I am currently going through the DCGAN tutorial 8. It performs weight initialisation using the following method.
Why did the author initialized conv layers with numbers from the normal distribution of mean 0 and batch norm layers with weights from normal distribution of mean 1?
What is the intuition of using two different normal distributions for initialising weights?
# custom weights initialization called on netG and netD
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)