function [trainedNet validationPredictions] = network_main()
%UNTITLED Summary of this function goes here
%   Detailed explanation goes here
ads = audioDatastore('onlineDataset','IncludeSubfolders',true,'LabelSource','foldernames');
labelTable = countEachLabel(ads);
numClasses = size(labelTable,1);
numAudios = length(ads.Labels);
% [adsTrain, adsValidation] = splitEachLabel(ads,0.8);
% % K-fold validation
num_folds=5;
for fold_idx=1:num_folds
    fprintf('processing %d among %d folds \n',fold_idx,num_folds);
    test_idx=fold_idx:num_folds:numAudios;
    adsTest=subset(ads,test_idx);
    
    train_idx=setdiff(1:length(ads.Files),test_idx);
    adsTrain=subset(ads,train_idx);
    
    overlapPercentage=75;
    
    [trainFeatures,trainLabels] = vggishPreprocess(adsTrain,overlapPercentage);
    [validationFeatures,validationLabels,segmentsPerFile] = vggishPreprocess(adsTest,overlapPercentage);
    
    clear net;
    net = vggish;
    lgraph = layerGraph(net.Layers);
    lgraph = removeLayers(lgraph,'regressionoutput');
    lgraph.Layers(end);
    layers = lgraph.Layers;
   for i = 1:23
        if isprop(layers(i),'WeightLearnRateFactor')
            layers(i).WeightLearnRateFactor = 0;
        end
        if isprop(layers(i),'WeightL2Factor')
            layers(i).WeightL2Factor = 0;
        end
        if isprop(layers(i),'BiasLearnRateFactor')
            layers(i).BiasLearnRateFactor = 0;
        end
        if isprop(layers(i),'BiasL2Factor')
            layers(i).BiasL2Factor = 0;
        end
    end
%     lgraph = addLayers(lgraph,dropoutLayer('Name','drop'));
    lgraph = addLayers(lgraph,fullyConnectedLayer(numClasses,'Name','FCFinal'));
    lgraph = addLayers(lgraph,softmaxLayer('Name','softmax'));
    lgraph = addLayers(lgraph,classificationLayer('Name','classOut'));

%     lgraph = connectLayers(lgraph,'EmbeddingBatch','drop');
    lgraph = connectLayers(lgraph,'EmbeddingBatch','FCFinal');
    lgraph = connectLayers(lgraph,'FCFinal','softmax');
    lgraph = connectLayers(lgraph,'softmax','classOut');
    miniBatchSize = 32;
     options = trainingOptions('adam',...
        'MaxEpochs',20,'MiniBatchSize',miniBatchSize,...
        'Shuffle','every-epoch', ...
         'ValidationData',{validationFeatures,validationLabels}, ...
        'Verbose',false, ...
        'Plots','training-progress',...
         'ValidationPatience',7,'ExecutionEnvironment','gpu','InitialLearnRate',0.001);
    
   [trainedNet, netInfo] = trainNetwork(trainFeatures,trainLabels,lgraph,options);
    validationPredictions = classify(trainedNet,validationFeatures);
    idx = 1;
    for ii = 1:numel(adsTest.Files)
        validationPredictionsPerFile(1,test_idx(ii)) = mode(validationPredictions(idx:idx+segmentsPerFile(ii)-1));
        idx = idx + segmentsPerFile(ii);
    end
    save(['net',num2str(fold_idx),'.mat'],'trainedNet');
    save(['info',num2str(fold_idx),'.mat'],'netInfo');
end
actual_labels=ads.Labels;

% Confusion Matrix
figure;
plotconfusion(actual_labels,validationPredictionsPerFile')
title('Confusion Matrix: VGGish');
end