tf.keras.wrappers.scikit_learn.KerasClassifier

Implementation of the scikit-learn classifier API for Keras.

Compat aliases for migration

tf.compat.v1.keras.wrappers.scikit_learn.KerasClassifier

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

Methods

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(
    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(
    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(
    **params
)

Gets parameters for this estimator.

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

Returns
Dictionary of parameter names mapped to their values.

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(
    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(
    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(
    **params
)

Sets the parameters of this estimator.

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

Returns
self

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/wrappers/scikit_learn/KerasClassifier