diff --git a/tutorials/09 - Image Captioning/sample.py b/tutorials/09 - Image Captioning/sample.py
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+++ b/tutorials/09 - Image Captioning/sample.py	
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+import os
+import numpy as np
+import torch
+import pickle
+import matplotlib.pyplot as plt
+from PIL import Image
+from model import EncoderCNN, DecoderRNN
+from vocab import Vocabulary
+from torch.autograd import Variable
+
+# Hyper Parameters
+embed_size = 128
+hidden_size = 512
+num_layers = 1
+
+# Load vocabulary
+with open('./data/vocab.pkl', 'rb') as f:
+    vocab = pickle.load(f)
+    
+# Load an image array
+images = os.listdir('./data/val2014resized/')
+image_path = './data/val2014resized/' + images[12]  
+with open(image_path, 'r+b') as f:
+    img = np.asarray(Image.open(f))
+image = torch.from_numpy(img.transpose(2, 0, 1)).float().unsqueeze(0) / 255 - 0.5
+
+# Load the trained models
+encoder = torch.load('./encoder.pkl')
+decoder = torch.load('./decoder.pkl')
+
+# Encode the image
+feature = encoder(Variable(image).cuda())
+
+# Set initial states
+state = (Variable(torch.zeros(num_layers, 1, hidden_size).cuda()),
+         Variable(torch.zeros(num_layers, 1, hidden_size)).cuda())
+
+# Decode the feature to caption
+ids = decoder.sample(feature, state)
+
+words = []
+for id in ids:
+    word = vocab.idx2word[id.data[0, 0]]
+    words.append(word)
+    if  word == '<end>':
+        break    
+caption = ' '.join(words)
+
+# Display the image and generated caption
+plt.imshow(img)
+plt.show()
+print (caption)
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