Given that each cell of your Xdata contains a 1921x3 numeric array, and you're attempting to feed this into a 2D CNN with an imageInputLayer expecting input of size [1921 3 1], you need to convert your cell array into a 4D numeric array where each "image" is of size 1921x3 and the fourth dimension indexes each example in your dataset.
Here's how you can convert your cell array Xdata into the required 4D array format:
% Initialize a 4D array with zeros. Assuming the depth is 1 for grayscale images.
X_4D = zeros(1921, 3, 1, numel(Xdata));
for i = 1:numel(Xdata)
X_4D(:, :, 1, i) = Xdata{i};
end
Make sure your training and validation splits (XTrain, XValidation) are also converted to the correct format
XTrain_4D = X_4D(:, :, :, Train_index);
TTrain = Tdata(Train_index);
XValidation_4D = X_4D(:, :, :, Test_index);
TValidation = Tdata(Test_index);
The "SequencePaddingDirection" option in "trainingOptions" and "classify" is not necessary unless you are dealing with sequence data, which does not seem to be the case here.
options = trainingOptions("sgdm", ...
MiniBatchSize=miniBatchSize, ...
MaxEpochs=40, ...
ValidationData={XValidation_4D, TValidation}, ...
Plots="training-progress", ...
Verbose=0);
net = trainNetwork(XTrain_4D, TTrain, layers, options);
Hope it works after these modifications!
