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# Copyright 2015 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.
# ==============================================================================
"""Momentum for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class MomentumOptimizer(optimizer.Optimizer):
"""Optimizer that implements the Momentum algorithm.
@@__init__
"""
def __init__(self, learning_rate, momentum,
use_locking=False, name="Momentum", use_nesterov=False):
"""Construct a new Momentum optimizer.
Args:
learning_rate: A `Tensor` or a floating point value. The learning rate.
momentum: A `Tensor` or a floating point value. The momentum.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Momentum".
"""
super(MomentumOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._momentum = momentum
self._use_nesterov = use_nesterov
def _create_slots(self, var_list):
for v in var_list:
self._zeros_slot(v, "momentum", self._name)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
self._momentum_tensor = ops.convert_to_tensor(self._momentum,
name="momentum")
def _apply_dense(self, grad, var):
mom = self.get_slot(var, "momentum")
return training_ops.apply_momentum(
var, mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad,
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
use_locking=self._use_locking,
use_nesterov=self._use_nesterov).op
def _apply_sparse(self, grad, var):
mom = self.get_slot(var, "momentum")
return training_ops.sparse_apply_momentum(
var, mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad.values, grad.indices,
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
use_locking=self._use_locking,
use_nesterov=self._use_nesterov).op
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