hinge_embedding_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_HINGE_EMBEDDING_LOSS_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_HINGE_EMBEDDING_LOSS_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
HingeEmbeddingLoss<InputDataType, OutputDataType>::HingeEmbeddingLoss()
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
HingeEmbeddingLoss<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  TargetType temp = target - (target == 0);
  return (arma::accu(arma::max(1 - prediction % temp, 0.))) / target.n_elem;
}

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

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void HingeEmbeddingLoss<InputDataType, OutputDataType>::serialize(
    Archive& /* ar */,
    const uint32_t /* version */)
{
  // Nothing to do here.
}

} // namespace ann
} // namespace mlpack

#endif