FYP Code - working - CWT/DWT & classifier

rng(1)
datafolder = "C:\Users\leere\Documents\MATLAB\Centrifugal Pump";

ads = audioDatastore(datafolder, ...
    'IncludeSubfolders',true, ...
    'FileExtensions','.wav', ...
    'LabelSource','foldernames');

% save Image to folder, and create the relevant subfolder
datafolder_scallogram = "datafolder/structure_Scallogram";
catego=categories(ads.Labels);
for i = 1:1:length(catego)
    imgLoc = fullfile(datafolder_scallogram,char(catego{i}));
    mkdir(imgLoc)
end

helperCreateRGBfromTF(ads,datafolder_scallogram)


allImages = imageDatastore(datafolder_scallogram,...
    'IncludeSubfolders',true,...
    'LabelSource','foldernames');

rng(1)
[imgsTrain,imgsValidation,imgsTest] = splitEachLabel(allImages,0.6,0.2,'randomized');
disp(['Number of training images: ',num2str(numel(imgsTrain.Files))]);
countEachLabel(imgsTrain)
disp(['Number of validation images: ',num2str(numel(imgsValidation.Files))]);
countEachLabel(imgsValidation)
disp(['Number of validation images: ',num2str(numel(imgsTest.Files))]);
countEachLabel(imgsTest)



net = googlenet;
net.Layers(1)
inputSize = net.Layers(1).InputSize;
if isa(net,'SeriesNetwork')
  lgraph = layerGraph(net.Layers);
else
  lgraph = layerGraph(net);
end

[learnableLayer,classLayer] = findLayersToReplace(lgraph);
[learnableLayer,classLayer]
numClasses = numel(categories(imgsTrain.Labels));

if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
    newLearnableLayer = fullyConnectedLayer(numClasses, ...
        'Name','new_fc', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);

elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
    newLearnableLayer = convolution2dLayer(1,numClasses, ...
        'Name','new_conv', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
end

lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);

figure('Units','normalized','Position',[0.3 0.3 0.4 0.4]);
plot(lgraph)
ylim([0,10])

miniBatchSize = 16;
valFrequency = floor(numel(imgsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',6, ...
    'InitialLearnRate',3e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imgsValidation, ...
    'ValidationFrequency',valFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress');

reset(gpuDevice())
net = trainNetwork(imgsTrain,lgraph,options);

[YPred,probs] = classify(net,imgsTest);
accuracy = mean(YPred == imgsTest.Labels)

idx = randperm(numel(imgsTest.Files),4);
figure
for i = 1:4
    subplot(2,2,i)
    I = readimage(imgsTest,idx(i));
    imshow(I)
    label = YPred(idx(i));
    title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%");
end