recordMetrics

Record metric values for custom training loops

Since R2022b

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 in the training plot and the MetricData property of the TrainingProgressMonitor object monitor.

recordMetrics(monitor,step,metricName1=metricValue1,...,metricNameN=metricValueN) records multiple metric values.

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

Examples

Track Progress and Produce Training Plots

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 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, 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 or updateInfo and when you update the Progress property.

Specify Metric Values by Using Structure

Use a structure to record metric values.

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

Input Arguments

monitor — Training progress monitor

TrainingProgressMonitor object

Training progress monitor, specified as a TrainingProgressMonitor object.

step — Custom training loop step

numeric scalar | dlarray object

Custom training loop step, such as the iteration or epoch number, specified as a numeric scalar or dlarray object. The software uses this value as the x-coordinate in the training plot.

metricName — Metric name

string scalar | character vector

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

Data Types: char | string | cell

metricValue — Metric value

numeric scalar | dlarray object

Metric value, specified as a numeric scalar or dlarray object. The software uses this value as the y-coordinate in the training plot.

metricStructure — Metric names and values

structure

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

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

Data Types: struct

Tips

• 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.

• Use the groupSubPlot function to group multiple metrics in a single training subplot.

Version History

Introduced in R2022b