reconstruction_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_RECONSTRUCTION_LOSS_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_RECONSTRUCTION_LOSS_IMPL_HPP

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

namespace mlpack {
namespace ann  {

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

template<typename InputDataType, typename OutputDataType, typename DistType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
ReconstructionLoss<InputDataType, OutputDataType, DistType>::Forward(
    const PredictionType& prediction, const TargetType& target)
{
  dist = DistType(prediction);
  return -dist.LogProbability(target);
}

template<typename InputDataType, typename OutputDataType, typename DistType>
template<typename PredictionType, typename TargetType, typename LossType>
void ReconstructionLoss<InputDataType, OutputDataType, DistType>::Backward(
    const PredictionType& /* prediction */,
    const TargetType& target,
    LossType& loss)
{
  dist.LogProbBackward(target, loss);
  loss *= -1;
}

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

} // namespace ann
} // namespace mlpack

#endif