Merge pull request #134 from acfr/bugfix-doc

Fixed a bug in the docs and removed `LiveServer.jl` as a dependency
acfr · Oct 26, 2023 · 669f373 · 669f373
2 parents 9a0c19a + 823f7b9
commit 669f373
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "RobustNeuralNetworks"
 uuid = "a1f18e6b-8af1-433f-a85d-2e1ee636a2b8"
 authors = ["Nicholas H. Barbara", "Max Revay", "Ruigang Wang", "Jing Cheng", "Jerome Justin", "Ian R. Manchester"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -2,7 +2,6 @@
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RobustNeuralNetworks = "a1f18e6b-8af1-433f-a85d-2e1ee636a2b8"
 

diff --git a/docs/src/examples/lbdn_mnist.md b/docs/src/examples/lbdn_mnist.md
@@ -17,7 +17,7 @@ For details on how Lipschitz bounds increase classification robustness and relia
 Let's start by loading the training and test data. [`MLDatasets.jl`](https://juliaml.github.io/MLDatasets.jl/stable/) contains a number of common machine-learning datasets, including the [MNIST dataset](https://juliaml.github.io/MLDatasets.jl/stable/datasets/vision/#MLDatasets.MNIST). The following code loads the full dataset of 60,000 training images and 10,000 test images.
 
 !!! info "Working on the GPU"
-    Since we're dealing with images, we will load are data and models onto the GPU to speed up training. We'll be using [`CUDA.jl`](https://github.com/JuliaGPU/CUDA.jl). 
+    Since we're dealing with images, we will load our data and models onto the GPU to speed up training. We'll be using [`CUDA.jl`](https://github.com/JuliaGPU/CUDA.jl). 
 
     If you don't have a GPU on your machine, just switch to `dev = cpu`. If you have a GPU but not an NVIDIA GPU, switch out `CUDA.jl` with whichever GPU backend supports your device. For more information on training models on a GPU, see [here](https://fluxml.ai/Flux.jl/stable/gpu/).
 

diff --git a/src/Wrappers/LBDN/sandwich_fc.jl b/src/Wrappers/LBDN/sandwich_fc.jl
@@ -63,7 +63,7 @@ println(round.(y;digits=2))
 
 # output
 
-[3.62 4.74 3.58 8.75 3.64 3.0 0.73 1.16 1.0 1.73]
+[4.13 4.37 3.22 8.38 4.15 3.71 0.7 2.04 1.78 2.64]
 ```
 
 See also [`DenseLBDNParams`](@ref), [`DiffLBDN`](@ref).