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groupSubPlot

Group metrics in training plot

    Description

    example

    groupSubPlot(monitor,groupName,metricNames) groups the specified metrics in a single training subplot with the y-axis label groupName. By default, the software plots each ungrouped metric in its own training subplot.

    Examples

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    Use a TrainingProgressMonitor object to track training progress and produce training plots for custom training loops.

    Create a TrainingProgressMonitor object. The monitor automatically tracks the start time and the elapsed time. The timer starts when you create the object.

    Tip

    To ensure that the elapsed time accurately reflects the training time, make sure you create the TrainingProgressMonitor object close to the start of your custom training loop.

    monitor = trainingProgressMonitor;

    Before you start the training, specify names for the information and metric values.

    monitor.Info = ["LearningRate","Epoch","Iteration"];
    monitor.Metrics = ["TrainingLoss","ValidationLoss","TrainingAccuracy","ValidationAccuracy"];

    Specify the horizontal axis label for the training plot. Group the training and validation loss in the same subplot, and group the training and validation accuracy in the same plot.

    monitor.XLabel = "Iteration";
    groupSubPlot(monitor,"Loss",["TrainingLoss","ValidationLoss"]);
    groupSubPlot(monitor,"Accuracy",["TrainingAccuracy","ValidationAccuracy"]);
    

    During training:

    • Evaluate the Stop property at the start of each step in your custom training loop. When you click the Stop button in the Training Progress window, the Stop property changes to 1. Training stops if your training loop exits when the Stop property is 1.

    • Update the information values. The updated values appear in the Training Progress window.

    • Record the metric values. The recorded values appear in the training plot.

    • Update the training progress percentage based on the fraction of iterations completed.

    Note

    The following example code is a template. You must edit this training loop to compute your metric and information values. For a complete example that you can run in MATLAB, see Monitor Custom Training Loop Progress During Training.

    epoch = 0;
    iteration = 0;
    
    monitor.Status = "Running";
    
    while epoch < maxEpochs && ~monitor.Stop
        epoch = epoch + 1;
    
        while hasData(mbq) && ~monitor.Stop
            iteration = iteration + 1;
    
            % Add code to calculate metric and information values.
            % lossTrain = ...
    
           updateInfo(monitor, ...
                LearningRate=learnRate, ...
                Epoch=string(epoch) + " of " + string(maxEpochs), ...
                Iteration=string(iteration) + " of " + string(numIterations));
    
           recordMetrics(monitor,iteration, ...
                TrainingLoss=lossTrain, ...
                TrainingAccuracy=accuracyTrain, ...
                ValidationLoss=lossValidation, ...
                ValidationAccuracy=accuracyValidation);
    
            monitor.Progress = 100*iteration/numIterations;
        end
    end

    The Training Progress window shows animated plots of the metrics, and the information values, training progress bar, and elapsed time.

    Training Progress window. The first plot shows the training and validation loss and the second plot shows the training and validation accuracy.

    Input Arguments

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    Training progress monitor, specified as a TrainingProgressMonitor object.

    Name of the subplot group, specified as a string scalar or character vector. The software groups the specified metrics in a single training subplot with the y-axis label groupName.

    Data Types: char | string

    Metric names, specified as a string scalar, character vector, string array, or cell array of character vectors. Each metric name must be an element of the Metrics property of monitor and can only appear in one subplot.

    Data Types: char | string | cell

    Version History

    Introduced in R2022b