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tf.keras.wrappers.scikit_learn.KerasClassifier

TensorFlow 1 version View source on GitHub

Implementation of the scikit-learn classifier API for Keras.

Methods

check_params

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Checks for user typos in params.

Arguments
params dictionary; the parameters to be checked

Raises
ValueError if any member of params is not a valid argument.

filter_sk_params

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Filters sk_params and returns those in fn's arguments.

Arguments
fn arbitrary function
override dictionary, values to override sk_params

Returns
res dictionary containing variables in both sk_params and fn's arguments.

fit

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Constructs a new model with build_fn & fit the model to (x, y).

Arguments
x array-like, shape (n_samples, n_features) Training samples where n_samples is the number of samples and n_features is the number of features.
y array-like, shape (n_samples,) or (n_samples, n_outputs) True labels for x.
**kwargs dictionary arguments Legal arguments are the arguments of Sequential.fit

Returns
history object details about the training history at each epoch.

Raises
ValueError In case of invalid shape for y argument.

get_params

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Gets parameters for this estimator.

Arguments
**params ignored (exists for API compatibility).

Returns
Dictionary of parameter names mapped to their values.

predict

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Returns the class predictions for the given test data.

Arguments
x array-like, shape (n_samples, n_features) Test samples where n_samples is the number of samples and n_features is the number of features.
**kwargs dictionary arguments Legal arguments are the arguments of Sequential.predict_classes.

Returns
preds array-like, shape (n_samples,) Class predictions.

predict_proba

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Returns class probability estimates for the given test data.

Arguments
x array-like, shape (n_samples, n_features) Test samples where n_samples is the number of samples and n_features is the number of features.
**kwargs dictionary arguments Legal arguments are the arguments of Sequential.predict_classes.

Returns
proba array-like, shape (n_samples, n_outputs) Class probability estimates. In the case of binary classification, to match the scikit-learn API, will return an array of shape (n_samples, 2) (instead of (n_sample, 1) as in Keras).

score

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Returns the mean accuracy on the given test data and labels.

Arguments
x array-like, shape (n_samples, n_features) Test samples where n_samples is the number of samples and n_features is the number of features.
y array-like, shape (n_samples,) or (n_samples, n_outputs) True labels for x.
**kwargs dictionary arguments Legal arguments are the arguments of Sequential.evaluate.

Returns
score float Mean accuracy of predictions on x wrt. y.

Raises
ValueError If the underlying model isn't configured to compute accuracy. You should pass metrics=["accuracy"] to the .compile() method of the model.

set_params

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Sets the parameters of this estimator.

Arguments
**params Dictionary of parameter names mapped to their values.

Returns
self