dice_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_DICE_LOSS_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_DICE_LOSS_IMPL_HPP

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

namespace mlpack {
namespace ann  {

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

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type DiceLoss<InputDataType, OutputDataType>
    ::Forward(const PredictionType& prediction,
              const TargetType& target)
{
  return 1 - ((2 * arma::accu(target % prediction) + smooth) /
    (arma::accu(target % target) + arma::accu(
    prediction % prediction) + smooth));
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void DiceLoss<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  loss = -2 * (target * (arma::accu(prediction % prediction) +
    arma::accu(target % target) + smooth) - prediction *
    (2 * arma::accu(target % prediction) + smooth)) / std::pow(
    arma::accu(target % target) + arma::accu(prediction % prediction)
    + smooth, 2.0);
}

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void DiceLoss<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const uint32_t /* version */)
{
  ar(CEREAL_NVP(smooth));
}

} // namespace ann
} // namespace mlpack

#endif