forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathbias_op_test.py
More file actions
110 lines (91 loc) · 3.95 KB
/
bias_op_test.py
File metadata and controls
110 lines (91 loc) · 3.95 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
# 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.
# ==============================================================================
"""Functional tests for BiasAdd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
class BiasAddTest(tf.test.TestCase):
def _npBias(self, inputs, bias):
assert len(bias.shape) == 1
print(inputs.shape)
print(bias.shape)
assert inputs.shape[-1] == bias.shape[0]
return inputs + bias.reshape(([1] * (len(inputs.shape) - 1))
+ [bias.shape[0]])
def testNpBias(self):
self.assertAllClose(np.array([[11, 22, 33], [41, 52, 63]]),
self._npBias(np.array([[10, 20, 30], [40, 50, 60]]),
np.array([1, 2, 3])))
def _testBias(self, np_inputs, np_bias, use_gpu=False):
np_val = self._npBias(np_inputs, np_bias)
with self.test_session(use_gpu=use_gpu):
tf_val = tf.nn.bias_add(np_inputs, np_bias).eval()
self.assertAllClose(np_val, tf_val)
def _testAll(self, np_inputs, np_bias):
self._testBias(np_inputs, np_bias, use_gpu=False)
if np_inputs.dtype == np.float32 or np_inputs.dtype == np.float64:
self._testBias(np_inputs, np_bias, use_gpu=True)
def testInputDims(self):
with self.assertRaises(ValueError):
tf.nn.bias_add([1, 2], [1])
def testBiasVec(self):
with self.assertRaises(ValueError):
tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1, 2], shape=[1, 2]))
def testBiasInputsMatch(self):
with self.assertRaises(ValueError):
tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
tf.reshape([1], shape=[1]))
def testIntTypes(self):
for t in [np.int8, np.int16, np.int32, np.int64]:
self._testAll(np.array([[10, 20, 30], [40, 50, 60]]).astype(t),
np.array([1, 2, 3]).astype(t))
def testFloatTypes(self):
for t in [np.float32, np.float64]:
self._testAll(np.random.rand(4, 3, 3).astype(t),
np.random.rand(3).astype(t))
def testGradientTensor(self):
with self.test_session():
t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
dtype=tf.float64)
b = tf.constant([1.3, 2.4], dtype=tf.float64)
bo = tf.nn.bias_add(t, b)
err = tf.test.compute_gradient_error(t, [3, 2], bo, [3, 2])
print("bias add tensor gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientBias(self):
with self.test_session():
t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
dtype=tf.float64)
b = tf.constant([1.3, 2.4], dtype=tf.float64)
bo = tf.nn.bias_add(t, b)
err = tf.test.compute_gradient_error(b, [2], bo, [3, 2])
print("bias add bias gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientTensor4D(self):
with self.test_session():
s = [2, 3, 4, 2]
x = np.arange(1.0, 49.0).reshape(s).astype(np.float32)
t = tf.constant(x, shape=s, dtype=tf.float32)
b = tf.constant([1.3, 2.4], dtype=tf.float32)
bo = tf.nn.bias_add(t, b)
err = tf.test.compute_gradient_error(t, s, bo, s, x_init_value=x)
print("bias add tensor gradient err = ", err)
self.assertLess(err, 1e-3)
if __name__ == "__main__":
tf.test.main()