mindspore.ops.tensor_dot
- mindspore.ops.tensor_dot(x1, x2, axes)[source]
Computation of Tensor contraction on arbitrary axes between tensors a and b.
Contraction allows for the summation of products of elements of a and b on specified axes. The same number of axes must be specified for both x1 and x2, and values must be within range of number of dims of both a and b.
Selected dims in both inputs must also match.
axes = 0 leads to outer product axes = 1 leads to normal matrix multiplication when inputs both 2D. axes = 1 is the same as axes = ((1,),(0,) where both a and b are 2D. axes = 2 is the same as axes = ((1,2),(0,1)) where both a and b are 3D.
- Inputs:
x1 (Tensor) - First tensor in tensor_dot with datatype float16 or float32
x2 (Tensor) - Second tensor in tensor_dot with datatype float16 or float32
axes (Union[int, tuple(int), tuple(tuple(int)), list(list(int))]) - Single value or tuple/list of length 2 with dimensions specified for a and b each. If single value N passed, automatically picks up last N dims from a input shape and first N dims from b input shape in order as axes for each respectively.
- Outputs:
Tensor, the shape of the output tensor is \((N + M)\). Where \(N\) and \(M\) are the free axes not contracted in both inputs
- Raises
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> input_x1 = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32) >>> input_x2 = Tensor(np.ones(shape=[3, 1, 2]), mindspore.float32) >>> output = ops.tensor_dot(input_x1, input_x2, ((0,1),(1,2))) >>> print(output) [[2. 2. 2] [2. 2. 2] [2. 2. 2]]