def get_groundtruth(self):
feature_batch = []
targets_batch = []
board_positions = self.get_board_position()
shuffle(board_positions)
print("done shuffling")
print("generating evaluations on {} board positions...".format(
len(board_positions)))
# stockfish = Stockfish()
for index, board_position in enumerate(board_positions):
print(index)
stockfish = Stockfish()
feature_batch.append(board_to_feature(board_position))
targets_batch.append(stockfish.stockfish_eval(board_position, 10))
stockfish.kill_me()
feature_arr = np.asarray(feature_batch)
targets_arr = np.asarray(targets_batch)
np.save('features.txt', feature_arr)
np.save('values.txt', targets_arr)
def pretrain(model):
feature_batch = []
targets_batch = []
board_positions = get_board_position()
shuffle(board_positions)
print("Pretraining on {} board positions...".format(len(board_positions)))
stockfish = Stockfish()
for batch in range(Config.PRETRAIN_EPOCHS):
for index, board_position in enumerate(board_positions):
if (index + 1) % Config.minibatch_size != 0:
feature_batch.append(board_to_feature(board_position))
targets_batch.append(
stockfish.stockfish_eval(board_position, 10))
else:
feature_batch = torch.FloatTensor(feature_batch)
targets_batch = Variable(torch.FloatTensor(targets_batch))
do_backprop(feature_batch, targets_batch, model)
feature_batch = []
targets_batch = []
print("Completed batch {} of {}".format(batch, Config.PRETRAIN_EPOCHS))
