SquaredDifference¶
Versioned name : SquaredDifference-1
Category : Arithmetic binary
Short description : SquaredDifference performs element-wise subtract and square the result operation with two given tensors applying broadcasting rule specified in the auto_broadcast attribute.
Detailed description As a first step input tensors a and b are broadcasted if their shapes differ. Broadcasting is performed according to auto_broadcast attribute specification. As a second step Substract and Square the result operation is computed element-wise on the input tensors a and b according to the formula below:
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 must match
numpy - numpy broadcasting rules, description is available in Broadcast Rules For Elementwise Operations
Type : string
Default value : “numpy”
Required : no
Inputs
1 : A tensor of type T and arbitrary shape. Required.
2 : A tensor of type T and arbitrary shape. Required.
Outputs
1 : The result of element-wise subtract and square the result operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
Types
T : any numeric type.
Examples
Example 1 - no broadcasting
<layer ... type="SquaredDifference">
<data auto_broadcast="none"/>
<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>Example 2: numpy broadcasting
<layer ... type="SquaredDifference">
<data auto_broadcast="numpy"/>
<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>