hinge_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_HINGE_LOSS_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_HINGE_LOSS_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
HingeLoss<InputDataType, OutputDataType>::HingeLoss(const bool reduction):
  reduction(reduction)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
HingeLoss<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  TargetType temp = target - (target == 0);
  TargetType temp_zeros(size(target), arma::fill::zeros);

  PredictionType loss = arma::max(temp_zeros, 1 - prediction % temp);

  typename PredictionType::elem_type lossSum = arma::accu(loss);

  if (reduction)
    return lossSum;

  return lossSum / loss.n_elem;
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void HingeLoss<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  TargetType temp = target - (target == 0);
  loss = (prediction < (1 / temp)) % -temp;

  if (!reduction)
    loss /= target.n_elem;
}

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

} // namespace ann
} // namespace mlpack

#endif