Manage Experiments
R2026bUse 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.
Apps
| Experiment Manager | Create and run experiments to train and compare deep learning networks |
Objects
experiments.Monitor | Update results table and training plots for custom training experiments |
Functions
groupSubPlot | Group metrics in experiment training plot |
recordMetrics | Record metric values in experiment results table and training plot |
updateInfo | Update information columns in experiment results table |
yscale | Set training plot y-axis scale (linear or logarithmic) (Since R2024a) |
Topics
Configure Experiment
- Choose Strategy for Exploring Experiment Parameters
Choose between the exhaustive sweep, random sampling, and Bayesian optimization strategies for exploring your experiment parameters using Experiment Manager. - Tune PyTorch Model Using Experiment Manager
Tune hyperparameters of a PyTorch® model by sweeping over combinations of hyperparameter values using Experiment Manager. (Since R2026b) - Debug Deep Learning Experiments
Diagnose problems in your setup, training, and metric functions. (Since R2023a) - Keyboard Shortcuts for Experiment Manager
Navigate Experiment Manager using only your keyboard.
Experiment Using trainnet
- Compare Classification Network Architectures Using Experiment
Train a deep learning network for classification using Experiment Manager. - Compare Dropout Probabilities and Filter Configurations for Image Regression Using Experiment
Train a deep learning network for regression using Experiment Manager. - Evaluate Deep Learning Experiments by Using Metric Functions
Use metric functions to evaluate the results of an experiment.
Experiment Using Custom Training Function
- Use Bayesian Optimization in Custom Training Experiments
Create custom training experiments that use Bayesian optimization. - Run a Custom Training Experiment for Image Comparison
Train a twin neural network to identify similar images of handwritten characters. - Custom Training with Multiple GPUs in Experiment Manager
Configure multiple parallel workers to collaborate on each trial of a custom training experiment.
Transfer Learning
- Try Multiple Pretrained Networks for Transfer Learning
Configure an experiment that replaces layers of different pretrained networks for transfer learning. - Experiment with Weight Initializers for Transfer Learning
Configure an experiment that initializes the weights of convolution and fully connected layers using different weight initializers. - Audio Transfer Learning Using Experiment Manager
Configure an experiment that compares the performance of multiple pretrained networks applied to a speech command recognition task using transfer learning.
Reduce Experiment Execution Time
- Run Experiments in Parallel
Run multiple simultaneous trials or one trial at a time on multiple workers. - Offload Experiments as Batch Jobs to a Cluster
Run experiments on a cluster so you can continue working or close MATLAB®. (Since R2022a)
Related Information
Featured Examples
Tune Experiment Hyperparameters by Using Bayesian Optimization
Find optimal network hyperparameters and training options for convolutional neural networks.
Choose Training Configurations for LSTM Using Bayesian Optimization
Find optimal data architecture and network configurations for sequence-to-sequence regression using Bayesian optimization.
Use Experiment Manager to Train Generative Adversarial Networks (GANs)
Create a custom training experiment to generate images of flowers.
