No appropriate method, property, or field 'Lables' for class 'matlab.io​.datastore​.ImageData​store'.

I am trying to classify two class of images with resnet-50 in deep learning and getting an error 'No appropriate method, property, or field 'Lables' for class 'matlab.io.datastore.ImageDatastore'..

Can anyone help. This is my code

clc

%%Image Folder%%
outputFolder = fullfile('input folder');
rootFolder = fullfile(outputFolder, 'CNN_Class');
categories = {'a','b'}; 
%%Datastore for storing the images%%
imds = imageDatastore(fullfile(rootFolder, categories),'LabelSource', 'foldernames');
tbl=countEachLabel(imds);
minSetcount=min(tbl{:,2});
imds=splitEachLabel(imds,minSetcount,'randomize');
countEachLabel(imds);
 net=resnet50();
set(gca,'YLim',[150 170]);
net.Layers(1);
net.Layers(end);
numel(net.Layers(end).ClassNames);
%%Split into Training and Testing set%%
[trainingSet,testSet] = splitEachLabel(imds,0.8,'randomized');

imageSize=(net.Layers(1).InputSize);

augmentedTrainingset=augmentedImageDatastore(imageSize,trainingSet);

augmentedTestset=augmentedImageDatastore(imageSize,testSet);
w1=net.Layers(2).Weights;
w1=mat2gray(w1);
% figure
%montage(w1)
title('First Convolution Layer Weight')
featureLayer='fc1000';
trainingFeatures=activations(net,augmentedTrainingset,...
    featureLayer,'MiniBatchSize',32,'OutputAs','Columns');
trainingLabels=trainingSet.Labels;
Classifier=fitcecoc(trainingFeatures,trainingLabels,...
    'Learner','Linear','Coding','onevsall','ObservationsIn','Columns');

testFeatures=activations(net,augmentedTestset,...
    featureLayer,'MiniBatchSize',32,'OutputAs','Columns');
predictLabels=predict(Classifier,testFeatures,'ObservationsIn','Columns');
testLables=testSet.Lables;
confMat=confusionmat(testLables,predictLables);
confMat=bsxfun(@rdivide,confMat,sum(confMat,2));
mean(diag(confMat))

Answer 1

[dpb]

It's spelled "labels", not "lables"

testLabeles=testSet.Lables;

should be

testLabels=testSet.Labels;

Of course, let the auto-correct feature fix the other places it can and manually correct the other places as well. Judicious use of the text substitution can help typing here.

[Sunil Prajapati]

Thank you so much .