diff --git a/data/image_folder.py b/data/image_folder.py
index d0b4b308a82..80c0b9a2d93 100644
--- a/data/image_folder.py
+++ b/data/image_folder.py
@@ -44,8 +44,7 @@ def __init__(self, root, transform=None, return_paths=False,
         imgs = make_dataset(root)
         if len(imgs) == 0:
             raise(RuntimeError("Found 0 images in: " + root + "\n"
-                               "Supported image extensions are: " +
-                               ",".join(IMG_EXTENSIONS)))
+                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
 
         self.root = root
         self.imgs = imgs
diff --git a/models/base_model.py b/models/base_model.py
index 9cfb761897e..6de961b51a2 100644
--- a/models/base_model.py
+++ b/models/base_model.py
@@ -115,6 +115,7 @@ def get_image_paths(self):
 
     def update_learning_rate(self):
         """Update learning rates for all the networks; called at the end of every epoch"""
+        old_lr = self.optimizers[0].param_groups[0]['lr']
         for scheduler in self.schedulers:
             if self.opt.lr_policy == 'plateau':
                 scheduler.step(self.metric)
@@ -122,7 +123,7 @@ def update_learning_rate(self):
                 scheduler.step()
 
         lr = self.optimizers[0].param_groups[0]['lr']
-        print('learning rate = %.7f' % lr)
+        print('learning rate %.7f -> %.7f' % (old_lr, lr))
 
     def get_current_visuals(self):
         """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
diff --git a/train.py b/train.py
index 9ff8b3d3f15..2852652df82 100644
--- a/train.py
+++ b/train.py
@@ -40,7 +40,7 @@
         iter_data_time = time.time()    # timer for data loading per iteration
         epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch
         visualizer.reset()              # reset the visualizer: make sure it saves the results to HTML at least once every epoch
-
+        model.update_learning_rate()    # update learning rates in the beginning of every epoch.
         for i, data in enumerate(dataset):  # inner loop within one epoch
             iter_start_time = time.time()  # timer for computation per iteration
             if total_iters % opt.print_freq == 0:
@@ -75,4 +75,3 @@
             model.save_networks(epoch)
 
         print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
-        model.update_learning_rate()                     # update learning rates at the end of every epoch.