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1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15
16"""GradientDescent for TensorFlow."""
17from __future__ import absolute_import
18from __future__ import division
19from __future__ import print_function
20
21from tensorflow.python.framework import ops
22from tensorflow.python.ops import math_ops
23from tensorflow.python.ops import resource_variable_ops
24from tensorflow.python.training import optimizer
25from tensorflow.python.training import training_ops
26from tensorflow.python.util.tf_export import tf_export
27
28
29@tf_export(v1=["train.GradientDescentOptimizer"])
30class GradientDescentOptimizer(optimizer.Optimizer):
31  """Optimizer that implements the gradient descent algorithm.
32  """
33
34  def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
35    """Construct a new gradient descent optimizer.
36
37    Args:
38      learning_rate: A Tensor or a floating point value.  The learning
39        rate to use.
40      use_locking: If True use locks for update operations.
41      name: Optional name prefix for the operations created when applying
42        gradients. Defaults to "GradientDescent".
43
44    @compatibility(eager)
45    When eager execution is enabled, `learning_rate` can be a callable that
46    takes no arguments and returns the actual value to use. This can be useful
47    for changing these values across different invocations of optimizer
48    functions.
49    @end_compatibility
50    """
51    super(GradientDescentOptimizer, self).__init__(use_locking, name)
52    self._learning_rate = learning_rate
53    self._learning_rate_tensor = None
54
55  def _apply_dense(self, grad, var):
56    return training_ops.apply_gradient_descent(
57        var,
58        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
59        grad,
60        use_locking=self._use_locking).op
61
62  def _resource_apply_dense(self, grad, handle):
63    return training_ops.resource_apply_gradient_descent(
64        handle.handle, math_ops.cast(self._learning_rate_tensor,
65                                     grad.dtype.base_dtype),
66        grad, use_locking=self._use_locking)
67
68  def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices):
69    return resource_variable_ops.resource_scatter_add(
70        handle.handle, indices, -grad * self._learning_rate)
71
72  def _apply_sparse_duplicate_indices(self, grad, var):
73    delta = ops.IndexedSlices(
74        grad.values *
75        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
76        grad.indices, grad.dense_shape)
77    return var.scatter_sub(delta, use_locking=self._use_locking)
78
79  def _prepare(self):
80    learning_rate = self._call_if_callable(self._learning_rate)
81    self._learning_rate_tensor = ops.convert_to_tensor(
82        learning_rate, name="learning_rate")
83