How to dynamically freeze weights after compiling model in Keras?
Maybe your adversarial net(generator plus discriminator) are wrote in 'Model'.
However, even you set the d.trainable=False, the independent d net are set non-trainable, but the d in the whole adversarial net is still trainable. 
You can use the d_on_g.summary() before then after set d.trainable=False and you would know What I mean(pay attention to the trainable variables).
I would like to train a GAN in Keras. My final target is BEGAN, but I'm starting with the simplest one. Understanding how to freeze weights properly is necessary here and that's what I'm struggling with.
During the generator training time the discriminator weights might not be updated. I would like to freeze and unfreeze discriminator alternately for training generator and discriminator alternately. The problem is that setting trainable parameter to false on discriminator model or even on its' weights doesn't stop model to train (and weights to update). On the other hand when I compile the model after setting trainable to False the weights become unfreezable. I can't compile the model after each iteration because that negates the idea of whole training.
Because of that problem it seems that many Keras implementations are bugged or they work because of some non-intuitive trick in old version or something.