Types of Activation Function in Neural Network – Complete Guide

Written By The IoT Academy
Published on April 13th, 2024

Activation functions are super important in neural networks. Because they decide what the neurons do and how well the network works. They make the network able to understand complicated relationships in data by adding curves. Also, knowing about different types of activation functions and what they do helps make better neural networks. In this guide, we’ll explore the various types of activation function in neural networks, their properties, and their significance in neural network architectures.

Table of Contents

What is Neural Network?

A neural network is like a computer brain that’s made up of connected parts called neurons. So, it takes in information, processes it, and makes decisions. It learns by practicing examples with known answers. Neural networks are good at figuring out patterns and solving problems. Like sorting things into categories, guessing values, and spotting trends. As well as there are lots of uses for smart technology like AI, machine learning, and data analysis.

What are Activation Functions?

Activation functions are like decision-makers in a neural network. They decide if a neuron should be active or not based on its input. So, these functions help the network understand complex patterns in data. Common ones are Sigmoid, tanh, and ReLU. Sigmoid also takes values between 0 and 1, tanh between -1 and 1, and ReLU turns negative numbers to 0. Picking the right from many types of activation function in neural network is super important for the network to learn well and do tasks. Like sorting things, guessing values, and spotting patterns.

Linear Activation Functions

Linear types of activation function in neural networks, such as the identity function, give outputs that are just like the inputs. They’re used mostly for predicting numbers in regression tasks. But they’re not good at capturing complex patterns like nonlinear functions. They are simple and fast, but they’re not great for handling complicated tasks that need detailed data representations. Still, they’re important for some jobs where the input needs to be directly mapped to the output without any change.

Non-linear Activation Functions

Nonlinear types of activation function in neural networks, unlike linear ones, add curves and complexity to what the neural network learns. They help with spotting patterns, classifying things, and finding features in the data. Examples are sigmoid, tanh, and ReLU. These functions are crucial for understanding the complicated connections in data, letting the network accurately represent complex things. Even though they need more computer power than linear functions. Nonlinear activation functions in CNN(Convolutional Neural Networks) make the network much better at handling tough jobs in AI and machine learning.

Types of Activation Function in Neural Network

Activation functions are super important in neural networks because they help the network understand complicated patterns in data. By adding curves and bends to its learning process. Here are some common activation function used in neural networks:

Sigmoid: Sigmoid functions make the output go between 0 and 1. They’re handy in problems where we decide between two things and need to know the chance of each one happening.
Hyperbolic Tangent (Tanh): Tanh functions make the output go between -1 and 1. They’re like sigmoid functions but start from zero, which makes them helpful for training neural networks.
Rectified Linear Unit (ReLU): ReLU functions are easy and fast for computers to use. They say “zero” for negative numbers and just give the number for positive ones. As well as making the network able to learn more complicated things.
Leaky ReLU: Leaky ReLU is another common types of activation function in neural networks that helps stop ReLU from getting stuck by allowing a little bit of movement for negative numbers, keeping neurons active.
Exponential Linear Unit (ELU): ELU functions are like ReLU but handle negative numbers differently. Which helps keep the network’s average activity closer to zero and prevents a problem where the network stops learning.
Softmax: Softmax functions help in picking the right category by turning numbers into probabilities that add up to 1, making it easier to understand which category is most likely.
Swish: Swish functions mix ReLU and sigmoid, making a smoother curve than ReLU. Which can sometimes help the network work better.

All activation functions in neural networks have their features. Also, we pick the one that fits best with what the neural network needs to do and the kind of data it’s dealing with.

Specialized Activation Functions

Specialized activation function types in neural networks help with specific tasks or problems when training neural networks. Examples include:

SELU (Scaled Exponential Linear Unit): It’s made to keep the average and spread of activations steady. So training stays stable and we don’t need as much Batch Normalization.
Hard Sigmoid: Hard Sigmoid types of activation function in neural networks are a quicker option as compared to the sigmoid function. Handy in places with limited resources or when we need speed in our models.
Hard Swish: It’s like Swish but easier to calculate, giving a good balance between being fast to compute and performing well.
GELU (Gaussian Error Linear Unit): It was brought in to fix the vanishing gradient issue by using the Gaussian distribution’s cumulative distribution function.
PReLU (Parametric Rectified Linear Unit): It’s a type of Leaky ReLU where the slope of the negative part is learned. While training let the network adjust its behavior as it learns.
Maxout: It’s a special kind of function that’s like ReLU but can be more flexible. Because it looks at the highest value from different straight lines. Also, helping the network represent things in more detail.

These special activation functions are made for specific needs or problems in training neural networks. As well as offering better options than the usual ones.

Conclusion

Activation functions are really important for neural networks. Because they help solve many different kinds of problems by modeling tricky relationships. When developers and researchers know about the different activation functions, they can pick the best one for their neural network. Trying out different options and testing them is key to finding the right from all the types of activation function in neural network for a particular job. Which helps make better AI and machine learning tools.

Frequently Asked Questions

Q. Why only ReLU is used in CNN?

Ans. ReLU is commonly used in Convolutional Neural Networks (CNNs) because it’s simple, and works well for training deep networks. Also, helps in the prevention of some common problems. Its efficiency, ability to handle gradient issues and promotion of network sparsity make it a good fit for CNNs. Improving their ability to learn and generalize from data.

Q. What is the difference between ReLU and Softmax activation function?

Ans. Generally, ReLU is good for finding complex patterns in hidden layers. By giving out the highest value between 0 and the input. Softmax is for classification tasks, making sure scores turn into probabilities that add up to 1. It helps pick the right class out of many choices.

Q. Which is the best activation function for convolutional neural network?

Ans. ReLU is often seen as the best activation function for Convolutional Neural Networks. It deals well with gradient issues, encourages network sparsity, and helps networks learn faster during training. Because it’s simple and captures complex patterns effectively. It’s the go-to choice for most CNNs, making them better at tasks like computer vision.

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The IoT Academy

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