NN multidim regression with matrix as output
All you have to do is to match the sizes. Assuming you know what particle belongs to what center that shouldn't be too hard.
So in your case you should have a matrix of (1000000,3)(atoms) and a vector of (1000000,)(centers) as their labels. This means that each entry in the vector corresponds to one row in the atom matrix.
I want to build a simple NN for regression purposes; the dimension of my input data reads (100000,3): meaning I have 1mio particles and their corresponding x,y,z coordinates. Out of these particles I want to predict centers which the particles correspond to where the data of the centers read (1000,3).
my question is: since the input array should have the same number of samples as target arrays how can I solve this problem?
actually my mapping is from (100000,3) -> (1000,3) because on average about 100 particles belong to one center.
to train the model i will use many of those datasets with the right centers as output; after that i want to predict out of one new set of particle coordinates the corresponding centers.