diff --git a/loss.py b/loss.py
index 2bfadbd..9bbca5b 100644
--- a/loss.py
+++ b/loss.py
@@ -29,7 +29,7 @@ def get_vgg_features(input, layers, input_shape):
     vgg = VGG19(input, input_shape)
     outputs = [layer.output for layer in vgg.layers if layer.name in layers]
     return outputs
-  
+
 
 def calculate_content_loss(content_image, reconstructed_image,
                            content_weight, image_shape, batch_size):
@@ -37,22 +37,21 @@ def calculate_content_loss(content_image, reconstructed_image,
             content_image, CONTENT_LAYERS, image_shape)[0]
     reconstructed_content_features = get_vgg_features(
             reconstructed_image, CONTENT_LAYERS, image_shape)[0]
-   
+
     content_size = tensor_size(content_features) * batch_size
     content_loss = content_weight * (2 * l2_loss(
         reconstructed_content_features - content_features) / content_size)
-    
+
     return content_loss
-    
-def calculate_style_loss(style_image, reconstructed_image,
+
+def calculate_style_loss(style_vgg_features, reconstructed_image,
                          style_weight, style_image_shape, content_image_shape,
                          batch_size):
      # Get outputs of style and content images at VGG layers
-    style_vgg_features = get_vgg_features(
-            style_image, STYLE_LAYERS, style_image_shape)
+
     reconstructed_style_vgg_features = get_vgg_features(
             reconstructed_image, STYLE_LAYERS, content_image_shape)
-    
+
     # Calculate the style features of the style image and output image
     # Style features are the gram matrices of the VGG feature maps
     style_grams = []
@@ -70,7 +69,7 @@ def calculate_style_loss(style_image, reconstructed_image,
         features_T = tf.transpose(features, perm=[0,2,1])
         gram = tf.matmul(features_T, features) / features_size
         style_grams.append(gram)
-        
+
     # Output image style features
     for features in reconstructed_style_vgg_features:
         _, h, w, filters = K.int_shape(features)
@@ -80,20 +79,20 @@ def calculate_style_loss(style_image, reconstructed_image,
         features = K.reshape(features, np.array((batch_size, h * w, filters)))
         features_T = tf.transpose(features, perm=[0,2,1])
         gram = tf.matmul(features_T, features) / size
-        style_rec_grams.append(gram)       
-        
+        style_rec_grams.append(gram)
+
     # Calculate style loss
     style_losses = []
     for style_gram, style_rec_gram in zip(style_grams, style_rec_grams):
         style_gram_size = tensor_size(style_gram)
         l2 = l2_loss(style_rec_gram - style_gram)
         style_losses.append(2 * l2 / style_gram_size)
-    
+
     style_loss = style_weight * reduce(tf.add, style_losses) / batch_size
-    
+
     return style_loss
-    
-    
+
+
 def calculate_tv_loss(x, tv_weight, batch_size):
     tv_y_size = tensor_size(x[:,1:,:,:])
     tv_x_size = tensor_size(x[:,:,1:,:])
@@ -106,6 +105,8 @@ def calculate_tv_loss(x, tv_weight, batch_size):
 def create_loss_fn(style_image, content_weight,
                    style_weight, tv_weight, batch_size):
     style_image = tf.convert_to_tensor(style_image)
+    style_vgg_features = get_vgg_features(
+            style_image, STYLE_LAYERS, K.int_shape(style_image))
 
     def style_transfer_loss(y_true, y_pred):
         """
@@ -115,13 +116,13 @@ def style_transfer_loss(y_true, y_pred):
 
         content_image = y_true
         reconstructed_image = y_pred
-        
+
         content_loss = calculate_content_loss(content_image,
                                               reconstructed_image,
                                               content_weight,
                                               CONTENT_TRAINING_SIZE,
                                               batch_size)
-        style_loss = calculate_style_loss(style_image,
+        style_loss = calculate_style_loss(style_vgg_features,
                                           reconstructed_image,
                                           style_weight,
                                           K.int_shape(style_image),
diff --git a/vgg.py b/vgg.py
index f06a356..87608ba 100644
--- a/vgg.py
+++ b/vgg.py
@@ -46,15 +46,20 @@ def vgg_layers(img_input, input_shape):
 
     return x
 
+weights_file = None
+def cached_file_load():
+    global weights_file
+    if(weights_file is None):
+        weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                                WEIGHTS_PATH_NO_TOP,
+                                cache_subdir='models',
+                                file_hash='253f8cb515780f3b799900260a226db6')
+        weights_file = h5py.File(weights_path)
+    return weights_file
 
 def load_weights(model):
-    weights_path = get_file('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',
-                            WEIGHTS_PATH_NO_TOP,
-                            cache_subdir='models',
-                            file_hash='253f8cb515780f3b799900260a226db6')
-    f = h5py.File(weights_path)
+    f = cached_file_load()
     layer_names = [name for name in f.attrs['layer_names']]
-
     for layer in model.layers:
         if layer.name in layer_names:
             g = f[layer.name]