python构建深度神经网络(构建深度神经网络(DNN))
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)、数据调用和预处理
2)、神经网络类构建和方法建立)、神经网络类构建和方法建立
3)、代码测试文件)、代码测试文件
1)数据调用:)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)网络构建
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