How to get the performance of more neural networks at once using a for-loop?

Hi, I am trying to get the performance of more neural networks. So I created 100 networks at first.

% Train the Network
%[net,tr] = train(net,x,t);
% Train more networks for better performance 
numNN = 100;
network_array = cell(1, numNN);
for i = 1:numNN
  fprintf('Training %d/%d\n', i, numNN)
  network_array{i} = train(network, x, t,'CheckpointFile','MyCheckpoint','CheckpointDelay',120);
  save 20212101_Workspace_NN_Torsion % Biegung und Torsion
end

For one network the code for test and recalculation is

% Test the Network
y = net_hiddenlayersize6_sortiert(x);
e = gsubtract(t,y);
performance = perform(net_hiddenlayersize6_sortiert,t,y)

% Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net_hiddenlayersize6_sortiert,trainTargets,y)
valPerformance = perform(net_hiddenlayersize6_sortiert,valTargets,y)
testPerformance = perform(net_hiddenlayersize6_sortiert,testTargets,y)

So now I have to do it for 100 networks. Shall I use a for-loop here? My idea is like this:

% Test the Network, but for more networks
for l = 1:numNN
y{l} = network{1,l}(x);
end
e = gsubtract(t,y);

for l = 1:numNN
performance{l} = perform(network{1,l},t(l),y{1,l})
end

% Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(netzwerk,trainTargets,y)
valPerformance = perform(netzwerk,valTargets,y)
testPerformance = perform(netzwerk,testTargets,y)

I am kinda stuck here. How can I test and do the recalculation for 100 networks at once? I found an example here

https://ch.mathworks.com/help/deeplearning/ug/improve-neural-network-generalization-and-avoid-overfitting.html

%Then, ten neural networks are trained.

net = feedforwardnet(10);
numNN = 10;
nets = cell(1, numNN);
for i = 1:numNN
  fprintf('Training %d/%d\n', i, numNN)
  nets{i} = train(net, x1, t1);
end
%Next, each network is tested on the second dataset with both individual performances and the performance for the average output calculated.

perfs = zeros(1, numNN);
y2Total = 0;
for i = 1:numNN
  neti = nets{i};
  y2 = neti(x2);
  perfs(i) = mse(neti, t2, y2);
  y2Total = y2Total + y2;
end
perfs
y2AverageOutput = y2Total / numNN;
perfAveragedOutputs = mse(nets{1}, t2, y2AverageOutput) 

I appriciate any help!

Answer 1

[Vineet Joshi]

Hi

In order to run get the performance at once you can use the parallel performance capabilities of MATLAB.

The following documentations will help you in getting familiarized with the same. You can then customize it according to your application.

• Shallow Neural Networks with Parallel and GPU Computing
• Execute for-loop iterations in parallel on workers
• Parallel Computing Fundamentals

Hope this helps.

Thanks