Make Multinomial robust against batches #4169
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Seems like there are legit test errors. |
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Yeah, I'll go through those tomorrow. |
lucianopaz
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twiecki
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Oct 14, 2020
| @@ -597,14 +597,10 @@ def __init__(self, n, p, *args, **kwargs): | |||
| super().__init__(*args, **kwargs) | |||
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| p = p / tt.sum(p, axis=-1, keepdims=True) | |||
| n = np.squeeze(n) # works also if n is a tensor | |||
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pretty simple fix, just remove some code 👍
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Also needs a line in the release-notes. |
Codecov Report
@@ Coverage Diff @@
## master #4169 +/- ##
==========================================
+ Coverage 88.76% 88.77% +0.01%
==========================================
Files 89 89
Lines 14083 14079 -4
==========================================
- Hits 12501 12499 -2
+ Misses 1582 1580 -2
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Thanks @lucianopaz! |
AlexAndorra
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Oct 14, 2020
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Thanks @lucianopaz ! Funny to see that removing code fixed the issue 😅
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At the moment, our
Multinomialdistribution mangles thenparameter's shape. This makes it very difficult to work with batches that have more than 2 dimensions. This PR addresses the problem and adds a test for it.Thank your for opening a PR!
Depending on what your PR does, here are a few things you might want to address in the description: