Mish#

Versioned name: Mish-1

Category: Activation

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

OpenVINO description: This OP is as same as OpenVINO OP

Detailed Description

Mish is a self regularized non-monotonic neural activation function proposed in this [article](https://arxiv.org/abs/1908.08681v2).

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

\[Mish(x) = x \cdot tanh(SoftPlus(x)) = x \cdot tanh(ln(1 + e^x)).\]

Attributes: Mish operation has no attributes.

Inputs:

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

    • Type: T

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. Required.

    • Type: T

Types:

  • T: f32, f16, bf16.

  • Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.