forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathclip_ops_test.py
More file actions
241 lines (200 loc) · 7.86 KB
/
clip_ops_test.py
File metadata and controls
241 lines (200 loc) · 7.86 KB
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
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
# 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.
# ==============================================================================
"""Tests for tensorflow.ops.clip_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import tensorflow as tf
class ClipTest(tf.test.TestCase):
# ClipByValue test
def testClipByValue(self):
with self.test_session():
x = tf.constant([-5.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
np_ans = [[-4.4, 2.0, 3.0],
[4.0, 4.4, 4.4]]
clip_value = 4.4
ans = tf.clip_by_value(x, -clip_value, clip_value)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByValueNonFinite(self):
with self.test_session():
x = tf.constant([float('NaN'), float('Inf'), -float('Inf')])
np_ans = [float('NaN'), 4.0, -4.0]
clip_value = 4.0
ans = tf.clip_by_value(x, -clip_value, clip_value)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
# ClipByNorm tests
def testClipByNormClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Norm of x = sqrt(3^2 + 4^2) = 5
np_ans = [[-2.4, 0.0, 0.0],
[3.2, 0.0, 0.0]]
clip_norm = 4.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormNotClipped(self):
# No norm clipping when clip_norm >= 5
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Norm of x = sqrt(3^2 + 4^2) = 5
np_ans = [[-3.0, 0.0, 0.0],
[4.0, 0.0, 0.0]]
clip_norm = 6.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByNormZero(self):
# No norm clipping when norm = 0
with self.test_session():
x = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
# Norm = 0, no changes
np_ans = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]
clip_norm = 6.0
ans = tf.clip_by_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByGlobalNormClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormSupportsNone(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm((x0, None, x1, None), clip_norm)
self.assertTrue(ans[1] is None)
self.assertTrue(ans[3] is None)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[2].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
# ClipByGlobalNorm tests
def testClipByGlobalNormWithIndexedSlicesClipped(self):
# Norm clipping when clip_norm < 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.IndexedSlices(tf.constant([1.0, -2.0]),
tf.constant([3, 4]))
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
clip_norm = 4.0
# Answers are the original tensors scaled by 4.0/5.0
np_ans_0 = [[-1.6, 0.0, 0.0],
[3.2, 0.0, 0.0]]
np_ans_1 = [0.8, -1.6]
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].values.eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormNotClipped(self):
# No norm clipping when clip_norm >= 5
with self.test_session():
x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([1.0, -2.0])
# Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
np_ans_0 = [[-2.0, 0.0, 0.0],
[4.0, 0.0, 0.0]]
np_ans_1 = [1.0, -2.0]
clip_norm = 6.0
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 5.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByGlobalNormZero(self):
# No norm clipping when norm = 0
with self.test_session():
x0 = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
x1 = tf.constant([0.0, 0.0])
# Norm = 0, no changes
np_ans_0 = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]
np_ans_1 = [0.0, 0.0]
clip_norm = 6.0
ans, norm = tf.clip_by_global_norm([x0, x1], clip_norm)
tf_ans_1 = ans[0].eval()
tf_ans_2 = ans[1].eval()
tf_norm = norm.eval()
self.assertAllClose(tf_norm, 0.0)
self.assertAllClose(np_ans_0, tf_ans_1)
self.assertAllClose(np_ans_1, tf_ans_2)
def testClipByAverageNormClipped(self):
# Norm clipping when average clip_norm < 0.83333333
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-2.88, 0.0, 0.0],
[3.84, 0.0, 0.0]]
clip_norm = 0.8
ans = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormNotClipped(self):
# No norm clipping when average clip_norm >= 0.83333333
with self.test_session():
x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
np_ans = [[-3.0, 0.0, 0.0],
[4.0, 0.0, 0.0]]
clip_norm = 0.9
ans = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
def testClipByAverageNormZero(self):
# No norm clipping when average clip_norm = 0
with self.test_session():
x = tf.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
# Average norm = 0, no changes
np_ans = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]
clip_norm = 0.9
ans = tf.clip_by_average_norm(x, clip_norm)
tf_ans = ans.eval()
self.assertAllClose(np_ans, tf_ans)
if __name__ == "__main__":
tf.test.main()