For TF/Keras (or in general), what is the best way to define a multidimensional y target? Should this even be done?
Any sample x tries to predict several "values of interest". These values are returned as simple floats. Each value also has a corresponding category (binary) that classifies the value. Note that the binary classification corresponds to class A or B not a "yes" or "no".
y = [[1.0451 0], [1.1469, 1], [1.3571, 1], [1.0451, 0]]
Note that for my particular problem (which might be relevant to the posted problem), each y label has a different # of elements up to 5 (elements without values are zero padded [0, 0].
A possible solution:
Flatten y into a single vector. From your experience, how well do NN's establish the type of relationship between values like above?