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#ifndef _CUSHARED_LINEARITY_H_
#define _CUSHARED_LINEARITY_H_
#include "Component.h"
#include "Matrix.h"
#include "Vector.h"
namespace TNet {
class SharedLinearity : public UpdatableComponent
{
public:
SharedLinearity(size_t nInputs, size_t nOutputs, Component *pPred);
~SharedLinearity();
ComponentType GetType() const
{ return SHARED_LINEARITY; }
const char* GetName() const
{ return "<SharedLinearity>"; }
Component* Clone() const;
void PropagateFnc(const Matrix<BaseFloat>& X, Matrix<BaseFloat>& Y);
void BackpropagateFnc(const Matrix<BaseFloat>& X, Matrix<BaseFloat>& Y);
void ReadFromStream(std::istream& rIn);
void WriteToStream(std::ostream& rOut);
/// calculate gradient
void Gradient();
/// accumulate gradient from other components
void AccuGradient(const UpdatableComponent& src, int thr, int thrN);
/// update weights, reset the accumulator
void Update(int thr, int thrN);
protected:
Matrix<BaseFloat> mLinearity; ///< Matrix with neuron weights
Vector<BaseFloat> mBias; ///< Vector with biases
Matrix<BaseFloat>* mpLinearity;
Vector<BaseFloat>* mpBias;
Matrix<BaseFloat> mLinearityCorrection; ///< Matrix for linearity updates
Vector<BaseFloat> mBiasCorrection; ///< Vector for bias updates
Matrix<double> mLinearityCorrectionAccu; ///< Accumulator for linearity updates
Vector<double> mBiasCorrectionAccu; ///< Accumulator for bias updates
int mNInstances;
};
////////////////////////////////////////////////////////////////////////////
// INLINE FUNCTIONS
// SharedLinearity::
inline
SharedLinearity::
SharedLinearity(size_t nInputs, size_t nOutputs, Component *pPred)
: UpdatableComponent(nInputs, nOutputs, pPred),
mpLinearity(&mLinearity), mpBias(&mBias),
mNInstances(0)
{ }
inline
SharedLinearity::
~SharedLinearity()
{ }
inline
Component*
SharedLinearity::
Clone() const
{
SharedLinearity* ptr = new SharedLinearity(GetNInputs(),GetNOutputs(),NULL);
ptr->mpLinearity = mpLinearity;
ptr->mpBias = mpBias;
ptr->mLinearityCorrection.Init(mpLinearity->Rows(),mpLinearity->Cols());
ptr->mBiasCorrection.Init(mpBias->Dim());
ptr->mNInstances = mNInstances;
ptr->mLearningRate = mLearningRate;
return ptr;
}
} //namespace
#endif
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