automatic-differentiation
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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
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.)
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Summary:
The functions for the categorical distribution only accept a column vector, it would be great if it could accept also row vectors.
Description:
I use the categorical distribution to go over a matrix N_obs x N_probabilities, so it's more natural for me to use row vectors than column vectors.
Current functions:
real categorical_lpmf(ints y | vector theta)
real
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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.
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Some of them can be ported over from Zygote.
cf. FluxML/Zygote.jl#906
https://github.com/FluxML/Zygote.jl/blob/956cbcf3c572c0eb09c146189bb38b1b434634ff/src/lib/array.jl#L130
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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