Relu activation function

In the process of constructing a neural network, one of the decisions regarding the relu activation function you must make is which Activation Function to apply to the hidden layer and output layer. 

This article outlines several options for the relu activation function.

Values during the training period.

A Brief Introduction to Neural Networks in the sense relu activation function

Comparable to the human brain, Artificial Neural Networks consist of multiple layers that accomplish a certain purpose. Each layer has a diverse number of neurons that are analogous to the biological neurons in the human body; they become activated in response to stimuli, resulting in a corresponding action performed by the body. These neurons are interconnected to several activation function-powered layers is the relu activation function.

Through the process of forward propagation, information is transmitted from one layer to the. After obtaining the output variable, the loss function is determined. Back-propagation is used to update the weights and minimize the loss function with the aid of an optimizer; gradient descent is the most used optimizer technique. Multiple epochs are executed until the loss approaches the global minimum.

What is the function of activation?

The activation function is a simple mathematical function that transforms a given input into an output with a specified range. As their name suggests, they activate the neuron when output surpasses the function’s threshold value. Essentially, they are responsible for turning neurons ON and OFF. For each layer, the neuron receives the sum of the product of inputs and randomly initialized weights, as well as a static bias. On applying the activation function to this sum, an output is generated. Activation functions introduce non-linearity so that the network may learn complex patterns in data, such as images, text, movies, and audio. Without an activation function, our model will behave like a linear regression model with minimal capacity for learning.

What exactly is ReLU?

The rectified linear activation function, often known as ReLU, is a non-linear or piecewise linear function that outputs the input directly if it is positive and zeroes otherwise.

It is the most popular activation function in neural networks, particularly Convolutional Neural Networks (CNNs) and Multilayer perceptrons.

The positive value is returned unchanged, while 0.0 is returned for values less than (negative values) or equal to zero.

Now, we will test our function by providing some input values and plotting the resulting data using the library’s plot. The range of input values is between -5 and 10. This set of input data is applied to our created function.

The figure reveals that all negative values have been set to zero, while positive values have been returned unchanged. Noting that the input was a series of progressively increasing numbers, the output is a linear function with an increasing slope.

Why is ReLU a non-linear algorithm?

After graphing ReLU, it appears to be a linear function at first glance. In reality, it is a non-linear function essential for recognizing and learning complex correlations from training data.

It functions linearly for positive values and nonlinearly for negative values.

When employing an optimizer such as SGD (Stochastic Gradient Descent) during backpropagation, the function behaves like a linear function for positive values, making finding the gradient much simpler. This near linearity permits the preservation of attributes and facilitates gradient-based optimization of linear models.

Derivative ReLU requires the derivative of an activation function for updating the weights during error backpropagation. ReLU has a slope of 1 for positive values and 0 for negative ones. It ceases to be differentiable when the input x is zero, however it is reasonable to assume that x is zero and poses no practical issue.

ReLU’s advantages are:

 “Vanishing Gradient” stops earlier network layers from learning critical information during backpropagation. Sigmoid and tanh also saturate and have diminished sensitivity.

Among the benefits of ReLU are:

Derivative remains constant, or 1 for a positive input, which simplifies computation and minimizes the time required for the model to learn and minimize errors.

Representational Sparsity: It can yield a genuine zero.

Linear activation functions are easy to optimize and permit a smooth flow. 

Disadvantages of ReLU:

Exploding Gradient: This occurs when the gradient is accumulated; hence, the successive weight updates are significantly different. This leads to instability during convergence to the global minimum and instability in learning as well.

Dying ReLU: The issue of “dead neurons” develops when the neuron becomes stuck on the negative side and outputs zero continuously. Because the gradient of 0 is likewise 0, the neuron is unlikely to ever recover.



With this Open Genus article, about the relu activation function, you should have a comprehensive understanding of the ReLU (Rectified Linear Unit) Activation Function.

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