noisylinear.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_NOISYLINEAR_HPP
#define MLPACK_METHODS_ANN_LAYER_NOISYLINEAR_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann  {

template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class NoisyLinear
{
 public:
  NoisyLinear();

  NoisyLinear(const size_t inSize,
              const size_t outSize);

  NoisyLinear(const NoisyLinear&);

  /*
   * Reset the layer parameter.
   */
  void Reset();

  /*
   * Reset the noise parameters(epsilons).
   */
  void ResetNoise();

  /*
   * Reset the values of layer parameters (factorized gaussian noise).
   */
  void ResetParameters();

  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);

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

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

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

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

  size_t InputSize() const { return inSize; }

  size_t OutputSize() const { return outSize; }

  OutputDataType const& Gradient() const { return gradient; }
  OutputDataType& Gradient() { return gradient; }

  arma::mat& Bias() { return bias; }

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

 private:
  size_t inSize;

  size_t outSize;

  OutputDataType weights;

  OutputDataType weight;

  OutputDataType weightMu;

  OutputDataType weightSigma;

  OutputDataType weightEpsilon;

  OutputDataType bias;

  OutputDataType biasMu;

  OutputDataType biasSigma;

  OutputDataType biasEpsilon;

  OutputDataType delta;

  OutputDataType gradient;

  InputDataType inputParameter;

  OutputDataType outputParameter;
}; // class NoisyLinear

} // namespace ann
} // namespace mlpack

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

#endif