概述
本文学习Neural Networks and Deep Learning 在线免费书籍，用python构建神经网络识别手写体的一个总结。
代码主要包括两三部分：
1）、数据调用和预处理
2）、神经网络类构建和方法建立
3）、代码测试文件
1）数据调用：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2017-03-12 15:11
# @Author : CC
# @File : net_load_data.py
# @Software: PyCharm Community Edition
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）网络构建
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2017-03-12 16:07
# @Author : CC
# @File : net_network.py
import numpy as np
import random
class Network(object): #默认为基类?用于继承：print isinstance(network,object)
def __init__(self,sizes):
self.num_layers = len(sizes)
self.sizes = sizes
# print 'num_layers', self.num_layers
self.weight = [np.random.randn(a1, a2) for (a1, a2) in zip(sizes[1:], sizes[:-1])] #产生一个个数组
self.bias = [np.random.randn(a3,1) for a3 in sizes[1:]]
# print self.weight[0].shape #(20,10)
def SGD(self,train_data,min_batch_size,epoches,eta,test_data=False):
""" 1) 打乱样本，将训练数据划分成小批次
2）计算出反向传播梯度
3） 获得权重更新"""
if test_data: n_test = len(test_data)
n = len(train_data) #50000
random.shuffle(train_data) # 打乱
min_batches = [train_data[k:k+min_batch_size] for k in xrange(0,n,min_batch_size)] #提取批次数据
for k in xrange(0,epoches): #利用更新后的权值继续更新
random.shuffle(train_data) # 打乱
for min_batch in min_batches: #逐个传入，效率很低
self.updata_parameter(min_batch,eta)
if test_data:
num = self.evaluate(test_data)
print "the {0}th epoches: {1}/{2}".format(k,num,len(test_data))
else:
print 'epoches {0} completed'.format(k)
def forward(self,x):
"""获得各层激活值"""
for w,b in zip(self.weight,self.bias):
x = sigmoid(np.dot(w, x)+b)
return x
def updata_parameter(self,min_batch,eta):
"""1) 反向传播计算每个样本梯度值
2） 累加每个批次样本的梯度值
3） 权值更新"""
ndeltab = [np.zeros(b.shape) for b in self.bias]
ndeltaw = [np.zeros(w.shape) for w in self.weight]
for x,y in min_batch:
deltab,deltaw = self.backprop(x,y)
ndeltab = [nb +db for nb,db in zip(ndeltab,deltab)]
ndeltaw = [nw + dw for nw,dw in zip(ndeltaw,deltaw)]
self.bias = [b - eta * ndb/len(min_batch) for ndb,b in zip(ndeltab,self.bias)]
self.weight = [w - eta * ndw/len(min_batch) for ndw,w in zip(ndeltaw,self.weight)]
def backprop(self,x,y):
"""执行前向计算，再进行反向传播，返回deltaw,deltab"""
# [w for w in self.weight]
# print 'len',len(w)
# print "self.weight",self.weight[0].shape
# print w[0].shape
# print w[1].shape
# print w.shape
activation = x
activations = [x]
zs = []
# feedforward
for w, b in zip(self.weight, self.bias):
# print w.shape,activation.shape,b.shape
z = np.dot(w, activation) +b
zs.append(z) #用于计算f(z)导数
activation = sigmoid(z)
# print 'activation',activation.shape
activations.append(activation) # 每层的输出结果
delta = self.top_subtract(activations[-1],y) * dsigmoid(zs[-1]) #最后一层的delta,np.array乘,相同维度乘
deltaw = [np.zeros(w1.shape) for w1 in self.weight] #每一次将获得的值作为列表形式赋给deltaw
deltab = [np.zeros(b1.shape) for b1 in self.bias]
# print 'deltab[0]',deltab[-1].shape
deltab[-1] = delta
deltaw[-1] = np.dot(delta,activations[-2].transpose())
for k in xrange(2,self.num_layers):
delta = np.dot(self.weight[-k+1].transpose(),delta) * dsigmoid(zs[-k])
deltab[-k] = delta
deltaw[-k] = np.dot(delta,activations[-k-1].transpose())
return (deltab,deltaw)
def evaluate(self,test_data):
"""评估验证集和测试集的精度,标签直接一个数作为比较"""
z = [(np.argmax(self.forward(x)),y) for x,y in test_data]
zs = np.sum(int(a == b) for a,b in z)
# zk = sum(int(a == b) for a,b in z)
# print "zs/zk:",zs,zk
return zs
def top_subtract(self,x,y):
return (x - y)
def sigmoid(x):
return 1.0/(1.0+np.exp(-x))
def dsigmoid(x):
z = sigmoid(x)
return z*(1-z)
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_network as net net1 = net.Network([784,30,10]) min_batch_size = 10 eta = 3.0 epoches = 30 net1.SGD(train_data,min_batch_size,epoches,eta,test_data) print "complete"
4）结果
the 9th epoches: 9405/10000 the 10th epoches: 9420/10000 the 11th epoches: 9385/10000 the 12th epoches: 9404/10000 the 13th epoches: 9398/10000 the 14th epoches: 9406/10000 the 15th epoches: 9396/10000 the 16th epoches: 9413/10000 the 17th epoches: 9405/10000 the 18th epoches: 9425/10000 the 19th epoches: 9420/10000
总体来说这本书的实例，用来熟悉python和神经网络非常好。
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