/* 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.
==============================================================================*/

#ifndef TENSORFLOW_KERNELS_SOFTSIGN_OP_H_
#define TENSORFLOW_KERNELS_SOFTSIGN_OP_H_
// Functor definition for SoftsignOp and SoftsignGradOp, must be compilable by
// nvcc.

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"

namespace tensorflow {
namespace functor {

// Functor used by SoftsignOp to do the computations.
template <typename Device, typename T>
struct Softsign {
  // Computes Softsign activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    activations.device(d) =
        features / (features.abs() + features.constant(T(1)));
  }
};

// Functor used by SoftsignGradOp to do the computations.
template <typename Device, typename T>
struct SoftsignGrad {
  // Computes SoftsignGrad backprops.
  //
  // gradients: gradients backpropagated to the Softsign op.
  // features: inputs that were passed to the Softsign op.
  // backprops: gradients to backpropagate to the Softsign inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor backprops) {
    backprops.device(d) =
        gradients / (features.abs() + features.constant(T(1))).square();
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_KERNELS_SOFTSIGN_OP_H_
