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
|
"""Tests for Relu and ReluGrad."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class ReluTest(tf.test.TestCase):
def _npRelu(self, np_features):
return np.maximum(np_features, np.zeros(np_features.shape))
def testNpRelu(self):
self.assertAllClose(
np.array([[0.0, 0.7, 0.0, 0.3, 0.0],
[0.1, 0.0, 0.5, 0.0, 0.9]]),
self._npRelu(np.array([[-0.9, 0.7, -0.5, 0.3, -0.1],
[0.1, -0.3, 0.5, -0.7, 0.9]])))
def _testRelu(self, np_features, use_gpu=False):
np_relu = self._npRelu(np_features)
with self.test_session(use_gpu=use_gpu):
relu = tf.nn.relu(np_features)
tf_relu = relu.eval()
self.assertAllClose(np_relu, tf_relu)
self.assertShapeEqual(np_relu, relu)
def testNumbers(self):
for t in [np.int32, np.int64, np.float, np.double]:
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
if t in [np.float, np.double]:
self._testRelu(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
# The gradient test for ReLU is a bit tricky as the derivative is not well
# defined at around zero and we want to avoid that in terms of input values.
def testGradientFloat(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], name="x")
y = tf.nn.relu(x, name="relu")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32, order="F")
err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
print "relu (float) gradient err = ", err
self.assertLess(err, 1e-4)
def testGradientNaN(self):
with self.test_session():
# Note the NaN is injected as an input to the gradient calculation.
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, np.nan, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], name="x")
y = tf.nn.relu(x, name="relu")
grad_ys = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], name="ys")
g_op = tf.gradients(
[y], [x], grad_ys=[grad_ys], name="gradients")[0]
try:
g_op.op.run()
assert False, "ReluGrad should have failed due to CheckNumerics."
except Exception as e: # pylint: disable=broad-except
assert "ReluGrad input is not finite." in str(e)
def testGradientDouble(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], dtype=tf.float64, name="x")
y = tf.nn.relu(x, name="relu")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64, order="F")
err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
print "relu (double) gradient err = ", err
self.assertLess(err, 1e-10)
def testGradGradFloat(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], name="x")
y = tf.nn.relu(x, name="relu")
z = tf.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32, order="F")
err = gc.ComputeGradientError(x, [2, 5], z[0], [2, 5],
x_init_value=x_init)
print "relu (float) gradient of gradient err = ", err
self.assertLess(err, 1e-4)
def testGradGradDouble(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5], dtype=tf.float64, name="x")
y = tf.nn.relu(x, name="relu")
z = tf.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64, order="F")
err = gc.ComputeGradientError(x, [2, 5], z[0], [2, 5],
x_init_value=x_init)
print "relu (double) gradient of gradient err = ", err
self.assertLess(err, 1e-10)
class Relu6Test(tf.test.TestCase):
def _npRelu6(self, np_features):
sixes = np.copy(np_features)
sixes.fill(6.0)
return np.minimum(np.maximum(np_features, np.zeros(np_features.shape)),
sixes)
def testNpRelu6(self):
self.assertAllClose(
np.array([[0.0, 0.7, 0.0, 0.3, 6.0],
[0.1, 0.0, 6.0, 0.0, 0.9]]),
self._npRelu6(np.array([[-0.9, 0.7, -0.5, 0.3, 6.0],
[0.1, -0.3, 6.5, -0.7, 0.9]])))
def _testRelu6(self, np_features, use_gpu=False):
np_relu6 = self._npRelu6(np_features)
with self.test_session(use_gpu=use_gpu):
relu6 = tf.nn.relu6(np_features)
tf_relu6 = relu6.eval()
self.assertAllClose(np_relu6, tf_relu6)
self.assertShapeEqual(np_relu6, relu6)
def testNumbers(self):
for t in [np.int32, np.int64, np.float, np.double]:
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
if t in [np.float, np.double]:
self._testRelu6(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
# The gradient test for ReLU6 is a bit tricky as the derivative is
# not well defined at around zero and six and we want to avoid that
# in terms of input values.
def testGradientFloat(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
shape=[2, 5], name="x")
y = tf.nn.relu6(x, name="relu6")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
dtype=np.float32, order="F")
err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
print "relu6 (float) gradient err = ", err
self.assertLess(err, 1e-4)
def testGradientDouble(self):
with self.test_session():
x = tf.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
shape=[2, 5], dtype=tf.float64, name="x")
y = tf.nn.relu6(x, name="relu6")
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
dtype=np.float64, order="F")
err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
print "relu6 (double) gradient err = ", err
self.assertLess(err, 1e-10)
if __name__ == "__main__":
tf.test.main()
|
