(self, **sk_params)
Initialize the KerasClassifierForQlik.
        The compiled Keras model should be included in sk_params under the 'neural_net' keyword argument.
Initialize the KerasClassifierForQlik.
        The compiled Keras model should be included in sk_params under the 'neural_net' keyword argument.
Initialize the KerasClassifierForQlik . The compiled Keras model should be included in sk_params under the neural_net keyword argument .
def __init__(self, **sk_params):
        """
        Initialize the KerasClassifierForQlik.
        The compiled Keras model should be included in sk_params under the 'neural_net' keyword argument.
        """
        
        # Assign the parameters to a class variable
        self.sk_params = sk_params
        
        # Set build_fn to the function supplied in sk_params
        self.build_fn = self.sk_params.pop('build_fn')

        # DataFrame to contain history of every training cycle
        # This DataFrame will provide metrics such as loss for each run of the fit method
        # Columns will be ['iteration', 'epoch', 'loss'] and any other metrics being calculated during training
        self.histories = pd.DataFrame()
        self.iteration = 0
        
        # Check the parameters using the super class method
        self.check_params(self.sk_params)
def __init__ ( self , * * sk_params ) : # Assign the parameters to a class variable self . sk_params = sk_params # Set build_fn to the function supplied in sk_params self . build_fn = self . sk_params . pop ( 'build_fn' ) # DataFrame to contain history of every training cycle # This DataFrame will provide metrics such as loss for each run of the fit method # Columns will be ['iteration', 'epoch', 'loss'] and any other metrics being calculated during training self . histories = pd . DataFrame ( ) self . iteration = 0 # Check the parameters using the super class method self . check_params ( self . sk_params )