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python营造深度神经互连网,送你一份深度学习10大在线无需付费课程财富

2019年8月31日 - 4166am金沙下载
python营造深度神经互连网,送你一份深度学习10大在线无需付费课程财富

python构建深度神经网络(DNN),pythondnn

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。

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帮客之家。

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…

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。

图片 1

代码主要包括两三部分:

来源:机器之心

1)、数据调用和预处理

本文长度为2000字,建议阅读3分钟

2)、神经网络类构建和方法建立

本文为你分享10大深度学习免费在线课程,have
fun~

3)、代码测试文件

现在网络上有大量深度学习在线课程,Edgy Lab为大家找到了10大免费课程,帮助大家自学,助力职业生涯。他们研究了顶尖大学和技术公司开设的深度学习MOOC课程,包括针对初级、中级和高级学习者的课程,覆盖深度学习的大部分概念(从最基础的到最前沿)。不过这些课程都需要一些先决条件:了解数学基础知识、知道如何使用GitHub库,以及掌握编程语言,如Python。以下是课程列表。

1)数据调用:

1. 深度学习(Deep Learning by Google)

#!/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 

谷歌在在线课程平台Udacity上发布了深度学习专门课程。该课程持续12周,适合中级开发者,讲授深度学习的多方面知识,如如何构建和优化深度神经网络。该课程由谷歌首席科学家、谷歌大脑团队技术负责人Vincent Vanhoucke开发。

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) 

2. 神经网络和深度学习(Neural Networks and Deep Learning)

3)网络测试

这门课程由斯坦福大学和deeplearning.ai开设,授课人为斯坦福大学教授、Coursera创始人吴恩达,课程通过Class Central和Coursera平台发布。

#!/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

这门课程主要讲授深度学习的基础知识。课程结束时,你将掌握如何构建、训练和管理深度神经网络,以及如何在自己的项目中使用深度神经网络。

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 

3. 算法:设计和分析(Algorithms: Design and Analysis)

总体来说这本书的实例,用来熟悉python和神经网络非常好。

算法是深度学习和计算机科学的核心,这门斯坦福大学开设的课程将带你了解算法。

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。

这门课程适合有点编程经验的学习者,课程第一部分讲述「Big-oh」符号、数据排序和搜索、分治法(divide and conquer method)、随机算法、数据结构和图基元。

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学习完第一部分之后,可以注册学习第二部分,更深入地学习算法。

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