Does it make sense to flatten two different features into a single array 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.
I believe it does, for example, if you flatten both features into 1-D array and then concatenate them, they should still preserve most of the information. Because total number of feature vector still remains the same.