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jax.numpy.max — JAX documentation

jax.numpy.max(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]#

Return the maximum of the array elements along a given axis.

JAX implementation of numpy.max().

Parameters:

• a (ArrayLike) – Input array.

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

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

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

• where (ArrayLike | None) – int or array of boolean dtype, default=None. The elements to be used in the maximum. Array should be broadcast compatible to the input. initial must be specified when where is used.

• out (None) – Unused by JAX.

Returns:

An array of maximum values along the given axis.

Return type:

Array

See also

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

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

• jax.numpy.prod(): Compute the product of array elements along a given axis.

Examples

By default, jnp.max computes the maximum of elements along all the axes.

>>> x = jnp.array([[9, 3, 4, 5],
...                [5, 2, 7, 4],
...                [8, 1, 3, 6]])
>>> jnp.max(x)
Array(9, dtype=int32)

If axis=1, the maximum will be computed along axis 1.

>>> jnp.max(x, axis=1)
Array([9, 7, 8], dtype=int32)

If keepdims=True, ndim of the output will be same of that of the input.

>>> jnp.max(x, axis=1, keepdims=True)
Array([[9],
       [7],
       [8]], dtype=int32)

To include only specific elements in computing the maximum, you can use where. It can either have same dimension as input

>>> where=jnp.array([[0, 0, 1, 0],
...                  [0, 0, 1, 1],
...                  [1, 1, 1, 0]], dtype=bool)
>>> jnp.max(x, axis=1, keepdims=True, initial=0, where=where)
Array([[4],
       [7],
       [8]], dtype=int32)

or must be broadcast compatible with input.

>>> where = jnp.array([[False],
...                    [False],
...                    [False]])
>>> jnp.max(x, axis=0, keepdims=True, initial=0, where=where)
Array([[0, 0, 0, 0]], dtype=int32)

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