how can I extract predicted label and testlabel from already trained deep learning model. the code below gives error while running

조회 수: 5(최근 30일)
Bashir Abubakar
Bashir Abubakar 2021년 12월 3일
댓글: Walter Roberson 2021년 12월 4일
load('MyVGG19Model.mat');
imdsTest = imageDatastore("C:\Users\Bashir\Desktop\Training dataset\Test set", ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
imageAugmenter = imageDataAugmenter( ...
'RandRotation',[-90,90], ...
'RandScale',[1 1.1], ...
'RandXTranslation',[-3 3], ...
'RandYTranslation',[-3 3]);
imageSize = myvgg16.Layers(1).InputSize;
augmentedTestSet = augmentedImageDatastore(imageSize, imdsTest, 'DataAugmentation',imageAugmenter);
predictedLabels = predict(myvgg16, augmentedTestSet);
testLabels = imdsTest.Labels
% Tabulate the results using a confusion matrix.
confMat = confusionmat(testLabels, predictedLabels);
% Convert confusion matrix into percentage form
confMat = bsxfun(@rdivide,confMat,sum(confMat,2))
  댓글 수: 7
Walter Roberson
Walter Roberson 2021년 12월 4일
You could try
categorical(string(round(predictedLabels)))
but I would recommend looking more carefully at the values in predictedLabels. The values of the predictedLabels might possibly be class numbers, in which case you would want to use them to index the categories that were used in TestLabels .

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답변(1개)

yanqi liu
yanqi liu 2021년 12월 4일
yes,sir,as Walter Roberson said,may be ensure data class,such as
load('MyVGG19Model.mat');
imdsTest = imageDatastore("C:\Users\Bashir\Desktop\Training dataset\Test set", ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
imageAugmenter = imageDataAugmenter( ...
'RandRotation',[-90,90], ...
'RandScale',[1 1.1], ...
'RandXTranslation',[-3 3], ...
'RandYTranslation',[-3 3]);
imageSize = myvgg16.Layers(1).InputSize;
augmentedTestSet = augmentedImageDatastore(imageSize, imdsTest, 'DataAugmentation',imageAugmenter);
predictedLabels = predict(myvgg16, augmentedTestSet);
testLabels = imdsTest.Labels
% Tabulate the results using a confusion matrix.
confMat = confusionmat(double(testLabels), double(predictedLabels));
% Convert confusion matrix into percentage form
confMat = bsxfun(@rdivide,confMat,sum(confMat,2))

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