4 ms·

How is that different from e.g. Tensorflow? You can compose any function you want that has differentiable components, and (re)define the derivatives of elementa

• Setepenre 8y ago
  No tensorflow does not allow you to do that. Tensorflow differentiate a TF graph. Flux differentiate Julia's IR. Tensorflow has OP(eration)s that are differentiable that you can compose and that's it. If you want to implement something that is going to be differentiable you need to implement it using Tensorflow OPs or add your own OPs to [tensorflow] with their gradient. With Flux you can take code written by a random guy on the internet that never thought about using his stuff in ML and Flux will be able to differentiate it anyway. [1]: https://www.tensorflow.org/guide/extend/op https://www.tensorflow.org/guide/extend/op
  • 0-_-0 8y ago
    You mean like AutoGraph in Tensorflow? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/autograph https://github.com/tensorflow/tensorflow/tree/master/tensorf...
    • byt143 8y ago
      Still a very restricted subset of python, can't autodiff custom types etc
    • FridgeSeal 8y ago
      Still a small subset of the language and features They've also got to manually implement the Python -> autograph translation for a whole variety of language features (so any language features that get added or changed will break autograph until it's updated. Flux gets this essentially for free, for the entire Julia language, without the need to manually build that language -> tensorflow translation layer. With the added benefit of Julia's non-trivial performance benefits.