C / C ++ में एक साधारण तंत्रिका नेटवर्क का एक उदाहरण

inputNeurons = 100; //   outputNeurons =2; //   nlCount = 4; //  (    3,      1 list = (nnLay*) malloc((nlCount)*sizeof(nnLay)); inputs = (float*) malloc((inputNeurons)*sizeof(float)); targets = (float*) malloc((outputNeurons)*sizeof(float)); list[0].setIO(100,20); //  INPUTS/OUTPUTS    list[1].setIO(20,6); // -//- list[2].setIO(6,3); // -//- list[3].setIO(3,2); // -//-   

  struct nnLay{ int in; int out; float** matrix; float* hidden; float* errors; int getInCount(){return in;} int getOutCount(){return out;} float **getMatrix(){return matrix;} void updMatrix(float *enteredVal) { for(int ou =0; ou < out; ou++) { for(int hid =0; hid < in; hid++) { matrix[hid][ou] += (learnRate * errors[ou] * enteredVal[hid]); } matrix[in][ou] += (learnRate * errors[ou]); } }; void setIO(int inputs, int outputs) { in=inputs; out=outputs; hidden = (float*) malloc((out)*sizeof(float)); matrix = (float**) malloc((in+1)*sizeof(float)); for(int inp =0; inp < in+1; inp++) { matrix[inp] = (float*) malloc(out*sizeof(float)); } for(int inp =0; inp < in+1; inp++) { for(int outp =0; outp < out; outp++) { matrix[inp][outp] = randWeight; } } } void makeHidden(float *inputs) { for(int hid =0; hid < out; hid++) { float tmpS = 0.0; for(int inp =0; inp < in; inp++) { tmpS += inputs[inp] * matrix[inp][hid]; } tmpS += matrix[in][hid]; hidden[hid] = sigmoida(tmpS); } }; float* getHidden() { return hidden; }; void calcOutError(float *targets) { errors = (float*) malloc((out)*sizeof(float)); for(int ou =0; ou < out; ou++) { errors[ou] = (targets[ou] - hidden[ou]) * sigmoidasDerivate(hidden[ou]); } }; void calcHidError(float *targets,float **outWeights,int inS, int outS) { errors = (float*) malloc((inS)*sizeof(float)); for(int hid =0; hid < inS; hid++) { errors[hid] = 0.0; for(int ou =0; ou < outS; ou++) { errors[hid] += targets[ou] * outWeights[hid][ou]; } errors[hid] *= sigmoidasDerivate(hidden[hid]); } }; float* getErrors() { return errors; }; float sigmoida(float val) { return (1.0 / (1.0 + exp(-val))); } float sigmoidasDerivate(float val) { return (val * (1.0 - val)); }; }; 

 #include <QCoreApplication> #include <QDebug> #include <QTime> #include "myneuro.h" int main(int argc, char *argv[]) { QCoreApplication a(argc, argv); myNeuro *bb = new myNeuro(); //----------------------------------INPUTS----GENERATOR------------- qsrand((QTime::currentTime().second())); float *abc = new float[100]; for(int i=0; i<100;i++) { abc[i] =(qrand()%98)*0.01+0.01; } float *cba = new float[100]; for(int i=0; i<100;i++) { cba[i] =(qrand()%98)*0.01+0.01; } //---------------------------------TARGETS----GENERATOR------------- float *tar1 = new float[2]; tar1[0] =0.01; tar1[1] =0.99; float *tar2 = new float[2]; tar2[0] =0.99; tar2[1] =0.01; //--------------------------------NN---------WORKING--------------- bb->query(abc); qDebug()<<"_________________________________"; bb->query(cba); int i=0; while(i<100000) { bb->train(abc,tar1); bb->train(cba,tar2); i++; } qDebug()<<"___________________RESULT_____________"; bb->query(abc); qDebug()<<"______"; bb->query(cba); qDebug()<<"_______________THE____END_______________"; return a.exec(); } 

 #include "myneuro.h" #include <QDebug> myNeuro::myNeuro() { //-------- inputNeurons = 100; outputNeurons =2; nlCount = 4; list = (nnLay*) malloc((nlCount)*sizeof(nnLay)); inputs = (float*) malloc((inputNeurons)*sizeof(float)); targets = (float*) malloc((outputNeurons)*sizeof(float)); list[0].setIO(100,20); list[1].setIO(20,6); list[2].setIO(6,3); list[3].setIO(3,2); //----------------- // inputNeurons = 100; // outputNeurons =2; // nlCount = 2; // list = (nnLay*) malloc((nlCount)*sizeof(nnLay)); // inputs = (float*) malloc((inputNeurons)*sizeof(float)); // targets = (float*) malloc((outputNeurons)*sizeof(float)); // list[0].setIO(100,10); // list[1].setIO(10,2); } void myNeuro::feedForwarding(bool ok) { list[0].makeHidden(inputs); for (int i =1; i<nlCount; i++) list[i].makeHidden(list[i-1].getHidden()); if (!ok) { qDebug()<<"Feed Forward: "; for(int out =0; out < outputNeurons; out++) { qDebug()<<list[nlCount-1].hidden[out]; } return; } else { // printArray(list[3].getErrors(),list[3].getOutCount()); backPropagate(); } } void myNeuro::backPropagate() { //-------------------------------ERRORS-----CALC--------- list[nlCount-1].calcOutError(targets); for (int i =nlCount-2; i>=0; i--) list[i].calcHidError(list[i+1].getErrors(),list[i+1].getMatrix(), list[i+1].getInCount(),list[i+1].getOutCount()); //-------------------------------UPD-----WEIGHT--------- for (int i =nlCount-1; i>0; i--) list[i].updMatrix(list[i-1].getHidden()); list[0].updMatrix(inputs); } void myNeuro::train(float *in, float *targ) { inputs = in; targets = targ; feedForwarding(true); } void myNeuro::query(float *in) { inputs=in; feedForwarding(false); } void myNeuro::printArray(float *arr, int s) { qDebug()<<"__"; for(int inp =0; inp < s; inp++) { qDebug()<<arr[inp]; } } 

 #ifndef MYNEURO_H #define MYNEURO_H #include <iostream> #include <math.h> #include <QtGlobal> #include <QDebug> #define learnRate 0.1 #define randWeight (( ((float)qrand() / (float)RAND_MAX) - 0.5)* pow(out,-0.5)) class myNeuro { public: myNeuro(); struct nnLay{ int in; int out; float** matrix; float* hidden; float* errors; int getInCount(){return in;} int getOutCount(){return out;} float **getMatrix(){return matrix;} void updMatrix(float *enteredVal) { for(int ou =0; ou < out; ou++) { for(int hid =0; hid < in; hid++) { matrix[hid][ou] += (learnRate * errors[ou] * enteredVal[hid]); } matrix[in][ou] += (learnRate * errors[ou]); } }; void setIO(int inputs, int outputs) { in=inputs; out=outputs; hidden = (float*) malloc((out)*sizeof(float)); matrix = (float**) malloc((in+1)*sizeof(float)); for(int inp =0; inp < in+1; inp++) { matrix[inp] = (float*) malloc(out*sizeof(float)); } for(int inp =0; inp < in+1; inp++) { for(int outp =0; outp < out; outp++) { matrix[inp][outp] = randWeight; } } } void makeHidden(float *inputs) { for(int hid =0; hid < out; hid++) { float tmpS = 0.0; for(int inp =0; inp < in; inp++) { tmpS += inputs[inp] * matrix[inp][hid]; } tmpS += matrix[in][hid]; hidden[hid] = sigmoida(tmpS); } }; float* getHidden() { return hidden; }; void calcOutError(float *targets) { errors = (float*) malloc((out)*sizeof(float)); for(int ou =0; ou < out; ou++) { errors[ou] = (targets[ou] - hidden[ou]) * sigmoidasDerivate(hidden[ou]); } }; void calcHidError(float *targets,float **outWeights,int inS, int outS) { errors = (float*) malloc((inS)*sizeof(float)); for(int hid =0; hid < inS; hid++) { errors[hid] = 0.0; for(int ou =0; ou < outS; ou++) { errors[hid] += targets[ou] * outWeights[hid][ou]; } errors[hid] *= sigmoidasDerivate(hidden[hid]); } }; float* getErrors() { return errors; }; float sigmoida(float val) { return (1.0 / (1.0 + exp(-val))); } float sigmoidasDerivate(float val) { return (val * (1.0 - val)); }; }; void feedForwarding(bool ok); void backPropagate(); void train(float *in, float *targ); void query(float *in); void printArray(float *arr,int s); private: struct nnLay *list; int inputNeurons; int outputNeurons; int nlCount; float *inputs; float *targets; }; #endif // MYNEURO_H