How do I calculate top5/top1 deep learning errors in MATLAB?

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Hello everyone.
I implemented different CNN models (AlexNet, DensNET), and I want to compare them based on top1/top5 error, but I couldn't find any useful tips regarding this point.
Would be appreciated if someone could help me.

Answers (1)

Raynier Suresh
Raynier Suresh on 2 Dec 2020
Top 1 Accuracy:
Output from the model that is the output label with highest probability needs to be same as expected
You can use the below code for Top-1 Accuracy
[YPred,scores] = classify(net,imdsValidation)
YValidation = imdsValidation.Labels;
top1Accuracy = mean(YPred == YValidation)
Top 5 Accuracy:
Any of the top 5 probability label obtained from the network must match with the original label.
You can use the below code for Top-5 Accuracy
[~,scores] = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
[n,m] = size(scores);
idx = zeros(m,n);
for i=1:n
[~,idx(:,i)] = sort(scores(i,:),'descend');
idx = idx(1:5,:);
top5Classes = net.Layers(end).ClassNames(idx);
top5count = 0;
for i = 1:n
top5count = top5count + sum(YValidation(i,1) == top5Classes(:,i));
top5Accuracy = top5count/n
Refer the below links for more information:

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