Model.py 文件源码

def forward(self, qu, w, cand):
        qu = Variable(qu)
        cand = Variable(cand)
        embed_q = self.embed(qu)
        embed_cand = self.embed(cand)

        out, (self.h0, self.c0) = self.rnn(embed_q, (self.h0, self.c0))
        self.h0.detach_()
        self.c0.detach_()
        q_state = out[:,-1,:]

        f_fea_v = torch.mm(q_state, torch.transpose(embed_cand,0,1))

        score_n = F.log_softmax(f_fea_v)
        return score_n