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Visualization and Interpretability

Plot training progress, assess accuracy, explain predictions, and visualize features learned by a 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.

Deep Learning Visualization Methods


Deep Network DesignerDesign and visualize deep learning networks


trainingProgressMonitorMonitor and plot training progress for deep learning custom training loops (Seit R2022b)


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analyzeNetworkAnalyze deep learning network architecture
plotPlot neural network architecture
updateInfoUpdate information values for custom training loops (Seit R2022b)
recordMetricsRecord metric values for custom training loops (Seit R2022b)
groupSubPlotGroup metrics in training plot (Seit R2022b)
yscaleSet training plot y-axis scale (linear or logarithmic) (Seit R2024a)
accuracyMetricDeep learning accuracy metric (Seit R2023b)
aucMetricDeep learning area under ROC curve (AUC) metric (Seit R2023b)
fScoreMetricDeep learning F-score metric (Seit R2023b)
precisionMetricDeep learning precision metric (Seit R2023b)
recallMetricDeep learning recall metric (Seit R2023b)
rmseMetricDeep learning root mean squared error metric (Seit R2023b)
predictCompute deep learning network output for inference (Seit R2019b)
minibatchpredictMini-batched neural network prediction (Seit R2024a)
scores2labelConvert prediction scores to labels (Seit R2024a)
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 (Seit R2022b)
addMetricsCompute additional classification performance metrics (Seit R2022b)
averageCompute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (Seit R2022b)
plotPlot receiver operating characteristic (ROC) curves and other performance curves (Seit R2022b)
imageLIMEExplain network predictions using LIME (Seit R2020b)
occlusionSensitivityExplain network predictions by occluding the inputs (Seit R2019b)
deepDreamImageVisualize network features using deep dream
gradCAMExplain network predictions using Grad-CAM (Seit R2021a)
driseExplain object detection network predictions using D-RISE (Seit R2024a)


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


Training Progress and Performance