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leakyReluLayer

Leaky Rectified Linear Unit (ReLU) layer

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Description

A leaky ReLU layer performs a threshold operation, where any input value less than zero is multiplied by a fixed scalar.

This operation is equivalent to:

f ( x ) = { x , x ≥ 0 s c a l e * x , x < 0 .

Creation

Syntax

layer = leakyReluLayer
layer = leakyReluLayer(scale)
layer = leakyReluLayer(___“名字”,名称)

Description

layer= leakyReluLayerreturns a leaky ReLU layer.

layer= leakyReluLayer(scale)returns a leaky ReLU layer with a scalar multiplier for negative inputs equal toscale.

example

layer= leakyReluLayer(___,'Name',Name)returns a leaky ReLU layer and sets the optionalNameproperty.

Properties

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Leaky ReLU

Scalar multiplier for negative input values, specified as a numeric scalar.

Example:0.4

Layer

Layer name, specified as a character vector or a string scalar. ForLayerarray input, thetrainNetwork,assembleNetwork,layerGraph, anddlnetworkfunctions automatically assign names to layers with name''.

Data Types:char|string

This property is read-only.

输入层的数量。这一层接受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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Open Live Script

Create a leaky ReLU layer with the name'leaky1'and a scalar multiplier for negative inputs equal to 0.1.

layer = leakyReluLayer(0.1,'Name','leaky1')
layer = LeakyReLULayer with properties: Name: 'leaky1' Hyperparameters Scale: 0.1000

Include a leaky ReLU layer in aLayerarray.

layers = [ imageInputLayer([28 28 1]) convolution2dLayer(3,16) batchNormalizationLayer leakyReluLayer maxPooling2dLayer(2,'Stride',2) convolution2dLayer(3,32) batchNormalizationLayer leakyReluLayer 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 '' Leaky ReLU Leaky ReLU with scale 0.01 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 '' Leaky ReLU Leaky ReLU with scale 0.01 9 '' Fully Connected 10 fully connected layer 10 '' Softmax softmax 11 '' Classification Output crossentropyex

                    

                   

                  

                  

                 

                

               

              

              

               
               
              

             

            

            

             
References

             

              
[1] Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng. "Rectifier nonlinearities improve neural network acoustic models." InProc. ICML, vol. 30, no. 1. 2013.

             

            

            

             
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 R2017b
            

             
See Also

             
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