Matlab "trainNetwork" error Predictors and responses must have the same number of observations

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SM
SM 2022-1-21
回答： Kumar Pallav 2022-2-1
Hi
I am using one of matlab dataset, (transmissionCasingData.csv), to use 1D convolution layer to train a network. Eventhough, the size of my predictors and response are the same, but matlab throws the error that the size of the predictors and response must be the same.
I am wondering if anyone have any idea how to resole the issue.
clear
clc
filename = "transmissionCasingData.csv";
tbl = readtable(filename,'TextType','String');
labelName = "GearToothCondition";
tbl = convertvars(tbl,labelName,'categorical');
categoricalInputNames = ["SensorCondition" "ShaftCondition"];
tbl = convertvars(tbl,categoricalInputNames,'categorical');
for i = 1:numel(categoricalInputNames)
name = categoricalInputNames(i);
oh = onehotencode(tbl(:,name));
tbl = addvars(tbl,oh,'After',name);
tbl(:,name) = [];
end
tbl = splitvars(tbl);
classNames = categories(tbl{:,labelName});
numObservations = size(tbl,1);
numObservationsTrain = floor(0.85*numObservations);
numObservationsTest = numObservations - numObservationsTrain;
idx = randperm(numObservations);
idxTrain = idx(1:numObservationsTrain);
idxTest = idx(numObservationsTrain+1:end);
tblTrain = tbl(idxTrain,:);
tblTest = tbl(idxTest,:);
numFeatures = size(tbl,2) - 1;
numClasses = numel(classNames);
%% Define Layers
classificationLayer];
%}
numFilters = 64;
filterSize = 5;
layers = [
% featureInputLayer(numFeatures)
sequenceInputLayer(numFeatures)
convolution1dLayer(filterSize,numFilters,Padding="causal")
convolution1dLayer(24,3,Padding="causal")
convolution1dLayer(24,3,Padding="causal")
convolution1dLayer(24,3,Padding="causal")
dropoutLayer(0.2)
convolution1dLayer(128,3,Padding="causal")
convolution1dLayer(128,3,Padding="causal")
convolution1dLayer(128,3,Padding="causal")
maxPooling1dLayer(3,Padding="same")
dropoutLayer(0.2)
reluLayer
softmaxLayer
classificationLayer]
miniBatchSize = 16;
options = trainingOptions('adam', ...
'MiniBatchSize',miniBatchSize, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false);
% Op=table2cell(tblTrain);
TragetData=(tblTrain.GearToothCondition);
% TragetData=table2cell(TragetData);
TrainData=(tblTrain(:,1:22));
TrainData_Cell=(table2cell(TrainData));
% net = trainNetwork(tblTrain,layers,options);
TrainData=(TrainData_Cell');
ResponseData=TragetData';
net = trainNetwork(TrainData,ResponseData,layers,options); % error happnes here
YPred = classify(net,tblTest,'MiniBatchSize',miniBatchSize);
YTest = tblTest{:,labelName};
accuracy = sum(YPred == YTest)/numel(YTest)
%{
The error is:
Error using trainNetwork (line 184)
Invalid training data. Predictors and responses must have the same number of
observations.
Error in test (line 85)
net = trainNetwork(TrainData,ResponseData,layers,options);
%}
  2 个评论
SM
SM 2022-1-24
Hi,
The "transmissionCasingData.csv" is an inbuild MATLAB dataset, and you should be able to access it by default.
However, I have attached the '.mat' file in case you do not have access it.
I am using MATLAB 2021b.
Thank you for your help in advance

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回答（1 个）

Kumar Pallav
Kumar Pallav 2022-2-1
The error is generally caused due to mismatch in shapes in the data provided to the trainNetwork. You may refer to a similar problem here to resolve the issue.

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