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Flux.jl
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readme tweaks

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Mike J Innes 2016-09-26 19:49:22 +01:00
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@ -9,7 +9,7 @@ Flux is designed to experiment with two core principles:
* *Walking the ladder of abstraction:* It should be possible to describe models at the highest level (e.g. the equations in the paper) or the lowest (e.g. custom GPU kernels) and mix and match the two. Model descriptions should be separated from their implementations, and changes to the way a model is used should never require changes to the model itself.
* *Cranking the lever of expression:* The same problems that come up when building ML models (composition and reuse, variable scoping, applying optimisations etc.) have already been solved by programming languages. If we think of building models as programming, we can reuse those solutions, greatly reducing the barriers to learning and using ML systems.
Right now Flux may be more appropriate for those interested in learning about neural networks than those with advanced needs in terms of features or performance. However, since we are able to make use of backends like TensorFlow and MXNet, more filling those needs is a very achievable goal as well.
Right now Flux is at a very early stage and may be more appropriate for those interested in learning about neural networks than those with advanced needs in terms of features or performance. However, since we are able to make use of backends like TensorFlow and MXNet, more filling those needs is a very achievable goal as well. See the [examples](/examples) for what currently works.
## How?
@ -57,7 +57,7 @@ end
Perceptron(in, out) = Perceptron(Dense(in, out))
```
This defines a simple perceptron layer which we can use in the same way as `Dense` above. We can draw arbitrary graphs, including those with splits, combines or recurrences, in a fully declarative way:
This defines a simple perceptron layer which we can use in the same way as `Dense` above. We can draw arbitrary graphs, including those with splits, combines or recurrences, in a fully declarative way [this API is a WIP]:
```julia
@net type SimpleRecurrent
@ -92,5 +92,3 @@ model = mxnet(model)
Flux.train!(model, ...)
```
See [examples](/examples) for more.