111 lines
5.0 KiB
Python
111 lines
5.0 KiB
Python
# Copyright 2018 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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"""Strategy and optimizer combinations for combinations.combine()."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.distribute import strategy_combinations as strategy_combinations_base
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from tensorflow.python.framework import test_combinations as combinations
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from tensorflow.python.keras.optimizer_v2 import adadelta as adadelta_keras_v2
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from tensorflow.python.keras.optimizer_v2 import adagrad as adagrad_keras_v2
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from tensorflow.python.keras.optimizer_v2 import adam as adam_keras_v2
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from tensorflow.python.keras.optimizer_v2 import adamax as adamax_keras_v2
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from tensorflow.python.keras.optimizer_v2 import ftrl as ftrl_keras_v2
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from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_keras_v2
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from tensorflow.python.keras.optimizer_v2 import nadam as nadam_keras_v2
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from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_keras_v2
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from tensorflow.python.training import adagrad
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from tensorflow.python.training import adam
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from tensorflow.python.training import ftrl
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from tensorflow.python.training import gradient_descent
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from tensorflow.python.training import rmsprop
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gradient_descent_optimizer_v1_fn = combinations.NamedObject(
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"GradientDescentV1",
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lambda: gradient_descent.GradientDescentOptimizer(0.001))
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adagrad_optimizer_v1_fn = combinations.NamedObject(
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"AdagradV1", lambda: adagrad.AdagradOptimizer(0.001))
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adam_optimizer_v1_fn = combinations.NamedObject(
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"AdamV1", lambda: adam.AdamOptimizer(0.001, epsilon=1))
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ftrl_optimizer_v1_fn = combinations.NamedObject(
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"FtrlV1", lambda: ftrl.FtrlOptimizer(0.001))
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rmsprop_optimizer_v1_fn = combinations.NamedObject(
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"RmsPropV1", lambda: rmsprop.RMSPropOptimizer(0.001))
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# TODO(shiningsun): consider adding the other v1 optimizers
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optimizers_v1 = [
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gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn,
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ftrl_optimizer_v1_fn, rmsprop_optimizer_v1_fn
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]
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adadelta_optimizer_keras_v2_fn = combinations.NamedObject(
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"AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001))
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adagrad_optimizer_keras_v2_fn = combinations.NamedObject(
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"AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001))
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adam_optimizer_keras_v2_fn = combinations.NamedObject(
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"AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0))
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adamax_optimizer_keras_v2_fn = combinations.NamedObject(
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"AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0))
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nadam_optimizer_keras_v2_fn = combinations.NamedObject(
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"NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0))
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ftrl_optimizer_keras_v2_fn = combinations.NamedObject(
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"FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001))
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gradient_descent_optimizer_keras_v2_fn = combinations.NamedObject(
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"GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.001))
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rmsprop_optimizer_keras_v2_fn = combinations.NamedObject(
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"RmsPropKerasV2", lambda: rmsprop_keras_v2.RMSprop(0.001))
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# TODO(shiningsun): consider adding the other v2 optimizers
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optimizers_v2 = [
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gradient_descent_optimizer_keras_v2_fn, adagrad_optimizer_keras_v2_fn
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]
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optimizers_v1_and_v2 = optimizers_v1 + optimizers_v2
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def distributions_and_v1_optimizers():
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"""A common set of combination with DistributionStrategies and Optimizers."""
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return combinations.combine(
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distribution=[
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strategy_combinations_base.one_device_strategy,
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strategy_combinations_base.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations_base.mirrored_strategy_with_two_gpus,
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],
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optimizer_fn=optimizers_v1)
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def distributions_and_v2_optimizers():
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"""A common set of combination with DistributionStrategies and Optimizers."""
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return combinations.combine(
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distribution=[
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strategy_combinations_base.one_device_strategy,
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strategy_combinations_base.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations_base.mirrored_strategy_with_two_gpus,
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],
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optimizer_fn=optimizers_v2)
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def distributions_and_v1_and_v2_optimizers():
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"""A common set of combination with DistributionStrategies and Optimizers."""
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return combinations.combine(
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distribution=[
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strategy_combinations_base.one_device_strategy,
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strategy_combinations_base.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations_base.mirrored_strategy_with_two_gpus,
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],
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optimizer_fn=optimizers_v1_and_v2)
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