PyTorch optimizer.step() Explained: Updating Model Parameters

This method is typically used in PyTorch to update the parameters of an optimizer during training.

After computing the gradients of the loss function with respect to the model parameters, we call 'optimizer.step()' to update the parameters based on the computed gradients.

For example, if we were using stochastic gradient descent (SGD) as our optimizer, the update rule would be:

new_param = old_param - learning_rate * gradient

where 'old_param' is the current value of the parameter, 'learning_rate' is a hyperparameter that determines the step size, and 'gradient' is the computed gradient with respect to the parameter.

'optimizer.step()' performs this update for all parameters in the model that are being optimized, based on the gradients that were computed in the previous step.

In summary, 'optimizer.step()' updates the model parameters according to the gradients computed in the previous step, using the specific optimization algorithm chosen.