### Learning TensorFlow #2 - Predicting chess pieces from images using a single-layer classifier

Let's train a tensorflow neural network to tell what piece is on a chess square. In the previous post discussed how to parse input images which contained a chessboard into 32x32 grayscale chess squares. Let's look again at our input and outputs. Input 32x32 grayscale normalized image of a single chess tile containing a piece or empty Example input tile, a black pawn Output A label for which piece we think it is, there are 6 white pieces and 6 black pieces, and 1 more for an empty square, so 13 possible choices. Let's define our output label as an integer value from 0-12, where 0 is an empty square, 1-6 is white King, Queen, Rook, Bishop, Knight Pawn, and then 7-12 are the same for black. A black pawn in this case would be 12 then. In a one-hot label vector, this would be [0, 0,0,0,0,0,0, 0,0,0,0,0,1], where the 12th index is 1 and the rest are zero. How do we generate training data where we know the labels? One way is to take screenshots of the