C++实现神经BP神经网络分享！

本文实例为大家分享了C++实现神经BP神经网络的具体代码，供大家参考，具体内容如下

BP.h

  #pragma once  #include<vector>  #include<stdlib.h>  #include<time.h>  #include<cmath>  #include<iostream>  using std::vector;  using std::exp;  using std::cout;  using std::endl;  class BP  {  private:   int studyNum;//允许学习次数   double h;//学习率   double allowError;//允许误差   vector<int> layerNum;//每层的节点数，不包括常量节点1   vector<vector<vector<double>>> w;//权重   vector<vector<vector<double>>> dw;//权重增量   vector<vector<double>> b;//偏置   vector<vector<double>> db;//偏置增量   vector<vector<vector<double>>> a;//节点值   vector<vector<double>> x;//输入   vector<vector<double>> y;//期望输出     void iniwb();//初始化w与b   void inidwdb();//初始化dw与db   double sigmoid(double z);//激活函数   void forward();//前向传播   void backward();//后向传播   double Error();//计算误差  public:   BP(vector<int>const& layer_num, vector<vector<double>>const & input_a0,   vector<vector<double>> const & output_y, double hh = 0.5, double allerror = 0.001, int studynum = 1000);   BP();   void setLayerNumInput(vector<int>const& layer_num, vector<vector<double>> const & input);   void setOutputy(vector<vector<double>> const & output_y);   void setHErrorStudyNum(double hh, double allerror,int studynum);   void run();//运行BP神经网络   vector<double> predict(vector<double>& input);//使用已经学习好的神经网络进行预测   ~BP();  };

BP.cpp

  #include "BP.h"  BP::BP(vector<int>const& layer_num, vector<vector<double>>const & input,   vector<vector<double>> const & output_y, double hh, double allerror,int studynum)  {   layerNum = layer_num;   x = input;//输入多少个节点的数据，每个节点有多少份数据   y = output_y;   h = hh;   allowError = allerror;   a.resize(layerNum.size());//有这么多层网络节点   for (int i = 0; i < layerNum.size(); i++)   {   a[i].resize(layerNum[i]);//每层网络节点有这么多个节点   for (int j = 0; j < layerNum[i]; j++)    a[i][j].resize(input[0].size());   }   a[0] = input;   studyNum = studynum;  }    BP::BP()  {   layerNum = {};   a = {};   y = {};   h = 0;   allowError = 0;  }    BP::~BP()  {  }    void BP::setLayerNumInput(vector<int>const& layer_num, vector<vector<double>> const & input)  {   layerNum = layer_num;   x = input;   a.resize(layerNum.size());//有这么多层网络节点   for (int i = 0; i < layerNum.size(); i++)   {   a[i].resize(layerNum[i]);//每层网络节点有这么多个节点   for (int j = 0; j < layerNum[i]; j++)    a[i][j].resize(input[0].size());   }   a[0] = input;  }      void BP::setOutputy(vector<vector<double>> const & output_y)  {   y = output_y;  }    void BP::setHErrorStudyNum(double hh, double allerror,int studynum)  {   h = hh;   allowError = allerror;   studyNum = studynum;  }    //初始化权重矩阵  void BP::iniwb()  {   w.resize(layerNum.size() - 1);   b.resize(layerNum.size() - 1);   srand((unsigned)time(NULL));   //节点层数层数   for (int l = 0; l < layerNum.size() - 1; l++)   {   w[l].resize(layerNum[l + 1]);   b[l].resize(layerNum[l + 1]);   //对应后层的节点   for (int j = 0; j < layerNum[l + 1]; j++)   {    w[l][j].resize(layerNum[l]);    b[l][j] = -1 + 2 * (rand() / RAND_MAX);    //对应前层的节点    for (int k = 0; k < layerNum[l]; k++)    w[l][j][k] = -1 + 2 * (rand() / RAND_MAX);   }   }  }      void BP::inidwdb()  {   dw.resize(layerNum.size() - 1);   db.resize(layerNum.size() - 1);   //节点层数层数   for (int l = 0; l < layerNum.size() - 1; l++)   {   dw[l].resize(layerNum[l + 1]);   db[l].resize(layerNum[l + 1]);   //对应后层的节点   for (int j = 0; j < layerNum[l + 1]; j++)   {    dw[l][j].resize(layerNum[l]);    db[l][j] = 0;    //对应前层的节点    for (int k = 0; k < layerNum[l]; k++)    w[l][j][k] = 0;   }   }  }    //激活函数  double BP::sigmoid(double z)  {   return 1.0 / (1 + exp(-z));  }    void BP::forward()  {   for (int l = 1; l < layerNum.size(); l++)   {   for (int i = 0; i < layerNum[l]; i++)   {    for (int j = 0; j < x[0].size(); j++)    {      a[l][i][j] = 0;//第l层第i个节点第j个数据样本    //计算变量节点乘权值的和    for (int k = 0; k < layerNum[l - 1]; k++)     a[l][i][j] += a[l - 1][k][j] * w[l - 1][i][k];    //加上节点偏置    a[l][i][j] += b[l - 1][i];    a[l][i][j] = sigmoid(a[l][i][j]);    }   }   }  }    void BP::backward()  {   int xNum = x[0].size();//样本个数   //daP第l层da，daB第l+1层da   vector<double> daP, daB;        for (int j = 0; j < xNum; j++)   {   //处理最后一层的dw   daP.clear();   daP.resize(layerNum[layerNum.size() - 1]);   for (int i = 0, l = layerNum.size() - 1; i < layerNum[l]; i++)   {    daP[i] = a[l][i][j] - y[i][j];    for (int k = 0; k < layerNum[l - 1]; k++)    dw[l - 1][i][k] += daP[i] * a[l][i][j] * (1 - a[l][i][j])*a[l - 1][k][j];    db[l - 1][i] += daP[i] * a[l][i][j] * (1 - a[l][i][j]);   }     //处理剩下层的权重w的增量Dw   for (int l = layerNum.size() - 2; l > 0; l--)   {    daB = daP;    daP.clear();    daP.resize(layerNum[l]);    for (int k = 0; k < layerNum[l]; k++)    {    daP[k] = 0;    for (int i = 0; i < layerNum[l + 1]; i++)     daP[k] += daB[i] * a[l + 1][i][j] * (1 - a[l + 1][i][j])*w[l][i][k];    //dw    for (int i = 0; i < layerNum[l - 1]; i++)     dw[l - 1][k][i] += daP[k] * a[l][k][j] * (1 - a[l][k][j])*a[l - 1][i][j];    //db    db[l-1][k] += daP[k] * a[l][k][j] * (1 - a[l][k][j]);    }   }     }      //计算dw与db平均值   for (int l = 0; l < layerNum.size() - 1; l++)   {   //对应后层的节点   for (int j = 0; j < layerNum[l + 1]; j++)   {    db[l][j] = db[l][j] / xNum;    //对应前层的节点    for (int k = 0; k < layerNum[l]; k++)    w[l][j][k] = w[l][j][k] / xNum;   }   }     //更新参数w与b   for (int l = 0; l < layerNum.size() - 1; l++)   {   for (int j = 0; j < layerNum[l + 1]; j++)   {    b[l][j] = b[l][j] - h * db[l][j];    //对应前层的节点    for (int k = 0; k < layerNum[l]; k++)    w[l][j][k] = w[l][j][k] - h * dw[l][j][k];   }   }  }    double BP::Error()  {   int l = layerNum.size() - 1;   double temp = 0, error = 0;   for (int i = 0; i < layerNum[l]; i++)   for (int j = 0; j < x[0].size(); j++)   {    temp = a[l][i][j] - y[i][j];    error += temp * temp;   }   error = error / x[0].size();//求对每一组样本的误差平均   error = error / 2;   cout << error << endl;   return error;  }    //运行神经网络  void BP::run()  {   iniwb();   inidwdb();   int i = 0;   for (; i < studyNum; i++)   {   forward();   if (Error() <= allowError)   {    cout << "Study Success!" << endl;    break;   }   backward();   }   if (i == 10000)   cout << "Study Failed!" << endl;  }    vector<double> BP::predict(vector<double>& input)  {   vector<vector<double>> a1;   a1.resize(layerNum.size());   for (int l = 0; l < layerNum.size(); l++)   a1[l].resize(layerNum[l]);   a1[0] = input;   for (int l = 1; l < layerNum.size(); l++)   for (int i = 0; i < layerNum[l]; i++)   {    a1[l][i] = 0;//第l层第i个节点第j个数据样本    //计算变量节点乘权值的和    for (int k = 0; k < layerNum[l - 1]; k++)    a1[l][i] += a1[l - 1][k] * w[l - 1][i][k];    //加上节点偏置    a1[l][i] += b[l - 1][i];    a1[l][i] = sigmoid(a1[l][i]);   }   return a1[layerNum.size() - 1];  }

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