192 lines
7.3 KiB
Python
192 lines
7.3 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Keras initializer serialization / deserialization."""
|
|
|
|
import threading
|
|
|
|
from tensorflow.python import tf2
|
|
from tensorflow.python.keras.initializers import initializers_v1
|
|
from tensorflow.python.keras.initializers import initializers_v2
|
|
from tensorflow.python.keras.utils import generic_utils
|
|
from tensorflow.python.keras.utils import tf_inspect as inspect
|
|
from tensorflow.python.ops import init_ops
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
# LOCAL.ALL_OBJECTS is meant to be a global mutable. Hence we need to make it
|
|
# thread-local to avoid concurrent mutations.
|
|
LOCAL = threading.local()
|
|
|
|
|
|
def populate_deserializable_objects():
|
|
"""Populates dict ALL_OBJECTS with every built-in initializer.
|
|
"""
|
|
global LOCAL
|
|
if not hasattr(LOCAL, 'ALL_OBJECTS'):
|
|
LOCAL.ALL_OBJECTS = {}
|
|
LOCAL.GENERATED_WITH_V2 = None
|
|
|
|
if LOCAL.ALL_OBJECTS and LOCAL.GENERATED_WITH_V2 == tf2.enabled():
|
|
# Objects dict is already generated for the proper TF version:
|
|
# do nothing.
|
|
return
|
|
|
|
LOCAL.ALL_OBJECTS = {}
|
|
LOCAL.GENERATED_WITH_V2 = tf2.enabled()
|
|
|
|
# Compatibility aliases (need to exist in both V1 and V2).
|
|
LOCAL.ALL_OBJECTS['ConstantV2'] = initializers_v2.Constant
|
|
LOCAL.ALL_OBJECTS['GlorotNormalV2'] = initializers_v2.GlorotNormal
|
|
LOCAL.ALL_OBJECTS['GlorotUniformV2'] = initializers_v2.GlorotUniform
|
|
LOCAL.ALL_OBJECTS['HeNormalV2'] = initializers_v2.HeNormal
|
|
LOCAL.ALL_OBJECTS['HeUniformV2'] = initializers_v2.HeUniform
|
|
LOCAL.ALL_OBJECTS['IdentityV2'] = initializers_v2.Identity
|
|
LOCAL.ALL_OBJECTS['LecunNormalV2'] = initializers_v2.LecunNormal
|
|
LOCAL.ALL_OBJECTS['LecunUniformV2'] = initializers_v2.LecunUniform
|
|
LOCAL.ALL_OBJECTS['OnesV2'] = initializers_v2.Ones
|
|
LOCAL.ALL_OBJECTS['OrthogonalV2'] = initializers_v2.Orthogonal
|
|
LOCAL.ALL_OBJECTS['RandomNormalV2'] = initializers_v2.RandomNormal
|
|
LOCAL.ALL_OBJECTS['RandomUniformV2'] = initializers_v2.RandomUniform
|
|
LOCAL.ALL_OBJECTS['TruncatedNormalV2'] = initializers_v2.TruncatedNormal
|
|
LOCAL.ALL_OBJECTS['VarianceScalingV2'] = initializers_v2.VarianceScaling
|
|
LOCAL.ALL_OBJECTS['ZerosV2'] = initializers_v2.Zeros
|
|
|
|
# Out of an abundance of caution we also include these aliases that have
|
|
# a non-zero probability of having been included in saved configs in the past.
|
|
LOCAL.ALL_OBJECTS['glorot_normalV2'] = initializers_v2.GlorotNormal
|
|
LOCAL.ALL_OBJECTS['glorot_uniformV2'] = initializers_v2.GlorotUniform
|
|
LOCAL.ALL_OBJECTS['he_normalV2'] = initializers_v2.HeNormal
|
|
LOCAL.ALL_OBJECTS['he_uniformV2'] = initializers_v2.HeUniform
|
|
LOCAL.ALL_OBJECTS['lecun_normalV2'] = initializers_v2.LecunNormal
|
|
LOCAL.ALL_OBJECTS['lecun_uniformV2'] = initializers_v2.LecunUniform
|
|
|
|
if tf2.enabled():
|
|
# For V2, entries are generated automatically based on the content of
|
|
# initializers_v2.py.
|
|
v2_objs = {}
|
|
base_cls = initializers_v2.Initializer
|
|
generic_utils.populate_dict_with_module_objects(
|
|
v2_objs,
|
|
[initializers_v2],
|
|
obj_filter=lambda x: inspect.isclass(x) and issubclass(x, base_cls))
|
|
for key, value in v2_objs.items():
|
|
LOCAL.ALL_OBJECTS[key] = value
|
|
# Functional aliases.
|
|
LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value
|
|
else:
|
|
# V1 initializers.
|
|
v1_objs = {
|
|
'Constant': init_ops.Constant,
|
|
'GlorotNormal': init_ops.GlorotNormal,
|
|
'GlorotUniform': init_ops.GlorotUniform,
|
|
'Identity': init_ops.Identity,
|
|
'Ones': init_ops.Ones,
|
|
'Orthogonal': init_ops.Orthogonal,
|
|
'VarianceScaling': init_ops.VarianceScaling,
|
|
'Zeros': init_ops.Zeros,
|
|
'HeNormal': initializers_v1.HeNormal,
|
|
'HeUniform': initializers_v1.HeUniform,
|
|
'LecunNormal': initializers_v1.LecunNormal,
|
|
'LecunUniform': initializers_v1.LecunUniform,
|
|
'RandomNormal': initializers_v1.RandomNormal,
|
|
'RandomUniform': initializers_v1.RandomUniform,
|
|
'TruncatedNormal': initializers_v1.TruncatedNormal,
|
|
}
|
|
for key, value in v1_objs.items():
|
|
LOCAL.ALL_OBJECTS[key] = value
|
|
# Functional aliases.
|
|
LOCAL.ALL_OBJECTS[generic_utils.to_snake_case(key)] = value
|
|
|
|
# More compatibility aliases.
|
|
LOCAL.ALL_OBJECTS['normal'] = LOCAL.ALL_OBJECTS['random_normal']
|
|
LOCAL.ALL_OBJECTS['uniform'] = LOCAL.ALL_OBJECTS['random_uniform']
|
|
LOCAL.ALL_OBJECTS['one'] = LOCAL.ALL_OBJECTS['ones']
|
|
LOCAL.ALL_OBJECTS['zero'] = LOCAL.ALL_OBJECTS['zeros']
|
|
|
|
|
|
# For backwards compatibility, we populate this file with the objects
|
|
# from ALL_OBJECTS. We make no guarantees as to whether these objects will
|
|
# using their correct version.
|
|
populate_deserializable_objects()
|
|
globals().update(LOCAL.ALL_OBJECTS)
|
|
|
|
# Utility functions
|
|
|
|
|
|
@keras_export('keras.initializers.serialize')
|
|
def serialize(initializer):
|
|
return generic_utils.serialize_keras_object(initializer)
|
|
|
|
|
|
@keras_export('keras.initializers.deserialize')
|
|
def deserialize(config, custom_objects=None):
|
|
"""Return an `Initializer` object from its config."""
|
|
populate_deserializable_objects()
|
|
return generic_utils.deserialize_keras_object(
|
|
config,
|
|
module_objects=LOCAL.ALL_OBJECTS,
|
|
custom_objects=custom_objects,
|
|
printable_module_name='initializer')
|
|
|
|
|
|
@keras_export('keras.initializers.get')
|
|
def get(identifier):
|
|
"""Retrieve a Keras initializer by the identifier.
|
|
|
|
The `identifier` may be the string name of a initializers function or class (
|
|
case-sensitively).
|
|
|
|
>>> identifier = 'Ones'
|
|
>>> tf.keras.initializers.deserialize(identifier)
|
|
<...keras.initializers.initializers_v2.Ones...>
|
|
|
|
You can also specify `config` of the initializer to this function by passing
|
|
dict containing `class_name` and `config` as an identifier. Also note that the
|
|
`class_name` must map to a `Initializer` class.
|
|
|
|
>>> cfg = {'class_name': 'Ones', 'config': {}}
|
|
>>> tf.keras.initializers.deserialize(cfg)
|
|
<...keras.initializers.initializers_v2.Ones...>
|
|
|
|
In the case that the `identifier` is a class, this method will return a new
|
|
instance of the class by its constructor.
|
|
|
|
Args:
|
|
identifier: String or dict that contains the initializer name or
|
|
configurations.
|
|
|
|
Returns:
|
|
Initializer instance base on the input identifier.
|
|
|
|
Raises:
|
|
ValueError: If the input identifier is not a supported type or in a bad
|
|
format.
|
|
"""
|
|
|
|
if identifier is None:
|
|
return None
|
|
if isinstance(identifier, dict):
|
|
return deserialize(identifier)
|
|
elif isinstance(identifier, str):
|
|
identifier = str(identifier)
|
|
return deserialize(identifier)
|
|
elif callable(identifier):
|
|
if inspect.isclass(identifier):
|
|
identifier = identifier()
|
|
return identifier
|
|
else:
|
|
raise ValueError('Could not interpret initializer identifier: ' +
|
|
str(identifier))
|