View source on GitHub |

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 | |
---|---|

`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. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/grad_pass_through