[mlpack-git] [mlpack] ANN Saving the network and reloading (#531)

[Joseph Mariadassou]

I had the same result when used make_tuple rather than tie. I fixed it
though in my own fork:
http://github.com/theSundayProgrammer/mlpack
fork:mytweaks
SHA1: 540dd7c3b3a5f3857eed922d446bfeb5016dbcd9

Although it is for CNN not FNN, I guess a similar change for FNN should
work too


On Thu, Mar 3, 2016 at 3:29 AM, sudarshan <notifications at github.com> wrote:

> Thank you. I checked out pr/536 and linked the include directories and lib
> from there. I got rid off all the compiler error. However, I get a 100%
> classification error when I use std::make_tuple, but when I use std::tie I
> get 4.95916%. This is my funciton:
>
> auto BuildFFN(MatType& trainData, MatType& trainLabels, MatType& testData, MatType& testLabels, const size_t hiddenLayerSize)
> {
>     // input layer
>     ann::LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
>     ann::BiasLayer<> inputBiasLayer(hiddenLayerSize);
>     ann::BaseLayer<PerformanceFunction> inputBaseLayer;
>
>     // hidden layer
>     ann::LinearLayer<> hiddenLayer1(hiddenLayerSize, trainLabels.n_rows);
>     ann::BiasLayer<> hiddenBiasLayer1(trainLabels.n_rows);
>     ann::BaseLayer<PerformanceFunction> outputLayer;
>
>     // output layer
>     OutputLayerType classOutputLayer;
>
>     auto modules = std::tie(inputLayer, inputBiasLayer, inputBaseLayer, hiddenLayer1, hiddenBiasLayer1, outputLayer);
>     ann::FFN<decltype(modules), decltype(classOutputLayer), ann::RandomInitialization, PerformanceFunctionType> net(modules, classOutputLayer);
>
>     net.Train(trainData, trainLabels);
>     arma::mat 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++;
>         }
>     }
>
>     std::cout << "Classification Error = " << (double(classificationError) / testData.n_cols) * 100 << "%" << std::endl;
>
>     return net;
> }
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/mlpack/mlpack/issues/531#issuecomment-191313956>.
>



-- 
Joseph Chakravarti Mariadassou
http://thesundayprogrammer.com


---
Reply to this email directly or view it on GitHub:
https://github.com/mlpack/mlpack/issues/531#issuecomment-191389288
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