Multiple nested for loops for machine learning model hyperparameters

I have a neural network and I am trying to build a nested loop to test multiple combinations of the follwoing two hyperparameters: filterSize and numBlocks. I have the code calculate the RMSE for each of the trials using leave out one vlaidation and then take the average overall. I am trying to test the follwing combination of filterSizes: 2, 3 ,4 and the number of Blocks: 3, 4; therefore, there should be a total of 6 avgRMSE values outputed. I am, however, getting an empty matrix with only 2 RMSE values, any suggestions on whats wrong with my code and how this can be fixed?

nfilterSize = [2 3 4];
nnumBlocks = [3 4];
numFilters = 80;
droupoutFactor = 0.005;
numFeatures = 8

%Iterate each combination of hyperparameters 
for j =1:length(nfilterSize)
    filterSize = nfilterSize(j);
    for k = length(nnumBlocks)
        numBlocks = nnumBlocks(k);

% Neural Network 
net = dlnetwork;

layer = sequenceInputLayer(numFeatures,Normalization="rescale-symmetric",Name="input");

net = addLayers(net,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(Name= "spat_drop_"+i,Probability=droupoutFactor)

    % Add and connect layers.
    net = addLayers(net,layers);
    net = connectLayers(net,outputName,"conv1_"+i);

layers = [
    fullyConnectedLayer(1)];

net = addLayers(net,layers);
net = connectLayers(net,outputName,"fc");

% Train the network
RMSEtot = 0;
for h = 1:length(table) %iterate over all data points

    validationdataX = table(h);
    validationdataY = velocity(h);

%Exclude the current index (i) for training
    trainingIndices = setdiff(1:length(table),h);
    traningdataX = table(trainingIndices);
    trainingdataY = velocity(trainingIndices);

      options = trainingOptions("adam", ...
        'MaxEpochs', 60, ...
        'MiniBatchSize', 1, ...
        'InputDataFormat', "CTB", ...
        'Metrics', "rmse", ...
        'Verbose', 0);

net = trainnet(traningdataX,trainingdataY,net,"mse",options);

Predval = minibatchpredict(net,validationdataX,InputDataFormats="CTB");
TrueVal = validationdataY;
TrueValue = cell2mat(TrueVal);
Predvalue = {Predval};
PredictedValue = cell2mat(Predvalue);

RMSE = rmse(PredictedValue,TrueValue)
RMSEtot = RMSEtot + RMSE;

end

%take average of all iterations after leave-out-one-validation
SumRMSE = RMSEtot;
AvgRMSE(j,k) = SumRMSE/(length(table))
    end
end

Answer 1

[Walter Roberson]

for j =1:length(nfilterSize)
%j is active at this level
    for k = length(nnumBlocks)
    %j and k are active at this level
for i = 1:numBlocks
%j and k and i are active at this level
for h = 1:length(table) %iterate over all data points
%j and k and i and h are active at this level
end
%j and k and i are active at this level
    end
%j and k are active at this level
end
%j is active at this level

You are missing an end matching for j

Possibly you have mismatched for/end structures in your actual code.

[Isabelle Museck]

I made sure that all the loops have matching end statments and now I am getting a 3x2 matrix for the AvgRMSE but the first column is only zeros as shown here:

I think there is an issue with my number of blocks loop or the placement of the end statements, but it is not clculating AvgRMSE values for the combination of numBlocks= 3 and filterSize = 2,3, and 4. Any thoughts on how to fix this in my code to get a 3x2 matrix with AvgRMSE outputs for all 6 of the combinations of hyperparameters?

nfilterSize = [2 3 4];
nnumBlocks = [3 4];
numFilters = 80;
droupoutFactor = 0.005;
numFeatures = 60

for j =1:length(nfilterSize)
    filterSize = nfilterSize(j)
    for k = length(nnumBlocks)
        numBlocks = nnumBlocks(k)

net = dlnetwork;

layer = sequenceInputLayer(numFeatures,Normalization="rescale-symmetric",Name="input");

net = addLayers(net,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(Name= "spat_drop_"+i,Probability=droupoutFactor)
        reluLayer
        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");

% Train the network
RMSEtot = 0;
for h = 1:length(table) %iterate over all data points

    validationdataX = table(h);
    validationdataY = velocity(h);

%Exclude the current index (i) for training
    trainingIndices = setdiff(1:length(table),h);
    traningdataX = table(trainingIndices);
    trainingdataY = velocity(trainingIndices);

      options = trainingOptions("adam", ...
        'MaxEpochs', 60, ...
        'MiniBatchSize', 1, ...
        'InputDataFormat', "CTB", ...
        'Metrics', "rmse", ...
        'Verbose', 0);

net = trainnet(traningdataX,trainingdataY,net,"mse",options);

Predval = minibatchpredict(net,validationdataX,InputDataFormats="CTB");
TrueVal = validationdataY;
TrueValue = cell2mat(TrueVal);
Predvalue = {Predval};
PredictedValue = cell2mat(Predvalue);

RMSE = rmse(PredictedValue,TrueValue)
RMSEtot = RMSEtot + RMSE;
end

SumRMSE = RMSEtot;
AvgRMSE(j,k) = SumRMSE/(length(IMU_table))
    end
end

[Isabelle Museck]

Nervermind!