mindspore.mint.mean

mindspore.mint.mean(input, *, dtype=None) → Tensor[source]

Compute the mean of the tensor.

Parameters: input (Tensor[Number]) – The input tensor.
Keyword Arguments: dtype (mindspore.dtype, optional) – The desired data type of returned tensor. Default None .
Returns: Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> x = mindspore.tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = mindspore.mint.mean(x)
>>> print(output)
5.0
>>> print(output.shape)
()

mindspore.mint.mean(input, dim, keepdim=False, *, dtype=None) → Tensor[source]

Compute the mean(s) of the tensor along the specified dimension(s).

Note

The dim with tensor type is only used for compatibility with older versions and is not recommended.

Parameters

input (Tensor[Number]) – The input tensor.
dim (Union[int, tuple(int), list(int), Tensor]) – Specify the dimension(s) for computation.
keepdim (bool) – Whether the output tensor has dim retained. Default False .

Keyword Arguments

dtype (mindspore.dtype, optional) – The desired data type of returned tensor. Default None .

Returns

Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> x = mindspore.tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = mindspore.mint.mean(x, 0, True)
>>> print(output)
[[[4. 4. 4. 4. 4. 4.]
  [5. 5. 5. 5. 5. 5.]
  [6. 6. 6. 6. 6. 6.]]]