jax.numpy.apply_over_axes

jax.numpy.apply_over_axes(func, a, axes)

Apply a function repeatedly over specified axes.

JAX implementation of numpy.apply_over_axes().

Parameters:

• func (Callable[[ArrayLike, int], Array]) – the function to apply, with signature func(Array, int) -> Array, and where y = func(x, axis) must satisfy y.ndim in [x.ndim, x.ndim - 1].

• a (ArrayLike) – N-dimensional array over which to apply the function.

• axes (Sequence[int]) – the sequence of axes over which to apply the function.

Returns:

An N-dimensional array containing the result of the repeated function application.

Return type:

Array

See also

• jax.numpy.apply_along_axis(): apply a 1D function along a single axis.

Examples

This function is designed to have similar semantics to typical associative jax.numpy reductions over one or more axes with keepdims=True. For example:

>>> x = jnp.array([[1, 2, 3],
...                [4, 5, 6]])

>>> jnp.apply_over_axes(jnp.sum, x, [0])
Array([[5, 7, 9]], dtype=int32)
>>> jnp.sum(x, [0], keepdims=True)
Array([[5, 7, 9]], dtype=int32)

>>> jnp.apply_over_axes(jnp.min, x, [1])
Array([[1],
       [4]], dtype=int32)
>>> jnp.min(x, [1], keepdims=True)
Array([[1],
       [4]], dtype=int32)

>>> jnp.apply_over_axes(jnp.prod, x, [0, 1])
Array([[720]], dtype=int32)
>>> jnp.prod(x, [0, 1], keepdims=True)
Array([[720]], dtype=int32)