# Matmul

Source:`R/gen-namespace-docs.R`

, `R/gen-namespace-examples.R`

, `R/gen-namespace.R`

`torch_matmul.Rd`

Matmul

## Arguments

- self
(Tensor) the first tensor to be multiplied

- other
(Tensor) the second tensor to be multiplied

## matmul(input, other, out=NULL) -> Tensor

Matrix product of two tensors.

The behavior depends on the dimensionality of the tensors as follows:

If both tensors are 1-dimensional, the dot product (scalar) is returned.

If both arguments are 2-dimensional, the matrix-matrix product is returned.

If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.

If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.

If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable). For example, if

`input`

is a \((j \times 1 \times n \times m)\) tensor and`other`

is a \((k \times m \times p)\) tensor,`out`

will be an \((j \times k \times n \times p)\) tensor.

## Examples

```
if (torch_is_installed()) {
# vector x vector
tensor1 = torch_randn(c(3))
tensor2 = torch_randn(c(3))
torch_matmul(tensor1, tensor2)
# matrix x vector
tensor1 = torch_randn(c(3, 4))
tensor2 = torch_randn(c(4))
torch_matmul(tensor1, tensor2)
# batched matrix x broadcasted vector
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(4))
torch_matmul(tensor1, tensor2)
# batched matrix x batched matrix
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(10, 4, 5))
torch_matmul(tensor1, tensor2)
# batched matrix x broadcasted matrix
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(4, 5))
torch_matmul(tensor1, tensor2)
}
#> torch_tensor
#> (1,.,.) =
#> -1.6344 -5.2313 0.2571 0.8802 -2.4961
#> -3.6743 -3.0672 -0.5211 0.9903 2.1357
#> -1.5367 -1.8580 -1.3778 -0.6833 5.5465
#>
#> (2,.,.) =
#> 1.9546 1.7773 -1.6320 5.4560 -2.5947
#> 2.7604 -1.6670 -0.8243 0.7185 -1.1604
#> -4.8251 -1.4487 2.6527 -1.9351 -2.4288
#>
#> (3,.,.) =
#> 4.0709 1.4427 -2.2523 1.9684 1.7632
#> 1.6224 -7.0804 -2.2227 4.8965 -3.1157
#> 2.0483 2.7188 -0.6189 0.6330 0.4951
#>
#> (4,.,.) =
#> 0.2188 2.2214 0.5632 -0.2542 -0.7546
#> 0.4243 -3.6394 1.8634 -1.9575 -4.9420
#> -0.4283 -1.9447 -1.7190 3.1359 1.0106
#>
#> (5,.,.) =
#> -0.0445 -0.3136 0.8806 -1.1479 -1.3528
#> 1.3692 -0.6014 -1.9937 1.5184 2.9016
#> 0.7310 2.0486 0.4198 -1.7983 1.1015
#>
#> (6,.,.) =
#> -1.3252 -2.0653 0.0881 -0.0067 0.0234
#> -0.0771 0.1078 0.1332 0.6176 -1.0592
#> -1.7423 -0.6105 2.1714 -2.7430 -2.0972
#>
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]
```