def XOR(x1, x2):
# 第1層
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
# 第2層
y = AND(s1, s2)
# 出力
return y
def XOR(x1, x2):
"""
x1 x2 | y
----------
0 0 | 0
0 1 | 1
1 0 | 1
1 1 | 0
"""
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
y = AND(s1, s2)
return y
def XOR(x1, x2):
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
return AND(s1, s2)
import numpy as np
def NAND(x1, x2):
x = np.array([x1, x2])
w = np.array([-0.5, -0.5])
b = 0.7
tmp = np.sum(w*x) + b
if tmp <= 0:
return 0
else:
return 1
if __name__ == '__main__':
for xs in [(0, 0), (1, 0), (0, 1), (1, 1)]:
y = NAND(xs[0], xs[1])
print(str(xs) + " -> " + str(y))
###########################################################
# coding: utf-8
import numpy as np
def OR(x1, x2):
x = np.array([x1, x2])
w = np.array([0.5, 0.5])
b = -0.2
tmp = np.sum(w*x) + b
if tmp <= 0:
return 0
def XOR(x1, x2):
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
y = AND(s1, s2)##至少有一个，且不相同，就是异或
return y
def XOR(x1, x2):
s1 = OR(x1, x2)
s2 = NAND(x1, x2)
y = AND(s1, s2)
return y
def XOR(x1, x2):
"""returns x1 XOR x2"""
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
return AND(s1, s2)
def XOR(x1, x2): # multi-layer perceptron
s1 = NAND(x1, x2) # layer 1
s2 = OR(x1, x2) # layer 1
y = AND(s1, s2) # layer 2
return y