cc80175c2704c0dbbfe908f6b678c84ef1741a56,onmt/Models.py,Encoder,forward,#Encoder#Any#Any#,28

Before Change

        if hidden is None:
            batch_size = emb.size(1)
            h_size = (self.layers * self.num_directions, batch_size, self.hidden_size)
            h_0 = Variable(emb.data.new(*h_size).zero_(), requires_grad=False)
            c_0 = Variable(emb.data.new(*h_size).zero_(), requires_grad=False)
            hidden = (h_0, c_0)

        outputs, hidden_t = self.rnn(emb, hidden)
        return hidden_t, outputs

After Change

    def forward(self, input, hidden=None):
        if isinstance(input, tuple):
            emb = pack(self.word_lut(input[0]), input[1])
        else:
            emb = self.word_lut(input)
        outputs, hidden_t = self.rnn(emb, hidden)

Instances