A small helper library for building directed acyclic graphs, trainable by gradient descent, in Theano
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README.rst

dagbldr

A small helper library for building directed acyclic graphs in Theano

What

Easy prototyping of out-of-core recurrent models with conditional structures, simple monitoring via html plots, many wrapped examples and tests, and a focus on node/function based code instead of an object-oriented approach.

One other goal is to make sharing experiment code easy - the entire library is serialized during training, and tries to have absolute minimal dependencies besides numpy, scipy, and Theano. Eventually I would like to save a single file which has all the codepaths used in an experiment.

Philosophically, there is similarity to Lasagne (another great neural network library) but written with my own research goals in mind.

WARNING

If you use this library, I will likely break your code. Someday I hope to have enough examples, tests, and experiments to have the API solid, but for now consider this "bleeding edge" type development.

Install

The typical install involves setting up a scientific Python environment using your preferred approach (I like Continuum Analytics Anaconda personally), plus the latest version of Theano.

Once this is done, clone this repo and run

python setup.py develop

Try running tests and examples to be sure install worked correctly.