LostTech.TensorFlow : API Documentation

Type KerasClassifier

Namespace tensorflow.keras.wrappers.scikit_learn

Parent BaseWrapper

Interfaces IKerasClassifier

Implementation of the scikit-learn classifier API for Keras.

Methods

Properties

Public instance methods

void check_params(IDictionary<object, object> params)

object check_params_dyn(object params)

IDictionary<object, object> filter_sk_params(PythonFunctionContainer fn, IDictionary<object, object> override)

IDictionary<object, object> filter_sk_params(object fn, IDictionary<object, object> override)

object filter_sk_params_dyn(object fn, object override)

object fit(object x, ndarray y, IDictionary<string, object> kwargs)

Constructs a new model with `build_fn` & fit the model to `(x, y)`.
Parameters
object x
ndarray y
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.fit`
Returns
object

object fit_dyn(object x, object y, IDictionary<string, object> kwargs)

Constructs a new model with `build_fn` & fit the model to `(x, y)`.
Parameters
object x
object y
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.fit`
Returns
object

object get_params(IDictionary<string, object> params)

object get_params_dyn(IDictionary<string, object> params)

object predict(object x, IDictionary<string, object> kwargs)

Returns predictions for the given test data.
Parameters
object 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.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.predict`.
Returns
object

object predict_dyn(object x, IDictionary<string, object> kwargs)

Returns predictions for the given test data.
Parameters
object 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.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.predict`.
Returns
object

object predict_proba(object x, IDictionary<string, object> kwargs)

Returns class probability estimates for the given test data.
Parameters
object 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.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.predict_classes`.
Returns
object

object predict_proba_dyn(object x, IDictionary<string, object> kwargs)

Returns class probability estimates for the given test data.
Parameters
object 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.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.predict_classes`.
Returns
object

object score(object x, object y, IDictionary<string, object> kwargs)

Returns the mean loss on the given test data and labels.
Parameters
object 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.
object y
array-like, shape `(n_samples,)` True labels for `x`.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`.
Returns
object

object score_dyn(object x, object y, IDictionary<string, object> kwargs)

Returns the mean loss on the given test data and labels.
Parameters
object 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.
object y
array-like, shape `(n_samples,)` True labels for `x`.
IDictionary<string, object> kwargs
dictionary arguments Legal arguments are the arguments of `Sequential.evaluate`.
Returns
object

BaseWrapper set_params(IDictionary<string, object> params)

object set_params_dyn(IDictionary<string, object> params)

Public properties

object build_fn get; set;

object classes_ get; set;

Sequential model get; set;

int n_classes_ get; set;

object PythonObject get;

IDictionary<string, int> sk_params get; set;