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

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Osama Tabbakh
Osama Tabbakh 2019-5-22
评论： Zezhong Wang 2021-5-26
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?
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Osama Tabbakh
Osama Tabbakh 2019-5-29
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);

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