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Fixed a bug in the docs and removed LiveServer.jl as a dependency #134

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Oct 26, 2023
Merged

Fixed a bug in the docs and removed LiveServer.jl as a dependency #134

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aff27db
Fixed a bug in the docs and removed LiveServer as a dependency
nic-barbara Oct 26, 2023
ae65074
Fixed typo
nic-barbara Oct 26, 2023
823f7b9
Version bump
nic-barbara Oct 26, 2023
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -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"
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1 change: 0 additions & 1 deletion docs/Project.toml
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Expand Up @@ -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"

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2 changes: 1 addition & 1 deletion docs/src/examples/lbdn_mnist.md
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Expand Up @@ -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/).

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2 changes: 1 addition & 1 deletion src/Wrappers/LBDN/sandwich_fc.jl
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Expand Up @@ -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).
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