automatic-differentiation
Here are 181 public repositories matching this topic...
I'm using TF 2.0, and I get this error when I import tangent, due to a list of non-differentiable functions that includes tf.to_float (line 60), which is deprecated:
https://www.tensorflow.org/versions/r1.14/api_docs/python/tf/to_float
-
Updated
Sep 5, 2020 - OCaml
I found that function mod2pi is not implemented yet, but mod works. Is there any list of implemented functions? Minimal working example is:
using Zygote
# This is working
gradient(x -> mod(x, 2pi), 1.)
# This is not
gradient(x -> mod2pi(x), 1.)
-
Updated
Jan 4, 2020 - Scala
-
Updated
Sep 12, 2020 - Nim
-
Updated
Sep 11, 2020 - Go
-
Updated
Sep 12, 2020 - Python
-
Updated
Aug 30, 2020 - C++
-
Updated
May 10, 2018 - Haskell
Description
@JamesYang007 reports in their own repo readme) that there is an issue with math/stan/math/rev/functor/adj_jac_apply.hpp on line 513 and suggests adding a stan::math:: qualifier to the use of apply to avoid conflict with C++17.
Expected Output
Compilation in C++17 on GCC 10 without having to hand tweak the code.
-
Updated
Aug 6, 2020 - C++
-
Updated
Aug 27, 2020 - Julia
-
Updated
Sep 7, 2020 - C++
-
Updated
Nov 16, 2016 - Python
-
Updated
Jul 9, 2020 - Python
-
Updated
Sep 11, 2020 - Kotlin
-
Updated
Jan 10, 2018 - Python
-
Updated
Sep 12, 2020 - Julia
profiles.h updates
At the moment profiles.h (in pkg/profiles) lacks many (any?) comments. Also lots of variables are declared somewhat separately from where they are associated with heap storage.
Both these make it a bit hard to read.
It would be nicer if it was called PROFILES.h too.
-
Updated
Jul 1, 2020 - Haskell
-
Updated
Sep 10, 2020 - Julia
-
Updated
Aug 28, 2020 - Julia
-
Updated
Apr 5, 2019 - C++
-
Updated
Aug 15, 2020 - Rust
-
Updated
Aug 19, 2020 - Julia
ChainRules.ignore
Zygote.ignore is a lovely piece of functionality that we aught to be able to provide at the ChainRulesCore-level. It would also be incredibly straightforward to implement.
It'll only work properly with Zygote-like ADs ofc, but it's an improvement over it not working at all.
-
Updated
Sep 11, 2020 - Python
-
Updated
Sep 1, 2020 - Julia
Improve this page
Add a description, image, and links to the automatic-differentiation topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the automatic-differentiation topic, visit your repo's landing page and select "manage topics."
In operations_broadcast_test.go there are some tests that are not yet filled in. The point is to test that broadcasting works for different shapes. The semantics of broadcast probably isn't clear, so please do send me a message for anything.
This is a good first issue for anyone looking to get interested