elu.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ELU_HPP
#define MLPACK_METHODS_ANN_LAYER_ELU_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann  {

template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class ELU
{
 public:
  ELU(const double alpha = 1.0);

  template<typename InputType, typename OutputType>
  void Forward(const InputType&& input, OutputType&& output);

  template<typename DataType>
  void Backward(const DataType&& input, DataType&& gy, DataType&& g);

  InputDataType const& InputParameter() const { return inputParameter; }
  InputDataType& InputParameter() { return inputParameter; }

  OutputDataType const& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }

  OutputDataType const& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }

  double const& Alpha() const { return alpha; }
  double& Alpha() { return alpha; }

  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);

 private:
  double Fn(const double x)
  {
    if (x < DBL_MAX)
      return (x > 0) ? x : alpha * (std::exp(x) - 1);
    return 1.0;
  }

  template<typename eT>
  void Fn(const arma::Mat<eT>& x, arma::Mat<eT>& y)
  {
    y = x;

    for (size_t i = 0; i < x.n_elem; i++)
    {
      y(i) = Fn(x(i));
    }
  }

  double Deriv(const double y)
  {
    return (y > 0) ? 1 : (y + alpha);
  }

  template<typename InputType, typename OutputType>
  void Deriv(const InputType& x, OutputType& y)
  {
    y = x;

    for (size_t i = 0; i < x.n_elem; i++)
    {
      y(i) = Deriv(x(i));
    }
  }

  OutputDataType delta;

  InputDataType inputParameter;

  OutputDataType outputParameter;

  double alpha;
}; // class ELU

} // namespace ann
} // namespace mlpack

// Include implementation.
#include "elu_impl.hpp"

#endif