In this project, I built a generative adversarial networks(GAN) to generate new images of faces. The project is part of fulfillment for a nanodegree at Udacity.
For best experience with managing dependency I advise you install Anconda or miniconda.
Create a virtual environment with conda
conda create --name deep-learning python=3
Activate environment.
source activate deep-learning
Install dependencies.
pip install -r requirements.txt
Download or clone this face-generation. Launch the app with jupyter-notebook.
jupyter-notebook dlnd_face_generation.ipynb
Download the celeba dataset. Unzip the folder and place it in this project's home directory, at the location processed_celeba_small/.
This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data processed_celeba_small/.
Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
Run all code cells in the notebook (This will take a very very long time to run on a CPU, preferably you should run on a GPU).
Notice that the generated faced though low-resolution are human faces.