标签：right frac log 推导 交叉 函数 theta partial left
目录
一 交叉熵原理
1 信息量
信息量的大小与信息发生的概率成反比。
公式如下：
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I(x)=-log (P(x))
I(x)=−log(P(x))
其中，
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I(x)为信息量,
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P(x)为某一事件发生的概率
2 信息熵(熵)
信息熵用来表示所有信息量的期望。
公式如下：
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H(\mathrm{X})=-\sum_{i=1}^{n} P\left(x_{i}\right) \log \left(P\left(x_{i}\right)\right)
H(X)=−i=1∑nP(xi)log(P(xi))
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(X=x 1, x 2, \ldots, x n)
(X=x1,x2,…,xn)
3 相对熵(KL散度)
使用KL散度来衡量对于同一随机变量的两个单独概率分布之间的差异。
公式如下：
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D_{K L}(p \| q)=\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(\frac{p\left(x_{i}\right)}{q\left(x_{i}\right)}\right)
DKL(p∥q)=i=1∑np(xi)log(q(xi)p(xi))
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P(x)表示样本的真实分布，
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Q(x)表示模型所预测的分布。
KL散度越小，表示
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Q(x)使其分布逼近
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4 交叉熵
交叉熵=相对熵-信息熵
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H(p, q)=\left[-\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(q\left(x_{i}\right)\right)\right]
H(p,q)=[−i=1∑np(xi)log(q(xi))]
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\begin{gathered} D_{K L}(p \| q)=\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(\frac{p\left(x_{i}\right)}{q\left(x_{i}\right)}\right) \\ =\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(p\left(x_{i}\right)\right)-\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(q\left(x_{i}\right)\right) \\ =H(p(x))+\left[-\sum_{i=1}^{n} p\left(x_{i}\right) \log \left(q\left(x_{i}\right)\right)\right] \end{gathered}
DKL(p∥q)=i=1∑np(xi)log(q(xi)p(xi))=i=1∑np(xi)log(p(xi))−i=1∑np(xi)log(q(xi))=H(p(x))+[−i=1∑np(xi)log(q(xi))]
训练网络时输入数据与标签已经确定，即
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P(x)确定，信息熵为常量。KL值越小，预测结果越好，需最小化KL散度，即用交叉熵损失函数计算。
5 小结
交叉熵源于信息论，主要用于度量两个概率分布间的差异性。
在线性回归问题中，常使用MSE作为损失函数；在分类问题中常使用交叉熵作为损失函数，在输出层使用softmax将输出的结果进行处理，使其多个分类的预测值和为1，再通过交叉熵来计算损失。
二 推导
1 Logistic交叉熵损失函数
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J(\theta)=-\frac{1}{m} \sum_{i=1}^{m} y^{(i)} \log \left(h_{\theta}\left(x^{(i)}\right)\right)+\left(1-y^{(i)}\right) \log \left(1-h_{\theta}\left(x^{(i)}\right)\right)
J(θ)=−m1i=1∑my(i)log(hθ(x(i)))+(1−y(i))log(1−hθ(x(i)))
导数：
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\frac{\partial}{\partial \theta_{j}} J(\theta)=\frac{1}{m} \sum_{i=1}^{m}\left(h_{\theta}\left(x^{(i)}\right)-y^{(i)}\right) x_{j}^{(i)}
∂θj∂J(θ)=m1i=1∑m(hθ(x(i))−y(i))xj(i)
推导
对于logistic回归，m组样本，输入样本
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x^{(i)}=\left(1, x_{1}^{(i)}, x_{2}^{(i)}, \ldots, x_{p}^{(i)}\right)^{T}
x(i)=(1,x1(i),x2(i),…,xp(i))T，为
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p+1维向量（考虑bias）；
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y(i)表示类别，此处取0或1；模型的参数为
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\theta=\left(\theta_{0}, \theta_{1, \ldots,} \theta_{p}\right)^{T}
θ=(θ0,θ1,…,θp)T
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\theta^{T} x^{(i)}:=\theta_{0}+\theta_{1} x_{1}^{(i)}+\cdots+\theta_{p} x_{p}^{(i)} .
θTx(i):=θ0+θ1x1(i)+⋯+θpxp(i).
假设函数定义为：
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h_{\theta}\left(x^{(i)}\right)=\frac{1}{1+e^{{-\theta ^T}x^{(i)}}}
hθ(x(i))=1+e−θTx(i)1
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\begin{gathered} P\left(\hat{y}^{(i)}=1 \mid x^{(i)} ; \theta\right)=h_{\theta}\left(x^{(i)}\right) \\ P\left(\hat{y}^{(i)}=0 \mid x^{(i)} ; \theta\right)=1-h_{\theta}\left(x^{(i)}\right) \\ \log P\left(\hat{y}^{(i)}=1 \mid x^{(i)} ; \theta\right)=\log h_{\theta}\left(x^{(i)}\right)=\log \frac{1}{1+e^{{-\theta ^{T}} x^{(i)}}} \\ \log P\left(\hat{y}^{(i)}=0 \mid x^{(i)} ; \theta\right)=\log \left(1-h_{\theta}\left(x^{(i)}\right)\right)=\log \frac{e^{-\theta^{T} x^{(i)}}}{1+e^{-\theta^{T} x^{(i)}}} \end{gathered}
P(y^(i)=1∣x(i);θ)=hθ(x(i))P(y^(i)=0∣x(i);θ)=1−hθ(x(i))logP(y^(i)=1∣x(i);θ)=loghθ(x(i))=log1+e−θTx(i)1logP(y^(i)=0∣x(i);θ)=log(1−hθ(x(i)))=log1+e−θTx(i)e−θTx(i)
对于第
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\begin{gathered} I\left\{y^{(i)}=1\right\} \log P\left(\hat{y}^{(i)}=1 \mid x^{(i)} ; \theta\right)+I\left\{y^{(i)}=0\right\} \log P\left(\hat{y}^{(i)}=0 \mid x^{(i)} ; \theta\right) \\ =y^{(i)} \log P\left(\hat{y}^{(i)}=1 \mid x^{(i)} ; \theta\right)+\left(1-y^{(i)}\right) \log P\left(\hat{y}^{(i)}=0 \mid x^{(i)} ; \theta\right) \\ =y^{(i)} \log \left(h_{\theta}\left(x^{(i)}\right)\right)+\left(1-y^{(i)}\right) \log \left(1-h_{\theta}\left(x^{(i)}\right)\right) \end{gathered}
I{y(i)=1}logP(y^(i)=1∣x(i);θ)+I{y(i)=0}logP(y^(i)=0∣x(i);θ)=y(i)logP(y^(i)=1∣x(i);θ)+(1−y(i))logP(y^(i)=0∣x(i);θ)=y(i)log(hθ(x(i)))+(1−y(i))log(1−hθ(x(i)))
对于
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J(\theta)=-\frac{1}{m} \sum_{i=1}^{m} y^{(i)} \log \left(h_{\theta}\left(x^{(i)}\right)\right)+\left(1-y^{(i)}\right) \log \left(1-h_{\theta}\left(x^{(i)}\right)\right)
J(θ)=−m1i=1∑my(i)log(hθ(x(i)))+(1−y(i))log(1−hθ(x(i)))
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J
J取负号的原因：表征正确的概率值越大，模型对数据的表达能力越好；但在衡量模型优劣时表现误差的损失函数且越小越好。两相矛盾，所以令损失函数对表征正确的组合对数概率取反。
求导
第一步：
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\begin{gathered} J(\theta)=-\frac{1}{m} \sum_{i=1}^{m} y^{(i)} \log \left(h_{\theta}\left(x^{(i)}\right)\right)+\left(1-y^{(i)}\right) \log \left(1-h_{\theta}\left(x^{(i)}\right)\right)\\ =-\frac{1}{m} \sum_{i=1}^{m}\left[-y^{(i)}\left(\log \left(1+e^{-\theta^{T} x^{(i)}}\right)\right)+\left(1-y^{(i)}\right)\left(-\theta^{T} x^{(i)}-\log \left(1+e^{-\theta^{T} x^{(i)}}\right)\right)\right] \\ =-\frac{1}{m} \sum_{i=1}^{m}\left[y^{(i)} \theta^{T} x^{(i)}-\theta^{T} x^{(i)}-\log \left(1+e^{-\theta^{T} x^{(i)}}\right)\right] \\ =-\frac{1}{m} \sum_{i=1}^{m}\left[y^{(i)} \theta^{T} x^{(i)}-\log e^{\theta^{T} x^{(i)}}-\log \left(1+e^{-\theta^{T} x^{(i)}}\right)\right]_{} \\ =-\frac{1}{m} \sum_{i=1}^{m}\left[y^{(i)} \theta^{T} x^{(i)}-\left(\log e^{\theta^{T} x^{(i)}}+\log \left(1+e^{-\theta^{T} x^{(i)}}\right)\right)\right]_{} \\ =-\frac{1}{m} \sum_{i=1}^{m}\left[y^{(i)} \theta^{T} x^{(i)}-\log \left(e^{\theta^{T} x^{(i)}}+1\right)\right] \end{gathered}
J(θ)=−m1i=1∑my(i)log(hθ(x(i)))+(1−y(i))log(1−hθ(x(i)))=−m1i=1∑m[−y(i)(log(1+e−θTx(i)))+(1−y(i))(−θTx(i)−log(1+e−θTx(i)))]=−m1i=1∑m[y(i)θTx(i)−θTx(i)−log(1+e−θTx(i))]=−m1i=1∑m[y(i)θTx(i)−logeθTx(i)−log(1+e−θTx(i))]=−m1i=1∑m[y(i)θTx(i)−(logeθTx(i)+log(1+e−θTx(i)))]=−m1i=1∑m[y(i)θTx(i)−log(eθTx(i)+1)]
第二步：
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\begin{gathered} \frac{\partial}{\partial \theta_{j}} J(\theta)=\frac{\partial}{\partial \theta_{j}}\left(\frac{1}{m} \sum_{i=1}^{m}\left[\log \left(1+e^{\theta^{T} x^{(i)}}\right)-y^{(i)} \theta^{T} x^{(i)}\right]\right) \\ =\frac{1}{m} \sum_{i=1}^{m}\left(\frac{x_{j}^{(i)} e^{\theta^{T} x^{(i)}}}{1+e^{\theta^{T} x^{(i)}}}-y^{(i)} x_{j}^{(i)}\right) \\ =\frac{1}{m} \sum_{i=1}^{m}\left(h_{\theta}\left(x^{(i)}\right)-y^{(i)}\right) x_{j}^{(i)} \end{gathered}
∂θj∂J(θ)=∂θj∂(m1i=1∑m[log(1+eθTx(i))−y(i)θTx(i)])=m1i=1∑m(1+eθTx(i)xj(i)eθTx(i)−y(i)xj(i))=m1i=1∑m(hθ(x(i))−y(i))xj(i)
2 Softmax交叉熵损失函数
公式：
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a_{i}=\frac{e^{z _{i}}}{\sum_{k} e^{z _{k}}},z_{i}=\sum_{j} w_{i j} x_{i j}+b
ai=∑kezkezi,zi=∑jwijxij+b
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导数：
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\frac{\partial C}{\partial z_{i}}=a_{i}-y_{i}
∂zi∂C=ai−yi
推导：
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\frac{\partial C}{\partial z_{i}}=\sum_{j}\left(\frac{\partial C_{j}}{\partial a_{j}} \frac{\partial a_{j}}{\partial z_{i}}\right)
∂zi∂C=j∑(∂aj∂Cj∂zi∂aj)
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\frac{\partial C_{j}}{\partial a_{j}}=\frac{\partial\left(-y_{j} \ln a_{j}\right)}{\partial a_{j}}=-y_{j} \frac{1}{a_{j}}
∂aj∂Cj=∂aj∂(−yjlnaj)=−yjaj1
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\frac{\partial a_{j}}{\partial z_{i}}
∂zi∂aj有如下两种情况：
（1）
i
=
j
i=j
i=j
∂
a
i
∂
z
i
=
∂
(
e
z
i
∑
k
e
z
k
)
∂
z
i
=
∑
k
e
z
k
e
z
i
−
(
e
z
i
)
2
(
∑
k
e
z
k
)
2
=
(
e
z
i
∑
k
e
z
k
)
(
1
−
e
z
i
∑
k
e
z
k
)
=
a
i
(
1
−
a
i
)
\frac{\partial a_{i}}{\partial z_{i}}=\frac{\partial\left(\frac{e^{z _{i}}}{\sum_{k} e^{z _{k}}}\right)}{\partial z_{i}}=\frac{\sum_{k} e^{z _{k}} e^{z _{i}}-\left(e^{z _{i}}\right)^{2}}{\left(\sum_{k} e^{z _{k}}\right)^{2}}\\ =\left(\frac{e^{z_{i}}}{\sum_{k} e^{z k}}\right)\left(1-\frac{e^{z_{i}}}{\sum_{k} e^{z k}}\right)=a_{i}\left(1-a_{i}\right)
∂zi∂ai=∂zi∂(∑kezkezi)=(∑kezk)2∑kezkezi−(ezi)2=(∑kezkezi)(1−∑kezkezi)=ai(1−ai)
（2）
i
≠
j
i \neq j
i=j
∂
a
j
∂
z
i
=
∂
(
e
z
j
∑
k
e
z
k
)
∂
z
i
=
−
e
z
j
(
1
∑
k
e
z
k
)
2
e
z
i
=
−
a
i
a
j
\frac{\partial a_{j}}{\partial z_{i}}=\frac{\partial\left(\frac{e^{z _{j}}}{\sum k e^{z_{k}}}\right)}{\partial z_{i}}=-e^{z_{ j}}\left(\frac{1}{\sum_{k} e^{z k}}\right)^{2} e^{z_ {i}}=-a_{i} a_{j}
∂zi∂aj=∂zi∂(∑kezkezj)=−ezj(∑kezk1)2ezi=−aiaj
综上：
∂
C
∂
z
i
=
∑
j
(
∂
C
j
∂
a
j
∂
a
j
∂
z
i
)
=
∑
j
≠
i
(
∂
C
j
∂
a
j
∂
a
j
∂
z
i
)
+
∑
i
=
j
(
∂
C
j
∂
a
j
∂
a
j
∂
z
i
)
=
∑
j
≠
i
−
y
j
1
a
j
(
−
a
i
a
j
)
+
(
−
y
i
1
a
i
)
(
a
i
(
1
−
a
i
)
)
=
∑
j
≠
i
a
i
y
j
+
(
−
y
i
(
1
−
a
i
)
)
=
∑
j
≠
i
a
i
y
j
+
a
i
y
i
−
y
i
=
a
i
∑
j
y
j
−
y
i
\begin{aligned} &\frac{\partial C}{\partial z_{i}}=\sum_{j}\left(\frac{\partial C_{j}}{\partial a_{j}} \frac{\partial a_{j}}{\partial z_{i}}\right)=\sum_{j \neq i}\left(\frac{\partial C_{j}}{\partial a_{j}} \frac{\partial a_{j}}{\partial z_{i}}\right)+\sum_{i=j}\left(\frac{\partial C_{j}}{\partial a_{j}} \frac{\partial a_{j}}{\partial z_{i}}\right) \\ &=\sum_{j \neq i}-y_{j} \frac{1}{a_{j}}\left(-a_{i} a_{j}\right)+\left(-y_{i} \frac{1}{a_{i}}\right)\left(a_{i}\left(1-a_{i}\right)\right) \\ &=\sum_{j \neq i} a_{i} y_{j}+\left(-y_{i}\left(1-a_{i}\right)\right) \\ &=\sum_{j \neq i} a_{i} y_{j}+a_{i} y_{i}-y_{i} \\ &=a_{i} \sum_{j} y_{j}-y_{i} \end{aligned}
∂zi∂C=j∑(∂aj∂Cj∂zi∂aj)=j=i∑(∂aj∂Cj∂zi∂aj)+i=j∑(∂aj∂Cj∂zi∂aj)=j=i∑−yjaj1(−aiaj)+(−yiai1)(ai(1−ai))=j=i∑aiyj+(−yi(1−ai))=j=i∑aiyj+aiyi−yi=aij∑yj−yi
针对分类问题，
y
i
yi
yi最终只会有一个类别是1，其他类别都是0
所以
∂
C
∂
z
i
=
a
i
−
y
i
\frac{\partial C}{\partial z_{i}}=a_{i}-y_{i}
∂zi∂C=ai−yi
附录 求导公式和法则
基本初等函数求导公式
(1)
(
C
)
′
=
0
\quad(C)^{\prime}=0
(C)′=0
(2)
(
x
μ
)
′
=
μ
x
μ
−
1
\quad\left(x^{\mu}\right)^{\prime}=\mu x^{\mu-1}
(xμ)′=μxμ−1
(3)
(
sin
x
)
′
=
cos
x
(\sin x)^{\prime}=\cos x
(sinx)′=cosx
(4)
(
cos
x
)
′
=
−
sin
x
(\cos x)^{\prime}=-\sin x
(cosx)′=−sinx
(5)
(
tan
x
)
′
=
sec
2
x
(\tan x)^{\prime}=\sec ^{2} x
(tanx)′=sec2x
(6)
(
cot
x
)
′
=
−
csc
2
x
(\cot x)^{\prime}=-\csc ^{2} x
(cotx)′=−csc2x
(7)
(
sec
x
)
′
=
sec
x
tan
x
(\sec x)^{\prime}=\sec x \tan x
(secx)′=secxtanx
(8)
(
csc
x
)
′
=
−
csc
x
cot
x
(\csc x)^{\prime}=-\csc x \cot x
(cscx)′=−cscxcotx
(9)
(
a
x
)
′
=
a
x
ln
a
\left(a^{x}\right)^{\prime}=a^{x} \ln a
(ax)′=axlna
(10)
(
e
x
)
′
=
e
x
\left(\mathrm{e}^{x}\right)^{\prime}=\mathrm{e}^{x}
(ex)′=ex
(11)
(
log
a
x
)
′
=
1
x
ln
a
\left(\log _{a} x\right)^{\prime}=\frac{1}{x \ln a}
(logax)′=xlna1
(12)
(
ln
x
)
′
=
1
x
(\ln x)^{\prime}=\frac{1}{x}
(lnx)′=x1,
(13)
(
arcsin
x
)
′
=
1
1
−
x
2
(\arcsin x)^{\prime}=\frac{1}{\sqrt{1-x^{2}}}
(arcsinx)′=1−x2
1
(14)
(
arccos
x
)
′
=
−
1
1
−
x
2
(\arccos x)^{\prime}=-\frac{1}{\sqrt{1-x^{2}}}
(arccosx)′=−1−x2
1
(15)
(
arctan
x
)
′
=
1
1
+
x
2
(\arctan x)^{\prime}=\frac{1}{1+x^{2}}
(arctanx)′=1+x21
(16)
(
arccot
x
)
′
=
−
1
1
+
x
2
(\operatorname{arccot} x)^{\prime}=-\frac{1}{1+x^{2}}
(arccotx)′=−1+x21
求导法则
设
u
=
u
(
x
)
,
v
=
v
(
x
)
u=u(x), v=v(x)
u=u(x),v=v(x) 都可导, 则
(1)
(
u
±
v
)
′
=
u
′
±
v
′
\quad(u \pm v)^{\prime}=u^{\prime} \pm v^{\prime}
(u±v)′=u′±v′
(2)
(
C
u
)
′
=
C
u
′
(
C
(C u)^{\prime}=C u^{\prime}(C
(Cu)′=Cu′(C 是常数)
(3)
(
u
v
)
′
=
u
′
v
+
u
v
′
\quad(u v)^{\prime}=u^{\prime} v+u v^{\prime}
(uv)′=u′v+uv′
(4)
(
u
v
)
′
=
u
′
v
−
u
v
′
v
2
\left(\frac{u}{v}\right)^{\prime}=\frac{u^{\prime} v-u v^{\prime}}{v^{2}}
(vu)′=v2u′v−uv′
复合函数求导法则
设
y
=
f
(
u
)
y=f(u)
y=f(u), 而
u
=
φ
(
x
)
u=\varphi(x)
u=φ(x) 且
f
(
u
)
f(u)
f(u) 及
φ
(
x
)
\varphi(x)
φ(x) 都可导, 则复合函数
y
=
f
[
φ
(
x
)
]
y=f[\varphi(x)]
y=f[φ(x)] 的导数为
d
y
d
x
=
d
y
d
u
⋅
d
u
d
x
或
y
′
=
f
′
(
u
)
⋅
φ
′
(
x
)
\frac{d y}{d x}=\frac{d y}{d u} \cdot \frac{d u}{d x} \text { 或 } y^{\prime}=f^{\prime}(u) \cdot \varphi^{\prime}(x)
dxdy=dudy⋅dxdu 或 y′=f′(u)⋅φ′(x)
标签：right,frac,log,推导,交叉,函数,theta,partial,left 来源： https://blog.csdn.net/weixin_50008473/article/details/120505614
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