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Deep Learning Visualization

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.

Apps

Deep Network DesignerDesign, visualize, and train deep learning networks

Functions

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analyzeNetworkAnalyze deep learning network architecture
plotPlot neural network layer graph
activationsCompute deep learning network layer activations
predictPredict responses using trained deep learning neural network
classifyClassify data using trained deep learning neural network
predictAndUpdateStatePredict responses using a trained recurrent neural network and update the network state
classifyAndUpdateStateClassify data using a trained recurrent neural network and update the network state
resetStateReset state parameters of neural network
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

Properties

ConfusionMatrixChart PropertiesConfusion matrix chart appearance and behavior

Topics