搭建简单的三层神经网络

# -*- coding: utf-8 -*-

import numpy as np

#损失函数
def sigmoid(x,deriv = False):
    if (deriv == True):
        return x * (1-x)
    return 1/(1 + np.exp(-x))

#建立输入输出数组
x = np.array([[0,0,1],
              [0,1,1],
              [1,0,1],
              [1,1,1],
              [0,0,1]
              ])
print(x.shape)

y = np.array([[0],
              [1],
              [1],
              [0],
              [0]
              ])
print(y.shape)
    
np.random.seed(1)

#搭建三层神经网络
w0 = 2 * np.random.random((3,4)) - 1
w1 = 2 * np.random.random((4,1)) - 1

print(w0)
print(w1)

for j in range(60000):
    l0 = x
    l1 = sigmoid(np.dot(l0,w0))
    l2 = sigmoid(np.dot(l1,w1))
    
#    损失值
    l2_error = y - l2
    if (j % 10000 == 0):
        print('Error' + str(np.mean(np.abs(l2_error)))) 
#        反向传播求导
        l2_delta = l2_error * sigmoid(l2,deriv = True)
        l1_error = l2_delta.dot(w1.T)
        l1_delta = l1_error * sigmoid(l1,deriv = True)
       
#       更新权重
        w1 += l1.T.dot(l2_delta)
        w0 += l0.T.dot(l1_delta)