binary_cross_entropy_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_CROSS_ENTROPY_ERROR_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_CROSS_ENTROPY_ERROR_IMPL_HPP

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

namespace mlpack {
namespace ann  {

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

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
BCELoss<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  typedef typename PredictionType::elem_type ElemType;

  ElemType loss = -arma::accu(target % arma::log(prediction + eps) +
      (1. - target) % arma::log(1. - prediction + eps));
  if (reduction)
    loss /= prediction.n_elem;
  return loss;
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void BCELoss<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  loss = (1. - target) / (1. - prediction + eps) - target / (prediction + eps);
  if (reduction)
    loss /= prediction.n_elem;
}

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

} // namespace ann
} // namespace mlpack

#endif