contrib.keras.wrappers.scikit_learn.KerasRegressor

tf.contrib.keras.wrappers.scikit_learn.KerasRegressor

class tf.contrib.keras.wrappers.scikit_learn.KerasRegressor

Defined in tensorflow/contrib/keras/python/keras/wrappers/scikit_learn.py.

Implementation of the scikit-learn regressor API for Keras.

Methods

__init__

__init__(
    build_fn=None,
    **sk_params
)

check_params

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

filter_sk_params(
    fn,
    override=None
)

Filters sk_params and return those in fn's arguments.

Arguments:

fn : arbitrary function
override: dictionary, values to override sk_params

Returns:

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

fit

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 in 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

get_params(**params)

Gets parameters for this estimator.

Arguments:

**params: ignored (exists for API compatiblity).

Returns:

Dictionary of parameter names mapped to their values.

predict

predict(
    x,
    **kwargs
)

Returns predictions for the given test data.

Arguments:

x: array-like, shape `(n_samples, n_features)`
    Test samples where n_samples in 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

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 in 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

set_params(**params)

Sets the parameters of this estimator.

Arguments:

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

Returns:

self