tf.grad_pass_through

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Creates a grad-pass-through op with the forward behavior provided in f.

Aliases:

  • tf.compat.v1.grad_pass_through
  • tf.compat.v2.grad_pass_through
tf.grad_pass_through(f)

Use this function to wrap any op, maintaining its behavior in the forward pass, but replacing the original op in the backward graph with an identity. For example:

x = tf.Variable(1.0, name="x")
z = tf.Variable(3.0, name="z")

with tf.GradientTape() as tape:
  # y will evaluate to 9.0
  y = tf.grad_pass_through(x.assign)(z**2)
# grads will evaluate to 6.0
grads = tape.gradient(y, z)

Another example is a 'differentiable' moving average approximation, where gradients are allowed to flow into the last value fed to the moving average, but the moving average is still used for the forward pass:

x = ... # Some scalar value
# A moving average object, we don't need to know how this is implemented
moving_average = MovingAverage()
with backprop.GradientTape() as tape:
  # mavg_x will evaluate to the current running average value
  mavg_x = tf.grad_pass_through(moving_average)(x)
grads = tape.gradient(mavg_x, x) # grads will evaluate to 1.0

Args:

  • f: function f(*x) that returns a Tensor or nested structure of Tensor outputs.

Returns:

A function h(x) which returns the same values as f(x) and whose gradients are the same as those of an identity function.

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