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:

\[o_{i} = a_{i} \mod b_{i}\]

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>