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Controls how gradient computation behaves when y does not depend on x.
The gradient of y with respect to x can be zero in two different ways: there could be no differentiable path in the graph connecting x to y (and so we can statically prove that the gradient is zero) or it could be that runtime values of tensors in a particular execution lead to a gradient of zero (say, if a relu unit happens to not be activated). To allow you to distinguish between these two cases you can choose what value gets returned for the gradient when there is no path in the graph from x to y:
NONE
: Indicates that [None] will be returned if there is no path from x to yZERO
: Indicates that a zero tensor will be returned in the shape of x.NONE
ZERO
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/UnconnectedGradients