Wrappers for the Scikit-Learn API

You can use Sequential Keras models (single-input only) as part of your Scikit-Learn workflow via the wrappers found at keras.wrappers.scikit_learn.py.

There are two wrappers available:

keras.wrappers.scikit_learn.KerasClassifier(build_fn=None, **sk_params), which implements the Scikit-Learn classifier interface,

keras.wrappers.scikit_learn.KerasRegressor(build_fn=None, **sk_params), which implements the Scikit-Learn regressor interface.


  • build_fn: callable function or class instance
  • sk_params: model parameters & fitting parameters

build_fn should construct, compile and return a Keras model, which will then be used to fit/predict. One of the following three values could be passed to build_fn:

  1. A function
  2. An instance of a class that implements the call method
  3. None. This means you implement a class that inherits from either KerasClassifier or KerasRegressor. The call method of the present class will then be treated as the default build_fn.

sk_params takes both model parameters and fitting parameters. Legal model parameters are the arguments of build_fn. Note that like all other estimators in scikit-learn, 'build_fn' should provide default values for its arguments, so that you could create the estimator without passing any values to sk_params.

sk_params could also accept parameters for calling fit, predict, predict_proba, and score methods (e.g., nb_epoch, batch_size). fitting (predicting) parameters are selected in the following order:

  1. Values passed to the dictionary arguments of fit, predict, predict_proba, and score methods
  2. Values passed to sk_params
  3. The default values of the keras.models.Sequential fit, predict, predict_proba and score methods

When using scikit-learn's grid_search API, legal tunable parameters are those you could pass to sk_params, including fitting parameters. In other words, you could use grid_search to search for the best batch_size or nb_epoch as well as the model parameters.