Counting 3,039 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Last Commit
Aug. 20, 2018
Jul. 31, 2017


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.

In this version, the interface to TensorFlow is done via python-code generation and a suitable runtime system.


The compiled documentation should be found on hackage.


TypedFlow comes with two examples of neural networks:

To running the examples can be done like so:

nix-env -iA nixpkgs.haskellPackages.styx
nix-env -iA nixpkgs.cabal2nix
styx configure
cd examples/seq2seq

Latest Releases
 Oct. 26 2017