X = np.array([0,0,1],[0,1,1],[1,0,1],[1,1,1]])
y = np.array([[0,1,1,0]]).T
sysn0= 2*np.random.random((3,4))-1
syn1 = 2*np.random.random((4,1))-1
for j in xrange(60000):
l1 = 1/(1+np.exp(-(-np.dot(X,syn0))))
l2 = 1/(1+np.exp(-(-np.dot(l1,syn1))))
l2_delta = (y-l2)*(l2*1-l2))
l1_delta = l2_deltadot(syn1.T)*(l1*1-l1))
syn1 += l1.T.dot(l2_delta)
syn0 += X.T.dot(l1_delta)
11行Python代码实现----------两层神经网络的前向和反向传播
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转载自blog.csdn.net/weixin_38246633/article/details/83036135
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