mindspore.ops.ReduceMean
- class mindspore.ops.ReduceMean(keep_dims=False)[source]
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.
Note
The axis with tensor type is only used for compatibility with older versions and is not recommended.
- Parameters
keep_dims (bool) – If
True
, keep these reduced dimensions and the length is 1. IfFalse
, don't keep these dimensions. Default:False
.
- Inputs:
x (Tensor[Number]) - The input tensor.
axis (Union[int, tuple(int), list(int), Tensor]) - The dimensions to reduce. Default:
()
, reduce all dimensions. Only constant value is allowed. Must be in the range [-r, r).
- Outputs:
Tensor, has the same dtype as the x.
If axis is
()
, and keep_dims isFalse
, the output is a 0-D tensor representing the mean 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) or list(int), set as (1, 2), and keep_dims is
False
, the shape of output is \((x_0, x_3, ..., x_R)\).If axis is 1-D Tensor, set as [1, 2], and keep_dims is
False
, the shape of output is \((x_0, x_3, ..., x_R)\).
- Raises
TypeError – If keep_dims is not a bool.
TypeError – If x is not a Tensor.
TypeError – If axis is not one of the following: int, tuple, list or Tensor.
ValueError – If axis is out of range.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = ops.ReduceMean(keep_dims=True) >>> output = op(x, 1) >>> 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 = op(x) >>> print(output) [[[5.]]] >>> print(output.shape) (1, 1, 1) >>> # case 2: Reduces a dimension along the axis 0 >>> output = op(x, 0) >>> 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 = op(x, 1) >>> 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 = op(x, 2) >>> print(output) [[[ 2.] [ 2.] [ 2.]] [[ 4.] [ 5.] [ 6.]] [[ 6.] [ 8.] [10.]]]