Welcome to SciKeras’s documentation!

The goal of scikeras is to make it possible to use Keras/TensorFlow with sklearn. This is achieved by providing a wrapper around Keras that has an Scikit-Learn interface. SciKeras is the successor to tf.keras.wrappers.scikit_learn, and offers many improvements over the TensorFlow version of the wrappers. See Migration for a more details.

SciKeras tries to make things easy for you while staying out of your way. If you are familiar with Scikit-Learn and Keras, you don’t have to learn any new concepts, and the syntax should be well known.

Overall, SciKeras aims at being as flexible as Keras while having a clean interface as Scikit-Learn.

• Installation
  • Users Installation
  • Developer Installation
• Quickstart
  • Training a model
  • In an sklearn Pipeline
  • Grid search
  • What’s next?
• Migrating from tf.keras.wrappers.scikit_learn
  • Why switch to SciKeras
  • Changes to your code
• Tutorials
  • Basic usage
  • MLPClassifier and MLPRegressor in SciKeras
  • Meta Estimators in SciKeras
  • Data Transformers
  • Autoencoders in SciKeras
  • SciKeras Benchmarks
• Advanced Usage of SciKeras Wrappers
  • Wrapper Classes
  • Arguments to model_build_fn
  • Routed parameters
  • Custom Scorers
• SciKeras API
  • Wrappers
  • Utilities