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,.,.) = 
#>   0.4953 -0.0931 -0.6012  0.3149  0.4956
#>  -0.8852 -0.8143  2.5032  1.5431  3.9407
#>   0.2427  0.4619 -1.6010 -0.1447  1.1771
#> 
#> (2,.,.) = 
#>   2.1702  2.7736 -8.8175 -3.2974 -4.1093
#>  -0.1470  1.8487 -4.4137 -1.2524  2.5574
#>   0.3700 -0.5519  0.7760  0.8085 -0.8392
#> 
#> (3,.,.) = 
#>   1.0952  0.8036 -3.0625 -0.9351 -2.5530
#>  -1.9992 -2.0946  7.0350  2.6163  4.0652
#>  -0.7806  1.0579 -1.0802 -1.3288 -1.2548
#> 
#> (4,.,.) = 
#>  -1.0307  0.4309  0.1326 -0.0340  2.4011
#>   1.5600  0.3610 -2.4803 -0.5294 -4.5023
#>   0.5848  1.4312 -4.2854 -1.3027  1.4004
#> 
#> (5,.,.) = 
#>   0.0092  0.4273 -0.4270 -1.1245 -3.4985
#>  -0.2728  0.1605  0.5047 -0.8748 -2.8190
#>  -1.0135  0.3970  0.5684 -0.5930  0.1720
#> 
#> (6,.,.) = 
#>   1.1472 -0.4599 -0.6486  0.4600  0.3944
#>  -2.1068 -0.8513  4.0317  1.7759  6.1749
#>  -0.7362 -1.1713  2.6907  2.3224  6.6571
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]