我的c ++代码比javascript代码慢很多

Question

我有一个使用相同数据的项目，在我的c ++代码中需要17秒来训练100个数据，同时在这个项目的javascript代码中

https://github.com/CodingTrain/Toy-Neural-Network-JS它只运行大约10秒来训练2400数据，请有人帮我解决错误，我需要为我的本科毕业论文完成我的项目。

我已经建立了2个项目，其中一个（这个）与cavascript中的javascript代码（有点）是相同的神经网络，但仍然给出相同的结果

NeuralNetwork::NeuralNetwork(int a,int b,int c)
{
    this->numInput = a;
    this->numHidden = b;
    this->numOutput = c;
    std::vector<double> vec(a, 0.1);
    for (int i = 0; i < b; ++i) {
        this->weightIH.push_back(vec);
    }
    std::vector<double> vec2(b, 0.1);
    for (int i = 0; i < c; ++i) {
        this->weightHO.push_back(vec2);
    }

}


NeuralNetwork::~NeuralNetwork()
{
}

std::vector<double> NeuralNetwork::tambahbias(std::vector<double> a) {
    int size = a.size();
    for (int i = 0; i < size; ++i) {
        a[i] = a[i] + 1;
    }

    return a;
}

std::vector<double> NeuralNetwork::activate(std::vector<double> a) {
    int size = a.size();
    for (int i = 0; i < size; ++i) {
        a[i] = a[i] / (1 + abs(a[i]));
    }
    return a;
}

std::vector<double> NeuralNetwork::derivation(std::vector<double> a) {
    int size = a.size();
    for (int i = 0; i < size; ++i) {
        a[i] = a[i] * (1 - a[i]);
    }
    return a;
}

std::vector<double> NeuralNetwork::hitungError(std::vector<double> a, std::vector<double> b) {
    int size = a.size();
    for (int i = 0; i < size; ++i) {
        a[i] = b[i] - a[i];
    }

    return a;
}


    void NeuralNetwork::train(std::vector<double> a, std::vector<double> target) {
        std::vector<double> hidden(numHidden);
        for (int i = 0; i < numHidden; ++i) {
            for (int j = 0; j < numInput; ++j) {
                hidden[i] += a[j] * weightIH[i][j];
            }
        }
        hidden = tambahbias(hidden);
        hidden = activate(hidden);
        std::vector<double> output(numOutput);
        for (int i = 0; i < numOutput; ++i) {
            for (int j = 0; j < numHidden; ++j) {
                output[i] += hidden[j] * weightHO[i][j];
            }
        }
        output = tambahbias(output);
        output = activate(output);
        std::vector<double> errorO(numOutput);
        errorO = hitungError(output, target);
        std::vector<double> gradO(numOutput);
        gradO = derivation(output);
        for (int i = 0; i < numOutput; ++i) {
            gradO[i] = gradO[i] * errorO[i] * 0.1;
        }
        for (int i = 0; i < numOutput; ++i) {
            for (int j = 0; j < numHidden; ++j) {
                weightHO[i][j] += (gradO[i] * hidden[j]);
            }
        }
        std::vector<double> gradH(numHidden);
        std::vector<double> derH(numHidden);
        derH = derivation(hidden);
        for (int i = 0; i < numHidden; ++i) {
            for (int j = 0; j < numOutput; ++j) {
                gradH[i] = gradO[j] * weightHO[j][i];
            }
            gradH[i] = gradH[i] * derH[i] * 0.1;
        }
        for (int i = 0; i < numHidden; ++i) {
            for (int j = 0; j < numInput; ++j) {
                weightIH[i][j] += (gradH[i] * a[j]);
            }
        }


    }

Answer 1

您正在将所有std::vector s复制到函数中：

void NeuralNetwork::train(std::vector<double> a, std::vector<double> target) 

改为使用引用：

void NeuralNetwork::train(const std::vector<double>& a, const std::vector<double>& target)

复制向量是空间和时间中的O(n)操作，使用两者中的引用都是O(1) 。

当您在修改向量后再次复制向量时，无法修改const std::vector引用：

std::vector<double> NeuralNetwork::derivation(std::vector<double> a)

改为使用非const引用：

void NeuralNetwork::derivation(std::vector<double>& a)

Answer 2

事实证明我只是一个白痴，谁不知道调试/发布，让这个程序发布只是解决问题，谢谢大家的帮助