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 InputType, typename TargetType>
  typename InputType::elem_type Forward(const InputType& input,
                                        const TargetType& target);
 
  template<typename InputType, typename TargetType, typename OutputType>
  void Backward(const InputType& input,
                const TargetType& target,
                OutputType& output);
 
  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 unsigned int /* 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