Why the GAN Discriminator shows wrong classes? - pytorch

Question

I created a GAN for text and using VanillaGAN training approach, but the main problem is the sequences that are created are good, but the main problem is that when I use nn.sigmoid to see the discriminator labels it shows [0] for data that is created which are completely real, and it is not correct.
Here is my Discriminator code:
class Classifier(nn.Module):
def __init__(self, hidden_size, hidden_size2, dropout):
super().__init__()
self.FC1 = nn.Sequential(
nn.Linear(512, hidden_size),
nn.LeakyReLU(0.1),
nn.Dropout(dropout))
self.FC2 = nn.Sequential(
nn.Linear(hidden_size, hidden_size2),
nn.LeakyReLU(0.1),
nn.Dropout(dropout)
)
self.FC3 = nn.Linear(hidden_size2, 1)
self.dropout = nn.Dropout(dropout)
self.bach = nn.BatchNorm1d(512)
self.bach2 = nn.BatchNorm1d(hidden_size)
self.bach3 = nn.BatchNorm1d(64)
def forward(self, x):
z = self.dropout(x)
z = self.bach(z)
z = self.FC1(z)
z = self.bach2(z)
z = self.FC2(z)
z = self.bach3(z)
out = self.FC3(z)
return out
As input to this Classifier, hidden states of real and fake sequences are feed into this network.
My loss function is BCEWithlogits and this is the Train class
# to create real labels (1s)
def label_real(size):
data = torch.ones(size, 1)
return data.to(device)
# to create fake labels (0s)
def label_fake(size):
data = torch.zeros(size, 1)
return data.to(device)
# function to train the discriminator network
def train_discriminator(optimizer, data_real, data_fake):
b_size = data_real.size(1)
real_label = label_real(b_size)
fake_label = label_fake(b_size)
optimizer.zero_grad()
output_real = discriminator(data_real)
loss_real = criterion(output_real, real_label)
output_fake = discriminator(data_fake)
loss_fake = criterion(output_fake, fake_label)
loss_real.backward()
loss_fake.backward()
optimizer.step()
return loss_real + loss_fake
I use nn.sigmoid after training and on testing the model. Please help me to know what is wrong with my neural network?