交叉熵(Corss Entropy)损失函数定义
二分类问题:
= - (y
+ (1-y)
)
多分类问题:
= -
y

激活函数:
SoftMAX 定义(多分类)
= 
Sigmoid 定义(二分类)
=
Sigmoid 交叉熵损失求导:
=
-
(
为期望值,
为预测值)
SoftMax 交叉熵损失求导(是不是很简单):
=
Loss =
(
为正则化惩罚)
参数更新:
(
为学习率,注意是反向梯度)
训练过程

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