三层神经网络(输入、隐藏、输出):
import numpy as np def nonlin(x, deriv=False): if (deriv == True): return x * (1 - x) return 1 / (1 + np.exp(-x)) X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) y = np.array([[0], [1], [1], [0]]) np.random.seed(1) # randomly initialize our weights with mean 0 syn0 = 2 * np.random.random((3, 4)) - 1 syn1 = 2 * np.random.random((4, 1)) - 1 for j in range(60000): # Feed forward through layers 0, 1, and 2 l0 = X l1 = nonlin(np.dot(l0, syn0)) l2 = nonlin(np.dot(l1, syn1)) # how much did we miss the target value? l2_error = y - l2 if (j % 10000) == 0: print("Error:" + str(np.mean(np.abs(l2_error)))) print(syn0,syn1) # in what direction is the target value? # were we really sure? if so, don't change too much. l2_delta = l2_error * nonlin(l2, deriv=True) # how much did each l1 value contribute to the l2 error (according to the weights)? l1_error = l2_delta.dot(syn1.T) # in what direction is the target l1? # were we really sure? if so, don't change too much. l1_delta = l1_error * nonlin(l1, deriv=True) syn1 += l1.T.dot(l2_delta) syn0 += l0.T.dot(l1_delta)