# Copyright 2017 Google Inc.
#
# 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
#
# https://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.
# ==============================================================================
"""Utilities to configure TF optimizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from absl import flags
FLAGS = flags.FLAGS
flags.DEFINE_string('optimizer', 'sgd',
'Which optimizer to use. Valid values are: '
'momentum, sgd, adagrad, adam, rmsprop.')
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_float('momentum', 0.9, 'Momentum.')
flags.DEFINE_float('learning_rate_decay_factor', 0.94,
'Learning rate decay factor.')
flags.DEFINE_float('num_epochs_per_decay', 2.0,
'Number of epochs after which learning rate decays.')
flags.DEFINE_float('rmsprop_decay', 0.9, 'Decay term for RMSProp.')
flags.DEFINE_float('adam_beta1', 0.9, 'Gradient decay term for Adam.')
flags.DEFINE_float('adam_beta2', 0.999, 'Gradient^2 decay term for Adam.')
flags.DEFINE_float('epsilon', 1e-8, 'Epsilon term for RMSProp and Adam.')
def optimizer_from_flags():
lr = FLAGS.learning_rate
if FLAGS.optimizer == 'momentum':
return tf.train.MomentumOptimizer(lr, FLAGS.momentum)
elif FLAGS.optimizer == 'sgd':
return tf.train.GradientDescentOptimizer(lr)
elif FLAGS.optimizer == 'adagrad':
return tf.train.AdagradOptimizer(lr)
elif FLAGS.optimizer == 'adam':
return tf.train.AdamOptimizer(learning_rate=lr,
beta1=FLAGS.adam_beta1,
beta2=FLAGS.adam_beta2,
epsilon=FLAGS.epsilon)
elif FLAGS.optimizer == 'rmsprop':
return tf.train.RMSPropOptimizer(lr, FLAGS.rmsprop_decay,
momentum=FLAGS.momentum,
epsilon=FLAGS.epsilon)
else:
raise ValueError('Unknown optimizer: %s' % FLAGS.optimizer)
