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rmsprop.py
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rmsprop.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.
# ==============================================================================
"""One-line documentation for rmsprop module.
rmsprop algorithm [tieleman2012rmsprop]
A detailed description of rmsprop.
- maintain a moving (discounted) average of the square of gradients
- divide gradient by the root of this average
mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon)
delta = - mom
The centered version additionally maintains a moving (discounted) average of the
gradients, and uses that average to estimate the variance:
mean_grad = decay * mean_square{t-1} + (1-decay) * gradient
mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t /
sqrt(mean_square - mean_grad**2 + epsilon)
delta = - mom
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class RMSPropOptimizer(optimizer.Optimizer):
"""Optimizer that implements the RMSProp algorithm.
See the [paper](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
"""
def __init__(self,
learning_rate,
decay=0.9,
momentum=0.0,
epsilon=1e-10,
use_locking=False,
centered=False,
name="RMSProp"):
"""Construct a new RMSProp optimizer.
Note that in dense implement of this algorithm, m_t and v_t will
update even if g is zero, but in sparse implement, m_t and v_t
will not update in iterations g is zero.
Args:
learning_rate: A Tensor or a floating point value. The learning rate.
decay: Discounting factor for the history/coming gradient
momentum: A scalar tensor.
epsilon: Small value to avoid zero denominator.
use_locking: If True use locks for update operation.
centered: If True, gradients are normalized by the estimated variance of
the gradient; if False, by the uncentered second moment. Setting this to
True may help with training, but is slightly more expensive in terms of
computation and memory. Defaults to False.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "RMSProp".
"""
super(RMSPropOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._decay = decay
self._momentum = momentum
self._epsilon = epsilon
self._centered = centered
# Tensors for learning rate and momentum. Created in _prepare.
self._learning_rate_tensor = None
self._decay_tensor = None
self._momentum_tensor = None
self._epsilon_tensor = None
def _create_slots(self, var_list):
for v in var_list:
init_rms = init_ops.ones_initializer(dtype=v.dtype)
self._get_or_make_slot_with_initializer(v, init_rms, v.get_shape(),
v.dtype, "rms", self._name)
if self._centered:
self._zeros_slot(v, "mg", self._name)
self._zeros_slot(v, "momentum", self._name)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
self._decay_tensor = ops.convert_to_tensor(self._decay, name="decay")
self._momentum_tensor = ops.convert_to_tensor(self._momentum,
name="momentum")
self._epsilon_tensor = ops.convert_to_tensor(self._epsilon,
name="epsilon")
def _apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.apply_centered_rms_prop(
var,
mg,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
else:
return training_ops.apply_rms_prop(
var,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
def _resource_apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.resource_apply_centered_rms_prop(
var.handle,
mg.handle,
rms.handle,
mom.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
math_ops.cast(self._decay_tensor, grad.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, grad.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, grad.dtype.base_dtype),
grad,
use_locking=self._use_locking)
else:
return training_ops.resource_apply_rms_prop(
var.handle,
rms.handle,
mom.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
math_ops.cast(self._decay_tensor, grad.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, grad.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, grad.dtype.base_dtype),
grad,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.sparse_apply_centered_rms_prop(
var,
mg,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
else:
return training_ops.sparse_apply_rms_prop(
var,
rms,
mom,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
if self._centered:
mg = self.get_slot(var, "mg")
return training_ops.resource_sparse_apply_centered_rms_prop(
var.handle,
mg.handle,
rms.handle,
mom.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
math_ops.cast(self._decay_tensor, grad.dtype),
math_ops.cast(self._momentum_tensor, grad.dtype),
math_ops.cast(self._epsilon_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
else:
return training_ops.resource_sparse_apply_rms_prop(
var.handle,
rms.handle,
mom.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
math_ops.cast(self._decay_tensor, grad.dtype),
math_ops.cast(self._momentum_tensor, grad.dtype),
math_ops.cast(self._epsilon_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)