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...and that's why you are using Keras instead.

I like Keras, but I always find myself having to write TF code whenever I need to implement something more interesting. And debugging, already hard in pure TF, is more complex due to the extra layer.

IMO, learning TF or pytorch is more effective at least in the current state of affairs.

That's true. Keras excels when you have some pre-baked DNN you need to implement quickly; if you plan to do some advanced things, you get back to backend anyway.

But that is precisely how you should be using Keras!

* If you are implementing a standard model (that's 90% of industry use cases, and a large fraction of research use cases as well), Keras primitives considerably simplify your workflow and make you a lot more productive.

* When you need to implement something highly customized or unusual, you can revert back to writing pure TensorFlow code, which will integrate seamlessly with your Keras workflow (via custom layers, functions etc).

Basically, Keras increases your productivity for common use cases, without any flexibility cost for rare/custom use cases. It is meant to be used together with TF, not as a replacement for TF.

You also created the backend abstraction that let you customize a lot without real "raw TensorFlow". Thank you Mr. Chollet.

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