https://github.com/anselmoo/autokeras
AutoML library for deep learning
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: acm.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.1%) to scientific vocabulary
Repository
AutoML library for deep learning
Basic Info
- Host: GitHub
- Owner: Anselmoo
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: http://autokeras.com/
- Size: 43 MB
Statistics
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
Official Website: autokeras.com
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone.
Learning resources
- A short example.
```python import autokeras as ak
clf = ak.ImageClassifier() clf.fit(xtrain, ytrain) results = clf.predict(x_test) ```
- Official website tutorials.
- The book of Automated Machine Learning in Action.
- The LiveProjects of Image Classification with AutoKeras.
 
 
Installation
To install the package, please use the pip installation as follows:
shell
pip3 install autokeras
Please follow the installation guide for more details.
Note: Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0.
Community
Ask your questions on our GitHub Discussions.
Contributing Code
Here is how we manage our project.
We pick the critical issues to work on from GitHub issues. They will be added to this Project. Some of the issues will then be added to the milestones, which are used to plan for the releases.
Refer to our Contributing Guide to learn the best practices.
Thank all the contributors!
Cite this work
Haifeng Jin, Qingquan Song, and Xia Hu. "Auto-keras: An efficient neural architecture search system." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019. (Download)
Biblatex entry:
bibtex
@inproceedings{jin2019auto,
title={Auto-Keras: An Efficient Neural Architecture Search System},
author={Jin, Haifeng and Song, Qingquan and Hu, Xia},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1946--1956},
year={2019},
organization={ACM}
}
Acknowledgements
The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.
Owner
- Name: Anselm Hahn
- Login: Anselmoo
- Kind: user
- Location: Switzerland
- Repositories: 100
- Profile: https://github.com/Anselmoo
GitHub Events
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Dependencies
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/setup-python v1 composite
- codecov/codecov-action v1 composite
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/setup-python v1 composite
- codecov/codecov-action v1 composite
- mcr.microsoft.com/vscode/devcontainers/python 3.8 build
- tensorflow/tensorflow 2.3.0 build
- Sphinx *
- jupyter *
- keras-autodoc *
- mkdocs *
- mkdocs-material *
- pygments *
- pymdown-extensions *
- keras-tuner >=1.1.0
- packaging *
- pandas *
- tensorflow >=2.8.0