diff --git a/facelib/FANSegmentator.py b/facelib/FANSegmentator.py
index 1bdc2f3..21b4f07 100644
--- a/facelib/FANSegmentator.py
+++ b/facelib/FANSegmentator.py
@@ -47,8 +47,8 @@ class FANSegmentator(object):
                         self.model.get_layer (s).set_weights ( d[s] )
                 except:
                     io.log_err("Unable to load VGG11 pretrained weights from vgg11_enc_weights.npy")
-                    
-                conv_weights_list = []                
+
+                conv_weights_list = []
                 for layer in self.model.layers:
                     if 'CA.' in layer.name:
                         conv_weights_list += [layer.weights[0]] #Conv2D kernel_weights
@@ -58,8 +58,8 @@ class FANSegmentator(object):
             inp_t = Input ( (resolution, resolution, 3) )
             real_t = Input ( (resolution, resolution, 1) )
             out_t = self.model(inp_t)
-            
-            #loss = K.mean(10*K.square(out_t-real_t))            
+
+            #loss = K.mean(10*K.square(out_t-real_t))
             loss = K.mean(10*K.binary_crossentropy(real_t,out_t) )
 
             out_t_diff1 = out_t[:, 1:, :, :] - out_t[:, :-1, :, :]
@@ -68,10 +68,10 @@ class FANSegmentator(object):
             total_var_loss = K.mean( 0.1*K.abs(out_t_diff1), axis=[1, 2, 3] ) + K.mean( 0.1*K.abs(out_t_diff2), axis=[1, 2, 3] )
 
             opt = Adam(lr=0.0001, beta_1=0.5, beta_2=0.999, tf_cpu_mode=2)
-            
+
             self.train_func = K.function  ( [inp_t, real_t], [K.mean(loss)], opt.get_updates( [loss,total_var_loss], self.model.trainable_weights) )
 
-            
+
     def __enter__(self):
         return self
 
@@ -118,19 +118,19 @@ class FANSegmentator(object):
             x = BlurPool(filt_size=3)(x) #x = MaxPooling2D()(x)
 
             x1 = x = Conv2D(ngf*2, kernel_size=3, strides=1, padding='same', activation='relu', name='features.3')(x)
-            x = BlurPool(filt_size=3)(x) 
+            x = BlurPool(filt_size=3)(x)
 
             x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.6')(x)
             x2 = x = Conv2D(ngf*4, kernel_size=3, strides=1, padding='same', activation='relu', name='features.8')(x)
-            x = BlurPool(filt_size=3)(x) 
+            x = BlurPool(filt_size=3)(x)
 
             x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.11')(x)
             x3 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.13')(x)
-            x = BlurPool(filt_size=3)(x) 
+            x = BlurPool(filt_size=3)(x)
 
             x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.16')(x)
             x4 = x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', activation='relu', name='features.18')(x)
-            x = BlurPool(filt_size=3)(x) 
+            x = BlurPool(filt_size=3)(x)
 
             x = Conv2D(ngf*8, kernel_size=3, strides=1, padding='same', name='CA.1')(x)