Why is LSTM structure derivation better than RNN?

1. RNN

Recurrent Neural Network (RNN) is a neural network model for processing sequence data. Different from traditional neural networks, RNN has memory ability when processing sequence data, and can capture the temporal dependence in the sequence. Each time step of RNN will receive the current input and the hidden state of the previous time step, and then output the hidden state and prediction result of the current time step. This structure makes RNN perform well in processing time series data, natural language processing and other tasks.

However, traditional RNNs also suffer from problems such as vanishing gradients and exploding gradients, which limit their ability to deal with long sequence data. In order to solve these problems, some improved RNN structures have emerged, such as long short-term memory network (LSTM) and gated recurrent unit (GRU). These structures introduce gating mechanisms that can better capture long-term dependencies and thus perform better when dealing with long sequence data.

The structure and working principle of RNN are as follows:

1. Cell State: In RNN, each time step maintains a hidden state (Hidden State) and a cell state (Cell State). The hidden state is the carrier for transmitting information in the time series, while the unit state is similar to the memory unit, which is responsible for recording the information of the previous time step.

2. Loop structure: The key to RNN is its loop structure. At each time step, the RNN calculates the hidden state and unit state of the current time step based on the current input and the hidden state of the previous time step. This recurrent mechanism allows RNNs to preserve historical information when processing sequence data.

3. Gating mechanism (LSTM and GRU): Traditional RNN is prone to gradient disappearance or gradient explosion when processing long sequences, which limits its learning ability. To solve this problem, LSTM and GRU introduce a gating mechanism, which can selectively update and forget information, so as to better capture long-term dependencies.

4. Output prediction: At each time step, RNN can input the hidden state as a feature to subsequent neural network layers for various tasks such as classification, prediction, etc.

Applications of RNNs include:

• Language Modeling: Predicting the next word or character, for natural language processing tasks.
• Machine Translation: Translating one language into another.
• Speech Recognition: Converting sound signals into text.
• Time series forecasting: predict future time series data, such as stock prices, temperature, etc.
• Natural Language Generation: Generate natural language text, such as chatbots.

In short, RNN is a neural network model suitable for processing sequence data. It has a cyclic structure and memory ability, and is suitable for a variety of sequence data processing tasks.

2. LSTM

Long Short-Term Memory (LSTM) is a special kind of recurrent neural network (RNN), which is specially designed to solve the problems of gradient disappearance and long-term dependence that occur when traditional RNN processes long sequence data. LSTM introduces a gating mechanism, which can better capture the long-term dependencies in the sequence, making it perform well in processing time series data, natural language processing and other tasks.

The main feature of LSTM is the introduction of three gating units: Forget Gate, Input Gate and Output Gate, and a Cell State. These gating units allow LSTM to selectively forget, update, and output information, thus efficiently processing long sequence data.

The structure of LSTM is as follows:

1. Forget Gate: The Forget Gate determines what information can be forgotten from the cell state. It outputs a value between 0 and 1 based on the hidden state of the previous time step and the current input to decide whether the information in the cell state is retained or forgotten.

2. Input Gate: The input gate determines which new information can be added to the cell state. It uses the hidden state from the previous time step and the current input to calculate a new candidate value, and then decides how much to update the cell state based on a value between 0 and 1.

3. Cell State: The cell state is used to store long-term memory information. Through the operation of the forget gate and the input gate, the cell state can be updated as needed.

4. Output Gate: The output gate determines the hidden state of the output, and the part of the cell state to be output. It outputs a value between 0 and 1, depending on the current hidden state and input, to control the amount of cell state output.

The gating mechanism of LSTM enables it to selectively remember and forget information, so as to better capture long-term dependencies in sequences. This makes LSTMs great at dealing with time series data, natural language processing tasks, and more. In practical applications, LSTM is often used in combination with other neural network layers to build more complex models, such as text generation, sentiment analysis, machine translation and other tasks.

3. Why is LSTM structure derivation better than RNN?

Compared with the traditional RNN (circular neural network), LSTM (long short-term memory network) performs better in processing long sequence data, mainly because LSTM introduces a gating mechanism, which can more effectively capture long-term dependencies in the sequence and avoid The influence of gradient disappearance and gradient explosion problems in traditional RNN is eliminated. The following is a brief derivation and explanation of the advantages of LSTM over RNN:

1. Gating mechanism: LSTM introduces forgetting gate, input gate and output gate. These gating mechanisms enable LSTM to selectively forget, update and output information. The forget gate and input gate allow the network to selectively retain and update the cell state, effectively solving the gradient disappearance problem in traditional RNN, so that it can better handle long sequence data.

2. Long-term dependence: When traditional RNN processes long sequences, it is difficult to capture long-term dependencies due to the influence of gradient disappearance. The gating mechanism of LSTM can preserve long-term memory, allowing information to be passed persistently in the cell state, so as to better capture long-term dependencies in sequences.

3. Prevent gradient explosion: The gating mechanism in LSTM can also help the network prevent the gradient explosion problem, because the gating mechanism can limit the propagation range of the gradient, thereby stabilizing the network training process.

4. Locality: The gating mechanism of LSTM allows it to selectively focus on important parts in the sequence, so as to better deal with local patterns in the sequence.

In short, the advantage of LSTM compared with traditional RNN in processing long sequence data lies in the gating mechanism it introduces. These mechanisms enable the network to better capture long-term dependencies and avoid the problems of gradient disappearance and gradient explosion. It performs better on time series data, natural language processing tasks, etc. Especially in long sequence data and tasks that need to capture complex time dependencies, LSTM usually performs better than traditional RNN.