flexible_relu_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_FLEXIBLERELU_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_FLEXIBLERELU_IMPL_HPP

#include "flexible_relu.hpp"
#include<algorithm>

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
FlexibleReLU<InputDataType, OutputDataType>::FlexibleReLU(
    const double alpha) : userAlpha(alpha)
{
  this->alpha.set_size(1, 1);
  this->alpha(0) = userAlpha;
}

template<typename InputDataType, typename OutputDataType>
void FlexibleReLU<InputDataType, OutputDataType>::Reset()
{
  alpha(0) = userAlpha;
}

template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename OutputType>
void FlexibleReLU<InputDataType, OutputDataType>::Forward(
    const InputType& input, OutputType& output)
{
  output = arma::clamp(input, 0.0, DBL_MAX) + alpha(0);
}

template<typename InputDataType, typename OutputDataType>
template<typename DataType>
void FlexibleReLU<InputDataType, OutputDataType>::Backward(
    const DataType& input, const DataType& gy, DataType& g)
{
  g = gy % arma::clamp(arma::sign(input), 0.0, 1.0);
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void FlexibleReLU<InputDataType, OutputDataType>::Gradient(
    const arma::Mat<eT>& input,
    const arma::Mat<eT>& error,
    arma::Mat<eT>& gradient)
{
  if (gradient.n_elem == 0)
  {
    gradient.set_size(1, 1);
  }

  gradient(0) = arma::accu(error) / input.n_cols;
}


template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void FlexibleReLU<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const uint32_t /* version*/)
{
  ar(CEREAL_NVP(alpha));
}

} // namespace ann
} // namespace mlpack

#endif