dot

Matrix product of two tensors.

The behavior depends on the dimensionality of the tensors as follows:

1. If both tensors are 1-dimensional, the dot product (scalar) is returned.

2. If both arguments are 2-dimensional, the matrix-matrix product is returned.

3. 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, depending on the implementation the prepended dimension might be removed.

4. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.

5. 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 broadcast (and thus must be broadcastable). For example, if input is a (j × 1 × n × n) tensor and other is a (k × n × n) tensor, out will be a (j × k × n × n) tensor.

For more information: https://pytorch.org/docs/stable/generated/torch.matmul.html

Return

a mathematical product of two tensors.

Parameters