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Make betainc derivative more numerically stable by using log1p (and handling NaN's in the case when a = 1 or b = 1).

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PiperOrigin-RevId: 289918587
Change-Id: I266fd10c0e34ad30291d061636926c945fe0f824
This commit is contained in:
Srinivas Vasudevan 2020-01-15 12:56:06 -08:00 committed by TensorFlower Gardener
parent 18369ac065
commit 86fa42f516
1 changed files with 4 additions and 2 deletions
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6
tensorflow/python/ops/math_grad.py
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@ -944,8 +944,10 @@ def _BetaincGrad(op, grad):
log_beta = (
gen_math_ops.lgamma(a) + gen_math_ops.lgamma(b) -
gen_math_ops.lgamma(a + b))
partial_x = math_ops.exp((b - 1) * math_ops.log(1 - x) +
(a - 1) * math_ops.log(x) - log_beta)
# We use xlog1py and xlogy since the derivatives should tend to
# zero one one of the tails when a is 1. or b is 1.
partial_x = math_ops.exp(math_ops.xlog1py(b - 1, -x) +
math_ops.xlogy(a - 1, x) - log_beta)
# TODO(b/36815900): Mark None return values as NotImplemented
if compat.forward_compatible(2020, 3, 14):