Function gemm

python/singa/autograd.py:3772–3798

Init a General Matrix multiplication(Gemm) operator. Compute `Y = alpha * A' * B' + beta * C`, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). `A' =

(A, B, C=None, alpha=1.0, beta=1.0, transA=0, transB=0)

def gemm(A, B, C=None, alpha=1.0, beta=1.0, transA=0, transB=0):
    """
    Init a General Matrix multiplication(Gemm) operator. Compute `Y = alpha *
    A' * B' + beta * C`, where input tensor A has shape (M, K) or (K, M), input
    tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to
    shape (M, N), and output tensor Y has shape (M, N).
    `A' = transpose(A)` if transA else A
    `B' = transpose(B)` if transB else B
    Args:
        A (Tensor): The shape of A should be (M, K) if transA is 0, or
            (K, M) if transA is non-zero.
        B (Tensor): The shape of B should be (K, N) if transB is 0, or
            (N, K) if transB is non-zero.
        C (Tensor): (optional), Optional input tensor C. If not specified,
            the computation is done as if C is a scalar 0. The shape of C
            should be unidirectional broadcastable to (M, N).
        alpha (float): Scalar multiplier for the product of input tensors A * B.
        beta (float): Scalar multiplier for input tensor C.
        ransA (int): Whether A should be transposed
        transB (int): Whether B should be transposed
    Returns:
        Tensor, the output
    """
    if C:
        return Gemm(alpha, beta, transA, transB)(A, B, C)[0]
    else:
        return Gemm(alpha, beta, transA, transB)(A, B)[0]


class GlobalAveragePool(Operator):