Explore the concepts, differences, advantages and disadvantages of RNN, LSTM and GRU

In time series data, there are dependencies between observations, so they are not independent of each other. However, traditional neural networks treat each observation as independent, which limits the model's ability to model time series data. To solve this problem, Recurrent Neural Network (RNN) was introduced, which introduced the concept of memory to capture the dynamic characteristics of time series data by establishing dependencies between data points in the network. Through recurrent connections, RNN can pass previous information into the current observation to better predict future values. This makes RNN a powerful tool for tasks involving time series data.

But how does RNN achieve this kind of memory?

RNN realizes memory through the feedback loop in the neural network. This is the main difference between RNN and traditional neural networks. Feedback loops allow information to be passed within layers, whereas feedforward neural networks have information passed only between layers. Therefore, there are different types of RNNs:

• Recurrent Neural Network (RNN)
• Long Short-Term Memory Network (LSTM)
• Gate Controlled Recurrent Unit Network (GRU)

This article will introduce the concepts, similarities and differences of RNN, LSTM and GRU, as well as some of their advantages and disadvantages.

Recurrent Neural Network (RNN)

Through a feedback loop, the output of an RNN unit is also used as input by the same unit. Therefore, every RNN has two inputs: past and present. Using past information creates short-term memory.

For a better understanding, the feedback loop of the RNN unit can be expanded. The length of the expanded cell is equal to the number of time steps of the input sequence.

You can see how past observations are passed through the unfolded network as hidden states. In each cell, the input from the current time step, the hidden state from the previous time step, and the bias are combined and then constrained through an activation function to determine the hidden state at the current time step.

RNN can be used for one-to-one, one-to-many, many-to-one and many-to-many predictions.

Advantages of RNN

Due to its short-term memory, RNN can process sequential data and identify patterns in historical data. In addition, RNN is able to handle inputs of different lengths.

Disadvantages of RNN

RNN has the problem of vanishing gradient descent. In this case, the gradient used to update the weights during backpropagation becomes very small. Multiplying weights with gradients close to zero prevents the network from learning new weights. Stopping learning causes the RNN to forget what it has seen in longer sequences. The problem of vanishing gradient descent increases with the number of network layers.

Because RNN only retains recent information, the model has problems when considering past observations. Therefore, RNN only has short-term memory but no long-term memory.

In addition, since RNN uses backpropagation to update weights in time, the network will also suffer from gradient explosion and, if the ReLu activation function is used, it will be affected by dead ReLu units. The former may cause convergence problems, while the latter may cause learning to cease.

Long Short-Term Memory (LSTM)

LSTM is a special type of RNN that solves the problem of gradient disappearance in RNN.

The key to LSTM is the cell state, which is passed from the input to the output of the cell. The cell state allows information to flow along the entire chain with only smaller linear actions through three gates. Therefore, the cell state represents the long-term memory of the LSTM. These three gates are called forget gate, input gate and output gate respectively. These gates act as filters and control the flow of information and determine which information is kept or ignored.

The forgetting gate determines how much long-term memory should be retained. For this purpose, a sigmoid function is used to account for the importance of the cell state. The output varies between 0 and 1, with 0 retaining no information and 1 retaining all information about the cell state.

The input gate determines what information is added to the cell state and thus to long-term memory.

The output gate determines which parts of the cell state build the output. Therefore, the output gate is responsible for short-term memory.

In general, the state is updated through the forget gate and the input gate.

Advantages of LSTM

The advantages of LSTM are similar to RNN, the main advantage is that they can capture both long-term and short-term patterns of sequences. Therefore, they are the most commonly used RNNs.

Disadvantages of LSTM

Due to the more complex structure, the computational cost of LSTM is higher, resulting in longer training time.

Since LSTM also uses the temporal backpropagation algorithm to update weights, LSTM has the disadvantages of backpropagation, such as dead ReLu units, gradient explosion, etc.

Gated Recurrent Unit (GRU)

Similar to LSTM, GRU solves the vanishing gradient problem of simple RNN. However, the difference from LSTM is that GRU uses fewer gates and does not have a separate internal memory, i.e., the cell state. Therefore, GRU relies entirely on hidden states as memory, leading to a simpler architecture.

The reset gate is responsible for short-term memory as it determines how much past information to retain and ignore.

The update gate is responsible for long-term memory and is comparable to the forget gate of LSTM.

The hidden state of the current time step is determined based on two steps:

First, determine the candidate hidden state. The candidate state is a combination of the current input and the hidden state of the previous time step and the activation function. The influence of the previous hidden state on the candidate hidden state is controlled by the reset gate.

The second step is to combine the candidate hidden state with the hidden state of the previous time step to generate the current hidden state. How the previous hidden state and the candidate hidden state are combined is determined by the update gate.

If the value given by the update gate is 0, the previous hidden state is completely ignored and the current hidden state is equal to the candidate hidden state. If the update gate gives a value of 1, the opposite is true.

Advantages of GRU

Due to its simpler architecture compared to LSTM, GRU has higher computational efficiency and faster training speed. Just requires less memory.

Additionally, GRU has been shown to be more effective for smaller sequences.

Disadvantages of GRU

Since GRUs do not have separate hidden states and cell states, they may not take into account past observations like LSTM .

Similar to RNN and LSTM, GRU may also suffer from the shortcomings of backpropagation and timely update of weights, namely dead ReLu units and gradient explosion.

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