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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.
# ==============================================================================
"""ProximalGradientDescent for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
# pylint: disable=unused-import
from tensorflow.python.ops import math_ops
# pylint: enable=unused-import
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export("train.ProximalGradientDescentOptimizer")
class ProximalGradientDescentOptimizer(optimizer.Optimizer):
# pylint: disable=line-too-long
"""Optimizer that implements the proximal gradient descent algorithm.
See this [paper](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf).
"""
def __init__(self, learning_rate, l1_regularization_strength=0.0,
l2_regularization_strength=0.0, use_locking=False,
name="ProximalGradientDescent"):
"""Construct a new proximal gradient descent optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning
rate to use.
l1_regularization_strength: A float value, must be greater than or
equal to zero.
l2_regularization_strength: A float value, must be greater than or
equal to zero.
use_locking: If True use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "GradientDescent".
"""
super(ProximalGradientDescentOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._l1_regularization_strength = l1_regularization_strength
self._l2_regularization_strength = l2_regularization_strength
self._l1_regularization_strength_tensor = None
self._l2_regularization_strength_tensor = None
def _apply_dense(self, grad, var):
return training_ops.apply_proximal_gradient_descent(
var,
self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
grad,
use_locking=self._use_locking).op
def _resource_apply_dense(self, grad, var):
return training_ops.resource_apply_proximal_gradient_descent(
var.handle,
self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
grad,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
return training_ops.sparse_apply_proximal_gradient_descent(
var,
self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
grad.values,
grad.indices,
use_locking=self._use_locking).op
def _resource_apply_sparse(self, grad, var, indices):
return training_ops.resource_sparse_apply_proximal_gradient_descent(
var.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
self._l1_regularization_strength_tensor = ops.convert_to_tensor(
self._l1_regularization_strength, name="l1_regularization_strength")
self._l2_regularization_strength_tensor = ops.convert_to_tensor(
self._l2_regularization_strength, name="l2_regularization_strength")
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