text: from numpy import load, expand_dims, asarray, savez_compressed
  from keras.models import load_model
  def get_embedding(model, face_pixels):
      face_pixels = face_pixels.astype('float32')
      mean, std = face_pixels.mean(), face_pixels.std()
      face_pixels = (face_pixels - mean) / std
      samples = expand_dims(face_pixels, axis=0)
      prediction = model.predict(samples)
      return prediction
  data = load('/home/akash-peace/Downloads/arya_jon_face_dataset.npz')
  trainx, trainy, testx, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
  model = load_model('/home/akash-peace/Downloads/facenet_keras.h5')
  embed_trainx = []
  for face_pixels in trainx:
      embedding = get_embedding(model, face_pixels)
      embed_trainx.append(embedding)
  embed_trainx = asarray(embed_trainx)
  embed_testx = []
  for face_pixels in testx:
      embedding = get_embedding(model, face_pixels)
      embed_testx.append(embedding)
  embed_testx = asarray(embed_testx)
  savez_compressed('/home/akash-peace/Downloads/arya_jon_embedded_face_dataset.npz', embed_trainx, trainy, embed_testx, testy)