Understanding the Intuition of tanh in LSTMs
Long Short-Term Memory (LSTM) networks are a powerful type of recurrent neural network (RNN) designed to handle sequential data, such as text, audio, and time series. One of the key components of LSTMs is the hyperbolic tangent function (tanh), which plays a crucial role in controlling the flow of information within the network.
The Role of tanh in LSTMs
tanh is primarily used in LSTM cells for two critical functions:
1. Gate Activation
- LSTMs employ “gates” to regulate the information that flows through the cell state. These gates are essentially sigmoid functions (outputting values between 0 and 1) that control how much information is allowed to pass.
- tanh is used in conjunction with the sigmoid function for gate activation. It scales the input values to the gates between -1 and 1, providing a more nuanced control over information flow.
2. Cell State Update
- The cell state, which stores long-term dependencies, is updated at each time step. tanh is applied to the candidate cell state, which represents the proposed updates to the current cell state.
- This squashes the candidate cell state values between -1 and 1, ensuring that the updates are kept within a reasonable range and don’t cause the cell state to explode.
Why tanh?
Using tanh in LSTMs offers several advantages:
- Gradient Flow Control: tanh’s bounded nature helps prevent exploding gradients, a common issue in RNNs where gradients can grow uncontrollably during backpropagation, leading to instability.
- Information Compression: tanh compresses the input values to a smaller range, which can be beneficial for improving the efficiency of the network and reducing the risk of overfitting.
- Smoothness and Differentiability: tanh is a smooth and differentiable function, making it suitable for gradient-based optimization techniques used to train neural networks.
Comparison with other Activation Functions
While tanh is commonly used in LSTMs, other activation functions can be employed as well. Some popular alternatives include:
| Activation Function | Advantages | Disadvantages |
|---|---|---|
| Sigmoid | Similar to tanh in terms of gradient flow control, but outputs values between 0 and 1. | Can suffer from vanishing gradients, particularly for very large negative inputs. |
| ReLU (Rectified Linear Unit) | Fast computation, less prone to vanishing gradients. | Can suffer from dying ReLU problem, where neurons can become inactive. |
Illustrative Code Example
import tensorflow as tf
# Define LSTM cell with tanh activation
lstm_cell = tf.keras.layers.LSTMCell(units=128, activation='tanh')
# Create LSTM layer
lstm_layer = tf.keras.layers.RNN(lstm_cell)
# Input data
input_data = tf.random.normal(shape=(10, 10, 1))
# Output from LSTM layer
output = lstm_layer(input_data)
Conclusion
tanh plays a vital role in LSTMs by controlling information flow through gates and ensuring stability during cell state updates. Its bounded nature, smoothness, and differentiability make it a suitable choice for this purpose. While other activation functions can be explored, tanh remains a widely adopted standard in LSTMs for handling sequential data effectively.