- If you specify sequences as a numeric or cell array, then you must also specify the responses argument.
how exactly to use the trainNetwork function?
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I want to use the function net = trainNetwork(sequences,layers,options) like this to train a recurrent network of the form lstm for identifying nonlinear systems. I have for training two sets of input data and one set of output data.
numResponses=1;
featureDimension=1;
numHiddenUnits=70;
miniBatchSize=300;
maxEpochs=1000;
layer=[...
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits,'OutputMode','last')
dropoutLayer(0.02)
fullyConnectedLayer(numResponses)
regressionLayer
];
options=trainingOptions('adam',...
'MaxEpochs',maxEpochs,...
'MiniBatchSize',miniBatchSize,...
'GradientThreshold',20,...
'Shuffle','once', ...
'Plots','training-progress',...
'ExecutionEnvironment','parallel',...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',200,...
'L2Regularization',1e-3,...
'LearnRateDropFactor',0.5,...
'Verbose',0,...
'Plots','training-progress');
C = num2cell(table2array(x1_train));
net = trainNetwork(C',layer,options);
Here is a part of my code.x1_train is a variable that contains the u1 regressor for the 2 tank system and I transformed it into a cell array because I understood that I had to do so that I could use this data in the train network function and I got the following error:
Error using trainNetwork
Not enough input arguments.
Error in sperproiectfinal1 (line 84)
net = trainNetwork(C',layer,options);
Caused by:
Error using nnet.internal.cnn.trainNetwork.DLTInputParser>iParseInputArguments
Not enough input arguments.
please help me with some steps that I should follow to use the function properly and be able to train my network with two sets of input data and one set of output data
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采纳的回答
Cris LaPierre
2023-12-4
You need to include a response input.
2 个评论
Cris LaPierre
2023-12-5
It should be part of your training data set. You need to create a 'labeled' data set for training.
When the input data is a numeric array or a cell array, specify the responses as one of the following.
- categorical vector of labels
- numeric array of numeric responses
- cell array of categorical or numeric sequences
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