Deep Learning with PyTorch

Defining the discriminator network

Let's quickly look at the following discriminator network code, and then discuss the key features of the discriminator network:

class Discriminator(nn.Module):    def __init__(self):        super(_netD, self).__init__()        self.main = nn.Sequential(            # input is (nc) x 64 x 64            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),            nn.LeakyReLU(0.2, inplace=True),            # state size. (ndf) x 32 x 32            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),            nn.BatchNorm2d(ndf * 2),            nn.LeakyReLU(0.2, inplace=True),            # state size. (ndf*2) x 16 x 16            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),            nn.BatchNorm2d(ndf * 4),            nn.LeakyReLU(0.2, inplace=True),            # state size. (ndf*4) x 8 x 8            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 8), ...