137 lines
5.7 KiB
Python
137 lines
5.7 KiB
Python
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# 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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import tensorflow.compat.v2 as tf
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from keras.optimizers import adam as adam_experimental
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from keras.optimizers.legacy import adadelta as adadelta_keras_v2
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from keras.optimizers.legacy import adagrad as adagrad_keras_v2
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from keras.optimizers.legacy import adam as adam_keras_v2
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from keras.optimizers.legacy import adamax as adamax_keras_v2
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from keras.optimizers.legacy import ftrl as ftrl_keras_v2
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from keras.optimizers.legacy import (
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gradient_descent as gradient_descent_keras_v2,
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)
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from keras.optimizers.legacy import nadam as nadam_keras_v2
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from keras.optimizers.legacy import rmsprop as rmsprop_keras_v2
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gradient_descent_optimizer_v1_fn = (
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tf.__internal__.test.combinations.NamedObject(
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"GradientDescentV1",
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lambda: tf.compat.v1.train.GradientDescentOptimizer(0.001),
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)
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)
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adagrad_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
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"AdagradV1", lambda: tf.compat.v1.train.AdagradOptimizer(0.001)
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)
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adam_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
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"AdamV1", lambda: tf.compat.v1.train.AdamOptimizer(0.001, epsilon=1)
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)
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ftrl_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
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"FtrlV1", lambda: tf.compat.v1.train.FtrlOptimizer(0.001)
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)
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rmsprop_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
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"RmsPropV1", lambda: tf.compat.v1.train.RMSPropOptimizer(0.001)
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)
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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,
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adagrad_optimizer_v1_fn,
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ftrl_optimizer_v1_fn,
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rmsprop_optimizer_v1_fn,
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]
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adadelta_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)
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)
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adagrad_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001)
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)
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adam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0)
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)
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adam_experimental_fn = tf.__internal__.test.combinations.NamedObject(
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"AdamExperimental", lambda: adam_experimental.Adam(0.001)
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)
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adamax_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0)
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)
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nadam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0)
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)
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ftrl_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001)
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)
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gradient_descent_optimizer_keras_v2_fn = (
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tf.__internal__.test.combinations.NamedObject(
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"GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.001)
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)
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)
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rmsprop_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
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"RmsPropKerasV2", lambda: rmsprop_keras_v2.RMSprop(0.001)
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)
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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,
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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
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Optimizers."""
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return tf.__internal__.test.combinations.combine(
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distribution=[
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tf.__internal__.distribute.combinations.one_device_strategy,
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tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call, # noqa: E501
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],
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optimizer_fn=optimizers_v1,
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)
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def distributions_and_v2_optimizers():
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"""A common set of combination with DistributionStrategies and
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Optimizers."""
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return tf.__internal__.test.combinations.combine(
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distribution=[
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tf.__internal__.distribute.combinations.one_device_strategy,
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tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call, # noqa: E501
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],
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optimizer_fn=optimizers_v2,
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)
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def distributions_and_v1_and_v2_optimizers():
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"""A common set of combination with DistributionStrategies and
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Optimizers."""
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return tf.__internal__.test.combinations.combine(
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distribution=[
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tf.__internal__.distribute.combinations.one_device_strategy,
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tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus, # noqa: E501
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tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call, # noqa: E501
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],
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optimizer_fn=optimizers_v1_and_v2,
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)
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