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# Copyright 2023 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.
"""Optimizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import tensorflow as tf, tf_keras
class OptimizerFactory(object):
"""Class to generate optimizer function."""
def __init__(self, params):
"""Creates optimized based on the specified flags."""
if params.type == 'momentum':
self._optimizer = functools.partial(
tf_keras.optimizers.SGD,
momentum=params.momentum,
nesterov=params.nesterov)
elif params.type == 'adam':
self._optimizer = tf_keras.optimizers.Adam
elif params.type == 'adadelta':
self._optimizer = tf_keras.optimizers.Adadelta
elif params.type == 'adagrad':
self._optimizer = tf_keras.optimizers.Adagrad
elif params.type == 'rmsprop':
self._optimizer = functools.partial(
tf_keras.optimizers.RMSprop, momentum=params.momentum)
else:
raise ValueError('Unsupported optimizer type `{}`.'.format(params.type))
def __call__(self, learning_rate):
return self._optimizer(learning_rate=learning_rate)
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