July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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第 4章 神经网络的学习
118
没有必要那么频繁地记录识别精度（只要从大方向上大致把握识别精度的推
移就可以了）。因此，我们才会每经过一个
epoch
就记录一次训练数据的识别
精度。
把从上面的代码中得到的结果用图表示的话，如图4
-
12 所示。
图4-12 训练数据和测试数据的识别精度的推移（横轴的单位是epoch）
图4
-
12 中，实线表示训练数据的识别精度，虚线表示测试数据的识别精
度。如图所示，随着
epoch
的前进（学习的进行），我们发现使用训练数据和
测试数据评价的识别精度都提高了，并且，这两个识别精度基本上没有差异（两
条线基本重叠在一起）。因此，可以说这次的学习中没有发生过拟合的现象。
4.6
小结
本章中，我们介绍了神经网络的学习。首先，为了能顺利进行神经网络
的学习，我们导入了损失函数这个指标。以这个损失函数为基准，找出使它
4.6 小结
119
的值达到最小的权重参数，就是神经网络学习的目标。为了找到尽可能小的
损失函数值，我们介绍了使用函数斜率的梯度法。
本章所学的内容
•
机器学习中使用的数据集分为训练数据和测试数据。
•
神经网络用训练数据进行学习，并用测试数据评价学习到的模型的
泛化能力。
•
神经网络的学习以损失函数为指标，更新权重参数，以使损失函数
的值减小。
•
利用某个给定的微小值的差分求导数的过程，称为数值微分。 ...
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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.