multiply_merge.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_HPP
#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_HPP

#include <mlpack/prereqs.hpp>

// #include "../visitor/delete_visitor.hpp"
// #include "../visitor/delta_visitor.hpp"
// #include "../visitor/output_parameter_visitor.hpp"

#include "layer_types.hpp"

namespace mlpack {
namespace ann  {

template<
    typename InputType = arma::mat,
    typename OutputType = arma::mat
>
class MultiplyMergeType : public MultiLayer<InputType, OutputType>
{
 public:
  MultiplyMergeType(const bool model = false, const bool run = true);

  MultiplyMerge(const MultiplyMerge& layer);

  MultiplyMerge(MultiplyMerge&& layer);

  MultiplyMerge& operator=(const MultiplyMerge& layer);

  MultiplyMerge& operator=(MultiplyMerge&& layer);

  ~MultiplyMergeType();

  MultiplyMergeType* Clone() const { return new MultiplyMergeType(*this); }

  void Forward(const InputType& /* input */, OutputType& output);

  void Backward(const InputType& /* input */,
                const OutputType& gy,
                OutputType& g);

  /*
   * Calculate the gradient using the output delta and the input activation.
   *
   * @param input The input parameter used for calculating the gradient.
   * @param error The calculated error.
   * @param gradient The calculated gradient.
   */
  void Gradient(const InputType& input,
                const OutputType& error,
                OutputType& gradient);

  OutputType const& Parameters() const { return weights; }
  OutputType& Parameters() { return weights; }

  size_t WeightSize() const { return 0; }

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

 private:
  bool run;

  bool ownsLayer;

  OutputType weights;
}; // class MultiplyMergeType

// Standard MultiplyMerge layer.
typedef MultiplyMergeType<arma::mat, arma::mat> MultiplyMerge;

} // namespace ann
} // namespace mlpack

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

#endif