Nan problem ( validation loss and mini batch loss) in Transfer Learning with Googlenet
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I am trying to use Googlenet for transfer Learning on a data set ( Images) , with three classes as output. I am using the same script that has been used successfully with ResNet50 before. As both networks shares the same input layer ( 224 224 3) ,I thought it would be easy to do another test on the same data set using Google net . Neither using Deep Network Designer nor editing the original Googlenet ( substituting the last layers ) by script have helped to avoid getting the same error that is :
after the first iteration both the mini batch loss and the validation loss go to NAN .
I think I am missing something here as it doesnt make sense to me .
I have attached the script plus a screen shot of the output that shows the Nan .
augmenter = imageDataAugmenter(...
'RandRotation',[0 15]);
path = fullfile('C:\','Big J','My Exp','training');
imds=imageDatastore(path,'IncludeSubfolder',true,'LabelSource','Foldernames');
inputSize=[224 224];
imds.ReadFcn = @(loc)imresize(imread(loc),inputSize);
[TrainDataStore,ValDataStore] = splitEachLabel(imds,0.8,'randomize');
Traindatasource = augmentedImageSource([224 224],TrainDataStore,'DataAugmentation',augmenter);
%%
net=googlenet;
lgraph = layerGraph(net);
lgraph = removeLayers(lgraph, {'loss3-classifier','prob','output'});
figure('Units','normalized','Position',[0.1 0.1 0.8 0.8]);plot(lgraph);
newLayers = [
fullyConnectedLayer(3,'Name','fc-3','WeightLearnRateFactor',20,'BiasLearnRateFactor', 20) % increase the learning factor of FC layer to speed up learning
softmaxLayer('Name','softmax')
classificationLayer('Name','classoutput')];
lgraph = addLayers(lgraph,newLayers);
figure('Units','normalized','Position',[0.1 0.1 0.8 0.8]);plot(lgraph);
lgraph = connectLayers(lgraph,'pool5-drop_7x7_s1','fc-3');
figure('Units','normalized','Position',[0.1 0.1 0.8 0.8]);plot(lgraph);
%%
options = trainingOptions('sgdm',...
'ExecutionEnvironment','gpu',...
'InitialLearnRate',0.01,...
'L2Regularization', 0.0001,...
'MaxEpochs',15,...
'MiniBatchSize',32,...
'Momentum',0.9,...
'Shuffle','once',...
'Verbose',1,...
'VerboseFrequency',50,...
'ValidationData',ValDataStore,...
'ValidationFrequency',50,...
'ValidationPatience',Inf,...
'Plots','training-progress' );
%%
net = trainNetwork(Traindatasource,lgraph,options);
%%
G_net_test =net;
save('G_net_test.mat', 'G_net_test');
![error.jpg](https://www.mathworks.com/matlabcentral/answers/uploaded_files/233267/error.jpeg)
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