[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
branch: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 6:33 AM, Joe Mariadassou <joe.mariadassou at gmail.com>
wrote:

> 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
>
>


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


---
Reply to this email directly or view it on GitHub:
https://github.com/mlpack/mlpack/issues/531#issuecomment-191417423
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