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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4.5 学习算法的实现
109
求出神经网络的梯度后，接下来只需根据梯度法，更新权重参数即可。
在下一节中，我们会以2 层神经网络为例，实现整个学习过程。
为了对应形状为多维数组的权重参数
W
，这里使用的n
umerical_
gradient()
和之前的实现稍有不同。不过，改动只是为了对应多维
数组，所以改动并不大。这里省略了对代码的说明，想知道细节的
读者请参考源代码（
common/gradient.py
）。
4.5
学习算法的实现
关于神经网络学习的基础知识，到这里就全部介绍完了。“损失函
数 ”“
mini-batch
”“梯度”“梯度下降法”等关键词已经陆续登场，这里我们
来确认一下神经网络的学习步骤，顺便复习一下这些内容。神经网络的学习
步骤如下所示。
前提
神经网络存在合适的权重和偏置，调整权重和偏置以便拟合训练数据的
过程称为“学习”。神经网络的学习分成下面 4 个步骤。
步骤1（mini-batch）
从训练数据中随机选出一部分数据，这部分数据称为mini-batch。我们
的目标是减小 mini-batch 的损失函数的值。
步骤2（计算梯度）
为了减小mini-batch 的损失函数的值，需要求出各个权重参数的梯度。
梯度表示损失函数的值减小最多的方向。
步骤3（更新参数）
将权重参数沿梯度方向进行微小更新。
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