Q: Negative/positive generator loss in WGAN with gradient penalty my discriminator loss is defined as:
tf.reduce_mean(D(fake)) - tf.reduce_mean(D(real))+ 10 * gradient penalty
my generator loss is defined as:
q* (-tf.reduce_mean(D(fake))) +p*ssim(fake, real) +mse(fake, real)
D refers to the Discriminator network. fake refers to the image generated by the generator and real refers to the image from the ground-truth dataset. p and q are parameters used to balance different loss functions. 
in some models, the value tf.reduce_mean(D(fake)) is always positive but in some other models, this value is always negative. How do I interpret it?
Does it mean I did something wrong if this value is positive? Because, I think, the discriminator is trying to distinguish between real and fake images and therefore tf.reduce_mean(D(fake)) should be negative eventually.
Should I adjust the parameters or train discriminator more?
