深度学习之感知器AND（与）实现C++代码

利用深度学习的感知器算法实现AND（与）操作

感知器类头文件：

#include <vector>#include <iostream>using namespace std;class Perceptron//感知器{public:    Perceptron(){}    Perceptron(const vector<vector<double>> &input, const int times, const double rate);    double UsePerceptron(const vector<double> &input);//使用生成的权值进行计算    void CheckWeight();//打印权值private:    vector<double> weightOutput;//计算出的权值向量    double bias;//偏置量    double Func(double x);//激活函数（阶跃函数）};

感知器类实现文件：

#include "Perceptron.h"Perceptron::Perceptron(const vector<vector<double>> &input, const int times, const double rate){    int sampleNum = input.size();//共有多少组数据    int weightNum = input[0].size() - 1;//每组数据自变量个数（最后一个为该组结果）    for (int i = 0; i < weightNum; i++)    {        weightOutput.push_back(0);    }    bias = 0;//初始化    for (int trainNum = 0; trainNum < times; trainNum++)//训练次数    {        for (int i = 0; i < sampleNum; i++)        {            double sum = bias;            for (int j = 0; j < weightNum; j++)            {                sum += input[i][j] * weightOutput[j];            }            sum = Func(sum);//每次迭代的结果            sum = input[i][weightNum] - sum;//与真实值的差            for (int j = 0; j < weightNum; j++)            {                weightOutput[j] += rate*sum*input[i][j];            }            bias += rate*sum;//更新权值        }    }}double Perceptron::Func(double x)//激活函数{    if (x > 0)        return 1;    else        return 0;}double Perceptron::UsePerceptron(const vector<double> &input){    double output = bias;    for (unsigned int i = 0; i < input.size(); i++)        output += input[i] * weightOutput[i];    return Func(output);}void Perceptron::CheckWeight(){    for (unsigned i = 0; i < weightOutput.size(); i++)        cout << "W: "<<weightOutput[i] << " " << endl;    cout << "bias: "<<bias << endl;}

主函数测试感知器：

#include <iostream>#include "Perceptron.h"using namespace std;int main(){    vector<vector<double>> AND = {        { 0, 0, 0 },        { 0, 1, 0 },        { 1, 0, 0 },        { 1, 1, 1 }    };    Perceptron Cand(AND, 10, 0.1);    Cand.CheckWeight();    vector<double> test = { 1, 1 };    cout << Cand.UsePerceptron(test) << endl;}

结果显示：