Why accept Matlab no vector as a response when I use CNN with residual connection?

I train my own CNN model like this tutorial:

https://de.mathworks.com/help/deeplearning/examples/train-residual-network-for-image-classification.html

but I need no classification layer at the end my neural network, but regression layer. That's why I need to define a vector for each sample. All in all, I have to train a matrix(40*num_of_samples), which must be converted into a 4D array like this tutorial:

https://de.mathworks.com/help/deeplearning/examples/train-a-convolutional-neural-network-for-regression.html;jsessionid=d711ac701ec8c18b8d81ba7651a4

I get this error:

Invalid validation data. Y must be a vector of categorical responses.

Does someone know how I can fix this?

[Shivam Sardana]

Can you please provide more information about inputs and outputs to your CNN? It will be good if you can provide code sample.

[Osama Tabbakh]

Let say this is my input images:

X_Train(:,:,3,3000) = rand(100);

my outputs are defined like this:

Y_4D_train=randn(1,1,40,3000);

and the data for validation:

X_Val(:,:,3,1000) = rand(100);

Y_4D_Val =randn(1,1,40,1000);

and then I did like the tutorial:

https://de.mathworks.com/help/deeplearning/examples/train-residual-network-for-image-classification.html

imageSize = [100 100 3];

pixelRange = [-4 4];

imageAugmenter = imageDataAugmenter( ...

'RandXReflection',true, ...

'RandXTranslation',pixelRange, ...

'RandYTranslation',pixelRange);

augimdsTrain = augmentedImageDatastore(imageSize,X_Train,Y_4D_train, ...

'DataAugmentation',imageAugmenter, ...

'OutputSizeMode','randcrop');

...

...

At the end:

trainedNet = trainNetwork(augimdsTrain,lgraph,options);

[Zezhong Wang]

Same problem here.