October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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290
｜
第
10
章
良好的默认设置。对于输出层，这实际上取决于你的任务。
迭代次数
在大多数情况下，实际上不需要调整训练迭代次数，只需使用提前停止即可。
最佳学习率取决于其他超参数，尤其是批量大小，因此如果你修改了任何超
参数，请确保也更新学习率。
有关调整神经网络超参数的更多最佳实践，请查看 Leslie Smith 的 2018 年优秀论文
注 27
。
到此结束我们对人工神经网络及其在 Keras 中实现的介绍。在接下来的几章中，我们将
讨论训练非常深度网络的技术。我们还将探索如何使用 TensorFlow 的较底层 API 自定
义模型，以及如何使用 Data API 有效地加载和预处理数据。我们还将深入探讨其他流行
的神经网络架构：用于图像处理的卷积神经网络、用于顺序数据的递归神经网络、用于
表征学习的自动编码器以及用于建模和生成数据的生成式对抗网络
注 28
。
12
10.4 练习题
1. TensorFlow Playground 是由 TensorFlow 团队构建的神经网络模拟器。在本练习
中，你只需单击几下即可训练几个二元分类器，并对模型的架构及其超参数进行调
整，以直观了解神经网络的工作方式及其超参数的功能。花一些时间来探索以下
内容：
a. 由神经网络学习到的模式。单击 Run 按钮（左上方），尝试训练默认的神经网
络。请注意它如何快速找到适合分类任务的最优解。第一隐藏层中的神经元已
经学会了简单模式，而第二隐藏层中的神经元已经学会了将第一隐藏层的简单
模式组合为更复杂的模式。通常，层数越多，模式越复杂。 ...
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