fakestatic.py 文件源码

def forward(self, x, lengths):
        batch_size = x.size(0)
        max_len = max(lengths)

        emb = Variable(torch.from_numpy(
            self.initial_embeddings.take(x.numpy(), 0)),
            volatile=not self.training)
        inp = Variable(torch.FloatTensor(emb.size()), volatile=not self.training)
        h0 = Variable(torch.FloatTensor(1, batch_size, self.model_dim), volatile=not self.training)

        _, hn = self.rnn(emb, h0)

        h = F.relu(self.l0(F.dropout(hn.squeeze(), 0.5, self.training)))
        h = F.relu(self.l1(F.dropout(h, 0.5, self.training)))
        y = F.log_softmax(h)
        return y