# Function Differences with torch.std [](https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/source_en/note/api_mapping/pytorch_diff/std.md) The following mapping relationships can be found in this file. | PyTorch APIs | MindSpore APIs | | :-------------------: | :-----------------------: | | torch.std | mindspore.ops.std | | torch.Tensor.std | mindspore.Tensor.std | ## torch.std ```python torch.std(input, dim, unbiased=True, keepdim=False, *, out=None) ``` For more information, see [torch.std](https://pytorch.org/docs/1.8.1/generated/torch.std.html). ## mindspore.ops.std ```python mindspore.ops.std(input, axis=None, ddof=0, keepdims=False) ``` For more information, see [mindspore.ops.std](https://www.mindspore.cn/docs/en/r2.0/api_python/ops/mindspore.ops.std.html). ## Differences PyTorch: Output the standard deviation of the Tensor in each dimension, or the standard deviation of the specified dimension according to `dim`. If `unbiased` is True, use Bessel for correction; if False, use bias estimation to calculate the standard deviation. `keepdim` controls whether the output and input dimensions are the same. MindSpore: Output the standard deviation of the Tensor in each dimension, or the standard deviation of the specified dimension according to `axis`. If `ddof` is a boolean, it has the same effect as `unbiased`; if `ddof` is an integer, the divisor used in the calculation is N-ddof, where N denotes the number of elements. `keepdim` controls whether the output and the input have the same dimensionality. | Categories | Subcategories | PyTorch | MindSpore | Differences | | --- |---------------|---------| --- |-------------| | Parameters | Parameter 1 | input | input | Same function, different parameter names | | | Parameter 2 | dim | axis | Same function, different parameter names | | | Parameter 3 | unbiased | ddof | `ddof` is the same as `unbiased` when it is a boolean value | | | Parameter 4 | keepdim | keepdims | Same function, different parameter names | | | Parameter 5 | out | - | MindSpore does not have this parameter | ### Code Example ```python # PyTorch import torch input = torch.tensor([[[9, 7, 4, -10], [-9, -2, 1, -2]]], dtype=torch.float32) print(torch.std(input, dim=2, unbiased=True, keepdim=True)) # tensor([[[8.5829], # [4.2426]]]) # MindSpore import mindspore as ms input = ms.Tensor([[[9, 7, 4, -10], [-9, -2, 1, -2]]], ms.float32) print(ms.ops.std(input, axis=2, ddof=True, keepdims=True)) # [[[8.582929 ] # [4.2426405]]] ```