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
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]]