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keras-team/autokeras

9,312PythonAudience · dataComplexity · 3/5Setup · moderate

TLDR

A Python library that automatically searches for the best machine learning model architecture for your data, you hand it your dataset and it returns a trained model without you needing to tune settings manually.

Mindmap

mindmap
  root((AutoKeras))
    What it does
      Auto model search
      Architecture tuning
      Trains on your data
    Tech stack
      Python
      Keras
      TensorFlow
    Audience
      Beginners
      Researchers
      Practitioners
    Use cases
      Image classification
      Baseline models
      Fast experiments
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Things people build with this

USE CASE 1

Get a working image classifier without writing model architecture code, AutoKeras finds the best one for your dataset automatically.

USE CASE 2

Generate a baseline machine learning model quickly to compare against your hand-tuned models.

USE CASE 3

Explore machine learning as a beginner by running a complete training pipeline with just three lines of code.

USE CASE 4

Use AutoKeras in a research workflow to find competitive model architectures without exhaustive manual experimentation.

Tech stack

PythonKerasTensorFlow

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Python 3.7+ and TensorFlow 2.8.0+, which can take several minutes to download and install.

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In plain English

AutoKeras is a Python library that automates the process of building machine learning models. Normally, training a machine learning model requires choosing an architecture, tuning many settings by hand, and iterating through experiments. AutoKeras tries to handle that search automatically, so you can get a working model with far less manual effort. The library is built on top of Keras, which is itself a widely-used Python library for building neural networks. AutoKeras adds a layer on top of it that tries different model configurations on your behalf and returns the one that performs best on your data. The code example in the README shows the idea: you create a classifier, call fit with your training data, and call predict on new data, the same three steps you would use in any basic machine learning library, but the internal search for the right model structure happens automatically. The project comes from a research lab at Texas A&M University and has been published in an academic journal. It supports tasks like image classification, though the full list of supported task types is on the project's website rather than in the README itself. Installing it requires Python 3.7 or higher and TensorFlow 2.8.0 or higher, two common requirements in the Python machine learning ecosystem. The package is available via pip, the standard Python package installer. This tool is aimed at people who want to apply machine learning without spending a lot of time on model design. It is particularly useful for researchers or practitioners who want a baseline model quickly, or for beginners learning about machine learning who want results before they understand all the technical details.

Copy-paste prompts

Prompt 1
Write a complete Python script using AutoKeras to train an image classifier on a folder of labeled images, evaluate its accuracy, and save the best model to disk.
Prompt 2
Show me how to use AutoKeras to classify text reviews as positive or negative, include loading data, fitting the model, and running predictions on new text samples.
Prompt 3
How do I limit the number of model architectures AutoKeras tries during its search to make training finish in under 30 minutes on a laptop?
Prompt 4
Give me a Python example of using AutoKeras for a structured data regression problem, predicting house prices from a CSV file with numeric and categorical columns.
Prompt 5
Compare the model AutoKeras found for my dataset against a manually built Keras model by printing accuracy and training time for both.
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