KnetNLPModels.jl

An NLPModels Interface to Knet

https://github.com/JSO-Boneyard/KnetNLPModels.jl

Science Score: 54.0%

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  • CITATION.cff file
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  • Scientific vocabulary similarity
    Low similarity (14.5%) to scientific vocabulary

Keywords

deep-learning julia machine-learning neural-networks nlpmodels optimization
Last synced: 6 months ago · JSON representation ·

Repository

An NLPModels Interface to Knet

Basic Info
  • Host: GitHub
  • Owner: JSO-Boneyard
  • License: other
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 758 KB
Statistics
  • Stars: 8
  • Watchers: 3
  • Forks: 4
  • Open Issues: 9
  • Releases: 6
Topics
deep-learning julia machine-learning neural-networks nlpmodels optimization
Created about 4 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation Zenodo

README.md

KnetNLPModels : An NLPModels Interface to Knet

| Documentation | Linux/macOS/Windows | Coverage | DOI | |:-----------------:|:-------------------------------:|:------------:|:-------:| | docs-stable docs-dev | build-gh | codecov | doi |

⚠️ Deprecated Package

This package is currently deprecated and no further maintenance or updates are planned. If you are interested in reviving or maintaining this package, feel free to reach out — we’d be happy to discuss or support such efforts.

How to Cite

If you use KnetNLPModels.jl in your work, please cite using the format given in CITATION.bib.

Compatibility

Julia ≥ 1.6.

How to install

This module can be installed with the following command: julia pkg> add KnetNLPModels pkg> test KnetNLPModels

Synopsis

KnetNLPModels is an interface between Knet.jl's classification neural networks and NLPModels.jl.

A KnetNLPModel gives the user access to: - the values of the neural network variables/weights w; - the value of the objective/loss function L(X, Y; w) at w for a given minibatch (X,Y); - the gradient ∇L(X, Y; w) of the objective/loss function at w for a given minibatch (X,Y).

In addition, it provides tools to: - switch the minibatch used to evaluate the neural network; - change the minibatch size; - measure the neural network's accuracy at the current w.

How to use

Check the tutorial.

How to Cite

If you use KnetNLPModels.jl in your work, please cite using the format given in CITATION.bib.

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

Owner

  • Name: JSO-Boneyard
  • Login: JSO-Boneyard
  • Kind: organization

Citation (CITATION.bib)

@Misc{raynaud2022,
  author = {Jean Bigeon and Dominique Orban and Paul Raynaud},
  title = {{KnetNLPModels.jl}: An {NLPModels} Interface to {Knet}},
  month = {June},
  howpublished = {\url{https://github.com/JuliaSmoothOptimizers/KnetNLPModels.jl}},
  year = {2022},
  DOI = {10.5281/zenodo.6984500}
}

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