I’m training a DCGAN on a dataset of 10k images. I want to expirement with adding and taking away convolutions in attempt to balance out the generator and discriminator. Whats the rule to do this in practice?

Here is my generator:

self.main = nn.Sequential( nn.ConvTranspose2d(100, 512, 4, 1, 0, bias = False), nn.BatchNorm2d(512), # We normalize all the features along the dimension of the batch. nn.ReLU(True), # We apply a ReLU rectification to break the linearity. nn.ConvTranspose2d(512, 256, 4, 2, 1, bias = False), nn.BatchNorm2d(256), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(256, 128, 4, 2, 1, bias = False), nn.BatchNorm2d(128), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(128, 64, 4, 2, 1, bias = False),
nn.BatchNorm2d(64), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(64, 3, 4, 2, 1, bias = False), nn.Tanh() )

What input and output values should I have for the 6th layer?