torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)



(Tensor) matrix to be added


(Tensor) the first vector of the outer product


(Tensor) the second vector of the outer product


(Number, optional) multiplier for input (\(\beta\))


(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))

addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor

Performs the outer-product of vectors vec1 and vec2 and adds it to the matrix input.

Optional values beta and alpha are scaling factors on the outer product between vec1 and vec2 and the added matrix input respectively.

$$ \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2}) $$ If vec1 is a vector of size n and vec2 is a vector of size m, then input must be broadcastable with a matrix of size \((n \times m)\) and out will be a matrix of size \((n \times m)\).

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers


if (torch_is_installed()) { vec1 = torch_arange(1., 4.) vec2 = torch_arange(1., 3.) M = torch_zeros(c(3, 2)) torch_addr(M, vec1, vec2) }
#> torch_tensor #> 1 2 #> 2 4 #> 3 6 #> [ CPUFloatType{3,2} ]