TensorFlow.js: Generative Adversarial Network

Demonstrates the generator part of the ACGAN in the browser.


This is the web demo part of the dual-environment TensorFlow.js example of Auxiliary Classifier Generative Adversarial Network (ACGAN).

The training code is in gan.js, which runs in Node.js using tfjs-node or tfjs-node-gpu. In this web page, we load the generator part of a pre-trained GAN to generate MNIST images. How real those generated MNIST images look depends on how well the model has been trained. After 5 epochs of training, you should start seeing reasonable images. After 15 epochs, the images should start to look good. Close-to-perfect images (ones that are hard to distinguish from the real ones) should appear toward the end of the default 100 epochs of training.

The ACGAN is a type of GAN. It consists of

Loading model...
Fake images (1 example per class)
Real images for comparison (10 examples per class)