How do I tune the hyperparameters for SGD?

How do I tune the hyperparameters for SGD?

Tuning hyperparameters for Stochastic Gradient Descent (SGD) involves adjusting parameters like learning rate, momentum, and batch size. What specific aspect are you interested in?

I'm particularly interested in the learning rate. How should I choose it?

The learning rate is crucial; too high can cause divergence, while too low can slow convergence. A common approach is to start with a small value like 0.01 and adjust based on performance.

What methods can I use to find the optimal learning rate?

You can use techniques like grid search, random search, or more advanced methods like learning rate schedules or the learning rate finder method.

Can you explain the learning rate finder method?

Sure! The learning rate finder gradually increases the learning rate during training and plots the loss. You can then choose a learning rate where the loss decreases rapidly.