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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.ops.SquaredDifference

class mindspore.ops.SquaredDifference

Subtracts the second input tensor from the first input tensor element-wise and returns square of it.

Inputs of x and y comply with the implicit type conversion rules to make the data types consistent. The inputs must be two tensors or one tensor and one scalar. When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

$$ou{t}_i=(x_i-y_i)\ast (x_i-y_i)=(x_i-y_i{)}^2$$

Inputs:

• x (Union[Tensor, Number, bool]) - The first input is a number, or a bool, or a tensor.

• y (Union[Tensor, Number, bool]) - The second input is a number, or a bool when the first input is a tensor, or a tensor.

Outputs:

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises

TypeError – if x and y is not a Number or a bool or a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 6.0]), mindspore.float32)
>>> squared_difference = ops.SquaredDifference()
>>> output = squared_difference(x, y)
>>> print(output)
[1. 4. 9.]