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.

App

Deep Network Designer

Progetta, visualizza e addestra le reti di Deep Learning

Funzioni

espandi tutto

Plots

`analyzeNetwork`	Analyze deep learning network architecture
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (Da R2022b)
`updateInfo`	Update information values for custom training loops (Da R2022b)
`recordMetrics`	Record metric values for custom training loops (Da R2022b)
`groupSubPlot`	Group metrics in training plot (Da R2022b)
`plot`	Plot neural network architecture

Feature Visualization

`predict`	Compute deep learning network output for inference (Da R2019b)
`minibatchpredict`	Mini-batched neural network prediction (Da R2024a)
`scores2label`	Convert prediction scores to labels (Da R2024a)
`gradCAM`	Explain network predictions using Grad-CAM (Da 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 (Da R2022b)
`addMetrics`	Compute additional classification performance metrics (Da R2022b)
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Da R2022b)

Proprietà

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

Argomenti

Interpretability

Visualizzazione delle attivazioni della rete LSTM
Questo esempio mostra come analizzare e visualizzare le feature apprese dalle reti LSTM tramite l'estrazione delle attivazioni.
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
Scoprire le capacità del Deep Learning in MATLAB^® utilizzando le reti neurali convoluzionali per la classificazione e la regressione, incluse le reti preaddestrate e il transfer learning, nonché l’addestramento su GPU, CPU, cluster e cloud.
Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.

Training Progress and Performance

Monitoraggio dei progressi dell’addestramento in Deep Learning
Questo esempio mostra come monitorare il processo di addestramento delle reti di Deep Learning.
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.

Esempi in primo piano

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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Previsione delle serie temporali tramite il Deep Learning

Questo esempio mostra come prevedere i dati delle serie temporali utilizzando una rete con memoria a breve e lungo termine (LSTM).

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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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