lstm_tagger_ph.py 文件源码

def __call__(self, ws, ss, ps, ts):
        """
        xs [(w,s,p,y), ..., ]
        w: word, s: suffix, p: prefix, y: label
        """
        batchsize, length = ts.shape
        ys = self.forward(ws, ss, ps)[1:-1]
        ts = [F.squeeze(x, 0) for x in F.split_axis(F.transpose(ts), length, 0)]
        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 /= length
        chainer.report({
            "loss": loss,
            "accuracy": acc
            }, self)
        return loss