I am currently going through the DCGAN tutorial. 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)
```