cosine_embedding_loss.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_COSINE_EMBEDDING_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_COSINE_EMBEDDING_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann  {

template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class CosineEmbeddingLoss
{
 public:
  CosineEmbeddingLoss(const double margin = 0.0,
                      const bool similarity = true,
                      const bool takeMean = false);

  template <typename PredictionType, typename TargetType>
  typename PredictionType::elem_type Forward(const PredictionType& prediction,
                                             const TargetType& target);

  template<typename PredictionType, typename TargetType, typename LossType>
  void Backward(const PredictionType& prediction,
                const TargetType& target,
                LossType& loss);

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

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

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

  bool TakeMean() const { return takeMean; }
  bool& TakeMean() { return takeMean; }

  double Margin() const { return margin; }
  double& Margin() { return margin; }

  bool Similarity() const { return similarity; }
  bool& Similarity() { return similarity; }

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

 private:
  OutputDataType delta;

  InputDataType inputParameter;

  OutputDataType outputParameter;

  double margin;

  bool similarity;

  bool takeMean;
}; // class CosineEmbeddingLoss

} // namespace ann
} // namespace mlpack

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

#endif