mindspore.ops.mean

mindspore.ops.mean(x, axis=(), keep_dims=False)[源代码]

Reduces a dimension of a tensor by averaging all elements in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Parameters

  • x (Tensor[Number]) – The input tensor. The dtype of the tensor to be reduced is number. \((N,*)\) where \(*\) means, any number of additional dimensions, its rank should be less than 8.

  • axis (Union[int, tuple(int), list(int)]) – The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Assume the rank of x is r, and the value range is [-r,r).

  • keep_dims (bool) – If true, keep these reduced dimensions and the length is 1. If false, don’t keep these dimensions. Default: False.

Returns

Tensor, has the same data type as input tensor.

  • If axis is (), and keep_dims is False, the output is a 0-D tensor representing the product of all elements in the input tensor.

  • If axis is int, set as 1, and keep_dims is False, the shape of output is \((x_0, x_2, ..., x_R)\).

  • If axis is tuple(int), set as (1, 2), and keep_dims is False, the shape of output is \((x_0, x_3, ..., x_R)\).

Raises

  • TypeError – If x is not a Tensor.

  • TypeError – If axis is not one of the following: int, tuple or list.

  • TypeError – If keep_dims is not a bool.

  • ValueError – If axis is out of range.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.mean(x, 1, keep_dims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> x = 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 = ops.mean(x)
>>> print(output)
5.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along the axis 0
>>> output = ops.mean(x, 0, True)
>>> print(output)
[[[4. 4. 4. 4. 4. 4.]
  [5. 5. 5. 5. 5. 5.]
  [6. 6. 6. 6. 6. 6.]]]
>>> # case 3: Reduces a dimension along the axis 1
>>> output = ops.mean(x, 1, True)
>>> print(output)
[[[2. 2. 2. 2. 2. 2.]]
 [[5. 5. 5. 5. 5. 5.]]
 [[8. 8. 8. 8. 8. 8.]]]
>>> # case 4: Reduces a dimension along the axis 2
>>> output = ops.mean(x, 2, True)
>>> print(output)
[[[ 2.]
  [ 2.]
  [ 2.]]
 [[ 4.]
  [ 5.]
  [ 6.]]
 [[ 6.]
  [ 8.]
  [10.]]]