What is Gated recurrent unit and its working

Introduction

In this blog, we would discuss the Gated recurrent unit and its work. A Gated recurrent unit (GRU) is a type of recurrent neural network (RNN) that can learn long-term dependencies. It is similar to a long short-term memory (LSTM) network but uses a simpler architecture and fewer parameters. A GRU is composed of two parts: a reset gate and an update gate. The reset gate determines how much of the past information to forget, while the update gate determines how much of the past information to keep and how much of the present information to add.

 

 

The GRU can be trained using backpropagation through time (BPTT) or stochastic gradient descent (SGD). SGD is generally faster and requires less memory, but BPTT can be more accurate. The GRU has been successful in a variety of tasks, including language modeling, machine translation, and image captioning.

 

 

 

What is a Gated recurrent unit?

A gated recurrent unit (GRU) is a type of recurrent neural network (RNN) that has gating mechanisms to control the flow of information. The gating mechanisms allow the model to learn when to update the hidden state and when to forget about the previous hidden state. The GRU was first proposed in 2014 by Kyunghyun Cho et al. and has since been widely used in various applications such as machine translation, natural language processing, and time series prediction.

 

 

Gated recurrent units are a type of recurrent neural network that can learn long-term dependencies. They are similar to long short-term memory networks, but they are more efficient to train. GRUs have two gates, a reset gate and an update gate. The reset gate determines how much of the past information to forget and the update gate determines how much of the past information to keep. The gates are trained using a sigmoid function. The output of the gates are multiplied with the input vector. The output of the GRU is the sum of the outputs of the gates. GRUs are typically used for tasks such as machine translation and speech recognition.

 

 

 

Working of Gated recurrent unit

 

• Consider the vectors representing the current input and the prior hidden state.

 

• Perform element-wise multiplication (Hadamard Product) between the relevant vector and the corresponding weights for each gate to determine the parameterized current input and previously hidden state vectors for each gate.

 

• Apply the appropriate activation function to the parameterized vectors for each gate element. The list of gates with the activation function to be used for the gate is provided below.

 

• The method for determining the Current Memory Gate differs a little. First, the previously hidden state vector and the reset gate’s Hadamard product are determined. This vector is then added to the parameterized current input vector after being parameterized.

 

 

 

 

 

• To begin, a vector of ones with the same dimensions as the input is defined in order to calculate the current hidden state. This vector will be known as one and will have the mathematical symbol one. Do the update gate and the previously hidden state vector’s Hadamard Product first. Then, after subtracting the update gate from ones to create a new vector, compute the Hadamard Product of the fresh vector and the active memory gate. The currently hidden state vector is obtained by adding the two vectors.

 

 

 

Advantages

• The main advantage of the GRU over other RNNs is that it can learn long-term dependencies without the need for complex architecture. This is due to the fact that the gating mechanisms allow the model to selectively update the hidden state based on the current input.

 

• Another advantage of the GRU is that it is computationally efficient. This is because the gating mechanisms allow the model to update the hidden state without having to perform a full forward pass through the network.

 

• The ability to better retain long-term information

 

• The ability to forget irrelevant information 

 

• The ability to model sequences of data 

 

 

 

Disadvantages

• The main disadvantage of the GRU is that it is harder to train than other RNNs. This is due to the fact that the gating mechanisms can introduce a lot of noise into the training process. 

 

• The need for more training data 

 

• The need for more computational resources

 

 

Also, read what is Naive Bayes and its works.