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Use OneHotArrays #105

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merged 11 commits into from
Jul 24, 2022
Merged

Use OneHotArrays #105

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5780213
Update adapt.jl
YichengDWu Jul 23, 2022
6e78360
Update Project.toml
YichengDWu Jul 23, 2022
a69fd27
format
YichengDWu Jul 23, 2022
35a2c1f
delete repeated code
YichengDWu Jul 23, 2022
358a971
Merge branch 'avik-pal:main' into main
YichengDWu Jul 23, 2022
b5c3e94
using OneHotArrays
YichengDWu Jul 24, 2022
97e764d
Update main.jl
YichengDWu Jul 24, 2022
a02e0dd
CompatHelper: add new compat entry for OneHotArrays at version 0.1 fo…
Jul 24, 2022
446d38f
Merge pull request #2 from MilkshakeForReal/compathelper/new_version/…
YichengDWu Jul 24, 2022
e1a565a
Update main.jl
YichengDWu Jul 24, 2022
c4ad363
Merge branch 'main' of https://github.com/MilkshakeForReal/Lux.jl
YichengDWu Jul 24, 2022
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21 changes: 7 additions & 14 deletions examples/NeuralODE/main.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,27 +5,22 @@ using Lux
using Pkg #hide
Pkg.activate(joinpath(dirname(pathof(Lux)), "..", "examples")) #hide
using ComponentArrays, CUDA, DiffEqSensitivity, NNlib, Optimisers, OrdinaryDiffEq, Random,
Statistics, Zygote
Statistics, Zygote, OneHotArrays
import MLDatasets: MNIST
import MLDataUtils: convertlabel, LabelEnc
import MLUtils: DataLoader, splitobs
CUDA.allowscalar(false)

# ## Loading MNIST
## Use MLDataUtils LabelEnc for natural onehot conversion
function onehot(labels_raw)
return convertlabel(LabelEnc.OneOfK, labels_raw, LabelEnc.NativeLabels(collect(0:9)))
end

function loadmnist(batchsize, train_split)
## Load MNIST: Only 1500 for demonstration purposes
N = 1500
imgs = MNIST.traintensor(1:N)
labels_raw = MNIST.trainlabels(1:N)
dataset = MNIST(; split=:train)
imgs = dataset.features[:, :, 1:N]
labels_raw = dataset.targets[1:N]

## Process images into (H,W,C,BS) batches
x_data = Float32.(reshape(imgs, size(imgs, 1), size(imgs, 2), 1, size(imgs, 3)))
y_data = onehot(labels_raw)
y_data = onehotbatch(labels_raw, 0:9)
(x_train, y_train), (x_test, y_test) = splitobs((x_data, y_data); at=train_split)

return (
Expand Down Expand Up @@ -88,8 +83,6 @@ function create_model()
end

# ## Define Utility Functions
get_class(x) = argmax.(eachcol(x))

logitcrossentropy(y_pred, y) = mean(-sum(y .* logsoftmax(y_pred); dims=1))

function loss(x, y, model, ps, st)
Expand All @@ -102,8 +95,8 @@ function accuracy(model, ps, st, dataloader)
st = Lux.testmode(st)
iterator = CUDA.functional() ? CuIterator(dataloader) : dataloader
for (x, y) in iterator
target_class = get_class(cpu(y))
predicted_class = get_class(cpu(model(x, ps, st)[1]))
target_class = onecold(cpu(y))
predicted_class = onecold(cpu(model(x, ps, st)[1]))
total_correct += sum(target_class .== predicted_class)
total += length(target_class)
end
Expand Down
2 changes: 2 additions & 0 deletions examples/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
ParameterSchedulers = "d7d3b36b-41b8-4d0d-a2bf-768c6151755e"
Expand Down Expand Up @@ -54,6 +55,7 @@ MLDatasets = "0.5, 0.7"
MLUtils = "0.2"
Metalhead = "0.7"
NNlib = "0.8"
OneHotArrays = "0.1"
Optimisers = "0.2"
OrdinaryDiffEq = "6"
ParameterSchedulers = "0.3"
Expand Down