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,.,.) = 
#>  -0.1923  0.4107 -0.1598  0.4698 -0.3438
#>   0.8307 -0.7669 -1.6040  0.2487  1.5457
#>  -1.5150  2.3265  2.6129  0.9229 -4.5403
#> 
#> (2,.,.) = 
#>  -0.8151 -2.1910 -0.7668  1.7592  2.3295
#>  -0.0131  0.1066 -0.7571  1.6599 -0.6498
#>   0.5685 -3.0548 -1.0582 -1.8672  5.1993
#> 
#> (3,.,.) = 
#>   0.2584  0.8887  0.5973 -0.9306 -1.0329
#>   0.2357  0.1567 -0.8951  2.0399 -1.2004
#>   1.7337  1.9915  1.2468 -3.7848 -2.0507
#> 
#> (4,.,.) = 
#>   0.6965 -0.4939 -1.1063  0.3131  0.7353
#>   1.0965  1.9808  0.3649 -2.3069 -1.4287
#>   1.8218  0.9201 -0.9613 -2.1385  0.2080
#> 
#> (5,.,.) = 
#>  -0.4136 -2.4539 -0.7182  0.9711  2.7323
#>   0.6499  4.0714 -0.1628 -0.4510 -3.7500
#>   0.1350 -1.6332  0.0697 -0.8396  1.9719
#> 
#> (6,.,.) = 
#>   0.2667 -0.0097 -1.1592  0.4403  0.8256
#>   0.2429  1.9702  0.0767 -0.5251 -1.6359
#>   1.7233 -3.8393 -0.7589 -2.6883  4.7548
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]