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)
目录
输出结果
实现代码
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)
