diff --git a/Deep Learning/Dogs_vs_cats_CNN.py b/Deep Learning/Dogs_vs_cats_CNN.py
index 73f0a7f..6a443a3 100644
--- a/Deep Learning/Dogs_vs_cats_CNN.py	
+++ b/Deep Learning/Dogs_vs_cats_CNN.py	
@@ -1,14 +1,17 @@
-
-# coding: utf-8
-
-# In[37]:
-
-
 import numpy as np
+import random
+import pickle
 import matplotlib.pyplot as plt
+%matplotlib inline
 import cv2
 import os
 from tqdm import tqdm
+import tensorflow as tf
+from tensorflow import keras
+from keras.models import Sequential
+from keras.layers import Activation, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
+from keras.callbacks import TensorBoard
+import time
 
 DIR='C:/Users/Asus/Downloads/ML DATASETS/Dogs Vs Cats/'
 CATEGORIES = ['dog', 'cat']
@@ -18,28 +21,16 @@
 for img in tqdm(os.listdir(train_path)):
     label = img.split('.')[0]
     img_array = cv2.imread(os.path.join(train_path, img), cv2.IMREAD_GRAYSCALE)
-    plt.imshow(img_array, cmap='gray')
+    plt.imshow(img_array, cmap='red')
     plt.show()
     break
 
-
-# In[38]:
-
-
 print(img_array)
 
-
-# In[39]:
-
-
 new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE), 1)
-plt.imshow(new_array, cmap="gray")
+plt.imshow(new_array, cmap="red")
 plt.show()
 
-
-# In[40]:
-
-
 training_data = []
 
 def create_train_data():
@@ -54,24 +45,9 @@ def create_train_data():
             pass
         
 create_train_data()
-
-
-# In[41]:
-
-
 print(len(training_data))
-
-
-# In[42]:
-
-
-import random
 random.shuffle(training_data)
 
-
-# In[43]:
-
-
 X=[]
 y=[]
 for features,labels in training_data:
@@ -79,13 +55,6 @@ def create_train_data():
     y.append(labels)
     
 X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
-
-
-# In[44]:
-
-
-import pickle
-
 pickle_out = open("X.pickle", "wb")
 pickle.dump(X, pickle_out)
 pickle_out.close()
@@ -94,23 +63,6 @@ def create_train_data():
 pickle.dump(y, pickle_out)
 pickle_out.close()
 
-
-# In[45]:
-
-
-#################################################################
-
-
-# In[46]:
-
-
-import tensorflow as tf
-from tensorflow import keras
-from keras.models import Sequential
-from keras.layers import Activation, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
-from keras.callbacks import TensorBoard
-import time
-
 X = pickle.load(open("X.pickle", "rb"))
 y = pickle.load(open("y.pickle", "rb"))
 
@@ -140,17 +92,11 @@ def create_train_data():
 
 model.fit(X, y, batch_size=32, epochs=10, validation_split=0.1, callbacks=[tensorboard])
 
-
-# In[47]:
-
-
 predict=['Dog', 'Cat']
 def prepare(filepath):
     image_array=cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
     new_img = cv2.resize(image_array, (IMG_SIZE, IMG_SIZE))
     return new_img.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
-
 prediction=model.predict([prepare('D:\250px-Gatto_europeo4.jpg')])
 print(predict[int(prediction[0][0])])
-    
-
+