old_contents: from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
  from keras.preprocessing import image
  import numpy as np
  
  model = VGG16(weights='imagenet')
  model.summary()
  
  img = image.load_img('elephant.jpg', target_size=(224, 224))
  x = image.img_to_array(img)
  print(x)
  print(x.shape)
  
  x = np.expand_dims(x, axis=0)
  print(x.shape)
  
  preds = model.predict(preprocess_input(x))
  print(preds.shape)
  
new_contents: from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
  from keras.preprocessing import image
  import numpy as np
  
  model = VGG16(weights='imagenet')
  model.summary()
  
  img = image.load_img('elephant.jpg', target_size=(224, 224))
  x = image.img_to_array(img)
  
  x = np.expand_dims(x, axis=0)
  
  preds = model.predict(preprocess_input(x))
  results = decode_predictions(preds)[0]
  
  for result in results:
      print(result)
  
subject: Modify to output top5-results