Experiment Manager App
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
|Experiment Manager||Design and run experiments to train and compare deep learning networks|
|Update results table and training plots for custom training experiments|
- 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.
Find errors in your experiment setup, metric, and training functions.