Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

some cleanup #2503

Merged
merged 2 commits into from
Oct 20, 2024
Merged

some cleanup #2503

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9c2f285
cleanup
CarloLucibello Oct 20, 2024
ee46ef7
fix doctest
CarloLucibello Oct 20, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions docs/src/tutorials/logistic_regression.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -140,8 +140,8 @@ Note, all the `flux_*` variables in this tutorial would be general, that is, the
julia> flux_model = Chain(Dense(4 => 3), softmax)
Chain(
Dense(4 => 3), # 15 parameters
NNlib.softmax,
)
softmax,
)
```

A [`Dense(4 => 3)`](@ref Dense) layer denotes a layer with four inputs (four features in every data point) and three outputs (three classes or labels). This layer is the same as the mathematical model defined by us above. Under the hood, Flux too calculates the output using the same expression, but we don't have to initialize the parameters ourselves this time, instead Flux does it for us.
Expand Down
4 changes: 2 additions & 2 deletions test/runtests.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,9 @@ using Functors: fmapstructure_with_path

## Uncomment below to change the default test settings
# ENV["FLUX_TEST_AMDGPU"] = "true"
ENV["FLUX_TEST_CUDA"] = "true"
# ENV["FLUX_TEST_CUDA"] = "true"
# ENV["FLUX_TEST_METAL"] = "true"
ENV["FLUX_TEST_CPU"] = "false"
# ENV["FLUX_TEST_CPU"] = "false"
# ENV["FLUX_TEST_DISTRIBUTED_MPI"] = "true"
# ENV["FLUX_TEST_DISTRIBUTED_NCCL"] = "true"
ENV["FLUX_TEST_ENZYME"] = "false" # We temporarily disable Enzyme tests since they are failing
Expand Down
21 changes: 9 additions & 12 deletions test/train.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -155,18 +155,15 @@ for (trainfn!, name) in ((Flux.train!, "Zygote"), (train_enzyme!, "Enzyme"))
pen2(x::AbstractArray) = sum(abs2, x)/2
opt = Flux.setup(Adam(0.1), model)

@test begin
trainfn!(model, data, opt) do m, x, y
err = Flux.mse(m(x), y)
l2 = sum(pen2, Flux.params(m))
err + 0.33 * l2
end

diff2 = model.weight .- init_weight
@test diff1 ≈ diff2

true
end broken = VERSION >= v"1.11"
trainfn!(model, data, opt) do m, x, y
err = Flux.mse(m(x), y)
l2 = sum(pen2, Flux.params(m))
err + 0.33 * l2
end

diff2 = model.weight .- init_weight
@test diff1 ≈ diff2

end

# Take 3: using WeightDecay instead. Need the /2 above, to match exactly.
Expand Down
12 changes: 5 additions & 7 deletions test/utils.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -273,13 +273,11 @@ end
@testset "params gradient" begin
m = (x=[1,2.0], y=[3.0]);

@test begin
# Explicit -- was broken by #2054 / then fixed / now broken again on julia v1.11
gnew = gradient(m -> (sum(norm, Flux.params(m))), m)[1]
@test gnew.x ≈ [0.4472135954999579, 0.8944271909999159]
@test gnew.y ≈ [1.0]
true
end broken = VERSION >= v"1.11"
# Explicit -- was broken by #2054 / then fixed / now broken again on julia v1.11
gnew = gradient(m -> (sum(norm, Flux.params(m))), m)[1]
@test gnew.x ≈ [0.4472135954999579, 0.8944271909999159]
@test gnew.y ≈ [1.0]


# Implicit
gold = gradient(() -> (sum(norm, Flux.params(m))), Flux.params(m))
Expand Down
Loading