In terms of the output feature map dimensions, there is a time "T" dimension that has to be eliminated in order to match the output dimensions, which can usually be done by indexing1dLayer. So the layers array is added before the fullyConnectedLayer.
% Here use simple data, for demonstration purposes only
XTrain = rand(3,200,1000); % dims "CTB"
TTrain = categorical(randi(4,1000,1));
% define my layers
numClasses = numel(categories(TTrain));
layers = [inputLayer(size(XTrain),"CTB");
flattenLayer;
selfAttentionLayer(6,48);
% lstmLayer(20,OutputMode="last"); % use lstmLayer is ok!
layerNormalizationLayer;
indexing1dLayer; % Add this!!!
fullyConnectedLayer(numClasses);
softmaxLayer];
net = dlnetwork(layers);
% train network
lossFcn = "crossentropy";
options = trainingOptions("adam", ...
MaxEpochs=1, ...
InitialLearnRate=0.01,...
Shuffle="every-epoch", ...
GradientThreshold=1, ...
Verbose=true);
netTrained = trainnet(XTrain,TTrain,net,lossFcn,options);
Iteration Epoch TimeElapsed LearnRate TrainingLoss
_________ _____ ___________ _________ ____________
1 1 00:00:02 0.01 1.5374
7 1 00:00:06 0.01 1.5272
Training stopped: Max epochs completed
-------------------------Off-topic interlude-------------------------------
I am currently looking for a job in the field of CV algorithm development, based in Shenzhen, Guangdong, China. I would be very grateful if anyone is willing to offer me a job or make a recommendation. My preliminary resume can be found at: https://cuixing158.github.io/about/ . Thank you!
Email: cuixingxing150@gmail.com
