diff --git a/requirements.txt b/requirements.txt
index 3ed1fc1..2759b7a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,7 +5,6 @@ jdcal==1.3
 odfpy==1.3.5
 olefile==0.44
 openpyxl==2.4.9
-pkg-resources==0.0.0
 pytz==2017.3
 records==0.5.2
 SQLAlchemy==1.1.14
diff --git a/sqlnet/lib/dbengine.py b/sqlnet/lib/dbengine.py
index 619c8b1..2acbe27 100644
--- a/sqlnet/lib/dbengine.py
+++ b/sqlnet/lib/dbengine.py
@@ -18,7 +18,7 @@ def __init__(self, fdb):
     def execute_query(self, table_id, query, *args, **kwargs):
         return self.execute(table_id, query.sel_index, query.agg_index, query.conditions, *args, **kwargs)
 
-    def execute(self, table_id, select_index, aggregation_index, conditions, lower=True):
+    def execute(self, table_id, select_index, aggregation_index, conditions, lower=True, isGold=False):
         if not table_id.startswith('table'):
             table_id = 'table_{}'.format(table_id.replace('-', '_'))
         table_info = self.db.query('SELECT sql from sqlite_master WHERE tbl_name = :name', name=table_id).all()[0].sql.replace('\n','')
@@ -47,6 +47,12 @@ def execute(self, table_id, select_index, aggregation_index, conditions, lower=T
         if where_clause:
             where_str = 'WHERE ' + ' AND '.join(where_clause)
         query = 'SELECT {} AS result FROM {} {}'.format(select, table_id, where_str)
+
+        if isGold:
+            print("Gold query: " + query)
+        else:
+            print("Pred query: " + query)
+
         #print query
         out = self.db.query(query, **where_map)
         return [o.result for o in out]
diff --git a/sqlnet/utils.py b/sqlnet/utils.py
index 2311671..939de88 100644
--- a/sqlnet/utils.py
+++ b/sqlnet/utils.py
@@ -127,6 +127,11 @@ def to_batch_query(sql_data, idxes, st, ed):
         table_ids.append(sql_data[idxes[i]]['table_id'])
     return query_gt, table_ids
 
+def pretty_print(vis_data):
+    print('question:', vis_data[0])
+    print('headers: (%s)'%(' || '.join(vis_data[1])))
+    print('query:', vis_data[2])
+
 def epoch_train(model, optimizer, batch_size, sql_data, table_data, pred_entry):
     model.train()
     perm=np.random.permutation(len(sql_data))
@@ -137,12 +142,13 @@ def epoch_train(model, optimizer, batch_size, sql_data, table_data, pred_entry):
 
         q_seq, col_seq, col_num, ans_seq, query_seq, gt_cond_seq = \
                 to_batch_seq(sql_data, table_data, perm, st, ed)
+        
         gt_where_seq = model.generate_gt_where_seq(q_seq, col_seq, query_seq)
         gt_sel_seq = [x[1] for x in ans_seq]
         score = model.forward(q_seq, col_seq, col_num, pred_entry,
                 gt_where=gt_where_seq, gt_cond=gt_cond_seq, gt_sel=gt_sel_seq)
         loss = model.loss(score, ans_seq, pred_entry, gt_where_seq)
-        cum_loss += loss.data.cpu().numpy()[0]*(ed - st)
+        cum_loss += loss.data.cpu().numpy()*(ed - st)
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()
@@ -175,11 +181,12 @@ def epoch_exec_acc(model, batch_size, sql_data, table_data, db_path):
 
         for idx, (sql_gt, sql_pred, tid) in enumerate(
                 zip(query_gt, pred_queries, table_ids)):
+            print("question:", sql_data[idx]["question"])
             ret_gt = engine.execute(tid,
-                    sql_gt['sel'], sql_gt['agg'], sql_gt['conds'])
+                    sql_gt['sel'], sql_gt['agg'], sql_gt['conds'], isGold=True)
             try:
                 ret_pred = engine.execute(tid,
-                        sql_pred['sel'], sql_pred['agg'], sql_pred['conds'])
+                        sql_pred['sel'], sql_pred['agg'], sql_pred['conds'], isGold=False)
             except:
                 ret_pred = None
             tot_acc_num += (ret_gt == ret_pred)
@@ -198,6 +205,8 @@ def epoch_acc(model, batch_size, sql_data, table_data, pred_entry):
         ed = st+batch_size if st+batch_size < len(perm) else len(perm)
 
         q_seq, col_seq, col_num, ans_seq, query_seq, gt_cond_seq, raw_data = to_batch_seq(sql_data, table_data, perm, st, ed, ret_vis_data=True)
+        # print(raw_data[0])
+        
         raw_q_seq = [x[0] for x in raw_data]
         raw_col_seq = [x[1] for x in raw_data]
         query_gt, table_ids = to_batch_query(sql_data, perm, st, ed)
@@ -242,10 +251,10 @@ def epoch_reinforce_train(model, optimizer, batch_size, sql_data, table_data, db
         for idx, (sql_gt, sql_pred, tid) in enumerate(
                 zip(query_gt, pred_queries, table_ids)):
             ret_gt = engine.execute(tid,
-                    sql_gt['sel'], sql_gt['agg'], sql_gt['conds'])
+                    sql_gt['sel'], sql_gt['agg'], sql_gt['conds'], isGold=True)
             try:
                 ret_pred = engine.execute(tid,
-                        sql_pred['sel'], sql_pred['agg'], sql_pred['conds'])
+                        sql_pred['sel'], sql_pred['agg'], sql_pred['conds'], isGold=False)
             except:
                 ret_pred = None
 
diff --git a/train.py b/train.py
index ed0cab5..64477f9 100644
--- a/train.py
+++ b/train.py
@@ -82,7 +82,7 @@
         print "Init dev acc_ex: %s"%epoch_exec_acc(
                 model, BATCH_SIZE, val_sql_data, val_table_data, DEV_DB)
         torch.save(model.cond_pred.state_dict(), cond_m)
-        for i in range(100):
+        for i in range(1):
             print 'Epoch %d @ %s'%(i+1, datetime.datetime.now())
             print ' Avg reward = %s'%epoch_reinforce_train(
                 model, optimizer, BATCH_SIZE, sql_data, table_data, TRAIN_DB)
@@ -121,7 +121,7 @@
             torch.save(model.cond_pred.state_dict(), cond_m)
             if args.train_emb:
                 torch.save(model.cond_embed_layer.state_dict(), cond_e)
-        for i in range(100):
+        for i in range(1):
             print 'Epoch %d @ %s'%(i+1, datetime.datetime.now())
             print ' Loss = %s'%epoch_train(
                     model, optimizer, BATCH_SIZE,