optimizer.step()

This function is used in PyTorch to update the parameters of the model during training.

After computing the gradients of the parameters with respect to the loss function, the optimizer.step() function is called to update the parameters using the calculated gradients.

This function updates the parameters in-place, so calling it repeatedly will cause the parameters to be updated multiple times.

Here's an example of how optimizer.step() is used:

# Define optimizer and loss function
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

# Perform forward pass and compute loss
outputs = model(inputs)
loss = criterion(outputs, labels)

# Compute gradients and update parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()

In this example, the optimizer is a stochastic gradient descent optimizer with a learning rate of 0.01. The loss function is the cross-entropy loss.

After performing a forward pass with the inputs and computing the loss, the optimizer is zeroed out using optimizer.zero_grad() to clear any previously computed gradients.

Then, the loss.backward() function is called to compute the gradients of the loss with respect to the parameters. Finally, optimizer.step() is called to update the parameters using the computed gradients.