133 lines
5.2 KiB
Python
133 lines
5.2 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=invalid-name
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"""Built-in optimizer classes.
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For more examples see the base class `tf.keras.optimizers.Optimizer`.
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"""
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.optimizer_v1 import Optimizer
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from tensorflow.python.keras.optimizer_v1 import TFOptimizer
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from tensorflow.python.keras.optimizer_v2 import adadelta as adadelta_v2
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from tensorflow.python.keras.optimizer_v2 import adagrad as adagrad_v2
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from tensorflow.python.keras.optimizer_v2 import adam as adam_v2
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from tensorflow.python.keras.optimizer_v2 import adamax as adamax_v2
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from tensorflow.python.keras.optimizer_v2 import ftrl
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from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
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from tensorflow.python.keras.optimizer_v2 import nadam as nadam_v2
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from tensorflow.python.keras.optimizer_v2 import optimizer_v2
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from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_v2
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from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
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from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
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from tensorflow.python.training import optimizer as tf_optimizer_module
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from tensorflow.python.util.tf_export import keras_export
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@keras_export('keras.optimizers.serialize')
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def serialize(optimizer):
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"""Serialize the optimizer configuration to JSON compatible python dict.
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The configuration can be used for persistence and reconstruct the `Optimizer`
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instance again.
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>>> tf.keras.optimizers.serialize(tf.keras.optimizers.SGD())
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{'class_name': 'SGD', 'config': {'name': 'SGD', 'learning_rate': 0.01,
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'decay': 0.0, 'momentum': 0.0,
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'nesterov': False}}
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Args:
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optimizer: An `Optimizer` instance to serialize.
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Returns:
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Python dict which contains the configuration of the input optimizer.
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"""
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return serialize_keras_object(optimizer)
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@keras_export('keras.optimizers.deserialize')
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def deserialize(config, custom_objects=None):
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"""Inverse of the `serialize` function.
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Args:
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config: Optimizer configuration dictionary.
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custom_objects: Optional dictionary mapping names (strings) to custom
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objects (classes and functions) to be considered during deserialization.
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Returns:
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A Keras Optimizer instance.
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"""
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# loss_scale_optimizer has a direct dependency of optimizer, import here
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# rather than top to avoid the cyclic dependency.
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from tensorflow.python.keras.mixed_precision import loss_scale_optimizer # pylint: disable=g-import-not-at-top
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all_classes = {
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'adadelta': adadelta_v2.Adadelta,
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'adagrad': adagrad_v2.Adagrad,
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'adam': adam_v2.Adam,
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'adamax': adamax_v2.Adamax,
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'nadam': nadam_v2.Nadam,
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'rmsprop': rmsprop_v2.RMSprop,
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'sgd': gradient_descent_v2.SGD,
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'ftrl': ftrl.Ftrl,
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'lossscaleoptimizer': loss_scale_optimizer.LossScaleOptimizer,
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# LossScaleOptimizerV1 deserializes into LossScaleOptimizer, as
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# LossScaleOptimizerV1 will be removed soon but deserializing it will
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# still be supported.
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'lossscaleoptimizerv1': loss_scale_optimizer.LossScaleOptimizer,
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}
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# Make deserialization case-insensitive for built-in optimizers.
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if config['class_name'].lower() in all_classes:
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config['class_name'] = config['class_name'].lower()
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return deserialize_keras_object(
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config,
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module_objects=all_classes,
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custom_objects=custom_objects,
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printable_module_name='optimizer')
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@keras_export('keras.optimizers.get')
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def get(identifier):
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"""Retrieves a Keras Optimizer instance.
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Args:
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identifier: Optimizer identifier, one of
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- String: name of an optimizer
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- Dictionary: configuration dictionary. - Keras Optimizer instance (it
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will be returned unchanged). - TensorFlow Optimizer instance (it
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will be wrapped as a Keras Optimizer).
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Returns:
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A Keras Optimizer instance.
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Raises:
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ValueError: If `identifier` cannot be interpreted.
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"""
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if isinstance(identifier, (Optimizer, optimizer_v2.OptimizerV2)):
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return identifier
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# Wrap legacy TF optimizer instances
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elif isinstance(identifier, tf_optimizer_module.Optimizer):
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opt = TFOptimizer(identifier)
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backend.track_tf_optimizer(opt)
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return opt
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elif isinstance(identifier, dict):
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return deserialize(identifier)
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elif isinstance(identifier, str):
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config = {'class_name': str(identifier), 'config': {}}
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return deserialize(config)
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else:
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raise ValueError(
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'Could not interpret optimizer identifier: {}'.format(identifier))
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