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 rese

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.