jax.numpy.max#

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 ofnumpy.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 resultwith 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 usedin the maximum. Array should be broadcast compatible to the input.initial must be specified whenwhere 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 givenaxis.

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

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

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)

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

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

Ifkeepdims=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 usewhere. 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)