nn alternatives and similar packages
Based on the "AI" category.
Alternatively, view nn alternatives based on common mentions on social networks and blogs.
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tensor-safe
A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. -
moo
Genetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II. -
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HaVSA
HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau. -
Etage
A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
WorkOS - The modern identity platform for B2B SaaS
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README
ηn
A tiny neural network ðŸ§
This small neural network is based on the backpropagation algorithm.
Usage
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
Hacking
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
Contributing
You want to contribute to this project? Wow, thanks! So please just fork it and send me a pull request.