Monitor deep learning training progress using built-in plots of network accuracy and loss. To improve network performance, you can tune training options and use Bayesian optimization to search for optimal hyperparameters. To investigate trained networks, you can visualize features learned by a network and create deep dream visualizations. Test your trained network by making predictions using new data.
|Deep Network Designer||Edit and build deep learning networks|
|Compute deep learning network layer activations|
|Predict responses using a trained deep learning neural network|
|Classify data using a trained deep learning neural network|
|Predict responses using a trained recurrent neural network and update the network state|
|Classify data using a trained recurrent neural network and update the network state|
|Reset the state of a recurrent neural network|
|Visualize network features using deep dream|
|Determine how input data affects output activations by occluding input|
|Create confusion matrix chart for classification problem|
|ConfusionMatrixChart Properties||Confusion matrix chart appearance and behavior|
|Sort classes of confusion matrix chart|
Learn how to set up training parameters for a convolutional neural network
Learn how to save checkpoint networks while training a convolutional neural network and resume training from a previously saved 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.
Learn how to improve the accuracy of deep learning networks.
This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.
When you train networks for deep learning, it is often useful to monitor the training progress.
Grad-CAM explains why a network makes a decision.
This example shows how to use occlusion sensitivity maps to understand why a deep neural network makes a classification decision.
This example shows how to use class activation mapping (CAM) to investigate and explain the predictions of a deep convolutional neural network for image classification.
This example shows how to use the
tsne function to view activations in a trained network.
This example shows how to feed an image to a convolutional neural network and display the activations of different layers of the network.
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
This example shows how to visualize the features learned by convolutional neural networks.