python构建深度神经网络(续)


Posted in Python onMarch 10, 2018

这篇文章在前一篇文章:python构建深度神经网络(DNN)的基础上,添加了一下几个内容:

1) 正则化项

2) 调出中间损失函数的输出

3) 构建了交叉损失函数

4) 将训练好的网络进行保存,并调用用来测试新数据

1  数据预处理

#!/usr/bin/env python 
# -*- coding: utf-8 -*- 
# @Time : 2017-03-12 15:11 
# @Author : CC 
# @File : net_load_data.py 
 
from numpy import * 
import numpy as np 
import cPickle 
def load_data(): 
 """载入解压后的数据,并读取""" 
 with open('data/mnist_pkl/mnist.pkl','rb') as f: 
  try: 
   train_data,validation_data,test_data = cPickle.load(f) 
   print " the file open sucessfully" 
   # print train_data[0].shape #(50000,784) 
   # print train_data[1].shape #(50000,) 
   return (train_data,validation_data,test_data) 
  except EOFError: 
   print 'the file open error' 
   return None 
 
def data_transform(): 
 """将数据转化为计算格式""" 
 t_d,va_d,te_d = load_data() 
 # print t_d[0].shape # (50000,784) 
 # print te_d[0].shape # (10000,784) 
 # print va_d[0].shape # (10000,784) 
 # n1 = [np.reshape(x,784,1) for x in t_d[0]] # 将5万个数据分别逐个取出化成(784,1),逐个排列 
 n = [np.reshape(x, (784, 1)) for x in t_d[0]] # 将5万个数据分别逐个取出化成(784,1),逐个排列 
 # print 'n1',n1[0].shape 
 # print 'n',n[0].shape 
 m = [vectors(y) for y in t_d[1]] # 将5万标签(50000,1)化为(10,50000) 
 train_data = zip(n,m) # 将数据与标签打包成元组形式 
 n = [np.reshape(x, (784, 1)) for x in va_d[0]] # 将5万个数据分别逐个取出化成(784,1),排列 
 validation_data = zip(n,va_d[1]) # 没有将标签数据矢量化 
 n = [np.reshape(x, (784, 1)) for x in te_d[0]] # 将5万个数据分别逐个取出化成(784,1),排列 
 test_data = zip(n, te_d[1]) # 没有将标签数据矢量化 
 # print train_data[0][0].shape #(784,) 
 # print "len(train_data[0])",len(train_data[0]) #2 
 # print "len(train_data[100])",len(train_data[100]) #2 
 # print "len(train_data[0][0])", len(train_data[0][0]) #784 
 # print "train_data[0][0].shape", train_data[0][0].shape #(784,1) 
 # print "len(train_data)", len(train_data) #50000 
 # print train_data[0][1].shape #(10,1) 
 # print test_data[0][1] # 7 
 return (train_data,validation_data,test_data) 
def vectors(y): 
 "赋予标签" 
 label = np.zeros((10,1)) 
 label[y] = 1.0 #浮点计算 
 return label

2 网络定义和训练

#!/usr/bin/env python 
# -*- coding: utf-8 -*- 
# @Time : 2017-03-28 10:18 
# @Author : CC 
# @File : net_network2.py 
 
from numpy import * 
import numpy as np 
import operator 
import json 
# import sys 
 
class QuadraticCost(): 
 """定义二次代价函数类的方法""" 
 @staticmethod 
 def fn(a,y): 
  cost = 0.5*np.linalg.norm(a-y)**2 
  return cost 
 @staticmethod 
 def delta(z,a,y): 
  delta = (a-y)*sig_derivate(z) 
  return delta 
 
class CrossEntroyCost(): 
 """定义交叉熵函数类的方法""" 
 @staticmethod 
 def fn(a, y): 
  cost = np.sum(np.nan_to_num(-y*np.log(a)-(1-y)*np.log(1-a))) # not a number---0, inf---larger number 
  return cost 
 @staticmethod 
 def delta(z, a, y): 
  delta = (a - y) 
  return delta 
 
class Network(object): 
 """定义网络结构和方法""" 
 def __init__(self,sizes,cost): 
  self.num_layer = len(sizes) 
  self.sizes = sizes 
  self.cost = cost 
  # print "self.cost.__name__:",self.cost.__name__ # CrossEntropyCost 
  self.default_weight_initializer() 
 def default_weight_initializer(self): 
  """权值初始化""" 
  self.bias = [np.random.rand(x, 1) for x in self.sizes[1:]] 
  self.weight = [np.random.randn(y, x)/float(np.sqrt(x)) for (x, y) in zip(self.sizes[:-1], self.sizes[1:])] 
 
 def large_weight_initializer(self): 
  """权值另一种初始化""" 
  self.bias = [np.random.rand(x, 1) for x in self.sizes[1:]] 
  self.weight = [np.random.randn(y, x) for x, y in zip(self.sizes[:-1], self.sizes[1:])] 
 def forward(self,a): 
  """forward the network""" 
  for w,b in zip(self.weight,self.bias): 
   a=sigmoid(np.dot(w,a)+b) 
  return a 
 
 def SGD(self,train_data,min_batch_size,epochs,eta,test_data=False, 
   lambd = 0, 
   monitor_train_cost = False, 
   monitor_train_accuracy = False, 
   monitor_test_cost=False, 
   monitor_test_accuracy=False 
   ): 
  """1)Set the train_data,shuffle; 
   2) loop the epoches, 
   3) set the min_batches,and rule of update""" 
  if test_data: n_test=len(test_data) 
  n = len(train_data) 
  for i in xrange(epochs): 
   random.shuffle(train_data) 
   min_batches = [train_data[k:k+min_batch_size] for k in xrange(0,n,min_batch_size)] 
 
   for min_batch in min_batches: # 每次提取一个批次的样本 
    self.update_minbatch_parameter(min_batch,eta,lambd,n) 
   train_cost = [] 
   if monitor_train_cost: 
    cost1 = self.total_cost(train_data,lambd,cont=False) 
    train_cost.append(cost1) 
    print "epoche {0},train_cost: {1}".format(i,cost1) 
   if monitor_train_accuracy: 
    accuracy = self.accuracy(train_data,cont=True) 
    train_cost.append(accuracy) 
    print "epoche {0}/{1},train_accuracy: {2}".format(i,epochs,accuracy) 
   test_cost = [] 
   if monitor_test_cost: 
    cost1 = self.total_cost(test_data,lambd) 
    test_cost.append(cost1) 
    print "epoche {0},test_cost: {1}".format(i,cost1) 
   test_accuracy = [] 
   if monitor_test_accuracy: 
    accuracy = self.accuracy(test_data) 
    test_cost.append(accuracy) 
    print "epoche:{0}/{1},test_accuracy:{2}".format(i,epochs,accuracy) 
  self.save(filename= "net_save") #保存网络网络参数 
 
 def total_cost(self,train_data,lambd,cont=True): 
  cost1 = 0.0 
  for x,y in train_data: 
   a = self.forward(x) 
   if cont: y = vectors(y) #将测试样本标签化为矩阵 
   cost1 += (self.cost).fn(a,y)/len(train_data) 
  cost1 += lambd/len(train_data)*np.sum(np.linalg.norm(weight)**2 for weight in self.weight) #加上权值项 
  return cost1 
 def accuracy(self,train_data,cont=False): 
  if cont: 
   output1 = [(np.argmax(self.forward(x)),np.argmax(y)) for (x,y) in train_data] 
  else: 
   output1 = [(np.argmax(self.forward(x)), y) for (x, y) in train_data] 
  return sum(int(out1 == y) for (out1, y) in output1) 
 def update_minbatch_parameter(self,min_batch, eta,lambd,n): 
  """1) determine the weight and bias 
   2) calculate the the delta 
   3) update the data """ 
  able_b = [np.zeros(b.shape) for b in self.bias] 
  able_w=[np.zeros(w.shape) for w in self.weight] 
  for x,y in min_batch: #每次只取一个样本? 
   deltab,deltaw = self.backprop(x,y) 
   able_b =[a_b+dab for a_b, dab in zip(able_b,deltab)] #实际上对dw,db做批次累加,最后小批次取平均 
   able_w = [a_w + daw for a_w, daw in zip(able_w, deltaw)] 
  self.weight = [weight - eta * (dw) / len(min_batch)- eta*(lambd*weight)/n for weight, dw in zip(self.weight,able_w) ] 
  #增加正则化项:eta*lambda/m *weight 
  self.bias = [bias - eta * db / len(min_batch) for bias, db in zip(self.bias, able_b)] 
 
 def backprop(self,x,y): 
  """" 1) clacu the forward value 
   2) calcu the delta: delta =(y-f(z)); deltak = delta*w(k)*fz(k-1)' 
   3) clacu the delta in every layer: deltab=delta; deltaw=delta*fz(k-1)""" 
  deltab = [np.zeros(b.shape) for b in self.bias] 
  deltaw = [np.zeros(w.shape) for w in self.weight] 
  zs = [] 
  activate = x 
  activates = [x] 
  for w,b in zip(self.weight,self.bias): 
   z =np.dot(w, activate) +b 
   zs.append(z) 
   activate = sigmoid(z) 
   activates.append(activate) 
   # backprop 
  delta = self.cost.delta(zs[-1],activates[-1],y) #调用不同代价函数的方法求梯度 
  deltab[-1] = delta 
  deltaw[-1] = np.dot(delta ,activates[-2].transpose()) 
  for i in xrange(2,self.num_layer): 
   z = zs[-i] 
   delta = np.dot(self.weight[-i+1].transpose(),delta)* sig_derivate(z) 
   deltab[-i] = delta 
   deltaw[-i] = np.dot(delta,activates[-i-1].transpose()) 
  return (deltab,deltaw) 
 
 def save(self,filename): 
  """将训练好的网络采用json(java script object notation)将对象保存成字符串保存,用于生产部署 
  encoder=json.dumps(data) 
  python 原始类型(没有数组类型)向 json 类型的转化对照表: 
   python    json 
   dict    object 
  list/tuple   arrary 
  int/long/float  number 
  .tolist() 将数组转化为列表 
  >>> a = np.array([[1, 2], [3, 4]]) 
  >>> list(a) 
  [array([1, 2]), array([3, 4])] 
  >>> a.tolist() 
  [[1, 2], [3, 4]] 
  """ 
  data = {"sizes": self.sizes,"weight": [weight.tolist() for weight in self.weight], 
    "bias": ([bias.tolist() for bias in self.bias]), 
    "cost": str(self.cost.__name__)} 
  # 保存网络训练好的权值,偏置,交叉熵参数。 
  f = open(filename, "w") 
  json.dump(data,f) 
  f.close() 
 
def load_net(filename): 
 """采用data=json.load(json.dumps(data))进行解码, 
 decoder = json.load(encoder) 
 编码后和解码后键不会按照原始data的键顺序排列,但每个键对应的值不会变 
 载入训练好的网络用于测试""" 
 f = open(filename,"r") 
 data = json.load(f) 
 f.close() 
 # print "data[cost]", getattr(sys.modules[__name__], data["cost"])#获得属性__main__.CrossEntropyCost 
 # print "data[cost]", data["cost"], data["sizes"] 
 net = Network(data["sizes"], cost=data["cost"]) #网络初始化 
 net.weight = [np.array(w) for w in data["weight"]] #赋予训练好的权值,并将list--->array 
 net.bias = [np.array(b) for b in data["bias"]] 
 return net 
 
def sig_derivate(z): 
 """derivate sigmoid""" 
 return sigmoid(z) * (1-sigmoid(z)) 
 
def sigmoid(x): 
 sigm=1.0/(1.0+exp(-x)) 
 return sigm 
 
def vectors(y): 
 """赋予标签""" 
 label = np.zeros((10,1)) 
 label[y] = 1.0 #浮点计算 
 return label

3) 网络测试

#!/usr/bin/env python 
# -*- coding: utf-8 -*- 
# @Time : 2017-03-12 15:24 
# @Author : CC 
# @File : net_test.py 
 
import net_load_data 
# net_load_data.load_data() 
train_data,validation_data,test_data = net_load_data.data_transform() 
 
import net_network2 as net 
cost = net.QuadraticCost 
cost = net.CrossEntroyCost 
lambd = 0 
net1 = net.Network([784,50,10],cost) 
min_batch_size = 30 
eta = 3.0 
epoches = 2 
net1.SGD(train_data,min_batch_size,epoches,eta,test_data, 
   lambd, 
   monitor_train_cost=True, 
   monitor_train_accuracy=True, 
   monitor_test_cost=True, 
   monitor_test_accuracy=True 
   ) 
print "complete"

4 调用训练好的网络进行测试

#!/usr/bin/env python 
# -*- coding: utf-8 -*- 
# @Time : 2017-03-28 17:27 
# @Author : CC 
# @File : forward_test.py 
 
import numpy as np 
# 对训练好的网络直接进行调用,并用测试样本进行测试 
import net_load_data #导入测试数据 
import net_network2 as net 
train_data,validation_data,test_data = net_load_data.data_transform() 
net = net.load_net(filename= "net_save")  #导入网络 
output = [(np.argmax(net.forward(x)),y) for (x,y) in test_data] #测试 
print sum(int(y1 == y2) for (y1,y2) in output)  #输出最终值

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持三水点靠木。

Python 相关文章推荐
详解Django中的权限和组以及消息
Jul 23 Python
深入了解Python数据类型之列表
Jun 24 Python
python常见排序算法基础教程
Apr 13 Python
python正则表达式爬取猫眼电影top100
Feb 24 Python
Windows下安装Django框架的方法简明教程
Mar 28 Python
PyCharm+PySpark远程调试的环境配置的方法
Nov 29 Python
Django中的cookie和session
Aug 27 Python
Pytorch中的variable, tensor与numpy相互转化的方法
Oct 10 Python
Python的对象传递与Copy函数使用详解
Dec 26 Python
在tensorflow下利用plt画论文中loss,acc等曲线图实例
Jun 15 Python
python和JavaScript哪个容易上手
Jun 23 Python
Python竟然能剪辑视频
May 25 Python
python构建深度神经网络(DNN)
Mar 10 #Python
Python使用numpy实现BP神经网络
Mar 10 #Python
python实现日常记账本小程序
Mar 10 #Python
python实现简单神经网络算法
Mar 10 #Python
TensorFlow saver指定变量的存取
Mar 10 #Python
TensorFLow用Saver保存和恢复变量
Mar 10 #Python
tensorflow创建变量以及根据名称查找变量
Mar 10 #Python
You might like
javascript实现的像java、c#之类的sleep暂停的函数代码
2010/03/04 Javascript
jQuery获取文本节点之 text()/val()/html() 方法区别
2011/03/01 Javascript
High Performance JavaScript(高性能JavaScript)读书笔记分析
2011/05/05 Javascript
JQuery与JSon实现的无刷新分页代码
2011/09/13 Javascript
动态加载JS文件的三种方法
2013/11/08 Javascript
将HTML的左右尖括号等转义成实体形式的两种实现方式
2014/05/04 Javascript
JavaScript实现N皇后问题算法谜题解答
2014/12/29 Javascript
jQuery中:visible选择器用法实例
2014/12/30 Javascript
jquery实现经典的淡入淡出选项卡效果代码
2015/09/22 Javascript
深入理解JavaScript中的对象复制(Object Clone)
2016/05/18 Javascript
Web程序员必备的7个JavaScript函数
2016/06/14 Javascript
AngularJs html compiler详解及示例代码
2016/09/01 Javascript
JavaScript 总结几个提高性能知识点(推荐)
2017/02/20 Javascript
js is_valid_filename验证文件名的函数
2017/07/19 Javascript
vue.js-div滚动条隐藏但有滚动效果的实现方法
2018/03/03 Javascript
详解mpvue开发小程序小总结
2018/07/25 Javascript
微信小程序中this.data与this.setData的区别详解
2018/09/17 Javascript
JavaScript实现的开关灯泡点击切换特效示例
2019/07/08 Javascript
vue v-for 使用问题整理小结
2019/08/04 Javascript
vue中实现图片压缩 file文件的方法
2020/05/28 Javascript
vue开发chrome插件,实现获取界面数据和保存到数据库功能
2020/12/01 Vue.js
为python设置socket代理的方法
2015/01/14 Python
python安装oracle扩展及数据库连接方法
2017/02/21 Python
Python人工智能之路 jieba gensim 最好别分家之最简单的相似度实现
2019/08/13 Python
tensorflow转换ckpt为savermodel模型的实现
2020/05/25 Python
利用python实现平稳时间序列的建模方式
2020/06/03 Python
keras打印loss对权重的导数方式
2020/06/10 Python
Python脚本破解压缩文件口令实例教程(zipfile)
2020/06/14 Python
Python调用REST API接口的几种方式汇总
2020/10/19 Python
我的画教学反思
2014/04/28 职场文书
歌颂祖国演讲稿
2014/05/04 职场文书
乡镇组织委员个人整改措施
2014/09/16 职场文书
长江三峡导游词
2015/01/31 职场文书
个人年终总结开头
2015/03/06 职场文书
社区党建工作总结2015
2015/05/13 职场文书
自荐信大全
2019/03/21 职场文书