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Python model.BaseModel方法代码示例

本文整理汇总了Python中model.BaseModel方法的典型用法代码示例。如果您正苦于以下问题:Python model.BaseModel方法的具体用法?Python model.BaseModel怎么用?Python model.BaseModel使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在model的用法示例。


在下文中一共展示了model.BaseModel方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: pretrain

# 需要导入模块: import model [as 别名]
# 或者: from model import BaseModel [as 别名]
def pretrain(model, dataloader):
    """
    Pre-normalizes a model (i.e., PreNormLayer layers) over the given samples.

    Parameters
    ----------
    model : model.BaseModel
        A base model, which may contain some model.PreNormLayer layers.
    dataloader : tf.data.Dataset
        Dataset to use for pre-training the model.
    Return
    ------
    number of PreNormLayer layers processed.
    """
    model.pre_train_init()
    i = 0
    while True:
        for batch in dataloader:
            c, ei, ev, v, n_cs, n_vs, n_cands, cands, best_cands, cand_scores = batch
            batched_states = (c, ei, ev, v, n_cs, n_vs)

            if not model.pre_train(batched_states, tf.convert_to_tensor(True)):
                break

        res = model.pre_train_next()
        if res is None:
            break
        else:
            layer, name = res

        i += 1

    return i 
开发者ID:ds4dm,项目名称:learn2branch,代码行数:35,代码来源:03_train_gcnn.py

示例2: main

# 需要导入模块: import model [as 别名]
# 或者: from model import BaseModel [as 别名]
def main():
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
    use_gpu = torch.cuda.is_available()
    if args.use_cpu: use_gpu = False

    logging.basicConfig(level=logging.INFO)

    if use_gpu:
        print("Currently using GPU {}".format(args.gpu_devices))
        cudnn.benchmark = True
        torch.cuda.manual_seed_all(args.seed)
    else:
        print("Currently using CPU (GPU is highly recommended)")

    logging.info("Initializing model...")
    # model = BaseModel(args, use_gpu)
    model = BertForSequenceClassification.from_pretrained(args.bert_model,
                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(-1),
                num_labels=2)

    if args.resume:
        model.load_state_dict(torch.load(args.load_model))

    if use_gpu:
        model = model.cuda()

    params = sum(np.prod(p.size()) for p in model.parameters())
    logging.info("Number of parameters: {}".format(params))

    if not os.path.isdir(args.save_dir):
        os.mkdir(args.save_dir)

    train_dataset = BertDataset(args.input_train, "train")
    dev_dataset = BertDataset(args.input_dev, "dev")
    test_dataset = BertDataset(args.input_test, "test")

    train_examples = len(train_dataset)

    train_dataloader = \
        BertDataLoader(train_dataset, mode="train", max_len=args.max_len, batch_size=args.batch_size, num_workers=4, shuffle=True)
    dev_dataloader = \
        BertDataLoader(dev_dataset, mode="dev", max_len=args.max_len, batch_size=args.batch_size, num_workers=4, shuffle=False)
    test_dataloader = \
        BertDataLoader(test_dataset, mode="test", max_len=args.max_len, batch_size=int(args.batch_size / 2), num_workers=4, shuffle=False)

    trainer = Trainer(args, model, train_examples, use_gpu)

    if args.resume == False:
        logging.info("Beginning training...")
        trainer.train(train_dataloader, dev_dataloader)

    prediction, id = trainer.predict(test_dataloader)

    with open(os.path.join(args.save_dir, "MG1833039.txt"), "w", encoding="utf-8") as f:
        for index in range(len(prediction)):
            f.write("{}\t{}\n".format(id[index], prediction[index]))

    logging.info("Done!") 
开发者ID:tracy-talent,项目名称:curriculum,代码行数:63,代码来源:main.py


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