-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathrmsprop.py
150 lines (131 loc) · 6.09 KB
/
rmsprop.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
# Copyright 2015 Google Inc. 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
"""
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 constant_op
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_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).
@@__init__
"""
def __init__(self,
learning_rate=0.001,
decay=0.9,
momentum=0.0,
epsilon=1e-10,
use_locking=False,
name="RMSProp"):
"""Construct a new RMSProp optimizer.
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.
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
# 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:
val = constant_op.constant(1.0, dtype=v.dtype, shape=v.get_shape())
self._get_or_make_slot(v, val, "rms", self._name)
self._zeros_slot(v, "momentum", self._name)
self._zeros_slot(v, "sparse_grad", 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")
return training_ops.apply_rms_prop(
var, rms, mom,
self._learning_rate_tensor,
self._decay_tensor,
self._momentum_tensor,
self._epsilon_tensor,
grad, use_locking=self._use_locking).op
def _apply_sparse(self, grad, var):
# ms_t = decay * ms + (1 - decay) * (g_t * g_t)
ms = self.get_slot(var, "rms") # should not be named rms when it's ms
print('---SPARSE TIME---')
print('lr: ' + str(self._learning_rate_tensor.get_shape()))
print('decay: ' + str(self._decay_tensor.get_shape()))
print('momentum: ' + str(self._momentum_tensor.get_shape()))
print('epsilon: ' + str(self._epsilon_tensor.get_shape()))
print('ms: ' + str(ms.get_shape()))
print('grad.values: ' + str(grad.values.get_shape()))
ms_scaled_g_values = (grad.values * grad.values) * \
(1 - self._decay_tensor)
print('ms_scaled_g_values:' + str(ms_scaled_g_values.get_shape()))
# no clue what these ops does
ms_t = state_ops.assign(ms, ms * self._decay_tensor,
use_locking=self._use_locking)
print('ms_t: ' + str(ms_t.get_shape()))
ms_t = state_ops.scatter_add(ms_t, grad.indices, ms_scaled_g_values,
use_locking=self._use_locking)
print('ms_t: ' + str(ms_t.get_shape()))
rms = math_ops.sqrt(ms_t)
print('rms: ' + str(rms.get_shape()))
rms += self._epsilon_tensor
print('rms: ' + str(rms.get_shape()))
mom = self.get_slot(var, "momentum")
print('mom: ' + str(mom.get_shape()))
sparse_grad = self.get_slot(var, "sparse_grad")
sparse_grad_t = state_ops.assign(sparse_grad, sparse_grad, use_locking=self._use_locking)
sparse_grad_t = state_ops.scatter_add(sparse_grad, grad.indices, grad.values*self._learning_rate, use_locking=self._use_locking)
mom_scaled_g_values = sparse_grad_t / rms
print('mom_scaled_g_values: ' + str(mom.get_shape()))
mom_t = state_ops.assign(mom, mom * self._momentum_tensor,
use_locking=self._use_locking)
print('mom_t: ' + str(mom_t.get_shape()))
mom_t += mom_scaled_g_values
# mom_t = state_ops.scatter_add(mom_t, grad.indices, mom_scaled_g_values,
# use_locking=self._use_locking)
print('mom_t: ' + str(mom_t.get_shape()))
var_update = state_ops.assign_sub(var, mom_t,
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, ms_t, mom_t])