This implementation slightly adjusts the convolutional autoencoder implementation found here: https://github.com/mikesj-public/dcgan-autoencoder

The main adjustments are:

• Altering the net input size
• Adding additional convolutional layers to the front of the autoencoder
• Toggling Pooling before MSE
• Adjusting the encoding cost multiplier
• Adding functionality for printing the entire test set
• Adjusting dataprocessing.py to create a masked image dataset

The instructions for running the code are copied below from the original implementation:

I assume knowledge of IPython (Jupyter), pip and virtualenv (not complicated to learn if not). The following should work on unix systems. Working in a virtualenv, run

pip install -r /path/to/requirements.txt

You should download the CelebA dataset from website (you're looking for a file called img_align_celeba.zip). Unzip into this directory then run

./dataprocessing.py

This will crop the images to the right size and store them in HDF5 format.

Next run the dcgan notbook.