Deep Learning with PyTorch

Generator

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

class Generator(nn.Module):    def __init__(self):        super(Generator, self).__init__()        self.main = nn.Sequential(            # input is Z, going into a convolution            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),            nn.BatchNorm2d(ngf * 8),            nn.ReLU(True),            # state size. (ngf*8) x 4 x 4            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),            nn.BatchNorm2d(ngf * 4),            nn.ReLU(True),            # state size. (ngf*4) x 8 x 8            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),            nn.BatchNorm2d(ngf * 2),            nn.ReLU(True),            # state size. (ngf*2) x 16 x 16            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),            nn.BatchNorm2d(ngf), nn.ReLU(True), ...