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# swishLayer

## Description

A swish activation layer applies the swish function on the layer inputs.

The swish operation is given by $f\left(x\right)=\frac{x}{1+{e}^{-x}}$.

## Creation

### Syntax

``layer = swishLayer``
``layer = swishLayer('Name',Name)``

### Description

````layer = swishLayer` creates a swish layer.```

example

````layer = swishLayer('Name',Name)` creates a swish layer and sets the optional `Name` property using a name-value argument. For example, `swishLayer('Name','swish1')` creates a swish layer with the name `'swish1'`.```

## Properties

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Layer name, specified as a character vector or a string scalar. For `Layer` array input, the `trainNetwork`, `assembleNetwork`, `layerGraph`, and `dlnetwork` functions automatically assign names to layers with `Name` set to `''`.

Data Types: `char` | `string`

This property is read-only.

Number of inputs of the layer. This layer accepts a single input only.

Data Types: `double`

This property is read-only.

Input names of the layer. This layer accepts a single input only.

Data Types: `cell`

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: `double`

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: `cell`

## Examples

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Create a swish layer with the name `'swish1'`.

`layer = swishLayer('Name','swish1')`
```layer = SwishLayer with properties: Name: 'swish1' Learnable Parameters No properties. State Parameters No properties. Show all properties ```

Include a swish layer in a `Layer` array.

```layers = [ ... imageInputLayer([28 28 1]) convolution2dLayer(5,20) batchNormalizationLayer swishLayer maxPooling2dLayer(2,'Stride',2) fullyConnectedLayer(10) softmaxLayer classificationLayer]```
```layers = 8x1 Layer array with layers: 1 '' Image Input 28x28x1 images with 'zerocenter' normalization 2 '' Convolution 20 5x5 convolutions with stride [1 1] and padding [0 0 0 0] 3 '' Batch Normalization Batch normalization 4 '' Swish Swish 5 '' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0] 6 '' Fully Connected 10 fully connected layer 7 '' Softmax softmax 8 '' Classification Output crossentropyex ```

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## Extended Capabilities

### GPU Code GenerationGenerate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Introduced in R2021a

## Support

#### Introducing Deep Learning with MATLAB

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