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

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    0 0 1
0 1 1
1 0 1
1 1 0

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 library  import numpy as np     # define Unit Step Function  def unitStep(v):      if v >= 0:          return 1     else:          return 0    # design Perceptron Model  def perceptronModel(x, w, b):      v = np.dot(w, x) + b      y = unitStep(v)      return y     # NOT Logic Function  # wNOT = -1, bNOT = 0.5  def NOT_logicFunction(x):      wNOT = -1     bNOT = 0.5     return perceptronModel(x, wNOT, bNOT)     # AND Logic Function  # w1 = 1, w2 = 1, bAND = -1.5  def 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 sequence  def NAND_logicFunction(x):      output_AND = AND_logicFunction(x)      output_NOT = NOT_logicFunction(output_AND)      return output_NOT     # testing the Perceptron Model  test1 = 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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Improved By : Akanksha_Rai