反向传播

符号设定¶

\(t\): 真实标签
\(o_q\): 神经元\(q\)的输出, 即激活函数激活后的输出
\(w_{pq}\): 从神经元\(p\)到神经元\(q\)的权重
\(\eta\): 学习率
\(z_q\): 神经元\(q\)的加权输入, 即激活函数激活前的输出
\(f(z_q)\): 激活函数

前向传播¶

对于每一个神经元\(q\), 有加权输入\(z_q = \sum_p w_{pq}o_p\). 经过激活函数激活\(o_q=f(z_q)=\frac{1}{1+e^{-z_q}}\). 这样一直传播到最后的输出神经元\(v\), 选择误差函数为MSE, 计算误差为\(L=\frac{1}{2}(t_v-o_v)^2\).

反向传播¶

目标是通过调整权重来最小化损失函数\(L\), 核心思想是对损失函数\(L\)相对于权重\(w_{pq}\)的偏导数进行计算, 并根据该梯度更新权重.

输出层梯度计算¶

对于输出层的神经元\(v\), 和前一层的某一个神经元\(u_t\), 我们需要计算损失函数\(L\)对于\(w_{u_t v}\)的导数, 有\(\frac{\partial L}{\partial w_{u_t v}}=\frac{\partial L}{\partial o_v}\cdot \frac{\partial o_v}{\partial z_v}\cdot \frac{\partial z_v}{\partial w_{u_tv}}\). 其中, \(\frac{\partial L}{\partial o_v}=-(t_v-o_v)\); \(\frac{\partial o_v}{\partial z_v}=o_v(1-o_v)\); \(\frac{\partial z_v}{\partial w_{u_t v}}=\frac{\partial (w_{u_1v}o_{u_1}+...+w_{u_t v}o_{u_t}+...w_{u_n v}o_{u_n}+b_v)}{\partial w_{u_t}}=o_{u_t}\). 将这三个部分结合起来, 我们得到该权重的导数为: \(\frac{\partial L}{\partial w_{u_t v}}=-(t_v-o_v)\cdot o_v(1-o_v)\cdot o_{u_t}\). 我们将\(\delta_v\)定义为\(\delta_v = \frac{\partial L}{\partial o_v}\cdot \frac{\partial o_v}{\partial z_v}=\frac{\partial L}{\partial z_v}=(t_v-o_v)\cdot o_v(1-o_v)\), 因此, 该导数可以写为\(\frac{\partial L}{\partial w_{u_t v}}=-\delta_v o_{u_t}\). 而梯度就是\(L\)对所有的权重求导得到的一个向量: \(\nabla L=(\frac{\partial L}{\partial w_{u_1}v},..., \frac{\partial L}{\partial w_{u_t}v},..., \frac{\partial L}{\partial w_{u_n}v})\)

隐藏层梯度计算¶

对于隐藏层的某一个神经元\(q\), 和前一层的某一个神经元\(p_t\), 我们需要计算损失函数\(L\)对于权重\(w_{p_tq}\)的导数., 有\(\frac{\partial L}{\partial w_{p_tq}}=\frac{\partial L}{\partial z_q}\cdot \frac{\partial z_q}{\partial w_{p_tq}}\). 其中, \(\frac{\partial z_q}{\partial w_{p_tq}}=\frac{\partial (w_{p_1q} o_{p_1}+...+w_{p_tq} o_{p_t}+...+w_{p_mq} o_{p_m}+b_q)}{\partial w_{p_t q}}=o_{p_t}\). 现在, 我们需要计算损失\(L\)对于\(z_q\)的偏导数\(\frac{\partial L}{\partial z_q}\), 注意, 隐藏层的神经元\(q\)不能直接影响损失\(L\), 但是能通过后续的层传递给\(L\). 根据链式法则, 我们有\(\frac{\partial L}{\partial z_q}=\sum_k \frac{\partial L}{\partial o_k}\cdot \frac{\partial o_k}{\partial z_q}\), 这里的\(k\)表示\(q\)的后续一层的神经元\(k\). 我们首先来计算\(\frac{\partial o_k}{\partial z_q}\), \(\frac{\partial o_k}{\partial z_q} = \frac{\partial o_k}{\partial z_k}\cdot \frac{\partial z_k}{\partial z_q}\), 其中, \(o_k=f(z_k)\), 有\(\frac{\partial o_k}{\partial z_k}=f'(z_k)\); 而\(\frac{\partial z_k}{\partial z_q}=\frac{\partial z_k}{\partial o_q}\cdot \frac{\partial o_q}{\partial z_q}=\frac{\partial (w_{q_1k}o_{q_1}+...+w_{qk}o_{q}+...+w_{q_rk}o_{q_r})}{\partial o_q}=w_{qk}\cdot f'(z_q)\), 所以\(\frac{\partial o_k}{\partial z_q}=f'(z_k)\cdot w_{qk}\cdot f'(z_q)\). 从而有\(\frac{\partial L}{\partial z_q}=\sum_k \frac{\partial L}{\partial o_k}\cdot f'(z_k)\cdot w_{qk}\cdot f'(z_q)\). 我们定义\(\delta_q = \frac{\partial L}{\partial z_q}=\sum_k \frac{\partial L}{\partial o_k}\cdot f'(z_k)\cdot w_{qk}\cdot f'(z_q)\). 而\(\frac{\partial L}{\partial o_k}\cdot f'(z_k)=\delta_k\), 所以有\(\delta_q=\sum_k \delta_k w_{qk}\cdot f'(z_q)\), 而\(f'(z_q)=o_q(1-o_q)\), 所以最终有\(\delta_q=o_q(1-o_q)\sum_k \delta_kw_{qk}\). 而梯度就是\(L\)对所有的权重求导得到的一个向量\(\nabla L = (\frac{\partial L}{\partial w_{p_1 q}},..., \frac{\partial L}{\partial w_{p_t q}},..., \frac{\partial L}{\partial w_{p_m q}})\).