0db408d1eea2ce5c1d9b20fe61cb519e059d6cef,examples/basic_tagger.py,,main,#Any#Any#,23

Before Change

    train_X, train_y = zip(*train_data)
    dev_X, dev_y = zip(*check_data)
    dev_y = model.ops.flatten(dev_y)
    with model.begin_training(train_X, train_y) as (trainer, optimizer):
        trainer.batch_size = 8
        trainer.nb_epoch = 10
        trainer.dropout = 0.2

After Change

            lambda: print(model.evaluate(dev_X, dev_y)))
        for X, y in trainer.iterate(train_X, train_y):
            yh, backprop = model.begin_update(X, drop=trainer.dropout)
            backprop([yh[i]-y[i] for i in range(len(yh))], optimizer)
    with model.use_params(optimizer.averages):
        print(model.evaluate(dev_X, dev_y))
 

Instances