Commit 29550762 authored by A. Unique TensorFlower's avatar A. Unique TensorFlower Committed by TensorFlower Gardener
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Fixes unit tests for inverse hyperbolic functions that were failing because...

Fixes unit tests for inverse hyperbolic functions that were failing because numeric gradients were computed too close to a branch cut (for complex arguments) or singularity (for real arguments) where the function is not differentiable (See, e.g., http://mathworld.wolfram.com/BranchCut.html). This change moves the test points away from the branch cut/singularity.

Improves precision of double precision numerical gradients by using a smaller step size delta (the optimal for symmetric difference approximation with functions computed with O(epsilon) error is epsilon^(1/3), so for double64 it is ~1e-5).

PiperOrigin-RevId: 163706297
parent 99b190a1
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