Deep Learning Tuning
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.
|Deep Network Designer||Design, visualize, and train deep learning networks|
Learn how to set up training parameters for a convolutional neural network.
This example shows how to apply Bayesian optimization to deep learning and find optimal network hyperparameters and training options for convolutional neural networks.
This example shows how to run multiple deep learning experiments on your local machine.
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.
This example shows how to compare the accuracy of training networks with ReLU, leaky ReLU, ELU, and swish activation layers.
Use Experiment Manager to tune the hyperparameters of a network trained in Deep Network Designer.
Learn how to improve the accuracy of deep learning networks.
This example shows how to train a neural network that is robust to adversarial examples using a Jacobian regularization scheme .
This example shows how to create a custom He weight initialization function for convolution layers followed by leaky ReLU layers.
This example shows how to train deep learning networks with different weight initializers.