https://github.com/probcomp/genflux.jl
https://github.com/probcomp/genflux.jl
Last synced: 12 days ago
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- Host: GitHub
- URL: https://github.com/probcomp/genflux.jl
- Owner: probcomp
- Created: 2020-12-10T15:40:26.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-09T17:18:08.000Z (almost 4 years ago)
- Last Synced: 2025-05-08T22:57:20.753Z (11 months ago)
- Language: Julia
- Size: 20.5 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# GenFlux.jl
[](https://probcomp.github.io/GenFlux.jl/dev)
`GenFlux.jl` is Gen DSL which implements [the generative function interface](https://www.gen.dev/dev/ref/gfi/#Generative-function-interface-1) to allow the usage of [Flux.jl models](https://github.com/FluxML/Flux.jl) as Gen generative functions.
---
([full example available here](https://github.com/femtomc/GenFlux.jl/blob/master/examples/mnist.jl))
```julia
g = @genflux Chain(Conv((5, 5), 1 => 10; init = glorot_uniform64),
MaxPool((2, 2)),
x -> relu.(x),
Conv((5, 5), 10 => 20; init = glorot_uniform64),
x -> relu.(x),
MaxPool((2, 2)),
x -> flatten(x),
Dense(320, 50; initW = glorot_uniform64),
Dense(50, 10; initW = glorot_uniform64),
softmax)
```
Now you can use `g` as a modelling component in your probabilistic programs:
```julia
@gen function f(xs::Vector{Float64})
probs ~ g(xs)
[{:y => i} ~ categorical(p |> collect) for (i, p) in enumerate(eachcol(probs))]
end
```
Allowing you to train the parameters of `g` via gradient descent [on the objective](https://www.gen.dev/dev/ref/gfi/#Gen.accumulate_param_gradients!):
```julia
update = ParamUpdate(Flux.ADAM(5e-5, (0.9, 0.999)), g)
for i = 1 : 1500
# Create trace from data
(xs, ys) = next_batch(loader, 100)
constraints = choicemap([(:y => i) => y for (i, y) in enumerate(ys)]...)
(trace, weight) = generate(f, (xs,), constraints)
# Increment gradient accumulators
accumulate_param_gradients!(trace)
# Perform ADAM update and then resets gradient accumulators
apply!(update)
println("i: $i, weight: $weight")
end
```
```julia
test_accuracy = mean(f(test_x) .== test_y)
println("Test set accuracy: $test_accuracy")
# Test set accuracy: 0.9392
```