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Mike J Innes 2016-08-18 14:47:49 +01:00
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@ -6,11 +6,13 @@ Flux is a programming model for building neural networks, implemented in Julia.
## Why?
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 is designed to be much more intuitive. 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)`). More importantly, Flux is fully declarative, so there's no more mental juggling of multiple execution paths as you read imperative graph-building code.
Flux's semantics include native support for recurrent loops, which it can automatically unroll for you – never do it by hand again.
Most frameworks intrinsically couple the model (what you'd find in a paper) with its implementation (details like batching and loop unrolling). This greatly increases the overhead involved in both getting a model to work and changing it afterwards. Flux's solution is to distinguish between a *description* of a model and the model itself, just like the class/object distinction. Once you instantiate a model you can alter its implementation as simply as with a call to `batch(model, 100)` or `unroll(model, 10)`.
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.
Flux natively supports for recurrent loops, which it can automatically unroll for you – never do it by hand again.
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?