When I have N samples of 2 different features (different scale, different meaning etc.), but whose values are scalar numbers of the same format.
Does it make sense to flatten the data into a 1D array of length [N*2] and feed it in this 1D shape into the neural network. Or must I keep them separate (as columns or otherwise)? I will standardize each feature before combining them into the single array.
(I want to do this for convenience reasons i.e. not deal with multidimensional arrays).
The data is input for a classification problem.