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Experiment Manager App

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

Find optimal training options for neural networks by sweeping through a range of hyperparameter values or 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. Monitor your progress by using training plots. Use confusion matrices and custom metric functions to evaluate your trained network. Refine your experiments by sorting and filtering. Use annotations to record your observations.


Experiment ManagerDesign and run experiments to train and compare deep learning networks


experiments.MonitorUpdate results table and training plots for custom training experiments


groupSubPlotGroup metrics in experiment training plot
recordMetricsRecord metric values in experiment results table and training plot
updateInfoUpdate information columns in experiment results table



Debug Code Before and After Running Experiments

Diagnose problems in your experiment setup, metric, and training functions.