Understanding Activation Functions
An activation function in a neural network determines how the weighted sum of the input is transformed into an output from a node or nodes in a layer of the network. Without activation functions, neural networks would simply be linear models, incapable of handling complex data patterns. Activation functions can be broadly categorized into linear and non-linear functions.
Why Use Activation Functions?
- Non-Linearity: Activation functions introduce non-linearity into the network, allowing it to learn and model complex data.
- Differentiability: Most activation functions are differentiable, which is essential for backpropagation, the algorithm used to train neural networks.
- Bounded Output: Some activation functions, like Sigmoid and Tanh, produce bounded outputs, which can be useful in certain types of neural networks.
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