TrainNodeCvANN.h

// Artificial Neural Network (based on OpenCV) training class interface
// Written by Sergey G. Kosov in 2017 for Project X
#pragma once

#include "TrainNode.h"

namespace DirectGraphicalModels
{
    class CSamplesAccumulator;

    typedef struct TrainNodeCvANNParams {
        word    numLayers;                          
        double  weightScale;                        
        double  momentumScale;                      
        int     maxCount;                           
        double  epsilon;                            
        int     term_criteria_type;                 
        size_t  maxSamples;                         

        TrainNodeCvANNParams() {}
        TrainNodeCvANNParams(word _numLayers, double _weightScale, double _momentumScale, int _maxCount, double _epsilon, int _term_criteria_type, int _maxSamples) : numLayers(_numLayers), weightScale(_weightScale), maxCount(_maxCount), epsilon(_epsilon), term_criteria_type(_term_criteria_type), maxSamples(_maxSamples) {}
    } TrainNodeCvANNParams;

    const TrainNodeCvANNParams TRAIN_NODE_CV_ANN_PARAMS_DEFAULT =   TrainNodeCvANNParams(
                                                                    5,      // Num layers
                                                                    0.0001, // Backpropagation Weight Scale
                                                                    0.1,    // Backpropagation Momentum Scale
                                                                    100,    // The maximum number of iterations (time / accuracy)
                                                                    0.01,   // The desired accuracy or change in parameters at which the iterative algorithm stops 
                                                                    TermCriteria::MAX_ITER | TermCriteria::EPS, // Termination cirteria (according the the two previous parameters)
                                                                    0       // Maximum number of samples to be used in training. 0 means using all the samples
                                                                    );

    // ====================== OpenCV Artificial Neural Network Train Class =====================
    class CTrainNodeCvANN : public CTrainNode {
    public:
        DllExport CTrainNodeCvANN(byte nStates, word nFeatures, TrainNodeCvANNParams params = TRAIN_NODE_CV_ANN_PARAMS_DEFAULT);
        DllExport CTrainNodeCvANN(byte nStates, word nFeatures, size_t maxSamples);
        DllExport virtual ~CTrainNodeCvANN(void);

        DllExport void  reset(void);
        DllExport void  save(const std::string &path, const std::string &name = std::string(), short idx = -1) const;
        DllExport void  load(const std::string &path, const std::string &name = std::string(), short idx = -1);

        DllExport void  addFeatureVec(const Mat &featureVector, byte gt);

        DllExport void  train(bool doClean = false);


    protected:
        DllExport void  saveFile(FILE *pFile) const { }
        DllExport void  loadFile(FILE *pFile) { }
        DllExport void  calculateNodePotentials(const Mat &featureVector, Mat &potential, Mat &mask) const;


    private:
        void          init(TrainNodeCvANNParams params);        // This function is called by both constructors


    protected:
        Ptr<ml::ANN_MLP>              m_pANN;                   
        CSamplesAccumulator         * m_pSamplesAcc;            
    };
}