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mindspore.ops.all

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

Reduces a dimension of input by the “logical AND” 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.

Parameters

input (Tensor) – Input Tensor, has the shape $(N, *)$ where $*$ means, any number of additional dimensions.
axis (Union[int, tuple(int), list(int)], 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 AND” of all elements in the input Tensor.
If axis is int, such as 2, and keep_dims is False , the shape of output is $(i n p u t_{1}, i n p u t_{3}, . . ., i n p u t_{R})$ .
If axis is tuple(int), such as (2, 3), and keep_dims is False, the shape of output is $(i n p u t_{1}, i n p u t_{4}, . . ., i n p u t_{R})$ .

Raises

TypeError – If keep_dims is not a bool.
TypeError – If input is not a Tensor.
TypeError – If axis is not one of the following: int, tuple or list.

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 "logicalAND" of all elements in the dimension.
>>> output = ops.all(x, keep_dims=True)
>>> print(output)
[[False]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.all(x, axis=0)
>>> print(output)
[ True False]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.all(x, axis=1)
>>> print(output)
[False True]