# relu activation function

What is the relu activation function to employ in the hidden layer and at the output layer of the network is one of the decisions you get to make while creating a neural network.

Some of the options for the relu activation function are discussed in this article.

The biological neurons in the human body that activate under specific conditions and cause a related action to be taken by the body in response are the model for artificial neural networks.

Artificial neural networks are made up of several interconnected layers of artificial neurons that are relu activation function, activated by activation functions that enable an ON/OFF switch. Similar to conventional machine learning techniques, neural networks learn specific values during the training stage.

## Synopsis of Neural Networks

Forward propagation, or the transfer of information from the input layer to the output layer, is the process by which information moves from one layer to the next. Once the relu activation function output variable has been collected, the loss function is calculated. With the aid of an optimizer—the gradient descent optimizer algorithm is the most used—back-propagation is performed to update the weights and decrease the loss function. Up until the loss converges to the global minima, several epochs are run.

## An activation function is what?

A straightforward mathematical formula called an activation function converts an input into an output that must fall within a particular range. In essence, they are in charge of turning the neuron ON and off. To help the network understand complicated patterns in the data, such as in the case of photos, text, videos, or sounds, activation functions add non-linearity into the system. Without an activation function, our model will operate like a linear regression model, which has a constrained capacity for learning.

## ReLU: What is it?

The rectified linear activation function, often known as ReLU, is a non-linear or piecewise linear function that, if the input is positive, outputs the input directly; if not, it outputs zero.

We’ll now put the function to the test by providing some input values, and we’ll use the library’s plot to visualize the results. The possible input values range from -5 to 10. Because we provided a series of values that increased one after the other as inputs, the output is linear with an increasing slope.

## ReLU is non-linear, why?

After graphing ReLU, it appears to be a linear function at first glance. However, it is a non-linear function that is necessary to recognize and understand complicated correlations from the training data.

For positive values, it behaves as a linear function; for negative values, it behaves as a non-linear activation function.

When doing backpropagation, an optimizer like SGD (Stochastic Gradient Descent) behaves like a linear function for positive values, making finding the gradient much simpler.

Derivative Regarding ReLU: During the backpropagation of the error, updating the weights calls for the derivative of an activation function. For positive values and zero for negative values, ReLU’s slope is 1.

## Benefits of ReLU:

When the network is backpropagating, the “Vanishing Gradient” stops the earlier layers from learning critical information. Given that the output of a sigmoid function varies from 0 to 1, it is preferable to utilize it solely at the output layer when dealing with regression or binary classification-related challenges. Additionally, the sigmoid and the tanh saturate and are less sensitive.

## The following are a few benefits of ReLU:

Simpler Computation:

The derivative stays constant, or is 1, given a positive input, which shortens the learning period for the model and speeds up error minimization.

Representational Sparsity: It can produce a real value of zero.

Linearity: Smooth flow and easy optimization are two benefits of linear activation functions. ReLU’s drawbacks include:

bursting gradient This happens when the gradient accumulates, and it results in significant changes in the ensuing weight updates. As a result, this leads to instability both during convergence to the global minima and during the learning process.

It is doubtful that the neuron will ever recover because the gradient of zero is also zero. When the learning rate is too high or the negative bias is significant, this occurs.