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focal Loss Layer evaluation

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I have created simple CNN for semantic segmentation and repalced last layer with focal loss layer to use focal loss fucntion instead of pixel classification function.
Network = [
imageInputLayer([256 256 3],"Name","imageinput")
convolution2dLayer([3 3],128,"Name","conv_1","BiasLearnRateFactor",2,"Padding","same")
transposedConv2dLayer([3 3],2,"Name","transposed-conv","Cropping","same")
after training the network, I used,
pxdsResults = semanticseg(imdsTest,Trained_network, ...
'MiniBatchSize',5, ...
'WriteLocation',tempdir, ...
for test images but I got error the following error;
Error using semanticseg>iFindAndAssertNetworkHasOnePixelClassificationLayer (line 584)
The network must have a pixel classification layer.
Error in semanticseg>iParseInputs (line 377)
pxLayerID = iFindAndAssertNetworkHasOnePixelClassificationLayer(net);
Error in semanticseg (line 216)
params = iParseInputs(I, net, varargin{:});
Now its obvious that last layer must be pixel classification layer. but if I am using focal loss layer how to evaluate this?

Accepted Answer

Bhargavi Maganuru
Bhargavi Maganuru on 6 Jul 2020
Focal loss layer to a semantic segmentation or object classification deep learning network has been added in future release 2020b. In the earlier versions, you can use either PixelClassificationLayer or DicePixelClassificationLayer or a ClassificationLayer as the last layer in the network.
Bhargavi Maganuru
Bhargavi Maganuru on 6 Jul 2020
You can use ClassficationLayer as the last layer in the network. For more information about ClassficationLayer, refer

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