# focalLossLayer

Create focal loss layer using focal loss function

## Description

A focal loss layer predicts object classes using focal loss. Add the focal loss layer to train an object detection, semantic segmentation, or a classification network when imbalance exists between foreground and background classes. To compensate for class imbalance, the focal loss function multiplies the cross entropy function with a modulating factor that increases the sensitivity of the network to misclassified observations.

## Creation

### Description

example

layer = focalLossLayer creates a focal loss layer for deep learning networks. For information on how to use focal loss layer in an object detection network, see Create SSD Object Detection Network.

example

layer = focalLossLayer(Name,Value) sets properties of the focal loss layer by using one or more name-value pair arguments. Enclose each property name in quotes.

For example, focalLossLayer('Name','focalloss') creates a focal loss layer with the name 'focalloss' and the specified balancing and focusing parameters.

## Properties

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Balancing parameter of the focal loss function, specified as a positive real number. The Alpha value scales the loss function linearly and is typically set to 0.25. If you decrease Alpha, increase Gamma.

Focusing parameter of the focal loss function, specified as a positive real number. Increasing the value of Gamma increases the sensitivity of the network to misclassified observations.

Object classes that the network is trained to detect, specified as a string vector, categorical vector, cell array of character vectors, or 'auto'. When you set Classes to 'auto', the classes are automatically set at training time. When you specify a string vector or cell array of character vectors, then the elements of Classes are sorted according to the output of the categories function.

Data Types: string | categorical | cell | char

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork (Deep Learning Toolbox), assembleNetwork (Deep Learning Toolbox), layerGraph (Deep Learning Toolbox), and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name ''.

Data Types: char | string

## Examples

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Specify class names.

classes = ["Vehicle","Background"];

Specify the balancing paramete, and focusing parameter of the focal loss function. Create a focal loss layer named "focallosslayer" for the two classes, displaying results.

layer = focalLossLayer('Classes',classes,'Name','focallosslayer')
layer =
FocalLossLayer with properties:

Name: 'focallosslayer'

Hyperparameters
Gamma: 2
Alpha: 0.2500
Classes: [2x1 categorical]
LossFunction: 'focalLoss'

Create a DeepLab v3+ network based on ResNet-18.

imageSize = [480 640 3];
numClasses = 5;
network = 'resnet18';
lgraph = deeplabv3plusLayers(imageSize,numClasses,network,'DownsamplingFactor',16)
lgraph =
LayerGraph with properties:

Layers: [100x1 nnet.cnn.layer.Layer]
Connections: [113x2 table]
InputNames: {'data'}
OutputNames: {'classification'}

Display the output layer of the network. The output layer of the DeepLab v3+ network is a Pixel Classification Layer that uses cross-entropy loss to predict the categorical label for every pixel in an input 2-D image.

lgraph.Layers(end)
ans =
PixelClassificationLayer with properties:

Name: 'classification'
Classes: 'auto'
ClassWeights: 'none'
OutputSize: 'auto'

Hyperparameters
LossFunction: 'crossentropyex'

Replace the output Pixel Classification Layer with the Focal Loss Layer to handle imbalanced classes in data.

layer = focalLossLayer("Name","focalloss");
lgraph = replaceLayer(lgraph,"classification",layer);

Display the network.

analyzeNetwork(lgraph);

Create a 3-D U-Net network for semantic segmentation by using unet3dLayers function. Set the encoder-decoder depth to 2 and specify the number of output channels for the first convolution layer as 16.

imageSize = [128 128 128 3];
numClasses = 5;
lgraph = unet3dLayers(imageSize,numClasses,'EncoderDepth',2,...
'NumFirstEncoderFilters',16);
figure
plot(lgraph)

Create a focal loss layer and replace the Segmentation-Layer in the network with the focal loss layer. The layer predicts the categorical label for every voxel in an input 3-D volume.

layer = focalLossLayer("Name","focalloss");
lgraph = replaceLayer(lgraph,"Segmentation-Layer",layer)
lgraph =
LayerGraph with properties:

Layers: [40x1 nnet.cnn.layer.Layer]
Connections: [41x2 table]
InputNames: {'ImageInputLayer'}
OutputNames: {'focalloss'}

Display the network.

analyzeNetwork(lgraph);

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## References

[1] Lin, Tsung-Yi, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. "Focal Loss for Dense Object Detection." In 2017 IEEE® International Conference on Computer Vision (ICCV), 2999–3007. Venice: IEEE, 2017. https://doi.org/10.1109/ICCV.2017.324.

## Version History

Introduced in R2020a