**Neural Network Tutorials - Herong's Tutorial Examples** - 1.20, by Dr. Herong Yang

What Is GRU (Gated Recurrent Unit)

This section provides a quick introduction of GRU (Gated Recurrent Unit), which is a simplified version of the LSTM (Long Short-Term Memory) recurrent neural network model. GRU uses only one state vector and two gate vectors, reset gate and update gate.

**What Is GRU (Gated Recurrent Unit)?**
GRU is a simplified version of the LSTM (Long Short-Term Memory) recurrent neural network model.
GRU uses only one state vector and two gate vectors, reset gate and update gate,
as described in this tutorial.

1. If we follow the same presentation style as the lSTM model used in the previous tutorial, we can present the GRU model as information flow diagram as shown below (on the right).

2. Similar to the LSTM model, the reset gate vector and the update gate vector are calculated as below.

Reset gate vector: r = sigmoid(Wgr_{t}· x_{t}+ Ugr_{t}· s_{t-1}) Update gate vector: u = sigmoid(Wgu_{t}· x_{t}+ Ugu_{t}· s_{t-1})

3. Note that reset gate vector, r, creates only one gate function
to control the flow of s_{t-1} into the input recursive function Ri().
But the update gate vector, u, creates a pair of gate functions
to control the flow of s_{t-1} and Ri() into final outputs,
s_{t} and y_{t}.

4. The RGU model can also be expressed in matrix format as shown below.

5. If you are reading other GRU documentations, you may see different presentations of the GRU model. Some examples are listed below.

Table of Contents

Deep Playground for Classical Neural Networks

Building Neural Networks with Python

Simple Example of Neural Networks

TensorFlow - Machine Learning Platform

PyTorch - Machine Learning Platform

CNN (Convolutional Neural Network)

►RNN (Recurrent Neural Network)

What Is RNN (Recurrent Neural Network)

What Is LSTM (Long Short-Term Memory)