updateInfo - Update information columns in experiment results table - MATLAB (original) (raw)
Update information columns in experiment results table
Since R2021a
Syntax
Description
updateInfo([monitor](#mw%5F82368a63-1025-45b4-ac3c-dd0f304a02da),[infoName](#mw%5Feedadef9-4ed0-46d8-8f16-038a2890246b)=[infoValue](#mw%5Fb9c85f87-757f-445e-a0c8-50a4a6be4cc0)) updates the specified information column for a trial in the Experiment Manager results table.
updateInfo([monitor](#mw%5F82368a63-1025-45b4-ac3c-dd0f304a02da),infoName1=infoValue1,...,infoNameN=infoValueN) updates multiple information columns for a trial.
updateInfo([monitor](#mw%5F82368a63-1025-45b4-ac3c-dd0f304a02da),[infoStructure](#mw%5F65ac032b-4e5a-4a65-9c27-31979a214ae7)) updates the information columns using the values specified by the structureinfoStructure.
Examples
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 update values of information columns in the results table.
structure.GradientDecayFactor = gradientDecayFactor; structure.SquaredGradientDecayFactor = squaredGradientDecayFactor; updateInfo(monitor,structure);
Input Arguments
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.
Information column name, specified as a string or character vector. This name must be an element of the Info property of theexperiments.Monitor object monitor.
Data Types: char | string
Information column value, specified as a numeric scalar, string, character vector, or dlarray object.
Information column names and values, specified as a structure. Names must be elements of the Info property of the experiments.Monitor object monitor and can appear in any order in the structure.
Example: struct(GradientDecayFactor=gradientDecayFactor,SquaredGradientDecayFactor=squaredGradientDecayFactor)
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.
Version History
Introduced in R2021a