name: neural version: 0.1.0.1 cabal-version: >=1.10 build-type: Simple license: MIT license-file: LICENSE copyright: Copyright: (c) 2016 Lars Bruenjes maintainer: brunjlar@gmail.com homepage: https://github.com/brunjlar/neural bug-reports: https://github.com/brunjlar/neural/issues synopsis: Neural Networks in native Haskell description: The goal of `neural` is to provide a modular and flexible neural network library written in native Haskell. . Features include . * /composability/ via instances and , . * /automatic differentiation/ for automatic gradient descent/ backpropagation training (using Edward Kmett's fabulous library). . The idea is to be able to easily define new components and wire them up in flexible, possibly complicated ways (convolutional deep networks etc.). . Two examples are included as proof of concept: . * A simple neural network that approximates the sqrt function on [0,4]. . * A slightly more complicated neural network that solves the famous problem. . The library is still very much experimental at this point. category: Machine Learning author: Lars Bruenjes extra-source-files: .travis.yml .gitignore .ghci stack.yaml README.markdown source-repository head type: git location: https://github.com/brunjlar/neural.git library exposed-modules: Numeric.Neural Numeric.Neural.Layer Numeric.Neural.Model Numeric.Neural.Normalization Numeric.Neural.Pipes Data.MyPrelude Data.Utils Data.Utils.Analytic Data.Utils.Arrow Data.Utils.List Data.Utils.Matrix Data.Utils.Random Data.Utils.Stack Data.Utils.Statistics Data.Utils.Traversable Data.Utils.Vector build-depends: base >=4.7 && <5, ad >=4.3.2 && <4.4, array >=0.5.1.0 && <0.6, deepseq >=1.4.1.1 && <1.5, directory >=1.2.2.0 && <1.3, filepath >=1.4.0.0 && <1.5, ghc-typelits-natnormalise >=0.4.1 && <0.5, hspec >=2.2.2 && <2.3, lens ==4.13.*, MonadRandom >=0.4.2.2 && <0.5, mtl >=2.2.1 && <2.3, parallel >=3.2.1.0 && <3.3, pipes >=4.1.8 && <4.2, profunctors ==5.2.*, STMonadTrans >=0.3.3 && <0.4, text >=1.2.2.1 && <1.3, transformers >=0.4.2.0 && <0.5, typelits-witnesses >=0.2.0.0 && <0.3, vector >=0.11.0.0 && <0.12 default-language: Haskell2010 hs-source-dirs: src ghc-options: -Wall -fexcess-precision -optc-O3 -optc-ffast-math executable iris main-is: iris.hs build-depends: base >=4.7 && <5, attoparsec >=0.13.0.1 && <0.14, neural >=0.1.0.1 && <0.2, text >=1.2.2.1 && <1.3 default-language: Haskell2010 hs-source-dirs: examples/iris ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math executable sqrt main-is: sqrt.hs build-depends: base >=4.7 && <5, MonadRandom >=0.4.2.2 && <0.5, neural >=0.1.0.1 && <0.2 default-language: Haskell2010 hs-source-dirs: examples/sqrt ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math test-suite neural-test type: exitcode-stdio-1.0 main-is: Spec.hs build-depends: base >=4.7 && <5, hspec >=2.2.2 && <2.3, MonadRandom >=0.4.2.2 && <0.5, neural >=0.1.0.1 && <0.2 default-language: Haskell2010 hs-source-dirs: test other-modules: Utils.MatrixSpec Utils.VectorSpec ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math test-suite neural-doctest type: exitcode-stdio-1.0 main-is: doctest.hs build-depends: base >=4.7 && <5, doctest >=0.10.1 && <0.11, neural >=0.1.0.1 && <0.2 default-language: Haskell2010 hs-source-dirs: doctest ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math