Deep Learning(0902_day1)

퍼셉트론¶

• AND 게이트

In [18]:

import numpy as np

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
    

def AND(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(AND(0, 0), AND(0, 1), AND(1, 0), AND(1, 1))

• NAND 게이트

In [31]:

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

print(NAND(0, 0), NAND(0, 1), NAND(1, 0), NAND(1, 1))

1 1 1 0

• OR 게이트

In [32]:

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
    else:
        return 1

print(OR(0, 0), OR(0, 1), OR(1, 0), OR(1, 1))

0 1 1 1

In [33]:

def XOR(x1, x2):
    s1 = NAND(x1, x2)
    s2 = OR(x1, x2)
    y = AND(s1, s2)
    return y

print(XOR(0, 0), XOR(0, 1), XOR(1, 0), XOR(1, 1))

0 1 1 0