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Mike J Innes 2016-08-15 17:24:20 +01:00
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## What?
Flux is an experimental machine perception / ANN library for Julia. It's designed to make experimenting with novel layer types and architectures really fast, without sacrificing runtime speed.
Flux is a programming model for building neural networks, implemented in Julia.
## Why?
Flux has a few key differences from other libraries:
Flux is designed to be much more intuitive than traditional frameworks. For starters, that means having a simple notation for models that's as close to the mathematical description as possible (like `σ(W*x + b)`). But it's deeper than syntax; we also reuse concepts from regular programming languages (like the class/object distinction) to create principled semantics. Flux is fully declarative, so there's no more mental juggling of multiple execution paths as you read imperative graph-building code.
* Flux's [graph-based DSL](https://github.com/MikeInnes/Flow.jl), which provides optimisations and automatic differentiation, is very tightly integrated with the language. This means nice syntax for your equations (`σ(W*x+b)` anyone?) and no unwieldy `compile` steps.
* The graph DSL directly is used to represent models (not just computations), so custom architectures – and in particular, recurrent models – are easy to express.
* Those fancy features are completely optional. You can implement functionality in a Torch-like fashion if you wish, since layers are simply objects that satisfy a small interface.
* Flux is written in [Julia](http://julialang.org), which means there's no "dropping down" to C. It's Julia all the way down, and you can prototype both high-level architectures and high-performance GPU kernels from the same language. This also makes the library itself very easy to understand and extend.
Flux's semantics include native support for recurrent loops, which it can automatically unroll for you – never do it by hand again.
Future work will also include:
* Integration with other backends, so that models can be described using Flux and run using (say) TensorFlow.
* Carrying out runtime optimisations of the graph, in particular to handle small matrices efficiently.
But it's also designed to be extremely flexible. Flux supports multiple backends – MXNet to begin with and TensorFlow in future – transparently taking advantage of all their features rather than providing a lowest common denominator. Flux's design allows for custom layer types – say custom GPU kernels – to be implemented in pure Julia, for backends that support it.
## How?