lstm_tagger.py 文件源码

def __call__(self, xs):
        """
        xs [(w,s,p,y), ..., ]
        w: word, s: suffix, p: prefix, y: label
        """
        batchsize = len(xs)
        ws, ss, ps, ts = zip(*xs)
        ys = self.forward(ws, ss, ps)
        loss = reduce(lambda x, y: x + y,
            [F.softmax_cross_entropy(y, t) for y, t in zip(ys, ts)])

        acc = reduce(lambda x, y: x + y,
            [F.accuracy(y, t, ignore_label=IGNORE) for y, t in zip(ys, ts)])

        acc /= batchsize
        chainer.report({
            "loss": loss,
            "accuracy": acc
            }, self)
        return loss