Skip to content

Tests for DeepONet layer #36

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

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Tests for DeepONet layer #36

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
bdc4dac
added project.toml
ba2tripleO Jun 1, 2022
ea38a5f
Added the small dataset via upload, left out by git push
ba2tripleO Jun 1, 2022
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
6 changes: 6 additions & 0 deletions test/Project.toml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,6 @@
[deps]
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
MAT = "23992714-dd62-5051-b70f-ba57cb901cac"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Binary file added test/burgerset.mat
View file
Open in desktop
Binary file not shown.
50 changes: 48 additions & 2 deletions test/deeponet.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
using Test, Random, Flux
using Test, Random, Flux, MAT

@testset "DeepONet" begin
@testset "dimensions" begin
Expand All @@ -14,4 +14,50 @@ using Test, Random, Flux
# Accept only Int as architecture parameters
@test_throws MethodError DeepONet((32.5,64,72), (24,48,72), σ, tanh)
@test_throws MethodError DeepONet((32,64,72), (24.1,48,72))
end
end

#Just the first 16 datapoints from the Burgers' equation dataset
a = [0.83541104, 0.83479851, 0.83404712, 0.83315711, 0.83212979, 0.83096755, 0.82967374, 0.82825263, 0.82670928, 0.82504949, 0.82327962, 0.82140651, 0.81943734, 0.81737952, 0.8152405, 0.81302771]
sensors = collect(range(0, 1, length=16))'

model = DeepONet((16, 22, 30), (1, 16, 24, 30), σ, tanh; init_branch=Flux.glorot_normal, bias_trunk=false)

model(a,sensors)

#forward pass
@test size(model(a, sensors)) == (1, 16)

mgrad = Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)

#gradients
@test !iszero(Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)[1])
@test !iszero(Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)[2])

#training
vars = matread("burgerset.mat")

xtrain = vars["a"][1:280, :]'
xval = vars["a"][end-19:end, :]'

ytrain = vars["u"][1:280, :]
yval = vars["u"][end-19:end, :]

grid = collect(range(0, 1, length=1024))'
model = DeepONet((1024,1024,1024),(1,1024,1024),gelu,gelu)

learning_rate = 0.001
opt = ADAM(learning_rate)

parameters = params(model)

loss(xtrain,ytrain,sensor) = Flux.Losses.mse(model(xtrain,sensor),ytrain)

evalcb() = @show(loss(xval,yval,grid))

Flux.@epochs 400 Flux.train!(loss, parameters, [(xtrain,ytrain,grid)], opt, cb = evalcb)

ỹ = model(xval, grid)

diffvec = vec(abs.((yval .- ỹ)))
mean_diff = sum(diffvec)/length(diffvec)
@test mean_diff < 0.4