text: import tensorflow as tf
  from tensorflow.keras.applications.vgg16 import VGG16 
  from tensorflow.keras import models
  from tensorflow.keras.preprocessing import image
  from tensorflow.keras.applications.vgg16 import preprocess_input
  import numpy as np
  import cv2
  
  # prebuild model with pre-trained weights on imagenet
  base_model = VGG16(weights='imagenet', include_top=True)
  print (base_model)
  for i, layer in enumerate(base_model.layers):
  	print (i, layer.name, layer.output_shape)
  
  # extract features from block4_pool block
  model = models.Model(inputs=base_model.input, 
  	outputs=base_model.get_layer('block4_pool').output)
  
  img_path = 'cat.jpg'
  img = image.load_img(img_path, target_size=(224, 224))
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  x = preprocess_input(x)
  
  # get the features from this block
  features = model.predict(x)
  print(features)
  
  