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; }

  size_t InputShape() const
  {
    return size;
  }

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