Test or validate a model using (same number of images form each subfolder)

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Hi, i have three subfolders (good, moderate and severe) with images(3608, 406 and 200 images respectivly)
1-the accuracy is (98.18), is it ok?
2-after training my model,how to take (100 images from each subfolder to test (validate) the model) ?
3-what is the code for confusion matrix
Thanks
my code is next:
imds = imageDatastore('C:\Users\Rayan\Desktop\9_8_balance_data\R_9_1_GSM', ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.85,'randomized');
numTrainImages = numel(imdsTrain.Labels);
idx = randperm(numTrainImages,16);
net = resnet50;
inputSize = net.Layers(1).InputSize;
lgraph = layerGraph(net);
% call the find layers to Replace at : edit(fullfile(matlabroot,'examples','nnet','main','findLayersToReplace.m'))
edit(fullfile(matlabroot,'examples','nnet','main','findLayersToReplace.m'))
[learnableLayer,classLayer] = findLayersToReplace(lgraph);
[learnableLayer,classLayer] %#ok<NOPTS>
numClasses = numel(categories(imdsTrain.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);
layers = lgraph.Layers;
connections = lgraph.Connections;
layers(1:20) = freezeWeights(layers(1:20));
lgraph = createLgraphUsingConnections(layers,connections);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain)
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
miniBatchSize=10;
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',10, ...
'InitialLearnRate',0.0007, ...
'Shuffle','every-epoch', ...
'ValidationFrequency',valFrequency, ...
'ValidationData',augimdsValidation, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options);
[YPred,probs] = classify(net,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels);
idx = randperm(numel(imdsValidation.Files),100);
R=1;
for j =1:25
figure(j)
for i = 1:4
subplot(2,2,i)
I = readimage(imdsValidation,idx(R));
imshow(I)
label = YPred(idx(R));
title(string(label) + ", " + num2str(100*max(probs(idx(R),:)),3) + "%");
R=R+1;
end
end

Accepted Answer

Image Analyst
Image Analyst on 4 Jul 2022
Regarding accuracy. It might be. But let's say you were building a dog and cat trainer and gave it 10000 images of dogs and 30 images of cats. Then, once trained, you give it the test set comprised of both dogs and cats - 9818 dog images and 172 cat images - and it called every single one a dog. That's 98.18 accurate. Is it good enough? It didn't find a single cat but it was 98.18% accurate.

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