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
|
# 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.argmax_op."""
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.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class GradientCorrectnessTest(test.TestCase):
def testMultipleOutputChainedGradients(self):
with self.cached_session() as sess:
x = constant_op.constant(1.0, dtype=dtypes.float32)
yexp = math_ops.exp(x)
yexplog = math_ops.log(yexp)
grads = gradients_impl.gradients([yexp, yexplog], [x])
grad_vals = sess.run(grads)
exp1_plus_one = (1.0 + np.exp(1.0)).astype(np.float32)
# [dexp(x)/dx + d(log(exp(x)))/dx] @ x=1 == exp(1) + 1
self.assertAllClose(grad_vals[0], exp1_plus_one)
def testIdentityGradient(self):
x = constant_op.constant(3.)
dx_dx, = gradients_impl.gradients(x, x)
with self.cached_session() as sess:
self.assertAllClose(1., sess.run(dx_dx))
def testIntegerIdentityGradient(self):
x = constant_op.constant(3)
dx_dx, = gradients_impl.gradients(x, x)
with self.cached_session() as sess:
self.assertAllClose(1, sess.run(dx_dx))
def testGradientWithIntegerPath(self):
x = constant_op.constant([3.9, 4.1])
k = math_ops.to_float(math_ops.to_int32(x))
y = x * k
dy_dx, = gradients_impl.gradients(y, x)
with self.cached_session() as sess:
self.assertAllClose([3., 4.], sess.run(dy_dx))
def testNoIntegerGradient1(self):
x = constant_op.constant([3.9, 4.1])
k = math_ops.to_float(math_ops.to_int32(x))
y = k * k
dy_dx, = gradients_impl.gradients(y, x)
self.assertIsNone(dy_dx)
def testNoIntegerGradient2(self):
k = constant_op.constant([3, 4])
x = math_ops.to_float(k)
y = x * x
dy_dk, = gradients_impl.gradients(y, k)
self.assertIsNone(dy_dk)
def testNoIntegerGradient3(self):
k = constant_op.constant([3, 4])
m = k * k
dm_dk, = gradients_impl.gradients(m, k)
self.assertIsNone(dm_dk)
def testNoIntegerGradient4(self):
k = constant_op.constant([3, 4])
m = k * k * k
dm_dk, = gradients_impl.gradients(m, k)
self.assertIsNone(dm_dk)
def testNoIntegerGradient5(self):
k = constant_op.constant([3, 4])
m = k * k
n = m * m
dn_dk, = gradients_impl.gradients(n, k)
self.assertIsNone(dn_dk)
def testNoIntegerGradient6(self):
k = constant_op.constant(3)
x = math_ops.to_float(k)
grad_1, = gradients_impl.gradients(k * k, k)
grad_2, = gradients_impl.gradients(x * x, k)
grad_3, = gradients_impl.gradients(math_ops.square(k), k)
grad_4, = gradients_impl.gradients(math_ops.square(x), k)
self.assertIsNone(grad_1)
self.assertIsNone(grad_2)
self.assertIsNone(grad_3)
self.assertIsNone(grad_4)
if __name__ == '__main__':
test.main()
|
