# Gated [Recurrent](Recurrent.md) Unit (GRU) -  - Simplified [LSTM)](Long Short Term Memory (LSTM|Long Short Term Memory (LSTM|[LSTM)](LSTM)](LSTM)](Long Short Term Memory (LSTM|Long Short Term Memory (LSTM|[LSTM)](LSTM).md).md) - It has an input and forget gate, no output gate - Faster than LSTM in training, but does not perform well in many tasks - Tries to forget what is not important ## The Math - Two gates, [Sigmoid](Sigmoid.md) - Reset : $g_r = \sigma(W_{hr}h_{t-1} + W_{xr}x_t + b_r)$ - Update : $g_u = \sigma(W_{hu}h_{t-1} + W_{xu}x_t + b_u)$ - Hidden state proposal - $\hat h_t = tanh(W_{xh}x_t + W_{hh}g_r\cdot h_{t-1} + b_h)$ - Final hidden state - Linear [Interpolation](Interpolation.md) between last hidden state and proposal - $h_t = (1-g_u)\cdot h_{t-1} + g_u \cdot \hat h_t$