What are the differences between optimizers in PyTorch?

What are the differences between optimizers in PyTorch?

In PyTorch, optimizers like SGD, Adam, and RMSprop have different update rules and performance characteristics. SGD is simple and effective for many tasks, while Adam adapts the learning rate for each parameter, often leading to faster convergence.

Can you explain how Adam optimizer works?

Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps an exponentially decaying average of past gradients (momentum) and an exponentially decaying average of past squared gradients (adaptive learning rate). This helps in adjusting the learning rate for each parameter.

When should I use RMSprop instead of Adam?

RMSprop is particularly useful for non-stationary objectives, like in recurrent neural networks. It can perform better than Adam in some cases, especially when dealing with noisy gradients.

What are the best practices for choosing an optimizer?

Best practices include starting with Adam for most tasks due to its adaptive nature, then experimenting with SGD for fine-tuning. Always monitor your model's performance and adjust as needed.