# 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:

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

## Creation

### Description

layer = eluLayer creates an ELU layer.

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

example

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

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### ELU

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

### Layer

Layer name, specified as a character vector or a string scalar. To include a layer in a layer graph, you must specify a nonempty, unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

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

Data Types: double

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

Data Types: cell

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

Data Types: double

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

Data Types: cell

## Examples

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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

Show 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
classificationLayer]
layers =
11x1 Layer array with layers:

1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
2   ''   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   ''   Max Pooling             2x2 max pooling with stride [2  2] and padding [0  0  0  0]
6   ''   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
11   ''   Classification Output   crossentropyex

## 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

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

Introduced in R2019a