jax.numpy.prod

jax.numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False, initial=None, where=None, promote_integers=True)

Return product of the array elements over a given axis.

JAX implementation of numpy.prod().

Parameters:

• a (ArrayLike) – Input array.

• axis (Axis | None) – int or array, default=None. Axis along which the product to be computed. If None, the product is computed along all the axes.

• dtype (DTypeLike | None | None) – The type of the output array. Default=None.

• keepdims (bool) – bool, default=False. If true, reduced axes are left in the result with size 1.

• initial (ArrayLike | None | None) – int or array, Default=None. Initial value for the product.

• where (ArrayLike | None | None) – int or array, default=None. The elements to be used in the product. Array should be broadcast compatible to the input.

• promote_integers (bool) – bool, default=True. If True, then integer inputs will be promoted to the widest available integer dtype, following numpy’s behavior. If False, the result will have the same dtype as the input. promote_integers is ignored if dtype is specified.

• out (None | None) – Unused by JAX.

Returns:

An array of the product along the given axis.

Return type:

Array

See also

• jax.numpy.sum(): Compute the sum of array elements over a given axis.

• jax.numpy.max(): Compute the maximum of array elements over given axis.

• jax.numpy.min(): Compute the minimum of array elements over given axis.

Examples

By default, jnp.prod computes along all the axes.

>>> x = jnp.array([[1, 3, 4, 2],
...                [5, 2, 1, 3],
...                [2, 1, 3, 1]])
>>> jnp.prod(x)
Array(4320, dtype=int32)

If axis=1, product is computed along axis 1.

>>> jnp.prod(x, axis=1)
Array([24, 30,  6], dtype=int32)

If keepdims=True, ndim of the output is equal to that of the input.

>>> jnp.prod(x, axis=1, keepdims=True)
Array([[24],
       [30],
       [ 6]], dtype=int32)

To include only specific elements in the sum, you can use a``where``.

>>> where=jnp.array([[1, 0, 1, 0],
...                  [0, 0, 1, 1],
...                  [1, 1, 1, 0]], dtype=bool)
>>> jnp.prod(x, axis=1, keepdims=True, where=where)
Array([[4],
       [3],
       [6]], dtype=int32)
>>> where = jnp.array([[False],
...                    [False],
...                    [False]])
>>> jnp.prod(x, axis=1, keepdims=True, where=where)
Array([[1],
       [1],
       [1]], dtype=int32)