View source on GitHub |
Exposes custom classes/functions to Keras deserialization internals.
tf.keras.utils.CustomObjectScope(
custom_objects
)
Under a scope with custom_object_scope(objects_dict)
, Keras methods such
as keras.models.load_model()
or
keras.models.model_from_config()
will be able to deserialize any
custom object referenced by a saved config (e.g. a custom layer or metric).
Example:
Consider a custom regularizer my_regularizer
:
layer = Dense(3, kernel_regularizer=my_regularizer)
# Config contains a reference to `my_regularizer`
config = layer.get_config()
...
# Later:
with custom_object_scope({'my_regularizer': my_regularizer}):
layer = Dense.from_config(config)
Args | |
---|---|
custom_objects
|
Dictionary of {str: object} pairs,
where the str key is the object name.
|
Methods
__enter__
__enter__()
__exit__
__exit__(
*args, **kwargs
)