LambdaNet is an artificial neural network library written in Haskell that abstracts network creation, training, and use as higher order functions. The benefit of this approach is that it provides a framework in which users can: quickly iterate through network designs by using different functional components as well as experiment by writing small functional components to extend the library.
It comes with a pre-defined set of functions that can be composed in many ways to operate on real-world data. LambdaNet can be installed through Cabal and using it to rapidly prototype networks using built-in functions requires only a minimal level of Haskell knowledge (although getting the data into the right form may be more difficult).
However, extending the library may require a more in-depth knowledge of Haskell and functional programming techniques.
Currently, the library only provides connectFully, a function which creates a fully connected feed-forward network. Simply, the connectivity function takes in the number of neurons in layer l and the number of neurons in layer l + 1, and returns a boolean matrix of integers (0/1) that represents the connectivity graph of the layers -- a 0 means two neurons are not connected and a 1 means they are.
It also defines two distributions : uniforms - A trivial function that returns a stream of uniformly distributed random numbers and normals - A slightly less-trivial function that uses the Box-Muller transform to create a stream of numbers ~ N(0, 1). Work is being done to offer a student t-distribution, which would require support for a chi-squared distribution transformation.
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