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mindspore.ops.kron

mindspore.ops.kron(input, other)

Computes the Kronecker product $input\otimes other$, denoted by ⊗, of input and other.

If input is a $(a_0$ input $a_1$ input … input $a_n)$ Tensor and other is a $(b_0$ input $b_1$ input … input $b_n)$ Tensor, the result will be a $(a_0\ast b_0$ input $a_1\ast b_1$ input … input $a_n\ast b_n)$ Tensor with the following entries:

$$(input\otimes other{)}_{k_0,k_1,...k_n}=inpu{t}_{i_0,i_1,...i_n}\ast othe{r}_{j_0,j_1,...j_n},$$

where $k_t=i_t\ast b_t+j_t$ for 0 ≤ t ≤ n. If one Tensor has fewer dimensions than the other it is unsqueezed until it has the same number of dimensions.

Note

Supports real-valued and complex-valued inputs.

Parameters

• input (Tensor) – Input Tensor, has the shape $(r0,r1,...,rN)$.

• other (Tensor) – Input Tensor, has the shape $(s0,s1,...,sN)$.

Returns

Tensor, has the shape $(r0\ast s0,r1\ast s1,...,rN\ast sN)$.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If other is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>> from mindspore import ops
>>> input = Tensor(np.array([[0, 1, 2], [3, 4, 5]])).astype(np.float32)
>>> other = Tensor(np.array([[-1, -2, -3], [-4, -6, -8]])).astype(np.float32)
>>> output = ops.kron(input, other)
>>> print(output)
[[  0.   0.   0.  -1.  -2.  -3.  -2.  -4.  -6.]
 [  0.   0.   0.  -4.  -6.  -8.  -8. -12. -16.]
 [ -3.  -6.  -9.  -4.  -8. -12.  -5. -10. -15.]
 [-12. -18. -24. -16. -24. -32. -20. -30. -40.]]