model.py 文件源码

def forward(self, inputs):
        # set up batch size
        batch_size = inputs.size(0)

        # compute hidden and cell
        hidden = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_size).cuda())
        cell = Variable(torch.zeros(self.num_layers * 2, batch_size, self.hidden_size).cuda())
        hidden_cell = (hidden, cell)

        # recurrent neural networks
        outputs, _ = self.rnn.forward(inputs, hidden_cell)
        outputs = outputs.contiguous().view(-1, self.hidden_size * 2)

        # compute classifications by outputs
        outputs = self.classifier.forward(outputs)
        outputs = F.softmax(outputs)
        outputs = outputs.view(batch_size, -1, self.num_classes)
        return outputs