model.py 文件源码

def forward(self, pos_u, pos_v, neg_u, neg_v):
        losses = []
        emb_u = []
        for i in range(len(pos_u)):
            emb_ui = self.u_embeddings(Variable(torch.LongTensor(pos_u[i])))
            emb_u.append(np.sum(emb_ui.data.numpy(), axis=0).tolist())
        emb_u = Variable(torch.FloatTensor(emb_u))
        emb_v = self.v_embeddings(Variable(torch.LongTensor(pos_v)))
        score = torch.mul(emb_u, emb_v)
        score = torch.sum(score, dim=1)
        score = F.logsigmoid(score)
        losses.append(sum(score))

        neg_emb_u = []
        for i in range(len(neg_u)):
            neg_emb_ui = self.u_embeddings(Variable(torch.LongTensor(neg_u[i])))
            neg_emb_u.append(np.sum(neg_emb_ui.data.numpy(), axis=0).tolist())
        neg_emb_u = Variable(torch.FloatTensor(neg_emb_u))
        neg_emb_v = self.v_embeddings(Variable(torch.LongTensor(neg_v)))
        neg_score = torch.mul(neg_emb_u, neg_emb_v)
        neg_score = torch.sum(neg_score, dim=1)
        neg_score = F.logsigmoid(-1 * neg_score)
        losses.append(sum(neg_score))

        return -1 * sum(losses)