ML之NN：利用神经网络的BP算法解决XOR类(异或非)问题(BP solve XOR Problem)

ML之NN：利用神经网络的BP算法解决XOR类(异或非)问题(BP solve XOR Problem)

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输出结果

实现代码

#BP solve XOR Problem
import numpy as np

X = np.array ([[1, 0, 0],
               [1, 0, 1],
               [1, 1, 0],
               [1, 1, 1]])
#标签
Y = np.array ([[0, 1, 1, 0]])
V = np.random.randn(3,4)*2-1
W = np.random.randn(4,1)*2-1
print (V)
print (W)
#设置学习率
lr = 0.11 

def update():  #更新权值的函数
    global X,Y,W,V,lr
    L1=sigmoid(np.dot(X,V))
    L2=sigmoid(np.dot(L1,W))
    L2_delta=(Y.T-L2)*dsigmoid(L2)
    L1_delta=L2_delta.dot(W.T)*dsigmoid(L1)

    W_C=lr*L1.T.dot(L2_delta)
    V_C=lr*X.T.dot(L1_delta)
    W=W+W_C
    V=V+V_C
for i in range(20000):
    update()
    if i%500==0:
        L1=sigmoid(np.dot(X,V))  #隐藏层输出4*4
        L2=sigmoid(np.dot(L1,W)) #输出层输出4*1
        print("error:",np.mean(np.abs(Y.T-L2)))

L1=sigmoid(np.dot(X,V))
L2=sigmoid(np.dot(L1,W))
print(L2)

def judge(x):
    if x>=0.5:
        return 1
    else:
        return 0
for i in map(judge,L2):
    print(i)