Mod#
Versioned name: Mod-1
Category: Arithmetic binary
Short description: Mod performs an element-wise modulo 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 Mod operation is computed element-wise on the input tensors a and b according to the formula below:
Mod operation computes a reminder of a truncated division. It is the same behavior like in C programming language: truncated(x / y) * y + truncated_mod(x, y) = x
. The sign of the result is equal to a sign of a dividend. The result of division by zero is undefined.
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 modulo 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="Mod">
<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="Mod">
<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>