tf.keras.legacy.saving.deserialize_keras_object

Turns the serialized form of a Keras object back into an actual object.

tf.keras.legacy.saving.deserialize_keras_object(
    identifier,
    module_objects=None,
    custom_objects=None,
    printable_module_name='object'
)

This function is for mid-level library implementers rather than end users.

Importantly, this utility requires you to provide the dict of module_objects to use for looking up the object config; this is not populated by default. If you need a deserialization utility that has preexisting knowledge of built-in Keras objects, use e.g. keras.layers.deserialize(config), keras.metrics.deserialize(config), etc.

Calling deserialize_keras_object while underneath the SharedObjectLoadingScope context manager will cause any already-seen shared objects to be returned as-is rather than creating a new object.

identifier the serialized form of the object.
module_objects A dictionary of built-in objects to look the name up in. Generally, module_objects is provided by midlevel library implementers.
custom_objects A dictionary of custom objects to look the name up in. Generally, custom_objects is provided by the end user.
printable_module_name A human-readable string representing the type of the object. Printed in case of exception.

The deserialized object.

Example:

A mid-level library implementer might want to implement a utility for retrieving an object from its config, as such:

def deserialize(config, custom_objects=None):
   return deserialize_keras_object(
     identifier,
     module_objects=globals(),
     custom_objects=custom_objects,
     name="MyObjectType",
   )

This is how e.g. keras.layers.deserialize() is implemented.