def XOR(x):
a1 = NAND(x)
a2 = OR(x)
a3 = AND(np.array([a1, a2]))
y = identity(a3) #출력함수 적용 지점 : q
return y
def forward_propagation(network, x):
w1, w2, w3 = network['w1'], network['w2'], network['w3']
b1, b2, b3 = network['b1'], network['b2'], network['b3']
a1 = np.dot(x, w1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, w2) + b2
z2 = sigmoid(a2)
a3 = np.dot(z2, w3) + b3
y = identity(a3)
return y
# 출력함수(출력층 활성함수) σ() - 항등함수(Identity Function)
import os
import sys
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import identity
except ImportError:
print('Library Module Can Not Found')
x = np.arange(-10, 10, 0.1)
y = identity(x)
plt.plot(x, y)
plt.show()
# 3층 신경망 신호 전달 구현6: 출력층 출력함수 σ() 적용
import os
import sys
from pathlib import Path
try:
sys.path.append(os.path.join(os.getcwd()))
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from ex05 import a3
from common import identity
except ImportError:
print('Library Module Can Not Found')
print('\n= 신호 전달 구현6: 출력층 출력함수 σ() 적용 ======================')
print(f'a3 dimension: {a3.shape}') # 2 vector
y = identity(a3)
print(f'y = {y}')