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深度学习可视化

绘制训练进度、评估准确度、解释预测以及将网络学习的特征可视化

使用内置的网络准确度和损失图监控训练进度。使用可视化方法,如 Grad-CAM、遮挡敏感度、LIME 和 Deep Dream,研究经过训练的网络。

App

深度网络设计器设计、可视化和训练深度学习网络

函数

全部展开

analyzeNetworkAnalyze deep learning network architecture
plotPlot neural network layer graph
activations计算深度学习网络层激活
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

属性

ConfusionMatrixChart PropertiesConfusion matrix chart appearance and behavior

主题