Auto-keras is an open source automatic machine learning library proposed by Haifeng Jin and Qingquan Song, assistant professor hu Xia and his two doctoral students at Texas A&M University. The ultimate goal of Auto-Keras is to allow deep learning to be easily accessible to experts in all fields with little background in data science or machine learning. Auto-keras provides a series of functions to automatically search the networks and hyperparameters of deep learning models. \

When it comes to automating machine learning, Google’s AutoML is firmly in place. AutoML is based on Google’s latest image recognition technology Neural Architecture Search (NAS). NAS is an algorithm that searches for the best neural network architecture based on a particular dataset to perform a particular task on that dataset. AutoML is a set of machine learning tools that can easily train high-performance deep networks without requiring users to master deep learning or AI knowledge. All you need is tagging data! \

However, they identified three flaws with Google AutoML:

First, you have to pay.

Second, because on the cloud, you have to configure Docker containers and Kubernetes.

Third, Google can’t keep your data safe and private.

AutoKeras uses the ENAS approach based on Keras, a very easy-to-use deep learning database. ENAS is the latest version of NAS, thus making AutoKeras efficient, easy to install, adjustable parameters, and easy to modify.

AutoKeras architecture:

PIP install autokeras

Sample:

import autokeras as ak

clf = ak.ImageClassifier()

clf.fit(x_train, y_train)

results = clf.predict(x_test)

Paper Download:

Arxiv.org/abs/1806.10…

Official website:

autokeras.com/

Citation:

@online{jin2018efficient,
  author       = {Haifeng Jin and Qingquan Song and Xia Hu},
  title        = {Efficient Neural Architecture Search with Network Morphism},
  date         = {2018- 0627 -},
  year         = {2018},
  eprintclass  = {cs.LG},
  eprinttype   = {arXiv},
  eprint       = {cs.LG/1806.10282}},Copy the code

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