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:
- Defines the model using
torch.nn.Linear()
- Defines the loss function using
torch.nn.MSELoss()
- Defines the optimizer using
torch.optim.SGD()
- Generates fake data using
torch.randn()
- Performs a forward pass using
model(x)
- Computes and prints the loss using
loss_fn(y_hat, y)
- Zeroes the gradients using
optimizer.zero_grad()
- Performs a backward pass using
loss.backward()
- Updates the weights using
optimizer.step()
PyTorch also provides APIs for data loading, visualization, and more. For more information, see the PyTorch documentation.
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