机器学习--BP神经网络的C++实现

来源：互联网发布：c语言编写的交易程序编辑：程序博客网时间：2024/04/27 21:59
激活函数:Sigmoid
使用的是周志华老师的《机器学习》一书上的更新公式。
一共有三层，第一层是三维的，第二层是4维，输出层是1维。
这里写图片描述
#include <iostream>#include <cstdlib>#include <ctime>#include <cmath>using namespace std;#define  innode 3  //输入结点数  #define  outnode 1 //输出结点数  #define  trainsample 8//BP训练样本数 #define  INF 99999999 //定义无穷大//初始化权值void initialValue(double **weight1,double **weight2,double *bias1,double *bias2,int n1,int n2,int n3){    for(int i=0; i<n1; i++)    {        for(int j=0; j<n2; j++)        {            //用来设置rand()产生随机数时的随机数种子。参数seed必须是个整数，如果每次seed都设相同值，rand()所产生的随机数值每次就会一样            srand(time(NULL));            weight1[i][j]=rand()%100/(100.0);        }    }    for(int i=0; i<n2; i++)    {        for(int j=0; j<n3; j++)        {            srand(time(NULL));            weight2[i][j]=rand()%100/(100.0);        }    }    for(int i=0; i<n2; i++)    {        srand(time(NULL));        bias1[i]=rand()%100/(100.0);    }    for(int i=0; i<n3; i++)    {        srand(time(NULL));        bias2[i]=rand()%100/(100.0);    }}double sigmoid(double x){    return 1/(1+exp(-x));}//计算样本实际输出void  computeY(double **weight1,double **weight2,double *bias1,double *bias2,int n1,int n2,int n3,double X[innode],double predictY[outnode],double *hideY){    double sum=0;    //计算隐层输出    for(int i=0; i<n2; i++)    {        for(int j=0; j<n1; j++)        {            sum += weight1[j][i]*X[j];        }        sum=sigmoid(sum-bias1[i]);        hideY[i]=sum;    }    sum=0;    //计算最后一层输出    for(int i=0; i<n3; i++)    {        for(int j=0; j<n2; j++)        {            sum += weight2[j][i]*hideY[i];        }        sum=sigmoid(sum-bias2[i]);        predictY[i]=sum;    }}//计算输出神经元的梯度void computeOutputDY(int n3,double predictY[outnode],double Y[outnode],double *outputDweight){    for(int i=0; i<n3; i++)    {        outputDweight[i]=predictY[i]*(1-predictY[i])*(Y[i]-predictY[i]);    }}//计算隐藏神经元的梯度void computeHideDY(double **weight2,int n2,int n3,double *hideY,double *outputDweight,double *hideDweight){    for(int i=0; i<n2; i++)    {        double sum=0;        for(int j=0; j<n3; j++)        {            sum+=weight2[i][j]*outputDweight[j];        }        hideDweight[i]=hideY[i]*(1-hideY[i])*sum;    }}//更新权值void updateWeight(double **weight1,double **weight2,double *bias1,double *bias2,int n1,int n2,int n3,double X[innode],double *hideY,double *outputDweight,double *hideDweight,double ratio){    for(int i=0; i<n1; i++)    {        for(int j=0; j<n2; j++)        {            weight1[i][j]+=ratio*hideDweight[j]*X[i];        }    }    for(int i=0; i<n2; i++)    {        for(int j=0; j<n3; j++)        {            weight2[i][j]+=ratio*outputDweight[j]*hideY[i];        }    }    for(int i=0; i<n2; i++)    {        bias1[i]-=ratio*hideDweight[i];    }    for(int i=0; i<n3; i++)    {        bias2[i]-=ratio*outputDweight[i];    }}//计算均方误差double computeError(double predictY[outnode],double Y[outnode],int n){    double error=0.0;    for(int i=0; i<n; i++)    {        error += ((predictY[i]-Y[i])*(predictY[i]-Y[i]));    }    return error;}int main(){    //三层神经网络，各层的维度    int n1=0,n2=0,n3=0;    cout<<"输入各层的维度：";    cin>>n1>>n2>>n3;    //输入层与隐层的连接权n1xn2，隐层与输出层的连接权n2xn3    double **weight1=new double*[n1];    for(int i=0;i<n1;i++)        weight1[i]=new double[n2];    double **weight2=new double*[n2];    for(int i=0;i<n2;i++)        weight2[i]=new double[n3];    //隐藏层的梯度项，输出层的梯度项    double *outputDweight = new double[n3];    double *hideDweight = new double[n2];    //隐层的偏置，输出层的偏置    double *bias1 = new double[n2];    double *bias2 = new double[n3];    //输入样本  n1=3,n3=1    double X[trainsample][innode]= {{0,0,0},{0,0,1},{0,1,0},{0,1,1},{1,0,0},{1,0,1},{1,1,0},{1,1,1}};      //期望输出样本      double Y[trainsample][outnode]={{0},{0.1429},{0.2857},{0.4286},{0.5714},{0.7143},{0.8571},{1.0000}};      //实际输出样本    double **predictY=new double*[trainsample];    for(int i=0;i<trainsample;i++)        predictY[i]=new double[outnode];    //隐层输出样本    double *hideY=new double[n2];    //权值，偏置初始化    initialValue(weight1,weight2,bias1,bias2,n1,n2,n3);    double error=INF;    //学习率    double ratio=0.5;    while(error>0.005)    {        error=0.0;        for(int i=0; i<trainsample; i++)        {            //计算输出层的值            computeY(weight1,weight2,bias1,bias2,n1,n2,n3,X[i],predictY[i],hideY);            //计算输出层的梯度项            computeOutputDY(n3,predictY[i],Y[i],outputDweight);            //计算隐层的梯度项            computeHideDY(weight2,n2,n3,hideY,outputDweight,hideDweight);            //更新权值            updateWeight(weight1,weight2,bias1,bias2,n1,n2,n3,X[i],hideY,outputDweight,hideDweight,ratio);            //计算均方误差            error += computeError(predictY[i],Y[i],outnode);        }        error=0.5*error;    }    //输出网络的输出与实际输出的对应    for(int i=0; i<trainsample; i++)    {        for(int j=0; j<outnode; j++)        {            cout<<"predictY[i][j]::"<<predictY[i][j]<<"----"<<"Y[i][j]::"<<Y[i][j]<<endl;        }    }    //释放空间    for(int i=0;i<n1;i++)    {        delete []  weight1[i];      }    for(int i=0;i<n2;i++)    {        delete []  weight2[i];    }    delete [] weight1;    delete [] weight2;    delete [] bias1;    delete [] bias2;}
0 0