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脚本之家。