new_contents: 
  from tensorflow import gfile
  from keras.applications.vgg19 import VGG19
  from keras.preprocessing import image
  from keras.applications.vgg19 import preprocess_input
  from keras.models import Model
  import numpy as np
  
  base_model = VGG19(weights='imagenet')
  model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc2').output)
  
  batch_size=10
  reach_last_file=False
  idx = np.array([0,batch_size])
  
  files = gfile.Glob("C:/Users/Administrator/Downloads/ILSVRC2016_DET_test_new.tar/ILSVRC/Data/DET/test/*.jpeg")
  out_file = gfile.Open("C:/Users/Public/Pictures/Sample Pictures/aa.csv", "w+")
  
  out_file.write("filename,"+",".join(["feature"+str(i) for i in range(1,4097)])+"\n")
  while not reach_last_file:
      if idx[1] == len(files):
          reach_last_file = True
  
      files0=files[idx[0]:idx[1]]
      imgs = [image.load_img(files0[i], target_size=(224, 224)) for i in range(len(files0))]
      x = [image.img_to_array(img) for img in imgs]
      x = np.expand_dims(x, axis=0)
      x = np.squeeze(x,0)
      x = preprocess_input(x)
  
      block4_pool_features = model.predict(x)
      for i in range(len(files0)):
          out_file.write(files0[i]+","+ ",".join(["%f" % i for i in block4_pool_features[i]]) + "\n")
  
      idx += batch_size
      idx[1] = min([idx[1],len(files)])
  out_file.close()
  
subject: Create imagenet.py