title: Can you explain difference between tensorflow loading and hdf5 loading in keras model
body: <p>I was trying to load the keras model which I saved during my training.So I went to <a href="https://keras.io/api/models/model_saving_apis/#save_model-function" rel="nofollow noreferrer">keras documentation</a> where I saw this.</p>
  
  <blockquote>
    <p>Only topological loading (by_name=False) is supported when loading
    weights from the TensorFlow format. Note that topological loading
    differs slightly between TensorFlow and HDF5 formats for user-defined
    classes inheriting from tf.keras.Model: HDF5 loads based on a
    flattened list of weights, while the TensorFlow format loads based on
    the object-local names of attributes to which layers are assigned in
    the Model's constructor.</p>
  </blockquote>
  
  <p>Could you please explain the above one?</p>