tf.keras.wrappers.scikit_learn.KerasClassifier
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
View source
set_params(
**params
)
Sets the parameters of this estimator.
| Arguments |
|---|
**params | Dictionary of parameter names mapped to their values. |