diff --git a/train.py b/train.py
index 4b8a3a2a..3c968f18 100644
--- a/train.py
+++ b/train.py
@@ -72,6 +72,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
     print(" | > Epoch {}/{}".format(epoch, c.epochs))
     progbar = Progbar(len(data_loader.dataset) / c.batch_size)
     n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
+    progbar_display = {}
     for num_iter, data in enumerate(data_loader):
         start_time = time.time()
 
@@ -127,6 +128,7 @@ def train(model, criterion, data_loader, optimizer, epoch):
             attention_loss = criterion(alignments, M, mel_lengths_var)
             loss += mk * attention_loss
             avg_attn_loss += attention_loss.data[0]
+            progbar_display['attn_loss'] = attention_loss.data[0]
 
         # backpass and check the grad norm
         loss.backward()
@@ -139,14 +141,14 @@ def train(model, criterion, data_loader, optimizer, epoch):
 
         step_time = time.time() - start_time
         epoch_time += step_time
+        
+        progbar_display['total_loss'] =  loss.data[0]
+        progbar_display['linear_loss'] = linear_loss.data[0]
+        progbar_display['mel_loss'] = mel_loss.data[0]
+        progbar_display['grad_norm'] = grad_norm
 
         # update
-        progbar.update(num_iter+1, values=[('total_loss', loss.data[0]),
-                                           ('linear_loss',
-                                            linear_loss.data[0]),
-                                           ('mel_loss', mel_loss.data[0]),
-                                           ('attn_loss', attention_loss.data[0]),
-                                           ('grad_norm', grad_norm)])
+        progbar.update(num_iter+1, values=[tuple(progbar_display.iteritems())])
         avg_linear_loss += linear_loss.data[0]
         avg_mel_loss += mel_loss.data[0]