recurrent.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_HPP
#define MLPACK_METHODS_ANN_LAYER_RECURRENT_HPP
 
#include <mlpack/core.hpp>
 
#include "../visitor/delete_visitor.hpp"
#include "../visitor/delta_visitor.hpp"
#include "../visitor/copy_visitor.hpp"
#include "../visitor/output_parameter_visitor.hpp"
 
#include "layer_types.hpp"
#include "add_merge.hpp"
#include "sequential.hpp"
 
namespace mlpack {
namespace ann  {
 
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat,
    typename... CustomLayers
>
class Recurrent
{
 public:
  Recurrent();
 
  Recurrent(const Recurrent&);
 
  template<typename StartModuleType,
           typename InputModuleType,
           typename FeedbackModuleType,
           typename TransferModuleType>
  Recurrent(const StartModuleType& start,
            const InputModuleType& input,
            const FeedbackModuleType& feedback,
            const TransferModuleType& transfer,
            const size_t rho);
 
  template<typename eT>
  void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);
 
  template<typename eT>
  void Backward(const arma::Mat<eT>& /* input */,
                const arma::Mat<eT>& gy,
                arma::Mat<eT>& g);
 
  /*
   * Calculate the gradient using the output delta and the input activation.
   *
   * @param input The input parameter used for calculating the gradient.
   * @param error The calculated error.
   * @param gradient The calculated gradient.
   */
  template<typename eT>
  void Gradient(const arma::Mat<eT>& input,
                const arma::Mat<eT>& error,
                arma::Mat<eT>& /* gradient */);
 
  std::vector<LayerTypes<CustomLayers...> >& Model() { return network; }
 
  bool Deterministic() const { return deterministic; }
  bool& Deterministic() { return deterministic; }
 
  OutputDataType const& Parameters() const { return parameters; }
  OutputDataType& Parameters() { return parameters; }
 
  OutputDataType const& OutputParameter() const { return outputParameter; }
  OutputDataType& OutputParameter() { return outputParameter; }
 
  OutputDataType const& Delta() const { return delta; }
  OutputDataType& Delta() { return delta; }
 
  OutputDataType const& Gradient() const { return gradient; }
  OutputDataType& Gradient() { return gradient; }
 
  size_t const& Rho() const { return rho; }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);
 
 private:
  DeleteVisitor deleteVisitor;
 
  CopyVisitor<CustomLayers...> copyVisitor;
 
  LayerTypes<CustomLayers...> startModule;
 
  LayerTypes<CustomLayers...> inputModule;
 
  LayerTypes<CustomLayers...> feedbackModule;
 
  LayerTypes<CustomLayers...> transferModule;
 
  size_t rho;
 
  size_t forwardStep;
 
  size_t backwardStep;
 
  size_t gradientStep;
 
  bool deterministic;
 
  bool ownsLayer;
 
  OutputDataType parameters;
 
  LayerTypes<CustomLayers...> initialModule;
 
  LayerTypes<CustomLayers...> recurrentModule;
 
  std::vector<LayerTypes<CustomLayers...> > network;
 
  LayerTypes<CustomLayers...> mergeModule;
 
  DeltaVisitor deltaVisitor;
 
  OutputParameterVisitor outputParameterVisitor;
 
  std::vector<arma::mat> feedbackOutputParameter;
 
  OutputDataType delta;
 
  OutputDataType gradient;
 
  OutputDataType outputParameter;
 
  arma::mat recurrentError;
}; // class Recurrent
 
} // namespace ann
} // namespace mlpack
 
// Include implementation.
#include "recurrent_impl.hpp"
 
#endif