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,.,.) = 
#>  -0.7096 -2.4952 -0.5448 -1.6013 -0.9051
#>  -2.7673  1.0499 -1.9971 -0.6267  0.0458
#>  -3.0538  0.0669 -0.9028  0.6094  0.5897
#> 
#> (2,.,.) = 
#>  -5.6211 -3.3761 -0.1902 -3.6141 -1.8894
#>   3.6859  0.2111 -3.5261  2.8575  0.2995
#>  -1.4334  2.3205 -0.7313  3.4281  1.7564
#> 
#> (3,.,.) = 
#>   2.8008 -4.4372 -1.0890  2.2385 -2.8341
#>   2.7623 -3.3589 -0.8144  0.3101  1.6893
#>   0.1959 -0.2360 -1.9945  1.0803 -1.5615
#> 
#> (4,.,.) = 
#>   2.6185  3.0292  0.7357 -0.5216  3.0430
#>   4.1578  6.5513  0.8269 -1.0677 -1.3759
#>  -0.5773 -3.3140  0.8970  0.1639  0.6223
#> 
#> (5,.,.) = 
#>   0.2924  2.5694  1.3157 -2.0076  3.0194
#>   1.8499 -2.1976  2.1450 -1.0396 -0.9372
#>   1.1715  5.0453  3.7745  2.8443 -1.5944
#> 
#> (6,.,.) = 
#>   3.8417 -1.9900  0.6235 -0.6033  0.6244
#>   1.4074 -0.6490 -1.0878 -1.2655  2.3180
#>  -1.3387 -7.5901 -8.9160 -4.7847 -0.4269
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]