test datasets\n","train_loader = torch.utils.data.DataLoader(\n","    train_dataset, batch_size=512, shuffle=True, num_workers=2\n",")\n","\n","test_loader = torch.utils.data.DataLoader(\n","    test_dataset, batch_size=512, shuffle=False, num_workers=2\n",")\n","\n","print(\"Num training examples: {}\".format(len(train_dataset)))\n","print(\"Num test examples: {}\".format(len(test_dataset)))\n","\n","\n","# List of class labels\n","classes = [\n","    'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n","    'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'\n","]"]},{"cell_type":"markdown","metadata":{"id":"F_G06d-GWvGw"},"source":["## 5. Visualizing Examples from the FashionMNIST Dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WvIF1OOuWzqG"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Function to unnormalize and display an image\n","def imshow(img):\n","    img = img / 2 + 0.5  # Unnormalize\n","    npimg = img.numpy()\n","    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n","    plt.show()\n","\n","# Get a batch of training data\n","dataiter = iter(train_loader)\n","images, labels = next(dataiter)\n","\n","# Display the images in a grid along with their labels\n","imshow(torchvision.utils.make_grid(images[:16]))\n","print(\" -- \".join(f\"{classes[labels[j]]}\" for j in range(8)))\n","print(\" -- \".join(f\"{classes[labels[j]]}\" for j in range(8,16)))"]},{"cell_type":"markdown","metadata":{"id":"3N2pcw66SKm1"},"source":["With the dataset loaded and the DataLoader created, we can now train our neural network using the FashionMNIST dataset. Let's modify our previous SimpleNet example to handle 28x28 images and 10 output classes."]},{"cell_type":"markdown","metadata":{"id":"Zq7XH2nwUlA_"},"source":["## 6. Training on the CPU = Slow!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yMrm_5c7RTdM"},"outputs":[],"source":["import torch.nn.functional as F\n","\n","# Define the neural network for FashionMNIST\n","class FashionMNISTNet(nn.Module):\n","    def __init__(self):\n","        super(FashionMNISTNet, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 16, 3)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.conv2 = nn.Conv2d(16, 32, 3)\n","        self.fc1 = nn.Linear(32 * 5 * 5, 128)\n","        self.fc2 = nn.Linear(128, 64)\n","        self.fc3 = nn.Linear(64, 10)\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = x.view(-1, 32 * 5 * 5)\n","        x = F.relu(self.fc1(x))\n","        x = F.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x\n","\n","# Create an instance of the neural network\n","net = FashionMNISTNet()\n","print(net)\n","\n","# Define the loss function and the optimizer\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n","\n","# Train the neural network using the FashionMNIST dataset\n","num_epochs = 5\n","for epoch in range(num_epochs):\n","    running_loss = 0.0\n","    for i, (inputs, labels) in enumerate(train_loader, 0):\n","        # Zero the gradients\n","        optimizer.zero_grad()\n","\n","        # Forward pass\n","        outputs = net(inputs)\n","\n","        # Compute the loss\n","        loss = criterion(outputs, labels)\n","\n","        # Backward pass\n","        loss.backward()\n","        optimizer.step()\n","\n","        # Update the running loss\n","        running_loss += loss.item()\n","\n","    # Print the average loss for this epoch\n","    avg_loss = running_loss / (i + 1)\n","    print(f\"Epoch [{epoch + 1}/{num_epochs}], Loss: {avg_loss:.4f}\")\n","print(\"Training finished.\")"]},{"cell_type":"markdown","metadata":{"id":"f_hfcVZsUqhg"},"source":["## 7. Training on the GPU = Faster!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n4F3cP8tUelq"},"outputs":[],"source":["import torch.nn.functional as F\n","\n","# Define the neural network for FashionMNIST\n","class FashionMNISTNet(nn.Module):\n","    def __init__(self):\n","        super(FashionMNISTNet, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 16, 3)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.conv2 = nn.Conv2d(16, 32, 3)\n","        self.fc1 = nn.Linear(32 * 5 * 5, 128)\n","        self.fc2 = nn.Linear(128, 64)\n","        self.fc3 = nn.Linear(64, 10)\n","\n","    def forward(self, x):\n","        x = self.pool(F.relu(self.conv1(x)))\n","        x = self.pool(F.relu(self.conv2(x)))\n","        x = x.view(-1, 32 * 5 * 5)\n","        x = F.relu(self.fc1(x))\n","        x = F.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x\n","\n","# IMPORTANT CODE BELOW\n","# Check if GPU is available\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(\"Using device:\", device)\n","\n","# Create an instance of the neural network\n","net = FashionMNISTNet()\n","print(net)\n","\n","# Move the model to the GPU if available\n","net.to(device)\n","\n","# Define the loss function and the optimizer\n","criterion = nn.CrossEntropyLoss()\n","optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n","\n","# Train the neural network using the FashionMNIST dataset\n","num_epochs = 5\n","for epoch in range(num_epochs):\n","    running_loss = 0.0\n","    for i, (inputs, labels) in enumerate(train_loader, 0):\n","\n","        # Move the inputs and labels to the GPU if available\n","        inputs = inputs.to(device)\n","        labels = labels.to(device)\n","\n","        # Zero the gradients\n","        optimizer.zero_grad()\n","\n","        # Forward pass\n","        outputs = net(inputs)\n","\n","        # Compute the loss\n","        loss = criterion(outputs, labels)\n","\n","        # Backward pass\n","        loss.backward()\n","        optimizer.step()\n","\n","        # Update the running loss\n","        running_loss += loss.item()\n","\n","    # Print the average loss for this epoch\n","    avg_loss = running_loss / (i + 1)\n","    print(f\"Epoch [{epoch + 1}/{num_epochs}], Loss: {avg_loss:.4f}\")\n","print(\"Training finished.\")"]},{"cell_type":"markdown","metadata":{"id":"utNlnm2eTUNi"},"source":["Now that we have trained our neural network, let's evaluate its performance on the test dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_XdttlMDSh4-"},"outputs":[],"source":["# Test the neural network\n","correct = 0\n","total = 0\n","\n","# Set the model to evaluation mode\n","net.eval()\n","\n","# Disable gradient calculation\n","with torch.no_grad():\n","    for inputs, labels in test_loader:\n","\n","        # Move the inputs and labels to the GPU if available\n","        inputs = inputs.to(device)\n","        labels = labels.to(device)\n","\n","        # Forward pass\n","        outputs = net(inputs)\n","\n","        # Get the predicted class\n","        _, predicted = torch.max(outputs.data, 1)\n","\n","        # Update the total number of samples and correct predictions\n","        total += labels.size(0)\n","        correct += (predicted == labels).sum().item()\n","\n","# Calculate the accuracy\n","accuracy = 100 * correct / total\n","print(f\"Accuracy: {accuracy:.2f}%\")"]},{"cell_type":"markdown","metadata":{"id":"pP9kK2tNXoze"},"source":["Not bad!\n","\n","Let's inspect number of total parameters and training parameters in the model:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M4j7D84vXppz"},"outputs":[],"source":["total_params = sum(p.numel() for p in net.parameters())\n","print(f'{total_params:,} total parameters.')\n","total_trainable_params = sum(\n","    p.numel() for p in net.parameters() if p.requires_grad)\n","print(f'{total_trainable_params:,} training parameters.')\n"]},{"cell_type":"markdown","metadata":{"id":"bihhzYrwNQXl"},"source":["Now that we have a trained model, if we want to adapt the model to another dataset with only 5 classes, we can freeze earlier layers and only train on the last fully-connected layer."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"knv8XBrYMV61"},"outputs":[],"source":["# Freeze earlier layers\n","for param in net.parameters():\n","    param.requires_grad = False\n","\n","n_inputs = net.fc3.in_features\n","n_classes = 5\n","net.fc3 = nn.Linear(n_inputs, n_classes)\n","total_params = sum(p.numel() for p in net.parameters())\n","print(f'{total_params:,} total parameters.')\n","total_trainable_params = sum(\n","    p.numel() for p in net.parameters() if p.requires_grad)\n","print(f'{total_trainable_params:,} training parameters.')"]},{"cell_type":"markdown","metadata":{"id":"BI6bxZK_Nw7c"},"source":["## 8. Pre-trained weights\n","\n","PyTorch has many pretrained models we can use. All of these models have been trained on Imagenet which consists of millions of images across 1000 categories. We want to freeze the early layers of these pretrained models and replace the classification module with our own.\n","\n","\n","PyTorch API for using pre-trained weights: https://pytorch.org/vision/stable/models.html\n","\n","The approach for using a pre-trained image recognition model is well-established:\n","\n","1. Load in pre-trained weights from a network trained on a large dataset\n","2. Freeze all the weights in the lower (convolutional) layers\n","    * Layers to freeze can be adjusted depending on similarity of task to large training dataset\n","3. Replace the classifier (fully connected) part of the network with a custom classifier\n","    * Number of outputs must be set equal to the number of classes\n","4. Train only the custom classifier (fully connected) layers for the task\n","    * Optimizer model classifier for smaller dataset\n","\n","\n","We will demonstrate an example of loading a pre-trained Resnet model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EScgp3xhOsht"},"outputs":[],"source":["from torchvision import models\n","\n","model = models.resnet50(pretrained=True)\n","\n","print(model)\n","\n","for param in model.parameters():\n","    param.requires_grad = False\n","\n","n_inputs = model.fc.in_features\n","model.fc = nn.Sequential(\n","    nn.Linear(n_inputs, 256), nn.ReLU(), nn.Dropout(0.2),\n","    nn.Linear(256, n_classes), nn.LogSoftmax(dim=1))"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.13.2"}},"nbformat":4,"nbformat_minor":0}