add multi-thread for end-to-end training
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rtz19970824
parent
fcaa571b42
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2e8662889f
@ -119,13 +119,12 @@ class ResNet(object):
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zip(self.black_var_list, self.white_var_list)]
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# training hyper-parameters:
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self.window_length = 900
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self.window_length = 500
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self.save_freq = 5000
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self.training_data = {'states': deque(maxlen=self.window_length), 'probs': deque(maxlen=self.window_length),
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'winner': deque(maxlen=self.window_length), 'length': deque(maxlen=self.window_length)}
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# training or not
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self.training = False
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self.use_latest = False
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def _build_network(self, scope, residual_block_num):
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"""
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@ -188,16 +187,16 @@ class ResNet(object):
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feed_dict={self.x: eval_state, self.is_training: False})
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def check_latest_model(self):
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if self.training:
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if self.use_latest:
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black_ckpt_file = tf.train.latest_checkpoint(self.save_path + "black/")
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if self.black_ckpt_file != black_ckpt_file:
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if self.black_ckpt_file != black_ckpt_file and black_ckpt_file is not None:
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self.black_ckpt_file = black_ckpt_file
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print('Loading model from {}...'.format(self.black_ckpt_file))
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self.black_saver.restore(self.sess, self.black_ckpt_file)
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print('Black Model Updated!')
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white_ckpt_file = tf.train.latest_checkpoint(self.save_path + "white/")
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if self.white_ckpt_file != white_ckpt_file:
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if self.white_ckpt_file != white_ckpt_file and white_ckpt_file is not None:
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self.white_ckpt_file = white_ckpt_file
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print('Loading model from {}...'.format(self.white_ckpt_file))
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self.white_saver.restore(self.sess, self.white_ckpt_file)
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@ -234,7 +233,7 @@ class ResNet(object):
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:param target: a string, which to optimize, can only be "both", "black" and "white"
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:param mode: a string, how to optimize, can only be "memory" and "file"
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"""
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self.training = True
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self.use_latest = True
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if mode == 'memory':
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pass
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if mode == 'file':
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@ -401,5 +400,5 @@ class ResNet(object):
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if __name__ == "__main__":
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model = ResNet(board_size=8, action_num=65, history_length=1, black_checkpoint_path="./checkpoint/black", white_checkpoint_path="./checkpoint/white")
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model.train(mode="file", data_path="./data/", batch_size=128, save_path="./checkpoint/")
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model = ResNet(board_size=9, action_num=82, history_length=8, black_checkpoint_path="./checkpoint/black", white_checkpoint_path="./checkpoint/white")
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model.train(mode="file", data_path="./data/", batch_size=128, save_path="./go-v2/")
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137
AlphaGo/play.py
137
AlphaGo/play.py
@ -3,6 +3,7 @@ import sys
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import re
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import time
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import os
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import threading
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from game import Game
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from engine import GTPEngine
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from utils import Data
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@ -17,6 +18,67 @@ else:
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import _pickle as cPickle
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def play(engine, data_path):
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data = Data()
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role = ["BLACK", "WHITE"]
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color = ['b', 'w']
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pattern = "[A-Z]{1}[0-9]{1}"
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space = re.compile("\s+")
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size = {"go": 9, "reversi": 8}
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show = ['.', 'X', 'O']
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# evaluate_rounds = 100
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game_num = 0
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while True:
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# while game_num < evaluate_rounds:
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engine._game.model.check_latest_model()
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num = 0
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pass_flag = [False, False]
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print("Start game {}".format(game_num))
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# end the game if both palyer chose to pass, or play too much turns
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while not (pass_flag[0] and pass_flag[1]) and num < size[engine._game.name] ** 2 * 2:
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turn = num % 2
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board = engine.run_cmd(str(num) + ' show_board')
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board = eval(board[board.index('['):board.index(']') + 1])
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for i in range(size[engine._game.name]):
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for j in range(size[engine._game.name]):
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print show[board[i * size[engine._game.name] + j]] + " ",
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print "\n",
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data.boards.append(board)
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move = engine.run_cmd(str(num) + ' genmove ' + color[turn])[:-1]
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print("\n" + role[turn] + " : " + str(move)),
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num += 1
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match = re.search(pattern, move)
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if match is not None:
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# print "match : " + str(match.group())
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pass_flag[turn] = False
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else:
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# print "no match"
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pass_flag[turn] = True
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prob = engine.run_cmd(str(num) + ' get_prob')
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prob = space.sub(',', prob[prob.index('['):prob.index(']') + 1])
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prob = prob.replace('[,', '[')
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prob = prob.replace('],', ']')
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prob = eval(prob)
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data.probs.append(prob)
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score = engine.run_cmd(str(num) + ' get_score')
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print("Finished : {}".format(score.split(" ")[1]))
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if eval(score.split(" ")[1]) > 0:
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data.winner = utils.BLACK
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if eval(score.split(" ")[1]) < 0:
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data.winner = utils.WHITE
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engine.run_cmd(str(num) + ' clear_board')
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current_time = strftime("%Y%m%d_%H%M%S", gmtime())
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if os.path.exists(data_path + current_time + ".pkl"):
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time.sleep(1)
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current_time = strftime("%Y%m%d_%H%M%S", gmtime())
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with open(data_path + current_time + ".pkl", "wb") as file:
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cPickle.dump(data, file)
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data.reset()
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game_num += 1
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if __name__ == '__main__':
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"""
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Starting two different players which load network weights to evaluate the winning ratio.
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@ -27,6 +89,7 @@ if __name__ == '__main__':
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parser.add_argument("--data_path", type=str, default="./data/")
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parser.add_argument("--black_weight_path", type=str, default=None)
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parser.add_argument("--white_weight_path", type=str, default=None)
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parser.add_argument("--save_path", type=str, default="./go/")
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parser.add_argument("--debug", type=bool, default=False)
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parser.add_argument("--game", type=str, default="go")
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args = parser.parse_args()
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@ -46,69 +109,15 @@ if __name__ == '__main__':
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debug=args.debug)
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engine = GTPEngine(game_obj=game, name='tianshou', version=0)
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data = Data()
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role = ["BLACK", "WHITE"]
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color = ['b', 'w']
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thread_list = []
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thread_train = threading.Thread(target=game.model.train, args=("file",),
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kwargs={'data_path':args.data_path, 'batch_size':128, 'save_path':args.save_path})
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thread_play = threading.Thread(target=play, args=(engine, args.data_path))
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thread_list.append(thread_train)
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thread_list.append(thread_play)
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pattern = "[A-Z]{1}[0-9]{1}"
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space = re.compile("\s+")
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size = {"go":9, "reversi":8}
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show = ['.', 'X', 'O']
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for t in thread_list:
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t.start()
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evaluate_rounds = 100
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game_num = 0
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try:
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while True:
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#while game_num < evaluate_rounds:
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start_time = time.time()
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game.model.check_latest_model()
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num = 0
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pass_flag = [False, False]
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print("Start game {}".format(game_num))
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# end the game if both palyer chose to pass, or play too much turns
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while not (pass_flag[0] and pass_flag[1]) and num < size[args.game] ** 2 * 2:
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turn = num % 2
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board = engine.run_cmd(str(num) + ' show_board')
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board = eval(board[board.index('['):board.index(']') + 1])
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for i in range(size[args.game]):
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for j in range(size[args.game]):
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print show[board[i * size[args.game] + j]] + " ",
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print "\n",
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data.boards.append(board)
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start_time = time.time()
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move = engine.run_cmd(str(num) + ' genmove ' + color[turn])[:-1]
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print("\n" + role[turn] + " : " + str(move)),
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num += 1
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match = re.search(pattern, move)
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if match is not None:
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# print "match : " + str(match.group())
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play_or_pass = match.group()
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pass_flag[turn] = False
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else:
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# print "no match"
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play_or_pass = ' PASS'
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pass_flag[turn] = True
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prob = engine.run_cmd(str(num) + ' get_prob')
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prob = space.sub(',', prob[prob.index('['):prob.index(']') + 1])
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prob = prob.replace('[,', '[')
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prob = prob.replace('],', ']')
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prob = eval(prob)
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data.probs.append(prob)
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score = engine.run_cmd(str(num) + ' get_score')
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print("Finished : {}".format(score.split(" ")[1]))
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if eval(score.split(" ")[1]) > 0:
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data.winner = utils.BLACK
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if eval(score.split(" ")[1]) < 0:
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data.winner = utils.WHITE
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engine.run_cmd(str(num) + ' clear_board')
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file_list = os.listdir(args.data_path)
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current_time = strftime("%Y%m%d_%H%M%S", gmtime())
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if os.path.exists(args.data_path + current_time + ".pkl"):
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time.sleep(1)
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current_time = strftime("%Y%m%d_%H%M%S", gmtime())
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with open(args.data_path + current_time + ".pkl", "wb") as file:
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picklestring = cPickle.dump(data, file)
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data.reset()
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game_num += 1
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except KeyboardInterrupt:
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pass
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for t in thread_list:
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t.join()
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