mindspore.ops.batch_dot

mindspore.ops.batch_dot(x1, x2, axes=None)

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

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:

Ascend GPU CPU

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 a 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)