urnn.py 文件源码

def _modReLU(self, h, bias):
        """
        sign(z)*relu(z)
        """
        batch_size = h.size(0)
        sign = torch.sign(h)
        bias_batch = (bias.unsqueeze(0)
                      .expand(batch_size, *bias.size()))
        return sign * functional.relu(torch.abs(h) + bias_batch)