Main Content

dlhdl.layer.mishLayer

Mish layer

Since R2024a

expand all in page

    Description

    Use mishLayer objects to apply the mish function to the layer inputs.

    This equation describes the mish operation:

    f(x)=xtanh(ln(1+ex))

    Creation

    Syntax

    layer = dlhdl.layer.mishLayer(Name)

    Description

    layer = dlhdl.layer.mishLayer(Name) creates a mish layer with the name specified by Name. For example, dlhdl.layer.mishLayer("mish1") creates a mish layer with the name "mish1".

    example

    Properties

    expand all

    Layer name, specified as a character vector or string scalar. For Layer array input, the trainnet and dlnetwork functions automatically assign names to layers with the name "".

    The mishLayer 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

    collapse all

    Create a mish layer with the name "mish1".

    layer = dlhdl.layer.mishLayer("mish1")
    layer = 
  mishLayer with properties:

    Name: 'mish1'
   Learnable Parameters
    No properties.
   State Parameters
    No properties.
  Show all properties

    Include the mish layer in a Layer array.

     layers = [imageInputLayer([20,20,3],'Normalization',"none",'Name','input')
                convolution2dLayer([5 5],3,'Padding',[1 2 1 2],'Stride',[1 1],'Name', 'conv')
                batchNormalizationLayer('Name','batchnorm')
                dlhdl.layer.mishLayer("mish1")
                convolution2dLayer([5 5],3,'Padding',[1 2 1 2],'Stride',[2 2],'Name', 'conv')
                batchNormalizationLayer('Name','batchnorm')
                swishLayer('Name','swish')]

    layers = 
  7×1 Layer array with layers:

     1   'input'       Image Input             20×20×3 images
     2   'conv'        2-D Convolution         3 5×5 convolutions with stride [1  1] and padding [1  2  1  2]
     3   'batchnorm'   Batch Normalization     Batch normalization
     4   'mish1'       dlhdl.layer.mishLayer   Custom mish Layer
     5   'conv'        2-D Convolution         3 5×5 convolutions with stride [2  2] and padding [1  2  1  2]
     6   'batchnorm'   Batch Normalization     Batch normalization
     7   'swish'       Swish                   Swish

    Algorithms

    expand all

    Version History

    Introduced in R2024a

    See Also

    |