Fix doc & PEP8 for AdversarialTrainer

Author: Irina Nicolae

art/defences/adversarial_trainer.py

@@ -15,7 +15,9 @@ class AdversarialTrainer:
     training on all adversarial data and other common setups. If multiple attacks are specified, they are rotated
     for each batch. If the specified attacks have as target a different model, then the attack is transferred. The
     `ratio` determines how many of the clean samples in each batch are replaced with their adversarial counterpart.
-    When the attack targets the current classifier, only successful adversarial samples are used.
+
+     .. warning:: Both successful and unsuccessful adversarial samples are used for training. In the case of
+                  unbounded attacks (e.g., DeepFool), this can result in invalid (very noisy) samples being included.
     """
     def __init__(self, classifier, attacks, ratio=.5):
         """
@@ -71,7 +73,7 @@ def fit(self, x, y, batch_size=128, nb_epochs=20):
         self._precomputed_adv_samples = []
         for attack in self.attacks:
             if 'targeted' in attack.attack_params:
-                if attack.targeted:                       
+                if attack.targeted:
                     raise NotImplementedError("Adversarial training with targeted attacks is \
                                                currently not implemented")
 
@@ -153,7 +155,7 @@ def fit(self, x, y, **kwargs):
 
         # Generate adversarial samples for each attack
         for i, attack in enumerate(self.attacks):
-            if 'targeted' in attack.attack_params and attack.targeted:                       
+            if 'targeted' in attack.attack_params and attack.targeted:
                     raise NotImplementedError("Adversarial training with targeted attacks is \
                                                currently not implemented")
 

art/defences/adversarial_trainer_unittest.py

@@ -169,17 +169,15 @@ def test_two_attacks(self):
 
         logger.info('Accuracy before adversarial training: %.2f%%', (acc * 100))
         logger.info('\nAccuracy after adversarial training: %.2f%%', (acc_new * 100))
-        
-    
+
     def test_targeted_attack_error(self):
         """
         Test the adversarial trainer using a targeted attack, which will currently result in a
         NotImplementError.
 
         :return: None
         """
-        
-        (x_train, y_train), (x_test, y_test) = self.mnist
+        (x_train, y_train), (_, _) = self.mnist
         params = {'nb_epochs': 2, 'batch_size': BATCH_SIZE}
 
         classifier = self.classifier_k
@@ -294,14 +292,14 @@ def test_targeted_attack_error(self):
 
         :return: None
         """
-        
-        (x_train, y_train), (x_test, y_test) = self.mnist
+        (x_train, y_train), (_, _) = self.mnist
         params = {'nb_epochs': 2, 'batch_size': BATCH_SIZE}
 
         classifier = self.classifier_k
         adv = FastGradientMethod(classifier, targeted=True)
         adv_trainer = StaticAdversarialTrainer(classifier, attacks=adv)
         self.assertRaises(NotImplementedError, adv_trainer.fit, x_train, y_train, **params)
 
+
 if __name__ == '__main__':
     unittest.main()