可视化和验证

可视化神经网络行为、解释预测并使用序列和表格数据验证稳健性

在训练期间和训练后，可视化深度网络。使用内置的网络准确度和损失图监控训练进度。为了研究经过训练的网络，您可以使用可视化方法，如 Grad-CAM。

使用深度学习验证方法来评估深度神经网络的属性。例如，您可以验证网络的稳健性属性，计算网络输出边界，并找到对抗示例。

App

深度网络设计器

设计和可视化深度学习网络

函数

全部展开

绘图

`analyzeNetwork`	Analyze deep learning network architecture
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (自 R2022b 起)
`updateInfo`	Update information values for custom training loops (自 R2022b 起)
`recordMetrics`	Record metric values for custom training loops (自 R2022b 起)
`groupSubPlot`	Group metrics in training plot (自 R2022b 起)
`plot`	绘制神经网络架构

特征可视化

`predict`	Compute deep learning network output for inference (自 R2019b 起)
`minibatchpredict`	Mini-batched neural network prediction (自 R2024a 起)
`scores2label`	Convert prediction scores to labels (自 R2024a 起)
`gradCAM`	Explain network predictions using Grad-CAM (自 R2021a 起)
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart
`rocmetrics`	Receiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers (自 R2022b 起)
`addMetrics`	Compute additional classification performance metrics (自 R2022b 起)
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (自 R2022b 起)

属性

ConfusionMatrixChart Properties	Confusion matrix chart appearance and behavior
ROCCurve Properties	Receiver operating characteristic (ROC) curve appearance and behavior (自 R2022b 起)

主题

可解释性

Visualize Activations of LSTM Network
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
Interpret Deep Learning Time-Series Classifications Using Grad-CAM
This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data.
View Network Behavior Using tsne
This example shows how to use the tsne function to view activations in a trained network.
在 MATLAB 中进行深度学习
通过使用卷积神经网络进行分类和回归来探索 MATLAB^® 的深度学习能力，包括预训练网络和迁移学习，以及在 GPU、CPU、集群和云上进行训练。
Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.

训练进度和性能

监控深度学习训练进度
此示例说明如何监控深度学习网络的训练进度。
Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
ROC Curve and Performance Metrics
Use rocmetrics to examine the performance of a classification algorithm on a test data set.

精选示例

Investigate Spectrogram Classifications Using LIME

Use locally interpretable model-agnostic explanations (LIME) to investigate the robustness of a deep convolutional neural network trained to classify spectrograms. LIME is a technique for visualizing which parts of an observation contribute to the classification decision of a network. This example uses the imageLIME function to understand which features in the spectrogram data are most important for classification.

打开实时脚本