mindspore.mint.count_nonzero

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mindspore.mint.count_nonzero(input, dim=None)[source]

Counts the number of non-zero values in the tensor input along the given dim. If no dim is specified then all non-zeros in the tensor are counted.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • input (Tensor) – Input data is used to count non-zero numbers. With shape \((*)\) where \(*\) means, any number of additional dimensions.

  • dim (Union[int, tuple(int), list(int)], optional) – The dimension to reduce. Default value: None, which indicates that the number of non-zero elements is calculated. If dim is None, all elements in the tensor are summed up.

Returns

Tensor, number of nonzero element across dim specified by dim.

Raises

  • TypeError – If input is not tensor.

  • TypeError – If dim is not int, tuple(int), list(int) or None.

  • ValueError – If any value in dim is not in range [-x.ndim, x.ndim).

Supported Platforms:

Ascend

Examples

>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> import mindspore
>>> # case 1: each value specified.
>>> x = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
>>> nonzero_num = mint.count_nonzero(input=x, dim=[0, 1])
>>> print(nonzero_num)
[[3]]
>>> # case 2: all value is default.
>>> nonzero_num = mint.count_nonzero(input=x)
>>> print(nonzero_num)
3
>>> # case 3: dim value was specified 0.
>>> nonzero_num = mint.count_nonzero(input=x, dim=[0,])
>>> print(nonzero_num)
[1 2 0]
>>> # case 4: dim value was specified 1.
>>> nonzero_num = mint.count_nonzero(input=x, dim=[1,])
>>> print(nonzero_num)
[1 2]