What are the key parameters for optimizers in PyTorch?

What are the key parameters for optimizers in PyTorch?

In PyTorch, key parameters for optimizers include learning rate, weight decay, and momentum. Would you like to know more about each?

Yes, please explain the learning rate and its impact.

The learning rate determines how much to adjust the model's weights with respect to the loss gradient. A high learning rate can lead to overshooting the minimum, while a low rate may slow down convergence.

What about weight decay? How does it work?

Weight decay is a regularization technique that adds a penalty to the loss function based on the size of the weights. It helps prevent overfitting by discouraging overly complex models.

Can you give an example of setting these parameters in code?

Sure! Here's a simple example:

import torch.optim as optim
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.0001, momentum=0.9)