LSTM cell and their functions?

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is designed to address the vanishing gradient problem and effectively capture long-term dependencies in sequential data. An LSTM cell contains several key components that enable it to retain and update information over time. The main components of an LSTM cell and their functions are as follows:

1. Cell State (Ct):
– The cell state represents the long-term memory of the LSTM cell.
– It allows information to flow through the cell by selectively updating or preserving information through the use of gates.
– The cell state is passed along from one time step to the next, allowing the LSTM to capture long-term dependencies.

2. Input Gate (i):
– The input gate determines how much of the new input information should be stored in the cell state.
– It takes input from the current time step’s input (Xt) and the previous time step’s hidden state (ht-1) and processes them through a sigmoid activation function.
– The result is a value between 0 and 1, which represents the amount of information to let into the cell state.

3. Forget Gate (f):
– The forget gate controls the amount of information to discard from the cell state.
– It takes input from the current time step’s input (Xt) and the previous time step’s hidden state (ht-1) and processes them through a sigmoid activation function.
– The output of the forget gate, ranging from 0 to 1, determines the amount of information to forget from the cell state.

4. Output Gate (o):
– The output gate determines how much of the cell state should be exposed as the output of the LSTM cell.
– It takes input from the current time step’s input (Xt) and the previous time step’s hidden state (ht-1) and processes them through a sigmoid activation function.
– The output of the output gate is multiplied by the cell state (Ct) that has been passed through a tanh activation function. This produces the output of the LSTM cell (ht).

5. Cell State Update:
– The cell state update combines the new input information (Xt) with the previous cell state (Ct-1).
– The input gate (i) and a tanh activation function are applied to the input and previous hidden state, resulting in a new candidate cell state (C~t).
– The candidate cell state is then combined with the forget gate (f) and the previous cell state (Ct-1) to update the cell state (Ct).

The combination of these components allows an LSTM cell to selectively retain and update information over multiple time steps, making it effective for tasks involving sequential data such as natural language processing, speech recognition, and time series analysis.