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>