Hi @colesbury
I used two loss function loss=loss1+loss2, and I expect to have different gradient when I use just loss=loss1,But the gradient flow and numbers was same.indeed adding second loss does not have any effect. Would you pleas help me with that? I try different second loss but the result does not have any change. The first loss is BCELoss and the second one is L1. I change the sigmoid function to Relu, But again the gradient from backward.() with loss2 and without loss2 is same!
netG = Generator(ngpu,nz,ngf).to(device)

optimizerG = optim.Adam(netG.parameters(), lr=lr2, betas=(beta1, 0.999))

netG.zero_grad()

label.fill_(real_label)  
label=label.to(device)
output = netD(fake).view(-1)
# Calculate G's loss based on this output
loss1 = criterion(output, label)


xxx=torch.histc(Gaussy.squeeze(1).view(-1).cpu(),100, min=0, max=1, out=None)
ddGaussy=xxx/xxx.sum()

xxx1=torch.histc(fake.squeeze(1).view(-1).cpu(),100, min=0, max=1, out=None)
ddFake=xxx1/xxx1.sum()
loss2=abs(ddGaussy-ddFake).sum()

# Calculate gradients for G with 2 loss

errG=loss1+loss2
errG.backward()

for param in netG.parameters():
            print(param.grad.data.sum())

# Update G
optimizerG.step()




 
## ------------------
class Generator(nn.Module):
    def __init__(self,ngpu,nz,ngf):
        super(Generator, self).__init__()
        self.ngpu=ngpu
        self.nz=nz
        self.ngf=ngf
        self.l1= nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(self.nz, self.ngf * 8, 3, 1, 0, bias=False),
            nn.BatchNorm2d(self.ngf * 8),
            nn.ReLU(True),)
            # state size. (ngf*8) x 4 x 4
        self.l2=nn.Sequential(nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 3, 1, 0, bias=False),
            nn.BatchNorm2d(self.ngf * 4),
            nn.ReLU(True),)
            # state size. (ngf*4) x 8 x 8
        self.l3=nn.Sequential(nn.ConvTranspose2d( self.ngf * 4, self.ngf * 2, 3, 1, 0, bias=False),
            nn.BatchNorm2d(self.ngf * 2),
            nn.ReLU(True),)
            # state size. (ngf*2) x 16 x 16
        self.l4=nn.Sequential(nn.ConvTranspose2d( self.ngf*2, 1, 3, 1, 0, bias=False),nn.Sigmoid()
#            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        out=self.l1(input)
        out=self.l2(out)
        out=self.l3(out)
        out=self.l4(out)
        print(out.shape)
        return out