mindspore.ops.dot

mindspore.ops.dot(x1, x2)

Computation a dot product between samples in two tensors.

Inputs:

• x1 (Tensor) - First tensor in Dot op with datatype float16 or float32 The rank must be greater than or equal to 2.

• x2 (Tensor) - Second tensor in Dot op with datatype float16 or float32 The rank must be greater than or equal to 2.

Outputs:

Tensor, dot product of x1 and x2.

Raises

• TypeError – If type of x1 and x2 are not the same.

• TypeError – If dtype of x1 or x2 is not float16 or float32.

• ValueError – If rank of x1 or x2 less than 2.

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x1 = Tensor(np.ones(shape=[2, 3]), mindspore.float32)
>>> input_x2 = Tensor(np.ones(shape=[1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input_x1, input_x2)
>>> print(output)
[[[3. 3.]]
 [[3. 3.]]]
>>> print(output.shape)
(2, 1, 2)
>>> input_x1 = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
>>> input_x2 = Tensor(np.ones(shape=[1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input_x1, input_x2)
>>> print(output)
[[[[3. 3.]]
  [[3. 3.]]]]
>>> print(output.shape)
(1, 2, 1, 2)
>>> input_x1 = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
>>> input_x2 = Tensor(np.ones(shape=[2, 3, 2]), mindspore.float32)
>>> output = ops.dot(input_x1, input_x2)
>>> print(output)
[[[[3. 3.]
   [3. 3.]]
  [[3. 3.]
   [3. 3.]]]]
>>> print(output.shape)
(1, 2, 2, 2)
>>> input_x1 = Tensor(np.ones(shape=[3, 2, 3]), mindspore.float32)
>>> input_x2 = Tensor(np.ones(shape=[2, 1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input_x1, input_x2)
>>> print(output)
[[[[[3. 3.]]
   [[3. 3.]]]
  [[[3. 3.]]
   [[3. 3.]]]]
 [[[[3. 3.]]
   [[3. 3.]]]
  [[[3. 3.]]
   [[3. 3.]]]]
 [[[[3. 3.]]
   [[3. 3.]]]
  [[[3. 3.]]
   [[3. 3.]]]]]
>>> print(output.shape)
(3, 2, 2, 1, 2)