tfp.math.custom_gradient  |  TensorFlow Probability (original) (raw)

Embeds a custom gradient into a Tensor.

tfp.math.custom_gradient(
    fx, gx, x, fx_gx_manually_stopped=False, name=None
)

This function works by clever application of stop_gradient. I.e., observe that:

h(x) = stop_gradient(f(x)) + stop_gradient(g(x)) * (x - stop_gradient(x))

is such that h(x) == stop_gradient(f(x)) andgrad[h(x), x] == stop_gradient(g(x)).

In addition to scalar-domain/scalar-range functions, this function also supports tensor-domain/scalar-range functions.

Partial Custom Gradient:

Suppose h(x) = htilde(x, y). Note that dh/dx = stop(g(x)) but dh/dy = None. This is because a Tensor cannot have only a portion of its gradient stopped. To circumvent this issue, one must manually stop_gradient the relevant portions of f, g. For example see the unit-test,test_works_correctly_fx_gx_manually_stopped.