import numpy as np
实现AND函数(与门):
# 最简单版本
def AND(x1, x2):
w1, w2, theta = 0.5, 0.5, 0.7
tmp = x1*w1 + x2*w2
if tmp <= theta:
return 0
elif tmp > theta:
return 1
print(AND(0, 0)) # 输出0
print(AND(1, 0)) # 输出0
print(AND(0, 1)) # 输出0
print(AND(1, 1)) # 输出1
0
0
0
1
# 使用权重和偏置的版本:
def AND1(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
print(AND1(0, 0)) # 输出0
print(AND1(1, 0)) # 输出0
print(AND1(0, 1)) # 输出0
print(AND1(1, 1)) # 输出1
0
0
0
1
实现NAND函数(与非门):
def NAND(x1, x2):
x = np.array([x1, x2])
w = np.array([-0.5, -0.5]) # 仅权重和偏置与AND不同!
b = 0.7
tmp = np.sum(w*x) + b
if tmp <= 0:
return 0
else:
return 1
print(NAND(0, 0)) # 输出1
print(NAND(1, 0)) # 输出1
print(NAND(0, 1)) # 输出1
print(NAND(1, 1)) # 输出0
1
1
1
0
实现OR函数(或门):
def OR(x1, x2):
x = np.array([x1, x2])
w = np.array([0.5, 0.5]) # 仅权重和偏置与AND不同!
b = -0.2
tmp = np.sum(w*x) + b
if tmp <= 0:
return 0
else:
return 1
print(OR(0, 0)) # 输出0
print(OR(1, 0)) # 输出1
print(OR(0, 1)) # 输出1
print(OR(1, 1)) # 输出1
0
1
1
1
实现异或门:
def XOR(x1, x2):
s1 = NAND(x1, x2)
s2 = OR(x1, x2)
y = AND(s1, s2)
return y
print(XOR(0, 0)) # 输出0
print(XOR(1, 0)) # 输出1
print(XOR(0, 1)) # 输出1
print(XOR(1, 1)) # 输出0
0
1
1
0