How can I tune optimizers in PyTorch for better performance?

How can I tune optimizers in PyTorch for better performance?

Tuning optimizers in PyTorch involves adjusting parameters like learning rate, momentum, and weight decay. What specific optimizer are you using?

I'm using Adam. What parameters should I focus on?

For Adam, focus on the learning rate and beta values. A common starting point is a learning rate of 0.001 and beta values of 0.9 and 0.999.

How do I know if my learning rate is too high or too low?

You can monitor the training loss. If it diverges or oscillates, your learning rate might be too high. If it decreases very slowly, it might be too low.

Are there any tools to help with this tuning process?

Yes, tools like Optuna or Ray Tune can help automate hyperparameter tuning, including optimizer settings.