[itdxer]

Usually each library has it's own architecture. This architecture based on some default classes that implements core functionality for models. I already have a chance to check source code for the popular neural network libraries and I found that their architecture requires some basic classes that are related to the Backpropagation algorithm and it's variation. In neural networks there are a lot of algorithms that really hard to compare and generalize. To make your idea possible I see two ways for that. First one is to change architecture for the main models classes in popular library. In practice for the popular libraries it's a huge amount of work. Even if you found possibility to generalize core architecture with other network that you want implement, probably, you will broke a big part of library that works before. The second one is build a new folder inside of existing library that contains localized code that don't have relation to the core part. How it differs from the decision of a single repository for a different library? In practice its a good way to separate something without relation, because further attempts to combine all in a big library will lead to breakage and bugs that are difficult to debug and fix.

[funkmaster_f]

Different strokes for different folks, man. Help contribute to the research/dev community by helping affect the research/dev community where it matters.