eluLayer

Exponential linear unit (ELU) layer

Description

An ELU activation layer performs the identity operation on positive inputs and an exponential nonlinearity on negative inputs.

The layer performs the following operation:

$$f\left(x\right)=\{\begin{array}{cc}x,& x\ge 0\\ \alpha \text{(exp(}x\text{) - 1)},& x<0\end{array}$$

The default value of α is 1. Specify a value of α for the layer by setting the Alpha property.

Creation

Syntax

layer = eluLayer

layer = eluLayer(alpha)

layer = eluLayer(___,'Name',Name)

Description

layer = eluLayer creates an ELU layer.

layer = eluLayer(alpha) creates an ELU layer and specifies the Alpha property.

layer = eluLayer(___,'Name',Name) additionally sets the optional Name property using any of the previous syntaxes. For example, eluLayer('Name','elu1') creates an ELU layer with the name 'elu1'.

Properties

ELU

Alpha — Nonlinearity parameter

1 (default) | finite real scalar

Nonlinearity parameter α, as a finite real scalar. The minimum value of the output of the ELU layer equals -α and the slope at negative inputs approaching 0 is α.

Layer

Name — Layer name

"" (default) | character vector | string scalar

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 ELULayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

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

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

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

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

Create ELU Layer

Create an exponential linear unit (ELU) layer with the name 'elu1' and a default value of 1 for the nonlinearity parameter Alpha.

layer = eluLayer(Name="elu1")

layer = 
  ELULayer with properties:

     Name: 'elu1'
    Alpha: 1

   Learnable Parameters
    No properties.

   State Parameters
    No properties.

Use properties method to see a list of all properties.

Include an ELU layer in a Layer array.

layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(3,16)
    batchNormalizationLayer
    eluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,32)
    batchNormalizationLayer
    eluLayer
    
    fullyConnectedLayer(10)
    softmaxLayer]

layers = 
  10x1 Layer array with layers:

     1   ''   Image Input           28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution       16 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   Batch Normalization   Batch normalization
     4   ''   ELU                   ELU with Alpha 1
     5   ''   2-D Max Pooling       2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     6   ''   2-D Convolution       32 3x3 convolutions with stride [1  1] and padding [0  0  0  0]
     7   ''   Batch Normalization   Batch normalization
     8   ''   ELU                   ELU with Alpha 1
     9   ''   Fully Connected       10 fully connected layer
    10   ''   Softmax               softmax

Algorithms

Layer Input and Output Formats

Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray objects. The format of a dlarray object is a string of characters in which each character describes the corresponding dimension of the data. The formats consist of one or more of these characters:

• "S" — Spatial

• "C" — Channel

• "B" — Batch

• "T" — Time

• "U" — Unspecified

For example, you can describe 2-D image data that is represented as a 4-D array, where the first two dimensions correspond to the spatial dimensions of the images, the third dimension corresponds to the channels of the images, and the fourth dimension corresponds to the batch dimension, as having the format "SSCB" (spatial, spatial, channel, batch).

ELULayer objects apply an element-wise operation and supports input data of any format. The layer and does not add or remove any dimensions, so it outputs data with the same format as its inputs.

References

[1] Clevert, Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter. "Fast and accurate deep network learning by exponential linear units (ELUs)." arXiv preprint arXiv:1511.07289 (2015).

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 R2019a