gru.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_GRU_HPP
#define MLPACK_METHODS_ANN_LAYER_GRU_HPP

#include <list>
#include <limits>

#include <mlpack/prereqs.hpp>

#include "../visitor/delta_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
>
class GRU
{
 public:
  GRU();

  GRU(const size_t inSize,
      const size_t outSize,
      const size_t rho = std::numeric_limits<size_t>::max());

  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 */);

  /*
   * Resets the cell to accept a new input. This breaks the BPTT chain starts a
   * new one.
   *
   * @param size The current maximum number of steps through time.
   */
  void ResetCell(const size_t size);

  bool Deterministic() const { return deterministic; }
  bool& Deterministic() { return deterministic; }

  size_t Rho() const { return rho; }
  size_t& Rho() { return rho; }

  OutputDataType const& Parameters() const { return weights; }
  OutputDataType& Parameters() { return weights; }

  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; }

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

  size_t InSize() const { return inSize; }

  size_t OutSize() const { return outSize; }

  size_t InputShape() const 
  {
    return inSize;
  }

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

 private:
  size_t inSize;

  size_t outSize;

  size_t rho;

  size_t batchSize;

  OutputDataType weights;

  LayerTypes<> input2GateModule;

  LayerTypes<> output2GateModule;

  LayerTypes<> outputHidden2GateModule;

  LayerTypes<> inputGateModule;

  LayerTypes<> hiddenStateModule;

  LayerTypes<> forgetGateModule;

  OutputParameterVisitor outputParameterVisitor;

  DeltaVisitor deltaVisitor;

  DeleteVisitor deleteVisitor;

  std::vector<LayerTypes<> > network;

  size_t forwardStep;

  size_t backwardStep;

  size_t gradientStep;

  std::list<arma::mat> outParameter;

  arma::mat allZeros;

  std::list<arma::mat>::iterator prevOutput;

  std::list<arma::mat>::iterator backIterator;

  std::list<arma::mat>::iterator gradIterator;

  arma::mat prevError;

  bool deterministic;

  OutputDataType delta;

  OutputDataType gradient;

  OutputDataType outputParameter;
}; // class GRU

} // namespace ann
} // namespace mlpack

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

#endif