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Backends · Flux
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Backends
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Native Integration
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Logistic Regression
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< a class = "edit-page" href = "https://github.com/MikeInnes/Flux.jl/tree/c05ef6286aa284f818bb0f3b90d4347efe8c29e6/docs/src/apis/backends.md" >
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Backends
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Basic Usage
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< pre > < code class = "language-julia" > model = Chain(Affine(10, 20), σ
xs = rand(10)< / code > < / pre >
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< p >
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Currently, Flux' s pure-Julia backend has no optimisations. This means that calling
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< pre > < code class = "language-julia" > model(rand(10)) #> [0.0650, 0.0655, ...]< / code > < / pre >
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directly won' t have great performance. In order to run a computationally intensive training process, we rely on a backend like MXNet or TensorFlow.
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This is easy to do. Just call either
< code > mxnet< / code >
or
< code > tf< / code >
on a model to convert it to a model of that kind:
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< pre > < code class = "language-julia" > mxmodel = mxnet(model, (10, 1))
mxmodel(xs) #> [0.0650, 0.0655, ...]
# or
tfmodel = tf(model)
tfmodel(xs) #> [0.0650, 0.0655, ...]< / code > < / pre >
< p >
These new models look and feel exactly like every other model in Flux, including returning the same result when you call them, and can be trained as usual using
< code > Flux.train!()< / code >
. The difference is that the computation is being carried out by a backend, which will usually give a large speedup.
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Native Integration
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Flux aims to provide high-level APIs that work well across backends, but in some cases you may want to take advantage of features specific to a given backend. In these cases it' s easy to " drop down" and use the backend' s API directly, where appropriate. For example:
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< pre > < code class = "language-julia" > using MXNet
Flux.loadmx()
mxmodel = mx.FeedForward(model)< / code > < / pre >
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This returns a standard
< code > mx.FeedForward< / code >
instance, just like you might have created using MXNet' s usual API. You can then use this with MXNet' s data provider implementation, custom optimisers, or distributed training processes.
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Same goes for TensorFlow, where it' s easy to create a
< code > Tensor< / code >
object:
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< pre > < code class = "language-julia" > using TensorFlow
Flux.loadtf()
x = placeholder(Float32)
y = Tensor(model, x)< / code > < / pre >
< p >
This makes makes it easy to take advantage of Flux' s model description and debugging tools while also getting the benefit of the work put into these backends. You can check out how this looks with the integration examples
< a href = "https://github.com/MikeInnes/Flux.jl/tree/master/examples" >
here
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