mindspore.Tensor.std

Tensor.std(axis=None, ddof=0, keepdims=False)[source]

Compute the standard deviation along the specified axis.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., $s t d = s q r t (m e a n (a b s (x - x . m e a n ()) * * 2))$ .

Return the standard deviation, which is computed for the flattened array by default, otherwise over the specified axis.

Note

Numpy arguments dtype, out and where are not supported.

Parameters

axis (Union[None, int, tuple(int)]) –
Axis or axes along which the standard deviation is computed. Default: None.

If None, compute the standard deviation of the flattened array.
ddof (int) – Means Delta Degrees of Freedom. The divisor used in calculations is $N - d d o f$ , where $N$ represents the number of elements. Default: 0.
keepdims – Default: False.

Returns

Standard deviation tensor.

Supported Platforms:: Ascend GPU CPU

See also

mindspore.Tensor.mean(): Reduce a dimension of a tensor by averaging all elements in the dimension.

mindspore.Tensor.var(): Compute the variance along the specified axis.

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input_x = Tensor(np.array([1, 2, 3, 4], dtype=np.float32))
>>> output = input_x.std()
>>> print(output)
1.118034