[Python][AI] XOR 예제

아래와 같이 1개의 은닉층을 가진 신경망을 구성해 보았습니다.

 

import numpy as np

def sigmoid(x):
    return 1/(1+np.exp(-x))


def DeltaSGD(W1, W2, X, D):
    alpha = 0.9
    
    idx = 0
    for k in X:
        a1 = np.dot(W1, k)
        z1 = sigmoid(a1)
        
        a2 = np.dot(W2, z1)
        y = sigmoid(a2)
        
        e = D[idx] - y
        delta = y * (1-y) * e
        
        e1 = W2 * delta
        delta1 = z1 * (1-z1) * e1

        dw1 = alpha * delta1
        dw1 = dw1.reshape(4,1)
        x = k.reshape(1,3)
        W1 += np.dot(dw1, x)
        
        dw2 = alpha * delta * z1
        W2 += dw2
        idx+=1

        
def Train(epoch, x, y, W1, W2):
    for ep in range (epoch):
        DeltaSGD(W1, W2, x, y)

        
def main():
    W1 = np.random.randn(4,3)
    W2 = np.random.randn(4)
    x = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
    y = np.array([0, 1, 1, 0])
    trainCount = 10000

    print('---Before Training---')
    print(W1)
    print(W2)
    print('---After Training---')
    Train(trainCount, x, y, W1, W2)
    print(W1)
    print(W2)
    
    print('---Result---')
    for k in x:
        a1 = np.dot(W1, k)
        z1 = sigmoid(a1)
        
        a2 = np.dot(W2, z1)
        y = sigmoid(a2)
        print(y) 
        
        
if __name__ == '__main__':       
    main()        

---Before Training--- [[-0.82542712 -0.65157477  0.09131077]  [-0.12367401  1.07601221 -1.43941252]  [-1.00401084 -0.04547823  0.97194216]  [ 0.02719329 -0.22932678 -0.45809196]] [ 0.16840571 -0.55206913  1.75255171 -0.54203599] ---After Training--- [[-6.3608461  -6.35976778  2.71126418]  [ 3.61918383  3.62562196 -5.65337702]  [-3.70117648 -3.70158904  5.76910815]  [-1.51981715 -1.56682777 -0.58710802]] [-11.12882426  -6.71646542   6.50908732  -1.52377049] ---Result--- 0.01083108954199271 0.9896067125952175 0.9895881791368779 0.01040259419256147