diff --git a/examples/pipeline/wiki_entity_linking/train_el.py b/examples/pipeline/wiki_entity_linking/train_el.py
index 2d7ede48d..3a7cd6186 100644
--- a/examples/pipeline/wiki_entity_linking/train_el.py
+++ b/examples/pipeline/wiki_entity_linking/train_el.py
@@ -33,9 +33,9 @@ class EL_Model:
     CUTOFF = 0.5
 
     INPUT_DIM = 300
-    ENTITY_WIDTH = 4 # 64
-    ARTICLE_WIDTH = 8 #  128
-    HIDDEN_WIDTH = 6 # 64
+    ENTITY_WIDTH = 4  # 64
+    ARTICLE_WIDTH = 8  # 128
+    HIDDEN_WIDTH = 6  # 64
 
     DROP = 0.00
 
@@ -71,7 +71,7 @@ class EL_Model:
         self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_random", calc_random=True)
         self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_random", calc_random=True)
         print()
-        self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_pre", calc_random=False)
+        self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_pre", avg=False)
         self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_pre", avg=False)
 
         instance_pos_count = 0
@@ -113,7 +113,7 @@ class EL_Model:
                             golds.append(float(0.0))
                             instance_neg_count += 1
 
-                for k in range(5):
+                for k in range(10):
                     print()
                     print("update", k)
                     print()
@@ -182,7 +182,7 @@ class EL_Model:
     def _predict(self, article_doc, entity, avg=False, apply_threshold=True):
         if avg:
             with self.article_encoder.use_params(self.sgd_article.averages) \
-                 and self.entity_encoder.use_params(self.sgd_article.averages):
+                 and self.entity_encoder.use_params(self.sgd_entity.averages):
                 doc_encoding = self.article_encoder([article_doc])[0]
                 entity_encoding = self.entity_encoder([entity])[0]
 
@@ -228,7 +228,7 @@ class EL_Model:
 
     @staticmethod
     def _encoder(in_width, hidden_width):
-        conv_depth = 1
+        conv_depth = 2
         cnn_maxout_pieces = 3
 
         with Model.define_operators({">>": chain}):
@@ -261,16 +261,10 @@ class EL_Model:
         return loss, d_scores
 
     def update(self, article_docs, entities, golds, apply_threshold=True):
-        print("article_docs", len(article_docs))
-        for a in article_docs:
-            print(a[0:10], a[-10:])
-            doc_encoding, bp_doc = self.article_encoder.begin_update([a], drop=self.DROP)
-            print(doc_encoding)
-
         doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=self.DROP)
         print("doc_encodings", len(doc_encodings), doc_encodings)
 
-        entity_encodings, bp_encoding = self.entity_encoder.begin_update(entities, drop=self.DROP)
+        entity_encodings, bp_entity = self.entity_encoder.begin_update(entities, drop=self.DROP)
         print("entity_encodings", len(entity_encodings), entity_encodings)
 
         concat_encodings = [list(entity_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
@@ -298,15 +292,19 @@ class EL_Model:
         # print("d_scores", d_scores)
 
         model_gradient = bp_model(d_scores, sgd=self.sgd)
-        # print("model_gradient", model_gradient)
+        print("model_gradient", model_gradient)
 
-        doc_gradient = [x[0:self.ARTICLE_WIDTH] for x in model_gradient]
-        # print("doc_gradient", doc_gradient)
-        entity_gradient = [x[self.ARTICLE_WIDTH:] for x in model_gradient]
-        # print("entity_gradient", entity_gradient)
+        doc_gradient = list()
+        entity_gradient = list()
+        for x in model_gradient:
+            doc_gradient.append(list(x[0:self.ARTICLE_WIDTH]))
+            entity_gradient.append(list(x[self.ARTICLE_WIDTH:]))
+
+        print("doc_gradient", doc_gradient)
+        print("entity_gradient", entity_gradient)
 
         bp_doc(doc_gradient)
-        bp_encoding(entity_gradient)
+        bp_entity(entity_gradient)
 
     def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
         id_to_descr = kb_creator._get_id_to_description(entity_descr_output)