Activation functions play a crucial role in neural networks, affecting how well they can learn and generalize from data. Here are the pros and cons of some commonly used activation functions: sigmoid, tanh (hyperbolic tangent), ReLU (Rectified Linear Unit), and leaky ReLU.
1. Sigmoid Activation Function:
Pros:
- Smooth and bounded output between 0 and 1, which can be interpreted as probabilities.
- Historically used in binary classification problems.
- Well-suited for the output layer of binary classification models.
Cons:
- Prone to vanishing gradient problem: Gradients become very small for extreme values, leading to slow convergence during training.
- Outputs are not zero-centered, which can lead to convergence issues when used in deep networks.
- Less common in hidden layers of deep networks due to the vanishing gradient issue.
2. Tanh (Hyperbolic Tangent) Activation Function:
Pros:
- Smooth and bounded output between -1 and 1, which helps mitigate vanishing gradient problems compared to sigmoid.
- Zero-centered output, which can help with faster convergence in deep networks compared to sigmoid.
Cons:
- Still prone to vanishing gradient issues, especially for very deep networks.
- Outputs can saturate, leading to slower learning in practice.
3. ReLU (Rectified Linear Unit) Activation Function:
Pros:
- Simple and computationally efficient: It involves only the element-wise max(0, x) operation.
- Accelerates training convergence: ReLU doesn't saturate for positive values, leading to faster convergence.
- Addresses vanishing gradient problems for most cases.
Cons:
- Prone to "dying ReLU" problem: Neurons can get stuck in a state where they always output zero if the weighted sum of inputs is consistently negative.
- Not zero-centered, which may lead to gradient issues for some optimization methods.
4. Leaky ReLU Activation Function:
Pros:
- Addresses the "dying ReLU" problem: Allows a small, non-zero gradient for negative inputs, preventing neurons from becoming inactive.
- Similar computational efficiency to ReLU.
Cons:
- Introduces a new hyperparameter (the leak slope) that needs to be tuned.
- Not as commonly used as ReLU, and not guaranteed to be better in all scenarios.
Choosing the right activation function depends on the specific problem, architecture, and empirical experimentation. ReLU variants (including leaky ReLU, Parametric ReLU, and Exponential Linear Unit) are popular choices for hidden layers in deep neural networks due to their faster convergence, but you may still encounter situations where sigmoid or tanh activations are preferred. It's common practice to experiment with different activation functions and architectures to find the best-performing combination for your specific task.