Sam Ansari

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

I've been wanting to learn how to use TensorFlow , an open-source library that google recently released for machine learning and other applications. The introductory tutorials are great, teaching how to classify written numbers, but it would be nice to try something different and new. So what sort of problems could we solve? Well, one problem that I'd been having involved chess. There's a web forum called Reddit , which has several subforums (they call them subreddits) where people can post about specific topics, in this case the one I'm interested in is the chess subreddit . About once or twice a day someone will publish a new post that links to an image of a 2D online chessboard in a certain layout. An example chess puzzle image posted on /r/chess They're either from games, or sometimes are called tactics puzzles, which is where a person is given a certain layout of chess pieces and tries to guess the next best move or series of moves for one of the sides