Enhancing Neural Network Performance: Selecting Activation Functions
For Hidden Layers
- ReLU: The default choice for hidden layers due to its simplicity and efficiency.
- Leaky ReLU: Use if you encounter the dying ReLU problem.
- Tanh: Consider if your data is centered around zero and you need a zero-centered activation function.
For Output Layers
- Linear: Use for regression problems where the output can take any value.
- Sigmoid: Suitable for binary classification problems.
- Softmax: Ideal for multi-class classification problems.
Choosing the Right Activation Function for Your Neural Network
Activation functions are a critical component in the design and performance of neural networks. They introduce non-linearity into the model, enabling it to learn and represent complex patterns in the data. Choosing the right activation function can significantly impact the efficiency and accuracy of a neural network. This article will guide you through the process of selecting the appropriate activation function for your neural network model.
Table of Content
- Understanding Activation Functions
- Choosing the Right Activation Function
- 1. Rectified Linear Unit (ReLU)
- 2. Leaky ReLU
- 3. Sigmoid
- 4. Hyperbolic Tangent (Tanh)
- 5. Softmax
- 6. Exponential Linear Unit (ELU)
- 7. Swish
- 8. Gated Linear Unit (GLU)
- 9. Softplus
- 10. Maxout
- Advantages and Disadvantages of Each Activation Function
- Enhancing Neural Network Performance: Selecting Activation Functions
- Practical Considerations for Optimizing Neural Networks