vanillaGAN.py
: Code taken from this
blog post. It trains a GAN on MNIST data.
- The generator takes a 100-dimensional noise vector and returns a 786-dimensional vector. This return vector is the same size as an MNIST image (28x28). In a way, it learns a mapping between the prior space (100-dim vector) and the MNIST data.
- The discriminator takes in an MNIST image and returns a scalar which represents the probability that the image passed in is a real MNIST image.
- Samples from the generator are generated every 1000 batches run over the MNIST data. The resulting samples can be viewed in the
out
folder. - Analysis: After ~300,000 training iterations (~1 hr), the generator creates passable MNIST digits.