def _python_properties_internal(self):
"""Returns dictionary of all python properties."""
# TODO(kathywu): Add support for metrics serialization.
# TODO(kathywu): Synchronize with the keras spec (go/keras-json-spec) once
# the python config serialization has caught up.
metadata = dict(
class_name=type(self.obj).__name__,
name=self.obj.name,
trainable=self.obj.trainable,
expects_training_arg=self.obj._expects_training_arg, # pylint: disable=protected-access
dtype=policy.serialize(self.obj._dtype_policy), # pylint: disable=protected-access
batch_input_shape=getattr(self.obj, '_batch_input_shape', None))
with generic_utils.skip_failed_serialization():
# Store the config dictionary, which may be used when reviving the object.
# When loading, the program will attempt to revive the object from config,
# and if that fails, the object will be revived from the SavedModel.
config = generic_utils.serialize_keras_object(self.obj)['config']
if config is not None:
metadata['config'] = config
if self.obj.input_spec is not None:
# Layer's input_spec has already been type-checked in the property setter.
metadata['input_spec'] = nest.map_structure(
lambda x: generic_utils.serialize_keras_object(x)
if x else None, self.obj.input_spec)
if (self.obj.activity_regularizer is not None
and hasattr(self.obj.activity_regularizer, 'get_config')):
metadata[
'activity_regularizer'] = generic_utils.serialize_keras_object(
self.obj.activity_regularizer)
return metadata
def get_config(obj):
with generic_utils.skip_failed_serialization():
# Store the config dictionary, which may be used when reviving the object.
# When loading, the program will attempt to revive the object from config,
# and if that fails, the object will be revived from the SavedModel.
config = generic_utils.serialize_keras_object(obj)['config']
if config is not None:
return {'config': config}
return {}