#!/usr/local/bin/python from Model import Model from Executor import Executor if __name__ == '__main__': model = Model() model.assemble_graph() silence_step = 0 skip_step = 20 exe = Executor(model, silence_step=silence_step, skip_step=skip_step) exe.train_and_dev() exe.restore_and_test()
if __name__ == '__main__': seed = 12345 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) n_gpu = torch.cuda.device_count() torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if n_gpu > 0: torch.cuda.manual_seed(seed) silence_step = 0 # 从哪步开始可以进行evaluate skip_step = 20 # 每隔多少步做evaluate config = DicToObj(**config_model) formatter = '%(asctime)s %(levelname)s %(message)s' config = generate_name_path(config, path_parser) train_logger = Logger(filename=config.saved_path + '/train.log', fmt=formatter).logger tb_logger = Tensorboard_Logger(config.saved_path) exe = Executor(model_name="stocknet", config=config, silence_step=silence_step, skip_step=skip_step, train_logger=train_logger, tb_logger=tb_logger) exe.apply(xavier_init) exe.train_and_dev(do_continue=False) exe.restore_and_test()