mindspore.Tensor.all

Tensor.all(axis=None, keep_dims=False) → Tensor

Reduces a dimension of self by the "logical AND" of all elements in the dimension, by default. And also can reduce a dimension of self along the axis. Determine whether the dimensions of the output and self 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

  • axis (Union[int, tuple(int), list(int), Tensor], optional) – The dimensions to reduce. Suppose the rank of self is r, axis must be in the range [-r, r). 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 self.

  • If axis is int, such as 2, and keep_dims is False , the shape of output is \((self_1, self_3, ..., self_R)\).

  • If axis is tuple(int) or list(int), such as (2, 3), and keep_dims is False , the shape of output is \((self_1, self_4, ..., self_R)\).

  • If axis is 1-D Tensor, such as [2, 3], and keep_dims is False , the shape of output is \((self_1, self_4, ..., self_R)\).

Raises

  • TypeError – If keep_dims is not a 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
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> # case 1: Reduces a dimension by the "logicalAND" of all elements in the dimension.
>>> output = Tensor.all(x, keep_dims=True)  # x.all(keep_dims=True)
>>> print(output)
[[False]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = Tensor.all(x, axis=0)  # x.all(axis=0)
>>> print(output)
[ True False]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = Tensor.all(x, axis=1)  #x.all(axis=1)
>>> print(output)
[False True]
Tensor.all(dim=None, keepdim=False) → Tensor

Reduces a dimension of self by the "logical AND" of all elements in the dimension, by default. And also can reduce a dimension of self along the dim. Determine whether the dimensions of the output and self are the same by controlling keepdim.

Note

The dim with tensor type is only used for compatibility with older versions and is not recommended.

Parameters

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

  • keepdim (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 dim is None , and keepdim is False , the output is a 0-D Tensor representing the "logical AND" of all elements in the self.

  • If dim is int, such as 2, and keepdim is False , the shape of output is \((self_1, self_3, ..., self_R)\).

  • If dim is tuple(int) or list(int), such as (2, 3), and keepdim is False , the shape of output is \((self_1, self_4, ..., self_R)\).

  • If dim is 1-D Tensor, such as [2, 3], and keepdim is False , the shape of output is \((self_1, self_4, ..., self_R)\).

Raises

  • TypeError – If keepdim is not a bool.

  • TypeError – If dim 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
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> # case 1: Reduces a dimension by the "logicalAND" of all elements in the dimension.
>>> output = Tensor.all(x, keepdim=True)  # x.all(keepdim=True)
>>> print(output)
[[False]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along dim 0.
>>> output = Tensor.all(x, dim=0)  # x.all(dim=0)
>>> print(output)
[ True False]
>>> # case 3: Reduces a dimension along dim 1.
>>> output = Tensor.all(x, dim=1)  #x.all(dim=1)
>>> print(output)
[False True]