我不确定如何引用此神经网络训练方法中的权重

Question

我正在用 C++ 编写 自己的 神经网络 class 实现。我不确定如何引用此语句的权重：

in = in + (inputs [l] * calcWeights [l]) ;

原因是因为权重可能 比输入多 。这是我的代码：

void Train (int numInputs, int numOutputs, double inputs [], double outputs []) {
    // Set the Random Seed:
    srand (time (0)) ;

    // Weights (n input(s) * n output(s) = n weight branch(es)):
    double calcWeights [numInputs * numOutputs] ;

    // Errors (n input(s) * n output(s) = n error branch(es)):
    double errors [numInputs * numOutputs] ;

    // Set the Weights to random:
    for (int j = 0 ; j < numInputs ; j = j + 1) {
        calcWeights [j] = ((-1 * numInputs) + (((double) rand ()) % (1 * numInputs))) ;
    }

    // Train:
    int i = 0 ;
    double in = 0 ;
    double out [numOutputs] ;
    while (i < 14999) {
        // Get the estimated output:
        for (int k = 0 ; k < numOutputs ; k = k + 1) {
            for (int l = 0 ; l < numInputs ; l = l + 1) {
                in = in + (inputs [l] * calcWeights [l]) ;
            }

            out [k] = in + GetBias () ;
        }

        for (int m = 0 ; m < numOutputs ; m = m + 1) {
            error [m] = outputs [m] - out [m]
        }

        // Increment the iterator:
        i = i + 1 ;
    }
}

Answer 1

根据您在评论中的说明，我相信稍微修改一下您的循环就会得到您想要的结果。

for (int k = 0 ; k < numOutputs ; k = k + 1) {
    in = 0; //Reset in to 0 at the beginning of each output loop

    for (int l = 0 ; l < numInputs ; l = l + 1) {
        in = in + (inputs [l] * calcWeights [l + k*numInputs]) ;
    }

    out [k] = in + GetBias () ;
}

您还应确保初始化以上所有权重。

for (int j = 0 ; j < (numInputs * numOutputs) ; j = j + 1) {
    calcWeights [j] = ((-1 * numInputs) + (((double) rand ()) % (1 * numInputs))) ;
}

对于几个样式选择，我只想指出您可以将 k = k + 1 替换为简单的 ++k。同样，您可以将 in = in + ...; 替换为 in += ...;

我不确定如何引用此神经网络训练方法中的权重

I'm not sure how to refer to the weights in this Neural Network Train method

c++

backpropagation

neural-network