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Creates a grad-pass-through op with the forward behavior provided in f.
Compat aliases for migration
See Migration guide for more details.
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
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Code samples licensed under the Apache 2.0 License.