Layers argument must be an array of layers or a layer graph.
1 visualización (últimos 30 días)
Mostrar comentarios más antiguos
XTrain = xlsread('R1_all_data.xlsx',1,'A1:G3788')';
YTrain = xlsread('R1_all_data.xlsx',1, 'H1:H3788')';
XTest = xlsread('R2_all_data.xlsx',1, 'A1:G3788')';
YTest = xlsread('R2_all_data.xlsx',1, 'H1:H3788')';
inputSize = 3788;
numResponses = 1;
numHiddenUnits = 5000;
layers = { sequenceInputLayer(inputSize)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer };
opts = trainingOptions('adam', 'MaxEpochs', 1000, 'GradientThreshold', 0.01, 'InitialLearnRate',0.0001);
net = trainNetwork(XTrain,YTrain,layers,opts);
YPred1=predict(net,XTest)
1 comentario
Respuestas (1)
Krishna
el 10 de Feb. de 2024
Hello PRAMOD,
It appears that the issue you're encountering stems from an improper initialization of the layers object. The mistake was made by using curly braces {} to initialize:
layers = { sequenceInputLayer(inputSize)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer }
Instead, you should initialize using square brackets [] like this:
layers = [ sequenceInputLayer(inputSize)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer ]
I hope this correction resolves your problem.
0 comentarios
Ver también
Categorías
Más información sobre Image Data Workflows en Help Center y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!