Matmul

Matmul

Usage

torch_matmul(self, other)

Arguments

self

(Tensor) the first tensor to be multiplied

other

(Tensor) the second tensor to be multiplied

Note

The 1-dimensional dot product version of this function does not support an `out` parameter.

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,.,.) = 
#>   6.1269  1.3612  0.6866 -3.8543 -1.1654
#>   1.5528  0.8489 -1.4175  1.1915  0.1632
#>  -1.2197 -0.3985 -0.0122  0.6675  0.5687
#> 
#> (2,.,.) = 
#>   4.5127  0.7491 -1.7496  0.9552  3.8570
#>  -5.5157 -1.1494  0.6715  1.3456 -1.3611
#>   0.9765  0.3039  1.3610 -2.6397 -2.5517
#> 
#> (3,.,.) = 
#>  -3.0810 -0.7864  0.0398  1.3468  0.3584
#>  -4.0864 -1.0721  0.8442  0.5483 -0.6800
#>   1.9305  0.8333  0.1393 -1.3895 -1.9398
#> 
#> (4,.,.) = 
#>  -3.8142 -0.6156 -0.2180  1.9256 -0.5659
#>  -6.0375 -1.0019  0.3333  1.8409 -1.9684
#>  -1.6487 -0.2011 -1.1990  2.5860  1.2929
#> 
#> (5,.,.) = 
#>   1.4946  0.4902 -0.4693  0.0062  0.1112
#>  -4.9825 -1.5976 -0.2041  2.9227  2.4289
#>  -2.6160 -0.0570 -1.3083  2.8375 -0.0867
#> 
#> (6,.,.) = 
#>   1.4341  0.8723 -0.6583 -0.0735 -1.3161
#>   2.5963  1.1872 -0.6945 -0.5008 -1.4558
#>  -2.1505 -0.4424 -0.1460  1.1587  0.0968
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]