Neural Networks for Art - DADACONF 0.1 - May 2016

Set-up:

• Install Docker
• Source the set-up script. This will download the nescessary Docker containers, create some folders in the working directory, and set-up some handy aliases for you:

  source set_up_containers.sh
  

Tools Included:

- Deep Dream (Inceptionism)

• Usage: deep-dream {input image path} {number of iterations, default 20} {scale change between iterations, default 0.1} {layer to maximize activations for, default inception_4c/output}. Saves images to /output.
• Source Docker Image: herval/deepdream
• More Links:
  • Google's iPython Notebook
  • A deep zoom I made
  • /r/deepdream subreddit

- Neural Doodle

• Usage: neural-doodle then any of the commands decribed in the repo (minus the python3 at the beginning). Saves intermediate frames to /frames and outputs to /samples. SLOW on CPU!
• Source Docker Image: alexjc/neural-doodle

- Char-rnn

• Usage: char-rnn opens an interactive shell with train.lua and sample.lua which can be used as described in the repo. Saves model checkpoints to /cv
• Source Docker Image: mbartoli/char-rnn
• More Links:
  • The Unreasonable Effectiveness of Recurrent Neural Networks - A really great blog-post by the author of the github repo, explaining the model with examples.
  • Music generated after training on Irish Folks songs
  • Sample outputs from my networks, trained on:
    • A collection of Trump debate transcripts
    • Every IMDB plot summary

Clean-up:

If you don't think you'll use these anymore, you should remove the Docker images to free up (~2 GB?) of space on your machine:

docker rmi herval/deepdream alexjc/neural-doodle mbartoli/char-rnn

The cv, frames, output, and samples folders, containing your inputs and outputs won't be deleted.