Validation of the prediction model with observed (new ) dataset

I have written a code in LSTM for prediction. The code is attached below. The predicted the values are matching with YTest dataset with an R squared value of 0.923. so doubt is that how can I validate this model with a new observed values. thanks in advance.

%%define LSTM architecture

inputSize = 8;

numResponses = 1;

numHiddenUnits = 100;

layers = [sequenceInputLayer(inputSize)

lstmLayer(numHiddenUnits)

fullyConnectedLayer(numResponses)

regressionLayer];

opts = trainingOptions('adam', ...

'MaxEpochs',500, ...

'GradientThreshold',1, ...

'InitialLearnRate',0.005, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropPeriod',125, ...

'LearnRateDropFactor',0.2, ...

'Verbose',0, ...

'Plots','training-progress');

net= trainNetwork(XTrain,YTrain,layers,opts);

%%predict

Ypred1= predict(net,XTest);

Answer 1

[Shubham Rawat]

Hi Athira,

You can use ‘validation data’ options with LSTM networks. But this option is available from R2018b.