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Deep Learning Experiments

Train networks under various 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


Create a Deep Learning Experiment for Classification

Train a deep learning network for classification using Experiment Manager.

Create a Deep Learning Experiment for Regression

Train a deep learning network for regression using Experiment Manager.

Use Experiment Manager to Train Networks in Parallel

Train deep networks in parallel using Experiment Manager.

Evaluate Deep Learning Experiments by Using Metric Functions

Use metric functions to evaluate the results of an experiment.

Tune Experiment Hyperparameters by Using Bayesian Optimization

Find optimal network hyperparameters and training options for convolutional neural networks.

Adapt Code Generated in Deep Network Designer for Use in Experiment Manager

Use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer.

Featured Examples