diff --git a/experiments/camera_demo.py b/experiments/camera_demo.py
index d7f5e2c..55ac6f9 100644
--- a/experiments/camera_demo.py
+++ b/experiments/camera_demo.py
@@ -11,7 +11,12 @@
 
 def run_demo(args, mirror=False):
 	style_model = Net(ngf=args.ngf)
-	style_model.load_state_dict(torch.load(args.model))
+	model_dict = torch.load(args.model)
+	model_dict_clone = model_dict.copy()
+	for key, value in model_dict_clone.items():
+		if key.endswith(('running_mean', 'running_var')):
+			del model_dict[key]
+	style_model.load_state_dict(model_dict, False)
 	style_model.eval()
 	if args.cuda:
 		style_loader = StyleLoader(args.style_folder, args.style_size)
@@ -57,8 +62,9 @@ def run_demo(args, mirror=False):
 			simg = style_v.cpu().data[0].numpy()
 			img = img.cpu().clamp(0, 255).data[0].numpy()
 		else:
-			simg = style_v.data().numpy()
+			simg = style_v.data.numpy()
 			img = img.clamp(0, 255).data[0].numpy()
+		simg = np.squeeze(simg)
 		img = img.transpose(1, 2, 0).astype('uint8')
 		simg = simg.transpose(1, 2, 0).astype('uint8')
 
diff --git a/experiments/main.py b/experiments/main.py
index 42e3933..37debf9 100644
--- a/experiments/main.py
+++ b/experiments/main.py
@@ -242,7 +242,12 @@ def evaluate(args):
     style = utils.preprocess_batch(style)
 
     style_model = Net(ngf=args.ngf)
-    style_model.load_state_dict(torch.load(args.model), False)
+    model_dict = torch.load(args.model)
+    model_dict_clone = model_dict.copy()
+    for key, value in model_dict_clone.items():
+        if key.endswith(('running_mean', 'running_var')):
+            del model_dict[key]
+    style_model.load_state_dict(model_dict, False)
 
     if args.cuda:
         style_model.cuda()
diff --git a/experiments/utils.py b/experiments/utils.py
index 131a984..bf9afcc 100644
--- a/experiments/utils.py
+++ b/experiments/utils.py
@@ -14,7 +14,7 @@
 import torch
 from PIL import Image
 from torch.autograd import Variable
-from torch.utils.serialization import load_lua
+from torchfile import load as load_lua
 
 from net import Vgg16
 
@@ -124,27 +124,3 @@ def get(self, i):
 
     def size(self):
         return len(self.files)
-
-def matSqrt(x):
-    U,D,V = torch.svd(x)
-    return U * (D.pow(0.5).diag()) * V.t()
-
-def color_match(src, dst):
-    src_flat = src.view(3,-1)
-    dst_flat = dst.view(3,-1)
-
-    src_mean = src_flat.mean(1, True)
-    src_std = src_flat.std(1, True)
-    src_norm = (src_flat - src_mean) / src_std
-
-    dst_mean = dst_flat.mean(1, True) 
-    dst_std = dst_flat.std(1, True)
-    dst_norm = (dst_flat - dst_mean) / dst_std
-
-    src_flat_cov_eye = src_norm @ src_norm.t() + Variable(torch.eye(3).cuda())
-    dst_flat_cov_eye = dst_norm @ dst_norm.t() + Variable(torch.eye(3).cuda())
-
-    src_flat_nrom_trans = matSqrt(dst_flat_cov_eye) * \
-        matSqrt(src_flat_cov_eye).inverse * src_norm
-    src_flat_transfer = src_flat_nrom_trans * dst_std + dst_mean
-    return src_flat_transfer.view_as(src)