mindspore.ops.ReduceMean

class mindspore.ops.ReduceMean(*args, **kwargs)[source]

Reduces a dimension of a tensor by averaging all elements in the dimension.

The dtype of the tensor to be reduced is number.

Parameters

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

Inputs:

  • input_x (Tensor[Number]) - The input tensor.

  • axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Must be in the range [-rank(input_x), rank(input_x)).

Outputs:

Tensor, has the same dtype as the input_x.

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

  • If axis is int, set as 2, and keep_dims is False, the shape of output is \((x_1, x_3, ..., x_R)\).

  • If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is \((x_1, x_4, ..., x_R)\).

Raises

  • TypeError – If keep_dims is not a bool.

  • TypeError – If input_x is not a Tensor.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = ops.ReduceMean(keep_dims=True)
>>> output = op(input_x, 1)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)