python构建深度神经网络（DNN）

#!/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 

#!/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) 

#!/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" 

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