mindspore.ops.MatMul

class mindspore.ops.MatMul(transpose_a=False, transpose_b=False)[source]

Multiplies matrix x and matrix y.

(O u t p u t)_{i j} = \sum_{k = 1}^{p} a_{i k} b_{k j} = a_{i 1} b_{1 j} + a_{i 2} b_{2 j} + \dots + a_{i p} b_{p j}, p \in N

where the $i, j$ indicates the output of the i-th row and j-th column element.

Parameters

transpose_x (bool) – If true, x is transposed before multiplication. Default: False.
transpose_y (bool) – If true, y is transposed before multiplication. Default: False.

Inputs:

x (Tensor) - The first tensor to be multiplied. The shape of the tensor is $(N, C)$ . If transpose_x is True, its shape must be $(N, C)$ after transpose.
y (Tensor) - The second tensor to be multiplied. The shape of the tensor is $(C, M)$ . If transpose_y is True, its shape must be $(C, M)$ after transpose.

Outputs:

Tensor, the shape of the output tensor is $(N, M)$ .

Raises

TypeError – If transpose_a or transpose_b is not a bool.
ValueError – If the column of matrix dimensions of x is not equal to the row of matrix dimensions of y.
ValueError – If length of shape of x or y is not equal to 2.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x = Tensor(np.ones(shape=[1, 3]), mindspore.float32)
>>> y = Tensor(np.ones(shape=[3, 4]), mindspore.float32)
>>> matmul = ops.MatMul()
>>> output = matmul(x, y)
>>> print(output)
[[3. 3. 3. 3.]]

check_shape_size(x1, x2)[source]: Check the shape size of inputs for MatMul.