83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""GradientDescent for TensorFlow."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.training import optimizer
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from tensorflow.python.training import training_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("train.GradientDescentOptimizer")
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class GradientDescentOptimizer(optimizer.Optimizer):
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"""Optimizer that implements the gradient descent algorithm.
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"""
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def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
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"""Construct a new gradient descent optimizer.
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Args:
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learning_rate: A Tensor or a floating point value. The learning
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rate to use.
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use_locking: If True use locks for update operations.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to "GradientDescent".
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@compatibility(eager)
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When eager execution is enabled, `learning_rate` can be a callable that
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takes no arguments and returns the actual value to use. This can be useful
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for changing these values across different invocations of optimizer
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functions.
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@end_compatibility
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"""
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super(GradientDescentOptimizer, self).__init__(use_locking, name)
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self._learning_rate = learning_rate
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self._learning_rate_tensor = None
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def _apply_dense(self, grad, var):
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return training_ops.apply_gradient_descent(
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var,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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grad,
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use_locking=self._use_locking).op
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def _resource_apply_dense(self, grad, handle):
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return training_ops.resource_apply_gradient_descent(
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handle.handle, math_ops.cast(self._learning_rate_tensor,
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grad.dtype.base_dtype),
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grad, use_locking=self._use_locking)
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def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices):
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return resource_variable_ops.resource_scatter_add(
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handle.handle, indices, -grad * self._learning_rate)
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def _apply_sparse_duplicate_indices(self, grad, var):
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delta = ops.IndexedSlices(
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grad.values *
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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grad.indices, grad.dense_shape)
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return var.scatter_sub(delta, use_locking=self._use_locking)
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def _prepare(self):
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learning_rate = self._call_if_callable(self._learning_rate)
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self._learning_rate_tensor = ops.convert_to_tensor(
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learning_rate, name="learning_rate")
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