tagger.py 文件源码

def train(args):
    model = EmbeddingTagger(args.model, 50, 20, 30)
    model.setup_training(args.embed)
    if args.initmodel:
        print('Load model from', args.initmodel)
        chainer.serializers.load_npz(args.initmodel, model)

    train = CCGBankDataset(args.model, args.train)
    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
    val = CCGBankDataset(args.model, args.val)
    val_iter = chainer.iterators.SerialIterator(
            val, args.batchsize, repeat=False, shuffle=False)
    optimizer = chainer.optimizers.SGD(lr=0.01)
    optimizer.setup(model)
    updater = training.StandardUpdater(train_iter, optimizer)
    trainer = training.Trainer(updater, (args.epoch, 'epoch'), args.model)

    val_interval = 5000, 'iteration'
    log_interval = 200, 'iteration'
    val_model = model.copy()

    trainer.extend(extensions.Evaluator(val_iter, val_model), trigger=val_interval)
    trainer.extend(extensions.dump_graph('main/loss'))
    trainer.extend(extensions.snapshot(), trigger=val_interval)
    trainer.extend(extensions.snapshot_object(
        model, 'model_iter_{.updater.iteration}'), trigger=val_interval)
    trainer.extend(extensions.LogReport(trigger=log_interval))
    trainer.extend(extensions.PrintReport([
        'epoch', 'iteration', 'main/loss', 'validation/main/loss',
        'main/accuracy', 'validation/main/accuracy',
    ]), trigger=log_interval)
    trainer.extend(extensions.ProgressBar(update_interval=10))

    trainer.run()