python构建深度神经网络（DNN）

本文学习Neural Networks and Deep Learning 在线免费书籍（http://neuralnetworksanddeeplearning.com/index.html），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 Editionfrom numpy import *import numpy as npimport cPickledef 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 Nonedef 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.pyimport numpy as npimport randomclass 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.pyimport net_load_data# net_load_data.load_data()train_data,validation_data,test_data = net_load_data.data_transform()import net_network as netnet1 = net.Network([784,30,10])min_batch_size = 10eta = 3.0epoches = 30net1.SGD(train_data,min_batch_size,epoches,eta,test_data)print "complete"

4） 结果

the 9th epoches: 9405/10000the 10th epoches: 9420/10000the 11th epoches: 9385/10000the 12th epoches: 9404/10000the 13th epoches: 9398/10000the 14th epoches: 9406/10000the 15th epoches: 9396/10000the 16th epoches: 9413/10000the 17th epoches: 9405/10000the 18th epoches: 9425/10000the 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。