main_code.txt

imds = imageDatastore("C:\Users\Kishor\Downloads\deeplearning_course_files\Roundworms\WormImages");

groundtruth = readtable("WormData");
imds.Labels = categorical(groundtruth.Status);

imshow(readimage(imds,1))
imshow(readimage(imds,2))
imshow(readimage(imds,3))

[trainImgs,testImgs] = splitEachLabel(imds,0.6,"randomized");

trainds = augmentedImageDatastore([224 224],trainImgs,"ColorPreprocessing","gray2rgb");
testds = augmentedImageDatastore([224 224],testImgs,"ColorPreprocessing","gray2rgb");

net = googlenet;
lgraph = layerGraph(net);
newFc = fullyConnectedLayer(2,"Name","new_fc")
lgraph = replaceLayer(lgraph,"loss3-classifier",newFc)
newOut = classificationLayer("Name","new_out")
lgraph = replaceLayer(lgraph,"output",newOut)

options = trainingOptions("sgdm","InitialLearnRate", 0.001, MaxEpochs=30);

wormsnet = trainNetwork(trainds,lgraph,options)

preds = classify(wormsnet,testds);

truetest = testImgs.Labels;
nnz(preds == truetest)/numel(preds)

confusionchart(truetest,preds);

idx = find(preds~=truetest)
if ~isempty(idx)
    imshow(readimage(testImgs,idx(1)))
    title(truetest(idx(1)))
end