Comparison and Architecture of LSTM, GRU and RNN. What Are the problems with RNN to process long sequences

Comparison and Architecture of LSTM, GRU, and RNN:

Recurrent neural networks (RNNs) are a type of neural network that are well-suited for processing sequential data, such as text, audio, and video. RNNs work by maintaining a hidden state that is updated as each element in the sequence is processed. This hidden state allows the RNN to remember information about previous elements in the sequence, which can be helpful for tasks such as language modeling, machine translation, and speech recognition.

There are three main types of RNNs:

• LSTM (Long Short-Term Memory): LSTMs are a type of RNN that are designed to address the vanishing gradient problem. LSTMs use a gating mechanism to control the flow of information through the network, which allows them to learn long-range dependencies.
• GRU (Gated Recurrent Unit): GRUs are a type of RNN that are similar to LSTMs, but they have a simpler gating mechanism. GRUs are often faster to train than LSTMs, but they may not be as effective for learning long-range dependencies.
• Vanilla RNNs: Vanilla RNNs are the simplest type of RNN. They do not have a gating mechanism, which makes them susceptible to the vanishing gradient problem.

Comparison of LSTM, GRU, and RNN

The following table compares the features of LSTM, GRU, and vanilla RNNs:

Architecture of LSTM

The architecture of an LSTM cell is shown below.

The LSTM cell has three gates: the input gate, the forget gate, and the output gate. The input gate controls how much of the current input is added to the cell state. The forget gate controls how much of the previous cell state is forgotten. The output gate controls how much of the cell state is outputted.

Architecture of GRU

The architecture of a GRU cell is shown below.

The GRU cell has two gates: the update gate and the reset gate. The update gate controls how much of the previous cell state is forgotten. The reset gate controls how much of the current input is added to the cell state.

Architecture of Vanilla RNN

The architecture of a vanilla RNN cell is shown below.

The vanilla RNN cell does not have any gates. The hidden state is simply updated by adding the current input to the previous hidden state.

Problems with RNN to process long sequences

RNNs can be effective for processing short sequences. However, they can have difficulty processing long sequences due to the vanishing gradient problem. The vanishing gradient problem occurs when the gradients of the weights in the RNN become very small as the length of the sequence increases. This can make it difficult for the network to learn long-range dependencies.

LSTM and GRU are able to address the vanishing gradient problem by using gating mechanisms to control the flow of information through the network. This allows them to learn long-range dependencies more effectively than vanilla RNNs.

Conclusion

LSTM, GRU, and vanilla RNNs are all types of RNNs that can be used for processing sequential data. LSTM and GRU are able to address the vanishing gradient problem more effectively than vanilla RNNs, making them a better choice for processing long sequences.