groupSubPlot - Group metrics in training plot - MATLAB (original) (raw)

Group metrics in training plot

Since R2022b

Syntax

Description

groupSubPlot([monitor](#mw%5F6253c844-b9b4-48a0-8c57-c68c28504221),[groupName](#mw%5F5a9f87f1-8780-4fd6-847c-4b61fa0b65cb),[metricNames](#mw%5F71fa6bff-91be-4bf5-869c-9143f42529e4)) 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.

To group metrics, all metrics must have the same y-axis scale. For more information, seeyscale.

example

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. Group the training and validation accuracy in the same plot.

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

Specify a logarithmic scale for the loss. You can also switch the y-axis scale by clicking the log scale button in the axes toolbar.

yscale(monitor,"Loss","log")

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 is1.
• 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.

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, as well as the information values, training progress bar, and elapsed time.

• The training plots update each time you call recordMetrics.
• The values under Information update each time you call updateInfo.
• The elapsed time updates each time you call recordMetrics orupdateInfo and when you update the Progress property.

Input Arguments

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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