Activation Functions in Neural Network

September 5, 2022

Neural Networks are an essential part of a technology-driven system that is revolutionizing our environment. But have you ever thought about how cutting-edge performance from Neural Networks is able to solve difficulties in the actual world? 

The answer is - Activation Functions.

The most important component of every neural network is its activation functions. Image categorization, language translation, object identification, and other extremely challenging problems in deep learning must be handled with the aid of neural networks and activation functions.

In this blog, we will explain what activation function in neural networks is, its purpose, types, and how to choose the best activation function each time you try to create a neural network-based model.

Table of Contents:

  1. What is an Activation Function in Neural Network?
  2. Purpose of an activation function in neural networks
  3. Types of Activation Functions
  4. Binary Step Function
  5. Linear Function
  6. ReLU( Rectified Linear unit) Activation function
  7. Sigmoid Activation Functions
  8. Tanh
  9. Softmax Activation Function 
  10. Exponential Linear Units (ELUs) Function
  11. How do you pick the ideal activation function?
  12. Conclusion.

What is an Activation Function in Neural Network?

Activation Functions in soft computing (AFs) are used by ANNs to carry out intricate calculations in the hidden layers before sending the result to the output nodes. The main goal of AFs, or activation functions, in soft computing is to give the neural network non-linear features. They do this to make it easier for deep networks to learn high-order polynomials with more than one degree by converting a node's linear input signals into non-linear output signals.

In short, The Activation Function's main job is to convert the node's weighted input sum into an output value that may either be fed into the following hidden layer or used as output.

Purpose of an activation function in neural networks

So now that we are aware of what Activation Function is and does.

The question is why is it required by neural networks? 

Activation functions are used to give the neural network some non-linearity. By incorporating nonlinearity, activation functions play a crucial role in training or building neural network-based models. Because neural networks are nonlinear, they may build sophisticated representations and functions depending on their inputs, which is not achievable with a straightforward linear regression model.

Additionally, because activation functions are differentiable, backpropagations to quantify gradient loss functions in neural networks may be performed with ease using an optimal technique.

Types of Activation Functions

In this section of the article, let’s discuss different types of Activation functions.

Linear Function

This activation function is a straightforward straight line, and it is directly proportional to the input's weighted sum of neurons. A line with a positive slope may cause the firing rate to rise as the input rate increases. Linear activation functions are superior at providing a wide range of activations.

Binary Step Function

When we attempt to bind output, this extremely basic activation function always comes to mind. It basically functions as a threshold-based classifier, where we choose a threshold value to determine whether a neuron should be activated or deactivated at the output.

Mathematically Binary Step function is represented as:

Sigmoid/Logistic Function

The sigmoid activation function is most frequently used because it performs its task very effectively; it essentially takes a probabilistic approach to decision-making and ranges from 0 to 1. Because this activation function's range is the smallest, predictions are more accurate when we use it to make decisions or predict outcomes.

Mathematically Sigmoid function is represented as:

Tanh Function 

With a variation in the output range of -1 to 1, the Tanh function is remarkably close to the sigmoid/logistic activation function and even has the same S-shape. Tanh's output value approaches 1.0 when the input is greater (more positive), whereas it approaches -1.0 when the input is smaller (more negative).

Mathematically Tanh function is represented as:

Softmax Activation Function 

The multi-class logistic regression or soft argmax function is another name for the softmax function. This is true because the softmax, a modification of logistic regression that may be applied to multi-class classification, has a formula with the sigmoid function, which is utilised in logistic regression. Only when the classes are mutually exclusive can a classifier employ the softmax function.

The mathematical Representation of the Softmax activation function is:

Rectified Linear Unit(ReLU) 

The rectified linear unit (ReLU) function, one of the most well-liked activation functions, is a fast-learning activation function that guarantees cutting-edge performance and excellent outcomes. The ReLU function provides significantly improved performance and generalisation in deep learning as compared to other activation functions like the sigmoid and tanh functions. Gradient-descent optimization techniques are simple to use on the function since it is approximately linear and retains the characteristics of linear models.

The mathematical representation of the ReLU activation function is:

Exponential Linear Units (ELUs) Function

Another activation function that is used to quicken the training of neural networks is the exponential linear units (ELUs) function. The ELU function's greatest benefit is its ability to solve the vanishing gradient problem by employing identity for positive values and enhancing the model's learning properties. ELUs have negative values that reduce computing complexity and accelerate learning by moving the mean unit activation closer to zero. The ELU is a fantastic substitute for the ReLU because it reduces bias shifts by training mean activation toward zero.

Mathematical representation of ELU Activation Function: 

The ELU hyperparameter α in this expression sets the saturation point for negative net inputs and is typically set to 1.0.


In this article, we touched upon various aspects of the activation function. Now the last piece of advice with which we will conclude the article is - “How to choose the activation function”.

Based on the kind of prediction problem you're dealing with—specifically the kind of predicted variable—you need to match your activation function for your output layer. 

As a general guideline, you should start by employing the ReLU activation function. If ReLU doesn't yield the best results then look for other activation functions. 

Depending on the kind of prediction issue you are addressing, you may choose the activation function for your output layer: Binary Classification—Sigmoid/Logistic Activation Function, Multiclass Classification—Softmax, Multilabel Classification—Sigmoid, Regression - Linear Activation Function, ReLU in a convolutional neural network (CNN) and Tanh or Sigmoid activation in recurrent neural network(RNN).

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