How to Classify New Dataset using Two trained models
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I have trained two models on a dataset
I want to Classify new data using the both the trained model. But Classify take one trained network. How can i do that?
Resnet50.mat
Resnet18.mat
rxTestPred = classify(resnet.trainedNet,rxTestFrames);
testAccuracy = mean(rxTestPred == rxTestLabels);
disp("Test accuracy: " + testAccuracy*100 + "%")
2 个评论
KSSV
2022-1-28
Question is not clear. What problem you have in using the trained model ofr new data?
hammad younas
2022-1-28
回答（1 个）
yanqi liu
2022-2-8
yes，sir，may be use different load variable，such as
net1 = load('Resnet50.mat')
net2 = load('Resnet18.mat')
rxTestPred = classify(net1.resnet.trainedNet,rxTestFrames);
testAccuracy = mean(rxTestPred == rxTestLabels);
disp("Resnet50 Test accuracy: " + testAccuracy*100 + "%")
rxTestPred = classify(net2.resnet.trainedNet,rxTestFrames);
testAccuracy = mean(rxTestPred == rxTestLabels);
disp("Resnet18 Test accuracy: " + testAccuracy*100 + "%")
3 个评论
Med Future
2022-2-8
@yanqi but the prediction are different, i want two models to give combine prediction. Not each model to give its prediction like you can say ensemble learning /majority voting
Nagwa megahed
2022-6-2
please i ask if you reach to how implement ensemble learning in matlab ?? as i need to perform ensemble learning between more than three different networks
David Willingham
2022-6-3
See this page for information on how to work with multi-input multi-output networks in MATLAB: Multiple-Input and Multiple-Output Networks
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