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Do you want the speed and functional power of JAX without losing the high-level convenience of model.fit? In this video, Google ML Developer Advocate Yufeng Guo (@yufengg) explains how Keras implements the principle of Progressive Disclosure of Complexity.
Learn how to take full control of your learning algorithms by overriding the train_step() and test_step() methods while keeping access to built-in callbacks, distribution support, and evaluation tools.
What You’ll Learn:
– Why override train_step instead of writing a loop from scratch?
– Understanding how to handle trainable variables, non-trainable variables, and optimizer states in a functional environment.
– Creating a compute_loss_and_updates function to manage forward passes and auxiliary data.
– Using jax.value_and_grad to compute gradients and losses simultaneously.
– Updating evaluation metrics using stateless_update_state.
Chapters:
0:00 – Introduction & The Default model.fit()
0:18 – Customizing Keras Training Loops
0:46 – Overriding train_step()
1:14 – Setting up the JAX Backend
1:26 – The Stateless train_step
2:11 – Stateless Loss Computation
3:04 – Taking Gradients in train_step
4:06 – How to pass around non-trainable variables
4:43 – Updating the Model Weights
5:05 – Handling Metrics
5:21 – Custom Evaluation Loops (overriding test_step)
Resources:
Complete Code Example →https://goo.gle/4eeSvlD
Keras Documentation → https://goo.gle/42Ebpv0
Keras Developer Guides →https://goo.gle/4um97N3
Subscribe to Google for Developers → https://goo.gle/developers
Speaker: Yufeng Guo
Products Mentioned: Google AI Read More Google for Developers
