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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 trainNetwork or define your own custom training function. Test different training configurations at the same time by running your experiment in parallel. Offload experiments as batch jobs in a remote cluster so that you can continue working or close your MATLAB® session while your experiment is running. Monitor your progress by using training plots. Use confusion matrices and custom metric functions to evaluate your trained network. Use visualizations, filters, and annotations to manage your experiment results and record your observations. Access past experiment definitions to keep track of the combinations of hyperparameters that produce each of your results.

For more information about setting up experiments, watch How to Set Up and Manage Experiments in MATLAB (5 min, 49 sec).


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


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


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



Debug Deep Learning Experiments

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