Assignment 6 Generative Adversarial Networks (GANs)

Assignment 6
In this assignment you will implement two different kinds of generative models: Variational Autoencoders
(VAEs) and Generative Adversarial Networks (GANs).
This assignment is due on Wednesday, December 9th at 11:59pm EST.
Q1: Variational Autoencoder (40 points)
The notebook variational_autoencoders.ipynb will walk you through the implementation of a VAE on the
MNIST dataset. This will allow you to generate new data, and to interpolate in the latent space.
Q2: Generative Adversarial Networks (60 points)
The notebook generative_adversarial_networks.ipynb will walk you through the implementation of fullyconnected and convolutional generative adversarial networks on the MNIST dataset.
Steps
1. Download the zipped assignment file
Click here to download the starter code
2. Unzip all and open the Colab file from the Drive
Once you unzip the downloaded content, please upload the folder to your Google Drive. Then, open each
*.ipynb notebook file with Google Colab by right-clicking the *.ipynb file. We recommend editing your
*.py file on Google Colab, set the ipython notebook and the code side by side. For more information on
using Colab, please see our Colab tutorial.
3. Work on the assignment
Work through the notebook, executing cells and writing code in *.py, as indicated. You can save your
work, both *.ipynb and *.py, in Google Drive (click “File” -> “Save”) and resume later if you don’t want
to complete it all at once.
While working on the assignment, keep the following in mind:
The notebook and the python file have clearly marked blocks where you are expected to write code.
Do not write or modify any code outside of these blocks.
Do not add or delete cells from the notebook. You may add new cells to perform scratch
computations, but you should delete them before submitting your work.
Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that
has been run.
4. Evaluate your implementation on Autograder
Once you want to evaluate your implementation, please submit the *.py , *.ipynb and other required
files to Autograder for grading your implementations in the middle or after implementing everything. You
can partially grade some of the files in the middle, but please make sure that this also reduces the daily
submission quota. Please check our Autograder tutorial for details.
5. Download .zip file
EECS 498-007 / 598-005
Deep Learning for Computer Vision
Fall 2020
Once you have completed a notebook, download the completed uniqueid_umid_A6.zip file, which is
generated from your last cell of the generative_adversarial_networks.ipynb notebook. Before
executing the last cell in this notebook, please manually run all the cells of notebook and save your
results so that the zip file includes all updates.
Make sure your downloaded zip file includes your most up-to-date edits; the zip file should include:
vae.py
variational_autoencoders.ipynb
gan.py
generative_adversarial_networks.ipynb
6. Submit your python and ipython notebook files to Autograder
When you are done, please upload your work to Autograder (UMich enrolled students only). Your
*.ipynb files SHOULD include all the outputs. Please check your outputs up to date before submitting
yours to Autograder.
Justin Johnson
justincj@umich.edu
Website for UMich EECS course
EECS 498-007 / 598-005: Deep Learning for Computer Vision