nonzero

paddle. nonzero ( x: Tensor, as_tuple=False, *, out: Tensor | None = None ) [source]

Return a tensor containing the indices of all non-zero elements of the input tensor. If as_tuple is True, return a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input. Given a n-Dimensional input tensor with shape [x_1, x_2, …, x_n], If as_tuple is False, we can get a output tensor with shape [z, n], where z is the number of all non-zero elements in the input tensor. If as_tuple is True, we can get a 1-D tensor tuple of length n, and the shape of each 1-D tensor is [z, 1].

Note

Alias Support: The parameter name input can be used as an alias for x. For example, nonzero(input=tensor_x) is equivalent to nonzero(x=tensor_x).

Parameters

x (Tensor) – The input tensor variable. alias: input.
as_tuple (bool, optional) – Return type, Tensor or tuple of Tensor.
out (Tensor|None, optional) – The output tensor. Default: None.

Returns

Tensor or tuple of Tensor, The data type is int64.

Examples

           >>> import paddle

>>> x1 = paddle.to_tensor([[1.0, 0.0, 0.0],
...                        [0.0, 2.0, 0.0],
...                        [0.0, 0.0, 3.0]])
>>> x2 = paddle.to_tensor([0.0, 1.0, 0.0, 3.0])
>>> out_z1 = paddle.nonzero(x1)
>>> print(out_z1)
Tensor(shape=[3, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 0],
 [1, 1],
 [2, 2]])

>>> out_z1_tuple = paddle.nonzero(x1, as_tuple=True)
>>> for out in out_z1_tuple:
...     print(out)
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 1, 2])
Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 1, 2])

>>> out_z2 = paddle.nonzero(x2)
>>> print(out_z2)
Tensor(shape=[2, 1], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1],
 [3]])

>>> out_z2_tuple = paddle.nonzero(x2, as_tuple=True)
>>> for out in out_z2_tuple:
...     print(out)
Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
[1, 3])

          