fixed discrepancies

Author: mathsinn

art/defences/adversarial_trainer.py

@@ -75,7 +75,7 @@ def fit(self, x, y, batch_size=128, nb_epochs=20):
 
             for batch_id in range(nb_batches):
                 # Create batch data
-                x_batch = x[ind[batch_id * batch_size:min((batch_id + 1) * batch_size, x.shape[0])]]
+                x_batch = x[ind[batch_id * batch_size:min((batch_id + 1) * batch_size, x.shape[0])]].copy()
                 y_batch = y[ind[batch_id * batch_size:min((batch_id + 1) * batch_size, x.shape[0])]]
 
                 # Choose indices to replace with adversarial samples
@@ -84,12 +84,7 @@ def fit(self, x, y, batch_size=128, nb_epochs=20):
 
                 # If source and target models are the same, craft fresh adversarial samples
                 if attack.classifier == self.classifier:
-                    labels_batch = np.argmax(y_batch, axis=1)
-                    x_adv = attack.generate(x_batch[adv_ids])
-                    y_adv = np.argmax(attack.classifier.predict(x_adv), axis=1)
-                    selected = np.array(y_adv != labels_batch[adv_ids])
-
-                    x_batch[adv_ids][selected] = x_adv[selected]
+                    x_batch[adv_ids] = attack.generate(x_batch[adv_ids])
 
                 # Otherwise, use precomputed adversarial samples
                 else: