MATLAB: (deep learning)how to get probability output of softmax in this code

deep learningDeep Learning Toolbox

%%












[XTrain,YTrain] = japaneseVowelsTrainData;
XTrain(1:5)
%%
figure
plot(XTrain{1}')
xlabel("Time Step")
title("Training Observation 1")
numFeatures = size(XTrain{1},1);
legend("Feature " + string(1:numFeatures),'Location','northeastoutside')
%%
numObservations = numel(XTrain);
for i=1:numObservations
    sequence = XTrain{i};
    sequenceLengths(i) = size(sequence,2);
end
%%
[sequenceLengths,idx] = sort(sequenceLengths);
XTrain = XTrain(idx);
YTrain = YTrain(idx);
%%
figure
bar(sequenceLengths)
ylim([0 30])
xlabel("Sequence")
ylabel("Length")
title("Sorted Data")
%%
miniBatchSize = 27;
%%
inputSize = 12;
numHiddenUnits = 100;
numClasses = 9;
layers = [ ...
    sequenceInputLayer(inputSize)
    bilstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer]
%%
maxEpochs = 100;
miniBatchSize = 27;
options = trainingOptions('adam', ...
    'ExecutionEnvironment','cpu', ...
    'GradientThreshold',1, ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'SequenceLength','longest', ...
    'Shuffle','never', ...
    'Verbose',0, ...
    'Plots','training-progress');
%%
net = trainNetwork(XTrain,YTrain,layers,options);
%%
[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);
%%
miniBatchSize = 27;
YPred = classify(net,XTest, ...
    'MiniBatchSize',miniBatchSize, ...
    'SequenceLength','longest');
%%
acc = sum(YPred == YTest)./numel(YTest)
this is mathworks example code for sequence classification.
'Ypred' shows me the predicted classes
but I want to know the result after softmax, before prediction, the probablity.
how to know?

Best Answer

self answering : use predict(net, X)
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