Tuning - MATLAB & Simulink (original) (raw)
Programmatically tune training options, resume training from a checkpoint, and investigate adversarial examples
To learn how to set options using the trainingOptions function, see Set Up Parameters and Train Convolutional Neural Network. After you identify some good starting options, you can automate sweeping of hyperparameters or try Bayesian optimization using Experiment Manager.
Investigate network robustness by generating adversarial examples. You can then use fast gradient sign method (FGSM) adversarial training to train a network robust to adversarial perturbations.
Apps
| Deep Network Designer | Design and visualize deep learning networks |
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Objects
| trainingProgressMonitor | Monitor and plot training progress for deep learning custom training loops (Since R2022b) |
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Functions
| trainingOptions | Options for training deep learning neural network |
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| trainnet | Train deep learning neural network (Since R2023b) |
Topics
- Set Up Parameters and Train Convolutional Neural Network
Learn how to set up training parameters for a convolutional neural network. - Deep Learning Using Bayesian Optimization
This example shows how to apply Bayesian optimization to deep learning and find optimal network hyperparameters and training options for convolutional neural networks. - Detect Issues During Deep Neural Network Training
This example shows how to automatically detect issues while training a deep neural network. - Train Deep Learning Networks in Parallel
This example shows how to run multiple deep learning experiments on your local machine. - Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule. - Compare Activation Layers
This example shows how to compare the accuracy of training networks with ReLU, leaky ReLU, ELU, and swish activation layers. - Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks. - Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training. - Profile Your Deep Learning Code to Improve Performance
This example shows how to profile deep learning training code to identify and resolve performance issues. (Since R2024b) - Specify Custom Weight Initialization Function
This example shows how to create a custom He weight initialization function for convolution layers followed by leaky ReLU layers. - Compare Layer Weight Initializers
This example shows how to train deep learning networks with different weight initializers. - Create Custom Deep Learning Training Plot
This example shows how to create a custom training plot that updates at each iteration during training of deep learning neural networks usingtrainnet. (Since R2023b) - Custom Stopping Criteria for Deep Learning Training
This example shows how to stop training of deep learning neural networks based on custom stopping criteria usingtrainnet. (Since R2023b)
Related Information
Featured Examples
Resume Training from Checkpoint Network
Save checkpoint networks while training a deep learning network and resume training from a previously saved network.
Generate Untargeted and Targeted Adversarial Examples for Image Classification
Use the fast gradient sign method (FGSM) and the basic iterative method (BIM) to generate adversarial examples for a pretrained neural network.
Generate Adversarial Examples for Semantic Segmentation
Generate adversarial examples for a semantic segmentation network using the basic iterative method (BIM).
Train Image Classification Network Robust to Adversarial Examples
Train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training.
Train Robust Deep Learning Network with Jacobian Regularization
Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme.