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You can actually implement most new layers or experimental ideas using frameworks like pytorch or tensorflow. They support fairly low-level primitives which are
by tehsauce 7y ago
You can actually implement most new layers or experimental ideas using frameworks like pytorch or tensorflow. They support fairly low-level primitives which are much more flexible than keras or pytorch sequential models.
That said C/C++ is still very useful for implementing high performance systems.
- bigmit37 7y agoAh. I haven’t played around with Pytorch custom layers enough so I am going to give it a try. I was initially trying to do it in keras but Keras was just using tensorflow layers for most operations so I couldn’t tweak the original tensorflow layers through keras easily.
- tehsauce 7y agoThe concept of "layers" is not in fact enforced by pytorch or tensorflow at all. This tutorial is a really nice overview of the levels of abstraction available in pytorch https://pytorch.org/tutorials/beginner/nn_tutorial.html https://pytorch.org/tutorials/beginner/nn_tutorial.html