105 lines
4.4 KiB
Python
105 lines
4.4 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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"""ProximalGradientDescent for TensorFlow."""
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_training_ops
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# pylint: disable=unused-import
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from tensorflow.python.ops import math_ops
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# pylint: enable=unused-import
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from tensorflow.python.training import optimizer
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["train.ProximalGradientDescentOptimizer"])
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class ProximalGradientDescentOptimizer(optimizer.Optimizer):
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# pylint: disable=line-too-long
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"""Optimizer that implements the proximal gradient descent algorithm.
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References:
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Efficient Learning using Forward-Backward Splitting:
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[Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting)
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([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
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"""
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def __init__(self, learning_rate, l1_regularization_strength=0.0,
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l2_regularization_strength=0.0, use_locking=False,
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name="ProximalGradientDescent"):
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"""Construct a new proximal 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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l1_regularization_strength: A float value, must be greater than or
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equal to zero.
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l2_regularization_strength: A float value, must be greater than or
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equal to zero.
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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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"""
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super(ProximalGradientDescentOptimizer, self).__init__(use_locking, name)
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self._learning_rate = learning_rate
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self._l1_regularization_strength = l1_regularization_strength
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self._l2_regularization_strength = l2_regularization_strength
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self._l1_regularization_strength_tensor = None
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self._l2_regularization_strength_tensor = None
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def _apply_dense(self, grad, var):
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return gen_training_ops.apply_proximal_gradient_descent(
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var,
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self._learning_rate_tensor,
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self._l1_regularization_strength_tensor,
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self._l2_regularization_strength_tensor,
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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, var):
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return gen_training_ops.resource_apply_proximal_gradient_descent(
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var.handle,
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self._learning_rate_tensor,
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self._l1_regularization_strength_tensor,
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self._l2_regularization_strength_tensor,
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grad,
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use_locking=self._use_locking)
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def _apply_sparse(self, grad, var):
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return gen_training_ops.sparse_apply_proximal_gradient_descent(
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var,
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self._learning_rate_tensor,
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self._l1_regularization_strength_tensor,
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self._l2_regularization_strength_tensor,
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grad.values,
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grad.indices,
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use_locking=self._use_locking).op
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def _resource_apply_sparse(self, grad, var, indices):
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return gen_training_ops.resource_sparse_apply_proximal_gradient_descent(
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var.handle,
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math_ops.cast(self._learning_rate_tensor, grad.dtype),
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math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
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math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
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grad,
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indices,
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use_locking=self._use_locking)
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def _prepare(self):
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self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
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name="learning_rate")
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self._l1_regularization_strength_tensor = ops.convert_to_tensor(
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self._l1_regularization_strength, name="l1_regularization_strength")
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self._l2_regularization_strength_tensor = ops.convert_to_tensor(
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self._l2_regularization_strength, name="l2_regularization_strength")
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