kl_divergence_impl.hpp

Go to the documentation of this file.

#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_KL_DIVERGENCE_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_KL_DIVERGENCE_IMPL_HPP

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

namespace mlpack {
namespace ann  {

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

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
KLDivergence<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  if (takeMean)
  {
    return arma::as_scalar(arma::mean(
        arma::mean(prediction % (arma::log(prediction) - arma::log(target)))));
  }
  else
  {
    return arma::accu(prediction % (arma::log(prediction) - arma::log(target)));
  }
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void KLDivergence<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  if (takeMean)
  {
    loss = arma::mean(arma::mean(
        arma::log(prediction) - arma::log(target) + 1));
  }
  else
  {
    loss = arma::accu(arma::log(prediction) - arma::log(target) + 1);
  }
}

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

} // namespace ann
} // namespace mlpack

#endif