mindspore.numpy.multi_dot
- mindspore.numpy.multi_dot(arrays)[source]
Computes the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order. multi_dot chains numpy.dot and uses optimal parenthesization of the matrices [1] <en.wikipedia.org/wiki/Matrix_chain_multiplication>. Depending on the shapes of the matrices, this can speed up the multiplication a lot. If the first argument is 1-D it is treated as a row vector. If the last argument is 1-D it is treated as a column vector. The other arguments must be 2-D.
Note
Numpy argument out is not supported.
- Parameters
arrays (sequence of array_like) – If the first argument is 1-D it is treated as row vector. If the last argument is 1-D it is treated as column vector. The other arguments must be 2-D.
- Returns
Tensor, the dot product of the supplied arrays.
- Raises
ValueError – arrays are not 2-D.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore.numpy as np >>> A = np.ones((10000, 100)) >>> B = np.ones((100, 1000)) >>> C = np.ones((1000, 5)) >>> D = np.ones((5, 333)) >>> output = np.multi_dot([A, B, C, D]) >>> print(output) [[500000. 500000. 500000. ... 500000. 500000. 500000.] [500000. 500000. 500000. ... 500000. 500000. 500000.] [500000. 500000. 500000. ... 500000. 500000. 500000.] ... [500000. 500000. 500000. ... 500000. 500000. 500000.] [500000. 500000. 500000. ... 500000. 500000. 500000.] [500000. 500000. 500000. ... 500000. 500000. 500000.]]