Optimizers are an essential part of everyone working in machine learning.
We all know that optimizers determine how the model will converge to the loss function during gradient descent. Therefore, using the right optimizer can increase the performance and training efficiency of the model.
In addition to the classic articles, many books explain the principles behind optimizers in simple terms.
However, I recently discovered that the performance of Keras 3 optimizers does not exactly match the mathematical algorithms described in these books, which made me a little anxious. I was worried that I might not understand something or that updates in the latest version of Keras would affect the optimizers.
So, I went through the source code of several common optimizers in Keras 3 and reviewed their use cases. Now I want to share this knowledge to save you time and help you master Keras 3 optimizers faster.
If you are not very familiar with the latest changes in Keras 3, here is a quick summary: Keras 3 integrates TensorFlow, PyTorch and JAX, allowing us to use cutting-edge deep learning frameworks easily through the Keras APIs.
