I am trying to use a different data for my Validation and it is saying that: Training and validation responses must have the same categories. To view the categories of the res

myfolder = 'C:\Users\Myname\Downloads\fall dataset\rgb';
dataDir = fullfile(myfolder);
imdir = fullfile(dataDir);
myfolder2 = 'C:\Users\Myname\Downloads\Validation';
dataDir2 = fullfile(myfolder2);
imdir2 = fullfile(dataDir2);

imds = imageDatastore(imdir, "IncludeSubfolders",true ,"LabelSource","foldernames");
imds2 = imageDatastore(imdir2,"IncludeSubfolders",true,"LabelSource","foldernames");
numTrainfiles =5172;
numValidfiles = 6598;
[imdsTrain] = splitEachLabel(imds,numTrainfiles,'randomized');
[imdsValidation] = splitEachLabel(imds2,numValidfiles,'randomized');
%definingarchitecture
inputSize = [ 240 320 3];
numClasses = numel(categories(imdsTrain.Labels));
numClasses2 = numel(categories(imdsValidation.Labels));
layers = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,20)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
%trainetwork

options = trainingOptions('sgdm', ...
    'MaxEpochs',4, ...
    'MiniBatchSize',64,...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
yvalidation = imdsValidation.Labels;
accuracy = mean(Ypred == yvalidation);

Answer 1

[Philip Brown]

It's likely that your training and validation folders contain different folder names, and those are being used as the class labels. For example, your training set has labels A, B, and C, but your validation set has labels A, B and D. This means your network never learns to classify into class D during training.