mindspore.ops.any

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mindspore.ops.any(input, axis=None, keep_dims=False)[source]

Reduces a dimension of input by the "logical OR" of all elements in the dimension, by default. And also can reduce a dimension of input 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

  • input (Tensor) – Input Tensor(bool),has the shape \((N, *)\) where \(*\) means, any number of additional dimensions.

  • axis (Union[int, tuple(int), list(int), Tensor], optional) – The dimensions to reduce. Suppose the rank of input is r, axis must be in the range [-rank(input), rank(input)). Default: None , all dimensions are reduced.

  • keep_dims (bool, optional) – If True , keep these reduced dimensions and the length is 1. If False , don't keep these dimensions. Default : False .

Returns

Tensor, the dtype is bool.

  • If axis is None , and keep_dims is False , the output is a 0-D Tensor representing the "logical OR" of all elements in the input Tensor.

  • If axis is int, such as 2, and keep_dims is False , the shape of output is \((input_1, input_3, ..., input_R)\).

  • If axis is tuple(int), such as (2, 3), and keep_dims is False , the shape of output is \((input_1, input_4, ..., input_R)\).

  • If axis is 1-D Tensor, such as [2, 3], and keep_dims is False , the shape of output is \((input_1, input_4, ..., input_R)\).

Raises

  • TypeError – If keep_dims is not a bool.

  • TypeError – If input is not a Tensor(bool).

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension.
>>> output = ops.any(x, keep_dims=True)
>>> print(output)
[[ True]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.any(x, axis=0)
>>> print(output)
[ True True]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.any(x, axis=1)
>>> print(output)
[ True True]