tf.keras.wrappers.scikit_learn.KerasClassifier     View source on GitHub    Implementation of the scikit-learn classifier API for Keras.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.wrappers.scikit_learn.KerasClassifier  
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.   
 


 Returns   self