Manage Experiments
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.
App
Experiment Manager | Design and run experiments to train and compare deep learning networks (Da R2020a) |
Oggetti
experiments.Monitor | Update results table and training plots for custom training experiments (Da R2021a) |
Funzioni
groupSubPlot | Group metrics in experiment training plot (Da R2021a) |
recordMetrics | Record metric values in experiment results table and training plot (Da R2021a) |
updateInfo | Update information columns in experiment results table (Da R2021a) |
Argomenti
- Create a Deep Learning Experiment for Classification
Train a deep learning network for classification using Experiment Manager. (Da R2020a)
- Create a Deep Learning Experiment for Regression
Train a deep learning network for regression using Experiment Manager. (Da R2020a)
- Use Experiment Manager to Train Networks in Parallel
Run multiple simultaneous trials or one trial at a time on multiple workers. (Da R2020b)
- Offload Deep Learning Experiments as Batch Jobs to a Cluster
Run experiments on a cluster so you can continue working or close MATLAB. (Da R2022a)
- Evaluate Deep Learning Experiments by Using Metric Functions
Use metric functions to evaluate the results of an experiment. (Da R2020a)
- Tune Experiment Hyperparameters by Using Bayesian Optimization
Find optimal network hyperparameters and training options for convolutional neural networks. (Da R2020b)
- Use Bayesian Optimization in Custom Training Experiments
Create custom training experiments that use Bayesian optimization. (Da R2021b)
- Generate Experiment Using Deep Network Designer
Use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer.
- Keyboard Shortcuts for Experiment Manager
Navigate Experiment Manager using only your keyboard.
Risoluzione dei problemi
Debug Deep Learning Experiments
Diagnose problems in your setup, training, and metric functions. (Da R2023a)