c++ return from template - c++

Question

I'm a bit new to generic programming with templates in C++ and have a question on how to return an object from a templated function. This is part of the neural network module of mlpack library. This is from the feedforward_network_test.cpp which can be found here. If I understand this correctly, the way the templated function BuildVanillaNetwork is setup, its possible to pass different types of network parameters to build the neural network. What I would like is for this function to return the FFN object that it builds, so that I can access it from where I call it. I made some small changes to the code over there:
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;
}
And here is the main function:
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;
}
When I compile this, I get the following error:
[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
Any help in fixing this is appreciated. Also, it'll be great if I could get links to a tutorial that explains the various concepts used in this code.
EDIT-1
I changed the function header to this:
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)
But I'm still getting error when compiling:
[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
Also, the signature of the FFN class (here) seems to be different than what I have in this function. Could that be an issue? If it is, how do I fix it since those typenames are not really "types" as far as I understand.
Thanks.

Answer 1

The problem is your definition of the BuildVanillaNetwork function as:
mlpack::ann::FFN<> BuildVanillaNetwork(...)
The error messages are usually hard to read by a human when they involve templates, but reading through the lines gives you something like this:
error: wrong number of template arguments (0, should be 4) ... provided for ‘template class mlpack::ann::FFN’
The rest of the errors are caused by this one (basically, because it doesn't understand the return type of that function, the compiler assumes it to be int, then it complains it can't convert net to int).
So you have to actually specify the template arguments for the return type. You're using decltype in the body of the function to deduce them (which happens at compile time, not runtime), but in the prototype it won't be that easy. There is a way to use decltype to declare the return type of a function, but it won't help you much in this case. So you might as well go on and write them explicitly.

Answer 2

mlpack::ann::FFN<> BuildVanillaNetwork(MatType& trainData,
Change it to something that includes the parameters from the template, somthing like:
mlpack::ann::FFN<
PerformanceFunction,
OutputLayerType,
PerformanceFunctionType,
MatType
> BuildVanillaNetwork(MatType& trainData,...
Or try decltype(auto) if your compiler supports it:
auto BuildVanillaNetwork(MatType& trainData,`...) -> decltype(auto) {...

Answer 3

You can use the following pattern to somewhat simplify the return type inference:
template<typename PerformanceFunction, typename OutputLayerType,
typename PerformanceFunctionType, typename MatType>
struct BuildVanillaNetworkHelper
{
using LinearLayer = mlpack::ann::LinearLayer<>;
using BiasLayer = mlpack::ann::BiasLayer<>;
using BaseLayer = mlpack::ann::BaseLayer<PerformanceFunction>;
using ModulesType = std::tuple<LinearLayer, BiasLayer, BaseLayer,
LinearLayer, BiasLayer, BaseLayer>;
using FFNType = mlpack::ann::FFN<ModulesType, OutputLayerType,
mlpack::ann::RandomInitialization, PerformanceFunctionType>;
};
template <typename PerformanceFunction,
typename OutputLayerType,
typename PerformanceFunctionType,
typename MatType = arma::mat,
typename Helper = BuildVanillaNetworkHelper<
PerformanceFunction, OutputLayerType,
PerformanceFunctionType, MatType>
>
typename Helper::FFNType BuildVanillaNetwork(...);