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Merge pull request #297 from FluxML/compathelper/new_version/2025-02-12-04-24-08-847-03479061932
CompatHelper: bump compat for Flux to 0.16, (keep existing compat)
2 parents 12f7ccb + b377c25 commit 958a320

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.github/workflows/ci.yml

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@@ -19,7 +19,7 @@ jobs:
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fail-fast: false
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matrix:
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version:
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- '1.9'
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- '1.10'
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- '1'
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os:
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- ubuntu-latest

Project.toml

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@@ -20,15 +20,15 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
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CategoricalArrays = "0.10"
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ColorTypes = "0.10.3, 0.11, 0.12"
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ComputationalResources = "0.3.2"
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Flux = "0.14, 0.15"
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Flux = "0.14, 0.15, 0.16"
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MLJModelInterface = "1.11"
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Metalhead = "0.9.3"
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Optimisers = "0.3.2, 0.4"
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ProgressMeter = "1.7.1"
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StatisticalMeasures = "0.1"
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Statistics = "<0.0.1, 1"
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Tables = "1.0"
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julia = "1.9"
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julia = "1.10"
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[extras]
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

test/mlj_model_interface.jl

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@@ -53,21 +53,21 @@ end
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model = MLJFlux.NeuralNetworkRegressor(; alpha=0, lambda=0.3, optimiser)
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chain = MLJFlux.regularized_optimiser(model, 1)
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@test chain isa Optimisers.OptimiserChain{
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Tuple{Optimisers.WeightDecay, Optimisers.Momentum}
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<:Tuple{Optimisers.WeightDecay, Optimisers.Momentum}
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}
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# alpha = 1:
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model = MLJFlux.NeuralNetworkRegressor(; alpha=1, lambda=0.3, optimiser)
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chain = MLJFlux.regularized_optimiser(model, 1)
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@test chain isa Optimisers.OptimiserChain{
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Tuple{Optimisers.SignDecay, Optimisers.Momentum}
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<:Tuple{Optimisers.SignDecay, Optimisers.Momentum}
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}
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# general case:
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model = MLJFlux.NeuralNetworkRegressor(; alpha=0.4, lambda=0.3, optimiser)
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chain = MLJFlux.regularized_optimiser(model, 1)
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@test chain isa Optimisers.OptimiserChain{
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Tuple{Optimisers.SignDecay, Optimisers.WeightDecay, Optimisers.Momentum}
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<:Tuple{Optimisers.SignDecay, Optimisers.WeightDecay, Optimisers.Momentum}
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}
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end
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@@ -133,8 +133,8 @@ mutable struct LisasBuilder
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n1::Int
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end
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# UndefVarError accepts two inputs from julia > v"1.9"
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_UndefVarError(var, scope) = @static if VERSION < v"1.10"
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# UndefVarError accepts two inputs from julia > v"1.10"
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_UndefVarError(var, scope) = @static if VERSION < v"1.11"
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UndefVarError(var)
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else
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UndefVarError(var, scope)

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