ReLU

Introduction

ReLU, or rectified line unit, is a type activation function that is used in artificial neural network. It can be described mathematically as

f(x) = max(0,x)

where x is the input of the function. If x is positive, the function returns x. If x is negative or zero, the function returns 0. This non-linear function allows the network to learn complex patterns from the data by introducing non-linearity.

Properties

ReLU is a popular choice for neural network activation functions due to its many useful properties. These include:

• It is easy to compute, since it only requires a comparison or a max operation.
• It is distinct, which means it can be used in backpropagation to train a network.
• It is not saturated, which means that the function's gradient does not decrease with increasing input. This prevents problems such as the disappearing gradient problem.

Variations

Literature has suggested several versions of the ReLU function:

• Leaky RELU: To avoid dead neurons, a small slope is added at the negative input.
• Parametric ReLU (PReLU), the slope is learned during training.
• Randomized ReLU (RReLU), the slope is chosen randomly during training.
• Exponential Linear unit (ELU), a similar unit to ReLU, has a nonzero value for inputs that are negative, which results in a smoother function.

Applications

ReLU is used frequently in deep learning and computer vision tasks, such as object detection and image classification. It is also used for natural language processing and speech identification.

Explain Like I'm 5 (ELI5)

ReLU is a math rule that tells a computer if a number has a positive or negative value. If the number is positive, it remains the same. If the number is negative, or zero, it becomes 0. This allows the computer to learn more difficult things.