[mlthoughts2018]

Keras still is the very best in terms of expressive models and end to end workflows. It leans heavily on the design idea that you should deliberately design for end to end use cases and all intermediate abstractions should be building blocks that serve precisely that purpose. This is discussed in [0] which IMO is something that deserves to be more widely talked about in software engineering. Lots of other disciplines of software engineering _say_ you should design this way, but in my experience it’s very rare no matter what discipline you’re in. Take TensorFlow itself. It’s a huge mess with no clear abstractions useful for end to end solutions. Just a hodge podge of disparate APIs and way too many underlying engineering concepts were elevated to abstractions for engineer (instead of user) convenience.

Constraining design by end to end use cases is a remarkably robust and useful process.

PyTorch is way better at having clean engineering abstractions than TensorFlow, but still falls short when things like “forward” or maintaining your own training loop and gradient metadata are necessary concepts for a practitioner’s end to end workflow.

[0]: https://blog.keras.io/user-experience-design-for-apis.html

[abakus]

In terms of model expressiveness, I made a functional NN building API for PyTorch (just like keras'), which offers the optimal balance of flexibility and expressiveness: https://github.com/blue-season/pywarm