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,.,.) = 
#>   2.7700  3.7597 -0.5840 -0.9228  1.1850
#>   0.2146 -1.0171  0.1117  0.0613  0.7060
#>  -0.8335 -1.2902  0.8080 -0.2263 -0.8075
#> 
#> (2,.,.) = 
#>  -1.3236  0.5524  2.3813 -1.8995  0.2905
#>   5.2242  2.2223 -2.6739  3.2640  1.0878
#>  -2.2081 -2.6137  3.7052 -0.2712  0.5142
#> 
#> (3,.,.) = 
#>   0.0276 -1.2610 -0.1775 -0.9561  0.7384
#>   0.6315 -3.6193 -2.3835 -3.4108 -2.9823
#>  -0.4199  2.7762  3.5225  4.3590  1.6647
#> 
#> (4,.,.) = 
#>   1.9706  0.8897 -1.7094  0.9065  0.6663
#>  -0.8588  0.0224  2.6829  2.5514 -2.3433
#>  -3.1659  3.5826  0.8190  0.8119  1.0245
#> 
#> (5,.,.) = 
#>  -1.3261  0.1733 -0.5291 -2.3699 -0.6561
#>   1.0878  3.9913  3.1729  1.1845 -0.7739
#>  -0.1118  1.0368 -1.6662  0.8605  1.1416
#> 
#> (6,.,.) = 
#>   0.2404  0.8618 -3.1487 -1.1204  4.1657
#>   0.6510  1.2810  0.9076  2.0881 -1.4185
#>   0.7884 -0.3808 -0.6798 -1.6016  2.4876
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]