What is the role of Adam optimizer in Keras and how do I choose the best learning rate for it

Question

Can you explain to me What is the role of Adam optimizer in Keras, and how do I choose the best learning rate for it?

Answer 1

Adam optimizer in Keras combines momentum and adaptive learning rates to efficiently optimize deep learning models, and the best learning rate can be found using a learning rate scheduler or a learning rate range test.

Here is the code snippet you can refer to:

In the above code, we are using the following approaches:

• Combines Momentum & RMSProp: Uses moving averages of gradients for adaptive updates.
• Adaptive Learning Rate: Each parameter gets an individual learning rate.
• Works Well for Most Models: Default settings (lr=0.001) are usually optimal.
• Efficient in Sparse Data: Performs well in NLP and sparse gradient problems.
• Handles Noisy Gradients: Reduces oscillations during optimization.

Hence, Adam optimizer in Keras is a versatile and adaptive optimization algorithm, and selecting the best learning rate through experimentation and scheduling can significantly improve model convergence.