1/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#ifndef TENSORFLOW_CORE_KERNELS_SOFTSIGN_OP_H_
17#define TENSORFLOW_CORE_KERNELS_SOFTSIGN_OP_H_
18// Functor definition for SoftsignOp and SoftsignGradOp, must be compilable by
19// nvcc.
20
21#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
22#include "tensorflow/core/framework/tensor_types.h"
23
24namespace tensorflow {
25namespace functor {
26
27// Functor used by SoftsignOp to do the computations.
28template <typename Device, typename T>
29struct Softsign {
30 // Computes Softsign activation.
31 //
32 // features: any shape.
33 // activations: same shape as "features".
34 void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
35 typename TTypes<T>::Tensor activations) {
36 activations.device(d) =
37 features / (features.abs() + features.constant(T(1)));
38 }
39};
40
41// Functor used by SoftsignGradOp to do the computations.
42template <typename Device, typename T>
43struct SoftsignGrad {
44 // Computes SoftsignGrad backprops.
45 //
46 // gradients: gradients backpropagated to the Softsign op.
47 // features: inputs that were passed to the Softsign op.
48 // backprops: gradients to backpropagate to the Softsign inputs.
49 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
50 typename TTypes<T>::ConstTensor features,
51 typename TTypes<T>::Tensor backprops) {
52 backprops.device(d) =
53 gradients / (features.abs() + features.constant(T(1))).square();
54 }
55};
56
57} // namespace functor
58} // namespace tensorflow
59
60#endif // TENSORFLOW_CORE_KERNELS_SOFTSIGN_OP_H_
61