I want to use Deep Network Designer to design a network which inputs a feature vector and outputs a classification. Simple case:
Data: 10000 labeled observations each with 5 features i.e. available
data matrix: X = [observations , features] -> size(X) = [ 10000 , 5 ]
label vector: y = [label] -> size(y) = [ 10000 , 1 ].
Classes: A binary classification problem, say class 'A' and class 'B'
What I strugle with is how to use the Deep Network Designer to setup; correct input for the feature vector (only image and sequence input layers are available) and a trainable network with correct output.
Thank you for your time