RNN

pchavanne · Jan 28, 2017 · cb31894 · cb31894
1 parent 0760f89
commit cb31894
Showing 1 changed file with 15 additions and 15 deletions.
diff --git a/yadll/layers.py b/yadll/layers.py
@@ -606,7 +606,7 @@ class BatchNormalization(Layer):
     Normalize the input layer over each mini-batch according to [1]_:
 
     .. math::
-        \hat{x} = \frac{x - E[x]}{\sqrt(Var[x] + \epsilon)}
+        \hat{x} = \frac{x - E[x]}{\sqrt{Var[x] + \epsilon}}
 
         y = \gamma * \hat{x} + \beta
 
@@ -696,27 +696,27 @@ class LSTM(Layer):
     Long Short Term Memory
 
     .. math ::
-        i_t &= \sigma(x_t.W_i + h_{t-1}.U_i + b_i) & \texte{Input gate}\\
-        f_t &= \sigma(x_t.W_f + h_{t-1}.U_f + b_f) & \texte{Forget gate}\\
-        \tilde{C_t} &= \tanh(x_t.W_c + h_{t-1}.U_c + b_c) & \texte{Cell gate}\\
-        C_t &= f_t * C_{t-1} + i_t * \tilde{C_t} & \texte{Cell state}\\
-        o_t &= \sigma(x_t.W_o + h_{t-1}.U_o + b_o) & \texte{Output gate}\\
-        h_t &= o_t * \tanh(C_t) & \texte{Hidden state}\\
+        i_t &= \sigma(x_t.W_i + h_{t-1}.U_i + b_i) & \text{Input gate}\\
+        f_t &= \sigma(x_t.W_f + h_{t-1}.U_f + b_f) & \text{Forget gate}\\
+        \tilde{C_t} &= \tanh(x_t.W_c + h_{t-1}.U_c + b_c) & \text{Cell gate}\\
+        C_t &= f_t * C_{t-1} + i_t * \tilde{C_t} & \text{Cell state}\\
+        o_t &= \sigma(x_t.W_o + h_{t-1}.U_o + b_o) & \text{Output gate}\\
+        h_t &= o_t * \tanh(C_t) & \text{Hidden state}\\
 
     with Peephole connections:
 
     .. math ::
-        i_t &= \sigma(x_t.W_i + h_{t-1}.U_i + C_{t-1}.P_i + b_i) & \texte{Input gate}\\
-        f_t &= \sigma(x_t.W_f + h_{t-1}.U_f + C_{t-1}.P_f + b_f) & \texte{Forget gate}\\
-        \tilde{C_t} &= \tanh(x_t.W_c + h_{t-1}.U_c + b_c) & \texte{Cell gate}\\
-        C_t &= f_t * C_{t-1} + i_t * \tilde{C_t} & \texte{Cell state}\\
-        o_t &= \sigma(x_t.W_o + h_{t-1}.U_o + C_t.P_o + b_o) & \texte{Output gate}\\
-        h_t &= o_t * \tanh(C_t) & \texte{Hidden state}\\
+        i_t &= \sigma(x_t.W_i + h_{t-1}.U_i + C_{t-1}.P_i + b_i) & \text{Input gate}\\
+        f_t &= \sigma(x_t.W_f + h_{t-1}.U_f + C_{t-1}.P_f + b_f) & \text{Forget gate}\\
+        \tilde{C_t} &= \tanh(x_t.W_c + h_{t-1}.U_c + b_c) & \text{Cell gate}\\
+        C_t &= f_t * C_{t-1} + i_t * \tilde{C_t} & \text{Cell state}\\
+        o_t &= \sigma(x_t.W_o + h_{t-1}.U_o + C_t.P_o + b_o) & \text{Output gate}\\
+        h_t &= o_t * \tanh(C_t) & \text{Hidden state}\\
 
     with tied forget and input gates:
 
     .. math ::
-        C_t &= f_t * C_{t-1} + (1 - f_t) * \tilde{C_t} & \texte{Cell state}\\
+        C_t &= f_t * C_{t-1} + (1 - f_t) * \tilde{C_t} & \text{Cell state}\\
 
     Parameters
     ----------
@@ -734,7 +734,7 @@ class LSTM(Layer):
     activation : `yadll.activations` function default is `yadll.activations.tanh`
         activation function
     last_only: boolean default is True
-        set to true if you only need the last element of the
+        set to true if you only need the last element of the output sequence
 
     References
     ----------