神经网络后面的层被freeze住，会影响前面的层的梯度吗？
答案是不会。
假设一个最简单的神经网络，它只有一个输入 x x x，一个隐藏层神经元 h h h，和一个输出层神经元 y y y，均方差损失 L L L，真实标签 t t t：
h = w 1 ⋅ x y = w 2 ⋅ h L = 1 2 ( y − t ) 2 \begin{gathered} h = w_1 \cdot x \\ y = w_2 \cdot h \\ L=\frac{1}{2}(y-t)^2 \end{gathered} h=w1⋅xy=w2⋅hL=21(y−t)2
以下分 w 2 w_2 w2是否被freeze住，即 w 2 w_2 w2.requires_grad是否为True来讨论。
情况1： w 2 w_2 w2.requires_grad为True
这种情况下， L L L对 w 1 w_1 w1的梯度为：
∂ L ∂ w 1 = ∂ L ∂ y ⋅ ∂ y ∂ h ⋅ ∂ h ∂ w 1 \frac{\partial L}{\partial w 1}=\frac{\partial L}{\partial y} \cdot \frac{\partial y}{\partial h} \cdot \frac{\partial h}{\partial w 1} ∂w1∂L=∂y∂L⋅∂h∂y⋅∂w1∂h
∂ L ∂ y = ∂ ∂ y ( 1 2 ( y − t ) 2 ) = y − t \frac{\partial L}{\partial y}=\frac{\partial}{\partial y}\left(\frac{1}{2}(y-t)^2\right)=y-t ∂y∂L=∂y∂(21(y−t)2)=y−t
∂ y ∂ h = ∂ ∂ h ( w 2 ⋅ h ) = w 2 \frac{\partial y}{\partial h}=\frac{\partial}{\partial h}\left(w_2 \cdot h\right)=w_2 ∂h∂y=∂h∂(w2⋅h)=w2
∂ h ∂ w 1 = ∂ ∂ w 1 ( w 1 ⋅ x ) = x \frac{\partial h}{\partial w_1}=\frac{\partial}{\partial w_1}\left(w_1 \cdot x\right)=x ∂w1∂h=∂w1∂(w1⋅x)=x
因此：
∂ L ∂ w 1 = ∂ L ∂ y ⋅ ∂ y ∂ h ⋅ ∂ h ∂ w 1 = ( y − t ) ⋅ w 2 ⋅ x \frac{\partial L}{\partial w 1}=\frac{\partial L}{\partial y} \cdot \frac{\partial y}{\partial h} \cdot \frac{\partial h}{\partial w 1} = (y-t) \cdot w_2 \cdot x ∂w1∂L=∂y∂L⋅∂h∂y⋅∂w1∂h=(y−t)⋅w2⋅x
情况2： w 2 w_2 w2.requires_grad为False
这种情况下， w 2 w_2 w2被视为一个常数，此时 L L L对 w 1 w_1 w1的梯度仍然为：
∂ L ∂ w 1 = ∂ L ∂ y ⋅ ∂ y ∂ h ⋅ ∂ h ∂ w 1 = ( y − t ) ⋅ w 2 ⋅ x \frac{\partial L}{\partial w 1}=\frac{\partial L}{\partial y} \cdot \frac{\partial y}{\partial h} \cdot \frac{\partial h}{\partial w 1} = (y-t) \cdot w_2 \cdot x ∂w1∂L=∂y∂L⋅∂h∂y⋅∂w1∂h=(y−t)⋅w2⋅x
因为无论 w 2 w_2 w2是否被freeze住， ∂ y ∂ h = ∂ ∂ h ( w 2 ⋅ h ) = w 2 \frac{\partial y}{\partial h}=\frac{\partial}{\partial h}\left(w_2 \cdot h\right)=w_2 ∂h∂y=∂h∂(w2⋅h)=w2这一点是不会变的。
在计算 w 1 w_1 w1的梯度时，我们并不需要 w 2 w_2 w2的梯度，而是只需要 w 2 w_2 w2这个参数值。