def play(location, option):
model: tf.keras.Model = tf.keras.models.load_model(location)
minimax = Minimax(model)
logo.show()
print('\n\n')
side = None
while side not in ['W', 'B']:
side = input('Play as:\nWhite (W) | Black (B) | Random (R) > ')
if side == 'R':
side = 'W' if np.random.uniform() < 0.5 else 'B'
side = side == 'W'
szr = serializer.Serializer(None)
if option == 'P':
pgn = chess.pgn.read_game(io.StringIO(input('Paste PGN:\n> ')))
for move in pgn.mainline_moves():
szr.board.push(move)
while not szr.board.is_game_over():
logo.show()
print('\n\n')
display_board(szr.board, side)
score = model.predict(np.expand_dims(szr.serialize(), 0))
print(f'ch0ss evaluation: {score}')
if szr.board.turn == side:
uci = None
while not szr.board.is_legal(uci):
if uci is not None:
print('Illegal move')
try:
uci = chess.Move.from_uci(input('Type your move:\n > '))
except ValueError:
print('Invalid move')
uci = None
if szr.board.is_legal(uci):
szr.board.push(uci)
else:
print('Illegal move.')
else:
print('ch0ss is thinking...')
move = minimax.search(szr.board, 3, side)
szr.board.push(move[1])
def test(location):
"""
Evaluates a given FEN using the model at `location`.
"""
model = tf.keras.models.load_model(location)
while True:
logo.show()
print('\n\n')
fen = input('Paste FEN\n').split()[0]
szr = serializer.Serializer(None)
bitboard = szr.serialize_fen(fen)
value = model.predict(np.expand_dims(bitboard, 0))
print(szr.board.unicode())
print(f'ch0ss evaluation: {value}\n\n\n')
input('Press anything to continue...')
random = np.arange(n)
np.random.shuffle(random)
szr = serializer.Serializer(None)
X = np.array(list(map(szr.serialize_fen, fens)), dtype=np.int8).reshape((n, 6, 8, 8))[
random]
y = np.array(list(map(np.mean, results)), dtype=np.float)[random]
assert(len(X) == len(y))
# split into train, test
cutoff = int(n * split)
return X[cutoff:], X[:cutoff], y[cutoff:], y[:cutoff]
if __name__ == '__main__':
logo.show()
print('\n\n')
if len(sys.argv) == 1 or not os.path.isfile(sys.argv[1]):
print('Dataset file location not found. Pass as an argument:')
print('\t> python parse_dataset.py ./dataset/raw/dataset.pgn\n')
else:
X_train, X_test, y_train, y_test, g = parse_dataset(
sys.argv[1], 1500000)
save_loc = f'./dataset/processed/dataset_{g}.npz'
np.savez(save_loc,
X_train=X_train, X_test=X_test,
y_train=y_train, y_test=y_test)
print(f'Saved dataset in {save_loc}')