homepage: ''
changelog-type: ''
hash: 0bb55d8dcd94597620c7fcf531742edb360ac2fb05fe77134c0ddb0e6bd26db9
test-bench-deps: {}
maintainer: jean-philippe.bernardy@gu.se
synopsis: Typed frontend to TensorFlow and higher-order deep learning
changelog: ''
basic-deps:
  base: ==4.*
  ghc-typelits-knownnat: -any
  pretty-compact: -any
  mtl: -any
all-versions:
- '0.9'
author: Jean-Philippe Bernardy
latest: '0.9'
description-type: haddock
description: ! 'TypedFlow is a typed, higher-order frontend to TensorFlow and a high-level
  library for deep-learning.


  The main design principles are:


  - To make the parameters of layers explicit. This choice makes sharing of parameters
  explicit and allows to implement "layers" as pure functions.


  - To provide as precise as possible types. Functions are explicit about the shapes
  and elements of the tensors that they manipulate (they are often polymorphic in
  shapes and elements though.)


  - To let combinators be as transparent as possible. If a NN layers is a simple tensor
  transformation it will be exposed as such.'
license-name: LGPL-3

Update from Hackage at 2017-10-26T14:18:05Z
Update from Hackage at 2017-10-26T14:18:05Z
