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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Design, visualize, and train deep learning networks

函数

全部展开

analyzeNetworkAnalyze deep learning network architecture
plotPlot neural network layer graph
activationsCompute deep learning network layer activations
predictPredict responses using a trained deep learning neural network
classifyClassify data using a 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 the state of a recurrent 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

使用深度学习对网络摄像头图像进行分类

此示例说明如何使用预训练的深度卷积神经网络 GoogLeNet 实时对来自网络摄像头的图像进行分类。

监控深度学习训练进度

在训练深度学习网络时,监控训练进度通常很有用。通过在训练过程中绘制各种指标,您可以了解训练的进度情况。例如,您可以确定网络准确度是否改善以及改善速度,还可以确定网络是否开始过拟合训练数据。

Understand Network Predictions Using Occlusion

This example shows how to use occlusion sensitivity maps to understand why a deep neural network makes a classification decision.

Interpret Deep Network Predictions on Tabular Data Using LIME

This example shows how to use the locally interpretable model-agnostic explanations (LIME) technique to understand the predictions of a deep neural network classifying tabular data.

Investigate Spectrogram Classifications Using LIME

This example shows how to use locally interpretable model-agnostic explanations (LIME) to investigate the robustness of a deep convolutional neural network trained to classify spectrograms.

Investigate Classification Decisions Using Gradient Attribution Techniques

This example shows how to use gradient attribution maps to investigate which parts of an image are most important for classification decisions made by a deep neural network.

使用类激活映射调查网络预测

此示例说明如何使用类激活映射 (CAM) 来调查和解释用于图像分类的深度卷积神经网络的预测。

Visualize Image Classifications Using Maximal and Minimal Activating Images

This example shows how to use a data set to find out what activates the channels of a deep neural network.

View Network Behavior Using tsne

This example shows how to use the tsne function to view activations in a trained network.

Monitor GAN Training Progress and Identify Common Failure Modes

Learn how to diagnose and fix some of the most common failure modes in GAN training.

可视化卷积神经网络的激活区域

此示例说明如何将图像馈送到卷积神经网络并显示网络的不同层的激活区域。通过将激活区域与原始图像进行比较,检查激活区域并发现网络学习的特征。发现较浅层中的通道学习颜色和边缘等简单特征,而较深层中的通道学习眼睛等复杂特征。以这种方式识别特征可以帮助您了解网络学习的内容。

Visualize Activations of LSTM Network

This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.

可视化卷积神经网络的特征

此示例说明如何可视化卷积神经网络学习的特征。

特色示例