Why number of class shows 1 ?

I write this code with using HAM 10000 dataset. But I have one problem. Accuracy always show %100. Also I realized when i write :

CLASS = categories(img.Labels);

CLASS gives value of 1 but normally i have 7 class. Can you help why it can be?

img = imageDatastore('HAM10000_images_part_1',...
    'IncludeSubfolders',true,...
    'LabelSource','foldernames');

figure
Images = length(img.Files);
perm = randperm(Images,40);
for i = 1:40
    subplot(8,5,i);
    imshow(img.Files{perm(i)});
end

imageSize = [64 64 3];
[imdsTrain,imdsTest] = splitEachLabel(img,0.7,'randomize');
datastore = augmentedImageDatastore(imageSize,imdsTrain); 



layers = [ 
    imageInputLayer(imageSize,'Name','input')  
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,64,'Padding','same') 
    batchNormalizationLayer
    reluLayer   
    maxPooling2dLayer(2,'Stride',2) 
    fullyConnectedLayer(7)
    softmaxLayer
    classificationLayer ];

options = trainingOptions('adam', ...
    'MaxEpochs',3,...
    'InitialLearnRate',0.01, ...
    'Verbose',true, ...
    'Plots','training-progress');

net = trainNetwork(datastore,layers,options);
YPred = classify(net,imdsTest);
YValidation = imdsTest.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation);

Answer 1

[Gaurav Garg]

Hi Ceren,

The given code and the model seem to work fine for me for a DigitDataset, with an accuracy of 99.4%.

Where you might be confused is with the output of the classification layer. A classification layer infers the number of classes from the output size of the previous layer. Hence, you are expected to get a single value as output and which you are getting as well. FOr more information on classification layer, you can refer here.