Keras-GAN | Keras implementations of Generative Adversarial Networks | Machine Learning library

As you can see from the accuracy plots you have in Tensorboard, your principal problem here is with the discriminator. Because its accuracy oscillates around 50-60% and doesn't improve. And this is very bad because the generator is downstream and can't train until the discriminator achieves decent accuracy. So what is wrong with the discriminator?

First, it is the way you train it. You feed it positive and negative samples in two separate batches. This can generate gradients pushing your model coefficients randomly in opposite directions with very poor convergence. If you combine both types of samples in a single batch, convergence will improve significantly.

Second, the batch size. 32 random points around a circle is too little for the model to feel the difference against 32 random points. You need to have a batch size of at least 256.

Third, the number of neurons in hidden layers. Actually, you have too many neurons for such simple data in both generator and discriminator. Having too many of them in the discriminator doesn't seem to do much harm, but having too many neurons in the generator makes it too unstable, the discriminator each time receives different training data and this is one more reason why it fails to train properly. If you put 16 and 32 hidden neurons instead of 64 and 128 into the generator, it will be much better.

And the last point: it is not only the circular form that makes your circle difficult to learn but also its size. It has a radius of 1, and 1 is the saturation value of your generator, so it is very easy for it to produce values around 1. And this makes additional trouble for the generator: it starts receiving fake data too close to the true data before it achieves decent accuracy.

To summarize:

1. Combine true and fake data into a single batch.
2. Use a larger batch size (at least 256).
3. Reduce the number of neurons at least in the generator (e.g. to 16 and 32).

Enjoy the result:

And one more thing: it is better to ask such questions in this community https://stats.stackexchange.com/.