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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.


Deep Network DesignerDesign, visualize, and train deep learning networks


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analyzeNetworkAnalyze deep learning network architecture
trainingProgressMonitorMonitor and plot training progress for deep learning custom training loops
updateInfoUpdate information values for custom training loops
recordMetricsRecord metric values for custom training loops
groupSubPlotGroup metrics in training plot
plotPlot neural network architecture
activationsCompute deep learning network layer activations
deepDreamImageVisualize network features using deep dream
occlusionSensitivityExplain network predictions by occluding the inputs
imageLIMEExplain network predictions using LIME
gradCAMExplain network predictions using Grad-CAM
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
addMetricsCompute additional classification performance metrics
averageCompute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem


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



Training Progress and Performance