mean_squared_logarithmic_error_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_LOGARITHMIC_ERROR_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTION_MEAN_SQUARED_LOGARITHMIC_ERROR_IMPL_HPP

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

namespace mlpack {
namespace ann  {

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

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
MeanSquaredLogarithmicError<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  return arma::accu(arma::square(arma::log(1. + target) -
      arma::log(1. + prediction))) / target.n_cols;
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void MeanSquaredLogarithmicError<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  loss = 2 * (arma::log(1. + prediction) - arma::log(1. + target)) /
      ((1. + prediction) * target.n_cols);
}

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

} // namespace ann
} // namespace mlpack

#endif