The @ operator calls the array’s __matmul__ method, not dot. This method is also present in the API as the function np.matmul. >>> a = np.random.rand(8,13,13) >>> b = np.random.rand(8,13,13) >>> np.matmul(a, b).shape (8, 13, 13) From the documentation: matmul differs from dot in two important ways. Multiplication by scalars is not allowed. Stacks of … Read more
This kind of question is recurring and should be answered more clearly than “MATLAB uses highly optimized libraries” or “MATLAB uses the MKL” for once on Stack Overflow. History: Matrix multiplication (together with Matrix-vector, vector-vector multiplication and many of the matrix decompositions) is (are) the most important problems in linear algebra. Engineers have been solving … Read more
From the documentation: The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator. The @ operator was introduced in Python 3.5. @= is matrix multiplication followed by assignment, as you would expect. They map to __matmul__, __rmatmul__ or __imatmul__ similar to how + and += map … Read more