layers_torch.py 文件源码

def forward(self, x):
        outputs = []
        h_t = Variable(torch.zeros(x.size(0), self.hidden_size).cuda())
        c_t = Variable(torch.zeros(x.size(0), self.hidden_size).cuda())


        for i, input_t in enumerate(x.chunk(x.size(1), dim=1)):
            input_t = input_t.contiguous().view(input_t.size()[0], 1)
            h_t, c_t = self.lstm1(input_t, (h_t, c_t))
            outputs += [c_t]
        outputs = torch.stack(outputs, 1).squeeze(2)
        shp=(outputs.size()[0], outputs.size()[1])
        out = outputs.contiguous().view(shp[0] *shp[1] , self.hidden_size)
        out = self.fc(out)
        out = out.view(shp[0], shp[1], self.num_classes)

        return out