poisson_nll_loss_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_POISSON_NLL_LOSS_IMPL_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_POISSON_NLL_LOSS_IMPL_HPP


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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
PoissonNLLLoss<InputDataType, OutputDataType>::PoissonNLLLoss(
    const bool logInput,
    const bool full,
    const typename InputDataType::elem_type eps,
    const bool mean):
    logInput(logInput),
    full(full),
    eps(eps),
    mean(mean)
{
  Log::Assert(eps >= 0, "Epsilon (eps) must be greater than or equal to zero.");
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType>
typename InputDataType::elem_type
PoissonNLLLoss<InputDataType, OutputDataType>::Forward(
    const PredictionType& prediction,
    const TargetType& target)
{
  PredictionType loss(arma::size(prediction));

  if (logInput)
    loss = arma::exp(prediction) - target % prediction;
  else
  {
    CheckProbs(prediction);
    loss = prediction - target % arma::log(prediction + eps);
  }

  if (full)
  {
    const auto mask = target > 1.0;
    const PredictionType approx = target % arma::log(target) - target
        + 0.5 * arma::log(2 * M_PI * target);
    loss.elem(arma::find(mask)) += approx.elem(arma::find(mask));
  }

  return mean ? arma::accu(loss) / loss.n_elem : arma::accu(loss);
}

template<typename InputDataType, typename OutputDataType>
template<typename PredictionType, typename TargetType, typename LossType>
void PoissonNLLLoss<InputDataType, OutputDataType>::Backward(
    const PredictionType& prediction,
    const TargetType& target,
    LossType& loss)
{
  loss.set_size(size(prediction));

  if (logInput)
    loss = (arma::exp(prediction) - target);
  else
    loss = (1 - target / (prediction + eps));

  if (mean)
    loss = loss / loss.n_elem;
}

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

} // namespace ann
} // namespace mlpack

#endif