From fe2446c271127c279f8bdb7bb83b86c79d602b89 Mon Sep 17 00:00:00 2001 From: joaquimg Date: Mon, 13 Jan 2025 21:22:47 -0800 Subject: [PATCH] format --- docs/src/examples/custom-relu.jl | 8 ++++---- docs/src/examples/polyhedral_project.jl | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/src/examples/custom-relu.jl b/docs/src/examples/custom-relu.jl index e6b440f2..ee3477c3 100644 --- a/docs/src/examples/custom-relu.jl +++ b/docs/src/examples/custom-relu.jl @@ -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); diff --git a/docs/src/examples/polyhedral_project.jl b/docs/src/examples/polyhedral_project.jl index dc9acdf2..b0f719db 100644 --- a/docs/src/examples/polyhedral_project.jl +++ b/docs/src/examples/polyhedral_project.jl @@ -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);