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scope 能让你命名变量的时候轻松很多. 同时也会在 reusing variable 代码中常常见到. 所以今天我们会来讨论下 tensorflow 当中的两种定义 scope 的方式. 最后并附加一个 RNN 运用 reuse variable 的例子.tf.name_scope()tf.variable_scope()RNN应用例子tf.name_scope()在 Tens

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.co

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('300x300')frm = tk.Frame(window)frm_l = tk.Frame(frm)frm_l.pack(side='left')frm_r = tk.Frame(frm)frm_r.pack(

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('300x300')l = tk.Label(window,text="",bg='yellow')l.pack()counter = 0def do_job():    global counter   

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('300x300')canvas = tk.Canvas(window,bg="blue",width=300,height=200)#image_file = canvas.PhotoImage(file='/baigong.

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('600x400')var1 = tk.StringVar()label = tk.Label(window,bg='yellow',width=30,heigh=2,text="empty")label.pack()d

#Pandas 导入导出#pandas可以读取与存取的资料格式有很多种，像csv、excel、json、html与pickle等…，#http://pandas.pydata.org/pandas-docs/stable/io.htmlimport pandas as pd#读取csvdata = pd.read_csv('students.csv')print(dat

# df.dropna()  df.fillna() df.isnull() np.any(df.isnull()) == Trueimport numpy as npimport pandas as pddates = pd.date_range('20170326',periods = 6)df = pd.DataFrame(np.arange(24).reshape(6,4)

我们可以根据自己的需求, 用 pandas 进行更改数据里面的值, 或者加上一些空的,或者有数值的列.首先建立了一个 6X4 的矩阵数据。dates = pd.date_range('20130101', periods=6)df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C'

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('300x400')var1 = tk.StringVar()label = tk.Label(window,bg='yellow',width=10,heigh=2,text="empty")label.pack()d

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('200x400')var1 = tk.StringVar()label = tk.Label(window,bg='yellow',width=6,heigh=2,textvariable=var1)label.pack(

import tkinter as tkwindow = tk.Tk()window.title("我的窗口")window.geometry('200x200')ent = tk.Entry(window,show = '*')ent.pack()def insertPoint():    var = ent.get()    text.insert(

import pandas as pdimport numpy as np#定义资料集df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])df3 = pd.DataFrame(

import multiprocessing as mpimport threading as tddef job(q):    res = 0    for i in range(1000):        res+= i+i**2+i**3    q.put(res)if __name__ == '__main__':    q = mp.Queue()

import multiprocessing as mpimport threading as tddef job(x):    return x*xdef multicore():    pool = mp.Pool(processes = 1)    res = pool.map(job,range(10000000))    print(res)  

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.co

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.co

# numpy 的关联性#Numpy copy & deep copyimport numpy as npa = np.arange(4)print(a)# =带来的赋值会带来关联性b = ac = ad = bprint(b,c,d)a[3] = 88print(a,b,c,d)print(a == d)#copy()的赋值方式没有关联性

#Numpy array 分割import numpy as np#建立3行4列arrayA = np.arange(16).reshape(4,4)print(A)"""[[ 0  1  2  3] [ 4  5  6  7] [ 8  9 10 11]] """# 纵向分割print(np.split(A,2,axis = 1))#横向分

对于一个array的合并，我们可以想到按行、按列等多种方式进行合并。首先先看一个例子：import numpy as np# numpy合并a = np.array([1,1,1])b = np.array([2,2,2])print(np.vstack((a,b)))  # 竖向合并"""[[1,1,1] [2,2,2]]"""print(np

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.co

#pandas中的merge和concat类似,但主要是用于两组有key column的数据,#统一索引的数据. 通常也被用在Database的处理当中.from __future__ import print_functionimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt'''#

import numpy as np#我们都知道，在元素列表或者数组中，我们可以用如同a[2]一样的表示方法，同样的，在Numpy中也有相对应的表示方法：A = np.arange(3,15)# array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])print(A)range(3,15)  #生成一个range object

通过上一节的学习，我们可以了解到一部分矩阵中元素的计算和查找操作。然而在日常使用中，对应元素的索引也是非常重要的。依然，让我们先从一个脚本开始 ：import numpy as npA = np.arange(2,14).reshape((3,4)) # array([[ 2, 3, 4, 5]#        [ 6, 7, 8, 9]#        [10,11,12

import numpy as npa = np.array([1,2,3,4])b = np.arange(4)print(a)print(b)print(a-b)print(a+b)print(a*b)print(b**2)print(b>2)print('************************************')c_dot

import multiprocessing as mpimport threading as tdimport timedef job(v,num,l):    l.acquire()    for _ in range(10):        time.sleep(0.1)        v.value += num        print(v.value

pandas 中选择数据的方法有很多种,一般我们会用到这几种.简单的筛选根据标签: loc根据序列: iloc根据混合的这两种: ix还有通过判断的筛选我们建立了一个 6X4 的矩阵数据。dates = pd.date_range('20130101', periods=6)df = pd.DataFrame(np.arange(24).reshape((6,4)

import pandas as pdimport numpy as np# series 的使用方法s = pd.Series([1,3,6,np.nan,44,1])print(s)print(s.dtype)#DataFrame 的创建：dates= pd.date_range('20170101',periods=6)df = pd.DataFr

import tkinter as tkwindow = tk.Tk()window.title("我的第一个视窗")window.geometry("200x100")#  这里显示视窗内容var = tk.StringVar()label = tk.Label(window,textvariable=var,font=('Arial',12),bg = 'yel

# if str.isdigit(): 判断输入为数字score = input('please input a score: \n')if score.isdigit():    score = int(score)    if score >= 90:        print('你的等级是:A')    elif score >= 80:        print

_init__可以理解成初始化class的变量，取自英文中initial 最初的意思.可以在运行时，给初始值附值，运行c=Calculator('bad calculator',18,17,16,15),然后调出每个初始值的值。看如下代码。# python 的面向对象的用法实例 __init__是用来初始化类的一些变量的，类似于java的构造方法class Person: 

#文件操作# 写入# text = 'this is first line \n this is second line \n this is last line'# myfile = open('my_file.txt','w')# myfile.write(text)# myfile.close()# 给文件添加内容 'a'=append 以增加内容的形式打开add

class 类class 定义一个类, 后面的类别首字母推荐以大写的形式定义，比如Calculator.class可以先定义自己的属性，比如该属性的名称可以写为name='Good Calculator'.class后面还可以跟def, 定义一个函数.比如def add(self,x,y): 加法, 输出print(x+y).其他的函数定义方法一样，注意这里的self 是默认值.#

安装numpysudo pip3 install numpy升级numpysudo pip3 install -U numpy

#全局和局部变量APPLE = 100a = Nonedef fun():    #global APPLE    a = 20    APPLE = 88    return a+100print(APPLE)print('a past=',a)print(fun())print('a now=',a)print('APPLE now is',AP

# def 函数def function(a,b):    return a+bprint("this is func result:")print(function(2,3))print("this is func result:",function(3,5))#默认函数参数def saleCar(price,color="red",brand="dazh

# if语句的使用x = -1y = 2z = 3i = 3if x>y:    print('x is greater than y')elif x    print('x is 小于 0')elif x    print('x is less to y')else:    print('x is equal to y')print('##

# for 迭代数组array = [1,3,5,7,9,118,18,81]for i in array:    print(i)    print('in for')print('out of for')print('############ range ###############')for j in range(1,10,2):    prin

1、自变量命名规则>>> apple = 7  ##赋值 数字>>> print(apple)7>>> apple = 'phone is 7 plus'>>> print(apple)phone is 7 plus>>> print(apple+1)Traceback (most recent call last):  File "", line 1, in

1、 加减乘除>>> 1+12>>> 4-13>>> 3*412>>> 4/31.3333333333333333>>> 4//31>>> 4//3   #整出 3 最近的整数1>>> 2、^符号与**python当中^符号，区别于Matlab，在python中，^用两个**表示，如3的平方为3**2 , **3表示立方，**