This repo trains a Deep Convolutional Generative Adversarial Network (DCGAN) to generate faces. The motivation for this program was the 4th project in the Udacity Deep Learning Nanodegree.

This project can be executed directly from within the dlnd_face_generation.ipynd Jupyter notebook. Due to the time required to train the model, it is recommended that a GPU is used. The output shown in the report and notebook were generated on AWS' Deep Learning AMI (Ubuntu 18.04) Version 26.0 with a p2.xlarge instance.

1. Launch your AWS instance and ssh into it.

   • Remember to add a "Custom TCP Rule" to your security group inbound rules to allow Jupyter notebooks.

   From AWS CLI

2. Configure Jupyter notebook

   jupyter notebook --generate-config
   sed -ie "s/#c.NotebookApp.ip = 'localhost'/#c.NotebookApp.ip = '*'/g" ~/.jupyter/jupyter_notebook_config.py

3. Clone the repo.

   git clone https://github.com/daniel-fudge/dlnd-DCGAN-Face-Generation.git

4. Download the input files (you can replaces with your own files) and unzip.

   cd dlnd-DCGAN-Face-Generation
   wget https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip
   unzip processed-celeba-small.zip
   rm *.zip
   rm -r __MACOSX

5. Activate Python 3 environment with PyTorch, CUDA 10 and MKL-DNN. Then install cv2.

   source activate pytorch_p36
   conda install opencv

6. Start Jupyter notebook

   jupyter notebook --ip=0.0.0.0 --no-browser

7. Record the long URL generated by the previous command, it should have the format http://[some_ip]:8888/?token=[your_token].

   From your local PC

8. Connect to the notebook from your browser by replacing [some_ip] with your instances IPv4 Public IP.

9. Open dlnd_face_generation.ipynb.

10. Read and/or run the cells at your leisure!! :)

Although it is not recommended to train on a local PC, you may want to run locally to debug.

1. Clone repo to PC.

   git clone https://github.com/daniel-fudge/dlnd-DCGAN-Face-Generation.git

2. Download processed-celeba-small.zip and unzip into dlnd-DCGAN-Face-Generation folder.

3. [OPTIONAL] Delete the zip file and any miscellaneous OS X files or folders such as __MACOSX or .DS_Store.

4. [OPTIONAL] Install Anaconda3.

5. [OPTIONAL] Create a virtual environment.

   conda update conda
   conda create -n pytorch_p36 python=3.7 
   conda activate pytorch_p36
   conda install pytorch
   conda install -c pytorch torchvision
   conda install opencv
   conda install matplotlib
   conda install tqdm

6. [OPTIONAL] Activate the virtual environment.

   conda activate pytorch_p36

7. Launch Jupyter.

   cd dlnd-DCGAN-Face-Generation
   jupyter notebook

8. Open dlnd_face_generation.ipynb.

9. Read and/or run the cells at your leisure!! :)

A report was generated from the Jupyter Notebook for those who prefer html files.

This code is copyright under the MIT License.

Please feel free to raise issues against this repo if you have any questions or suggestions for improvement.