I'm trying to train a neural network on evaluating chess positions if rather white (0.0) or black would win (1.0)
Currently the input consists of 4 bits per chess field (piece id 0 - 12, equals 64*4). Factors like castling are being ignored for now. Also, all training sets are random positions from popular games where it's white's turn and the desired output is the outcome of the game (0.0, 0.5, 1.0).
Are my input values the right choice? How many hidden layers / neurons for each layer should be used and what's the best learning rate? What type of NN's and which activation function would you recommend for this project?