norm¶

paddle.linalg. norm ( x, p=None, axis=None, keepdim=False, name=None ) [source]

Returns the matrix norm (the Frobenius norm, the nuclear norm and p-norm) or vector norm (the 1-norm, the Euclidean or 2-norm, and in general the p-norm) of a given tensor.

Whether the function calculates the vector norm or the matrix norm is determined as follows:

If axis is of type int, calculate the vector norm.
If axis is a two-dimensional array, calculate the matrix norm.
If axis is None, x is compressed into a one-dimensional vector and the vector norm is calculated.

Paddle supports the following norms:

porder	norm for matrices	norm for vectors
None(default)	frobenius norm	2_norm
fro	frobenius norm	not support
nuc	nuclear norm	not support
inf	max(sum(abs(x), dim=1))	max(abs(x))
-inf	min(sum(abs(x), dim=1))	min(abs(x))
0	not support	sum(x != 0)
1	max(sum(abs(x), dim=0))	as below
-1	min(sum(abs(x), dim=0))	as below
2	The maximum singular value of a matrix consisting of axis.	as below
-2	The minimum singular value of a matrix consisting of axis.	as below
other int or float	not support	sum(abs(x)^{porder})^ {(1 / porder)}

Parameters

x (Tensor) – The input tensor could be N-D tensor, and the input data type could be float32 or float64.
p (int|float|string, optional) – Order of the norm. Supported values are fro, nuc, 0, ±1, ±2, ±inf and any real number yielding the corresponding p-norm. Default value is None.
axis (int|list|tuple, optional) – The axis on which to apply norm operation. If axis is int or list(int)/tuple(int) with only one element, the vector norm is computed over the axis. If axis < 0, the dimension to norm operation is rank(input) + axis. If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis. Default value is None.
keepdim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result tensor will have fewer dimension than the input unless keepdim is true, default value is False.
name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.

Returns

results of norm operation on the specified axis of input tensor, it’s data type is the same as input’s Tensor.

Return type

Tensor

Examples

           >>> import paddle
>>> x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12
>>> print(x)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-12., -11., -10., -9. ],
  [-8. , -7. , -6. , -5. ],
  [-4. , -3. , -2. , -1. ]],
 [[ 0. ,  1. ,  2. ,  3. ],
  [ 4. ,  5. ,  6. ,  7. ],
  [ 8. ,  9. ,  10.,  11.]]])

>>> # compute frobenius norm along last two dimensions.
>>> out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])
>>> print(out_fro)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[17.43559647, 16.91153526, 16.73320007, 16.91153526])

>>> # compute 2-order vector norm along last dimension.
>>> out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)
>>> print(out_pnorm)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[21.11871147, 13.19090557, 5.47722578 ],
 [3.74165750 , 11.22497177, 19.13112640]])

>>> # compute 2-order  norm along [0,1] dimension.
>>> out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])
>>> print(out_pnorm)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[15.75857544, 14.97978878, 14.69693947, 14.97978973])

>>> # compute inf-order  norm
>>> out_pnorm = paddle.linalg.norm(x, p=float("inf"))
>>> print(out_pnorm)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
12.)

>>> out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0)
>>> print(out_pnorm)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[12., 11., 10., 9. ],
 [8. , 7. , 6. , 7. ],
 [8. , 9. , 10., 11.]])

>>> # compute -inf-order  norm
>>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"))
>>> print(out_pnorm)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.)

>>> out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0)
>>> print(out_pnorm)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 1., 2., 3.],
 [4., 5., 6., 5.],
 [4., 3., 2., 1.]])

          