https://github.com/DADACONF/neural-nets-for-art

Accompanying material for workshop on Neural Networks for Art -- DADACONF 0.1
https://github.com/DADACONF/neural-nets-for-art

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Accompanying material for workshop on Neural Networks for Art -- DADACONF 0.1

• Host: GitHub
• URL: https://github.com/DADACONF/neural-nets-for-art
• Owner: DADACONF
• Created: 2016-05-06T06:36:13.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-05-07T06:08:15.000Z (over 8 years ago)
• Last Synced: 2024-08-02T05:13:14.031Z (3 months ago)
• Language: Shell
• Size: 2.93 KB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
## Neural Networks for Art - DADACONF 0.1 - May 2016

### Set-up:

- Install [Docker](https://docs.docker.com/mac/)

- 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](https://github.com/google/deepdream) (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](https://hub.docker.com/r/herval/deepdream/)

  - More Links:

    - Google's [iPython Notebook](https://github.com/google/deepdream/blob/master/dream.ipynb)

    - A [deep zoom](https://vimeo.com/132623267) I made

    - [/r/deepdream subreddit](https://www.reddit.com/r/deepdream)

##### - [Neural Doodle](https://github.com/alexjc/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](https://hub.docker.com/r/alexjc/neural-doodle/)

##### - [Char-rnn](https://github.com/karpathy/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](https://hub.docker.com/r/mbartoli/char-rnn)

  - More Links:

    - [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) - A really great blog-post by the author of the github repo, explaining the model with examples.

    - [Music](https://soundcloud.com/seaandsailor/sets/char-rnn-composes-irish-folk-music) generated after training on Irish Folks songs

    - Sample outputs from my networks, trained on:

      - [A collection of Trump debate transcripts](http://pastebin.com/jzR2UBpA)

      - [Every IMDB plot summary](http://pastebin.com/j2vj0Fsk)

### 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.