What is a learning rate scheduler in PyTorch?

What is a learning rate scheduler in PyTorch?

A learning rate scheduler in PyTorch is a tool that adjusts the learning rate during training to improve model performance.

Why is it important to adjust the learning rate?

Adjusting the learning rate helps in converging faster and avoiding local minima, leading to better model accuracy.

Can you give me an example of how to implement one?

Sure! You can use torch.optim.lr_scheduler to create a scheduler. For example, scheduler = StepLR(optimizer, step_size=30, gamma=0.1).

How do I choose the right learning rate for my model?

You can start with a common value like 0.001 and use techniques like learning rate range test to find the optimal rate.