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_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()