vr_class_reward.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP
#define MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP

#include <mlpack/prereqs.hpp>

#include "layer_types.hpp"

namespace mlpack {
namespace ann  {

template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class VRClassReward
{
 public:
  VRClassReward(const double scale = 1, const bool sizeAverage = true);

  template<typename InputType, typename TargetType>
  double Forward(const InputType& input, const TargetType& target);

  template<typename InputType, typename TargetType, typename OutputType>
  void Backward(const InputType& input,
                const TargetType& target,
                OutputType& output);

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

  OutputDataType& Delta() const {return delta; }
  OutputDataType& Delta() { return delta; }

  /*
   * Add a new module to the model.
   *
   * @param args The layer parameter.
   */
  template <class LayerType, class... Args>
  void Add(Args... args) { network.push_back(new LayerType(args...)); }

  /*
   * Add a new module to the model.
   *
   * @param layer The Layer to be added to the model.
   */
  void Add(LayerTypes<> layer) { network.push_back(layer); }

  std::vector<LayerTypes<> >& Model() { return network; }

  bool SizeAverage() const { return sizeAverage; }

  double Scale() const { return scale; }

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

 private:
  double scale;

  bool sizeAverage;

  double reward;

  OutputDataType delta;

  OutputDataType outputParameter;

  std::vector<LayerTypes<> > network;
}; // class VRClassReward

} // namespace ann
} // namespace mlpack

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

#endif