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., \(std = sqrt(mean(abs(x - x.mean())**2))\).

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 \(N - ddof\), 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