# neural - Neural Nets in native Haskell

## Motivation

The goal of this project is to provide a flexible framework for
neural networks
(and similar parameterized models) in Haskell.

There are already a couple of neural network libraries out there on Hackage, but as far as I can tell,
they either

- are wrappers for an engine written in another language or
- offer a limitted choice of network architectures, training algorithms or error functions
or are not easily extensible.

The goal of this library is to have an implementation in native Haskell (reasonably efficient)
which offers maximal flexibility.

Furthermore, gradient descent/backpropagation should work automatically, using
automatic differentiation.
This means that new and complicated activation functions and/or network architectures can be used without the need
to first calculate derivatives by hand.

In order to provide a powerful and flexible API, models are constructed using *components* which behave as if they implemented the
Arrow and ArrowChoice typeclasses.
They can therefore easily be combined and transformed.

Once a model has been constructed, it can be hooked up into a customized training algorithm using pipes,
so that various aspects of the algorithm (loading data, choosing random samples, reporting intermediate results, stop criterium etc.) can be defined in a modular,
decoupled way.

Even though neural networks are the primary motivation for this project, any other kind of model can be
defined in the same framework, whenever the model depends on a collection of numerical parameters in a differentiable
way. - One simple example for this would be linear regression.

## Examples

At the moment, three examples are included:

sqrt models the regression problem of approximating the square root function on the interval [0,4].

iris solves the famous Iris Flower classification problem.

MNIST tackles the equally famous MNIST problem of recognizing handwritten digits.