poisson_nll_loss.hpp

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#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_POISSON_NLL_LOSS_HPP
#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_POISSON_NLL_LOSS_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann  {

template <
  typename InputDataType = arma::mat,
  typename OutputDataType = arma::mat
>
class PoissonNLLLoss
{
 public:
  PoissonNLLLoss(const bool logInput = true,
                 const bool full = false,
                 const typename InputDataType::elem_type eps = 1e-08,
                 const bool mean = true);

  template<typename PredictionType, typename TargetType>
  typename InputDataType::elem_type Forward(const PredictionType& prediction,
                                            const TargetType& target);

  template<typename PredictionType, typename TargetType, typename LossType>
  void Backward(const PredictionType& prediction,
                const TargetType& target,
                LossType& loss);

  InputDataType& InputParameter() const { return inputParameter; }
  InputDataType& InputParameter() { return inputParameter; }

  OutputDataType& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }

  bool LogInput() const { return logInput; }
  bool& LogInput() { return logInput; }

  bool Full() const { return full; }
  bool& Full() { return full; }

  typename InputDataType::elem_type Eps() const { return eps; }
  typename InputDataType::elem_type& Eps() { return eps; }

  bool Mean() const { return mean; }
  bool& Mean() { return mean; }

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */);

 private:
  template<typename eT>
  void CheckProbs(const arma::Mat<eT>& probs)
  {
    for (size_t i = 0; i < probs.size(); ++i)
    {
      if (probs[i] > 1.0 || probs[i] < 0.0)
        Log::Fatal << "Probabilities cannot be greater than 1 "
                   << "or smaller than 0." << std::endl;
    }
  }

  InputDataType inputParameter;

  OutputDataType outputParameter;

  bool logInput;

  // approximation term.
  bool full;

  typename InputDataType::elem_type eps;

  bool mean;
}; // class PoissonNLLLoss

} // namespace ann
} // namespace mlpack

// Include implementation.
#include "poisson_nll_loss_impl.hpp"

#endif