Simple perceptron neural net to test boolean functions (CSE471).

perceptron_test.py

import math

def sig(x):
  return 1 / (1 + math.exp(-x))
  
b = [0,1]

# INPUT WEIGHTS #

w1 = [ 2, 1,-3,-4,-4, 4]
w2 = [-2,-3, 1, 4, 5,-4]
w3 = [ 3, 1,-3,-4,-3, 5]
w4 = [-4,-2,-2,-4,-4,-3]

W = [w1,w2,w3,w4]

def nn(X1,X2,w):
  H1 = sig(w[0]*X1+w[2]*X2+0.5)
  H2 = sig(w[1]*X1+w[3]*X2+0.5)
  Y  = sig(w[4]*H1+w[5]*H2+0.5)
  return round(Y)

print('┌──┐  w0  ┌──┐')       
print('│X1├─────>│H1│')       
print('└──┘      └──┘')       
print('    \     ^   \\ w4')      
print('  w1 \   /     \\')   
print('      \ /       >┌───┐')
print('       X         │ Y │')
print('      / \       >└───┘')
print('  w2 /   \     / ')   
print('    /     v   / w5')      
print('┌──┐  w3  ┌──┐')       
print('│X2├─────>│H2│')      
print('└──┘      └──┘')  

print('Weights = ',W)

print("┌────┬────┬────┬────┬────┬────┬────┐")
print("│X1  │X2  │AND │OR  │NAND│NOR │ NN │") 

for v in W:
  print("├────┼────┼────┼────┼────┼────┼────┤")   
  for i in b:
    for j in b:
      print("│ "+str(i),j,i and j,i or j, int(not(i and j)), int(not(i or j)),str(nn(i,j,v))+"  │ ",sep="  │ ")

print("└────┴────┴────┴────┴────┴────┴────┘")