3 ms·

Could you elaborate why this is so impressive? As I understand it, it's because things like TF or PyTorch can only AD specific things as part of a spec vs gener

by viksit 8y ago

Could you elaborate why this is so impressive? As I understand it, it's because things like TF or PyTorch can only AD specific things as part of a spec vs generic functions like this?

twtw 8y ago
Yeah, it's impressive because it's differentiating the language - it doesn't require putting 'tf.' in front of everything and hoping tf has the function you want.
FridgeSeal 8y ago
It's impressive and super super cool, because now any function you can write in Julia, the AD can differentiate. You don't have to hope that your AD/ML package has that function, you can just write it or find it in a package and punch it straight in. That's awesome.
- 0-_-0 8y ago
  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 elementary or composed functions.
  - 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.

by viksit 8y ago

Could you elaborate why this is so impressive? As I understand it, it's because things like TF or PyTorch can only AD specific things as part of a spec vs generic functions like this?

twtw 8y ago

Yeah, it's impressive because it's differentiating the language - it doesn't require putting 'tf.' in front of everything and hoping tf has the function you want.

FridgeSeal 8y ago

It's impressive and super super cool, because now any function you can write in Julia, the AD can differentiate. You don't have to hope that your AD/ML package has that function, you can just write it or find it in a package and punch it straight in. That's awesome.

0-_-0 8y ago
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 elementary or composed functions.
- 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.