def load_model(self):
from agent.model_connect4 import Connect4Model
model = Connect4Model(self.config)
if self.config.opts.new or not load_best_model_weight(model):
model.build()
save_as_best_model(model)
return model
def load_next_generation_model(self):
rc = self.config.resource
while True:
dirs = get_next_generation_model_dirs(self.config.resource)
if dirs:
break
logger.info(f"There is no next generation model to evaluate")
sleep(60)
model_dir = dirs[-1] if self.config.eval.evaluate_latest_first else dirs[0]
config_path = os.path.join(model_dir, rc.next_generation_model_config_filename)
weight_path = os.path.join(model_dir, rc.next_generation_model_weight_filename)
model = Connect4Model(self.config)
model.load(config_path, weight_path)
return model, model_dir
def load_model(self):
from agent.model_connect4 import Connect4Model
model = Connect4Model(self.config)
rc = self.config.resource
dirs = get_next_generation_model_dirs(rc)
if not dirs:
logger.debug(f"loading best model")
if not load_best_model_weight(model):
raise RuntimeError(f"Best model can not loaded!")
else:
latest_dir = dirs[-1]
logger.debug(f"loading latest model")
config_path = os.path.join(
latest_dir, rc.next_generation_model_config_filename)
weight_path = os.path.join(
latest_dir, rc.next_generation_model_weight_filename)
model.load(config_path, weight_path)
return model
def _load_model(self):
from agent.model_connect4 import Connect4Model
model = Connect4Model(self.config)
if not load_best_model_weight(model):
raise RuntimeError("best model not found!")
return model
def load_best_model(self):
model = Connect4Model(self.config)
load_best_model_weight(model)
return model