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geluLayer

Gaussian error linear unit (GELU) layer

    Description

    A Gaussian error linear unit (GELU) layer weights the input by its probability under a Gaussian distribution.

    This operation is given by

    GELU(x)=x2(1+erf(x2)),

    where erf denotes the error function.

    Creation

    Description

    example

    layer = geluLayer returns a GELU layer.

    layer = geluLayer(Name=Value) sets the optional Approximation and Name properties using name-value arguments. For example, geluLayer(Name="gelu") creates a GELU layer with the name "gelu".

    Properties

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    GELU

    Approximation method for the GELU operation, specified as one of these values:

    • 'none' — Do not use approximation.

    • 'tanh' — Approximate the underlying error function using

      erf(x2)tanh(2π(x+0.044715x3)).

    Tip

    In MATLAB®, computing the tanh approximation is typically less accurate, and, for large input sizes, slower than computing the GELU activation without using an approximation. Use the tanh approximation when you want to reproduce models that use this approximation, such as BERT and GPT-2.

    Layer

    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 the name ''.

    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 GELU layer.

    layer = geluLayer
    layer = 
      GELULayer with properties:
    
                 Name: ''
    
       Hyperparameters
        Approximation: 'none'
    
    

    Include a GELU layer in a Layer array.

    layers = [
        imageInputLayer([28 28 1])
        convolution2dLayer(5,20)
        geluLayer
        maxPooling2dLayer(2,Stride=2)
        fullyConnectedLayer(10)
        softmaxLayer
        classificationLayer]
    layers = 
      7×1 Layer array with layers:
    
         1   ''   Image Input             28×28×1 images with 'zerocenter' normalization
         2   ''   Convolution             20 5×5 convolutions with stride [1  1] and padding [0  0  0  0]
         3   ''   GELU                    GELU
         4   ''   Max Pooling             2×2 max pooling with stride [2  2] and padding [0  0  0  0]
         5   ''   Fully Connected         10 fully connected layer
         6   ''   Softmax                 softmax
         7   ''   Classification Output   crossentropyex
    

    Algorithms

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    References

    [1] Hendrycks, Dan, and Kevin Gimpel. "Gaussian error linear units (GELUs)." Preprint, submitted June 27, 2016. https://arxiv.org/abs/1606.08415

    Version History

    Introduced in R2022b