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