在C ++ 11中沒有用於調用'evaluate_net_weight'的匹配函數

Question

我正在做一個深度學習項目，其中我編寫了一些測試來評估神經網絡中的凈重。 該代碼看起來像是evaluate_net_weight ：

/*! 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;
}

該函數被調用如下：

// 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是我的輸入， Y是預測， lstm是網絡類型。 這些已經定義。

當我嘗試使用cmake生成程序時，通常會出現以下錯誤：

/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...

我無法弄清楚，為什么會發生此錯誤？ 如果有人可以幫助我解決這個問題，那就太好了。 謝謝！

Answer 1

在不同的范圍內，您對自定義類型Scalar_t定義可能有不同且相互沖突的定義。

從錯誤消息中，我們可以看到該函數期望一個typename TReference<double>::Scalar_t （相當於double ），但實際上您傳遞了一個Scalar_t類型的參數（可能在全局范圍內定義），正如某些程序員所提到的那樣，它等效於TMatrixT<double> ，這會導致錯誤。