torch_diag

input

(int, optional) the diagonal to consider

diagonal

(Tensor, optional) the output tensor.

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

 RStudio

torch_diag function

<ul>
<li>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
with the elements of <code>input</code> as the diagonal.</li>
<li>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
the diagonal elements of <code>input</code>.</li>
</ul>The argument <code>diagonal</code> controls which diagonal to consider:<ul>
<li>If <code>diagonal</code> = 0, it is the main diagonal.</li>
<li>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</li>
<li>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</li>
</ul>

torch_diag: Diag

Description

Arguments

diag(input, diagonal=0, out=None) -> Tensor

Description

Arguments

diag(input, diagonal=0, out=None) -&gt; Tensor

diag(input, diagonal=0, out=None) -> Tensor