Why does discriminiator accuracy falls to 0%, and is there a fix around this? I am training a Vanilla-GAN(or original GAN 2016) on a pokemon dataset https://www.kaggle.com/kvpratama/pokemon-images-dataset, for few epochs the discriminator has 100% accuracy over the real examples, but as the epochs pass it falls to 0% around 4-5 epochs.

One interesting effect however is that when I increase my batch size from 32 to 64, this effect is seemed to have a delayed effect. i.e It kicks in around 400-500 epochs.

Please help.
I figured out my answer a while back. SO here it goes... 


Ensure that your training data is normalized. This was what causing the discriminator's accuracy to fall. 
You need to decrease your dropouts as it can cause heavy bias if done ineffectively.
Remove any unnecessary layers in your discriminator as they can cause overfitting.
Use batch normalization.
Introduce dropouts in your generator.