text: import MusEEG
  import pandas
  import os
  import numpy as np
  
  save = True
  
  brain = MusEEG.classifier()
  train_inputs, train_targets, test_inputs, test_targets = brain.loadTrainingData(
      address=os.path.join(MusEEG.parentDir, 'data', 'training', 'batch1_batch2_320samples', 'bigChunks'),
      percentTrain=0.5,
      normalize=True)
  brain.build_model(inputShape=brain.inputShape,
                    hiddenNeurons=100,
                    hiddenActivation='elu',
                    numberOfTargets=max(train_targets) + 1,
                    regularization='l1_l2 ',
                    loss='sparse_categorical_crossentropy')
  
  brain.train_model(train_inputs, train_targets, nEpochs=50, verbose=2)
  brain.evaluate_model(test_inputs, test_targets)
  
  cm = brain.print_confusion(test_inputs, test_targets)
  
  cmdataframe = pandas.DataFrame(cm)
  
  print('hi')
  if save:
      prompt = input('should we save this model, ye great master?')
      if prompt == 'yes':
          name = input('what should we name it, ye great master?')
          brain.savemodel(name)
      else:
          print('oh ok')
  