tf.keras.ops.jvp

Computes a (forward-mode) Jacobian-vector product of fun.

tf.keras.ops.jvp(
    fun, primals, tangents, has_aux=False
)

fun Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard Python container of arrays or scalars.
primals The primal values at which the Jacobian of fun should be evaluated. Should be either a tuple or a list of arguments, and its length should be equal to the number of positional parameters of fun.
tangents The tangent vector for which the Jacobian-vector product should be evaluated. Should be either a tuple or a list of tangents, with the same tree structure and array shapes as primals.
has_aux Optional, bool. Indicates whether fun returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default is False.

If has_aux is False, returns a (primals_out, tangents_out) pair, where primals_out is fun(*primals), and tangents_out is the Jacobian-vector product of fun evaluated at primals with tangents. The tangents_out value has the same Python tree structure and shapes as primals_out.

If has_aux is True, returns a (primals_out, tangents_out, aux) tuple where aux is the auxiliary data returned by fun.

Example:

from keras import ops
a1, a2 = ops.convert_to_tensor(0.1), ops.convert_to_tensor(0.2)
primals, tangents = ops.jvp(ops.sin, (a1,), (a2,))
primals
0.09983342
tangents
0.19900084