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Python utils.get_lr方法代碼示例

本文整理匯總了Python中utils.get_lr方法的典型用法代碼示例。如果您正苦於以下問題:Python utils.get_lr方法的具體用法?Python utils.get_lr怎麽用?Python utils.get_lr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utils的用法示例。


在下文中一共展示了utils.get_lr方法的1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 utils [as 別名]
# 或者: from utils import get_lr [as 別名]
def main():
    args = parse_args()
    C = importlib.import_module(args.config).TrainConfig
    print("MODEL ID: {}".format(C.model_id))

    summary_writer = SummaryWriter(C.log_dpath)

    train_iter, val_iter, test_iter, vocab = build_loaders(C)

    model = build_model(C, vocab)

    optimizer = torch.optim.Adam(model.parameters(), lr=C.lr, weight_decay=C.weight_decay, amsgrad=True)
    lr_scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=C.lr_decay_gamma,
                                     patience=C.lr_decay_patience, verbose=True)

    best_val_scores = { 'CIDEr': 0. }
    best_epoch = 0
    best_ckpt_fpath = None
    for e in range(1, C.epochs + 1):
        ckpt_fpath = C.ckpt_fpath_tpl.format(e)

        """ Train """
        print("\n")
        train_loss = train(e, model, optimizer, train_iter, vocab, C.decoder.rnn_teacher_forcing_ratio,
                           C.reg_lambda, C.recon_lambda, C.gradient_clip)
        log_train(C, summary_writer, e, train_loss, get_lr(optimizer))

        """ Validation """
        val_loss = test(model, val_iter, vocab, C.reg_lambda, C.recon_lambda)
        val_scores = evaluate(val_iter, model, model.vocab)
        log_val(C, summary_writer, e, val_loss, val_scores)

        if e >= C.save_from and e % C.save_every == 0:
            print("Saving checkpoint at epoch={} to {}".format(e, ckpt_fpath))
            save_checkpoint(e, model, ckpt_fpath, C)

        if e >= C.lr_decay_start_from:
            lr_scheduler.step(val_loss['total'])
        if e == 1 or val_scores['CIDEr'] > best_val_scores['CIDEr']:
            best_epoch = e
            best_val_scores = val_scores
            best_ckpt_fpath = ckpt_fpath

    """ Test with Best Model """
    print("\n\n\n[BEST]")
    best_model = load_checkpoint(model, best_ckpt_fpath)
    test_scores = evaluate(test_iter, best_model, best_model.vocab)
    log_test(C, summary_writer, best_epoch, test_scores)
    save_checkpoint(best_epoch, best_model, C.ckpt_fpath_tpl.format("best"), C) 
開發者ID:hobincar,項目名稱:RecNet,代碼行數:51,代碼來源:train.py


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