NumpyNet is a very simple python framework for neural networks. It meant to be a teaching tool so that people can really get under the hood and learn the basics about how neural network are built and how they work.
It includes nice visualizations of the process so that the user can watch what is going on as the models learn and make predictions. Its only dependencies are numpy, which does the math, and visdom, which does the visualizations.
Grab NumpyNet:
git clone https://github.com/uptake/numpynet.git
cd numpynetInstall NumpyNet (will install visdom as well):
python setup.py installStart visdom server locally:
visdomOpen up http://localhost:8097 in a browser
Run a demo and have some fun:
python examples.pyCurrently this project is in its infancy. The basic functionality is there but there's still a lot to do. So get in there and add some issues you'd like to see or better yet contribute some code!
Take a look at our travis.yml for integration testing using Travis CI. For local testing use ./integration.sh.
Check out these resouces in concert with NumpyNet for a full appreciation of how a neural network works:
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