mindspore.nn.Norm

class mindspore.nn.Norm(axis=(), keep_dims=False)

Computes the norm of vectors, currently including Euclidean norm, i.e., \(L_2\)-norm.

\[norm(x) = \sqrt{\sum_{i=1}^{n} (x_i^2)}\]

Parameters

• axis (Union[tuple, int]) – The axis over which to compute vector norms. Default: ().

• keep_dims (bool) – If true, the axis indicated in axis are kept with size 1. Otherwise, the dimensions in axis are removed from the output shape. Default: False.

Inputs:

• x (Tensor) - Tensor which is not empty. The data type should be float16 or float32. \((N,*)\) where \(*\) means, any number of additional dimensions.

Outputs:

Tensor, output tensor with dimensions in ‘axis’ reduced to 1 will be returned if ‘keep_dims’ is True; otherwise a Tensor with dimensions in ‘axis’ removed is returned. The data type is the same with x.

Raises

• TypeError – If axis is neither an int nor a tuple.

• TypeError – If keep_dims is not a bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = nn.Norm(axis=0)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[4.472136 4.1231055 9.486833 6.0827627]
>>> print(output.shape)
(4,)
>>> net = nn.Norm(axis=0, keep_dims=True)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[4.472136 4.1231055 9.486833 6.0827627]
>>> print(output.shape)
(1, 4)
>>> net = nn.Norm(axis=1)
>>> x = Tensor(np.array([[4, 4, 9, 1], [2, 1, 3, 6]]), mindspore.float32)
>>> print(x.shape)
(2, 4)
>>> output = net(x)
>>> print(output)
[10.677078 7.071068]
>>> print(output.shape)
(2,)