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Accessible AutoML for deep learning.
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satyakesav and jhfjhfj1 [MRG] Text Regressor using BERT Model (#609)
* Working: Text Regressor module; Pending: replace Pytorch loss function to AutoKeras generic loss function

* change pytorch loss functions to AutoKeras standard loss function

* final clean-up

* Metric for Text Regressor

* Fix for classifier

* quick fix

* Adding evaluate for text supervised

* quick fix

* Tweak TextSupervised class

* remove unused import
Latest commit a1b0e27 Apr 11, 2019
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.github/ISSUE_TEMPLATE Update Aug 13, 2018
autokeras-docker [MRG] Update Auto-Keras Docker image (#611) Apr 11, 2019
autokeras [MRG] Text Regressor using BERT Model (#609) Apr 11, 2019
examples [MRG] Text Regressor using BERT Model (#609) Apr 11, 2019
mkdocs [MRG] Update Auto-Keras Docker image (#611) Apr 11, 2019
tests [MRG] Text Regressor using BERT Model (#609) Apr 11, 2019
.coveragerc [MRG]Fit param. Resolve issue #209 (#238) Oct 12, 2018
.gitignore [MRG] Object Detection (#396) Dec 26, 2018
.travis.yml update website badges and travis-ci coverage Apr 2, 2019 Create Aug 9, 2018 [MRG] Changing Docs from jhfjhfj1 to keras-team (#610) Apr 2, 2019
LICENSE.txt Update LICENSE.txt (#75) Aug 12, 2018 [MRG] Changing Docs from jhfjhfj1 to keras-team (#610) Apr 2, 2019 [MRG]Fit param. Resolve issue #209 (#238) Oct 12, 2018 merge (#128) Aug 24, 2018
logo.png Docs (#17) Jul 4, 2018 Aug 13, 2018
requirements.txt new release (#330) Nov 18, 2018
setup.cfg Update setup.cfg (#270) Oct 22, 2018 [MRG] Changing Docs from jhfjhfj1 to keras-team (#610) Apr 2, 2019


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Official Website:

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.


Here is a short example of using the package.

import autokeras as ak

clf = ak.ImageClassifier(), y_train)
results = clf.predict(x_test)

Cite this work

Auto-Keras: An Efficient Neural Architecture Search System. Haifeng Jin, Qingquan Song, and Xia Hu. arXiv:1806.10282.

Biblatex entry:

  author       = {Haifeng Jin and Qingquan Song and Xia Hu},
  title        = {Auto-Keras: An Efficient Neural Architecture Search System},
  date         = {2018-06-27},
  year         = {2018},
  eprintclass  = {cs.LG},
  eprinttype   = {arXiv},
  eprint       = {cs.LG/1806.10282},


You can use Gitter to communicate with people who also interested in Auto-Keras. Join the chat at

You can also follow us on Twitter @autokeras for the latest news.

Contributing Code

You can follow the Contributing Guide for details. The easist way to contribute is to resolve the issues with the "call for contributors" tag. They are friendly to beginners.

Support Auto-Keras

We accept donations on Open Collective. Thank every backer for supporting us!


Please note that this is a pre-release version of the Auto-Keras which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an “as is” and “as available” basis. Auto-Keras does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. Auto-Keras will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user’s own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.


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.

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