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# Copyright 2017 Google, Inc. 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.
# ==============================================================================
"""A trainable optimizer that learns a single global learning rate."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from learned_optimizer.optimizer import trainable_optimizer
class GlobalLearningRate(trainable_optimizer.TrainableOptimizer):
"""Optimizes for a single global learning rate."""
def __init__(self, initial_rate=1e-3, **kwargs):
"""Initializes the global learning rate."""
with tf.variable_scope(trainable_optimizer.OPTIMIZER_SCOPE):
initializer = tf.constant_initializer(initial_rate)
self.learning_rate = tf.get_variable("global_learning_rate", shape=(),
initializer=initializer)
super(GlobalLearningRate, self).__init__("GLR", [], **kwargs)
def _compute_update(self, param, grad, state):
return param - tf.scalar_mul(self.learning_rate, grad), state
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