aboutsummaryrefslogtreecommitdiffhomepage
path: root/tensorflow/examples/adding_an_op/zero_out_2_test.py
blob: 217bbbcffa3f9009008f76d951a3bad68bc8b85d (plain)
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
# 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.
# ==============================================================================

"""Test for version 2 of the zero_out op."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf


from tensorflow.examples.adding_an_op import zero_out_grad_2  # pylint: disable=unused-import
from tensorflow.examples.adding_an_op import zero_out_op_2


class ZeroOut2Test(tf.test.TestCase):

  def test(self):
    with self.test_session():
      result = zero_out_op_2.zero_out([5, 4, 3, 2, 1])
      self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0])

  def test_2d(self):
    with self.test_session():
      result = zero_out_op_2.zero_out([[6, 5, 4], [3, 2, 1]])
      self.assertAllEqual(result.eval(), [[6, 0, 0], [0, 0, 0]])

  def test_grad(self):
    with self.test_session():
      shape = (5,)
      x = tf.constant([5, 4, 3, 2, 1], dtype=tf.float32)
      y = zero_out_op_2.zero_out(x)
      err = tf.test.compute_gradient_error(x, shape, y, shape)
      self.assertLess(err, 1e-4)

  def test_grad_2d(self):
    with self.test_session():
      shape = (2, 3)
      x = tf.constant([[6, 5, 4], [3, 2, 1]], dtype=tf.float32)
      y = zero_out_op_2.zero_out(x)
      err = tf.test.compute_gradient_error(x, shape, y, shape)
      self.assertLess(err, 1e-4)


if __name__ == '__main__':
  tf.test.main()