alpha_dropout.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_HPP
#define MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann  {

template <typename InputDataType = arma::mat,
          typename OutputDataType = arma::mat>
class AlphaDropout
{
 public:
  AlphaDropout(const double ratio = 0.5,
               const double alphaDash = -alpha * lambda);

  template<typename eT>
  void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);

  template<typename eT>
  void Backward(const arma::Mat<eT>& /* input */,
                const arma::Mat<eT>& gy,
                arma::Mat<eT>& g);

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

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

  bool Deterministic() const { return deterministic; }
  bool& Deterministic() { return deterministic; }

  double Ratio() const { return ratio; }

  double A() const { return a; }

  double B() const { return b; }

  double AlphaDash() const {return alphaDash; }

  OutputDataType const& Mask() const {return mask;}

  void Ratio(const double r)
  {
    ratio = r;
    a = pow((1 - ratio) * (1 + ratio * pow(alphaDash, 2)), -0.5);
    b = -a * alphaDash * ratio;
  }

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */);

 private:
  OutputDataType delta;

  OutputDataType outputParameter;

  OutputDataType mask;

  double ratio;

  double alphaDash;

  bool deterministic;

  static constexpr double alpha = 1.6732632423543772848170429916717;

  static constexpr double lambda = 1.0507009873554804934193349852946;

  double a;

  double b;
}; // class AlphaDropout

} // namespace ann
} // namespace mlpack

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

#endif