# Mish¶

Versioned name : Mish-4

Category : Activation function

Short description : Mish is a Self Regularized Non-Monotonic Neural Activation Function.

Detailed description

Mish is a self regularized non-monotonic neural activation function proposed in this article.

Mish performs element-wise activation function on a given input tensor, based on the following mathematical formula:

$Mish(x) = x\cdot\tanh\big(SoftPlus(x)\big) = x\cdot\tanh\big(\ln(1+e^{x})\big)$

Attributes : Mish operation has no attributes.

Inputs :

• 1 : A tensor of type T and arbitrary shape. Required.

Outputs :

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

Types

• T : arbitrary supported floating-point type.

Example

<layer ... type="Mish">
<input>
<port id="0">
<dim>256</dim>
<dim>56</dim>
</port>
</input>
<output>
<port id="3">
<dim>256</dim>
<dim>56</dim>
</port>
</output>
</layer>