linear.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_LINEAR_HPP
#define MLPACK_METHODS_ANN_LAYER_LINEAR_HPP
 
#include <mlpack/prereqs.hpp>
#include <mlpack/methods/ann/regularizer/no_regularizer.hpp>
 
#include "layer_types.hpp"
 
namespace mlpack {
namespace ann  {
 
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat,
    typename RegularizerType = NoRegularizer
>
class Linear
{
 public:
  Linear();
 
  Linear(const size_t inSize,
         const size_t outSize,
         RegularizerType regularizer = RegularizerType());
 
  Linear(const Linear& layer);
 
  Linear(Linear&&);
 
  Linear& operator=(const Linear& layer);
 
  Linear& operator=(Linear&& layer);
 
  /*
   * Reset the layer parameter.
   */
  void Reset();
 
  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; }
 
  OutputDataType const& Weight() const { return weight; }
  OutputDataType& Weight() { return weight; }
 
  OutputDataType const& Bias() const { return bias; }
  OutputDataType& Bias() { return bias; }
 
  size_t WeightSize() const
  {
    return (inSize * outSize) + outSize;
  }
 
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);
 
 private:
  size_t inSize;
 
  size_t outSize;
 
  OutputDataType weights;
 
  OutputDataType weight;
 
  OutputDataType bias;
 
  OutputDataType delta;
 
  OutputDataType gradient;
 
  InputDataType inputParameter;
 
  OutputDataType outputParameter;
 
  RegularizerType regularizer;
}; // class Linear
 
} // namespace ann
} // namespace mlpack
 
// Include implementation.
#include "linear_impl.hpp"
 
#endif