1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Tests for Softsign and SoftsignGrad.""" 16 17 from __future__ import absolute_import 18 from __future__ import division 19 from __future__ import print_function 20 21 import numpy as np 22 23 from tensorflow.python.framework import constant_op 24 from tensorflow.python.ops import gradient_checker 25 from tensorflow.python.ops import nn_ops 26 import tensorflow.python.ops.nn_grad # pylint: disable=unused-import 27 from tensorflow.python.platform import test 28 29 30 class SoftsignTest(test.TestCase): 31 32 def _npSoftsign(self, np_features): 33 return np_features / (1 + np.abs(np_features)) 34 35 def _testSoftsign(self, np_features, use_gpu=False): 36 np_softsign = self._npSoftsign(np_features) 37 with self.test_session(use_gpu=use_gpu): 38 softsign = nn_ops.softsign(np_features) 39 tf_softsign = softsign.eval() 40 self.assertAllClose(np_softsign, tf_softsign) 41 self.assertShapeEqual(np_softsign, softsign) 42 43 def testNumbers(self): 44 for t in [np.float, np.double]: 45 self._testSoftsign( 46 np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t), 47 use_gpu=False) 48 self._testSoftsign( 49 np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t), 50 use_gpu=True) 51 52 def testGradient(self): 53 with self.test_session(): 54 x = constant_op.constant( 55 [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9], 56 shape=[2, 5], 57 name="x") 58 y = nn_ops.softsign(x, name="softsign") 59 x_init = np.asarray( 60 [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]], 61 dtype=np.float32, 62 order="F") 63 err = gradient_checker.compute_gradient_error( 64 x, [2, 5], y, [2, 5], x_init_value=x_init) 65 print("softsign (float) gradient err = ", err) 66 self.assertLess(err, 1e-4) 67 68 def testWarnInts(self): 69 # NOTE(irving): Actually I don't know how to intercept the warning, but 70 # let's make sure it runs. I promised I've looked, and there was a warning. 71 with self.test_session(): 72 nn_ops.softsign(constant_op.constant(7)).eval() 73 74 75 if __name__ == "__main__": 76 test.main() 77
