反向传播不工作为异或

过去两周我一直在学习反向传播，做了它背后的数学计算，并认为我对这个主题的理解足够好，可以用于我自己的实现(没有任何线性代数包等)。显然，我错了。下面你可以找到我能想到的最简单的网络示例:2个隐藏单元和1个输出单元。我试着学习异或函数。然而，这根本不起作用。预测总是在0.5附近。我不知道我在哪里搞砸了。也许有人能帮忙?

float sigmoid(float pX) {
    return 1.0f/(1.0f+exp(-1.0f*pX));
}
int main(int argc, char const *argv[]) {
// DEFINE XOR problem
float examples[4][2] = { {0,0} , {0,1}, {1,0}, {1,1}};
float labels[4] = {0, 1, 1, 0};
/* I want to use a network with two hidden neurons and 1 output neuron 
*/
// Weights from input to hidden neurons
float WInput[2][2];
float WInputBias[2];
// Weights from hidden to output neuron
float WOutput[2];
float WOutputBias;
// output of hidden layer to output neuron
float hidden[2];
// error for hidden layer
float error[2];
//output of network 
float yPred;
// randomly init weights
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<float> d(0, 0.1);
WInput[0][0] = d(gen); WInput[0][1] = d(gen); 
WInput[1][0] = d(gen); WInput[1][1] = d(gen); 
WInputBias[0] = d(gen); WInputBias[1] = d(gen);
WOutput[0] = d(gen); WOutput[1] = d(gen); WOutputBias = d(gen);
// do the learning
for(unsigned int i = 0; i < 1000; ++i) {
    for (unsigned int k = 0; k < 4; ++k) {
        float * input = &examples[k][0];
        float label = labels[k];
        // Compute forward pass
        hidden[0] = sigmoid(WInput[0][0]*input[0] + WInput[1][0]*input[1] + WInputBias[0]);
        hidden[1] = sigmoid(WInput[0][1]*input[0] + WInput[1][1]*input[1] + WInputBias[1]);
        yPred = sigmoid(WOutput[0]*hidden[0] + WOutput[1]*hidden[1] + WOutputBias);
        std :: cout << "Target / Prediction: " << label << " / " << yPred << std :: endl;
        // Backward pass with alpha = 0.1
        float outputError = -(label - yPred)*yPred*(1-yPred);
        WOutput[0] = WOutput[0] - 0.1f*outputError*hidden[0]; //hidden equals input from this layer
        WOutput[1] = WOutput[1] - 0.1f*outputError*hidden[1];
        WOutputBias = WOutputBias - 0.1f*outputError;
        error[0] = (WOutput[0]*outputError)*hidden[0]*(1-hidden[0]);
        error[1] = (WOutput[1]*outputError)*hidden[1]*(1-hidden[1]);
        WInput[0][0] = WInput[0][0] - 0.1f*error[0]*input[0];
        WInput[1][0] = WInput[1][0] - 0.1f*error[0]*input[1];
        WInput[0][1] = WInput[0][1] - 0.1f*error[1]*input[0];
        WInput[1][1] = WInput[1][1] - 0.1f*error[1]*input[1];
        WInputBias[0] = WInputBias[0] - 0.1f*error[0];
        WInputBias[1] = WInputBias[1] - 0.1f*error[1];
    }
    std :: cout << std :: endl;
    // getch();
} 
}