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 "../visitor/input_shape_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; }

  size_t InputShape() const;

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