focal Loss Layer evaluation
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") reluLayer("Name","relu_1") batchNormalizationLayer("Name","batchnorm") transposedConv2dLayer([3 3],2,"Name","transposed-conv","Cropping","same") reluLayer("Name","relu_3") softmaxLayer("Name","softmax") focalLossLayer(2,0.25,"Name","focal-loss")]; after training the network, I used, pxdsResults = semanticseg(imdsTest,Trained_network, ... 'MiniBatchSize',5, ... 'WriteLocation',tempdir, ... 'Verbose',false); 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?
John Michell answered .
2025-11-20
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.