mindspore.numpy.matmul

mindspore.numpy.matmul(x1, x2, dtype=None)[source]

Returns the matrix product of two arrays.

Note

Numpy arguments out, casting, order, subok, signature, and extobj are not supported. On GPU, the supported dtypes are np.float16 and np.float32. On CPU, the supported dtypes are np.float16 and np.float32.

Parameters

x1 (Tensor) – Input tensor, scalar not allowed.
x2 (Tensor) – Input tensor, scalar not allowed.
dtype (mindspore.dtype, optional) – Default: None . Overrides the dtype of the output Tensor.

Returns

Tensor or scalar, the matrix product of the inputs. This is a scalar only when both x1, x2 are 1-d vectors.

Raises

ValueError – If the last dimension of x1 is not the same size as the second-to-last dimension of x2, or if a scalar value is passed in.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x1 = np.arange(2*3*4).reshape(2, 3, 4).astype('float32')
>>> x2 = np.arange(4*5).reshape(4, 5).astype('float32')
>>> output = np.matmul(x1, x2)
>>> print(output)
[[[  70.   76.   82.   88.   94.]
[ 190.  212.  234.  256.  278.]
[ 310.  348.  386.  424.  462.]]
[[ 430.  484.  538.  592.  646.]
[ 550.  620.  690.  760.  830.]
[ 670.  756.  842.  928. 1014.]]]