[XTrain,YTrain]=japaneseVowelsTrainData;
XTrain(1:5)
figure
plot(XTrain{1}')
xlabel("Time Step");
title("Training Observation 1")
legend("Feature"+string(1:12),'Location','northeastoutside')
%prepare data for padding 野割
%get the sequence lengths for each observation
numObservations = numel(XTrain);
for i=1:numObservations
sequence=XTrain{i};
sequenceLengths(i)=size(sequence,2);
end
% sort by sequence lengths
[sequenceLengths,idx]=sort(sequenceLengths);
XTrain=XTrain(idx);
YTrain=YTrain(idx);
% view the sorted sequence in a bar chart
figure
bar(sequenceLengths)
ylim([0 30])
xlabel("Sequence")
ylabel("Length")
title("Sorted Data")
% minibatch
miniBatchSize=27;
%define LSTM network architecture
inputSize=12;
numHiddenUnits=100;
numClasses=9;
layers=[...
sequenceInputLayer(inputSize)
bilstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer
]
% training options
maxEpochs=100;
miniBatchSize=27;
options=trainingOptions('adam',...
'ExecutionEnvironment','cpu',...
'GradientThreshold',1,...
'MaxEpochs',maxEpochs,...
'MinibatchSize',miniBatchSize,...
'SequenceLength','longest',...
'Shuffle','never',...
'Verbose',0,...
'Plots','training-progress');
% train LSTM network
net=trainNetwork(XTrain,YTrain,layers,options);
% test LSTM network
% load the test data
[XTest,YTest]=japaneseVowelsTestData;
XTest(1:3)
%
numObservationsTest = numel(XTest);
for i=1:numObservationsTest
sequence = XTest{i};
sequenceLengthsTest(i)=size(sequence,2);
end
[sequenceLengthsTest,idx]=sort(sequenceLengthsTest);
XTest=XTest(idx);
YTest=YTest(idx);
% classify the test data
miniBatchSize =27;
YPred=classify(net,XTest,...
'MiniBatchSize',miniBatchSize,...
'SequenceLength','longest');
acc=sum(YPred==YTest)./numel(YTest)
