Baddbmm — torch_baddbmm • torch

Baddbmm

Usage

torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)

Arguments

self: (Tensor) the tensor to be added
batch1: (Tensor) the first batch of matrices to be multiplied
batch2: (Tensor) the second batch of matrices to be multiplied
beta: (Number, optional) multiplier for input ($\beta$)
alpha: (Number, optional) multiplier for $\mbox{batch1} \mathbin{@} \mbox{batch2}$ ($\alpha$)

baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor

Performs a batch matrix-matrix product of matrices in batch1 and batch2. input is added to the final result.

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

If batch1 is a $(b \times n \times m)$ tensor, batch2 is a $(b \times m \times p)$ tensor, then input must be broadcastable with a $(b \times n \times p)$ tensor and out will be a $(b \times n \times p)$ tensor. Both alpha and beta mean the same as the scaling factors used in torch_addbmm.

$$ \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i) $$ 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(10, 3, 5))
batch1 = torch_randn(c(10, 3, 4))
batch2 = torch_randn(c(10, 4, 5))
torch_baddbmm(M, batch1, batch2)
}
#> torch_tensor
#> (1,.,.) = 
#>   1.6657 -0.6742  0.4512 -3.2314  0.8295
#>   2.0840  3.4537  1.5246  0.4369 -1.3417
#>   0.8246 -0.4030  1.1787  1.0525  0.6282
#> 
#> (2,.,.) = 
#>   1.1660  1.2199 -1.4092  1.6581 -2.3506
#>  -0.3510 -0.2972  1.0932  3.2922 -1.8609
#>   0.0902 -0.7679  0.4225  2.6301 -1.6637
#> 
#> (3,.,.) = 
#>   1.5026 -1.2179 -3.3466  1.0576 -1.6512
#>  -2.7543  0.9854 -1.4523  1.0961  2.5613
#>   3.3064 -1.2371 -3.1061  1.9371 -0.7384
#> 
#> (4,.,.) = 
#>  -3.8763  4.0324  0.5153 -3.7112  1.9464
#>   2.9007 -1.3636  0.9369  3.3250 -1.9141
#>  -1.3285  3.9644 -1.3838 -0.5099 -0.7736
#> 
#> (5,.,.) = 
#>   0.6634  1.0391 -1.9443  1.2481  0.5775
#>   1.2183  3.6301 -0.8679 -1.9750 -0.5477
#>   1.9005 -0.3253 -2.8810  1.6825  0.8057
#> 
#> (6,.,.) = 
#>   0.1856  2.1649  1.2783  1.2708 -0.4000
#>  -1.1717 -2.7041  0.5744 -0.5783 -1.6064
#>   1.2987 -1.4754  3.0119 -1.4063  0.6659
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]