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def create_model():
    
    IMG_SHAPE = (160, 160, 3)
    base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights="imagenet")
    #base_model.summary()
    base_model.trainable = False
    global_average_layer = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
    prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid')(global_average_layer)
    model = tf.keras.models.Model(inputs=base_model.input, outputs=prediction_layer)
    #model.summary()

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def create_model():
    model = Sequential()

    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_WIDTH, IMG_HEIGHT, 3)))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))

    model.add(Conv2D(64, (3, 3), activation='relu'))

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def data_gen(img_names, batch_size, colorspace):
    c = 0
    n = img_names #List of training images
    random.shuffle(n)
        
    while (True):
        img = np.zeros((batch_size, 150, 150, 3)).astype('float')
        labels = []
      
        #initially from 0 to 16, c = 0. 

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_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip', origin=_URL, extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')

train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')  # directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')  # directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')  # directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')  # directory with our validation dog pictures

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import cv2
import os
from PIL import Image
import numpy as np
import tensorflow as tf

# assume all variables are defined. That is not the bug.
def create_img(path):
    #Function to load,normalize and return image 
    im = cv2.imread(path)

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import cv2
import dlib
import numpy as np

def shape_to_np(shape, dtype="int"):
	# initialize the list of (x, y)-coordinates
	coords = np.zeros((68, 2), dtype=dtype)
	# loop over the 68 facial landmarks and convert them
	# to a 2-tuple of (x, y)-coordinates
	for i in range(0, 68):