 What is Perceptron & Tutorial? Defined, Explained, & Explored | ACTE

# What is Perceptron & Tutorial? Defined, Explained, & Explored

Last updated on 21st Jun 2020, Blog, Tutorials

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What is an Artificial Neuron?

Considering the state of today’s world and to solve the problems around us we are trying to determine the solutions by understanding how nature works, this is also known as biomimicry. In the same way, to work like human brains, people developed artificial neurons that work similarly to biological neurons in a human being. An artificial neuron is a complex mathematical function, which takes input and weights separately, merge them together and pass it through the mathematical function to produce output.

Perceptron Learning Algorithm

Perceptron Algorithm is used in a supervised machine learning domain for classification. In classification, there are two types of linear classification and no-linear classification. Linear classification is nothing but if we can classify the data set by drawing a simple straight line then it can be called a linear binary classifier. Whereas if we cannot classify the data set by drawing a simple straight line then it can be called a non-linear binary classifier.

Perceptron Algorithm Block Diagram

Let us see the terminology of the above diagram.

1. Input: All the features of the model we want to train the neural network will be passed as the input to it, Like the set of features [X1, X2, X3…..Xn]. Where n represents the total number of features and X represents the value of the feature.

2. Weights: Initially, we have to pass some random values as values to the weights and these values get automatically updated after each training error that is the values are generated during the training of the model. In some cases, weights can also be called as weight coefficients.

3. Weights Sum: Each input value will be first multiplied with the weight assigned to it and the sum of all the multiplied values is known as a weighted sum. Popular Course in this category

Step or Activation Function

Activation function applies step rule which converts the numerical value to 0 or 1 so that it will be easy for data set to classify. Based on the type of value we need as output we can change the activation function. Sigmoid function, if we want values to be between 0 and 1 we can use a sigmoid function that has a smooth gradient as well.

Sign function, if we want values to be +1 and -1 then we can use sign function. The hyperbolic tangent function is a zero centered function making it easy for the multilayer neural networks. Relu function is highly computational but it cannot process input values that approach zero. It is good for the values that are both greater than and less than a Zero.

Bias

If you notice, we have passed value one as input in the starting and W0 in the weights section W0 is an element that adjusts the boundary away from origin to move the activation function left, right, up or down. since we want this to be independent of the input features, we add constant one in the statement so the features will not get affected by this and this value is known as Bias.

Perceptron algorithms can be divided into two types they are single layer perceptrons and multi-layer perceptron’s. In single-layer perceptron’s neurons are organized in one layer whereas in a multilayer perceptron’s a group of neurons will be organized in multiple layers. Every single neuron present in the first layer will take the input signal and send a response to the neurons in the second layer and so on.

Single Layer Perceptron

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Perceptron Learning Steps

• Features of the model we want to train should be passed as input to the perceptrons in the first layer.
• These inputs will be multiplied by the weights or weight coefficients and the production values from all perceptrons will be added.
• Adds the Bias value, to move the output function away from the origin.
• This computed value will be fed to the activation function (chosen based on the requirement, if a simple perceptron system activation function is step function).
• The result value from the activation function is the output value.

Features added with perceptron make in deep neural networks. Back Propagation is the most important feature in these.

Back Propagation

After performing the first pass (based on the input and randomly given inputs) error will be calculated and the back propagation algorithm performs an iterative backward pass and try to find the optimal values for weights so that the error value will be minimized. To minimize the error back propagation algorithm will calculate partial derivatives from the error function till each neuron’s specific weight, this process will give us complete transparency from total error value to a specific weight that is responsible for the error.

Perceptron Networks are single-layer feed-forward networks. These are also called Single Perceptron Networks. The Perceptron consists of an input layer, a hidden layer, and output layer.

The input layer is connected to the hidden layer through weights which may be inhibitory or excitery or zero (-1, +1 or 0). The activation function used is a binary step function for the input layer and the hidden layer.

The output is

Y= f (y)

The activation function is:

The weight updation takes place between the hidden layer and the output layer to match the target output. The error is calculated based on the actual output and the desired output.

If the output matches the target then no weight updation takes place. The weights are initially set to 0 or 1 and adjusted successively till an optimal solution is found.

The weights in the network can be set to any values initially. The Perceptron learning will converge to weight vector that gives correct output for all input training pattern and this learning happens in a finite number of steps.

The Perceptron rule can be used for both binary and bipolar inputs.

Learning Rule for Single Output Perceptron

• Let there be “n” training input vectors and x (n) and t (n) are associated with the target values.
• Initialize the weights and bias. Set them to zero for easy calculation.
• Let the learning rate be 1.
• The input layer has identity activation function so x (i)= s ( i).
• To calculate the output of the network:
• The activation function is applied over the net input to obtain an output. ### Best Perceptron Learning Algorithm Course from Real Time Experts

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• Now based on the output, compare the desired target value (t) and the actual output.
• Continue the iteration until there is no weight change. Stop once this condition is achieved.

Learning Rule for Multiple Output Perceptron

• Let there be “n” training input vectors and x (n) and t (n) are associated with the target values.
• Initialize the weights and bias. Set them to zero for easy calculation.
• Let the learning rate be 1.
• The input layer has identity activation function so x (i)= s ( i).
• To calculate the output of each output vector from j= 1 to m, the net input is:
• The activation function is applied over the net input to obtain an output.
•  Now based on the output, compare the desired target value (t) and the actual output and make weight adjustments.

w is the weight vector of the connection links between ith input and jth output neuron and t is the target output for the output unit j.

• Continue the iteration until there is no weight change. Stop once this condition is achieved.