c++ - 在 C++11 中没有用于调用 'evaluate_net_weight' 的匹配函数

Question

我正在从事一个深度学习项目，我在该项目中编写了一些测试来评估神经网络中的净权重。 evaluate_net_weight 的代码如下所示:

/*! Compute the loss of the net as a function of the weight at index (i,j) in
 *  layer l. dx is added as an offset to the current value of the weight. */
//______________________________________________________________________________
template <typename Architecture>
auto evaluate_net_weight(TDeepNet<Architecture> &net, std::vector<typename Architecture::Matrix_t> & X,
                         const typename Architecture::Matrix_t &Y, const typename Architecture::Matrix_t &W, size_t l,
                         size_t k, size_t i, size_t j, typename Architecture::Scalar_t xvalue) ->
   typename Architecture::Scalar_t
{
    using Scalar_t = typename Architecture::Scalar_t;

    Scalar_t prev_value = net.GetLayerAt(l)->GetWeightsAt(k).operator()(i,j);
    net.GetLayerAt(l)->GetWeightsAt(k).operator()(i,j) = xvalue;
    Scalar_t res = net.Loss(X, Y, W, false, false);
    net.GetLayerAt(l)->GetWeightsAt(k).operator()(i,j) = prev_value;
    //std::cout << "compute loss for weight  " << xvalue << "  " << prev_value << " result " << res << std::endl;
    return res;
}

函数调用如下:

// Testing input gate: input weights k = 0
    auto &Wi = layer->GetWeightsAt(0);
    auto &dWi = layer->GetWeightGradientsAt(0);
    for (size_t i = 0; i < (size_t) Wi.GetNrows(); ++i) {
        for (size_t j = 0; j < (size_t) Wi.GetNcols(); ++j) {
            auto f = [&lstm, &XArch, &Y, &weights, i, j](Scalar_t x) {
                return evaluate_net_weight(lstm, XArch, Y, weights, 0, 0, i, j, x);
            };
            ROOT::Math::Functor1D func(f);
            double dy = deriv.Derivative1(func, Wi(i,j), 1.E-5);
            Double_t dy_ref = dWi(i,j);

            // Compute relative error if dy != 0
            Double_t error;
            std::string errorType;
            if (std::fabs(dy_ref) > 1e-15) {
                error = std::fabs((dy - dy_ref) / dy_ref);
                errorType = "relative";
            } else {
                error = std::fabs(dy - dy_ref);
                errorType = "absolute";
            }

            if (debug) std::cout << "Input Gate: input weight gradients (" << i << "," << j << ") : (comp, ref) " << dy << ", " << dy_ref << std::endl;

            if (error >= maximum_error) {
                maximum_error = error;
                maxErrorType = errorType;
            }
        }
    }

XArch 是我的输入，Y 是预测，lstm 是指网络类型。这些已经定义好了。

当我尝试使用 cmake 构建程序时，我通常会遇到此错误:

/Users/harshitprasad/Desktop/gsoc-rnn/root/tmva/tmva/test/DNN/RNN/TestLSTMBackpropagation.h:385:24: error: 
      no matching function for call to 'evaluate_net_weight'
                return evaluate_net_weight(lstm, XArch, Y, weights, 0, 2, i, j, x);
                       ^~~~~~~~~~~~~~~~~~~
/Users/harshitprasad/Desktop/gsoc-rnn/root/tmva/tmva/test/DNN/RNN/TestLSTMBackpropagation.h:67:6: note: 
      candidate function [with Architecture = TMVA::DNN::TReference<double>] not viable: no known
      conversion from 'Scalar_t' (aka 'TMatrixT<double>') to 'typename TReference<double>::Scalar_t'
      (aka 'double') for 9th argument
auto evaluate_net_weight(TDeepNet<Architecture> &net, std::vector<typename Architecture::Matr...

我想不通，为什么会出现这个错误？如果有人可以帮助我解决这个问题，那就太好了。谢谢!

Answer 1

您的自定义类型可能有不同且相互冲突的定义 Scalar_t , 在不同的范围内。

从错误消息中，我们可以看到该函数需要一个typename TReference<double>::Scalar_t。 (相当于 double )，但你实际上传递了一个 Scalar_t 类型的参数(可能在全局范围内的某个地方定义)，相当于 TMatrixT<double> ，这会导致错误，正如一些程序员提到的那样。