How to plot performance graph after CNN training

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Satyabrata Nath
Satyabrata Nath el 23 de Mzo. de 2017
Respondida: Parag el 7 de Mzo. de 2025
I am new in deep learning and unable to plot performance graph after training my CNN architecture . My code is as follows :-
opts = trainingOptions('sgdm',
'Momentum', 0.9,
'InitialLearnRate', 0.001,
'LearnRateSchedule', 'piecewise',
'LearnRateDropFactor', 0.1,
'LearnRateDropPeriod', 8,
'L2Regularization', 0.004,
'MaxEpochs', 40,
'MiniBatchSize', 128,
'Verbose', true);
cifar10Net = trainNetwork(trainingImages, trainingLabels, layers, opts);
YTest = classify(cifar10Net, testImages);
accuracy = sum(YTest == testLabels)/numel(testLabels)

Respuestas (1)

Parag
Parag el 7 de Mzo. de 2025
Hi, the current code does not include instructions to plot the performance graph (training progress, accuracy, loss, etc.). However, MATLAB automatically displays the training progress plot by default when using the "trainingOptions" function with the "Plots" property set to "training-progress."
Modify the trainingOptions to include the Plots parameter:
Please refer to MATLAB code for the same
opts = trainingOptions('sgdm', ...
'Momentum', 0.9, ...
'InitialLearnRate', 0.001, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.1, ...
'LearnRateDropPeriod', 8, ...
'L2Regularization', 0.004, ...
'MaxEpochs', 40, ...
'MiniBatchSize', 128, ...
'Verbose', true, ...
'Plots', 'training-progress'); % Enable performance graph
This will display a real-time training progress plot, including accuracy, loss, and learning rate changes during training.

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