mindspore.ops.count_nonzero

mindspore.ops.count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32)[source]

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

Parameters

x (Tensor) – The input tensor.
axis (Union[int, tuple(int), list(int)], optional) – Specify the axis for computation. Default () , which counts all non-zero elements.
keep_dims (bool, optional) – Whether to maintain dimensions specified by axis. Default False , don't keep these dimensions.
dtype (Union[Number, mindspore.bool_], optional) – The data type returned. Default mstype.int32 .

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> # case 1: each value specified.
>>> x = mindspore.tensor([[0, 1, 0], [1, 1, 0]], mindspore.float32)
>>> nonzero_num = mindspore.ops.count_nonzero(x=x, axis=[0, 1], keep_dims=True, dtype=mindspore.int32)
>>> print(nonzero_num)
[[3]]
>>> # case 2: all value is default.
>>> nonzero_num = mindspore.ops.count_nonzero(x=x)
>>> print(nonzero_num)
3
>>> # case 3: axis value was specified 0.
>>> nonzero_num = mindspore.ops.count_nonzero(x=x, axis=[0,])
>>> print(nonzero_num)
[1 2 0]
>>> # case 4: axis value was specified 1.
>>> nonzero_num = mindspore.ops.count_nonzero(x=x, axis=[1,])
>>> print(nonzero_num)
[1 2]
>>> # case 5: keep_dims value was specified.
>>> nonzero_num = mindspore.ops.count_nonzero(x=x,  keep_dims=True)
>>> print(nonzero_num)
[[3]]
>>> # case 6: keep_dims and axis value was specified.
>>> nonzero_num = mindspore.ops.count_nonzero(x=x, axis=[0,], keep_dims=True)
>>> print(nonzero_num)
[[1 2 0]]