Implementation of the scikit-learn classifier API for Keras.
tf.keras.wrappers.scikit_learn.KerasClassifier(
build_fn=None, **sk_params
)
Methods
check_params
View source
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
View source
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
View source
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
View source
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
View source
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
View source
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
View source
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
View source
set_params(
**params
)
Sets the parameters of this estimator.
| Arguments |
|---|
**params
|
Dictionary of parameter names mapped to their values.
|
TensorFlow 2 version
View source on GitHub