Deserialization failure during load_model with unregistered custom objects

Automating Custom Object Resolution

To avoid manual registration during load_model, the recommended strategy in Keras 3 is to use the @keras.saving.register_keras_serializable decorator. This decorator registers the class or function in the Keras global serialization registry at the moment the module is imported, ensuring that load_model can resolve the object by its name without requiring an explicit custom_objects dictionary.

import keras

@keras.saving.register_keras_serializable(package="MyCustomOps")
class CustomLayer(keras.layers.Layer):
    def __init__(self, units=32, **kwargs):
        super().__init__(**kwargs)
        self.units = units

    def get_config(self):
        config = super().get_config()
        config.update({"units": self.units})
        return config

Interactive Sessions vs. Module Imports

The Keras .keras format saves the configuration (the class name and its parameters) and the weights, but it does not package the Python source code. This creates a critical distinction in how symbols are handled:

• Module-based Symbols: When a class is defined in a module and decorated with @register_keras_serializable, importing that module in the loading environment automatically populates the registry.
• Interactive/__main__ Symbols: Symbols defined in an interactive session or a script's __main__ scope are transient. Because they lack a permanent module path, they cannot be automatically resolved in a new process. You must either redefine the class in the new session or pass the class explicitly to the custom_objects argument in load_model.

Implementation Steps for Portability

1. Replace Lambda Layers: Lambda layers are difficult to serialize across environments. Replace them with a subclassed keras.layers.Layer that implements get_config.
2. Apply Decorators: Add @keras.saving.register_keras_serializable to all custom layers, loss functions, and metrics.
3. Centralize Definitions: Move custom objects from interactive notebooks into a dedicated Python module (e.g., model_utils.py).
4. Import Before Loading: Ensure the module containing the registered objects is imported before calling keras.models.load_model().

Verification

To verify the fix, attempt to load the model in a completely fresh Python process. If the model fails to load with a ValueError citing an "unknown layer," verify that the module containing the decorated class was imported before the load call.

Diagnostic Note: Are you using Lambda layers specifically? If so, the registration strategy differs as you must map the function name manually via custom_objects during loading, as Lambda layers do not support the registration decorator in the same way as class-based layers.