I understand that you are trying to train a "Seq2Seq model" by predicting the Sequence, as a Regression Model, as opposed to Classifying as shown in the example.
From your description of the data you are using, you seem to be using a 10x1 cell, with each row containing a different set of data points. The given problem is dissimar to the one example you have been using and hence you are left with an error.
To elaborate, the example you have been using employs a "1D Convulational Network". The data is in the form of a 1x1 cell, where the input data is the contents of the cell. The prediction made in the output is only dependant on the single cell data. Your code also works perfectly fine when doing the same, i.e. predicting the first data point of "IMUdata" to the corresponding value of "Predvar". You may use the following code for further clarity:
s = load("HumanActivityTrain.mat");
YTest{1} = rand(1,length(XTest{1})); % Initializing Random Variables to YTest for a regression problem
numfeatures = 3;
numFilters = 64;
filterSize = 5;
droupoutFactor = 0.005;
numBlocks = 4;
layer = sequenceInputLayer(numfeatures,Normalization="rescale-symmetric",Name="input");
net = dlnetwork(layer);
outputName = layer.Name;
for i = 1:numBlocks
dilationFactor = 2^(i-1);
layers = [
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i)
layerNormalizationLayer
spatialDropoutLayer(droupoutFactor,Name="spat_drop_"+i)
convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal")
layerNormalizationLayer
reluLayer
spatialDropoutLayer(droupoutFactor,Name="spat_drop2_"+i)
additionLayer(2,Name="add_"+i)];
% Add and connect layers.
net = addLayers(net,layers);
net = connectLayers(net,outputName,"conv1_"+i);
% Skip connection.
if i == 1
% Include convolution in first skip connection.
layer = convolution1dLayer(1,numFilters,Name="convSkip");
net = addLayers(net,layer);
net = connectLayers(net,outputName,"convSkip");
net = connectLayers(net,"convSkip","add_" + i + "/in2");
else
net = connectLayers(net,outputName,"add_" + i + "/in2");
end
% Update layer output name.
outputName = "add_" + i;
end
layers = [
fullyConnectedLayer(1)];
net = addLayers(net,layers);
net = connectLayers(net,outputName,"fc");
options = trainingOptions("adam", ...
MaxEpochs=60, ...
miniBatchSize=1, ...
InputDataFormats="CTB", ...
Plots="training-progress", ...
Metrics="rmse", ...
Verbose=0);
net = trainnet(XTest,YTest,net,"mse",options);
If this is how you intend the code to work, then you may retrain the network for each row in "IMUdata" and it would act as a "Transfer Learning" model.
However, from your test, I see that you would like to predict all 10 rows at a time and create generate outputs. Thus the input data is not of the form "3*T" but will be of the form "10*3*T". T-"Time Steps".
I would recommend looking at a 2D Convulational Layer for this problem. You may use the following links to understand how you can refactor your code according to your requirements:
Hope this helps!
