Python model.RNNModel方法代码示例

# 需要导入模块: import model [as 别名]
# 或者: from model import RNNModel [as 别名]
def generate_flow(epoch=3):
    """读取存储的模型，生成新词"""
    corpus = Corpus(train_dir)
    config = Config()
    config.vocab_size = len(corpus.dictionary)

    model = RNNModel(config)
    model_file = os.path.join(save_dir, model_name.format(epoch))
    assert os.path.exists(model_file), 'File %s does not exist.' % model_file
    model.load_state_dict(torch.load(model_file, map_location=lambda storage, loc: storage))

    word_list = generate(model, corpus.dictionary.idx2word, word_len=50)
    print(''.join(word_list)) 

# 需要导入模块: import model [as 别名]
# 或者: from model import RNNModel [as 别名]
def train():
    # 载入数据与配置模型
    print("Loading data...")
    corpus = Corpus(train_dir)
    print(corpus)

    config = Config()
    config.vocab_size = len(corpus.dictionary)
    train_data = batchify(corpus.train, config.batch_size)
    train_len = train_data.size(0)
    seq_len = config.seq_len

    print("Configuring model...")
    model = RNNModel(config)
    if use_cuda:
        model.cuda()
    print(model)

    criterion = nn.CrossEntropyLoss()
    lr = config.learning_rate  # 初始学习率
    start_time = time.time()

    print("Training and generating...")
    for epoch in range(1, config.num_epochs + 1):  # 多轮次训练
        total_loss = 0.0
        model.train()  # 在训练模式下dropout才可用。
        hidden = model.init_hidden(config.batch_size)  # 初始化隐藏层参数

        for ibatch, i in enumerate(range(0, train_len - 1, seq_len)):
            data, targets = get_batch(train_data, i, seq_len)  # 取一个批次的数据
            # 在每批开始之前，将隐藏的状态与之前产生的结果分离。
            # 如果不这样做，模型会尝试反向传播到数据集的起点。
            hidden = repackage_hidden(hidden)
            model.zero_grad()

            output, hidden = model(data, hidden)
            loss = criterion(output.view(-1, config.vocab_size), targets)
            loss.backward()  # 反向传播

            # `clip_grad_norm` 有助于防止RNNs/LSTMs中的梯度爆炸问题。
            torch.nn.utils.clip_grad_norm(model.parameters(), config.clip)
            for p in model.parameters():  # 梯度更新
                p.data.add_(-lr, p.grad.data)

            total_loss += loss.data  # loss累计

            if ibatch % config.log_interval == 0 and ibatch > 0:  # 每隔多少个批次输出一次状态
                cur_loss = total_loss[0] / config.log_interval
                elapsed = get_time_dif(start_time)
                print("Epoch {:3d}, {:5d}/{:5d} batches, lr {:2.3f}, loss {:5.2f}, ppl {:8.2f}, time {}".format(
                    epoch, ibatch, train_len // seq_len, lr, cur_loss, math.exp(cur_loss), elapsed))
                total_loss = 0.0
        lr /= 4.0  # 在一轮迭代完成后，尝试缩小学习率

        # 每隔多少轮次保存一次模型参数
        if epoch % config.save_interval == 0:
            torch.save(model.state_dict(), os.path.join(save_dir, model_name.format(epoch)))

        print(''.join(generate(model, corpus.dictionary.idx2word))) 

# 需要导入模块: import model [as 别名]
# 或者: from model import RNNModel [as 别名]
def build_model():
    """Build the model according to CLI arguments

    Global Dependencies:
        - corpus
        - args
    """
    # noise for soise sampling in NCE
    noise = build_unigram_noise(
        torch.FloatTensor(corpus.vocab.idx2count)
    )

    norm_term = 'auto' if args.norm_term == -1 else args.norm_term
    # setting up NCELoss modules
    if args.index_module == 'linear':
        criterion = IndexLinear(
            args.emsize,
            ntoken,
            noise=noise,
            noise_ratio=args.noise_ratio,
            norm_term=norm_term,
            loss_type=args.loss,
            reduction='none',
        )
        model = RNNModel(
            ntoken, args.emsize, args.nhid, args.nlayers,
            criterion=criterion, dropout=args.dropout,
        )
    elif args.index_module == 'gru':
        if args.nlayers != 1:
            logger.warning('Falling into one layer GRU due to Index_GRU supporting')
        nce_criterion = IndexGRU(
            ntoken, args.emsize, args.nhid,
            args.dropout,
            noise=noise,
            noise_ratio=args.noise_ratio,
            norm_term=norm_term,
        )
        model = GenModel(
            criterion=nce_criterion,
        )
    else:
        logger.error('The index module [%s] is not supported yet' % args.index_module)
        raise(NotImplementedError('index module not supported'))

    if args.cuda:
        model.cuda()

    logger.info('model definition:\n %s', model)
    return model