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

x1 or x2 first dimension 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)