BYU CS classes

To learn about deconvolutions, variable sharing, trainable variables, and generative adversarial models.

For this lab, you will need to implement a generative adversarial network (GAN). Specifically, we will be using the technique outlined in the paper Improved Training of Wasserstein GANs.

You should turn in an iPython notebook that shows a two plots. The first plot should be random samples from the final generator. The second should show interpolation between two faces by interpolating in z space.

You must also turn in your code, but your code does not need to be in a notebook, if it's easier to turn it in separately (but please zip your code and notebook together in a single zip file).

NOTE: this lab is complex. Please read through the entire spec before diving in.

Your code/image will be graded on the following:

• 20% Correct implementation of discriminator
• 20% Correct implementation of generator
• 20% Correct implementation of loss functions
• 20% Correct sharing of variables
• 10% Correct training of subsets of variables
• 10% Tidy and legible final image

The dataset you will be using is the "celebA" dataset, a set of 202,599 face images of celebrities. Each image is 178×218. You should download the “aligned and cropped” version of the dataset. Here is a direct download link (1.4G), and here is additional information about the dataset.

This lab will help you develop several new tensorflow skills, as well as understand some best practices needed for building large models. In addition, we'll be able to create networks that generate neat images!

tf.gradients