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python构建深度神经网络（续）

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这篇CFSDN的博客文章python构建深度神经网络（续）由作者收集整理，如果你对这篇文章有兴趣，记得点赞哟.

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 。

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#!/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 。

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#!/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 。

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#!/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 。

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#!/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我.

原文链接：http://blog.csdn.net/Ychan_cc/article/details/67640185 。

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