if args.do_train:
        train_pyreader.start()
        steps = 0
        if warmup_steps > 0:
            graph_vars["learning_rate"] = scheduled_lr
        #current_step = 2500
        ce_info = []
        time_begin = time.time()
        last_epoch = 0
        current_epoch = 0
        while True:
            try:
                steps += 1
                if steps % args.skip_steps != 0:
                    train_exe.run(fetch_list=[])
                else:
                    outputs = evaluate(
                        train_exe,
                        train_program,
                        train_pyreader,
                        graph_vars,
                        "train",
                        metric=args.metric,
                        is_classify=args.is_classify,
                        is_regression=args.is_regression)

                    if args.verbose:
                        verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
                        )
                        verbose += "learning rate: %f" % (
                            outputs["learning_rate"]
                            if warmup_steps > 0 else args.learning_rate)
                        log.info(verbose)

                    current_example, current_epoch = reader.get_train_progress()
                    time_end = time.time()
                    used_time = time_end - time_begin

总是从数据头部开始继续训练，如何从中断的部分开始？