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Manage Experiments

Train networks under multiple initial conditions, interactively tune training options, and evaluate your results

Use the Experiment Manager app to find optimal training options for neural networks by sweeping through a range of hyperparameter values or by using Bayesian optimization. Use the built-in function trainnet or define your own custom training function. Monitor your progress by using training plots. Use confusion matrices and custom metric functions to evaluate your trained network.

This page contains information about experiments for your AI workflows. For general information about using the app, see Experiment Manager.


Experiment Manager Design and run experiments to train and compare deep learning networks (Since R2020a)


experiments.MonitorUpdate results table and training plots for custom training experiments (Since R2021a)


groupSubPlotGroup metrics in experiment training plot (Since R2021a)
recordMetricsRecord metric values in experiment results table and training plot (Since R2021a)
updateInfoUpdate information columns in experiment results table (Since R2021a)
yscaleSet training plot y-axis scale (linear or logarithmic) (Since R2024a)



Debug Deep Learning Experiments

Diagnose problems in your setup, training, and metric functions. (Since R2023a)