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# 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 Softsign and SoftsignGrad."""
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.ops import gradient_checker
from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
from tensorflow.python.platform import test
class SoftsignTest(test.TestCase):
def _npSoftsign(self, np_features):
return np_features / (1 + np.abs(np_features))
def _testSoftsign(self, np_features, use_gpu=False):
np_softsign = self._npSoftsign(np_features)
with self.test_session(use_gpu=use_gpu):
softsign = nn_ops.softsign(np_features)
tf_softsign = softsign.eval()
self.assertAllClose(np_softsign, tf_softsign)
self.assertShapeEqual(np_softsign, softsign)
def testNumbers(self):
for t in [np.float, np.double]:
self._testSoftsign(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=False)
self._testSoftsign(
np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
use_gpu=True)
def testGradient(self):
with self.cached_session():
x = constant_op.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 = nn_ops.softsign(x, name="softsign")
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 = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("softsign (float) gradient err = ", err)
self.assertLess(err, 1e-4)
def testNoInts(self):
with self.cached_session():
with self.assertRaisesRegexp(
TypeError,
"'features' has DataType int32 not in list of allowed values"):
nn_ops.softsign(constant_op.constant(7)).eval()
if __name__ == "__main__":
test.main()
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