My DC-GAN on grayscale face images is not training well - machine-learning

Question

So I trained by python/pytorch DC-GAN (deep convolutional GAN) for 30 epochs on grayscale faces, and my GAN pretty much failed. I added batch normalization and leaky relu's to my generator and discriminator (I heard those are ways to make the GAN converge), and the Adam optimizer. My GAN still only putting out random grayscale pixels (nothing even remotely related to faces.) I have no problem with the discriminator, my discriminator works very well. I then implemented weight decay of 0.01 on my discriminator to make my GAN train better (since my discriminator was doing better than my generator) but to no avail. Finally, I tried training the GAN for more epochs, 60 epochs. My GAN still generates just random pixels, sometimes outputting completely black.
The GAN training method I used worked for the MNIST dataset (but I used a way simpler GAN architecture for that.)
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 4, 3)
self.conv2 = nn.Conv2d(4, 8, 3)
self.bnorm1 = nn.BatchNorm2d(8)
self.conv3 = nn.Conv2d(8, 16, 3)
self.conv4 = nn.Conv2d(16, 32, 3)
self.bnorm2 = nn.BatchNorm2d(32)
self.conv5 = nn.Conv2d(32, 4, 3)
self.fc1 = nn.Linear(5776, 1024)
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(512, 1)
def forward(self, x):
pred = F.leaky_relu(self.conv1(x.reshape(-1,1,48,48)))
pred = F.leaky_relu(self.bnorm1(self.conv2(pred)))
pred = F.leaky_relu(self.conv3(pred))
pred = F.leaky_relu(self.bnorm2(self.conv4(pred)))
pred = F.leaky_relu(self.conv5(pred))
pred = pred.reshape(-1, 5776)
pred = F.leaky_relu(self.fc1(pred))
pred = F.leaky_relu(self.fc2(pred))
pred = torch.sigmoid(self.fc3(pred))
return pred
class Generator(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(512, 1024)
self.fc2 = nn.Linear(1024, 2048)
self.fc3 = nn.Linear(2048, 5776)
self.convT1 = nn.ConvTranspose2d(4, 32, 3)
self.convT2 = nn.ConvTranspose2d(32, 16, 3)
self.bnorm1 = nn.BatchNorm2d(16)
self.convT3 = nn.ConvTranspose2d(16, 8, 3)
self.convT4 = nn.ConvTranspose2d(8, 4, 3)
self.bnorm2 = nn.BatchNorm2d(4)
self.convT5 = nn.ConvTranspose2d(4, 1, 3)
def forward(self, x):
pred = F.leaky_relu(self.fc1(x))
pred = F.leaky_relu(self.fc2(pred))
pred = F.leaky_relu(self.fc3(pred))
pred = pred.reshape(-1, 4, 38, 38)
pred = F.leaky_relu(self.convT1(pred))
pred = F.leaky_relu(self.bnorm1(self.convT2(pred)))
pred = F.leaky_relu(self.convT3(pred))
pred = F.leaky_relu(self.bnorm2(self.convT4(pred)))
pred = torch.sigmoid(self.convT5(pred))
return pred
import torch.optim as optim
discriminator = discriminator.to("cuda")
generator = generator.to("cuda")
discriminator_losses = []
generator_losses = []
for epoch in range(30):
for data,label in tensor_dataset:
data = data.to("cuda")
label = label.to("cuda")
batch_size = data.size(0)
real_labels = torch.ones(batch_size, 1).to("cuda")
fake_labels = torch.zeros(batch_size, 1).to("cuda")
noise = torch.randn(batch_size, 512).to("cuda")
D_real = discriminator(data)
D_fake = discriminator(generator(noise))
D_real_loss = F.binary_cross_entropy(D_real, real_labels)
D_fake_loss = F.binary_cross_entropy(D_fake, fake_labels)
D_loss = D_real_loss+D_fake_loss
d_optim.zero_grad()
D_loss.backward()
d_optim.step()
noise = torch.randn(batch_size, 512).to("cuda")
D_fake = discriminator(generator(noise))
G_loss = F.binary_cross_entropy(D_fake, real_labels)
g_optim.zero_grad()
G_loss.backward()
g_optim.step()
discriminator_losses.append(D_loss)
generator_losses.append(G_loss)
print(epoch)

Answer 1

I'm also new to Deep learning and GAN models, but this method solved similar problem for my DCGAN project. Use kernel size at least 4*4: it's my guess, but it seems that small kernels can't catch details in image, no matter how deep the network is. Other tips I found are mostly from here:(same link from above)
https://machinelearningmastery.com/how-to-train-stable-generative-adversarial-networks/