mindspore.ops.norm
- mindspore.ops.norm(input_x, axis, p=2, keep_dims=False, epsilon=1e-12)[source]
Returns the matrix norm or vector norm of a given tensor.
\[output = sum(abs(input)**p)**(1/p)\]- Parameters
input_x (Tensor) – Input tensor. The dtype must be float32 or float16.
axis (Union[int,list,tuple]) – Specifies which dimension or dimensions of input to calculate the norm across.
p (int) – The order of norm. Default: 2. p is greater than or equal to 0.
keep_dims (bool) – Whether the output tensors have dim retained or not. Default: False.
epsilon (float) – A value added to the denominator for numerical stability. Default: 1e-12.
- Returns
Tensor, has the same dtype as input, which shape depends on the args axis. For example, if the size of input is (2, 3, 4), axis is [0, 1], Outputs’ shape will be (4,).
- Raises
TypeError – If input is not a Tensor.
TypeError – If dtype of input is not one of: float16, float32.
TypeError – If p is not an int.
TypeError – If axis is not an int, a tuple or a list.
TypeError – If axis is a tuple or a list, but the element of axis is not an int.
TypeError – If keep_dims is not a bool.
TypeError – If epsilon is not a float.
ValueError – If the element of axis is out of the range [-len(input.shape), len(input.shape)).
ValueError – If the length of shape of axis is bigger than the length of shape of input.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> input_x = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32)) >>> output = ops.norm(input_x, [0, 1], p=2) >>> print(output) [ 9.165152 10.954452]