单层和双层神经网络反向传播公式推导(从矩阵求导的角度)

最近在跟着Andrew Ng老师学习深度神经网络.在学习浅层神经网络(两层)的时候,推导反向传播公式遇到了一些困惑,网上没有找到系统推导的过程.后来通过学习矩阵求导相关技巧,终于搞清楚了.首先从最简单的logistics回归(单层神经网络)开始.

logistics regression中的梯度下降法

单训练样本的logistics regression

输入训练样本为$x$,网络权重为$w$和$b$,其中$x$为列向量,向量维度为$(n_0,1)$,$w$为行向量,向量维度为$(1,n_0)$,$b$为标量.则神经网络的输出为$$a = \sigma(z),z = wx + b$$其中,$\sigma()$函数为sigmoid函数,其定义为$$\sigma(x) = \frac{1}{1+e^{-x}}$$
网络的loss函数定义为:$$ l(a) = -(yloga+(1-y)log(1-a))$$
其中,$y$为训练样本标签,对于logistics regression$y = 0/1$.

  1. 下面首先求解$\frac{\partial l}{\partial z}$:
    $$\begin{aligned}
    \frac{\partial l}{\partial z} &= -(ylog\sigma(z)+(1-y)log(1-\sigma(z)))^{'}\
    &= -{ylog^{'}\sigma(z)\sigma^{'}(z)+(1-y)log^{'}(1-\sigma(z))(-\sigma^{'}(z))}\
    &= -{y\frac{1}{\sigma(z)}\sigma(z)(1-\sigma(z))-(1-y)\frac{1}{1-\sigma(z)}\sigma(z)(1-\sigma(z))}\
    &= -{y(1-\sigma(z))-(1-y)\sigma(z)}\
    &= \sigma(z)-y \
    &= a-y
    \end{aligned}$$
  2. 下面求解$\frac{\partial l}{\partial w}$:
    由于w为行向量,上面的求导为标量对向量的求导.可以按照标量对向量求导的定义来计算,即$$\frac{\partial l}{\partial w} = [\frac{\partial l}{\partial w_1},\frac{\partial l}{\partial w_2},...,\frac{\partial l}{\partial w_{n_0}}]$$当然此处可以利用标量求导的链式法则,将$\frac{\partial l}{\partial w_i} = \frac{\partial l}{\partial z}\frac{\partial z}{\partial w_i}$带入进行计算.
    但是,为了与后续向量化实现和两层神经网络的求导相一致,此处利用矩阵求导的法则进行计算,虽然有杀鸡用牛刀的嫌疑.首先明确一点,标量的链式求导法则并不适用于向量,不能相当然的套用,我就是犯了这个错误,在自己推导公式时百思不得其解.但是矩阵求导也有类似与标量的链式法则,下面直接给出公式:
    $$ dl = tr(\frac{\partial l^{T}}{\partial W}dW)$$
    其中,dl指的是标量l的微分,W为矩阵,tr为迹运算.若dl,dW能满足上面这种形式,则dW前面部分就是标量l对矩阵W的导数.此处简单的举个例子:
    $f = a^{T}Xb$,$f$为标量,$a,b$为列向量,$X$为矩阵,求$\frac{\partial f}{\partial W}$,解答过程如下:
    $$\begin{aligned}
    df &= da^{T}Xb\
    &= a^{T}dXb\
    &= tr(a^{T}dXb)\
    &= tr(ba^{T}dX)\
    &= tr((ab^{T})^{T})dX \
    \end{aligned}$$
    对照上面的公式,可得$\frac{\partial f}{\partial W} = ab^{T}$.上面的推导过程用了部分矩阵微分公式如$d(XY) = dXY+XdY$,还包括迹运算的技巧,如$tr(ABC) = tr(CAB) = tr(BAC)$,更详细的关于矩阵求导的内容请参考博主叠加态的猫
  3. 下面求解$\frac{\partial l}{\partial b}$:
    由于b为标量,可以简单求得$\frac{\partial l}{\partial b} = \frac{\partial l}{\partial z}$

m个训练样本的logistics regression向量化实现

单次输入的训练样本是$X$,$X$为矩阵,维度为$(n_{0},m)$.网络权重为$\boldsymbol{w}$和$b$,$\boldsymbol{w}$为行向量,向量维度为$(1,n_0)$,$\boldsymbol{b}=\overrightarrow{1}^{T}b$.则神经网络的输出为$$\boldsymbol{a} = \sigma(\boldsymbol{z}),\boldsymbol{z} = \boldsymbol{w}X + \boldsymbol{b}$$|
$\boldsymbol{z},\boldsymbol{a}$均为行向量,维度为$(1,m)$.cost函数定义为:
$$ J(\boldsymbol{a})=-\frac{1}{m}\sum_{i=1}^{m}l(a_i)$$
也可以定义为矩阵的形式:
$$ J(\boldsymbol{a})=-\frac{1}{m}[\boldsymbol{y}log\boldsymbol{a}^{T}+(\overrightarrow{1}^{T}-\boldsymbol{y})log(\overrightarrow{1}-\boldsymbol{y}^{T})]$$
$\overrightarrow{1}$为全为1的列向量

  1. 下面首先求解$\frac{\partial J}{\partial \boldsymbol{z}}$:
    不管通过标量对向量求导的定义,或者利用矩阵"链式法则"都能求得:
    $$\frac{\partial J}{\partial \boldsymbol{z}}=\frac{1}{m}(\boldsymbol{a}-\boldsymbol{z})$$
    注意此处J对z的导数与Andrew Ng老师的结果有点区别,多了一个$\frac{1}{m}$,私以为严格按照求导公式,$\frac{1}{m}$是该有的,虽然Andrew Ng老师在dw,db前加上了$\frac{1}{m}$,所以对最终的迭代并无影响.
  2. 下面求解$\frac{\partial J}{\partial \boldsymbol{w}}$:
    $$\begin{aligned}
    d\boldsymbol{z} &= d(\boldsymbol{w}X+\boldsymbol{b})\
    &= d\boldsymbol{w}X\
    \end{aligned}$$
    已知$dJ=tr(\frac{\partial J^{T}}{\partial \boldsymbol{z}}d\boldsymbol{z})$,将上式带入可得:
    $$\begin{aligned}
    dJ &= tr(\frac{\partial J^{T}}{\partial \boldsymbol{z}}d\boldsymbol{w}X)\
    &= tr(\frac{X\partial J^{T}}{\partial \boldsymbol{z}}d\boldsymbol{w})\
    &= tr((\frac{\partial J}{\partial \boldsymbol{z}}X^{T})^{T}d\boldsymbol{w})\
    \end{aligned}$$
    因此,$\frac{\partial J}{\partial \boldsymbol{w}}=\frac{\partial J}{\partial \boldsymbol{z}}X^{T}$
  3. 下面求解$\frac{\partial J}{\partial b}$:
    $$\begin{aligned}
    dJ &= tr(\frac{\partial J^{T}}{\partial \boldsymbol{z}}d\boldsymbol{b})\
    &= tr(\frac{\partial J^{T}}{\partial \boldsymbol{z}}\overrightarrow{1}^{T}db)\
    &= tr(\overrightarrow{1}^{T}\frac{\partial J^{T}}{\partial \boldsymbol{z}})db\
    &= tr(\frac{\partial J}{\partial \boldsymbol{z}}\overrightarrow{1})^{T}db\
    \end{aligned}$$
    因此,$\frac{\partial J}{\partial b}=\frac{\partial J}{\partial \boldsymbol{z}}\overrightarrow{1}$

双层神经网络中的梯度下降法

神经网络的输入,隐含层,输出层神经元个数分别为$n_0,n_1,n_2=1$,其中隐含层激活函数为$g()$,参数为$W_1,\boldsymbol{b_1}$,$W_1$为矩阵,维度$(n_1,n_0)$,$\boldsymbol{b_1}$为列向量,维度$(n_1,1)$.输出层激活函数选择sigmoid函数,参数为$\boldsymbol{w_2},b_2$,$\boldsymbol{w_2}为行向量,维度为$(n_1,1)$,$b_2$为标量.

单个训练样本推导

输入$\boldsymbol{x}$,则网络的正向传递过程如下:
$$\begin{aligned}
\boldsymbol{z_1}&=W_1\boldsymbol{x}+\boldsymbol{b_1}\
\boldsymbol{a_1}&=g(\boldsymbol{z_1})\
z_2&=\boldsymbol{w_2}\boldsymbol{a_1}+b_2\
a_2&=\sigma(z_2)\
\end{aligned}$$
loss函数定义与logistics regression相同

  1. 首先求解$\frac{\partial l}{\partial z_2}$:
    与logistics regression方式相同,可得$\frac{\partial l}{\partial z_2}=a_2-y$
  2. 下面求解$\frac{\partial l}{\partial \boldsymbol{w_2}}$:
    与logistics regression方式相同,可得$\frac{\partial l}{\partial \boldsymbol{w_2}}=\frac{\partial l}{\partial z_2}\boldsymbol{a_1}^{T}$
  3. 相同方式可求解$\frac{\partial l}{\partial b_2}=\frac{\partial l}{\partial z_2}$
  4. 求解$\frac{\partial l}{\partial \boldsymbol{z_1}}$:
    $$\begin{aligned}
    dl &= tr(\frac{\partial l^{T}}{\partial z_2}d(\boldsymbol{w_2}g(\boldsymbol{z_1})+b_2)\
    &= tr(\frac{\partial l^{T}}{\partial z_2}\boldsymbol{w_2}dg(\boldsymbol{z_1}))\
    &= tr(\frac{\partial l^{T}}{\partial z_2}\boldsymbol{w_2}(g^{'}(\boldsymbol{z_1})d\boldsymbol{z_1}))\
    &= tr((\boldsymbol{w_2}^{T}\frac{\partial l}{\partial z_2})^{T}(g^{'}(\boldsymbol{z_1})
    d\boldsymbol{z_1}))\
    &= tr((\boldsymbol{w_2}^{T}\frac{\partial l}{\partial z_2}g^{'}(\boldsymbol{z_1}))^{T}d\boldsymbol{z_1})\
    \end{aligned}$$
    因此,$\frac{\partial l}{\partial \boldsymbol{z_1}}=\boldsymbol{w_2}^{T}\frac{\partial l}{\partial z_2}
    g^{'}(\boldsymbol{z_1})$,其中为逐元素相乘,上面公式推导过程中运用了迹的性质,$tr(A^{T}(BC))=tr((A*B)^{T}C)$
  5. 求解$\frac{\partial l}{\partial W_1}$:
    $$\begin{aligned}
    dl &= tr(\frac{\partial l^{T}}{\partial \boldsymbol{z_1}}d\boldsymbol{z_1})\
    &= tr(\frac{\partial l^{T}}{\partial \boldsymbol{z_1}}dW_1\boldsymbol{x})\
    &= tr(\boldsymbol{x}\frac{\partial l^{T}}{\partial \boldsymbol{z_1}}dW_1)\
    &= tr((\frac{\partial l}{\partial \boldsymbol{z_1}}\boldsymbol{x}^{T})^{T}dW_1)\
    \end{aligned}$$
    因此,$\frac{\partial l}{\partial W_1}=\frac{\partial l}{\partial \boldsymbol{z_1}}\boldsymbol{x}^{T}$
  6. 求解$\frac{\partial l}{\partial \boldsymbol{b_1}}$:
    $$\begin{aligned}
    dl &= tr(\frac{\partial l^{T}}{\partial \boldsymbol{z_1}}d\boldsymbol{z_1})\
    &= tr(\frac{\partial l^{T}}{\partial \boldsymbol{z_1}}d\boldsymbol{b_1})\
    \end{aligned}$$
    因此,$\frac{\partial l}{\partial \boldsymbol{b_1}}=\frac{\partial J}{\partial \boldsymbol{z_1}}$

m个训练样本向量化实现的推导

输入$X$,$X$为矩阵,维度为$(n_1,m)$则网络的正向传递过程如下:
$$\begin{aligned}
Z_1&=W_1X+\boldsymbol{b_1}\overrightarrow{1}^{T}\
A_1&=g(Z_1)\
\boldsymbol{z_2}&=\boldsymbol{w_2}A_1+b_2\overrightarrow{1}^{T}\
\boldsymbol{a_2}&=\sigma(\boldsymbol{z_2})\
\end{aligned}$$

  1. 下面首先求解$\frac{\partial J}{\partial \boldsymbol{z_2}}$:
    与logistics regression中方法相同,可得$\frac{\partial J}{\partial \boldsymbol{z_2}}=\frac{1}{m}(\boldsymbol{a_2}-\boldsymbol{Y})$
  2. 下面求解$\frac{\partial J}{\partial \boldsymbol{w_2}}$:与logistics regression中方法相同,可得$\frac{\partial J}{\partial \boldsymbol{w_2}}=\frac{\partial J}{\partial \boldsymbol{z_2}}\boldsymbol{a_1}^{T}$
  3. 下面求解$\frac{\partial J}{\partial b_2}$:同logistics regression可得$\frac{\partial J}{\partial b_2}=\frac{\partial J}{\partial \boldsymbol{z_2}}\overrightarrow{1}$
  4. 下面求解$\frac{\partial J}{\partial Z_1}$:
    与单个训练样本方法相同,可得$\frac{\partial J}{\partial Z_1}=\boldsymbol{w_2}^{T}\frac{\partial J}{\partial \boldsymbol{z_2}}*g^{'}(\boldsymbol{z_1})$
  5. 求解$\frac{\partial J}{\partial W_1}$:
    与单个训练样本方法相同,可得$\frac{\partial J}{\partial W_1}=\frac{\partial J}{\partial Z_1}\boldsymbol{X}^{T}$
  6. 求解$\frac{\partial J}{\partial \boldsymbol{b_1}}$:
    $$\begin{aligned}
    dJ &= tr(\frac{\partial J^{T}}{\partial Z_1}d\boldsymbol{b_1}\overrightarrow{1}^{T})\
    &= tr(\overrightarrow{1}^{T}\frac{\partial J^{T}}{\partial Z_1}d\boldsymbol{b_1})\
    &= tr((\frac{\partial J}{\partial Z_1}\overrightarrow{1})^{T}d\boldsymbol{b_1})\
    \end{aligned}$$
    因此,$\frac{\partial J}{\partial \boldsymbol{b_1}}=\frac{\partial J}{\partial Z_1}\overrightarrow{1}$

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转载自www.cnblogs.com/hello-ai/p/10885202.html
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