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torch.diag#

torch.diag(input,diagonal=0,*,out=None)→Tensor#

  • Ifinput is a vector (1-D tensor), then returns a 2-D square tensorwith the elements ofinput as the diagonal.

  • Ifinput is a matrix (2-D tensor), then returns a 1-D tensor withthe diagonal elements ofinput.

The argumentdiagonal controls which diagonal to consider:

  • Ifdiagonal = 0, it is the main diagonal.

  • Ifdiagonal > 0, it is above the main diagonal.

  • Ifdiagonal < 0, it is below the main diagonal.

Parameters

  • input (Tensor) – the input tensor.

  • diagonal (int,optional) – the diagonal to consider

Keyword Arguments

out (Tensor,optional) – the output tensor.

See also

torch.diagonal() always returns the diagonal of its input.

torch.diagflat() always constructs a tensor with diagonal elementsspecified by the input.

Examples:

Get the square matrix where the input vector is the diagonal:

>>>a=torch.randn(3)>>>atensor([ 0.5950,-0.0872, 2.3298])>>>torch.diag(a)tensor([[ 0.5950, 0.0000, 0.0000],        [ 0.0000,-0.0872, 0.0000],        [ 0.0000, 0.0000, 2.3298]])>>>torch.diag(a,1)tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],        [ 0.0000, 0.0000,-0.0872, 0.0000],        [ 0.0000, 0.0000, 0.0000, 2.3298],        [ 0.0000, 0.0000, 0.0000, 0.0000]])

Get the k-th diagonal of a given matrix:

>>>a=torch.randn(3,3)>>>atensor([[-0.4264, 0.0255,-0.1064],        [ 0.8795,-0.2429, 0.1374],        [ 0.1029,-0.6482,-1.6300]])>>>torch.diag(a,0)tensor([-0.4264,-0.2429,-1.6300])>>>torch.diag(a,1)tensor([ 0.0255, 0.1374])