Implementation of Perceptron Algorithm for AND Logic Gate with 2-bit Binary Input

Implementation of Perceptron Algorithm for NAND Logic Gate with 2-bit Binary Input

Last Updated : 05 May, 2025

A Perceptron is one of the simplest types of deep learning models. It is used to make decisions based on input values and is like a basic brain cell that can "learn" to give output like 0 or 1 from given inputs. In this article, we will show how a perceptron can be used to perform the task of NAND logic gate. A NAND gate gives output of 0 only when both inputs are 1. In all other cases the output is 1. NAND Truth Table is:

Input 1	Input 2	Output
0	0	1
0	1	1
1	0	1
1	1	0

It is the opposite of the AND gate that's why it is called NAND gate (Not of And gate).

Perceptron for NAND Gate

A perceptron takes binary inputs, multiplies them with weights, adds a bias and passes the result through an activation function usually a step function.

y = \Theta(w_1 x_1 + w_2 x_2 + b)

where:

x_1, x_2: Inputs
w_1, w_2:Weights for the inputs
b: Bias
\Theta(z): Step function

Step Function:

\Theta(z) = \begin{cases}1 & \text{if } z \geq 0 \\0 & \text{if } z < 0\end{cases}

Choose Weights and Bias for NAND

We need to find w_1, w_2,and, b such that the perceptron behaves like a NAND gate. Let’s use this configuration:

w_1 = -1
w_2 = -1
b = 1.5

The inputs x_1 \,and\,x_2go into the perceptron. The weights and bias are used to calculate the weighted sum. and the step function determines the output.

NA_p — Nand gate implementation

Let’s implement this into Python.

Step 1: Define the Step Activation Function

Python

import numpy as np

def step_function(z):
    return 1 if z >= 0 else 0

Step 2: Perceptron logic for 2 inputs

Python

def perceptron(x1, x2, w1, w2, b):
    z = (x1 * w1) + (x2 * w2) + b
    return step_function(z)

Step 3: Set weights and bias

Python

w1 = -1
w2 = -1
b = 1.5

Step 4: Test all combinations

Python

print("NAND Gate Output:")
for x1 in [0, 1]:
    for x2 in [0, 1]:
        output = perceptron(x1, x2, w1, w2, b)
        print(f"NAND({x1}, {x2}) = {output}")

Output:

NAND-Gate-output-of-Perceptron — NAND Gate Output

The code correctly predicts the NAND gate outputs for all input combinations. The perceptron outputs matches expected truth table.

Implementation of Perceptron Algorithm for AND Logic Gate with 2-bit Binary Input

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