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.

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.


  • 1: input - multidimensional input tensor. Required.

    • Type: T


  • 1: output - multidimensional output tensor with shape and type matching the input tensor. Required.

    • Type: T


  • 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.