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：