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joaquimg committed Jan 14, 2025
1 parent ede3364 commit fe2446c
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Showing 2 changed files with 8 additions and 8 deletions.
8 changes: 4 additions & 4 deletions docs/src/examples/custom-relu.jl
Original file line number Diff line number Diff line change
Expand Up @@ -76,13 +76,13 @@ m = Flux.Chain(

N = 1000 # batch size
## Preprocessing train data
imgs = MLDatasets.MNIST(split=:train).features[:,:,1:N]
labels = MLDatasets.MNIST(split=:train).targets[1:N]
imgs = MLDatasets.MNIST(; split = :train).features[:, :, 1:N]
labels = MLDatasets.MNIST(; split = :train).targets[1:N]
train_X = float.(reshape(imgs, size(imgs, 1) * size(imgs, 2), N)) # stack images
train_Y = Flux.onehotbatch(labels, 0:9);
## Preprocessing test data
test_imgs = MLDatasets.MNIST(split=:test).features[:,:,1:N]
test_labels = MLDatasets.MNIST(split=:test).targets[1:N];
test_imgs = MLDatasets.MNIST(; split = :test).features[:, :, 1:N]
test_labels = MLDatasets.MNIST(; split = :test).targets[1:N];
test_X = float.(reshape(test_imgs, size(test_imgs, 1) * size(test_imgs, 2), N))
test_Y = Flux.onehotbatch(test_labels, 0:9);

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8 changes: 4 additions & 4 deletions docs/src/examples/polyhedral_project.jl
Original file line number Diff line number Diff line change
Expand Up @@ -122,13 +122,13 @@ m = Flux.Chain(

M = 500 # batch size
## Preprocessing train data
imgs = MLDatasets.MNIST(split=:train).features[:,:,1:M]
labels = MLDatasets.MNIST(split=:train).targets[1:M]
imgs = MLDatasets.MNIST(; split = :train).features[:, :, 1:M]
labels = MLDatasets.MNIST(; split = :train).targets[1:M]
train_X = float.(reshape(imgs, size(imgs, 1) * size(imgs, 2), M)) # stack images
train_Y = Flux.onehotbatch(labels, 0:9);
## Preprocessing test data
test_imgs = MLDatasets.MNIST(split=:test).features[:,:,1:M]
test_labels = MLDatasets.MNIST(split=:test).targets[1:M]
test_imgs = MLDatasets.MNIST(; split = :test).features[:, :, 1:M]
test_labels = MLDatasets.MNIST(; split = :test).targets[1:M]
test_X = float.(reshape(test_imgs, size(test_imgs, 1) * size(test_imgs, 2), M))
test_Y = Flux.onehotbatch(test_labels, 0:9);

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