japanese_tagger.py 文件源码

def __call__(self, ws, cs, ls, ts):
        h_w = self.emb_word(ws) #_(batchsize, windowsize, word_dim)
        h_c = self.emb_char(cs) # (batchsize, windowsize, max_char_len, char_dim)
        batchsize, windowsize, _, _ = h_c.data.shape
        # (batchsize, windowsize, char_dim)
        h_c = F.sum(h_c, 2)
        h_c, ls = F.broadcast(h_c, F.reshape(ls, (batchsize, windowsize, 1)))
        h_c = h_c / ls
        h = F.concat([h_w, h_c], 2)
        h = F.reshape(h, (batchsize, -1))
        # ys = self.linear1(h)
        h = F.relu(self.linear1(h))
        h = F.dropout(h, ratio=.5, train=self.train)
        ys = self.linear2(h)

        loss = F.softmax_cross_entropy(ys, ts)
        acc = F.accuracy(ys, ts)
        chainer.report({
            "loss": loss,
            "accuracy": acc
            }, self)
        return loss