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Added support for 'softplus' and 'logSigmoid' and their VJPs. #148
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eaplatanios
changed the title
Shashi456
reviewed
May 31, 2019
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Is this blocked on any other PR? If not, could you resolve the conflicts? |
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I don't think it's blocked. I'll resolve the conflicts later today because I'm currently at a conference. |
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@rxwei I finally updated this and ran tests locally. It should be ready to merge. The only failing tests I see have to do with the lazy tensor implementation and so are not relevant to this PR. |
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Also verified that all CI tests pass. |
rxwei
reviewed
Jun 21, 2019
rxwei
approved these changes
Jun 21, 2019
| @@ -923,6 +923,14 @@ internal func _vjpSigmoid<T: TensorFlowFloatingPoint>( | |||
| (sigmoid(x), { v in Raw.sigmoidGrad(x, dy: v) }) | |||
| } | |||
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| /// Returns the log-sigmoid of the specified tensor element-wise. Specifically, | |||
| /// `y = log(1 / (1 + exp(-x)))`. For numerical stability, we use `y = -softplus(-x)`. | |||
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Suggested change
| /// `y = log(1 / (1 + exp(-x)))`. For numerical stability, we use `y = -softplus(-x)`. | |
| /// `log(1 / (1 + exp(-x)))`. For numerical stability, we use `-softplus(-x)`. |
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