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swishLayer

Swish layer

Since R2021a

    Description

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

    The swish operation is given by f(x)=x1+ex.

    Creation

    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 trainnet and dlnetwork functions automatically assign names to layers with the name "".

    The SwishLayer object stores this property as a character vector.

    Data Types: char | string

    This property is read-only.

    Number of inputs to the layer, returned as 1. This layer accepts a single input only.

    Data Types: double

    This property is read-only.

    Input names, returned as {'in'}. This layer accepts a single input only.

    Data Types: cell

    This property is read-only.

    Number of outputs from the layer, returned as 1. This layer has a single output only.

    Data Types: double

    This property is read-only.

    Output names, returned as {'out'}. 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.
    
    Use properties method to see a list of 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]
    layers = 
      7x1 Layer array with layers:
    
         1   ''   Image Input           28x28x1 images with 'zerocenter' normalization
         2   ''   2-D Convolution       20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
         3   ''   Batch Normalization   Batch normalization
         4   ''   Swish                 Swish
         5   ''   2-D 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
    

    Algorithms

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    Extended Capabilities

    C/C++ Code Generation
    Generate C and C++ code using MATLAB® Coder™.

    GPU Code Generation
    Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

    Version History

    Introduced in R2021a