yscale

Set training plot y-axis scale (linear or logarithmic)

Since R2024a

Syntax

yscale(monitor,axesName,scale)

Description

yscale(monitor,axesName,scale) specifies the y-axis scale as linear or logarithmic in the axes specified by axesName. Specify scale as "linear" or "log".

Note

To switch the y-axis scale, you can also click the log scale button in the axes toolbar.

Examples

Track Training Progress for Custom Training Loop

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.

Track Experiment Progress, Display Information and Record Metric Values, and Produce Training Plots

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);

Input Arguments

monitor — Metric monitor

TrainingProgressMonitor object | experiments.Monitor object

Metric monitor, specified as a TrainingProgressMonitor or an experiments.Monitor object.

axesName — Name of plot axes

string | character array

Name of plot axes, specified as a string or a character array. The name of the axes must match the name of a metric. If you use the groupSubPlot function to group metrics, then axesName must be the name of the group.

Data Types: char | string

scale — Axis scale

"linear" (default) | "log"

Axis scale, specified as one of these values.

Value	Description	Example
"linear"	Linear scale
"log"	Log scale Note When you specify a logarithmic scale, the axes might exclude coordinates in some cases: If the coordinates include positive and negative values, only the positive values are displayed. If the coordinates are all negative, all of the values are displayed on a logarithmic scale with the appropriate sign. Zero values are not displayed.

Version History

Introduced in R2024a