tagger.py 文件源码

def __call__(self, xs, ts):
        """
        Inputs:
            xs (tuple(Variable, Variable, Variable)):
                each of Variables is of dim (batchsize,)
            ts Variable:
                (batchsize)
        """
        words, suffixes, caps = xs[:,:7], xs[:, 7:14], xs[:, 14:]
        h_w = self.emb_word(words)
        h_c = self.emb_caps(caps)
        h_s = self.emb_suffix(suffixes)
        h = F.concat([h_w, h_c, h_s], 2)
        batchsize, ntokens, hidden = h.data.shape
        h = F.reshape(h, (batchsize, ntokens * hidden))
        ys = self.linear(h)

        loss = F.softmax_cross_entropy(ys, ts)
        acc = F.accuracy(ys, ts)

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