Addmm

Usage

torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)

Arguments

self

(Tensor) matrix to be added

mat1

(Tensor) the first matrix to be multiplied

mat2

(Tensor) the second matrix to be multiplied

beta

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

alpha

(Number, optional) multiplier for $mat1@mat2$ ($\alpha$)

addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor

Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.

If mat1 is a $(n\times m)$ tensor, mat2 is a $(m\times p)$ tensor, then input must be broadcastable with a $(n\times p)$ tensor and out will be a $(n\times p)$ tensor.

alpha and beta are scaling factors on matrix-vector product between mat1 and mat2 and the added matrix input respectively.

$$\text{out}=\beta \text{input}+\alpha ({\text{mat1}}_i@{\text{mat2}}_i)$$ For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Examples

if (torch_is_installed()) {

M = torch_randn(c(2, 3))
mat1 = torch_randn(c(2, 3))
mat2 = torch_randn(c(3, 3))
torch_addmm(M, mat1, mat2)
}
#> torch_tensor
#>  1.3950 -0.6360 -0.5194
#>  0.4232 -2.0003  0.1454
#> [ CPUFloatType{2,3} ]