Swish¶

Versioned name : Swish-4

Category : Activation function

Short description : Swish performs element-wise activation function on a given input tensor.

Detailed description

Swish operation is introduced in this article.

Swish is a smooth, non-monotonic function. The non-monotonicity property of Swish distinguishes itself from most common activation functions. It performs element-wise activation function on a given input tensor, based on the following mathematical formula:

\[Swish(x) = x\cdot \sigma(\beta x) = x \left(1 + e^{-(\beta x)}\right)^{-1}\]

where β corresponds to beta scalar input.

Attributes : Swish operation has no attributes.

Inputs :

1 : data. A tensor of type T and arbitrary shape. Required.
2 : beta. A non-negative scalar value of type T. Multiplication parameter for the sigmoid. Default value 1.0 is used. Optional.

Outputs :

1 : The result of element-wise Swish function applied to the input tensor data. A tensor of type T and the same shape as data input tensor.

Types

T : arbitrary supported floating-point type.

Examples

Example: Second input beta provided

<layer ... type="Swish">
    <input>
        <port id="0">
            <dim>256</dim>
            <dim>56</dim>
        </port>
        <port id="1">  <!-- beta value: 2.0 -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>256</dim>
            <dim>56</dim>
        </port>
    </output>
</layer>

Example: Second input beta not provided

<layer ... type="Swish">
    <input>
        <port id="0">
            <dim>128</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>128</dim>
        </port>
    </output>
</layer>

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