Effacer les filtres
Effacer les filtres

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

8 vues (au cours des 30 derniers jours)
%%
[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?

Réponse acceptée

Yeon Hwee Bae
Yeon Hwee Bae le 22 Sep 2020
self answering : use predict(net, X)

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R2019a

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