Deep Learning Visualization
Monitor training progress using built-in plots of network accuracy and loss. Investigate trained networks using visualization techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream.
|Deep Network Designer||Design, visualize, and train deep learning networks|
Feature Visualization and Prediction
|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|
|Explain network predictions by occluding the inputs|
|Explain network predictions using LIME|
|Explain network predictions using Grad-CAM|
|Create confusion matrix chart for classification problem|
|Sort classes of confusion matrix chart|
|ConfusionMatrixChart Properties||Confusion matrix chart appearance and behavior|
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.
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 the locally interpretable model-agnostic explanations (LIME) technique to understand the predictions of a deep neural network classifying tabular data.
This example shows how to use locally interpretable model-agnostic explanations (LIME) to investigate the robustness of a deep convolutional neural network trained to classify spectrograms.
This example shows how to use gradient attribution maps to investigate which parts of an image are most important for classification decisions made by a deep neural network.
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 a data set to find out what activates the channels of a deep neural network.
This example shows how to use the
tsne function to view activations in a trained network.
Learn how to diagnose and fix some of the most common failure modes in GAN training.
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.
Learn about and compare deep learning visualization methods.