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Mish specification refactoring (#5035)

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* Review spec of Mish operation

* Add minor changes

   * Updated reference paper to a newer version
   * Fix typo in SoftPlus op

* Minor change in example section

* Fix minor wording issues
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Gabriele Galiero Casay 2021-04-01 15:08:24 +02:00 committed by GitHub
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**Versioned name**: *Mish-4*
**Category**: *Activation*
**Category**: *Activation function*
**Short description**: Mish is a Self Regularized Non-Monotonic Neural 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 the [article](https://arxiv.org/abs/1908.08681).
**Detailed description**
**Attributes**: operation has no attributes.
*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:
\f[
Mish(x) = x\cdot\tanh\big(SoftPlus(x)\big) = x\cdot\tanh\big(\ln(1+e^{x})\big)
\f]
**Attributes**: *Mish* operation has no attributes.
**Inputs**:
* **1**: Input tensor *x* of any floating point type T. Required.
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
**Outputs**:
* **1**: Floating point tensor with shape and type matching the input tensor.
* **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*: any floating point type.
* *T*: arbitrary supported floating-point type.
**Mathematical Formulation**
For each element from the input tensor calculates corresponding
element in the output tensor with the following formula:
\f[
Mish(x) = x*tanh(ln(1.0+e^{x}))
\f]
**Examples**
**Example**
```xml
<layer ... type="Mish">