Training Neural Networks using Multi-Class output

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Anirudh Roy
Anirudh Roy on 23 Nov 2020
Commented: Anirudh Roy on 5 Dec 2020
The Deep Learning toolbox supports classification based training (from feature based data) for ony 1 label per sample. I have a MxD training set (D number of features and M number of samples). Each output should be characterized by 'T' number of labels (ie final output MxT). My question is how do i get around this limitation ? (The labels are mutually exclusive)

Answers (1)

Raynier Suresh
Raynier Suresh on 1 Dec 2020
One way to obtain multiple labels for a single sample is to branch the network and have multiple classification layers or regression layers.
Refer the below link for designing and training multi-output networks :
  1 Comment
Anirudh Roy
Anirudh Roy on 5 Dec 2020
Branching isnt probably ging to solve my problem, stacking of classification layer would. But yeah i get the idea, ill have to customize the whole thing (loop and layer)

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