python-pytorchHow can a Python engineer use PyTorch?

PyTorch is a popular open source library for deep learning used by Python engineers. It allows developers to quickly and easily build, train, and deploy deep learning models. With PyTorch, engineers can build and train neural networks with dynamic computational graphs.

For example, the following code block shows a simple linear regression model built with PyTorch:

import torch

# Define the model
model = torch.nn.Linear(in_features=1, out_features=1)

# Define the loss function
loss_fn = torch.nn.MSELoss()

# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Train the model
for i in range(1000):
    # Generate fake data
    x = torch.randn(1)
    y = 3*x + 2

    # Forward pass
    y_hat = model(x)

    # Compute and print loss
    loss = loss_fn(y_hat, y)
    print(f'Iteration {i+1}: Loss = {loss.item():.4f}')

    # Zero the gradients
    optimizer.zero_grad()

    # Backward pass
    loss.backward()

    # Update weights
    optimizer.step()

The code above trains a linear regression model using PyTorch. It does the following:

1. Defines the model using torch.nn.Linear()
2. Defines the loss function using torch.nn.MSELoss()
3. Defines the optimizer using torch.optim.SGD()
4. Generates fake data using torch.randn()
5. Performs a forward pass using model(x)
6. Computes and prints the loss using loss_fn(y_hat, y)
7. Zeroes the gradients using optimizer.zero_grad()
8. Performs a backward pass using loss.backward()
9. Updates the weights using optimizer.step()

PyTorch also provides APIs for data loading, visualization, and more. For more information, see the PyTorch documentation.

Edit this code on GitHub