This small neural network is based on the backpropagation algorithm.
A minimal usage example would look like this:
import AI.Nn (new
,predict
,train)
main :: IO ()
main = do
{- Creates a new network with two inputs,
two hidden layers and one output -}
network <- new [2, 2, 1]
{- Train the network for a common logical AND,
until the maximum error of 0.01 is reached -}
let trainedNetwork = train 0.01 network [([0, 0], [0])
,([0, 1], [0])
,([1, 0], [0])
,([1, 1], [1])]
{- Predict the learned values -}
let r00 = predict trainedNetwork [0, 0]
let r01 = predict trainedNetwork [0, 1]
let r10 = predict trainedNetwork [1, 0]
let r11 = predict trainedNetwork [1, 1]
{- Print the results -}
putStrLn $ printf "0 0 -> %.2f" (head r00)
putStrLn $ printf "0 1 -> %.2f" (head r01)
putStrLn $ printf "1 0 -> %.2f" (head r10)
putStrLn $ printf "1 1 -> %.2f" (head r11)
The result should be something like:
0 0 -> -0.02
0 1 -> -0.02
1 0 -> -0.01
1 1 -> 1.00
To start hacking simply clone this repository and make sure that stack is installed. Then simply hack around and build the project with:
> stack build --file-watch
You want to contribute to this project? Wow, thanks! So please just fork it and send me a pull request.