def pick_model_move(self, color):
if self.model.Nfeat == 15:
board_feature_planes = Features.make_feature_planes_stones_3liberties_4history_ko(
self.board, color)
Normalization.apply_featurewise_normalization_B(
board_feature_planes)
elif self.model.Nfeat == 21:
board_feature_planes = Features.make_feature_planes_stones_4liberties_4history_ko_4captures(
self.board, color).astype(np.float32)
Normalization.apply_featurewise_normalization_C(
board_feature_planes)
else:
assert False
feature_batch = Symmetry.make_symmetry_batch(board_feature_planes)
feed_dict = {self.feature_planes: feature_batch}
logit_batch = self.sess.run(self.logits, feed_dict)
move_logits = Symmetry.average_plane_over_symmetries(
logit_batch, self.model.N)
softmax_temp = 1.0
move_probs = softmax(move_logits, softmax_temp)
# zero out illegal moves
for x in xrange(self.model.N):
for y in xrange(self.model.N):
ind = self.model.N * x + y
if not self.board.play_is_legal(x, y, color):
move_probs[ind] = 0
sum_probs = np.sum(move_probs)
if sum_probs == 0: return Move.Pass() # no legal moves, pass
move_probs /= sum_probs # re-normalize probabilities
pick_best = True
if pick_best:
move_ind = np.argmax(move_probs)
else:
move_ind = sample_from(move_probs)
move_x = move_ind / self.model.N
move_y = move_ind % self.model.N
self.last_move_probs = move_probs.reshape((self.board.N, self.board.N))
return Move(move_x, move_y)
def pick_book_move(self, color):
if self.book:
book_move = Book.get_book_move(self.board, self.book)
if book_move:
print "playing book move", book_move
return Move(book_move[0], book_move[1])
print "no book move"
else:
print "no book"
return None
def generate_move(self, color, cleanup=False):
self.push_state()
if channel_from_color(color) == gvg.player_channel:
self.board = go_board.switch_player_perspec(self.board)
move = self.pick_move(color)
if move.is_play():
go_board.make_move(self.board, [move.x, move.y], gvg.bot_channel,
gvg.player_channel)
go_board.show_board(self.board)
return Move(move.x, move.y)
def pick_model_move(self, color):
# If asked to make a move for enemy player, switch perspective as if we are them
if channel_from_color(color) == gvg.player_channel:
self.board = go_board.switch_player_perspec(self.board)
enc_board = go_board.get_encoded_board(self.board)
pred = np.asarray(self.model.predict(enc_board. \
reshape(1, gvg.board_size, gvg.board_size, gvg.enc_board_channels))). \
reshape(gvg.board_size * gvg.board_size)
prob_board = np.array(
self.model.predict(
go_board.get_encoded_board(
self.board.reshape(
gvg.board_size, gvg.board_size,
gvg.board_channels)).reshape(1, 19, 19, 20))).reshape(
(gvg.board_size, gvg.board_size))
self.last_move_probs = prob_board
move = go_learn.predict_move(self.board,
self.model,
prob_board=prob_board)
return Move(move[0], move[1])
def pick_move(self, color):
for x in xrange(self.board.N):
for y in xrange(self.board.N):
if self.board.play_is_legal(x, y, color):
return Move(x, y)
return Move.Pass()
def pick_move(self, color):
x,y = choose_move_alphabeta(self.board, self.policy, self.value, depth=3)
return Move(x,y)