def _genmove(self, color, game, flat_board):
flat_board = flat_board.reshape(1, len(flat_board))
inp = self.board_to_input(flat_board)
current_pred = self.model.predict(inp)
my_index = 0 if color == 'b' else 1
my_pred = current_pred[0, my_index]
my_value = BLACK if color == 'b' else WHITE
# We're still interested in the playable locations
playable_locations = game.get_playable_locations(color)
results = np.zeros(game.board.shape)
for move in playable_locations:
if move.is_pass:
continue
test_board = copy.deepcopy(game.board)
test_board.place_stone_and_capture_if_applicable_default_values(
move.to_matrix_location(), my_value)
inp = self.board_to_input(test_board.flatten())
pred_result = self.model.predict(inp)
# pred_result = self.softmax(pred_result)
results[move.to_matrix_location()] = pred_result[0, my_index]
results -= my_pred
row, col = np.unravel_index(results.argmax(), results.shape)
move = Move(col=col, row=row)
if (results[move.to_matrix_location()] <= 0):
move = Move(is_pass=True)
return move
def genmove(self, color, game) -> Move:
# We're still interested in the playable locations
playable_locations = game.get_playable_locations(color)
# Format the board and make predictions
inp = self.board_to_input(color, game.board)
pred_moves = self.model.predict(inp)
pred_moves = pred_moves.reshape(9, 9)
# print(pred_moves)
# print(playable_locations)
dummy_value = -10
potential_moves = np.array([[dummy_value] * 9] * 9, dtype=float)
for move in playable_locations:
# print(move)
if move.is_pass:
continue
loc = move.to_matrix_location()
potential_moves[loc[0]][loc[1]] = pred_moves[loc[0]][loc[1]]
potential_moves = self.softmax(potential_moves)
row, col = np.unravel_index(potential_moves.argmax(),
potential_moves.shape)
move = Move(col=col, row=row)
# if game.board[col,row] != 0:
# move = Move(is_pass = True)
# return move
if potential_moves[move.to_matrix_location()] == dummy_value:
move = Move(is_pass=True)
return move
def genmove(self, color, game) -> Move:
# We're still interested in the playable locations
playable_locations = game.get_playable_locations(color)
# Format the board and make predictions
inp = self.board_to_input(color, game.board)
bot_logger.debug('Input shape:', inp.shape)
bot_logger.debug('Input:', inp)
pred_moves = self.model.predict(inp)
# pred_moves = self.model.predict(np.zeros((1, 162)))
bot_logger.debug('This worked')
bot_logger.debug('Predicted moves:', pred_moves)
pred_moves = pred_moves.reshape(9, 9)
# print(pred_moves)
# print(playable_locations)
dummy_value = -10
potential_moves = np.array([[dummy_value] * 9] * 9, dtype=float)
for move in playable_locations:
# print(move)
if move.is_pass:
continue
loc = move.to_matrix_location()
potential_moves[loc[0]][loc[1]] = pred_moves[loc[0]][loc[1]]
# print([i for row in potential_moves for i in row])
potential_moves = self.softmax(potential_moves)
row, col = np.unravel_index(potential_moves.argmax(),
potential_moves.shape)
move = Move(col=col, row=row)
if (potential_moves[move.to_matrix_location()] == dummy_value
or potential_moves[move.to_matrix_location()] <
(1 / 81 + 0.0001)):
move = Move(is_pass=True)
return move
def genmove(self, color, game) -> Move:
my_index = 0 if color == 'b' else 1
# We're still interested in the playable locations
playable_locations = game.get_playable_locations(color)
inp = self.board_to_input(color, game.board)
current_pred = self.model.predict(inp)
# print('Current outcome prediction:', current_pred)
# assert (self.softmax(current_pred) == current_pred).all()
current_pred = self.softmax(current_pred)
my_pred = current_pred[0, my_index]
my_value = BLACK if color == 'b' else WHITE
results = np.zeros(game.board.shape)
for move in playable_locations:
if move.is_pass:
continue
test_board = copy.deepcopy(game.board)
test_board[move.to_matrix_location()] = my_value
inp = self.board_to_input(color, test_board)
pred_result = self.model.predict(inp)
pred_result = self.softmax(pred_result)
results[move.to_matrix_location()] = pred_result[0, my_index]
# print(results>0)
# print(my_pred)
results -= my_pred
# print(results>0)
""" `results` now contains our prediction of our win probabilities
for each move, adjusted by our current win probability. We can now
easily check if a move is worth playing by checking the
sign; If it is negative, our probability to win gets worse. In general
the higher the number in `results` the better the move."""
row, col = np.unravel_index(results.argmax(), results.shape)
move = Move(col=col, row=row)
if (results[move.to_matrix_location()] <= 0):
move = Move(is_pass=True)
# print('Returned move:', move.to_gtp(9))
return move
