mindspore.numpy.dot

mindspore.numpy.dot(a, b)[source]

Returns the dot product of two arrays.

Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). If both a and b are 2-D arrays, it is matrix multiplication. If either a or b is 0-D (scalar), it is equivalent to multiplication. If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b. If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b: dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])

Note

Numpy argument out is not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
On CPU, the supported dtypes are np.float16, np.float32 and np.float64.

Parameters:

a (Tensor) – input tensor
b (Tensor) – input tensor

Returns:

Tensor or scalar, the dot product of a and b. If a and b are both scalars or both 1-D arrays, then a scalar is returned; otherwise an array is returned

Raises:

ValueError – If the last dimension of a is not the same size as the second-to-last dimension of b.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> a = np.full((1, 3), 7).astype('float32')
>>> b = np.full((2, 3, 4), 5).astype('float32')
>>> output = np.dot(a, b)
>>> print(output)
[[[105. 105. 105. 105.]
[105. 105. 105. 105.]]]