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
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#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
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#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];} |
验证程序：
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#include"BP.h"int main(){ vector<int> layer_num = { 1, 10, 1 }; vector<vector<double>> input_a0 = { { 1,2,3,4,5,6,7,8,9,10 } }; vector<vector<double>> 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<double> input = { 0.5*j }; vector<double> output = bp.predict(input); for (auto i : output) cout << "j:" << 0.5*j <<" pridict:" << i << " "; cout << endl; } system("pause"); return 0;} |
输出：
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/weixin_42333471/article/details/90106465
