求注释简单神经网络代码

问题遇到的现象和发生背景 想知道这个神经网络的运行过程，最好能注释一下代码

#include
using namespace std;
double getMSEloss(double x1,double x2){
    return (x1 - x2)*(x1 - x2);
}
class NNetwork
{
    private:
    int epoches;
    double learning_rate;
    double w1,w2,w3,w4,w5,w6;
    double b1,b2,b3;
    public:
    NNetwork(int es,double lr);
    double sigmoid(double x);
    double deriv_sigmoid(double x);
    double forward(vector<double> data);
    void train(vectordouble>> data,vector<double> label);
    void predict(vectordouble>> test_data,vector<double> test_label);
};
NNetwork::NNetwork(int es,double lr):epoches(es),learning_rate(lr){
    // 超参数、参数初始化
    w1=w2=w3=w4=w5=w6=0;
    b1=b2=b3=0;
}
double NNetwork::sigmoid(double x){
    // 激活函数
    return 1/(1+exp(-x));
}
double NNetwork::deriv_sigmoid(double x){
    // 激活函数求导
    double y = sigmoid(x);
    return y*(1-y);
}
double NNetwork::forward(vector<double> data){
    // 前向传播
    double sum_h1 = w1 * data[0] + w2 * data[1] + b1;
    double h1 = sigmoid(sum_h1);
    double sum_h2 = w3 * data[0] + w4 * data[1] + b2;
    double h2 = sigmoid(sum_h2);
    double sum_o1 = w5 * h1 + w6 * h2 + b3;
    return sigmoid(sum_o1);
}
void NNetwork::train(vectordouble>> data,vector<double> label){
    for(int epoch=0;epochint total_n = data.size();
        for(int i=0;idouble> x = data[i];
            double sum_h1 = w1 * x[0] + w2 * x[1] + b1;
            double h1 = sigmoid(sum_h1);
            double sum_h2 = w3 * x[0] + w4 * x[1] + b2;
            double h2 = sigmoid(sum_h2);
            double sum_o1 = w5 * h1 + w6 * h2 + b3;
            double o1 = sigmoid(sum_o1);
            double pred = o1;
 
            double d_loss_pred = -2 * (label[i] - pred);
 
            double d_pred_w5 = h1 * deriv_sigmoid(sum_o1);
            double d_pred_w6 = h2 * deriv_sigmoid(sum_o1);
            double d_pred_b3 = deriv_sigmoid(sum_o1);
            
            double d_pred_h1 = w5 * deriv_sigmoid(sum_o1);
            double d_pred_h2 = w6 * deriv_sigmoid(sum_o1);
 
            double d_h1_w1 = x[0] * deriv_sigmoid(sum_h1);
            double d_h1_w2 = x[1] * deriv_sigmoid(sum_h1);
            double d_h1_b1 = deriv_sigmoid(sum_h1);
 
            double d_h2_w3 = x[0] * deriv_sigmoid(sum_h2);
            double d_h2_w4 = x[1] * deriv_sigmoid(sum_h2);
            double d_h2_b2 = deriv_sigmoid(sum_h2);
 
            w1 -= learning_rate * d_loss_pred * d_pred_h1 * d_h1_w1;
            w2 -= learning_rate * d_loss_pred * d_pred_h1 * d_h1_w2;
            b1 -= learning_rate * d_loss_pred * d_pred_h1 * d_h1_b1;
            w3 -= learning_rate * d_loss_pred * d_pred_h2 * d_h2_w3;
            w4 -= learning_rate * d_loss_pred * d_pred_h2 * d_h2_w4;
            b2 -= learning_rate * d_loss_pred * d_pred_h2 * d_h2_b2;
            w5 -= learning_rate * d_loss_pred * d_pred_w5;
            w6 -= learning_rate * d_loss_pred * d_pred_w6;
            b3 -= learning_rate * d_loss_pred * d_pred_b3; 
        }
        if(epoch%10==0){
            double loss = 0;
            for(int i=0;idouble pred = forward(data[i]);
                loss += getMSEloss(pred,label[i]);
            }
            cout<<"epoch "<" loss: "<void NNetwork::predict(vectordouble>> test_data,vector<double> test_label){
    int n = test_data.size();
    double cnt = 0;
    for(int i=0;idouble pred = forward(test_data[i]);
        pred = pred>0.5?1:0;
        cnt += (test_label[i]==pred);
    }
    cout<<"correct rate:"<int main(){
    vectordouble>> data = {{-2,-1},{25,6},{17,4},{-15,-6}};
    vector<double> label = {1,0,0,1};
    NNetwork network = NNetwork(1000,0.1);
    network.train(data,label);
    vectordouble>> test_data  = {{-3,-4},{-5,-4},{12,3},{-13,-4},{9,12}};
    vector<double> test_label = {1,1,0,1,0};
    network.predict(test_data,test_label);
    return 0;
}

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