recordMetrics

Record metric values in experiment results table and training plot

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    Syntax

    recordMetrics(monitor,step,metricName=metricValue)
    recordMetrics(monitor,step,metricName1=metricValue1,...,metricNameN=metricValueN)
    recordMetrics(monitor,step,metricStructure)

    Description

    recordMetrics(monitor,step,metricName=metricValue) records the specified metric value for a trial in the Experiment Manager results table and training plot.

    recordMetrics(monitor,step,metricName1=metricValue1,...,metricNameN=metricValueN) records values of multiple metrics for a trial.

    example

    recordMetrics(monitor,step,metricStructure) records the metric values specified by the structure metricStructure.

    example

    Examples

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    Use an experiments.Monitor object to track the progress of the training, display information and metric values in the experiment results table, and produce training plots for custom training experiments.

    Before starting the training, specify the names of the information and metric columns of the Experiment Manager results table.

    monitor.Info = ["GradientDecayFactor","SquaredGradientDecayFactor"];
monitor.Metrics = ["TrainingLoss","ValidationLoss"];

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

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

    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")

    Update the values of the gradient decay factor and the squared gradient decay factor for the trial in the results table.

    updateInfo(monitor, ...
    GradientDecayFactor=gradientDecayFactor, ...
    SquaredGradientDecayFactor=squaredGradientDecayFactor);

    After each iteration of the custom training loop, record the value of training and validation loss for the trial in the results table and the training plot.

    recordMetrics(monitor,iteration, ...
    TrainingLoss=trainingLoss, ...
    ValidationLoss=validationLoss);

    Update the training progress for the trial based on the fraction of iterations completed.

    monitor.Progress = 100 * (iteration/numIterations);

    Use a structure to record metric values in the results table and the training plot.

    structure.TrainingLoss = trainingLoss;
structure.ValidationLoss = validationLoss;
recordMetrics(monitor,iteration,structure);

    Input Arguments

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    Experiment monitor for the trial, specified as an experiments.Monitor object. When you run a custom training experiment, Experiment Manager passes this object as the second input argument of the training function.

    Custom training loop step, such as the iteration or epoch number, specified as one of these values:

    • numeric scalar

    • dlarray object scalar

    • numeric vector (since R2026a)

    • dlarray object vector (since R2026a)

    Experiment Manager uses this value as the x-coordinate in the training plot.

    To specify multiple values simultaneously, specify step as a vector of values (since R2026a). Specifying multiple values simultaneously can help speed up the training process. If the steps argument value is a vector, then the values must be strictly increasing and the number of steps must match the number of metric values.

    Before R2026a: The step argument value must be scalar.

    The step values must be greater than the values already recorded in the monitor object.

    Metric name, specified as a string or character vector. This name must be an element of the Metrics property of the experiments.Monitor object monitor.

    Data Types: char | string

    Metric value, specified as one of these values:

    • numeric scalar

    • dlarray object scalar

    • numeric vector (since R2026a)

    • dlarray object vector (since R2026a)

    The software uses this value as the y-coordinate values in the training plot.

    To specify multiple values simultaneously, specify metricValue as a vector of values (since R2026a). Specifying multiple values simultaneously can help speed up the training process. If the metricValue argument value is a vector, then the number of metric values must match the number of steps.

    Experiment Manager uses this value as the y-coordinate in the training plot.

    Metric names and values, specified as a structure. Names must be elements of the Metrics property of the experiments.Monitor object monitor and can appear in any order in the structure.

    Example: struct(TrainingLoss=trainingLoss,ValidationLoss=validationLoss)

    Data Types: struct

    Tips

    • Both information and metric columns display values in the results table for your experiment. Additionally, the training plot shows a record of the metric values. Use information columns for text and for numerical values that you want to display in the results table but not in the training plot.

    • Use the groupSubPlot function to define your training subplots before calling the function recordMetrics.

    • Because of the computation required to update graphics, updating the plot every iteration can slow down your training. To reduce the number of plot updates and speed up training, plot multiple values simultaneously by passing vectors of values to the step and metricValue arguments (since R2026a).

    Version History

    Introduced in R2021a

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    See Also

    Apps

    Objects

    Functions

    Topics