diff --git a/Readme.md b/Readme.md
index 79177e2..e10de90 100644
--- a/Readme.md
+++ b/Readme.md
@@ -12,8 +12,18 @@ A CNN based pytorch implementation on facial expression recognition (FER2013 and
 - sklearn (plot confusion matrix)
 
 ## Visualize for a test image by a pre-trained model ##
-- Firstly, download the pre-trained model from https://drive.google.com/open?id=1Oy_9YmpkSKX1Q8jkOhJbz3Mc7qjyISzU (or https://pan.baidu.com/s/1w2TAWzaqh8YvT-1I6rojAg, key:skg2) and then put it in the "FER2013_VGG19" folder; Next, Put the test image (rename as 1.jpg) into the "images" folder, then 
-- python visualize.py
+- Firstly, download the pre-trained model from https://drive.google.com/open?id=1Oy_9YmpkSKX1Q8jkOhJbz3Mc7qjyISzU (or https://pan.baidu.com/s/1w2TAWzaqh8YvT-1I6rojAg, key:skg2) and then put it in the "FER2013_VGG19" folder; Next, Put the test image (rename as 1.jpg) into the "images" folder, then if you use GPU
+
+```
+python visualize.py
+```
+
+or if you use CPU,
+
+```
+python visualize.py --cpu
+```
+
 
 ## FER2013 Dataset ##
 - Dataset from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
diff --git a/images/results/.gitkeep b/images/results/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/visualize.py b/visualize.py
index 5133971..bd43aeb 100644
--- a/visualize.py
+++ b/visualize.py
@@ -10,12 +10,18 @@
 import torch.nn.functional as F
 import os
 from torch.autograd import Variable
+import argparse
 
 import transforms as transforms
 from skimage import io
 from skimage.transform import resize
 from models import *
 
+parser = argparse.ArgumentParser(description='demo script')
+parser.add_argument('--cpu', help="use this option if you run this script on cpu", action='store_true')
+args = parser.parse_args()
+print(args)
+
 cut_size = 44
 
 transform_test = transforms.Compose([
@@ -39,15 +45,23 @@ def rgb2gray(rgb):
 class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 
 net = VGG('VGG19')
-checkpoint = torch.load(os.path.join('FER2013_VGG19', 'PrivateTest_model.t7'))
+
+if args.cpu:
+    checkpoint = torch.load(os.path.join('FER2013_VGG19', 'PrivateTest_model.t7'), map_location='cpu')
+else:
+    checkpoint = torch.load(os.path.join('FER2013_VGG19', 'PrivateTest_model.t7'))
 net.load_state_dict(checkpoint['net'])
-net.cuda()
+
 net.eval()
 
 ncrops, c, h, w = np.shape(inputs)
 
 inputs = inputs.view(-1, c, h, w)
-inputs = inputs.cuda()
+
+if not args.cpu:
+    net.cuda()
+    inputs = inputs.cuda()
+
 inputs = Variable(inputs, volatile=True)
 outputs = net(inputs)