Activation Function

What is Activation Function? Activation Function Explained.

Activation Function, in the context of neural networks and machine learning, refers to the process of calculating the output of a neuron or a layer in a neural network. The activation function determines whether and to what extent the neuron or layer “fires" or becomes activated based on the weighted sum of its inputs.

In a neural network, each neuron receives inputs from the previous layer or directly from the input data. These inputs are multiplied by corresponding weights, and the weighted sum is then passed through an activation function, which introduces non-linearity into the network.

The activation function is a crucial component of a neural network because it enables the network to model complex, non-linear relationships between inputs and outputs. Without an activation function, the network would simply perform linear transformations, resulting in limited learning and modeling capabilities.

Commonly used activation functions include:

Sigmoid function: This function maps the weighted sum to a value between 0 and 1, which can be interpreted as a probability or a binary activation. It has been widely used in the past but is less common in modern networks due to certain limitations, such as the vanishing gradient problem.

Rectified Linear Unit (ReLU): The ReLU activation function returns the input directly if it is positive, and zero otherwise. It is simple and computationally efficient, allowing for faster training of deep neural networks. ReLU has become a popular choice in many applications.

Hyperbolic tangent (tanh): The tanh function maps the weighted sum to a value between -1 and 1. It is similar to the sigmoid function but is symmetric around zero, which can make training more efficient in certain cases.

Softmax: The softmax function is often used as the activation function in the output layer of a neural network for multi-class classification problems. It normalizes the outputs of the layer, turning them into a probability distribution over the different classes.

The choice of activation function depends on the specific problem and network architecture. Different activation functions can have varying effects on the learning process, model performance, and gradient flow during training. Experimentation and empirical analysis are often performed to select the most suitable activation function for a given task.