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TensorFlow 1 version
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View source on GitHub
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Exposes custom classes/functions to Keras deserialization internals.
tf.keras.utils.CustomObjectScope(
*args
)
Used in the notebooks
| Used in the guide |
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Under a scope with custom_object_scope(objects_dict), Keras methods such
as tf.keras.models.load_model or tf.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 = layer.get_config() # Config contains a reference to `my_regularizer`
...
# Later:
with custom_object_scope({'my_regularizer': my_regularizer}):
layer = Dense.from_config(config)
Arguments | |
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*args
|
Dictionary or dictionaries of {name: object} pairs.
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Methods
__enter__
__enter__()
__exit__
__exit__(
*args, **kwargs
)