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Deep Learning Tuning and Visualization

Plot training progress, assess accuracy and make predictions, tune deep network training options, visualize features learned by a network

Monitor deep learning training progress using built-in or custom plots, and use the plots and results to assess model accuracy and loss. To improve network performance, you can tune training options or use Bayesian optimization to search for optimal hyperparameters. To explore results, you can visualize features learned by a network and create deep dream visualizations. Test your trained network by making predictions using new data.

Functions

trainingOptionsOptions for training neural network
trainNetworkTrain neural network for deep learning
activationsCompute convolutional neural network layer activations
predictPredict responses using a trained deep learning neural network
classifyClassify data using a trained deep learning neural network
deepDreamImageVisualize network features using deep dream

Topics

Classify Webcam Images Using Deep Learning

This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network AlexNet.

Set Up Parameters and Train Convolutional Neural Network

Learn how to set up training parameters for a convolutional neural network

Monitor Deep Learning Training Progress

When you train networks for deep learning, it is often useful to monitor the training progress.

Visualize Activations of a Convolutional Neural Network

This example shows how to feed an image to a convolutional neural network and display the activations of different layers of the network.

Visualize Features of a Convolutional Neural Network

This example shows how to visualize the features learned by convolutional neural networks.

Resume Training from a Checkpoint Network

Learn how to save checkpoint networks while training a convolutional neural network and resume training from a previously saved network

Deep Learning Using Bayesian Optimization

This example shows how to apply Bayesian optimization to deep learning and find optimal network parameters and training options for convolutional neural networks.

Featured Examples

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