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

• Last Updated : 08 Jul, 2020

In the field of Machine Learning, the Perceptron is a Supervised Learning Algorithm for binary classifiers. The Perceptron Model implements the following function: For a particular choice of the weight vector and bias parameter , the model predicts output for the corresponding input vector .

NAND logical function truth table for 2-bit binary variables, i.e, the input vector and the corresponding output    001
011
101
110

We can observe that, Now for the corresponding weight vector of the input vector to the AND node, the associated Perceptron Function can be defined as: Later on, the output of AND node is the input to the NOT node with weight . Then the corresponding output is the final output of the NAND logic function and the associated Perceptron Function can be defined as:  For the implementation, considered weight parameters are and the bias parameters are .

Python Implementation:

 # importing Python libraryimport numpy as np  # define Unit Step Functiondef unitStep(v):    if v >= 0:        return 1    else:        return 0  # design Perceptron Modeldef perceptronModel(x, w, b):    v = np.dot(w, x) + b    y = unitStep(v)    return y  # NOT Logic Function# wNOT = -1, bNOT = 0.5def NOT_logicFunction(x):    wNOT = -1    bNOT = 0.5    return perceptronModel(x, wNOT, bNOT)  # AND Logic Function# w1 = 1, w2 = 1, bAND = -1.5def AND_logicFunction(x):    w = np.array([1, 1])    bAND = -1.5    return perceptronModel(x, w, bAND)  # NAND Logic Function# with AND and NOT  # function calls in sequencedef NAND_logicFunction(x):    output_AND = AND_logicFunction(x)    output_NOT = NOT_logicFunction(output_AND)    return output_NOT  # testing the Perceptron Modeltest1 = np.array([0, 1])test2 = np.array([1, 1])test3 = np.array([0, 0])test4 = np.array([1, 0])  print("NAND({}, {}) = {}".format(0, 1, NAND_logicFunction(test1)))print("NAND({}, {}) = {}".format(1, 1, NAND_logicFunction(test2)))print("NAND({}, {}) = {}".format(0, 0, NAND_logicFunction(test3)))print("NAND({}, {}) = {}".format(1, 0, NAND_logicFunction(test4)))
Output:
NAND(0, 1) = 1
NAND(1, 1) = 0
NAND(0, 0) = 1
NAND(1, 0) = 1


Here, the model predicted output ( ) for each of the test inputs are exactly matched with the NAND logic gate conventional output ( ) according to the truth table for 2-bit binary input.
Hence, it is verified that the perceptron algorithm for NAND logic gate is correctly implemented.

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