old_contents: import unittest
  
  
  class TestMNISTTutorialTF(unittest.TestCase):
      def test_mnist_tutorial_tf(self):
          from tutorials import mnist_tutorial_pure_tf
  
          # Run the MNIST tutorial on a dataset of reduced size
          test_dataset_indices = {'train_start': 0,
                                  'train_end': 5000,
                                  'test_start': 0,
                                  'test_end': 333}
          report = mnist_tutorial_pure_tf.mnist_tutorial(**test_dataset_indices)
  
          # Check accuracy values contained in the AccuracyReport object
          self.assertTrue(report.clean_train_clean_eval > 0.85)
          self.assertTrue(report.clean_train_adv_eval < 0.07)
          self.assertTrue(report.adv_train_clean_eval > 0.8)
          self.assertTrue(report.adv_train_adv_eval > 0.14)
  
  
  if __name__ == '__main__':
      unittest.main()
  
new_contents: import unittest
  
  
  class TestMNISTTutorialTF(unittest.TestCase):
      def test_mnist_tutorial_tf(self):
          from tutorials import mnist_tutorial_pure_tf
  
          # Run the MNIST tutorial on a dataset of reduced size
          test_dataset_indices = {'train_start': 0,
                                  'train_end': 5000,
                                  'test_start': 0,
                                  'test_end': 333,
                                  'nb_epochs': 2}
          report = mnist_tutorial_pure_tf.mnist_tutorial(**test_dataset_indices)
  
          # Check accuracy values contained in the AccuracyReport object
          self.assertTrue(report.clean_train_clean_eval > 0.75)
          self.assertTrue(report.clean_train_adv_eval < 0.08)
          self.assertTrue(report.adv_train_clean_eval > 0.7)
          self.assertTrue(report.adv_train_adv_eval > 0.07)
  
  
  if __name__ == '__main__':
      unittest.main()
  
subject: make pure TF FGM tutorial test faster