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torch.triu
torch.triu(input, diagonal=0, *, out=None) → Tensor
-
Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
input
, the other elements of the result tensorout
are set to 0.The upper triangular part of the matrix is defined as the elements on and above the diagonal.
The argument
diagonal
controls which diagonal to consider. Ifdiagonal
= 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices for where are the dimensions of the matrix.- Parameters
- Keyword Arguments
-
out (Tensor, optional) – the output tensor.
Example:
>>> a = torch.randn(3, 3) >>> a tensor([[ 0.2309, 0.5207, 2.0049], [ 0.2072, -1.0680, 0.6602], [ 0.3480, -0.5211, -0.4573]]) >>> torch.triu(a) tensor([[ 0.2309, 0.5207, 2.0049], [ 0.0000, -1.0680, 0.6602], [ 0.0000, 0.0000, -0.4573]]) >>> torch.triu(a, diagonal=1) tensor([[ 0.0000, 0.5207, 2.0049], [ 0.0000, 0.0000, 0.6602], [ 0.0000, 0.0000, 0.0000]]) >>> torch.triu(a, diagonal=-1) tensor([[ 0.2309, 0.5207, 2.0049], [ 0.2072, -1.0680, 0.6602], [ 0.0000, -0.5211, -0.4573]]) >>> b = torch.randn(4, 6) >>> b tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.4333, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], [-0.9888, 1.0679, -1.3337, -1.6556, 0.4798, 0.2830]]) >>> torch.triu(b, diagonal=1) tensor([[ 0.0000, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [ 0.0000, 0.0000, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.0000, 0.0000, 0.0000, -1.0432, 0.9348, -0.4410], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.4798, 0.2830]]) >>> torch.triu(b, diagonal=-1) tensor([[ 0.5876, -0.0794, -1.8373, 0.6654, 0.2604, 1.5235], [-0.2447, 0.9556, -1.2919, 1.3378, -0.1768, -1.0857], [ 0.0000, 0.3146, 0.6576, -1.0432, 0.9348, -0.4410], [ 0.0000, 0.0000, -1.3337, -1.6556, 0.4798, 0.2830]])
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