SquaredDifference

Versioned name: SquaredDifference-1

Category: Arithmetic binary operation

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.

Attributes:

• 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: A tensor of type T. Required.
• 2: A tensor of type T. Required.

Outputs

• 1: The result of element-wise SquaredDifference operation. A tensor of type T.

Types

• T: any numeric type.

Detailed description Before performing arithmetic operation, input tensors a and b 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 a and b:

$o_{i} = (a_{i} - b_{i})^2$

Examples

Example 1

<layer ... type="SquaredDifference">
<input>
<port id="0">
<dim>256</dim>
<dim>56</dim>
</port>
<port id="1">
<dim>256</dim>
<dim>56</dim>
</port>
</input>
<output>
<port id="2">
<dim>256</dim>
<dim>56</dim>
</port>
</output>
</layer>

<layer ... type="SquaredDifference">
<input>
<port id="0">
<dim>8</dim>
<dim>1</dim>
<dim>6</dim>
<dim>1</dim>
</port>
<port id="1">
<dim>7</dim>
<dim>1</dim>
<dim>5</dim>
</port>
</input>
<output>
<port id="2">
<dim>8</dim>
<dim>7</dim>
<dim>6</dim>
<dim>5</dim>
</port>
</output>
</layer>