Cabal revisions of neural-0.2.0.0
Hackage metadata revisions edit the .cabal file after upload; each diff below is one revision.
revision 1
-name: neural-version: 0.2.0.0-cabal-version: >=1.10-build-type: Simple-license: MIT-license-file: LICENSE-copyright: Copyright: (c) 2016 Lars Bruenjes-maintainer: brunjlar@gmail.com-stability: provisional-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 arrow-like instances and- <https://hackage.haskell.org/package/pipes pipes>,- .- * /automatic differentiation/ for automatic gradient descent/ backpropagation training- (using Edward Kmett's fabulous <https://hackage.haskell.org/package/ad ad> 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.).- .- Three 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- <https://en.wikipedia.org/wiki/Iris_flower_data_set Iris flower> problem.- .- * A first (still simple) neural network for recognizing handwritten digits from the equally famous- <https://en.wikipedia.org/wiki/MNIST_database MNIST> database.- .- The library is still very much experimental at this point.-category: Machine Learning-author: Lars Bruenjes-tested-with: GHC ==7.10.3-extra-source-files:- .travis.yml- .gitignore- .ghci- stack.yaml- README.markdown--source-repository head- type: git- location: https://github.com/brunjlar/neural.git--source-repository this- type: git- location: https://github.com/brunjlar/neural.git- tag: 0.1.1.0--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.Pipes- 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,- bytestring >=0.10.6.0 && <0.11,- 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,- kan-extensions >=4.2.3 && <4.3,- lens ==4.13.*,- MonadRandom >=0.4.2.2 && <0.5,- monad-par >=0.3.4.7 && <0.4,- monad-par-extras >=0.3.3 && <0.4,- mtl >=2.2.1 && <2.3,- parallel >=3.2.1.0 && <3.3,- pipes >=4.1.8 && <4.2,- pipes-bytestring >=2.1.1 && <2.2,- pipes-safe >=2.2.3 && <2.3,- profunctors ==5.2.*,- reflection >=2.1.2 && <2.2,- 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 >=0.2.0.0 && <0.3,- 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 >=0.2.0.0 && <0.3- default-language: Haskell2010- hs-source-dirs: examples/sqrt- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math--executable MNIST- main-is: MNIST.hs- build-depends:- base >=4.7 && <5,- array >=0.5.1.0 && <0.6,- JuicyPixels >=3.2.7 && <3.3,- neural >=0.2.0.0 && <0.3,- pipes >=4.1.8 && <4.2,- pipes-zlib >=0.4.4 && <0.5- default-language: Haskell2010- hs-source-dirs: examples/MNIST- 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 >=0.2.0.0 && <0.3- 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,- Glob >=0.7.5 && <0.8- default-language: Haskell2010- hs-source-dirs: doctest- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math--benchmark neural-bench- type: exitcode-stdio-1.0- main-is: benchmark.hs- build-depends:- base >=4.7 && <5,- criterion >=1.1.1.0 && <1.2,- neural >=0.2.0.0 && <0.3- default-language: Haskell2010- hs-source-dirs: benchmark- ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math+name: neural +version: 0.2.0.0 +x-revision: 1 +cabal-version: >=1.10 +build-type: Simple +license: MIT +license-file: LICENSE +copyright: Copyright: (c) 2016 Lars Bruenjes +maintainer: brunjlar@gmail.com +stability: provisional +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 arrow-like instances and + <https://hackage.haskell.org/package/pipes pipes>, + . + * /automatic differentiation/ for automatic gradient descent/ backpropagation training + (using Edward Kmett's fabulous <https://hackage.haskell.org/package/ad ad> 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.). + . + Three 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 + <https://en.wikipedia.org/wiki/Iris_flower_data_set Iris flower> problem. + . + * A first (still simple) neural network for recognizing handwritten digits from the equally famous + <https://en.wikipedia.org/wiki/MNIST_database MNIST> database. + . + The library is still very much experimental at this point. +category: Machine Learning +author: Lars Bruenjes +tested-with: GHC ==7.10.3 +extra-source-files: + .travis.yml + .gitignore + .ghci + stack.yaml + README.markdown + +source-repository head + type: git + location: https://github.com/brunjlar/neural.git + +source-repository this + type: git + location: https://github.com/brunjlar/neural.git + tag: 0.2.0.0 + +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.Pipes + 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, + bytestring >=0.10.6.0 && <0.11, + 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, + kan-extensions >=4.2.3 && <4.3, + lens ==4.13.*, + MonadRandom >=0.4.2.2 && <0.5, + monad-par >=0.3.4.7 && <0.4, + monad-par-extras >=0.3.3 && <0.4, + mtl >=2.2.1 && <2.3, + parallel >=3.2.1.0 && <3.3, + pipes >=4.1.8 && <4.2, + pipes-bytestring >=2.1.1 && <2.2, + pipes-safe >=2.2.3 && <2.3, + profunctors ==5.2.*, + reflection >=2.1.2 && <2.2, + 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 >=0.2.0.0 && <0.3, + 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 >=0.2.0.0 && <0.3 + default-language: Haskell2010 + hs-source-dirs: examples/sqrt + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math + +executable MNIST + main-is: MNIST.hs + build-depends: + base >=4.7 && <5, + array >=0.5.1.0 && <0.6, + JuicyPixels >=3.2.7 && <3.3, + neural >=0.2.0.0 && <0.3, + pipes >=4.1.8 && <4.2, + pipes-zlib >=0.4.4 && <0.5 + default-language: Haskell2010 + hs-source-dirs: examples/MNIST + 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 >=0.2.0.0 && <0.3 + 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, + Glob >=0.7.5 && <0.8 + default-language: Haskell2010 + hs-source-dirs: doctest + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math + +benchmark neural-bench + type: exitcode-stdio-1.0 + main-is: benchmark.hs + build-depends: + base >=4.7 && <5, + criterion >=1.1.1.0 && <1.2, + neural >=0.2.0.0 && <0.3 + default-language: Haskell2010 + hs-source-dirs: benchmark + ghc-options: -Wall -threaded -rtsopts -with-rtsopts=-N -fexcess-precision -optc-O3 -optc-ffast-math