torch_baddbmm function

Baddbmm

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.

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)
}