layer_norm.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_LAYERNORM_HPP
#define MLPACK_METHODS_ANN_LAYER_LAYERNORM_HPP
 
#include <mlpack/prereqs.hpp>
 
namespace mlpack {
namespace ann  {
 
template <
  typename InputDataType = arma::mat,
  typename OutputDataType = arma::mat
>
class LayerNorm
{
 public:
  LayerNorm();
 
  LayerNorm(const size_t size, const double eps = 1e-8);
 
  void Reset();
 
  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);
 
  template<typename eT>
  void Gradient(const arma::Mat<eT>& input,
                const arma::Mat<eT>& error,
                arma::Mat<eT>& gradient);
 
  OutputDataType const& Parameters() const { return weights; }
  OutputDataType& Parameters() { return weights; }
 
  OutputDataType const& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }
 
  OutputDataType const& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }
 
  OutputDataType const& Gradient() const { return gradient; }
  OutputDataType& Gradient() { return gradient; }
 
  OutputDataType Mean() { return mean; }
 
  OutputDataType Variance() { return variance; }
 
  size_t InSize() const { return size; }
 
  double Epsilon() const { return eps; }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);
 
 private:
  size_t size;
 
  double eps;
 
  bool loading;
 
  OutputDataType gamma;
 
  OutputDataType beta;
 
  OutputDataType weights;
 
  OutputDataType mean;
 
  OutputDataType variance;
 
  OutputDataType gradient;
 
  OutputDataType delta;
 
  OutputDataType outputParameter;
 
  OutputDataType normalized;
 
  OutputDataType inputMean;
}; // class LayerNorm
 
} // namespace ann
} // namespace mlpack
 
// Include the implementation.
#include "layer_norm_impl.hpp"
 
#endif