Visualization and Verification

Visualize neural network behavior, explain predictions, and verify robustness using sequence and tabular data

Visualize deep networks during and after training. Monitor training progress using built-in plots of network accuracy and loss. To investigate trained networks, you can use visualization techniques such as Grad-CAM.

Use deep learning verification methods to assess the properties of deep neural networks. For example, you can verify the robustness properties of a network, compute network output bounds, and find adversarial examples.

Apps

Deep Network Designer

Design and visualize 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 (Since R2022b)
`updateInfo`	Update information values for custom training loops (Since R2022b)
`recordMetrics`	Record metric values for custom training loops (Since R2022b)
`groupSubPlot`	Group metrics in training plot (Since R2022b)
`plot`	Plot neural network architecture

Feature Visualization

`testnet`	Test deep learning neural network (Since R2024b)
`predict`	Compute deep learning network output for inference
`minibatchpredict`	Mini-batched neural network prediction (Since R2024a)
`scores2label`	Convert prediction scores to labels (Since R2024a)
`gradCAM`	Explain network predictions using Grad-CAM (Since R2021a)
`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 (Since R2022b)
`addMetrics`	Compute additional classification performance metrics (Since R2022b)
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Since R2022b)

Reproducibility

deep.gpu.deterministicAlgorithms Set determinism of deep learning operations on the GPU to get reproducible results (Since R2024b)

Properties

ConfusionMatrixChart Properties	Confusion matrix chart appearance and behavior
ROCCurve Properties	Receiver operating characteristic (ROC) curve appearance and behavior (Since R2022b)

Topics

Interpretability

Visualize Activations of LSTM Network
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
Interpret Deep Learning Time-Series Classifications Using Grad-CAM
This example shows how to 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.
View Network Behavior Using tsne
This example shows how to use the tsne function to view activations in a trained network.
Deep Learning in MATLAB
Discover deep learning capabilities in MATLAB^® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.

Training Progress and Performance

Monitor Deep Learning Training Progress
This example shows how to monitor the training progress of deep learning networks.
Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
ROC Curve and Performance Metrics
Use rocmetrics to examine the performance of a classification algorithm on a test data set.

Featured Examples

Investigate Spectrogram Classifications Using LIME

Use locally interpretable model-agnostic explanations (LIME) to investigate the robustness of a deep convolutional neural network trained to classify spectrograms. LIME is a technique for visualizing which parts of an observation contribute to the classification decision of a network. This example uses the imageLIME function to understand which features in the spectrogram data are most important for classification.

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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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Time Series Forecasting Using Deep Learning

Forecast time series data using a long short-term memory (LSTM) network.

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Train Speech Command Recognition Model Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a set of commands.

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Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

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