Python models.EncoderRNN方法代碼示例

本文整理匯總了Python中models.EncoderRNN方法的典型用法代碼示例。如果您正苦於以下問題：Python models.EncoderRNN方法的具體用法？Python models.EncoderRNN怎麽用？Python models.EncoderRNN使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類models的用法示例。

在下文中一共展示了models.EncoderRNN方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: import models [as 別名]
# 或者: from models import EncoderRNN [as 別名]
def main(opt):
    dataset = VideoDataset(opt, "test")
    opt["vocab_size"] = dataset.get_vocab_size()
    opt["seq_length"] = dataset.max_len
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                          rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"], bidirectional=opt["bidirectional"],
                             input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                             input_dropout_p=opt["input_dropout_p"],
                             rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    #model = nn.DataParallel(model)
    # Setup the model
    model.load_state_dict(torch.load(opt["saved_model"]))
    crit = utils.LanguageModelCriterion()

    test(model, crit, dataset, dataset.get_vocab(), opt)

開發者ID:xiadingZ，項目名稱:video-caption.pytorch，代碼行數:22，代碼來源:eval.py

示例2: main

# 需要導入模塊: import models [as 別名]
# 或者: from models import EncoderRNN [as 別名]
def main(opt):
    dataset = VideoDataset(opt, "test")
    opt["vocab_size"] = dataset.get_vocab_size()
    opt["seq_length"] = dataset.max_len
    if opt['beam_size'] != 1:
        assert opt["batch_size"] == 1
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"], opt['dim_vid'],
                          n_layers=opt['num_layers'],
                          rnn_cell=opt['rnn_type'],
                          bidirectional=opt["bidirectional"],
                          rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"],
                             n_layers=opt['num_layers'],
                             rnn_cell=opt['rnn_type'], bidirectional=opt["bidirectional"],
                             input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                             n_layers=opt['num_layers'],
                             rnn_cell=opt['rnn_type'], input_dropout_p=opt["input_dropout_p"],
                             rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    model = nn.DataParallel(model)
    # Setup the model
    model.load_state_dict(torch.load(opt["saved_model"]))
    crit = utils.LanguageModelCriterion()

    test(model, crit, dataset, dataset.get_vocab(), opt)

開發者ID:Sundrops，項目名稱:video-caption.pytorch，代碼行數:30，代碼來源:eval.py

示例3: main

# 需要導入模塊: import models [as 別名]
# 或者: from models import EncoderRNN [as 別名]
def main(opt):
    dataset = VideoDataset(opt, 'train')
    dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
    opt["vocab_size"] = dataset.get_vocab_size()
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            opt['dim_vid'],
            rnn_cell=opt['rnn_type'],
            n_layers=opt['num_layers'],
            bidirectional=opt["bidirectional"],
            rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(
            opt["dim_vid"],
            opt["dim_hidden"],
            n_layers=opt['num_layers'],
            bidirectional=opt["bidirectional"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            n_layers=opt['num_layers'],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"],
            bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    crit = utils.LanguageModelCriterion()
    rl_crit = utils.RewardCriterion()
    optimizer = optim.Adam(
        model.parameters(),
        lr=opt["learning_rate"],
        weight_decay=opt["weight_decay"])
    exp_lr_scheduler = optim.lr_scheduler.StepLR(
        optimizer,
        step_size=opt["learning_rate_decay_every"],
        gamma=opt["learning_rate_decay_rate"])

    train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)

開發者ID:Sundrops，項目名稱:video-caption.pytorch，代碼行數:49，代碼來源:train.py

示例4: main

# 需要導入模塊: import models [as 別名]
# 或者: from models import EncoderRNN [as 別名]
def main(opt):
    dataset = VideoDataset(opt, 'train')
    dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
    opt["vocab_size"] = dataset.get_vocab_size()
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            opt['dim_vid'],
            rnn_cell=opt['rnn_type'],
            n_layers=opt['num_layers'],
            rnn_dropout_p=opt["rnn_dropout_p"])
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(
            opt["dim_vid"],
            opt["dim_hidden"],
            bidirectional=opt["bidirectional"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"],
            bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder)
    model = model.cuda()
    crit = utils.LanguageModelCriterion()
    rl_crit = utils.RewardCriterion()
    optimizer = optim.Adam(
        model.parameters(),
        lr=opt["learning_rate"],
        weight_decay=opt["weight_decay"])
    exp_lr_scheduler = optim.lr_scheduler.StepLR(
        optimizer,
        step_size=opt["learning_rate_decay_every"],
        gamma=opt["learning_rate_decay_rate"])

    train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)

開發者ID:xiadingZ，項目名稱:video-caption.pytorch，代碼行數:47，代碼來源:train.py

示例5: main

# 需要導入模塊: import models [as 別名]
# 或者: from models import EncoderRNN [as 別名]
def main():
    voc = Lang('data/WORDMAP.json')
    print("voc.n_words: " + str(voc.n_words))

    train_data = SaDataset('train', voc)
    val_data = SaDataset('valid', voc)

    # Initialize encoder
    encoder = EncoderRNN(voc.n_words, hidden_size, encoder_n_layers, dropout)

    # Use appropriate device
    encoder = encoder.to(device)

    # Initialize optimizers
    print('Building optimizers ...')
    optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)

    best_acc = 0
    epochs_since_improvement = 0

    # Epochs
    for epoch in range(start_epoch, epochs):
        # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20
        if epochs_since_improvement == 20:
            break
        if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0:
            adjust_learning_rate(optimizer, 0.8)

        # One epoch's training
        train(epoch, train_data, encoder, optimizer)

        # One epoch's validation
        val_acc, val_loss = valid(val_data, encoder)
        print('\n * ACCURACY - {acc:.3f}, LOSS - {loss:.3f}\n'.format(acc=val_acc, loss=val_loss))

        # Check if there was an improvement
        is_best = val_acc > best_acc
        best_acc = max(best_acc, val_acc)

        if not is_best:
            epochs_since_improvement += 1
            print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
        else:
            epochs_since_improvement = 0

        # Save checkpoint
        save_checkpoint(epoch, encoder, optimizer, val_acc, is_best)

        # Reshuffle samples
        np.random.shuffle(train_data.samples)
        np.random.shuffle(val_data.samples)

開發者ID:foamliu，項目名稱:Sentiment-Analysis，代碼行數:53，代碼來源:train.py

注：本文中的models.EncoderRNN方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。