# 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,.,.) =
#> -0.6703 0.1748 0.1019 2.1782 -1.9164
#> 3.4863 0.7423 -0.8082 1.5387 -0.2886
#> 2.2476 0.9486 1.8962 -1.2999 1.4790
#>
#> (2,.,.) =
#> 0.9446 1.0571 1.4544 -0.2813 0.9670
#> 1.4007 -0.4533 0.5070 -0.2281 -0.8250
#> -0.2017 0.3509 0.6494 0.0332 0.1461
#>
#> (3,.,.) =
#> -3.4653 -1.6867 -0.8829 -0.4846 -1.2331
#> -0.6890 0.2529 1.7228 -0.7841 0.2751
#> -4.6621 0.6449 0.9571 2.8237 -2.0121
#>
#> (4,.,.) =
#> -0.3645 -0.8249 0.9828 -2.5264 0.8061
#> -5.7000 -0.6100 -0.2068 0.9414 -1.2617
#> 1.9504 1.7264 2.3723 -1.4703 2.5558
#>
#> (5,.,.) =
#> -0.3427 0.8398 1.4759 -0.6493 1.1072
#> 2.5539 -1.2103 -1.4570 -0.5723 -0.6212
#> 2.2649 -0.9920 -2.4079 0.2779 -0.6104
#>
#> (6,.,.) =
#> -1.8022 0.1544 -0.5616 0.9548 -0.2760
#> -0.6957 -2.7108 -2.1812 -1.5212 -1.2216
#> 2.8840 -0.3510 -1.7105 -1.5380 1.7126
#>
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]
```