Predicting a Chess Board Position using Pytorch
I want to predict a the current chess board using pytorch/keras. (Let's not worry about the input for now.)
How would I got about that?
A chess board has 8x8 positions (64) on each position could be a black or white piece (12) or no piece at all (1). I am planning on using this representation for the chess board (other suggestions are welcome!):
https://en.wikipedia.org/wiki/Board_representation_(computer_chess)#Square_list
For example:
 2 3 4 5 6 4 3 2
 1 1 1 1 1 1 1 1
 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0
-1-1-1-1-1-1-1-1
-2-3-4-5-6-4-3-2`

As far as I know it is not possible to predict something like this. Because the number of classes my final layer would have to predict are 448 (64x7) and I don't feel like a NN could do that. Additionally there is the problem that softmax wouldn't work (imo). Also the Loss function might become a problem as well.
Does someone have an idea on how to do that? Or could point me in the right direction, because multi-class classification isn't really the right term for this task. I was thinking about creating 6 networks that create a classification for each piece. So a 8x8 array that looks like this (for rooks):
 10000001
 00000000
 00000000
 00000000
 00000000
-1000000-1

But the problem is still quite similar.
I think creating 64 NNs that take care of one position each would simplify the problem a bit. But that would be a pain to train.
Looking forward to hearing your suggestions!

For anyone wondering how to do this. I think I figured it out:
You build a Softmax over the third dimension of a 8x8x13 array and get a 8x8 matrix with all the chess figures.
Thanks to @Prune. I will adapt my questions in the future.
