Fix typo of LSTM formula

06.understand_sentiment/README.cn.md

@@ -59,10 +59,10 @@ $$ h_t=F(x_t,h_{t-1})$$
 
 $F$由下列公式组合而成\[[7](#参考文献)\]：
 \begin{align}
-i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
-f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
-c_t & = f_t\odot c_{t-1}+i_t\odot tanh(W_{xc}x_t+W_{hc}h_{h-1}+b_c)\\\\
-o_t & = \sigma(W_{xo}x_t+W_{ho}h_{h-1}+W_{co}c_{t}+b_o)\\\\
+i_t & = \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_i)\\\\
+f_t & = \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_f)\\\\
+c_t & = f_t\odot c_{t-1}+i_t\odot tanh(W_{xc}x_t+W_{hc}h_{t-1}+b_c)\\\\
+o_t & = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}c_{t}+b_o)\\\\
 h_t & = o_t\odot tanh(c_t)\\\\
 \end{align}
 其中，$i_t, f_t, c_t, o_t$分别表示输入门，遗忘门，记忆单元及输出门的向量值，带角标的$W$及$b$为模型参数，$tanh$为双曲正切函数，$\odot$表示逐元素（elementwise）的乘法操作。输入门控制着新输入进入记忆单元$c$的强度，遗忘门控制着记忆单元维持上一时刻值的强度，输出门控制着输出记忆单元的强度。三种门的计算方式类似，但有着完全不同的参数，它们各自以不同的方式控制着记忆单元$c$，如图2所示：

06.understand_sentiment/README.md

@@ -61,10 +61,10 @@ $$ h_t=F(x_t,h_{t-1})$$
 
 $F$ contains following formulations\[[7](#references)\]：
 \begin{align}
-i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
-f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
-c_t & = f_t\odot c_{t-1}+i_t\odot \tanh(W_{xc}x_t+W_{hc}h_{h-1}+b_c)\\\\
-o_t & = \sigma(W_{xo}x_t+W_{ho}h_{h-1}+W_{co}c_{t}+b_o)\\\\
+i_t & = \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_i)\\\\
+f_t & = \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_f)\\\\
+c_t & = f_t\odot c_{t-1}+i_t\odot \tanh(W_{xc}x_t+W_{hc}h_{t-1}+b_c)\\\\
+o_t & = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}c_{t}+b_o)\\\\
 h_t & = o_t\odot \tanh(c_t)\\\\
 \end{align}
 

06.understand_sentiment/index.cn.html

@@ -101,10 +101,10 @@ $$ h_t=F(x_t,h_{t-1})$$
 
 $F$由下列公式组合而成\[[7](#参考文献)\]：
 \begin{align}
-i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
-f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
-c_t & = f_t\odot c_{t-1}+i_t\odot tanh(W_{xc}x_t+W_{hc}h_{h-1}+b_c)\\\\
-o_t & = \sigma(W_{xo}x_t+W_{ho}h_{h-1}+W_{co}c_{t}+b_o)\\\\
+i_t & = \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_i)\\\\
+f_t & = \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_f)\\\\
+c_t & = f_t\odot c_{t-1}+i_t\odot tanh(W_{xc}x_t+W_{hc}h_{t-1}+b_c)\\\\
+o_t & = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}c_{t}+b_o)\\\\
 h_t & = o_t\odot tanh(c_t)\\\\
 \end{align}
 其中，$i_t, f_t, c_t, o_t$分别表示输入门，遗忘门，记忆单元及输出门的向量值，带角标的$W$及$b$为模型参数，$tanh$为双曲正切函数，$\odot$表示逐元素（elementwise）的乘法操作。输入门控制着新输入进入记忆单元$c$的强度，遗忘门控制着记忆单元维持上一时刻值的强度，输出门控制着输出记忆单元的强度。三种门的计算方式类似，但有着完全不同的参数，它们各自以不同的方式控制着记忆单元$c$，如图2所示：

06.understand_sentiment/index.html

@@ -103,10 +103,10 @@ $$ h_t=F(x_t,h_{t-1})$$
 
 $F$ contains following formulations\[[7](#references)\]：
 \begin{align}
-i_t & = \sigma(W_{xi}x_t+W_{hi}h_{h-1}+W_{ci}c_{t-1}+b_i)\\\\
-f_t & = \sigma(W_{xf}x_t+W_{hf}h_{h-1}+W_{cf}c_{t-1}+b_f)\\\\
-c_t & = f_t\odot c_{t-1}+i_t\odot \tanh(W_{xc}x_t+W_{hc}h_{h-1}+b_c)\\\\
-o_t & = \sigma(W_{xo}x_t+W_{ho}h_{h-1}+W_{co}c_{t}+b_o)\\\\
+i_t & = \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_i)\\\\
+f_t & = \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_f)\\\\
+c_t & = f_t\odot c_{t-1}+i_t\odot \tanh(W_{xc}x_t+W_{hc}h_{t-1}+b_c)\\\\
+o_t & = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}c_{t}+b_o)\\\\
 h_t & = o_t\odot \tanh(c_t)\\\\
 \end{align}