The GAN loss is defined:

Prob_1 = D(real_data)
Prob_2 = D(G(z))
D_loss = log(prob_1) + (1 - log(prob_2))
G_loss = -log(D(G(z))

The loss are plotted here:
GAN_not_converge.png1280×960 48.4 KB

And as expected, the Prob_1 increases and Prob_2 decreases as the training goes, which means D is learning the right thing as expected.
Does this mean that the training of G and D are not balanced? G cannot keep up with D?
So intuitively, I think it would be better if G is trained more than D, but according to tips for training GAN 3, it would be a bad practice. (I tried other tips in this article like optimizers, activation functions, batchNorms etc.)
Would it be a good idea to train G more than D?
Or maybe there are some tips I should try?