TensorFlow 1 version | View source on GitHub |
Turns the serialized form of a Keras object back into an actual object.
tf.keras.utils.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.
Args | |
---|---|
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. |
Returns | |
---|---|
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.