tf.keras.wrappers.scikit_learn.KerasClassifier

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Implementation of the scikit-learn classifier API for Keras.

tf.keras.wrappers.scikit_learn.KerasClassifier(
    build_fn=None, **sk_params
)

Methods

check_params

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check_params(
    params
)

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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filter_sk_params(
    fn, override=None
)

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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fit(
    x, y, **kwargs
)

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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get_params(
    **params
)

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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predict(
    x, **kwargs
)

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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predict_proba(
    x, **kwargs
)

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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score(
    x, y, **kwargs
)

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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set_params(
    **params
)

Sets the parameters of this estimator.

Arguments:

• **params: Dictionary of parameter names mapped to their values.

Returns:

self