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 5章 误差反向传播法
154
5.7
误差反向传播法的实现
通过像组装乐高积木一样组装上一节中实现的层，可以构建神经网络。
本节我们将通过组装已经实现的层来构建神经网络。
5.7.1
神经网络学习的全貌图
在进行具体的实现之前，我们再来确认一下神经网络学习的全貌图。神
经网络学习的步骤如下所示。
前提
神经网络中有合适的权重和偏置，调整权重和偏置以便拟合训练数据的
过程称为学习。神经网络的学习分为下面 4 个步骤。
步骤1（mini-batch）
从训练数据中随机选择一部分数据。
步骤2（计算梯度）
计算损失函数关于各个权重参数的梯度。
步骤3（更新参数）
将权重参数沿梯度方向进行微小的更新。
步骤4（重 复）
重复步骤 1、步骤 2、步骤 3。
之前介绍的误差反向传播法会在步骤2 中出现。上一章中，我们利用数
值微分求得了这个梯度。数值微分虽然实现简单，但是计算要耗费较多的时
间。和需要花费较多时间的数值微分不同，误差反向传播法可以快速高效地
计算梯度。
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