imclab / simplelearn

A neural network library built on top of Theano.

Simplelearn: A library for machine learning research

Author: Matthew Koichi Grimes

Copyright 2015

Introduction

Simplelearn is a machine learning library that allows you to define complex models using simple parts. It it meant for those who want to quickly define new models and training algorithms. The code is written to be easily readable, understood, and extended.

All models in Simplelearn are directed acyclic graphs of function objects. It is therefore easy to design models that are more complicated than a simple stack of layers. Thanks to the Theano library, these models automatically compile to C/CUDA code, and are automatically differentiable.

It is easy to optimize loss functions and other outputs with respect to arbitrary variables, be they model parameters (training), input variables (inference), or both. Optimizing w.r.t. inputs is useful for visualizing deep features, or searching for pathological inputs that increase the loss function.

Currently Simplelearn only supports deterministic models (no RBMs).

Dependencies

Required:

• Theano
• nose
• h5py
• CUDA To run on the GPU.

Highly recommended:

• cuDNN To run convnets on the GPU.
• matplotlib To run many of the visualizers / examples.

Optional:

• sphinx To build the API docs yourself.

Documentation

Installation instructions are in simplelearn/INSTALL.md

Beginners should start by with the GitHub wiki, then explore the complete working examples in simplelearn/examples/.

License

I have licensed this library under the Apache 2.0 license. (full text) (wikipedia) (tl:dr).