mindspore.nn.Triu

class mindspore.nn.Triu[source]

Returns a tensor with elements below the kth diagonal zeroed.

Inputs:

x (Tensor) - The input tensor. The data type is Number. \((N,*)\) where \(*\) means, any number of additional dimensions.
k (Int) - The index of diagonal. Default: 0

Outputs:

Tensor, has the same type and shape as input x.

Raises

TypeError – If k is not an int.
ValueError – If length of shape of x is less than 1.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x)
>>> print(result)
[[ 1,  2,  3,  4],
 [ 0,  6,  7,  8],
 [ 0,  0, 12, 13],
 [ 0,  0,  0, 17]]))
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, 1)
>>> print(result)
[[ 0,  2,  3,  4],
 [ 0,  0,  7,  8],
 [ 0,  0,  0, 13],
 [ 0,  0,  0,  0]]))
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, 2)
>>> print(result)
[[ 0,  0,  3,  4],
 [ 0,  0,  0,  8],
 [ 0,  0,  0,  0],
 [ 0,  0,  0,  0]]))
>>> x = Tensor(np.array([[ 1,  2,  3,  4],
...                      [ 5,  6,  7,  8],
...                      [10, 11, 12, 13],
...                      [14, 15, 16, 17]]))
>>> triu = nn.Triu()
>>> result = triu(x, -1)
>>> print(result)
[[ 1,  2,  3,  4],
 [ 5,  6,  7,  8],
 [ 0,  11, 12, 13],
 [ 0,  0,  16, 17]]))