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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.


Deep Network DesignerProgetta, visualizza e addestra le reti di Deep Learning


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analyzeNetworkAnalyze deep learning network architecture
trainingProgressMonitorMonitor and plot training progress for deep learning custom training loops (Da R2022b)
updateInfoUpdate information values for custom training loops (Da R2022b)
recordMetricsRecord metric values for custom training loops (Da R2022b)
groupSubPlotGroup metrics in training plot (Da R2022b)
plotPlot neural network architecture
activationsCompute deep learning network layer activations
gradCAMExplain network predictions using Grad-CAM (Da R2021a)
confusionchartCreate confusion matrix chart for classification problem
sortClassesSort classes of confusion matrix chart
rocmetricsReceiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers (Da R2022b)
addMetricsCompute additional classification performance metrics (Da R2022b)
averageCompute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Da R2022b)


ConfusionMatrixChart PropertiesConfusion matrix chart appearance and behavior
ROCCurve PropertiesReceiver operating characteristic (ROC) curve appearance and behavior (Da R2022b)



Training Progress and Performance