Added mish layer doc opset (#1149)
* Added mish layer doc opset * Refactoring mish spec * Update mish spec * Change output description of Mish layer * Fix Mish according to review * Refactoring Mish and GELU spec according to code review * Update formula for ops in spec * Refactoring spec text * Update Mish opset * Change Mish version from 1 to 4 * Sort opset4 Co-authored-by: Your Name <you@example.com>
This commit is contained in:
parent
f2aba7cdf6
commit
ac5217d17f
@ -15,13 +15,15 @@ Gelu(x)=x*Φ(x), where Φ(x) is the Cumulative Distribution Function for Gaussia
|
||||
The following equivalent combination is recognized and fused into single Gelu op:
|
||||
|
||||
\f[
|
||||
Gelu(x) = 0.5*x*(1 + erf((x) / sqrt(2) )
|
||||
Gelu(x) = 0.5*x*(1.0 + erf((x) / \sqrt{2})
|
||||
\f]
|
||||
|
||||
Similarly, the following Gelu approximation (typical for the TensorFlow*) is recognized and fused into single Gelu op
|
||||
Similarly, the following Gelu approximation (typical for the TensorFlow*) is recognized and fused into single Gelu op
|
||||
|
||||
\f[
|
||||
Gelu(x) \approx 0.5*x*(1 + tanh((sqrt(2/pi)) * (x + 0.044715 * x ^ 3))
|
||||
Gelu(x) \approx 0.5x(1.0 + tanh(\sqrt{2.0/pi} * (x + 0.044715 * x ^ 3))
|
||||
\f]
|
||||
|
||||
**Inputs**:
|
||||
|
||||
* **1**: Multidimensional input tensor. Required.
|
||||
|
50
docs/ops/activation/Mish_4.md
Normal file
50
docs/ops/activation/Mish_4.md
Normal file
@ -0,0 +1,50 @@
|
||||
## Mish <a name="Mish"></a>
|
||||
|
||||
**Versioned name**: *Mish-4*
|
||||
|
||||
**Category**: *Activation*
|
||||
|
||||
**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).
|
||||
|
||||
**Attributes**: operation has no attributes.
|
||||
|
||||
**Inputs**:
|
||||
|
||||
* **1**: Input tensor *x* of any floating point type T. Required.
|
||||
|
||||
**Outputs**:
|
||||
|
||||
* **1**: Floating point tensor with shape and type matching the input tensor. Required.
|
||||
|
||||
**Types**
|
||||
|
||||
* *T*: any 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**
|
||||
|
||||
```xml
|
||||
<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>
|
||||
```
|
@ -77,6 +77,7 @@ declared in `namespace opset4`.
|
||||
* [MaxPool](pooling/MaxPool_1.md)
|
||||
* [Maximum](arithmetic/Maximum_1.md)
|
||||
* [Minimum](arithmetic/Minimum_1.md)
|
||||
* [Mish](activation/Mish_4.md)
|
||||
* [Mod](arithmetic/Mod_1.md)
|
||||
* [MVN](normalization/MVN_1.md)
|
||||
* [Multiply](arithmetic/Multiply_1.md)
|
||||
|
Loading…
Reference in New Issue
Block a user