old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  
  class DeepLearning:
      """
      Class for deel learning methods.
      """
      def __init__(self, **kwargs):
          self.dataset = _get_dataset()
          self.epochs = kwargs.pop('epochs', 0)
          self.batch_size = kwargs.pop('batch_size', 0)
          self.classes = kwargs.pop('classes', 1)
          self.hidden_layers = kwargs.pop('hidden_layers', None)
          self.noise_layers = kwargs.pop('noise_layers', None)
          self.n_folds = kwargs.pop('n_folds', 1)
  
          for key in kwargs.keys():
              print('Argument {} doesn\'t recognize.'.format(key))
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  import numpy as np
  
  
  class DeepLearning:
      """
      Class for deel learning methods.
      """
      def __init__(self, **kwargs):
          self.dataset = _get_dataset()
          self.epochs = kwargs.pop('epochs', 0)
          self.batch_size = kwargs.pop('batch_size', 0)
          self.classes = kwargs.pop('classes', 1)
          self.hidden_layers = kwargs.pop('hidden_layers', None)
          self.noise_layers = kwargs.pop('noise_layers', None)
          self.n_folds = kwargs.pop('n_folds', 1)
  
          for key in kwargs.keys():
              print('Argument {} doesn\'t recognize.'.format(key))
  
      def _get_dataset():
          """ Get dataset and unpack it. """
          unlabeled_train = np.load('unlabeled_feature.npz')['X']
  
          train_data = np.load('train_feature.npz')
          X_train, Y_train = train_data['X'], train_data['y']
  
          test_data = np.load('test_feature.npz')
          X_test, Y_test = test_data['X'], test_data['y']
  
          return unlabeled_train, (X_train, Y_train), \
                  (X_test, Y_test)
  
subject: Add get dataset.