Matmul
Source:R/gen-namespace-docs.R
, R/gen-namespace-examples.R
, R/gen-namespace.R
torch_matmul.Rd
Matmul
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 andother
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,.,.) =
#> -1.8151 0.5780 -0.1290 -1.8930 0.5097
#> -0.8777 -0.6976 -0.2187 1.4253 0.2219
#> -0.6841 1.3040 -1.1039 -0.4555 0.3386
#>
#> (2,.,.) =
#> 1.7396 -0.4245 0.8057 0.1974 -0.3215
#> 2.0949 0.4632 2.0046 -4.0647 -0.3736
#> -0.1693 0.1147 0.6495 -0.2027 1.5907
#>
#> (3,.,.) =
#> 2.1441 0.4117 0.2571 0.3958 0.0381
#> 0.5040 0.8120 0.5435 -1.2966 1.1500
#> -1.9426 -0.2980 -0.7680 -0.6991 -1.3861
#>
#> (4,.,.) =
#> 4.5067 -0.2358 3.4780 -2.1069 1.3877
#> -0.2883 0.2882 -0.8061 2.6827 1.6880
#> 1.0297 0.5074 0.9047 -2.5976 -0.3816
#>
#> (5,.,.) =
#> -0.0459 1.9817 -0.8511 -2.3165 -0.0819
#> 0.3907 0.7466 0.2695 -1.2724 0.5345
#> -4.6231 -0.0114 -3.3173 2.4813 -1.0749
#>
#> (6,.,.) =
#> 2.9665 0.7322 1.6268 -1.3085 1.3395
#> 0.6895 -2.1424 1.1066 1.8214 -0.6949
#> 2.0319 -0.4956 1.9697 -0.9556 0.7094
#>
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]