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 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.
# ==============================================================================
"""Tests for tensorflow.ops.numerics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import numerics
from tensorflow.python.platform import test
class VerifyTensorAllFiniteTest(test.TestCase):
def testVerifyTensorAllFiniteSucceeds(self):
x_shape = [5, 4]
x = np.random.random_sample(x_shape).astype(np.float32)
with self.test_session(use_gpu=True):
t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
t_verified = numerics.verify_tensor_all_finite(t,
"Input is not a number.")
self.assertAllClose(x, t_verified.eval())
def testVerifyTensorAllFiniteFails(self):
x_shape = [5, 4]
x = np.random.random_sample(x_shape).astype(np.float32)
my_msg = "Input is not a number."
# Test NaN.
x[0] = np.nan
with self.test_session(use_gpu=True):
with self.assertRaisesOpError(my_msg):
t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
t_verified = numerics.verify_tensor_all_finite(t, my_msg)
t_verified.eval()
# Test Inf.
x[0] = np.inf
with self.test_session(use_gpu=True):
with self.assertRaisesOpError(my_msg):
t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
t_verified = numerics.verify_tensor_all_finite(t, my_msg)
t_verified.eval()
class NumericsTest(test.TestCase):
def testInf(self):
with self.session(graph=ops.Graph()):
t1 = constant_op.constant(1.0)
t2 = constant_op.constant(0.0)
a = math_ops.div(t1, t2)
check = numerics.add_check_numerics_ops()
a = control_flow_ops.with_dependencies([check], a)
with self.assertRaisesOpError("Inf"):
a.eval()
def testNaN(self):
with self.session(graph=ops.Graph()):
t1 = constant_op.constant(0.0)
t2 = constant_op.constant(0.0)
a = math_ops.div(t1, t2)
check = numerics.add_check_numerics_ops()
a = control_flow_ops.with_dependencies([check], a)
with self.assertRaisesOpError("NaN"):
a.eval()
def testBoth(self):
with self.session(graph=ops.Graph()):
t1 = constant_op.constant([1.0, 0.0])
t2 = constant_op.constant([0.0, 0.0])
a = math_ops.div(t1, t2)
check = numerics.add_check_numerics_ops()
a = control_flow_ops.with_dependencies([check], a)
with self.assertRaisesOpError("Inf and NaN"):
a.eval()
def testPassThrough(self):
with self.session(graph=ops.Graph()):
t1 = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
checked = array_ops.check_numerics(t1, message="pass through test")
value = checked.eval()
self.assertAllEqual(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), value)
self.assertEqual([2, 3], checked.get_shape())
def testControlFlowCond(self):
predicate = array_ops.placeholder(dtypes.bool, shape=[])
_ = control_flow_ops.cond(predicate,
lambda: constant_op.constant([37.]),
lambda: constant_op.constant([42.]))
with self.assertRaisesRegexp(
ValueError,
r"`tf\.add_check_numerics_ops\(\) is not compatible with "
r"TensorFlow control flow operations such as `tf\.cond\(\)` "
r"or `tf.while_loop\(\)`\."):
numerics.add_check_numerics_ops()
def testControlFlowWhile(self):
predicate = array_ops.placeholder(dtypes.bool, shape=[])
_ = control_flow_ops.while_loop(lambda _: predicate,
lambda _: constant_op.constant([37.]),
[constant_op.constant([42.])])
with self.assertRaisesRegexp(
ValueError,
r"`tf\.add_check_numerics_ops\(\) is not compatible with "
r"TensorFlow control flow operations such as `tf\.cond\(\)` "
r"or `tf.while_loop\(\)`\."):
numerics.add_check_numerics_ops()
if __name__ == "__main__":
test.main()
|
