Activation Function
An activation function is a mathematical function used in artificial neurons to introduce non‑linearity into the model, enabling complex problems to be solved—this concept is clearly presented in the Innovatiana article Activation function: a hidden pillar of neural networks (https://www.innovatiana.com/en/post/activation-function-in-ai).
Why are activation functions essential?
- Introduce non-linearity: Without them, a multilayer network collapses into a linear structure, incapable of modeling complex relationships.
- Learn complex functions: They enable hierarchical representation learning for vision, NLP, and beyond.
- Decide neuron activation: They convert weighted input sums into usable outputs.
- Stabilize training: Some, like ReLU, help avoid saturation and encourage convergence.
Common activation functions
- Sigmoid: S-shaped, bounded between 0 and 1; suited for binary classification but prone to saturation.
- Tanh: Ranges between −1 and 1, zero-centered to improve convergence, but may still suffer from vanishing gradients.
- ReLU (Rectified Linear Unit): f(x)=max(0,x); simple, effective, widely used in CNNs.
- Modern variants: Include Leaky ReLU, PReLU, ELU, GELU, Swish—offer smoother gradients, adaptive behavior, or better performance.
Use cases
- Convolutional Neural Networks (CNNs): After convolution layers, a non-linear activation—typically ReLU—is applied to capture complex features; Softmax at the output layer creates multi‑class probability distributions.
Activation functions are often described as the heartbeat of neural networks because they give models the ability to capture nonlinear patterns. Without them, no matter how many layers we stack, the network would collapse into a simple linear mapping. By introducing nonlinearity, activation functions enable deep models to learn hierarchical representations—from edges and textures in images to abstract linguistic structures in text.
The choice of activation is not trivial: it directly affects gradient flow, training speed, and generalization. While ReLU remains a default choice in many architectures due to its simplicity and efficiency, it also comes with drawbacks such as “dying ReLUs” (neurons that stop activating). Modern alternatives like GELU or Swish, often used in Transformers, balance smoothness and flexibility, leading to state-of-the-art performance in vision and NLP.
From a practical perspective, activations are also tied to model interpretability and robustness. For instance, smooth functions like tanh can stabilize gradients in recurrent neural networks, while probabilistic activations like Softmax provide outputs that humans can directly interpret as class probabilities. This makes activation functions not just a mathematical trick, but a key design decision in AI systems.
Learn more on Innovatiana
- Deep dive (English): Activation function: a hidden pillar of neural networks – https://www.innovatiana.com/en/post/activation-function-in-ai
- CNN application: Scene classification in AI/Computer Vision – https://www.innovatiana.com/en/post/scene-classification-in-ai
