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fix paraynaud/KnetNLPModels.jl to JuliaSmoothOptimizers/KnetLNPModels… #20

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Jun 28, 2022
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fix paraynaud/KnetNLPModels.jl to JuliaSmoothOptimizers/KnetLNPModels… #20

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edfbd3c
fix paraynaud/KnetNLPModels.jl to JuliaSmoothOptimizers/KnetLNPModels…
Jun 28, 2022
ecd1a01
add updated .zenodo.json
Jun 28, 2022
e028da1
fix doi
Jun 28, 2022
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3 changes: 1 addition & 2 deletions .gitignore
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Expand Up @@ -23,5 +23,4 @@ docs/site/
# environment.
Manifest.toml

test/dvpt/*
retour/*
dvpt/*
6 changes: 3 additions & 3 deletions CITATION.bib
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@@ -1,8 +1,8 @@
@Misc{raynaud2022,
author = {P. Raynaud },
author = {Jean Bigeon, Dominique Orban, P. Raynaud},
title = {{KnetNLPModels.jl}:},
month = {Month},
howpublished = {\url{https://github.com/paraynaud/KnetNLPModels.jl}},
month = {June},
howpublished = {\url{https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl}},
year = {2022},
DOI = {}
}
26 changes: 15 additions & 11 deletions README.md
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Expand Up @@ -5,22 +5,22 @@
| [![docs-stable][docs-stable-img]][docs-stable-url] [![docs-dev][docs-dev-img]][docs-dev-url] | [![build-gh][build-gh-img]][build-gh-url] [![build-cirrus][build-cirrus-img]][build-cirrus-url] | [![codecov][codecov-img]][codecov-url] | [![doi][doi-img]][doi-url] |

[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://paraynaud.github.io/KnetNLPModels.jl/stable
[docs-stable-url]: https://JuliaSmoothOptimizers.github.io/KnetNLPModels.jl/stable
[docs-dev-img]: https://img.shields.io/badge/docs-dev-purple.svg
[docs-dev-url]: https://paraynaud.github.io/KnetNLPModels.jl/dev
[build-gh-img]: https://github.com/paraynaud/KnetNLPModels.jl/workflows/CI/badge.svg?branch=main
[build-gh-url]: https://github.com/paraynaud/KnetNLPModels.jl/actions
[build-cirrus-img]: https://img.shields.io/cirrus/github/paraynaud/KnetNLPModels.jl?logo=Cirrus%20CI
[build-cirrus-url]: https://cirrus-ci.com/github/paraynaud/KnetNLPModels.jl
[codecov-img]: https://codecov.io/gh/paraynaud/KnetNLPModels.jl/branch/main/graph/badge.svg
[codecov-url]: https://app.codecov.io/gh/paraynaud/KnetNLPModels.jl
[docs-dev-url]: https://JuliaSmoothOptimizers.github.io/KnetNLPModels.jl/dev
[build-gh-img]: https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl/workflows/CI/badge.svg?branch=main
[build-gh-url]: https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl/actions
[build-cirrus-img]: https://img.shields.io/cirrus/github/JuliaSmoothOptimizers/KnetNLPModels.jl?logo=Cirrus%20CI
[build-cirrus-url]: https://cirrus-ci.com/github/JuliaSmoothOptimizers/KnetNLPModels.jl
[codecov-img]: https://codecov.io/gh/JuliaSmoothOptimizers/KnetNLPModels.jl/branch/main/graph/badge.svg
[codecov-url]: https://app.codecov.io/gh/JuliaSmoothOptimizers/KnetNLPModels.jl
[doi-img]: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.822073-blue.svg
[doi-url]: https://doi.org/10.5281/zenodo.822073
[doi-url]: https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl/blob/main/CITATION.bib
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## How to install
This module can be installed with the following command:
```julia
julia> ] add https://github.com/paraynaud/KnetNLPModels.jl.git
julia> ] add https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl.git
pkg> test KnetNLPModels
```

Expand Down Expand Up @@ -148,4 +148,8 @@ The size of the new minibatch is the size define earlier.
The size of the training and test minibatch can be set to `1/p` the size of the dataset with:
```julia
set_size_minibatch!(DenseNetNLPModel, p) # p::Int > 1
```
```

## How to Cite

If you use KnetNLPModels.jl in your work, please cite using the format given in [`CITATION.bib`](https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl/blob/main/CITATION.bib).
2 changes: 1 addition & 1 deletion docs/make.jl
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Expand Up @@ -13,4 +13,4 @@ makedocs(
pages = Any["Home" => "index.md", "Tutorial" => "tutorial.md", "Reference" => "reference.md"],
)

deploydocs(repo = "github.com/paraynaud/KnetNLPModels.jl.git", devbranch = "main")
deploydocs(repo = "github.com/JuliaSmoothOptimizers/KnetNLPModels.jl.git", devbranch = "main")
4 changes: 2 additions & 2 deletions src/KnetNLPModels.jl
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Expand Up @@ -28,7 +28,7 @@ mutable struct KnetNLPModel{T, S, C <: Chain} <: AbstractKnetNLPModel{T, S}
minibatch_test
current_minibatch_training
current_minibatch_testing
w::S # :: Vector{T}
w::S
layers_g::Vector{Param}
nested_cuArray::Vector{CuArray{T, N, CUDA.Mem.DeviceBuffer} where N}
end
Expand All @@ -37,7 +37,7 @@ end
KnetNLPModel(chain_ANN; size_minibatch=100, data_train=MLDatasets.MNIST.traindata(Float32), data_test=MLDatasets.MNIST.testdata(Float32))

Build a `KnetNLPModel` from the neural network represented by `chain_ANN`.
`chain_ANN` is built using [Knet.jl](https://github.com/denizyuret/Knet.jl), see the [tutorial](https://paraynaud.github.io/KnetNLPModels.jl/dev/tutorial/) for more details.
`chain_ANN` is built using [Knet.jl](https://github.com/denizyuret/Knet.jl), see the [tutorial](https://JuliaSmoothOptimizers.github.io/KnetNLPModels.jl/dev/tutorial/) for more details.
The other data required are: an iterator over the training dataset `data_train`, an iterator over the test dataset `data_test` and the size of the minibatch `size_minibatch`.
Suppose `(xtrn,ytrn) = knetnlp.data_train`, then the size of each training minibatch will be `1/size_minibatch * length(ytrn)`.
By default, the other data are respectively set to the training dataset and test dataset of `MLDatasets.MNIST`, with each minibatch a hundredth of the dataset.
Expand Down