python_神经网络识别手写数字0-9

Posted 2020-11-27 太空堡垒啦啦啦

 

  1 import numpy as np
  2 import matplotlib.pyplot as plt

  5 import struct
  6 i=0
  7 #读取训练数据
  8 file_list=[\'train-images.idx3-ubyte\',\'train-labels.idx1-ubyte\',\'t10k-images.idx3-ubyte\',\'t10k-labels.idx1-ubyte\']
  9 with open(\'./sjwl/train-images.idx3-ubyte\',\'rb\') as f:
 10     header_data=f.read(16)
 11     image_data=[]
 12     image=[]
 13     while i<1000:
 14         im=f.read(28*28)
 15         image_data.append(im)
 16         tp=struct.unpack(\'784B\',image_data[i])
 17         image.append(tp)
 18         i=i+1
 19 with open(\'./sjwl/train-labels.idx1-ubyte\',\'rb\') as f:
 20     header_label=f.read(8)
 21     labels_data=[]
 22     labels=[]
 23     i=0
 24     while i<1000:
 25         la=f.read(1)
 26         labels_data.append(la)
 27         label=struct.unpack(\'>B\',labels_data[i])
 28         labels.append(label)
 29         i=i+1
 30 #sigmoid函数
 31 def sigmoid(x):
 32     y=1/(1+np.exp(-x))
 33     return y
 34 #学习率eta
 35 eta=0.2
 36 #创建隐层
 37 b=np.zeros((1,100))#隐层输出矩阵，隐层神经元个数
 38 alpha=np.zeros((1,100))#隐层神经元输入
 39 eh=np.zeros((1,100))#隐层梯度项
 40 #创建输出层
 41 y=np.zeros((1,10))#输出层输出
 42 beta=np.zeros((1,10))#输出层输入
 43 gj=np.zeros((1,10))#输出层梯度项
 44 #定义连接权
 45 v=np.random.rand(784,100)#输入层-隐层-连接权
 46 delt_v=np.zeros((784,100))#输入层-隐层-连接权-误差
 47 w=np.random.rand(100,10)#隐层-输出层-连接权
 48 delt_w=np.zeros((100,10))#隐层-输出层-连接权-误差
 49 #定义阈值
 50 thita=np.random.rand(1,10)#输出层神经元阈值
 51 delt_thita=np.zeros((1,10))#输出层神经元阈值-误差
 52 gamma=np.random.rand(1,100)#隐层神经元阈值
 53 delt_gamma=np.zeros((1,100))#隐层神经元阈值-误差
 54 #循环
 55 for daishu in range(1000):
 56     for n in range(100):
 57         image_=np.asarray(image[n])
 58         shuru=np.asarray(image_)
 59         shuru=shuru/255
 60         mubiao=np.asarray(labels[n])
 61         if mubiao[0]==0:
 62             mubiaojz=np.asarray([1,0,0,0,0,0,0,0,0,0])
 63         elif mubiao[0]==1:
 64             mubiaojz=np.asarray([0,1,0,0,0,0,0,0,0,0])
 65         elif mubiao[0]==2:
 66             mubiaojz=np.asarray([0,0,1,0,0,0,0,0,0,0])
 67         elif mubiao[0]==3:
 68             mubiaojz=np.asarray([0,0,0,1,0,0,0,0,0,0])
 69         elif mubiao[0]==4:
 70             mubiaojz=np.asarray([0,0,0,0,1,0,0,0,0,0])
 71         elif mubiao[0]==5:
 72             mubiaojz=np.asarray([0,0,0,0,0,1,0,0,0,0])
 73         elif mubiao[0]==6:
 74             mubiaojz=np.asarray([0,0,0,0,0,0,1,0,0,0])
 75         elif mubiao[0]==7:
 76             mubiaojz=np.asarray([0,0,0,0,0,0,0,1,0,0])
 77         elif mubiao[0]==8:
 78             mubiaojz=np.asarray([0,0,0,0,0,0,0,0,1,0])
 79         elif mubiao[0]==9:
 80             mubiaojz=np.asarray([0,0,0,0,0,0,0,0,0,1])
 81         #计算输出
 82         alpha=shuru@v
 83         alpha=alpha/100
 84         b=sigmoid(alpha-gamma)
 85         beta=b@w
 86         beta=beta/100
 87         y=sigmoid(beta-thita)
 88         #计算输出层神经元梯度项
 89         gj=y*(1-y)*(mubiaojz-y)
 90         #计算隐层神经元梯度项
 91         eh=b*(1-b)*(gj@w.T) 
 92         #更新链接权
 93         delt_w=eta*b.T@gj
 94         delt_thita=-eta*gj
 95         shuru=shuru.reshape(1,784)
 96         delt_v=eta*shuru.T@eh
 97         delt_gamma=-eta*eh
 98         w=w+delt_w
 99         thita=thita+delt_thita
100         v=v+delt_v
101         gamma=gamma+delt_gamma

运行完测试一下

数字0

输出结果矩阵y

代表数字0的y[0]=0.957864064，代表1-9的y[1:9]的结果接近于0，识别结果是数字0。