This is one thing that confuses me. Why Keras is still a separate brand? Why everything isn't under just tensorflow namespace instead of having to do tf.keras all the time. I really wish tf just had one API and just one thing to learn.
I doubt it, it's more likely that they are creating higher level abstractions atop the lower level ones and are advertising/documenting the higher level ones
Keras is part of the problem for me. It is rather rigid and it's hard to get around. Works super well for the regular use case. On the other hand, when you want to start doing custom stuff, it's hell.
https://medium.com/tensorflow/standardizing-on-keras-guidanc...