Error in predict function (LSTM NN trained in the Neural Net Fitting Toolbox)

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NATALIA ARREGUI GONZALEZ
编辑： NATALIA ARREGUI GONZALEZ 2020-5-4
Hello,
I have created a LSTM Neural Network in the Neural Net Fitting Toolbox, using 8 input variables.
The code that was generated and I exported is the following:
% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by Neural Fitting app
% This script assumes these variables are defined:
% X - input data.
% yRecord - target data.
x = X';
t = yRecord';
% Choose a Training Function
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainbr'; % Bayesian Regularization backpropagation.
% Create a Fitting Network
hiddenLayerSize = 25;
net1 = fitnet(hiddenLayerSize,trainFcn);
% Choose Input and Output Pre/Post-Processing Functions
net1.input.processFcns = {'removeconstantrows','mapminmax'};
net1.output.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing
net1.divideFcn = 'dividerand'; % Divide data randomly
net1.divideMode = 'sample'; % Divide up every sample
net1.divideParam.trainRatio = 70/100;
net1.divideParam.valRatio = 15/100;
net1.divideParam.testRatio = 15/100;
% Choose a Performance Function
net1.performFcn = 'mse'; % Mean Squared Error
% Choose Plot Functions
net1.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
% Train the Network
[net1,tr] = train(net1,x,t);
% Test the Network
y = net1(x);
e = gsubtract(t,y);
performance = perform(net1,t,y)
% Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net1,trainTargets,y)
valPerformance = perform(net1,valTargets,y)
testPerformance = perform(net1,testTargets,y)
% View the Network
view(net1)
I have exported the neural network into my workspace and now I would like to use it to predict some values into the future. I have tried to use the function predict but I keep getting the same mistake:
yPredtest = predict(net1,testingpred);
Error using predict (line 84)
No valid system or dataset was specified.
% testingpred is a matrix that contains the 8 input variables that I want to use
I have read the documentation and I don't find any proper solution, since I keep getting errors.
My knowledge in the Deep Learning Toolbox is limited, I would really appreciate if someone could provide a solution for this. I have been for some days trying several options and nothing has worked.
Many thanks,
Natalia

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