def _trigger_epoch(self):
if FEATURE:
if self.epoch_num % 1 == 0:
logger.info("update density network at epoch %d." %
(self.epoch_num))
cfg = PredictConfig(session_init=JustCurrentSession(),
model=Model(),
input_var_names=['state'],
output_var_names=[FEATURE])
self.offline_predictor = get_predict_func(cfg)
def run_submission(cfg, dump_dir='gym-submit'):
dirname = dump_dir
player = get_player(dumpdir=dirname)
# player = get_player("/home/piotr.milos")
predfunc = get_predict_func(cfg)
for k in range(10):
if k != 0:
player.restart_episode()
score = play_one_episode(player, predfunc)
print("Total:", score)
def _setup_graph(self):
self.sess = self.trainer.sess
self.async_predictor = MultiThreadAsyncPredictor(
self.trainer.get_predict_funcs(['state'], ['logitsT', 'pred_value'],
PREDICTOR_THREAD), batch_size=15)
# else:
# self.async_predictor = MultiThreadAsyncPredictor(
# self.trainer.get_predict_funcs(['state'], ['logitsT', 'pred_value', FEATURE],
# PREDICTOR_THREAD), batch_size=15)
if FEATURE:
logger.info("Initialize density network")
cfg = PredictConfig(
session_init=NewSession(),
model=Model(),
input_var_names=['state'],
output_var_names=[FEATURE])
self.offline_predictor = get_predict_func(cfg)
self.async_predictor.run()
def eval_model_multithread(cfg, nr_eval):
func = get_predict_func(cfg)
NR_PROC = min(multiprocessing.cpu_count() // 2, 8)
mean, max = eval_with_funcs([func] * NR_PROC, nr_eval)
logger.info("Average Score: {}; Max Score: {}".format(mean, max))
def play_model(cfg):
player = get_player(viz=0.01)
predfunc = get_predict_func(cfg)
while True:
score = play_one_episode(player, predfunc)
print("Total:", score)