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Update information values for custom training loops

Since R2022b



    updateInfo(monitor,infoName=infoValue) updates the specified information in the Training Progress window and saves the values in the InfoData property of the TrainingProgressMonitor object monitor.


    updateInfo(monitor,infoName1=infoValue1,...,infoNameN=infoValueN) updates multiple information values.


    updateInfo(monitor,infoStructure) updates the information using the values specified by the structure infoStructure.


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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.


    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";

    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.


    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.


    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, ...
            monitor.Progress = 100*iteration/numIterations;

    The Training Progress window shows animated plots of the metrics, as well as 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.

    Use a structure to update the information values.

    structure.GradientDecayFactor = gradientDecayFactor;
    structure.SquaredGradientDecayFactor = squaredGradientDecayFactor;

    Input Arguments

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    Training progress monitor, specified as a TrainingProgressMonitor object.

    Information name, specified as a string scalar or character vector. This name must be an element of the Info property of monitor.

    Data Types: char | string | cell

    Information value, specified as a numeric scalar, string scalar, character vector, or dlarray object.

    Information names and values, specified as a structure. Names must be elements of the Info property of monitor and can appear in any order in the structure.

    Example: struct(GradientDecayFactor=gradientDecayFactor,SquaredGradientDecayFactor=squaredGradientDecayFactor)

    Data Types: struct


    • The information values appear in the Training Progress window and the training plot shows a record of the metric values. Use information values for text and for numerical values that you want to display in the training window but not in the training plot.

    Version History

    Introduced in R2022b