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。

Use occlusion sensitivity maps to understand why a deep neural network makes a classification decision. Occlusion sensitivity is a simple technique for understanding which parts of an image are most important for a deep network's classification. You can measure a network's sensitivity to occlusion in different regions of the data using small perturbations of the data. Use occlusion sensitivity to gain a high-level understanding of what image features a network uses to make a particular classification, and to provide insight into the reasons why a network can misclassify an image.

Use locally interpretable model-agnostic explanations (LIME) to understand why a deep neural network makes a classification decision.

Configure an experiment that replaces layers of different pretrained networks for transfer learning.

Find optimal data architecture and network configurations for sequence-to-sequence regression using Bayesian optimization.