/
proximal_adagrad.py
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/
proximal_adagrad.py
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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.
# ==============================================================================
"""ProximalAdagrad for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
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
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=["train.ProximalAdagradOptimizer"])
class ProximalAdagradOptimizer(optimizer.Optimizer):
# pylint: disable=line-too-long
"""Optimizer that implements the Proximal Adagrad algorithm.
References:
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
([pdf](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf))
Efficient Learning using Forward-Backward Splitting:
[Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting)
([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
"""
def __init__(self, learning_rate, initial_accumulator_value=0.1,
l1_regularization_strength=0.0, l2_regularization_strength=0.0,
use_locking=False, name="ProximalAdagrad"):
"""Construct a new ProximalAdagrad optimizer.
Args:
learning_rate: A `Tensor` or a floating point value. The learning rate.
initial_accumulator_value: A floating point value.
Starting value for the accumulators, must be positive.
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 "Adagrad".
Raises:
ValueError: If the `initial_accumulator_value` is invalid.
"""
if initial_accumulator_value <= 0.0:
raise ValueError("initial_accumulator_value must be positive: %s" %
initial_accumulator_value)
super(ProximalAdagradOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._initial_accumulator_value = initial_accumulator_value
self._l1_regularization_strength = l1_regularization_strength
self._l2_regularization_strength = l2_regularization_strength
# Created in Initialize.
self._l1_regularization_strength_tensor = None
self._l2_regularization_strength_tensor = None
self._learning_rate_tensor = None
def _create_slots(self, var_list):
for v in var_list:
with ops.colocate_with(v):
val = constant_op.constant(self._initial_accumulator_value,
shape=v.get_shape(),
dtype=v.dtype.base_dtype)
self._get_or_make_slot(v, val, "accumulator", self._name)
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")
def _apply_dense(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.apply_proximal_adagrad(
var, acc, self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
grad, use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_apply_proximal_adagrad(
var.handle, acc.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):
acc = self.get_slot(var, "accumulator")
return training_ops.sparse_apply_proximal_adagrad(
var, acc, self._learning_rate_tensor,
self._l1_regularization_strength_tensor,
self._l2_regularization_strength_tensor,
grad.values, grad.indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_proximal_adagrad(
var.handle, acc.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)