从模板返回的 C++ 返回

我对C++中使用模板进行泛型编程有点陌生，并且对如何从模板化函数返回对象有疑问。这是 mlpack 库的神经网络模块的一部分。这是来自可以在这里找到的feedforward_network_test.cpp。如果我理解正确，模板化函数BuildVanillaNetwork的设置方式，可以传递不同类型的网络参数来构建神经网络。我希望这个函数返回它构建的 FFN 对象，以便我可以从我调用它的地方访问它。我对那里的代码做了一些小的更改：

template <typename PerformanceFunction,
         typename OutputLayerType,
         typename PerformanceFunctionType,
         typename MatType = arma::mat
         >
mlpack::ann::FFN<> BuildVanillaNetwork(MatType& trainData,
        MatType& trainLabels,
        MatType& testData,
        MatType& testLabels,
        const size_t hiddenLayerSize,
        const size_t maxEpochs,
        const double classificationErrorThreshold)
{
    // input layer
    mlpack::ann::LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
    mlpack::ann::BiasLayer<> inputBiasLayer(hiddenLayerSize);
    mlpack::ann::BaseLayer<PerformanceFunction> inputBaseLayer;
    // hidden layer
    mlpack::ann::LinearLayer<> hiddenLayer1(hiddenLayerSize, trainLabels.n_rows);
    mlpack::ann::BiasLayer<> hiddenBiasLayer1(trainLabels.n_rows);
    mlpack::ann::BaseLayer<PerformanceFunction> outputLayer;
    // output layer
    OutputLayerType classOutputLayer;
    auto modules = std::tie(inputLayer, inputBiasLayer, inputBaseLayer, hiddenLayer1, hiddenBiasLayer1, outputLayer);
    mlpack::ann::FFN<decltype(modules), decltype(classOutputLayer), mlpack::ann::RandomInitialization, PerformanceFunctionType> net(modules, classOutputLayer);
    net.Train(trainData, trainLabels);
    MatType prediction;
    net.Predict(testData, prediction);
    double classificationError;
    for (size_t i = 0; i < testData.n_cols; i++)
    {
        if (arma::sum(arma::sum(arma::abs(prediction.col(i) - testLabels.col(i)))) != 0)
        {
            classificationError++;
        }
    }
     classificationError = double(classificationError) / testData.n_cols;
    std::cout << "Classification Error = " << classificationError * 100 << "%" << std::endl;
    return net;
}

这是主要功能：

int main(int argc, char** argv)
{
    arma::mat dataset;
    mlpack::data::Load("../data/thyroid_train.csv", dataset, true);
    arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4, dataset.n_cols - 1);
    arma::mat trainLabels = dataset.submat(dataset.n_rows - 3, 0, dataset.n_rows - 1, dataset.n_cols - 1);
    mlpack::data::Load("../data/thyroid_test.csv", dataset, true);
    arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4, dataset.n_cols - 1);
    arma::mat testLabels = dataset.submat(dataset.n_rows - 3, 0, dataset.n_rows - 1, dataset.n_cols - 1);
    const size_t hiddenLayerSize = 8;
    const size_t maxEpochs = 200;
    const double classificationErrorThreshold = 0.1;
    auto myFFN = BuildVanillaNetwork<mlpack::ann::LogisticFunction, mlpack::ann::BinaryClassificationLayer, mlpack::ann::MeanSquaredErrorFunction>
        (trainData, trainLabels, testData, testLabels, hiddenLayerSize, maxEpochs, classificationErrorThreshold);
    return 0;
}

当我编译它时，我收到以下错误：

[100%] Building CXX object CMakeFiles/ff_nn.dir/src/ff_nn.cpp.o /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:24:18: error: wrong number of template arguments (0, should be 4)  mlpack::ann::FFN<> BuildVanillaNetwork(MatType& trainData,
                  ^ In file included from /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:16:0: /usr/local/include/mlpack/methods/ann/ffn.hpp:35:7: error: provided for ‘template<class LayerTypes, class OutputLayerType, class InitializationRuleType, class PerformanceFunction> class mlpack::ann::FFN’  class FFN
       ^ /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp: In instantiation of ‘int BuildVanillaNetwork(MatType&, MatType&, MatType&, MatType&, size_t, size_t, double) [with PerformanceFunction
= mlpack::ann::LogisticFunction; OutputLayerType = mlpack::ann::BinaryClassificationLayer; PerformanceFunctionType = mlpack::ann::MeanSquaredErrorFunction; MatType = arma::Mat<double>; size_t = long unsigned int]’: /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:83:112:   required from here /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:64:12: error: cannot convert ‘mlpack::ann::FFN<std::tuple<mlpack::ann::LinearLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BiasLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BaseLayer<mlpack::ann::LogisticFunction, arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::LinearLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BiasLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BaseLayer<mlpack::ann::LogisticFunction, arma::Mat<double>, arma::Mat<double> >&>, mlpack::ann::BinaryClassificationLayer, mlpack::ann::RandomInitialization, mlpack::ann::MeanSquaredErrorFunction>’ to ‘int’ in return
     return net;
            ^ make[2]: *** [CMakeFiles/ff_nn.dir/src/ff_nn.cpp.o] Error 1 make[1]: *** [CMakeFiles/ff_nn.dir/all] Error 2 make: *** [all] Error 2

任何解决此问题的帮助将不胜感激。此外，如果我能获得一个教程的链接，该教程解释了此代码中使用的各种概念，那就太好了。

编辑-1

我将函数标头更改为：

template <typename PerformanceFunction,
         typename OutputLayerType,
         typename PerformanceFunctionType,
         typename MatType = arma::mat
         >
mlpack::ann::FFN<PerformanceFunction, OutputLayerType, PerformanceFunctionType, MatType> BuildVanillaNetwork(MatType& trainData,
        MatType& trainLabels,
        MatType& testData,
        MatType& testLabels,
        const size_t hiddenLayerSize,
        const size_t maxEpochs,
        const double classificationErrorThreshold)

但是我在编译时仍然遇到错误：

[100%] Building CXX object CMakeFiles/ff_nn.dir/src/ff_nn.cpp.o
In file included from /home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:16:0:
/usr/local/include/mlpack/methods/ann/ffn.hpp: In instantiation of ‘class mlpack::ann::FFN<mlpack::ann::LogisticFunction, mlpack::ann::BinaryClassificationLayer, mlpack::ann::MeanSquaredErrorFunction, arma::Mat<double> >’:
/home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:83:112:   required from here
/usr/local/include/mlpack/methods/ann/ffn.hpp:361:55: error: incomplete type ‘std::tuple_size<mlpack::ann::LogisticFunction>’ used in nested name specifier
       size_t Max = std::tuple_size<LayerTypes>::value - 1,
                                                       ^
/usr/local/include/mlpack/methods/ann/ffn.hpp:369:55: error: incomplete type ‘std::tuple_size<mlpack::ann::LogisticFunction>’ used in nested name specifier
       size_t Max = std::tuple_size<LayerTypes>::value - 1,
                                                       ^
/home/username/project-yanack/mlpack_nn/src/ff_nn.cpp: In instantiation of ‘mlpack::ann::FFN<PerformanceFunction, OutputLayerType, PerformanceFunctionType, MatType> BuildVanillaNetwork(MatType&, MatType&, MatType&, MatType&, size_t, size_t, double) [with PerformanceFunction = mlpack::ann::LogisticFunction; OutputLayerType = mlpack::ann::BinaryClassificationLayer; PerformanceFunctionType = mlpack::ann::MeanSquaredErrorFunction; MatType = arma::Mat<double>; size_t = long unsigned int]’:
/home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:83:112:   required from here
/home/username/project-yanack/mlpack_nn/src/ff_nn.cpp:64:12: error: could not convert ‘net’ from ‘mlpack::ann::FFN<std::tuple<mlpack::ann::LinearLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BiasLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BaseLayer<mlpack::ann::LogisticFunction, arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::LinearLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BiasLayer<arma::Mat<double>, arma::Mat<double> >&, mlpack::ann::BaseLayer<mlpack::ann::LogisticFunction, arma::Mat<double>, arma::Mat<double> >&>, mlpack::ann::BinaryClassificationLayer, mlpack::ann::RandomInitialization, mlpack::ann::MeanSquaredErrorFunction>’ to ‘mlpack::ann::FFN<mlpack::ann::LogisticFunction, mlpack::ann::BinaryClassificationLayer, mlpack::ann::MeanSquaredErrorFunction, arma::Mat<double> >’
     return net;
            ^
make[2]: *** [CMakeFiles/ff_nn.dir/src/ff_nn.cpp.o] Error 1
make[1]: *** [CMakeFiles/ff_nn.dir/all] Error 2
make: *** [all] Error 2

此外，FFN 类（此处）的签名似乎与我在此函数中的签名不同。这可能是一个问题吗？如果是，我该如何解决它，因为据我所知，这些类型名并不是真正的"类型"。

谢谢。