def main(): # nn = NN(2, 1) # input = torch.DoubleTensor([[1,1,0,0],[1,0,1,0]]) # print("input", input) # output = nn.forward(input) # print("output", output) And = AND() Or = OR() Not = NOT() Xor = XOR() print(And(True, True), And(True, False), And(False, True), And(False, False)) print(Or(True, True), Or(True, False), Or(False, True), Or(False, False)) print(Not(True), Not(False)) print(Xor(True, True), Xor(True, False), Xor(False, True), Xor(False, False))
def test(): # ~~~~~~~~~~~~~~~~~Test Neural Network with batch input~~~~~~~~~~~~~~~~~~~# layers = [3, 4, 2] # [in,h1,...,out] print('layers', layers) model = NeuralNetwork(layers) # initialize neural network # input = torch.DoubleTensor([[1, 3, 1], [2, 2, 2]]) #2D DoubleTensor input = torch.DoubleTensor([1, 3, 1]) # 1D DoubleTensor if (input.numpy()).ndim is 1: pass else: input = torch.transpose(input, 0, 1) print('input', input) print('output', model.forward(input)) # ~~~~~~~~~~~~~~~~~Test getLayer method~~~~~~~~~~~~~~~~~~~# th0 = model.getLayer(0) print('th0=', th0) # th0[0]=-20; th0[1] =15; #th0[2] =15 #modify theta # print('th0m=', th0) # ~~~~~~~~~~~~~~~~~Test Logic Gates~~~~~~~~~~~~~~~~~~~# And = AND() print(And(False, False)) print(And(False, True)) print(And(True, False)) print(And(True, True)) Or = OR() print(Or(False, False)) print(Or(False, True)) print(Or(True, False)) print(Or(True, True)) Not = NOT() print(Not(False)) print(Not(True)) Xor = XOR() print(Xor(False, False)) print(Xor(False, True)) print(Xor(True, False)) print(Xor(True, True))
# for i in range(100): # xor_gate = XOR() # xor_gate.train() # mse = xor_gate._mse() # if mse >= .01: # wrong += 1 # else: # correct += 1 # print(wrong) # print(correct) # print(correct / (wrong + correct)) # exit() and_gate = AND() and_gate.train() or_gate = OR() or_gate.train() not_gate = NOT() not_gate.train() xor_gate = XOR() xor_gate.train() print("") print("AND GATE") print(and_gate(False, False))
from logic_gates import AND from logic_gates import OR from logic_gates import NOT from logic_gates import XOR print("------- AND -------") And = AND() print(And(True, True)) print(And(True, False)) print(And(False, True)) print(And(False, False)) print("------- OR -------") Or = OR() print(Or(True, True)) print(Or(True, False)) print(Or(False, True)) print(Or(False, False)) print("------- NOT -------") Not = NOT() print(Not(True)) print(Not(False)) print("------- XOR -------") Xor = XOR() print(Xor(True, True)) print(Xor(True, False)) print(Xor(False, True)) print(Xor(False, False))
print 'Test Input:', test_inp print 'Output:', model.forward(test_inp) print 'PART A Completed' print '---------------------------------------------------------------------' print '\nPART B: \t Neural Networks - Logic Gates' # Test Inputs test_input_1 = [[False, False], [False, True], [True, False], [True, True]] test_input_2 = [False, True] # AND Gate ----------------------------------------------------------------- print '\nInitializing Object for AND Gate ...' ANDCurr = AND() print 'Testing Output ...' for test_inp in test_input_1: print 'Input 0: ', test_inp[0],' \tInput 1: ', test_inp[1], ' \tOutput: ', \ ANDCurr(test_inp[0],test_inp[1]) # OR Gate ----------------------------------------------------------------- print '\nInitializing Object for OR Gate ...' ORCurr = OR() print 'Testing Output ...' for test_inp in test_input_1: print 'Input 0: ', test_inp[0],' \tInput 1: ', test_inp[1], ' \tOutput: ', \
import numpy as np import torch from logic_gates import AND,OR,NOT,XOR # Testing a=AND() c=OR() b=XOR() e=NOT() i=[[True,False],[True,True]] i1=[True,True] i2=[False,False] i3=[False,True] print("The input sequence for testing is:") print("1.",i) print("2.",i1) print("3.",i2) print("4.",i3) print("Training of AND gate") a.train() print("Testing of AND gate") print(a.forward(i)) print(a.forward(i1)) print(a.forward(i2)) print(a.forward(i3)) print("Training of XOR gate") b.train()
# coding: utf-8 # In[1]: from neural_network import NeuralNetwork from nn2 import NeuralNetwork2 import matplotlib.pyplot as plt import torch from logic_gates import AND from logic_gates import OR from logic_gates import NOT from logic_gates import XOR # In[2]: And = AND() #print(And.nn.theta) print(And(True, True)) print(And(True, False)) print(And(False, True)) print(And(False, False)) # In[3]: Or = OR() #print(Or.nn.theta) print(Or(True, True)) print(Or(True, False)) print(Or(False, True)) print(Or(False, False))
#from __future__ import print_function as print from neural_network import NeuralNetwork as NN import torch from logic_gates import AND, OR, NOT, XOR if __name__ == "__main__": nn = NN([2, 1]) and_gate = AND() or_gate = OR() not_gate = NOT() xor_gate = XOR() print(and_gate(False, False)) print(and_gate(False, True)) print(and_gate(True, False)) print(and_gate(True, True)) print(" ") print(or_gate(False, False)) print(or_gate(False, True)) print(or_gate(True, False)) print(or_gate(True, True)) print("") print(not_gate(True)) print(not_gate(False)) print("") print(xor_gate(False, False)) print(xor_gate(False, True))
from neural_network import NeuralNetwork from logic_gates import AND from logic_gates import OR from logic_gates import NOT from logic_gates import XOR print "Testing AND gate implementation.\nAND Truth Table:" _and = AND() print "INPUT_1\tINPUT_2\tAND_RESULT" print "True\tTrue\t" + str(_and(True, True)) print "True\tFalse\t" + str(_and(True, False)) print "False\tTrue\t" + str(_and(False, True)) print "False\tFalse\t" + str(_and(False, False)) print "\nTesting OR gate implementation.\nOR Truth Table:" _or = OR() print "INPUT_1\tINPUT_2\tOR_RESULT" print "True\tTrue\t" + str(_or(True, True)) print "True\tFalse\t" + str(_or(True, False)) print "False\tTrue\t" + str(_or(False, True)) print "False\tFalse\t" + str(_or(False, False)) print "\nTesting NOT gate implementation.\nNOT Truth Table:" _not = NOT() print "INPUT_1\tNOT_RESULT" print "True\t" + str(_not(True)) print "False\t" + str(_not(False)) print "\nTesting XOR gate implementation.\nXOR Truth Table:" _xor = XOR() print "INPUT_1\tINPUT_2\tXOR_RESULT"
from logic_gates import AND from logic_gates import OR from logic_gates import NOT from logic_gates import XOR # Initialize 4 types of Gates And = AND() And.train() Or = OR() Or.train() Not = NOT() Not.train() Xor = XOR() Xor.train() ' Test cases for 4 Gates for 1D Input' # Test cases for AND print( "\nDemonstrating AND Gate functionality using FeedForward Neural Network") print("And(False, False) = %r" % And.forward(False, False)) print("And(False, True) = %r" % And.forward(False, True)) print("And(True, False) = %r" % And.forward(True, False)) print("And(True, True) = %r" % And.forward(True, True)) # Test cases for OR print("\nDemonstrating OR Gate functionality using FeedForward Neural Network") print("Or(False, False) = %r" % Or.forward(False, False)) print("Or(False, True) = %r" % Or.forward(False, True)) print("Or(True, False) = %r" % Or.forward(True, False)) print("Or(True, True) = %r" % Or.forward(True, True))