How should I design my input layer for the following classification problem?
Input: 5 cards (from a deck of 52 cards) in a card game;
Output: some classification using a neural network
How should I model the input layer?
Option A: 5 one-hot encodings for the 5 cards, i.e. 5 one-hot vectors of length 52 = 260 input vector. For example
[
[0,0,0,0,0,0,1,...],
[1,0,0,0,0,0,0,...],
[0,0,0,0,0,1,0,...],
[0,0,1,0,0,0,0,...],
[0,0,0,0,1,0,0,...]
]
Option B: 5 hot encoding encompassing all 5 cards in one 52 element vector
[1,0,1,0,1,1,1,...]
What are the disadvantages between A and B?