tf.grad_pass_through

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

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.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