https://github.com/awentzonline/pytorch-cns

Generalized Compressed Network Search with PyTorch
https://github.com/awentzonline/pytorch-cns

Last synced: about 2 months ago
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Generalized Compressed Network Search with PyTorch

• Host: GitHub
• URL: https://github.com/awentzonline/pytorch-cns
• Owner: awentzonline
• Created: 2017-07-23T20:14:19.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-01T04:50:51.000Z (about 7 years ago)
• Last Synced: 2024-10-30T09:39:27.000Z (2 months ago)
• Language: Python
• Homepage:
• Size: 72.3 KB
• Stars: 26
• Watchers: 3
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
PyTorch-CNS

==============

An implementation of [Generalized Compressed Network Search](http://people.idsia.ch/~juergen/compressednetworksearch.html)

for PyTorch models.

This creates a genome per layer, rather than a single one for the entire model as is described in the paper.

There are two optimizers:

 * Asynchronous Gene Pool: a master list of genomes, sorted by fitness, is worked

 on by a pool of agents which draw fit genomes for mutation and evaluation.

 * Synchronous Score Swap: each worker maintains a full copy of all genomes and

 only fitness scores are exchanged.

Installation

------------

Install python package with `pip install pytorch-cns`

Install [redis](https://redis.io/) which is used as the datastore. The examples

expect a redis instance listening on localhost at the default port. You can change

this by passing a JSON dict of `StrictRedis` kwargs via the `--redis-params` command

line option.

AI Gym Examples

---------------

 * `aigym.py`: CartPole!

 * `atari.py`: Atari ram-based games

 * `atari_pix.py`: Atari pixel-based games

 * `atari_pixrnn_gpa.py`: Atari pixel-based games with a recurrent neural network,

 using the asynchronous gene pool optimizer.

 * `atari_pixrnn_ss.py`: Atari pixel-based games with a recurrent neural network,

 using the synchronous

Install additional requirements: `pip install gym atari_py box2d`

To run a pool of workers with default settings simply run the python file

(e.g. `python atari_pix.py`). If you make any changes to the hyperparameters

you'll to use the `--clear-store` flag which deletes the old gene pool upon start.

Use `--num-agents` to customize the number of child processes spawned.

Invoke the example with `python atari_pix.py --render --best` to run the simulation

with the fittest genome. This can be done at the same time as the workers are

running to monitor progress.

Image generation

----------------

These do not converge on anything at the moment. Maybe, if you are a real GANimal,

you can find the right configuration. The code here is hacked together from

the pytorch example repo.

`cnsdcgan.py`: DCGAN adapted from the PyTorch DCGAN example. Attempts to train

both the discriminator and generator with compressed network search.

`vggmse.py`: An autoencoder which uses VGG16 to calculate the loss.