Create model.py

model.py

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+
+import tensorflow as tf
+import pandas as pd
+import matplotlib.pyplot as plt
+import cv2
+import os
+import numpy as np
+
+#data directories
+
+DATADIR  = "/home/spidy/Documents/RJIT/PicData"
+CATEGORIES = ["sfw", "nsfw"]
+
+for category in CATEGORIES:
+    path = os.path.join(DATADIR, category) #path to sfw and nfsw dir
+    for img in os.listdir(path):
+        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
+        plt.imshow(img_array, cmap ="gray")
+        plt.show()
+        break
+    break
+
+
+# In[2]:
+
+
+print (img_array).shape
+
+
+# In[3]:
+
+
+IMG_SIZE = 80
+new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
+plt.imshow(new_array, cmap = 'gray')
+plt.show()
+
+
+# In[4]:
+
+
+training_data = []
+def create_training_data():
+    for category in  CATEGORIES:
+        path = os.path.join(DATADIR, category) #path to sfw and nsfw
+        class_num = CATEGORIES.index(category)
+        for img in os.listdir(path):
+            try:
+                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
+                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
+                training_data.append([new_array, class_num])
+            except Exception as e:
+                pass
+            
+        
+create_training_data()
+
+
+# In[5]:
+
+
+print(len(training_data))
+
+
+# In[6]:
+
+
+import random 
+random.shuffle(training_data)
+
+
+# In[7]:
+
+
+for sample in training_data[:10]:
+    print(sample[1])
+
+
+# In[8]:
+
+
+X = []
+y = []
+
+
+
+# In[9]:
+
+
+for features, label in training_data:
+    X.append(features)
+    y.append(label)
+    
+X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
+
+
+# In[10]:
+
+
+import pickle
+
+pickle_out = open("X.pickle", "wb")
+pickle.dump(X, pickle_out)
+pickle_out.close()
+
+pickle_out = open("y.pickle", "wb")
+pickle.dump(y, pickle_out)
+pickle_out.close()
+
+
+# In[11]:
+
+
+pickle_in = open("X.pickle", "rb")
+X =  pickle.load(pickle_in)
+                 
+
+
+# In[13]:
+
+
+X[1]
+
+
+# In[14]:
+
+
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
+import pickle
+
+X = pickle.load(open("X.pickle", "rb"))
+y = pickle.load(open("y.pickle", "rb"))
+
+X = X/255.0
+y = np.array(y)
+
+model = Sequential()
+model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
+model.add(Activation("relu"))
+model.add(MaxPooling2D(pool_size = (2, 2)))
+
+model.add(Conv2D(64, (3,3)))
+model.add(Activation("relu"))
+model.add(MaxPooling2D(pool_size = (2, 2)))
+
+model.add(Flatten())
+model.add(Dense(64))
+
+model.add(Dense(1))
+model.add(Activation('sigmoid'))
+
+model.compile(loss="binary_crossentropy", 
+             optimizer="adam",
+             metrics=['accuracy'])
+
+model.fit(X, y, batch_size=8, epochs=8, validation_split=0.1)
+
+