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 ( $β$ )
alpha: (Number, optional) multiplier for $batch1 @ batch2$ ( $α$ )

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.

${out}_{i} = β {input}_{i} + α ({batch1}_{i} @ {batch2}_{i})$ 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,.,.) = 
#>  -1.8747  2.7116 -1.9605 -2.5448 -0.7616
#>   0.5820 -1.9892  1.7117 -0.2892  0.4162
#>  -3.0926  0.4680  0.1686  1.7324 -1.0170
#> 
#> (2,.,.) = 
#>   1.9619  1.3288  0.9719  1.0108 -4.4842
#>  -2.5403  0.1741  0.3527  0.3679 -1.2192
#>   0.9423 -0.1049 -1.8414  0.1004  4.1637
#> 
#> (3,.,.) = 
#>   1.3505  0.4892  4.1143  2.3817 -0.9033
#>  -0.2163  0.0489 -2.4095 -3.3519  0.0691
#>   2.2633  3.3438  0.7845  1.4967  1.4554
#> 
#> (4,.,.) = 
#>   3.8104 -1.2033  1.3291 -0.4609 -0.7298
#>   3.1195 -0.1612  1.3787  1.7386 -1.7486
#>  -0.9602  2.3897  0.6790 -1.8008  2.2341
#> 
#> (5,.,.) = 
#>  -1.8901  1.0118  0.8525 -4.1708  3.7186
#>   2.2699  0.1018  0.8561  2.0228 -0.2011
#>  -2.1236  2.2618  0.8220  0.8760  0.8450
#> 
#> (6,.,.) = 
#>   1.5310 -0.1770  0.4190  1.5319 -0.5963
#>   1.8078  2.9346 -2.1238  0.1933  4.7776
#>   1.0082 -2.4974 -2.4940  0.0338 -2.2650
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]