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