[WARNING: This page is extremely outdated and hasn't been updated since 2010.]
/!\ HNN is undergoing a total rewrite. If you're interested in getting involved or just keeping an eye on it, check the project page and the mailing-list.
HNN (stands for Haskell Neural Network library) is an attempt at providing a simple but powerful and efficient library to deal with feed-forward neural networks in Haskell.
Why another neural network library ?
I tried HFANN and few other code I found before deciding to write my own library. But I wasn't satisfied with them. They are more comprehensive but they were not to be used the way I intended to, for a Haskell neural network library. Mine is much simpler, less comprehensive but is an attempt at easily creating, training and using neural networks in Haskell, without performance losses. Note : HNN is full-Haskell unlike HFANN which is a binding to a C library.
Get the code
hnn-0.1 is on Hackage, in the AI category. A simple :
- cabal install hnn
should install HNN for you. (You may need to do a cabal update before so that cabal will be aware of the new package named hnn -- and of course of the other new packages or new versions of the packages)
From the git repository
HNN is hosted on github :  The instructions to get it and build it are :
- git clone git://github.com/alpmestan/HNN.git
- cd HNN
- cabal install
After these commands, provided you have ghc 6.8 or later and the 'uvector' package installed, HNN will be installed just like any other library. To generate the documentation, you have to execute :
- cabal haddock
or you can just pass the --enable-documentation flag to cabal install. The documentation should then be in HNN/dist/doc/.
You can see the xor-3input.hs file for an example of use of the library (see HNN#Example on this page).
There is an online version of the documentation here :  but there should be the same doc soon on the hackage page of hnn.
Here is a simple example of use of the HNN library.
xor-3inputs.hs file :
module Main where import AI.HNN.Net import AI.HNN.Layer import AI.HNN.Neuron import Data.Array.Vector import Control.Arrow import Data.List alpha = 0.8 :: Double -- learning ratio epsilon = 0.001 :: Double -- desired maximal bound for the quad error layer1, layer2 :: [Neuron] layer1 = createSigmoidLayer 4 0.5 [0.5, 0.5, 0.5] -- the hidden layer layer2 = createSigmoidLayer 1 0.5 [0.5, 0.4, 0.6, 0.3] -- the output layer net = [layer1, layer2] -- the neural network finalnet = train alpha epsilon net [([1, 1, 1],), ([1, 0, 1],), ([1, 1, 0],), ([1, 0, 0],)] -- the trained neural network good111 = computeNet finalnet [1, 1, 1] good101 = computeNet finalnet [1, 0, 1] good110 = computeNet finalnet [1, 1, 0] good100 = computeNet finalnet [1, 0, 0] main = do putStrLn $ "Final neural network : \n" ++ show finalnet putStrLn " ---- " putStrLn $ "Output for [1, 1, 1] (~ 0): " ++ show good111 putStrLn $ "Output for [1, 0, 1] (~ 1): " ++ show good101 putStrLn $ "Output for [1, 1, 0] (~ 1): " ++ show good110 putStrLn $ "Output for [1, 0, 0] (~ 0): " ++ show good100
Compile it with ghc -O2 --make xor-3inputs.hs -o xor-3 and launch it. You should get something close to the following.
$ ./xor-3inputs Final neural network : [[Threshold : 0.5 Weights : toU [1.30887603787326,1.7689534867644316,2.2908214981696453],Threshold : 0.5 Weights : toU [-2.4792430791673947,4.6447786039112655,-4.932860802255383],Threshold : 0.5 Weights : toU [2.613377735822592,6.793687725768354,-5.324081206358496],Threshold : 0.5 Weights : toU [-2.5134194114492585,4.730152273922408,-5.021321916827272]],[Threshold : 0.5 Weights : toU [4.525235803191061,4.994126671590998,-8.2102354168462,5.147655509585701]]] ---- Output for [1, 1, 1] (~ 0): [2.5784449476436315e-2] Output for [1, 0, 1] (~ 1): [0.9711209812630944] Output for [1, 1, 0] (~ 1): [0.9830499812666017] Output for [1, 0, 0] (~ 0): [1.4605247804272069e-2]
Feedback, participation and all
If you have anything to say related to the HNN library, please send an email to alpmestan <a t> gmail <d o t> com. I'd be pleased to hear from any HNN user so don't hesitate ! Thank you.