Bp神经网络算法(使用C++编写)

#include "bp.h" #include "memory.h" #include "math.h" #include "time.h" #include "stdlib.h" #include "stdio.h" double sigmoid(double x) { return 1 / (1 + exp(- x)); } BpNet::BpNet(int nInput, int nHide, int nOutput) { _nInput = nInput; _nHide = nHide; _nOutput = nOutput; srand((unsigned)time(NULL)); _pplfWeight1 = new double *[_nInput]; for (int i = 0; i < _nInput; i++) { _pplfWeight1[i] = new double [_nHide]; for (int j = 0; j < _nHide; j++) _pplfWeight1[i][j] = rand() / (double)(RAND_MAX); } _pplfWeight2 = new double *[_nHide]; for (int i = 0; i < _nHide; i++) { _pplfWeight2[i] = new double [_nOutput]; for (int j = 0; j < _nOutput; j++) _pplfWeight2[i][j] = rand() / (double)(RAND_MAX); } _plfHideIn = new double[_nHide]; _plfHideOut = new double[_nHide]; _plfOutputIn = new double[_nOutput]; _plfOutputOut = new double[_nOutput]; f = sigmoid; //使用sigmoid激活函数 } BpNet::~BpNet() { delete []_plfHideIn; delete []_plfHideOut; delete []_plfOutputIn; delete []_plfOutputOut; for (int i = 0; i < _nInput; i++) delete []_pplfWeight1[i]; for (int i = 0; i < _nHide; i++) delete []_pplfWeight2[i]; delete []_pplfWeight1; delete []_pplfWeight2; } void BpNet::Classify(double plfInput[], double plfOutput[]) { memset(_plfHideIn, 0, sizeof(double) * _nHide); memset(_plfHideOut, 0, sizeof(double) * _nHide); memset(_plfOutputIn, 0, sizeof(double) * _nOutput); memset(_plfOutputOut, 0, sizeof(double) * _nOutput); //输入层到隐含层的正向传播 for (int i = 0; i < _nInput; i++) for (int j = 0; j < _nHide; j++) _plfHideIn[j] += plfInput[i] * _pplfWeight1[i][j]; for (int j = 0; j < _nHide; j++) _plfHideOut[j] = (*f)(_plfHideIn[j]); //隐含层到输出层的正向传播 for (int j = 0; j < _nHide; j++) for (int k = 0; k < _nOutput; k++) _plfOutputIn[k] += _plfHideOut[j] * _pplfWeight2[j][k]; for (int k = 0; k < _nOutput; k++) _plfOutputOut[k] = (*f)(_plfOutputIn[k]); if (plfOutput != NULL) memcpy(plfOutput, _plfOutputOut, sizeof(double) * _nOutput); } bool BpNet::Train(int n, double **pplfInput, double **pplfDesire) { const double a = 0.1; const double E = 0.01; double lfE = E + 1; //输入层-隐含层权系数增量 double **pplfDeltaWeight1 = new double *[_nInput]; for (int i = 0; i < _nInput; i++) { pplfDeltaWeight1[i] = new double [_nHide]; memset(pplfDeltaWeight1[i], 0, sizeof(double) * _nHide); } //隐含层-输出层权系数增量 double **pplfDeltaWeight2 = new double *[_nHide]; for (int i = 0; i < _nHide; i++) { pplfDeltaWeight2[i] = new double[_nOutput]; memset(pplfDeltaWeight2[i], 0, sizeof(double) * _nOutput); } int nCount = 0; while (lfE > E) { lfE = 0; //对每一个样本进行处理 for (int i = 0; i < n; i++) { double *plfInput = pplfInput[i]; //样本输入 double *plfDesire = pplfDesire[i]; //样本期望输出 //计算样本实际输出plfOutput Classify(plfInput, NULL); //计算误差测度 double lfEp = 0; for (int j = 0; j < _nOutput; j++) lfEp += (plfDesire[j] - _plfOutputOut[j]) * (plfDesire[j] - _plfOutputOut[j]); lfE += lfEp; //计算隐含层-输出层权系数增量 double *plfChange2 = new double[_nOutput]; for (int j = 0; j < _nOutput; j++) plfChange2[j] = _plfOutputOut[j] * (1 - _plfOutputOut[j]) * (plfDesire[j] - _plfOutputOut[j]); for (int j = 0; j < _nHide; j++) for (int k = 0; k < _nOutput; k++) pplfDeltaWeight2[j][k] = a * _plfHideOut[j] * plfChange2[k]; //计算输入层-隐含层权系数增量 double *plfChange1 = new double[_nHide]; memset(plfChange1, 0, sizeof(double) * _nHide); for (int j = 0; j < _nHide; j++) { for (int k = 0; k < _nOutput; k++) plfChange1[j] += _pplfWeight2[j][k] * plfChange2[k]; plfChange1[j] *= _plfHideOut[j] * (1 - _plfHideOut[j]); } for (int j = 0; j < _nInput; j++) for (int k = 0; k < _nHide; k++) pplfDeltaWeight1[j][k] = a * plfInput[j] * plfChange1[k]; delete []plfChange1; delete []plfChange2; //更新Bp网络权值 for (int i = 0; i < _nInput; i++) for (int j = 0; j < _nHide; j++) _pplfWeight1[i][j] += pplfDeltaWeight1[i][j]; for (int i = 0; i < _nHide; i++) for (int j = 0; j < _nOutput; j++) _pplfWeight2[i][j] += pplfDeltaWeight2[i][j]; } nCount++; if (nCount % 10 == 0) printf("%d%8.5lf\n", nCount, lfE); } for (int i = 0; i < _nInput; i++) delete []pplfDeltaWeight1[i]; for (int i = 0; i < _nHide; i++) delete []pplfDeltaWeight2[i]; delete [] pplfDeltaWeight1; delete [] pplfDeltaWeight2; return true; }