def __init__(self,
regularizer=None,
activity_regularizer=None,
use_operator=False,
var_name='v',
**kwargs):
"""Initializes the MultiplyLayer.
Args:
regularizer: The weight regularizer on the scalar variable.
activity_regularizer: The activity regularizer.
use_operator: If True, add using the * operator. If False, add using
tf.multiply.
var_name: The name of the variable. It can be useful to pass a name other
than 'v', to test having the attribute name (self.v) being different
from the variable name.
**kwargs: Passed to AssertTypeLayer constructor.
"""
self._regularizer = regularizer
if isinstance(regularizer, dict):
self._regularizer = regularizers.deserialize(
regularizer, custom_objects=globals())
self._activity_regularizer = activity_regularizer
if isinstance(activity_regularizer, dict):
self._activity_regularizer = regularizers.deserialize(
activity_regularizer, custom_objects=globals())
self._use_operator = use_operator
self._var_name = var_name
super(MultiplyLayer,
self).__init__(activity_regularizer=self._activity_regularizer,
**kwargs)
def _init_from_metadata(cls, metadata):
"""Create revived layer from metadata stored in the SavedModel proto."""
init_args = dict(
name=metadata['name'],
trainable=metadata['trainable'])
if metadata.get('dtype') is not None:
init_args['dtype'] = metadata['dtype']
if metadata.get('batch_input_shape') is not None:
init_args['batch_input_shape'] = metadata['batch_input_shape']
revived_obj = cls(**init_args)
with trackable.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
revived_obj._expects_training_arg = metadata['expects_training_arg']
config = metadata.get('config')
if generic_utils.validate_config(config):
revived_obj._config = config
if metadata.get('input_spec') is not None:
revived_obj.input_spec = recursively_deserialize_keras_object(
metadata['input_spec'],
module_objects={'InputSpec': input_spec.InputSpec})
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
if metadata.get('_is_feature_layer') is not None:
revived_obj._is_feature_layer = metadata['_is_feature_layer']
if metadata.get('stateful') is not None:
revived_obj.stateful = metadata['stateful']
# pylint:enable=protected-access
return revived_obj, _revive_setter
def _init_from_metadata(cls, metadata):
"""Create revived layer from metadata stored in the SavedModel proto."""
init_args = dict(name=metadata['name'],
trainable=metadata['trainable'])
if metadata.get('dtype') is not None:
init_args['dtype'] = metadata['dtype']
if metadata.get('batch_input_shape') is not None:
init_args['batch_input_shape'] = metadata['batch_input_shape']
revived_obj = cls(**init_args)
with trackable.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
revived_obj._expects_training_arg = metadata[
'expects_training_arg']
if metadata.get('config') is not None:
revived_obj._config = metadata['config']
if metadata.get('input_spec') is not None:
revived_obj.input_spec = recursively_deserialize_keras_object(
metadata['input_spec'],
module_objects={'InputSpec': input_spec.InputSpec})
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
if metadata.get('_is_feature_layer') is not None:
revived_obj._is_feature_layer = metadata['_is_feature_layer']
# Store attributes revived from SerializedAttributes in a un-tracked
# dictionary. The attributes are the ones listed in CommonEndpoints or
# "keras_api" for keras-specific attributes.
revived_obj._serialized_attributes = {'metadata': metadata}
# pylint:enable=protected-access
return revived_obj, _revive_setter
def _init_from_metadata(cls, metadata):
"""Create revived network from metadata stored in the SavedModel proto."""
revived_obj = cls(name=metadata['name'])
with trackable.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
if metadata.get('dtype') is not None:
revived_obj._dtype = metadata['dtype']
revived_obj.trainable = metadata['trainable']
revived_obj._expects_training_arg = metadata[
'expects_training_arg']
if metadata.get('config') is not None:
revived_obj._config = metadata['config']
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
# Store attributes revived from SerializedAttributes in a un-tracked
# dictionary. The attributes are the ones listed in CommonEndpoints or
# "keras_api" for keras-specific attributes.
revived_obj._serialized_attributes = {}
# pylint:enable=protected-access
return revived_obj
def _set_network_attributes_from_metadata(revived_obj):
"""Sets attributes recorded in the metadata."""
with trackable.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
metadata = revived_obj._serialized_attributes['metadata']
if metadata.get('dtype') is not None:
revived_obj._dtype = metadata['dtype']
revived_obj.trainable = metadata['trainable']
revived_obj._expects_training_arg = metadata['expects_training_arg']
if metadata.get('config') is not None:
revived_obj._config = metadata['config']
if metadata.get('activity_regularizer') is not None:
revived_obj.activity_regularizer = regularizers.deserialize(
metadata['activity_regularizer'])
def get(config):
return regularizers.deserialize(config=config,
custom_objects={
"ctx_cond_neg_ent": ctx_cond_neg_ent,
"l1_l2_tv": l1_l2_tv,
})
