def main():
log.info('Loading %s...', Game.__name__)
g = Game(4, 9, 4)
log.info('Loading %s...', nn.__name__)
nnet = nn(g)
if args.load_model:
log.info('Loading checkpoint "%s/%s"...', args.load_folder_file[0],
args.load_folder_file[1])
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
else:
log.warning('Not loading a checkpoint!')
log.info('Loading the Coach...')
c = Coach(g, nnet, args)
if args.load_model:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
log.info('Starting the learning process 🎉')
c.learn()
'--load_model',
dest='load_model',
action='store_true')
parser.add_argument('-loadf',
'--load_folder_file',
dest='load_folder_file',
type=str)
parser.add_argument('-iterexamp',
'--num_iters_example',
dest='numItersForTrainExamplesHistory',
type=int,
default=20)
args = parser.parse_args()
fh = open(os.path.join("..", "data", "puzzle1.txt"))
fcontent = fh.read()
fh.close()
sys.setrecursionlimit(10000)
g = game(fcontent)
nnet = nn(g, args)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file)
c = Coach(g, nnet, args)
if args.load_model:
print("Load trainExamples from file")
c.loadTrainExamples()
c.learn()
dropout_rate=configs.dropout_rate,
epochs=configs.num_epochs,
batch_size=configs.batch_size,
num_channels=configs.num_channels,
log_device_placement=configs.log_device_placement,
network_architecture=configs.network_architecture)
coach = Coach(game=game,
nnet=nnet,
pnet=pnet,
num_iters=configs.num_iters,
root_noise=configs.root_noise,
board_size=configs.board_size)
if configs.load_model:
logging.info("Loading training examples")
coach.loadTrainExamples()
if configs.web_server:
web = WebServer(game=game,
nnet=nnet,
checkpoint_folder=configs.checkpoint_dir,
c_puct=configs.c_puct,
num_mcst_sims=configs.num_mcts_sims)
web.start_web_server()
exit(0)
coach.learn(num_train_episodes=configs.num_episodes,
num_training_examples_to_keep=configs.maxlenOfQueue,
checkpoint_folder=configs.checkpoint_dir,
arena_tournament_size=configs.arena_size,
model_update__win_threshold=configs.update_threshold,
