Some common activation functions used in LSTM (Long Short-Term Memory) models include:

  1. Sigmoid Activation Function: The sigmoid activation function is typically used in the gates of LSTM units, such as the input gate, forget gate, and output gate. It maps the input to a value between 0 and 1, which helps control the flow of information through the LSTM cell.

  2. Tanh Activation Function: The hyperbolic tangent (tanh) activation function is used in the LSTM cell to control the input modulation and cell state updates. It maps the input to a value between -1 and 1, which allows the LSTM to capture both positive and negative relationships in the data.

  3. ReLU Activation Function: While not commonly used directly in the LSTM cell, the rectified linear unit (ReLU) activation function is often employed in the output layer of an LSTM model or in the dense layers connected to the LSTM layer. ReLU sets negative values to zero and keeps positive values unchanged, providing better gradient flow during training and promoting sparsity in the network.

  4. Softmax Activation Function: The softmax activation function is often used in the final layer of an LSTM model for multi-class classification tasks. It normalizes the output of the previous layer into a probability distribution over the different classes, ensuring that the predicted probabilities sum up to 1.

  It's important to note that the choice of activation functions depends on the specific problem, the characteristics of the data, and the desired behavior of the LSTM model. Experimentation and tuning may be required to find the most suitable activation functions for a given task.