GANs are a highly active topic for research. Generally GANs don’t converge well. A typical GAN loss should be something where G loss log(D(G(z))  maximizes and D loss log(D(x))+log(1-D(G(z)) minimizes. But that’s not the scenario all the time. Most of the time the discriminator gets fooled easily by the generator. To avoid this:

Update the discriminator more often than the generator.
Use WGAN with Gradient Penalty.

In some cases the discriminator gets an upperhand. To avoid this:

Add dropout and leaky relu in the discriminator layers.
Use label smoothing.
Add noise to both generated and real input before feeding them to the discriminator.

**Note: Don’t expect GAN to converge they may converge and may not. But usually if your generator and discriminator loss converges together then your GAN is good.