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import numpy as np # sigmoid function def nonlin(x,deriv=False): if(deriv==True): return x*(1-x) return 1/(1+np.exp(-x)) # input dataset X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ]) # output dataset y = np.array([[0,0,1,1]]).T print() # seed random numbers to make calculation # deterministic (just a good practice) ''' np.random.seed(1) ''' # initialize weights randomly with mean 0 syn0 = 2*np.random.random((3,1)) - 1 print(syn0) for iter in range(100): # forward propagation l0 = X l1 = nonlin(np.dot(l0,syn0)) # how much did we miss? l1_error = y - l1 # multiply how much we missed by the # slope of the sigmoid at the values in l1 l1_delta = l1_error * nonlin(l1,True) # update weights syn0 += np.dot(l0.T,l1_delta) print ("Output After Training:") print (l1)

是不是他就是先生成几个随机的数值，然后然后不断迭代他，计算他和准确值的差，

l1_delta = l1_error * nonlin(l1,True) # update

syn0 += np.dot(l0.T,l1_delta)

这里是什么意思？他是如何调整权重的?nonlin这个代码什么意思？l1_delta是不是就是新的权重？那么np.dot(l0.T,l1_delta)他又是怎么把他更新到新的矩阵里去的呢？