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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 | |