Why do I meet the error when using trainNetwork, it shows too many input arguments in Table?

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long feifei 2024-9-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2154305-why-do-i-meet-the-error-when-using-trainnetwork-it-shows-too-many-input-arguments-in-table

评论： long feifei 2024-9-21

在 MATLAB Online 中打开

When I use the official case given in Matlab, but Matlab always tell me the message:

"Error in using trainNetwork Too many input arguments..

Reason: Incorrect use of table Too many input arguments.."

load WaveformData
Error using load
Unable to find file or directory "WaveformData". It is used in these examples: 
  Run Sequence Forecasting Using a GRU Layer on an FPGA 
  Run Sequence Forecasting on FPGA by Using Deep Learning HDL Toolbox
  Detect Anomalies in Signals Using deepSignalAnomalyDetector
  Sequence Classification Using Deep Learning
  Train Network for Sequence Classification
  Interpret Deep Learning Time-Series Classifications Using Grad-CAM
  Get Started with Deep Network Designer 
  Pad or Truncate Both Sides of Sequence Data
  Pad Sequence Data to Same Length
  Train Sequence Classification Network Using Custom Training Loop
  Sequence Classification Using 1-D Convolutions
  Train Sequence Classification Network Using Data with Imbalanced Classes
  Time Series Forecasting Using Deep Learning
  Sequence-to-One Regression Using Deep Learning 
  Get Started with Time Series Forecasting
  Time Series Anomaly Detection Using Deep Learning
  Build Time Series Forecasting Network Using Deep Network Designer
numChannels = size(data{1},1);
idx = [3 4 5 12];
figure
tiledlayout(2,2)
for i = 1:4
    nexttile
    stackedplot(data{idx(i)}', ...
        DisplayLabels="Channel " + string(1:numChannels))
    
    xlabel("Time Step")
    title("Class: " + string(labels(idx(i))))
end
numObservations = numel(data);
[idxTrain,idxTest] = trainingPartitions(numObservations, [0.9 0.1]);
XTrain = data(idxTrain);
TTrain = labels(idxTrain);
XTest = data(idxTest);
TTest = labels(idxTest);
numHiddenUnits = 120;
numClasses = numel(categories(TTrain));
layers = [ ...
    sequenceInputLayer(numChannels)
    lstmLayer(numHiddenUnits,OutputMode="last")
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer]
options = trainingOptions("adam", ...
    MaxEpochs=150, ...
    InitialLearnRate=0.01,...
    Shuffle="every-epoch", ...
    GradientThreshold=1, ...
    Verbose=false, ...
    Plots="training-progress");
net = trainNetwork(XTrain,TTrain,layers,options);
%please note that this an official case in MATLAB shown below.
% "https://ww2.mathworks.cn/help/deeplearning/ref/trainnetwork.html#mw_408bdd15-2d34-4c0d-ad91-bc83942f7493"

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long feifei 2024-9-20

编辑：long feifei 2024-9-20

When I run in matlab on line script, it works. it doesn't show the error . Why doesn't work in R2022b? shown below

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Answer 1

Omega 2024-9-20

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2154305-why-do-i-meet-the-error-when-using-trainnetwork-it-shows-too-many-input-arguments-in-table#answer_1520260

Hi Long,

I understand that you are facing issues with the "trainNetwork" MATLAB function. I believe the reason for the issue is a version mismatch. You are using MATLAB R2022b Desktop and referring to an example mentioned in the "trainNetwork" documentation for MATLAB R2024a. The reason you are able to run the script on MATLAB Online is that MATLAB Online uses the latest version.

I suggest you refer to the following documentation link for MATLAB R2022b:

https://www.mathworks.com/help/releases/R2022b/deeplearning/ref/trainnetwork.html

Alternatively, you can update MATLAB to the latest version. Additionally, the "trainNetwork" function is not recommended from R2024a onwards, and you might want to consider using the "trainNet" function instead. The "trainNet" function has several advantages over the "trainNetwork" function.

For further information, you can refer to the following documentation links for the latest MATLAB versions:

"trainNetwork": https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html

"trainNet": https://www.mathworks.com/help/deeplearning/ref/trainnet.html

I hope it helps!