July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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322
｜
第
20
章
神经网络
讨论
正如我们在
20.7
节中讨论的，一般来说在前几个
epoch
中，训练集和测试集的误差都会
减小，但是到了某个节点，神经网络会开始“记住”测试集数据，导致训练集误差持续
减小而测试集误差开始增大。因为存在这种现象，所以一个最普遍且有效的对抗过拟合
的方法就是，监视训练过程并且在测试集误差开始增大时就结束训练。这个策略被称作
提前结束。
在
Keras
中，我们可以用一个回调函数来实施提前结束策略。回调函数是可以在训练
过程的某几个特定阶段应用的函数，比如在每个
epoch
结束的时候。在本节的解决方
案中，我们加入了
EarlyStopping(monitor='val_loss', patience=2)
，告诉程序我
们想监视每个
epoch
的测试集（验证集）损失，并且如果连续两个
epoch
测试集损失
的情况都没有得到改善，就中断训练。但是因为我们设定了
patience=2
，所以得不到
最佳模型，而会得到最佳模型之后的两个
epoch
的模型。因此，我们也可以添加一个
ModelCheckpoint
操作，在每个检查点（如果你有一个持续多日的训练由于某种原因被
打断，这时检查点就会很有用）之后把模型保存到文件中。这对我们会很有帮助，因为
如果设定
save_best_only=True
，
ModelCheckpoint
就会仅保存最佳模型。
20.10
通过
Dropout
减少过拟合
问题描述
减少过拟合。
解决方案
使用
Dropout
（丢弃）方法向网络结构中引入噪声 ：
#
加载库
import numpy ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.