experiments.Monitor - Update results table and training plots for custom training experiments - MATLAB (original) (raw)
Update results table and training plots for custom training experiments
Since R2021a
Creation
When you run a custom training experiment, Experiment Manager creates anexperiments.Monitor object for each trial of your experiment. Access the object as the second input argument of the training function.
Properties
Metrics — Metric column names
"" (default) | string | character vector | string array | cell array of character vectors
Metric column names, specified as a string, character vector, string array, or cell array of character vectors. Valid names begin with a letter, and can contain letters, digits, and underscores. These names appear as column headers in the experiment results table. Additionally, each metric appears in its own training subplot. To plot more than one metric in a single subplot, use the function groupSubPlot.
Example: monitor.Metrics = ["TrainingLoss","ValidationLoss"];
Data Types: char | string
Info — Information column names
"" (default) | string | character vector | string array | cell array of character vectors
Information column names, specified as a string, character vector, string array, or cell array of character vectors. Valid names begin with a letter, and can contain letters, digits, and underscores. These names appear as column headers in the experiment results table. The values in the information columns do not appear in the training plot.
Example: monitor.Info = ["GradientDecayFactor","SquaredGradientDecayFactor"];
Data Types: char | string
Stop — Request to stop trial
false or 0 (default) | true or 1
This property is read-only.
Request to stop trial, specified as a numeric or logical 1 (true) or 0 (false). The value of this property changes totrue when you click Stop in the Experiment Manager toolstrip or the results table.
Data Types: logical
Progress — Training progress
0 (default) | numeric scalar | dlarray
Training progress percentage, specified as a numeric scalar ordlarray object between 0 and 100.
Example: monitor.Progress = 17;
XLabel — Horizontal axis label
"" (default) | string | character vector
Horizontal axis label in the training plot, specified as a string or character vector.
Set this value before calling the function recordMetrics.
Example: monitor.XLabel = "Iteration";
Data Types: char | string
Status — Training status
"" (default) | string | character vector
Training status for a trial, specified as a string or character vector.
Example: monitor.Status = "Loading Data";
Data Types: char | string
MetricData — Metric column values
structure
Since R2022b
This property is read-only.
Metric column values, specified as a structure. Use the Metrics property to specify the field names for the structure. Each field is a matrix that contains the custom training loop step values and metric values recorded by therecordMetrics function.
Data Types: struct
InfoData — Information column values
structure
Since R2022b
This property is read-only.
Information column values, specified as a structure. Use theInfo property to specify the field names for the structure. Each field is a column vector that contains the values updated by the updateInfo function.
Data Types: struct
Object Functions
| groupSubPlot | Group metrics in experiment training plot |
|---|---|
| recordMetrics | Record metric values in experiment results table and training plot |
| updateInfo | Update information columns in experiment results table |
| yscale | Set training plot _y_-axis scale (linear or logarithmic) |
Examples
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);
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.
- An
experiments.Monitorobject has the same properties and object functions as a TrainingProgressMonitor object. Therefore, you can easily adapt your custom training loop plotting code for use in an Experiment Manager setup script. For more information, see Prepare Plotting Code for Custom Training Experiment.
Version History
Introduced in R2021a
R2022b: InfoData and MetricData properties
The properties InfoData and MetricData store the values of the information and metric columns for your trial.