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
|
# Copyright 2017 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.
# ==============================================================================
"""Tests for RMSProp optimizer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
class RmspropTest(xla_test.XLATestCase):
def _rmsprop_update_numpy(self,
var,
g,
mg,
rms,
mom,
lr,
decay=0.9,
momentum=0.0,
epsilon=1e-10,
centered=False):
rms_t = rms * decay + (1 - decay) * g * g
denom_t = rms_t + epsilon
if centered:
mg_t = mg * decay + (1 - decay) * g
denom_t -= mg_t * mg_t
else:
mg_t = mg
mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype)
var_t = var - mom_t
return var_t, mg_t, rms_t, mom_t
def testBasic(self):
for dtype in self.float_types:
for centered in [False, True]:
with self.cached_session(), self.test_scope():
# Initialize variables for numpy implementation.
var0_np = np.array([1.0, 2.0], dtype=dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype)
mg0_np = np.array([0.0, 0.0], dtype=dtype)
mg1_np = np.array([0.0, 0.0], dtype=dtype)
rms0_np = np.array([1.0, 1.0], dtype=dtype)
rms1_np = np.array([1.0, 1.0], dtype=dtype)
mom0_np = np.array([0.0, 0.0], dtype=dtype)
mom1_np = np.array([0.0, 0.0], dtype=dtype)
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
learning_rate = 3.0
rms_opt = rmsprop.RMSPropOptimizer(learning_rate, centered=centered)
rms_update = rms_opt.apply_gradients(
zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
mg0 = rms_opt.get_slot(var0, "mg")
self.assertEqual(mg0 is not None, centered)
mg1 = rms_opt.get_slot(var1, "mg")
self.assertEqual(mg1 is not None, centered)
rms0 = rms_opt.get_slot(var0, "rms")
self.assertTrue(rms0 is not None)
rms1 = rms_opt.get_slot(var1, "rms")
self.assertTrue(rms1 is not None)
mom0 = rms_opt.get_slot(var0, "momentum")
self.assertTrue(mom0 is not None)
mom1 = rms_opt.get_slot(var1, "momentum")
self.assertTrue(mom1 is not None)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], var0.eval())
self.assertAllClose([3.0, 4.0], var1.eval())
# Run 3 steps of RMSProp
for _ in range(3):
rms_update.run()
var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy(
var0_np,
grads0_np,
mg0_np,
rms0_np,
mom0_np,
learning_rate,
centered=centered)
var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy(
var1_np,
grads1_np,
mg1_np,
rms1_np,
mom1_np,
learning_rate,
centered=centered)
# Validate updated params
if centered:
self.assertAllCloseAccordingToType(mg0_np, mg0.eval())
self.assertAllCloseAccordingToType(mg1_np, mg1.eval())
self.assertAllCloseAccordingToType(rms0_np, rms0.eval())
self.assertAllCloseAccordingToType(rms1_np, rms1.eval())
self.assertAllCloseAccordingToType(mom0_np, mom0.eval())
self.assertAllCloseAccordingToType(mom1_np, mom1.eval())
self.assertAllCloseAccordingToType(var0_np, var0.eval())
self.assertAllCloseAccordingToType(var1_np, var1.eval())
if __name__ == "__main__":
test.main()
|
