mindspore.ops.std

mindspore.ops.std(input, axis=None, ddof=0, keepdims=False)

Compute the standard deviation of the tensor along a specified axis.

Parameters

• input (Tensor[Number]) – The input tensor.

• axis (Union[int, tuple(int)], optional) – Specify the axis for computation. If None , compute all elements in the input . Default None .

• ddof (Union[int, bool], optional) –

  Means Delta Degrees of Freedom. Default 0 .

  • If ddof is an integer, the divisor used in calculations is $N-ddof$, where $N$ represents the number of elements.

  • If ddof is a boolean, True and False correspond to when ddof is an integer 1 and 0 respectively.

  • If ddof is 0, 1, True or False, the supported device is only Ascend and CPU. In other cases, the supported device is Ascend, GPU and CPU.

• keepdims (bool, optional) – Whether the output tensor has dim retained. Default False .

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> input = mindspore.tensor([[1., 3, 4, 2],
...                           [4, 2, 5, 3],
...                           [5, 4, 2, 3]])
>>> # case 1: By default, compute the standard deviation of all elements.
>>> output = mindspore.ops.std(input)
>>> print(output)
1.2133516
>>>
>>> # case 2: Compute the standard deviation along axis 0.
>>> output = mindspore.ops.std(input, axis=0)
>>> print(output)
[1.6996732 0.8164966 1.2472192 0.4714045]
>>>
>>> # case 3: If keepdims=True, the output shape will be same of that of the input.
>>> output = mindspore.ops.std(input, axis=0, keepdims=True)
>>> print(output)
[[1.6996732 0.8164966 1.2472192 0.4714045]]
>>>
>>> # case 4: If ddof=1:
>>> output = mindspore.ops.std(input, axis=0, keepdims=True, ddof=1)
>>> print(output)
[[2.081666   1.         1.5275253  0.57735026]]
>>>
>>> # case 5: If ddof=True, same as ddof=1:
>>> output = mindspore.ops.std(input, axis=0, keepdims=True, ddof=True)
>>> print(output)
[[2.081666   1.         1.5275253  0.57735026]]
>>>
>>> # case 6: If ddof=False, same as ddof=0:
>>> output = mindspore.ops.std(input, axis=0, keepdims=True, ddof=False)
>>> print(output)
[[1.6996732 0.8164966 1.2472192 0.4714045]]