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.

Apps

Experiment Manager

Design 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

Topics

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
Run multiple simultaneous trials or one trial at a time on multiple workers.
Offload Experiments as Batch Jobs to Cluster
Run experiments on a cluster so that you can continue working or close MATLAB^® during training.
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.
Use Bayesian Optimization in Custom Training Experiments
Create custom training experiments that use Bayesian optimization.
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.

Troubleshooting

Debug Code Before and After Running Experiments

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

Related Information

Featured Examples

Try Multiple Pretrained Networks for Transfer Learning

Configure an experiment that replaces layers of different pretrained networks for transfer learning.

Open Example

Experiment with Weight Initializers for Transfer Learning

Configure an experiment that initializes the weights of convolution and fully connected layers using different weight initializers.

Open Example

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.

Open Example