FAQ

What is Activation Function ?

An activation function in neural networks is a mathematical equation that determines whether a neuron should be activated or not. It does this by taking the input signal and converting it into an output signal, which is necessary for the neurons in a network to transmit signals to each other. Activation functions are critical in allowing neural networks to learn complex patterns in data, as they introduce non-linear properties to the network. Here’s an overview of the role and types of activation functions:

Purpose of Activation Functions

Non-linearity: Without activation functions, a neural network would essentially become a linear regression model, which limits its ability to learn complex patterns. By introducing non-linear properties, activation functions allow neural networks to learn more complex decision boundaries.
Control of Output Range: Activation functions can normalize the output of each neuron to a limited range, such as between 0 and 1 or between -1 and 1. This standardization helps stabilize the learning process.
Efficiency and Simplicity: These functions help simplify the network operations during forward and backward propagation since they introduce fixed operations for each neuron’s output.

Common Types of Activation Functions

Sigmoid or Logistic

Function Description: The sigmoid function maps the input (x values) to values between 0 and 1. It is defined as f(x) = 1/(1 + e^-x) .
Major Applications: Commonly used in the output layer of binary classification models to predict probabilities, as it outputs values between 0 and 1.
Limitations: Prone to the vanishing gradient problem, which can drastically slow down the training process or cause it to plateau if gradients become too small.

Tanh (Hyperbolic Tangent)

Function Description: Tanh maps the input to values between -1 and 1, enhancing the model’s ability to generalize. It is defined as f(x) = tanh(x) = 2/((1 + e^-2x) - 1).
Major Applications: Used in hidden layers where data needs to be normalized around zero, thus aiding the learning process for subsequent layers.
Limitations: Also susceptible to the vanishing gradient problem, although it generally performs better than sigmoid in hidden layers due to its output range.

ReLU (Rectified Linear Unit)

Function Description: ReLU is one of the most widely used activation functions. Defined as f(x) = max(0, x), it outputs x if x is positive and 0 otherwise.
Major Applications: Extensively used in most deep learning networks for hidden layers due to its computational efficiency and the ability to enable faster convergence.
Limitations: Can suffer from the "dying ReLU" problem, where neurons can sometimes permanently die during training, causing a substantial portion of the network to become inactive.

Leaky ReLU

Function Description: A variant of ReLU designed to solve the dying neuron problem. It allows a small, positive gradient when the unit is not active and is defined as f(x) = max(α x, x), where α is a small coefficient.
Major Applications: Typically used to address the limitations of ReLU by allowing a small gradient when x < 0 to keep neurons alive.
Limitations: The value of &alpha needs careful tuning, and it can introduce complexity in the tuning process of the network.

Softmax

Function Description: The softmax function converts logits to probabilities by taking the exponentials of each output and then normalizing these values by dividing by the sum of all exponentials.
Major Applications: Predominantly used in the output layers of multi-class classification models to represent the probabilities of each class.
Limitations: Can be prone to numerical instability if not implemented with care, as it involves exponentiation of potentially large numbers.

Activation functions play a critical role in deep learning architectures, influencing both the speed at which a network learns and its ability to handle complex types of data. Each function has its specific uses, advantages, and limitations, which should be considered when designing neural network models.