BP神经网络-XOR运算

import numpy as npx = np.array([[1,0,0],             [1,0,1],             [1,1,0],             [1,1,1]])y = np.array([[0,1,1,0]])v = np.random.random((3,4))*2-1w = np.random.random((4,1))*2-1print(v)print(w)lr = 0.11def sigmoid(x):    return 1/(1+np.exp(-x))def dsigmoid(x):    return x*(1-x)def update():    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_cfor i in range(20000):    update()    if i % 500==0:        l1 = sigmoid(np.dot(x,v))        l2 = sigmoid(np.dot(l1,w))        print(np.mean(np.abs(y.T-l2)))l1 = sigmoid(np.dot(x,v))l2 = sigmoid(np.dot(l1,w))print(l2)