Class UnconnectedGradients

tensorflow/python/ops/unconnected_gradients.py:27–43

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 run

@tf_export("UnconnectedGradients")
class UnconnectedGradients(enum.Enum):
  """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 y
  * `ZERO`: Indicates that a zero tensor will be returned in the shape of x.
  """
  NONE = "none"
  ZERO = "zero"