triplet_margin_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_TRIPLET_MARGIN_IMPL_LOSS_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_TRIPLET_MARGIN_IMPL_LOSS_HPP

// In case it hasn't been included.
#include "triplet_margin_loss.hpp"

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
TripletMarginLoss<InputDataType, OutputDataType>::TripletMarginLoss(
    const double margin) : margin(margin)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
TripletMarginLoss<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  PredictionType anchor =
      prediction.submat(0, 0, prediction.n_rows / 2 - 1, prediction.n_cols - 1);
  PredictionType positive =
      prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1,
      prediction.n_cols - 1);
  return std::max(0.0, arma::accu(arma::pow(anchor - positive, 2)) -
      arma::accu(arma::pow(anchor - target, 2)) + margin) / anchor.n_cols;
}

template<typename InputDataType, typename OutputDataType>
template <
    typename PredictionType,
    typename TargetType,
    typename LossType
>
void TripletMarginLoss<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  PredictionType positive =
      prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1,
      prediction.n_cols - 1);
  loss = 2 * (target - positive) / target.n_cols;
}

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void TripletMarginLoss<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const unsigned int /* version */)
{
  ar(CEREAL_NVP(margin));
}

} // namespace ann
} // namespace mlpack

#endif