lstm_parser_old.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]
        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)]
        hs = [F.concat([h_f, h_b]) for h_f, h_b in zip(hs_f, hs_b)]

        cat_ys = [self.linear_cat2(
            F.dropout(F.elu(self.linear_cat1(h)), 0.5, train=self.train)) for h in hs]

        dep_ys = [self.biaffine(
            F.elu(F.dropout(self.linear_dep(h), 0.32, train=self.train)),
            F.elu(F.dropout(self.linear_head(h), 0.32, train=self.train))) for h in hs]

        return cat_ys, dep_ys