def load_model(self):
from chess_zero.agent.model_chess import ChessModel
model = ChessModel(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_model(self):
"""
Loads the next generation model from the appropriate directory. If not found, loads
the best known model.
"""
model = ChessModel(self.config)
rc = self.config.resource
dirs = get_next_generation_model_dirs(rc)
if not dirs:
logger.debug("loading best model")
if self.config.opts.new and not load_best_model_weight(model):
model.build()
save_as_best_model(model)
elif not load_best_model_weight(model):
raise RuntimeError("Best model can not loaded!")
else:
latest_dir = dirs[-1]
logger.debug("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):
"""
Load the current best model
:return ChessModel: current best model
"""
model = ChessModel(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_model(self):
model = ChessModel(self.config)
if self.config.opts.new or not load_newest_model_weight(self.config.resource, model):
model.build() # optimize will now _also_ build a new model from scratch if none exists.
save_as_newest_model(self.config.resource, model)
return model
def load_model(self):
model = ChessModel(self.config)
if self.config.opts.new or not load_best_model_weight(model):
model.build()
save_as_best_model(model)
return model
