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My love for Keras’ clear and well thought-out API design is matched by my distaste and frustration with TensorFlow’s :-/
TensorFlow is like C++: it's complex, but the parts you actually have to use are straightforward (math ops, gradient tape, http://tf.data , tf.distribute). And like C++, it's the fastest & most powerful option you have available. Just you have to use the right subset.
TF is just NumPy with gradients, TPU/GPU acceleration, and large-scale computation distribution. And the ability to export programs to mobile devices, javascript, etc. Basically, it's the layer of infrastructure you need if you're serious about ML.
TF isn't meant to be easy to use (that's more like Keras). It's meant to be powerful -- fast, scalable, production-grade. It's 100% the best infrastructure layer available today to build Keras. Though I like Jax as well (but it isn't nearly as mature).
Well, speaking of TF and C++... The one thing that still keep me away from TF (and unfortunately, Keras), is the limited support of the framework on the C++ side - as opposed to PyTorch, which has a 1:1 C++ mapping of almost all critical features.
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