What is activation function

Searched over 200M research papers

Research Analysis by Consensus

Consensus Meter

Yes

Possibly

Understanding Activation Functions in Neural Networks

What is an Activation Function?

An activation function in neural networks is a mathematical function that determines the output of a neuron given an input or set of inputs. It introduces non-linearity into the model, enabling the network to learn complex patterns in the data. Without activation functions, a neural network would simply be a linear regression model, incapable of handling the intricacies of real-world data .

Types of Activation Functions

Rectified Linear Unit (ReLU)

The Rectified Linear Unit (ReLU) is one of the most widely used activation functions in deep learning. It is defined as ( f(x) = \max(0, x) ). ReLU is popular because it helps mitigate the vanishing gradient problem, allowing models to learn faster and perform better.

Swish

Swish is a newer activation function discovered through automatic search techniques. It is defined as ( f(x) = x \cdot \text{sigmoid}(\beta x) ). Swish has been shown to outperform ReLU in deeper models and across various challenging datasets, making it a promising alternative .

Sigmoid and Tanh

Sigmoid and Tanh are traditional activation functions. The Sigmoid function maps input values to a range between 0 and 1, while Tanh maps inputs to a range between -1 and 1. These functions are useful for binary classification tasks but can suffer from vanishing gradient problems, making them less effective for deeper networks .

Trainable Activation Functions

Recent research has focused on trainable or adaptable activation functions, which can be optimized during the learning process. These functions adjust their parameters based on the data, potentially leading to better performance. Examples include adaptive versions of the linear sigmoidal activation function, which can model non-linear dependencies in data more effectively .

Novel Activation Functions

Linearized Sigmoidal Activation

The linearized sigmoidal activation function is a novel approach that maintains non-saturating behavior even with non-linear structures. It provides distinct activation behaviors for different data range segments, enhancing the network's ability to model complex data.

ACON

ACON is another innovative activation function that learns whether to activate neurons or not. It can be seen as a smooth approximation to ReLU and Swish, offering improved performance by optimizing the parameter switching between non-linear and linear states.

Performance Comparison

Empirical evaluations have shown that different activation functions can significantly impact the training dynamics and performance of neural networks. For instance, Swish has been found to improve top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2, simply by replacing ReLUs with Swish units. Similarly, the linearized sigmoidal activation function has outperformed state-of-the-art activation functions on benchmark datasets like CIFAR-10 and MNIST.

Conclusion

Activation functions are a critical component of neural networks, providing the necessary non-linearity to model complex data. While traditional functions like ReLU, Sigmoid, and Tanh have their merits, recent advancements in trainable and novel activation functions like Swish, linearized sigmoidal activation, and ACON offer promising improvements in performance. As research continues, the discovery and optimization of new activation functions will likely play a significant role in advancing the capabilities of neural networks.