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Last Commit
Apr. 18, 2017
Created
Nov. 21, 2016

DeepLearning.scala

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DifferentiableAny DifferentiableNothing DifferentiableSeq DifferentiableDouble DifferentiableFloat DifferentiableHList DifferentiableCoproduct DifferentiableINDArray

DeepLearning.scala is a DSL for creating complex neural networks.

With the help of DeepLearning.scala, regular programmers are able to build complex neural networks from simple code. You write code almost as usual, the only difference being that code based on DeepLearning.scala is differentiable, which enables such code to evolve by modifying its parameters continuously.

Features

Differentiable basic types

Like Theano and other deep learning toolkits, DeepLearning.scala allows you to build neural networks from mathematical formulas. It supports floats, doubles, GPU-accelerated N-dimensional arrays, and calculates derivatives of the weights in the formulas.

Differentiable ADTs

Neural networks created by DeepLearning.scala support ADT data structures (e.g. HList and Coproduct), and calculate derivatives using these data structures.

Differentiable control flow

Neural networks created by DeepLearning.scala may contain control flows like if/else/match/case in a regular language. Combined with ADT data structures, you can implement arbitrary algorithms inside neural networks, and still keep the variables within those algorithms differentiable and trainable.

Composability

Neural networks created by DeepLearning.scala are composable. You can create large networks by combining smaller networks. If two larger networks share sub-networks, the weights for a shared sub-network that are trained in one super-network will also affect the other super-network.

Static type system

All of the above features are statically type checked.

Roadmap

v1.0

Version 1.0 is the current version with all of the above features. The final version will be released in March 2017.

v2.0

  • Support for/while and other higher-order functions on differentiable Seqs.
  • Support for/while and other higher-order functions on GPU-accelerated differentiable N-dimensional arrays.

Version 2.0 will be released in Q2 2017.

v3.0

  • Support using custom case classes inside neural networks.
  • Support distributed models and distributed training on Spark.

Version 3.0 will be released in late 2017.

Links

Acknowledgements

DeepLearning.scala is heavily inspired by my colleague @MarisaKirisame. Originally, we worked together on a prototype of a deep learning framework, and eventually split our work into this project and DeepDarkFantasy.

@milessabin's shapeless provides a solid foundation for type-level programming as used in DeepLearning.scala.

Latest Releases
v1.0.0
 Mar. 27 2017
v1.0.0-RC11
 Mar. 27 2017
v1.0.0-RC9
 Mar. 26 2017
v1.0.0-RC8
 Mar. 23 2017
v1.0.0-RC7
 Mar. 10 2017