the architecture that gives 64 overall accuracy

answer_classifier_cached_features/inference.py

@@ -174,12 +174,12 @@ class AnswerInference():
             self.obj_atr_det_fc_bn = self.fc_bn(
                 self.obj_atr_det_packed,
                 2500,
-                'obj_atr_det_conv_bn')
+                'obj_atr_det_fc_bn')
 
             self.q_a_feat_fc_bn = self.fc_bn(
                 self.q_a_feat_packed,
                 2500,
-                'q_a_feat_conv_bn')
+                'q_a_feat_fc_bn')
 
             # (25*18 x 2500)
             self.per_answer_feat = tf.nn.relu(

object_attribute_classifier_cached_features/inference.py

@@ -78,10 +78,10 @@ class ObjectAttributeInference():
                     out_dim,
                     'fc',
                     func = None)
-                # fc2_out = layers.batch_norm(
-                #     fc2_out,
-                #     tf.constant(self.training))
-                # fc2_out = tf.nn.relu(fc2_out)
+                fc2_out = layers.batch_norm(
+                    fc2_out,
+                    tf.constant(self.training))
+                fc2_out = tf.nn.relu(fc2_out)
 
         return fc2_out
 
@@ -108,10 +108,10 @@ class ObjectAttributeInference():
                     out_dim,
                     'fc',
                     func = None)
-                # fc2_out = layers.batch_norm(
-                #     fc2_out,
-                #     tf.constant(self.training))
-                # fc2_out = tf.nn.relu(fc2_out)
+                fc2_out = layers.batch_norm(
+                    fc2_out,
+                    tf.constant(self.training))
+                fc2_out = tf.nn.relu(fc2_out)
         
         return fc2_out