Weights of Regression Partioned Model in Neural Network

Hey there,

I have a regression neural network (1 hidden layer with 10 neurons) which is cross validated and now I wonder how I can get the weight matrix.

    KFolds = observations; % LOOCV
    rng("default") % For reproducibility of the partition
    cvp = cvpartition(observations,"KFold",KFolds);
    lambda = (0:0.005:0.015);

    for i = 1:length(lambda)
        cvMdl = fitrnet(data_table,"Output","Lambda",lambda(i), ...
            "CVPartition",cvp,"LayerSizes",[10]);
        cvloss(i) = kfoldLoss(cvMdl); % MSE for cross-validated models
    end
    
    plot(lambda,cvloss)
    xlabel("Regularization Strength")
    ylabel("Cross-Validation Loss")
    [~,idx] = min(cvloss);
    bestLambda = lambda(idx)

    Mdl = fitrnet(data_table,"Output","Lambda",bestLambda, ...
        "CVPartition",cvp,"LayerSizes",[10],"Verbose",0);

With Mdl.W I get the scaled weights, but all values are the same in this vector and the length of this vector equals the number of observations.

I don't understand the meaning of this vector and how I can get the 'normal' weight matrix.

Thanks for your answers.