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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 differe
by 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 agoYou 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 agoStill a very restricted subset of python, can't autodiff custom types etc
- FridgeSeal 8y agoStill 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.