lstm_tagger.py 文件源码

def forward(self, ws, ss, ps):
        batchsize = len(ws)
        xp = chainer.cuda.get_array_module(ws[0])
        ws = map(self.emb_word, ws)
        ss = [F.reshape(self.emb_suf(s), (s.shape[0], 4 * self.afix_dim)) for s in ss]
        ps = [F.reshape(self.emb_prf(s), (s.shape[0], 4 * self.afix_dim)) for s in ps]
        # [(sentence length, (word_dim + suf_dim + prf_dim))]
        xs_f = [F.dropout(F.concat([w, s, p]),
            self.dropout_ratio, train=self.train) for w, s, p in zip(ws, ss, ps)]
        xs_b = [x[::-1] for x in xs_f]
        cx_f, hx_f, cx_b, hx_b = self._init_state(xp, batchsize)
        _, _, hs_f = self.lstm_f(hx_f, cx_f, xs_f, train=self.train)
        _, _, hs_b = self.lstm_b(hx_b, cx_b, xs_b, train=self.train)
        hs_b = [x[::-1] for x in hs_b]
        # ys: [(sentence length, number of category)]
        ys = [self.linear2(F.relu(
                self.linear1(F.concat([h_f, h_b]))))
                    for h_f, h_b in zip(hs_f, hs_b)]
        return ys