Which optimizer is better: Adam or RMSprop?

I'm trying to decide between Adam and RMSprop for my neural network. Which one is better?

Both Adam and RMSprop are popular optimizers, but their effectiveness can depend on the specific problem. Adam adapts the learning rate for each parameter, which can lead to faster convergence in many cases.

What are the main differences in how they update weights?

Adam uses both the first moment (mean) and the second moment (variance) of the gradients, while RMSprop only uses the second moment. This allows Adam to have more adaptive learning rates.

Are there any scenarios where RMSprop might outperform Adam?

Yes, RMSprop can perform better in some cases, especially in non-stationary objectives or when dealing with noisy gradients. It's worth experimenting with both.

How do I implement both optimizers in TensorFlow?

In TensorFlow, you can easily switch between them by using tf.keras.optimizers.Adam() and tf.keras.optimizers.RMSprop(). Just specify the optimizer when compiling your model.