Keras 2 to Keras 3 Serialization and Multi-Backend Interoperability

Model Portability across Keras Versions

Migrating a production application from Keras 2 to Keras 3 involves transitioning to a multi-backend architecture that supports TensorFlow, PyTorch, and JAX. To ensure consistency during this migration, the .keras zip-based archive format is the recommended standard for serialization over legacy HDF5 files.

Backend Compatibility Constraints

While the .keras format aims for cross-version portability, models utilizing framework-specific operations (such as non-Keras TensorFlow ops) may encounter failures when loaded into a different backend environment in Keras 3. Additionally, custom layers and loss functions must be handled via the custom_objects dictionary to prevent loading errors.

Given these constraints, there is uncertainty regarding the exact behavior of weight initialization and tensor handling when a model trained in Keras 2 is loaded into a Keras 3 environment using a non-TensorFlow backend.

• Does loading a Keras 2 model into Keras 3 with a PyTorch or JAX backend introduce numerical drift in output tensors?
• What is the recommended verification process to ensure functional parity across different backends without retraining?