mindspore.ops.kron

mindspore.ops.kron(x, y)[source]

Computes the Kronecker product \(x ⊗ y\), denoted by ⊗, of x and y.

If x is a \((a_{0}\) x \(a_{1}\) x … x \(a_{n})\) Tensor and y is a \((b_{0}\) x \(b_{1}\) x … x \(b_{n})\) Tensor, the result will be a \((a_{0}*b_{0}\) x \(a_{1}*b_{1}\) x … x \(a_{n}*b_{n})\) Tensor with the following entries:

\[(x ⊗ y)_{k_{0},k_{1},...k_{n}} = x_{i_{0},i_{1},...i_{n}} * y_{j_{0},j_{1},...j_{n}},\]

where \(k_{t} = i_{t} * b_{t} + j_{t}\) 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

  • x (Tensor) – Input Tensor, has the shape \((r0, r1, ... , rN)\).

  • y (Tensor) – Input Tensor, has the shape \((s0, s1, ... , sN)\).

Returns

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

Raises
Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>> from mindspore import ops
>>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]])).astype(np.float32)
>>> y = Tensor(np.array([[-1, -2, -3], [-4, -6, -8]])).astype(np.float32)
>>> output = ops.kron(x, y)
>>> 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.]]