Real data (fixed)
Generated G(z)
Discriminator field D(x)
⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.
This simulation illustrates the principles of Generative Adversarial Networks by actually running one: a generator and discriminator, each a small multilayer perceptron trained with hand-written backpropagation, compete in real time in your browser. The discriminator's confidence score across the 2D data plane is rendered as living 3D terrain — height and color track D(x) exactly — while cyan points mark the fixed real data distribution and magenta points show where the generator's noise-driven output currently lands. Raise the learning rate to watch the two networks destabilize each other and trigger mode collapse; lower it and watch the generator patiently climb toward every mode of the real distribution as the adversarial game approaches a Nash equilibrium.