Neural Network Backpropagation Optimization

Hi all,

I am training a shallow neural network with the following layers using the Neural Network Toolbox.

input layer - 100 (features)

hidden layer - 10 (neurons)

output layer - 100 (labels)

I used the following code to format the training set.

[inputs,~] = tonndata(features,1,0);

[labels,~] = tonndata(targets,1,0);

so, after these two lines, inputs and labels are two 1x5000 cell arrays, being each element in the arrays a 1x100 signal. Then I configure, initialize and train the network using Levenberg-Marquardt backpropagation like this:

net = configure(net,inputs,labels);

net = init(net);

[trained_net,perf] = train(net,inputs,labels);

the network view is as follows:

the issue is, during training, each epoch takes around 2 minutes to train, which seems to much. I know Levenberg-Marquardt algorithm involves the calculation of the inverse of the Jacobian, which takes time, but MATLAB's documentation literally says:

"trainlm is often the fastest backpropagation algorithm in the toolbox, and is highly recommended as a first-choice supervised algorithm, although it does require more memory than other algorithms."

I would like to know if anyone has any suggestion to reduce the training time or if I am missing something in the model setup.

Thanks in advance.