a7a5cdc598afd6afa3d0e9d355360bebc4947c29,snli/model.py,Encoder,forward,#Encoder#Any#,29

Before Change

        batch_size = inputs.size()[1]
        h0 = Variable(torch.zeros(self.config.n_cells, batch_size, self.config.d_hidden)).cuda()
        c0 = Variable(torch.zeros(self.config.n_cells, batch_size, self.config.d_hidden)).cuda()
        _, (hn, _) = self.rnn(inputs, (h0, c0))
        return hn[-1] if not self.config.bidirectional else hn[-2:].view(batch_size, -1)

After Change

    def forward(self, inputs):
        batch_size = inputs.size()[1]
        state_shape = self.config.n_cells, batch_size, self.config.d_hidden
        h0 = c0 = Variable(inputs.data.new(*state_shape).zero_())
        outputs, (ht, ct) = self.rnn(inputs, (h0, c0))
        return ht[-1] if not self.config.birnn else ht[-2:].view(batch_size, -1)

Instances