Minimum¶

Versioned name: Minimum-1

Category: Arithmetic binary

Short description: Minimum performs element-wise minimum 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 Minimum operation is computed element-wise on the input tensors a and b according to the formula below:

\[o_{i} = min(a_{i},\ b_{i})\]

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 minimum 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="Minimum">
    <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="Minimum">
    <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>