mindspore.ops.MatMul
- class mindspore.ops.MatMul(transpose_a=False, transpose_b=False)[source]
Multiplies matrix a and matrix b.
\[(Output)_{i j}=\sum_{k=1}^{p} a_{i k} b_{k j}=a_{i 1} b_{1 j}+a_{i 2} b_{2 j}+\cdots+a_{i p} b_{p j}, p\in N\]where the \(i,j\) indicates the output of the i-th row and j-th column element.
Note
If \(N * M\) cannot be divided by 16, the performance will be poor in ascend environment.
- Parameters
- Inputs:
a (Tensor) - The first tensor to be multiplied. The shape of the tensor is \((N, C)\). If transpose_a is True, its shape must be \((N, C)\) after transpose.
b (Tensor) - The second tensor to be multiplied. The shape of the tensor is \((C, M)\). If transpose_b 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 a is not equal to the row of matrix dimensions of b.
ValueError – If length of shape of a or b is not equal to 2.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> a = Tensor(np.ones(shape=[1, 3]), mindspore.float32) >>> b = Tensor(np.ones(shape=[3, 4]), mindspore.float32) >>> matmul = ops.MatMul() >>> output = matmul(a, b) >>> print(output) [[3. 3. 3. 3.]]