C++实现神经BP神经网络

#pragma once
#include
#include
#include
#include
#include
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 layerNum;//每层的节点数，不包括常量节点1
 vector>> w;//权重
 vector>> dw;//权重增量
 vector> b;//偏置
 vector> db;//偏置增量
 vector>> a;//节点值
 vector> x;//输入
 vector> 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(vectorconst& layer_num, vector>const & input_a0,
 vector> const & output_y, double hh = 0.5, double allerror = 0.001, int studynum = 1000);
 BP();
 void setLayerNumInput(vectorconst& layer_num, vector> const & input);
 void setOutputy(vector> const & output_y);
 void setHErrorStudyNum(double hh, double allerror,int studynum);
 void run();//运行BP神经网络
 vector predict(vector& input);//使用已经学习好的神经网络进行预测
 ~BP();
};

#include "BP.h"
BP::BP(vectorconst& layer_num, vector>const & input,
 vector> 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 const& layer_num, vector> 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 > 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  daP, daB;
 

 for (int j = 0; j  0; l--)
 {
  daB = daP;
  daP.clear();
  daP.resize(layerNum[l]);
  for (int k = 0; k  BP::predict(vector& input)
{
 vector> a1;
 a1.resize(layerNum.size());
 for (int l = 0; l 

#include"BP.h"

int main()
{
 vector layer_num = { 1, 10, 1 };
 vector> input_a0 = { { 1,2,3,4,5,6,7,8,9,10 } };
 vector> output_y = { {0,0,0,0,1,1,1,1,1,1} };

 BP bp(layer_num, input_a0,output_y,0.6,0.001, 2000);
 bp.run();
 for (int j = 0; j <30; j++)
 {
 vector input = { 0.5*j };
 vector output = bp.predict(input);
 for (auto i : output)
  cout <<"j:" <<0.5*j <<" pridict:" <