mindspore.numpy.norm(x, ord=None, axis=None, keepdims=False)[source]

Matrix or vector norm. This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the ord parameter.


Nuclear norm and 2-norm are not supported for matrices.

  • x (Union[int, float, bool, list, tuple, Tensor]) – Input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned.

  • ord (Union[None, 'fro', 'nuc', inf, -inf, int, float], optional) – Order of the norm. inf means numpy’s inf object. The default is None.

  • axis (Union[None, int, 2-tuple of integers], optional) – If axis is an integer, it specifies the axis of x along which to compute the vector norms. If axis is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed. If axis is None then either a vector norm (when x is 1-D) or a matrix norm (when x is 2-D) is returned. The default is None.

  • keepdims (boolean, optional) – If this is set to True, the axes which are normed over are left in the result as dimensions with size one. With this option the result will broadcast correctly against the original x.


Tensor, norm of the matrix or vector(s).


ValueError – If the norm order is not defined.

Supported Platforms:

Ascend GPU CPU


>>> import mindspore.numpy as np
>>> print(np.norm(np.arange(9).astype(np.float32)))