Hi All,
I found the following two ways of predicting performs differently:
(1).
for i=1:size(XTest,2)
[trainedNet,YTest(:,i)]=PredictAndUpdateState(trainedNet,XTest(:,i));
end
(2).
for i=1:size(XTest,2)
[YTest(:,i),state]=predict(trainedNet,XTest(:,i));
trainedNet.State=state;
end
I wonder that what is the reseason for this phenomenon? Or, what is the difference between the ways of predict() and PredictAndUpdateState() to update networks?
Any help will be appreciated!Hi All,
I found the following two ways of predicting performs differently:
(1).
for i=1:size(XTest,2)
[trainedNet,YTest(:,i)]=PredictAndUpdateState(trainedNet,XTest(:,i));
end
(2).
for i=1:size(XTest,2)
[YTest(:,i),state]=predict(trainedNet,XTest(:,i));
trainedNet.State=state;
end
I wonder that what is the reseason for this phenomenon? Or, what is the difference between the ways of predict() and PredictAndUpdateState() to update networks?
Any help will be appreciated! Hi All,
I found the following two ways of predicting performs differently:
(1).
for i=1:size(XTest,2)
[trainedNet,YTest(:,i)]=PredictAndUpdateState(trainedNet,XTest(:,i));
end
(2).
for i=1:size(XTest,2)
[YTest(:,i),state]=predict(trainedNet,XTest(:,i));
trainedNet.State=state;
end
I wonder that what is the reseason for this phenomenon? Or, what is the difference between the ways of predict() and PredictAndUpdateState() to update networks?
Any help will be appreciated! lstm, predictandupdatestate, predict, prediction, deep learning MATLAB Answers — New Questions

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