diff --git a/answer_classifier_cached_features/inference.py b/answer_classifier_cached_features/inference.py
index 8ddbe60a48f596bdb038f481eccfa3b273b9f1c2..5c115c6596381266d49c242e32c155e817a2d767 100644
--- a/answer_classifier_cached_features/inference.py
+++ b/answer_classifier_cached_features/inference.py
@@ -97,7 +97,7 @@ class AnswerInference():
                 self.per_region_answer_scores[j] = tf.nn.relu(
                     self.batch_norm(
                         self.per_region_answer_scores[j],
-                        is_training))                
+                        self.is_training))                
 
                 self.per_region_answer_scores[j] = layers.conv2d(
                     self.per_region_answer_scores[j],
diff --git a/answer_classifier_cached_features/train.py b/answer_classifier_cached_features/train.py
index 14f7f4e015a7cfe676840881b0692ebabe39e65d..e5d6fd3f3159a02ec4b1f4ebbda2c2abc64b608f 100644
--- a/answer_classifier_cached_features/train.py
+++ b/answer_classifier_cached_features/train.py
@@ -108,7 +108,7 @@ class graph_creator():
                 self.num_neg_answers + 1,
                 self.space_dim,
                 self.plh['keep_prob'],
-                training)
+                self.training)
 
             self.add_losses()
             self.add_accuracy_computation()
@@ -367,8 +367,7 @@ class graph_creator():
             attribute_loss_summary = tf.scalar_summary(
                 "loss_attribute", 
                 self.attribute_loss)
-            # self.object_loss = 0.0
-            # self.attribute_loss = 0.0
+
         else:
             self.object_loss = 0.0
             self.attribute_loss = 0.0