Is there a way to plot multiple neural network run results into one plot?

Hello,

I am utilizing a shallow neural network to analyze a large dataset. I'm running the data through the network 100 times to get an idea of the best fit. Is there a way to create a plot where the results from all 100 runs are combined into one figure? Currenlty the network produces just one graph for the last run through the network.

Thanks!

Answer 1

[Divya Gaddipati]

You can use the field OutputFcn of the trainingOptions function.

You can refer to the following example and change it according to your need.

clc; clear; close all;

% Data
[XTrain,YTrain] = digitTrain4DArrayData;

idx = randperm(size(XTrain,4),1000);
XValidation = XTrain(:,:,:,idx);
XTrain(:,:,:,idx) = [];
YValidation = YTrain(idx);
YTrain(idx) = [];

% Network
layers = [
    imageInputLayer([28 28 1])

    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer   

    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer   

    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer   

    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

max_epoch = 5;
miniBatchSize = 128;

% Number of iteration in an epoch with miniBatchSize as 128
total_iterations = round(length(YTrain)/miniBatchSize);

options = trainingOptions('sgdm', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',5, ...
'MiniBatchSize',miniBatchSize, ...
'ValidationData',{XValidation,YValidation}, ...
'Plots','training-progress', ...
'OutputFcn',@(info)savetrainingdata(info, total_iterations));

% Train network
net = trainNetwork(XTrain,YTrain,layers,options);


function stop = savetrainingdata(info, total_iterations) 

    stop = false; %prevents this function from ending trainNetwork prematurely 
    
    % Keep track of the training loss and accuracy for each iteration in an epoch
    persistent train_loss
    persistent train_acc
    persistent results
    
    if info.State == "start"
        train_loss = [];
        train_acc = [];
    end
    
    if info.State == "iteration"
        train_loss = [train_loss; info.TrainingLoss]; 
        train_acc = [train_acc; info.TrainingAccuracy]; 
    end
    
    % For each epoch, save the training loss and accuracy 
    if(info.State == "iteration" && info.Iteration == info.Epoch*total_iterations)
        all_val = [train_loss, train_acc];
        results{info.Epoch} = all_val; 
        % you can also plot the graph
        train_loss = [];
        train_acc = [];
    end
    
    if info.State == "done" %check if all epochs have completed    
        save('results.mat', 'results');
    end 
end 

Finally, you can load the results.mat and plot the training loss and accuracy for all the epochs.

For more information on how to use OutputFcn, refer to the link here

Hope this helps!

[Tyler Menz]

Thank you!! Is there a way to get this code to save the p-value and r2 the network makes as well?

[Divya Gaddipati]

Yes, you can add that in the fourth "if" loop (i.e., if(info.State == "iteration" && info.Iteration == info.Epoch*total_iterations))