[딥러닝] 딥러닝 활성화 함수 적용하기

 

딥러닝 7 공식 적용하기

from math import exp

x1,x2=0.05,0.10
w1,w2=0.15,0.20
w3,w4=0.25,0.30
b1,b2=0.35,0.35
w5,w6=0.40,0.45
w7,w8=0.50,0.55
b3,b4=0.60,0.60
y1T,y2T=0.01,0.99
lr=0.01

EPOCH=1000

for epoch in range(EPOCH):
    h1=x1*w1+x2*w2+1*b1
    h2=x1*w3+x2*w4+1*b2
    
    # ReLU feed forward
    h1=h1 if h1>0 else 0
    h2=h2 if h2>0 else 0
    
    y1=h1*w5+h2*w6+1*b3
    y2=h1*w7+h2*w8+1*b4
    
    # sigmoid feed forward
    y1=1/(1+exp(-y1))
    y2=1/(1+exp(-y2))
    
    E=(y1-y1T)**2/2+(y2-y2T)**2/2
    
    y1E=y1-y1T
    y2E=y2-y2T
    
    # sigmoid back propagation
    y1E=y1*(1-y1)*y1E
    y2E=y2*(1-y2)*y2E
    
    w5E=y1E*h1
    w6E=y1E*h2
    w7E=y2E*h1
    w8E=y2E*h2
    b3E=y1E*1
    b4E=y2E*1
    
    h1E=y1E*w5+y2E*w7
    h2E=y1E*w6+y2E*w8
    
    # ReLU back propagation
    h1E=h1E if h1>0 else 0
    h2E=h2E if h2>0 else 0
    
    w1E=h1E*x1
    w2E=h1E*x2
    w3E=h2E*x1
    w4E=h2E*x2
    b1E=h1E*1
    b2E=h2E*1
    
    w5=w5-lr*w5E
    w6=w6-lr*w6E
    w7=w7-lr*w7E
    w8=w8-lr*w8E
    b3=b3-lr*b3E
    b4=b4-lr*b4E
    
    w1=w1-lr*w1E
    w2=w2-lr*w2E
    w3=w3-lr*w3E
    w4=w4-lr*w4E
    b1=b1-lr*b1E
    b2=b2-lr*b2E
    
    if epoch % 100 == 99:
        print(f'epoch={epoch}')
        print(f'y1:{y1:6f}')
        print(f'y2:{y2:6f}')
        
    if E<0.0000001:
        break
    
print(f'w1,w3={w1:.6f},{w3:.6f}')
print(f'w2,w4={w2:.6f},{w4:.6f}')
print(f'b1,b2={b1:.6f},{b2:.6f}')
print(f'w5,w7={w5:.6f},{w7:.6f}')
print(f'w6,w8={w6:.6f},{w8:.6f}')
print(f'b3,b4={b3:.6f},{b4:.6f}')

 

 

 

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딥러닝 7공식에 linear 활성화 함수 적용

출력층에 linear 함수 적용해보기(출력층 : 보통 소프트맥스 사용, 이외에 시그모이드 등 사용)

from math import exp

x1,x2=0.05,0.10
w1,w2=0.15,0.20
w3,w4=0.25,0.30
b1,b2=0.35,0.35
w5,w6=0.40,0.45
w7,w8=0.50,0.55
b3,b4=0.60,0.60
y1T,y2T=0.01,0.99
lr=0.01

EPOCH=1000

for epoch in range(EPOCH):
    h1=x1*w1+x2*w2+1*b1
    h2=x1*w3+x2*w4+1*b2
    
    # ReLU feed forward
    h1=h1 if h1>0 else 0
    h2=h2 if h2>0 else 0
    
    y1=h1*w5+h2*w6+1*b3
    y2=h1*w7+h2*w8+1*b4
    
    # sigmoid feed forward
    # y1=1/(1+exp(-y1))
    # y2=1/(1+exp(-y2))

    E=(y1-y1T)**2/2+(y2-y2T)**2/2

    y1E=y1-y1T
    y2E=y2-y2T

    # sigmoid back propagation
    # y1E=y1*(1-y1)*y1E
    # y2E=y2*(1-y2)*y2E
     
    w5E=y1E*h1
    w6E=y1E*h2
    w7E=y2E*h1
    w8E=y2E*h2
    b3E=y1E*1
    b4E=y2E*1
    
    h1E=y1E*w5+y2E*w7
    h2E=y1E*w6+y2E*w8
    
    # ReLU back propagation
    h1E=h1E if h1>0 else 0
    h2E=h2E if h2>0 else 0
    
    w1E=h1E*x1
    w2E=h1E*x2
    w3E=h2E*x1
    w4E=h2E*x2
    b1E=h1E*1
    b2E=h2E*1
    
    w5=w5-lr*w5E
    w6=w6-lr*w6E
    w7=w7-lr*w7E
    w8=w8-lr*w8E
    b3=b3-lr*b3E
    b4=b4-lr*b4E
    
    w1=w1-lr*w1E
    w2=w2-lr*w2E
    w3=w3-lr*w3E
    w4=w4-lr*w4E
    b1=b1-lr*b1E
    b2=b2-lr*b2E
    
    if epoch % 100 == 99:
        print(f'epoch={epoch}')
        print(f'y1:{y1:6f}')
        print(f'y2:{y2:6f}')
        
    if E<0.0000001:
        break
    
print(f'w1,w3={w1:.6f},{w3:.6f}')
print(f'w2,w4={w2:.6f},{w4:.6f}')
print(f'b1,b2={b1:.6f},{b2:.6f}')
print(f'w5,w7={w5:.6f},{w7:.6f}')
print(f'w6,w8={w6:.6f},{w8:.6f}')
print(f'b3,b4={b3:.6f},{b4:.6f}')