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add debuf info for mcts and add softmax for the prior

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Dong Yan 2017-12-26 14:46:14 +08:00
parent 725fc2c04e
commit aa6b5434c6
2 changed files with 13 additions and 6 deletions
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15
AlphaGo/game.py
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@ -71,6 +71,13 @@ class Game:
mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color],
self.size ** 2 + 1, role=self.role, debug=self.debug, inverse=True) self.size ** 2 + 1, role=self.role, debug=self.debug, inverse=True)
mcts.search(max_step=100) mcts.search(max_step=100)
if self.debug:
file = open("mcts_debug.log", 'ab')
np.savetxt(file, mcts.root.Q, header="\nQ value : ", fmt='%.4f', newline=", ")
np.savetxt(file, mcts.root.W, header="\nW value : ", fmt='%.4f', newline=", ")
np.savetxt(file, mcts.root.N, header="\nN value : ", fmt="%d", newline=", ")
np.savetxt(file, mcts.root.prior, header="\nprior : ", fmt='%.4f', newline=", ")
file.close()
temp = 1 temp = 1
prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp) prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0] choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]
@ -119,7 +126,7 @@ class Game:
sys.stdout.flush() sys.stdout.flush()
if __name__ == "__main__": if __name__ == "__main__":
print("test game.py") game = Game(name="go", checkpoint_path="./checkpoint")
#file = open("debug.txt", "a") game.debug = True
#file.write("mcts check\n") game.think_play_move(utils.BLACK)
#file.close()

4
AlphaGo/model.py
View File

@ -80,7 +80,7 @@ class Data(object):
class ResNet(object): class ResNet(object):
def __init__(self, board_size, action_num, history_length=1, residual_block_num=20, checkpoint_path=None): def __init__(self, board_size, action_num, history_length=1, residual_block_num=10, checkpoint_path=None):
""" """
the resnet model the resnet model
@ -161,7 +161,7 @@ class ResNet(object):
'The length of history cannot meet the need of the model, given {}, need {}'.format(len(history), 'The length of history cannot meet the need of the model, given {}, need {}'.format(len(history),
self.history_length)) self.history_length))
state = self._history2state(history, color) state = self._history2state(history, color)
return self.sess.run([self.p, self.v], feed_dict={self.x: state, self.is_training: False}) return self.sess.run([tf.nn.softmax(self.p), self.v], feed_dict={self.x: state, self.is_training: False})
def _history2state(self, history, color): def _history2state(self, history, color):
""" """