Visualize Deep Neural Networks

Plot training progress, assess accuracy, explain predictions, and visualize features learned by an image network

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.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

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Plots

`analyzeNetwork`	Analyze deep learning network architecture
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops
`updateInfo`	Update information values for custom training loops
`recordMetrics`	Record metric values for custom training loops
`groupSubPlot`	Group metrics in training plot
`plot`	Plot neural network architecture

Feature Visualization

`activations`	Compute deep learning network layer activations
`deepDreamImage`	Visualize network features using deep dream
`occlusionSensitivity`	Explain network predictions by occluding the inputs
`imageLIME`	Explain network predictions using LIME
`gradCAM`	Explain network predictions using Grad-CAM
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart
`rocmetrics`	Receiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers
`addMetrics`	Compute additional classification performance metrics
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem

Properties

ConfusionMatrixChart Properties	Confusion matrix chart appearance and behavior
ROCCurve Properties	Receiver operating characteristic (ROC) curve appearance and behavior

Topics

Interpretability

Deep Learning Visualization Methods
Learn about and compare deep learning visualization methods.
Understand Network Predictions Using Occlusion
This example shows how to use occlusion sensitivity maps to understand why a deep neural network makes a classification decision.
Investigate Spectrogram Classifications Using LIME
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.
Investigate Classification Decisions Using Gradient Attribution Techniques
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.
Investigate Network Predictions Using Class Activation Mapping
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.
Visualize Image Classifications Using Maximal and Minimal Activating Images
This example shows how to use a data set to find out what activates the channels of a deep neural network.
View Network Behavior Using tsne
This example shows how to use the tsne function to view activations in a trained network.
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.

Training Progress and Performance

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 GoogLeNet.
Monitor Deep Learning Training Progress
This example shows how to monitor the training process of deep learning networks.
Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
Monitor GAN Training Progress and Identify Common Failure Modes
Learn how to diagnose and fix some of the most common failure modes in GAN training.
ROC Curve and Performance Metrics
Use rocmetrics to examine the performance of a classification algorithm on a test data set.
Compare Deep Learning Models Using ROC Curves
This example shows how to use receiver operating characteristic (ROC) curves to compare the performance of deep learning models.

Featured Examples

Detect Vanishing Gradients in Deep Neural Networks by Plotting Gradient Distributions

Monitor vanishing gradients while training a deep neural network.

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Explore Network Predictions Using Deep Learning Visualization Techniques

Investigate network predictions using deep learning visualization techniques.

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Grad-CAM Reveals the Why Behind Deep Learning Decisions

Use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions. Grad-CAM, invented by Selvaraju and coauthors [1], uses the gradient of the classification score with respect to the convolutional features determined by the network in order to understand which parts of the image are most important for classification. This example uses the GoogLeNet pretrained network for images.

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Interpret Deep Learning Time-Series Classifications Using Grad-CAM

Use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data.

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Understand Network Predictions Using LIME

Use locally interpretable model-agnostic explanations (LIME) to understand why a deep neural network makes a classification decision.

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Explore Semantic Segmentation Network Using Grad-CAM

Explore the predictions of a pretrained semantic segmentation network using Grad-CAM.

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Investigate Audio Classifications Using Deep Learning Interpretability Techniques

Use interpretability techniques to investigate the predictions of a deep neural network trained to classify audio data.

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Deep Dream Images Using GoogLeNet

Generate images using deepDreamImage with the pretrained convolutional neural network GoogLeNet.

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