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【Wrong Fix has been Closed】Fix: Replace the wrong symbol '+' with '=' in formulation related to chain rule #1317

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【Wrong Fix has been Closed】Fix: Replace the wrong symbol '+' with '=' in formulation related to chain rule #1317

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2 changes: 1 addition & 1 deletion chapter_recurrent-neural-networks/bptt.md
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Expand Up @@ -76,7 +76,7 @@ $h_t$既依赖于$h_{t-1}$又依赖于$w_h$，
其中$h_{t-1}$的计算也依赖于$w_h$。
因此，使用链式法则产生：

$$\frac{\partial h_t}{\partial w_h}= \frac{\partial f(x_{t},h_{t-1},w_h)}{\partial w_h} +\frac{\partial f(x_{t},h_{t-1},w_h)}{\partial h_{t-1}} \frac{\partial h_{t-1}}{\partial w_h}.$$
$$\frac{\partial h_t}{\partial w_h}= \frac{\partial f(x_{t},h_{t-1},w_h)}{\partial w_h} =\frac{\partial f(x_{t},h_{t-1},w_h)}{\partial h_{t-1}} \frac{\partial h_{t-1}}{\partial w_h}.$$
:eqlabel:`eq_bptt_partial_ht_wh_recur`

为了导出上述梯度，假设我们有三个序列$\{a_{t}\},\{b_{t}\},\{c_{t}\}$，
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