A python package for describing and serializing feed-forward (acyclic) signal processing graphs by decoupling topology and parameter from implementation in JSON. Currently, the primary purpose of optimus is to extend the functionality of Theano making it easier to build and reconstruct arbitrary neural networks in a language-agnostic manner.
Unfortunately, documentation is rather sparse at the moment. Please refer to the demos found in examples/ and the notebooks below in the interim.
What does optimus bring to Theano? Here is a quick demonstration:
- Introduction notebook (soon): a brief introduction to building, saving, and re-loading a network.
- Basic Neural Networks (soon): using built-in data from scikit-learn, train a simple neural network classifier.
- MNIST with a ConvNet: demonstration using optimus to build a ConvNet for the MNIST dataset, showing the basics of building a graph and training it with real data. https://github.com/
The easiest way to install optimus is with pip:
$ pip install git+git://github.com/ejhumphrey/optimus.git
Alternatively, you can clone the repository and do it the hard way:
$ cd ~/to/a/good/place
$ git clone https://github.com/ejhumphrey/optimus.git
$ cd optimus
$ python setup.py build
$ [sudo] python setup.py install
Clone the repository and run the tests directly; nose is recommended, and installed as a dependency:
$ cd {wherever_you_cloned_it}/optimus
$ nosetests
If you've made it this far, the mnist demo script, provided atexamples/mnist.py should run without a hitch.
...working on it...