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修正了一些符号 #47

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修正了一些符号 #47

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Update 21_手动推导反向传播公式BP.md
rainylt Oct 15, 2020
95ebf12
Update 21_手动推导反向传播公式BP.md
rainylt Oct 15, 2020
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4 changes: 2 additions & 2 deletions 计算机视觉/21_手动推导反向传播公式BP.md
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Expand Up @@ -27,7 +27,7 @@ $$

首先，根据神经网络误差函数的定义式，我们可以很容易地求出输出层的delta误差 $$\delta^L$$：
$$
\delta^L = \frac{\partial C}{\partial z^L} = \frac{\partial C}{\partial a^L} \frac{\partial a^L}{\partial z^L} = (a^L-y) \odot \delta'(z^L)
\delta^L = \frac{\partial C}{\partial z^L} = \frac{\partial C}{\partial a^L} \frac{\partial a^L}{\partial z^L} = (a^L-y) \odot \sigma'(z^L)
$$
公式中的 $$\odot$$ 表示 Hardmard 积，即对应逐元素相乘。注意输出层的 delta 误差 $$\delta^L$$ 与损失函数的定义相关，不同的损失函数得到不同的计算结果，在本文中损失函数以均方误差为例讲解。

Expand All @@ -44,7 +44,7 @@ $$
$$
又：
$$
z^{l+1} = W^{l+!}a^l+b^{l+!} = W^{l+!}\sigma(z^l)+b^{l+!}
z^{l+1} = W^{l+1}a^l+b^{l+1} = W^{l+1}\sigma(z^l)+b^{l+1}
$$
因此：
$$
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