57112455fc0288a68fc01a79ccd747caf5e5a696,onmt/Models.py,Decoder,forward,#Decoder#Any#Any#Any#Any#,99

Before Change

        batch_size = input.size(0)

        h_size = (batch_size, self.hidden_size)
        output = Variable(emb.data.new(*h_size).zero_(), requires_grad=False)

        // n.b. you can increase performance if you compute W_ih * x for all
        // iterations in parallel, but that"s only possible if
        // self.input_feed=False

After Change

            output, hidden = self.rnn(emb_t, hidden)
            output, attn = self.attn(output.squeeze(0), context.t())
            output = self.dropout(output.unsqueeze(0))
            outputs += [output]

        outputs = torch.cat(outputs, 0)

Instances