Group metrics in experiment training plot
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"]);
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 = (iteration/numIterations) * 100;
monitor— Experiment monitor
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.
title— Title of training subplot
Title of the training subplot, specified as a string or character vector.
metricNames— Metric names
Metric names, specified as a string, character vector, string array, or cell array
of character vectors. Each metric name must be an element of the
Metrics property of the
groupSubplot function to define your training subplots
before calling the function