SquaredDifference

Versioned name: SquaredDifference-1

Category: Arithmetic

Short description: SquaredDifference performs element-wise subtraction operation with two given tensors applying multi-directional broadcast rules, after that each result of the subtraction is squared.

OpenVINO description: This OP is as same as OpenVINO OP

Attributes:

• auto_broadcast

  • Description: specifies rules used for auto-broadcasting of input tensors.

  • Range of values:

    • none - no auto-broadcasting is allowed, all input shapes should match

    • numpy - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in ONNX docs.

  • Type: string

  • Default value: numpy

  • Required: no

Inputs

• 1: input_1 - the first input tensor. Required.

  • Type: T

• 2: input_2 - the second input tensor. Required.

  • Type: T

Outputs

• 1: output - the output tensor of SquaredDifference operation. Required.

  • Type: T

Types:

• T: f32, f16, bf16.

• Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.

Detailed description:

Before performing arithmetic operation, input_1 and input_2 are broadcasted if their shapes are different and auto_broadcast attributes is not none. Broadcasting is performed according to auto_broadcast value.

After broadcasting SquaredDifference does the following with the input tensors:

\[output_{i} = (input\_1_{i} - input\_2_{i}) ^ 2\]