tf.keras.wrappers.scikit_learn.KerasRegressor

Implementation of the scikit-learn regressor API for Keras.

Compat aliases for migration

See Migration guide for more details.

tf.keras.wrappers.scikit_learn.KerasRegressor(
    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.

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 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`.

Returns
`preds`	array-like, shape `(n_samples,)` Predictions.

score(
    x, y, **kwargs
)

Returns the mean loss 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,)` 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`.

set_params(
    **params
)

Sets the parameters of this estimator.

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

Returns
self