What Is RELU?

Rectified Linear Unit (ReLU) is a commonly used activation function in artificial neural networks. An activation function is a mathematical function that is applied to the output of a neuron in a neural network to determine whether the neuron should be activated or not.

ReLU is defined as follows:

f(x) = max(0, x)

In other words, the output of the ReLU function is the maximum of 0 and the input value. This means that if the input value is negative, the output will be 0, and if the input value is positive, the output will be the input value itself.

ReLU has several advantages as an activation function:

• It is simple to implement and computationally efficient.
• It does not saturate, meaning that the output of the function does not become significantly smaller as the input value increases. This can help prevent the “vanishing gradient” problem, which can occur when the output of the activation function becomes too small and the gradient of the function becomes difficult to calculate.
• It has been shown to improve the training speed of neural networks and to produce better results in some cases.

Overall, ReLU is a widely used and effective activation function that is well-suited for many types of neural network architectures.