Official Website: autokeras.com
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()
clf.fit(x_train, y_train)
results = clf.predict(x_test)
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Auto-Keras: Efficient Neural Architecture Search with Network Morphism. Haifeng Jin, Qingquan Song, and Xia Hu. arXiv:1806.10282.
Biblatex entry:
@online{jin2018efficient,
author = {Haifeng Jin and Qingquan Song and Xia Hu},
title = {Auto-Keras: Efficient Neural Architecture Search with Network Morphism},
date = {2018-06-27},
year = {2018},
eprintclass = {cs.LG},
eprinttype = {arXiv},
eprint = {cs.LG/1806.10282},
}
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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.