mindspore.ops.batch_dot

mindspore.ops.batch_dot(x1, x2, axes=None)[source]

Computation of batch dot product between samples in two tensors containing batch dims.

Warning

This interface is deprecated and will be removed after version 2.9.0.

Note

The first dimension of x1 or x2 is batch size. Datatype must be float32 and the rank must be greater than or equal to 2.

\[output = x1[batch, :] · x2[batch, :]\]

Parameters:

x1 (Tensor) – The first input tensor.
x2 (Tensor) – The second input tensor.
axes (Union[int, tuple(int), list(int)]) – Specify the axes for computation. Default None .

Returns:

Tensor

Supported Platforms:: Deprecated

Examples

>>> import mindspore
>>> # case 1: axes is a tuple(axes of `x1` , axes of `x2` )
>>> x1 = mindspore.ops.ones([2, 2, 3])
>>> x2 = mindspore.ops.ones([2, 3, 2])
>>> axes = (-1, -2)
>>> output = mindspore.ops.batch_dot(x1, x2, axes)
>>> print(output)
[[[3. 3.]
  [3. 3.]]
 [[3. 3.]
  [3. 3.]]]
>>> print(output.shape)
(2, 2, 2)
>>> x1 = mindspore.ops.ones([2, 2], mindspore.float32)
>>> x2 = mindspore.ops.ones([2, 3, 2], mindspore.float32)
>>> axes = (1, 2)
>>> output = mindspore.ops.batch_dot(x1, x2, axes)
>>> print(output)
[[2. 2. 2.]
 [2. 2. 2.]]
>>> print(output.shape)
(2, 3)
>>>
>>> # case 2: axes is None
>>> x1 = mindspore.ops.ones([6, 2, 3, 4], mindspore.float32)
>>> x2 = mindspore.ops.ones([6, 5, 4, 8], mindspore.float32)
>>> output = mindspore.ops.batch_dot(x1, x2)
>>> print(output.shape)
(6, 2, 3, 5, 8)
>>>
>>> # case 3: axes is an int data.
>>> x1 = mindspore.ops.ones([2, 2, 4])
>>> x2 = mindspore.ops.ones([2, 5, 4, 5])
>>> output = mindspore.ops.batch_dot(x1, x2, 2)
>>> print(output.shape)
(2, 2, 5, 5)