用python做youtube自动化下载器!附完整代码!

如何使用 Python 下载 Youtube 音乐和视频 本文将讨论如何使用 Python 编程语言轻松地从 Youtube 下载音乐和视频。我们将使用 Python 的原因是它是一种轻量级、快速且易于使用的软件开发工具。我们将使用两个库来实现我们的目标argparsepytube并且可以使用 python 包安装程序 (pip) 安装它。 阅读详情

流程

1. post

根据思路里的第一步,我们首先需要用 post 方式取到加密后的js字段,笔者使用了 requests第三方库来执行,关于爬虫可以参考 我之前的文章

i. 先把post中的headers格式化

# set the headers or the website will not return information
    # the cookies in here you may need to change
    headers = {
        "cache-Control": "no-cache",
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,"
                  "*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
        "content-type": "application/x-www-form-urlencoded",
        "cookie": "lang=en; country=CN; uid=fd94a82a406a8dd4; sfHelperDist=72; reference=14; "
                  "clickads-e2=90; poropellerAdsPush-e=63; promoBlock=64; helperWidget=92; "
                  "helperBanner=42; framelessHdConverter=68; inpagePush2=68; popupInOutput=9; "
                  "_ga=GA1.2.799702638.1610248969; _gid=GA1.2.628904587.1610248969; "
                  "PHPSESSID=030393eb0776d20d0975f99b523a70d4; x-requested-with=; "
                  "PHPSESSUD=islilfjn5alth33j9j8glj9776; _gat_helperWidget=1; _gat_inpagePush2=1",
        "origin": "https://en.savefrom.net",
        "pragma": "no-cache",
        "referer": "https://en.savefrom.net/1-youtube-video-downloader-4/",
        "sec-ch-ua": "\"Google Chrome\";v=\"87\", \"Not;A Brand\";v=\"99\",\"Chromium\";v=\"87\"",
        "sec-ch-ua-mobile": "?0",
        "sec-fetch-dest": "iframe",
        "sec-fetch-mode": "navigate",
        "sec-fetch-site": "same-origin",
        "sec-fetch-user": "?1",
        "upgrade-insecure-requests": "1",
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
                      "Chrome/87.0.4280.88 Safari/537.36"}

其中 cookie 部分可能要改,然后最好以你们浏览器上的为主,具体每个参数的含义不是本文范围,可以自行去搜索引擎搜

ii.然后把参数也格式化

# set the parameter, we can get from chrome
    kv = {"sf_url": url,
          "sf_submit": "",
          "new": "1",
          "lang": "en",
          "app": "",
          "country": "cn",
          "os": "Windows",
          "browser": "Chrome"}

其中 sf_url 字段是我们要下载的youtube视频的url,其他参数都不变

iii. 最后再执行 requests 库的post请求

# do the POST request
    r = requests.post(url="https://en.savefrom.net/savefrom.php", headers=headers,
                      data=kv)
    r.raise_for_status()

注意是 data=kv

iv. 封装成一个函数

import requests

def gethtml(url):
    # set the headers or the website will not return information
    # the cookies in here you may need to change
    headers = {
        "cache-Control": "no-cache",
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,"
                  "*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
        "content-type": "application/x-www-form-urlencoded",
        "cookie": "lang=en; country=CN; uid=fd94a82a406a8dd4; sfHelperDist=72; reference=14; "
                  "clickads-e2=90; poropellerAdsPush-e=63; promoBlock=64; helperWidget=92; "
                  "helperBanner=42; framelessHdConverter=68; inpagePush2=68; popupInOutput=9; "
                  "_ga=GA1.2.799702638.1610248969; _gid=GA1.2.628904587.1610248969; "
                  "PHPSESSID=030393eb0776d20d0975f99b523a70d4; x-requested-with=; "
                  "PHPSESSUD=islilfjn5alth33j9j8glj9776; _gat_helperWidget=1; _gat_inpagePush2=1",
        "origin": "https://en.savefrom.net",
        "pragma": "no-cache",
        "referer": "https://en.savefrom.net/1-youtube-video-downloader-4/",
        "sec-ch-ua": "\"Google Chrome\";v=\"87\", \"Not;A Brand\";v=\"99\",\"Chromium\";v=\"87\"",
        "sec-ch-ua-mobile": "?0",
        "sec-fetch-dest": "iframe",
        "sec-fetch-mode": "navigate",
        "sec-fetch-site": "same-origin",
        "sec-fetch-user": "?1",
        "upgrade-insecure-requests": "1",
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
                      "Chrome/87.0.4280.88 Safari/537.36"}
    # set the parameter, we can get from chrome
    kv = {"sf_url": url,
          "sf_submit": "",
          "new": "1",
          "lang": "en",
          "app": "",
          "country": "cn",
          "os": "Windows",
          "browser": "Chrome"}
    # do the POST request
    r = requests.post(url="https://en.savefrom.net/savefrom.php", headers=headers,
                      data=kv)
    r.raise_for_status()
    # get the result
    return r.text

2. 调用解密函数

i. 分析

这其中的难点在于在python里执行javascript代码,而晚上的解决方法有 PyV8 等,本文选用 execjs 。在思路部分我们可以发现js部分的最后几行是解密函数,所以我们只需要在 execjs 中先执行一遍全部,然后再单独执行解密函数就好了

ii. 先取出js部分

# target(youtube address) url
    url = "https://www.youtube.com/watch?v=YPvtz1lHRiw"
    # get the target text
    reo = gethtml(url)
    # Remove the code from the head and tail (we need the javascript part, information store with encryption in js part)
    reo = reo.split("<script type=\"text/javascript\">")[1].split("</script>")[0]

这里其实可以用正则,不过由于笔者正则表达式还不太熟练就直接用 split 了

iii. 取第一个解密函数作为我们用的解密函数

当你多取几次不同视频的结果,你就会发现每次的解密函数都不一样,不过位置都是还是在固定行数

# split each line(help us find the decrypt function in last few line)
    reA = reo.split("\n")
    # get the depcrypt function
    name = reA[len(reA) - 3].split(";")[0] + ";"

所以 name 就是我们的解密函数了(变量名没取太好hhh)

iv. 用execjs执行

# use execjs to execute the js code, and the cwd is the result of `npm root -g`(the path of npm in your computer)
    ct = execjs.compile(reo)
    # do the decryption
    text = ct.eval(name.split("=")[1].replace(";", ""))

其中只取 = 后面的和去掉分号是指指执行这个函数而不用赋值,当先执行赋值+解密然后取值也不是不可以

但是我们可以发现马上就报错了(要是有这么简单就好了)

1. this也就是window变量不存在

如果没记错是报错 this 或者 $b ,笔者尝试把全部 this 去掉或者把全部框在一个 class 里面(这样子this就变成那个class了)不过都没有成功,然后发现在 npm 下有个 jsdom 可以在 execjs 里模拟window变量(其实应该有更好方法的),所以我们需要下载 npm 和里面的 jsdom ,然后改写以上代码

addition = """
    const jsdom = require("jsdom");
    const { JSDOM } = jsdom;
    const dom = new JSDOM(`<!DOCTYPE html><p>Hello world</p>`);
    window = dom.window;
    document = window.document;
    XMLHttpRequest = window.XMLHttpRequest;
    """
    # use execjs to execute the js code, and the cwd is the result of `npm root -g`(the path of npm in your computer)
    ct = execjs.compile(addition + reo, cwd=r'C:\Users\xxx\AppData\Roaming\npm\node_modules')

其中

  • cwd 字段是 npm root -g 的结果,也就是npm的modules路径
  • addition 是用来模拟 window 的
    但是我们又可以发现下一个错误

2. alert不存在

这个错误是因为在 execjs 下执行 alert 函数是没有意义的,因为我们没有浏览器让他弹窗,且原本 alert 函数的定义是来源 window 而我们自定义了 window ,所以我们要在代码前重写覆盖 alert 函数(相当于定义一个alert)

# override the alert function, because in the code there has one place using
    # and we cannot do the alerting in execjs(it is meaningless) however, if we donnot override, the code will raise a error
    reo = reo.replace("(function(){", "(function(){\nthis.alert=function(){};")

v. 整合代码

# target(youtube address) url
    url = "https://www.youtube.com/watch?v=YPvtz1lHRiw"
    # get the target text
    reo = gethtml(url)
    # Remove the code from the head and tail (we need the javascript part, information store with encryption in js part)
    reo = reo.split("<script type=\"text/javascript\">")[1].split("</script>")[0]
    # override the alert function, because in the code there has one place using
    # and we cannot do the alerting in execjs(it is meaningless) however, if we donnot override, the code will raise a error
    reo = reo.replace("(function(){", "(function(){\nthis.alert=function(){};")
    # split each line(help us find the decrypt function in last few line)
    reA = reo.split("\n")
    # get the depcrypt function
    name = reA[len(reA) - 3].split(";")[0] + ";"
    # add jsdom into the execjs because the code will use(maybe there is a solution without jsdom, but i have no idea)
    addition = """
    const jsdom = require("jsdom");
    const { JSDOM } = jsdom;
    const dom = new JSDOM(`<!DOCTYPE html><p>Hello world</p>`);
    window = dom.window;
    document = window.document;
    XMLHttpRequest = window.XMLHttpRequest;
    """
    # use execjs to execute the js code, and the cwd is the result of `npm root -g`(the path of npm in your computer)
    ct = execjs.compile(addition + reo, cwd=r'C:\Users\19308\AppData\Roaming\npm\node_modules')
    # do the decryption
    text = ct.eval(name.split("=")[1].replace(";", ""))

3. 分析解密结果

i. 取关键json

运行完上面的部分,解密结果就存在text里了,而我们在思路中可以发现,真正对我们重要的就是存在 window.parent.sf.videoResult.show() 里的json,所以用正则表达式取这一部分的json

# get the result in json
    result = re.search('show\((.*?)\);;', text, re.I | re.M).group(0).replace("show(", "").replace(");;", "")

ii. 格式化json

python可以格式化json的库有很多,这里笔者用了 json 库(记得import)

# use `json` to load json
    j = json.loads(result)

iii. 取下载地址

接下来就到了最后一步,根据思路里和json格式化工具我们可以发现 j["url"][num]["url"] 就是下载链接,而 num 是我们要的视频格式(不同分辨率和类型)

# the selection of video(in this case, num=1 mean the video is
    # - 360p known from j["url"][num]["quality"]
    # - MP4 known from j["url"][num]["type"]
    # - audio known from j["url"][num]["audio"]
    num = 1
    downurl = j["url"][num]["url"]
    # do some download
    # thanks :)
    # - EOF -

3. 全部代码

# -*- coding: utf-8 -*-
# @Time: 2021/1/10
# @Author: Eritque arcus
# @File: Youtube.py
# @License: MIT
# @Environment:
#           - windows 10
#           - python 3.6.2
# @Dependence:
#           - jsdom in npm(windows also can use)
#           - requests, execjs, re, json in python
import requests
import execjs
import re
import json


def gethtml(url):
    # set the headers or the website will not return information
    # the cookies in here you may need to change
    headers = {
        "cache-Control": "no-cache",
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,"
                  "*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
        "accept-encoding": "gzip, deflate, br",
        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
        "content-type": "application/x-www-form-urlencoded",
        "cookie": "lang=en; country=CN; uid=fd94a82a406a8dd4; sfHelperDist=72; reference=14; "
                  "clickads-e2=90; poropellerAdsPush-e=63; promoBlock=64; helperWidget=92; "
                  "helperBanner=42; framelessHdConverter=68; inpagePush2=68; popupInOutput=9; "
                  "_ga=GA1.2.799702638.1610248969; _gid=GA1.2.628904587.1610248969; "
                  "PHPSESSID=030393eb0776d20d0975f99b523a70d4; x-requested-with=; "
                  "PHPSESSUD=islilfjn5alth33j9j8glj9776; _gat_helperWidget=1; _gat_inpagePush2=1",
        "origin": "https://en.savefrom.net",
        "pragma": "no-cache",
        "referer": "https://en.savefrom.net/1-youtube-video-downloader-4/",
        "sec-ch-ua": "\"Google Chrome\";v=\"87\", \"Not;A Brand\";v=\"99\",\"Chromium\";v=\"87\"",
        "sec-ch-ua-mobile": "?0",
        "sec-fetch-dest": "iframe",
        "sec-fetch-mode": "navigate",
        "sec-fetch-site": "same-origin",
        "sec-fetch-user": "?1",
        "upgrade-insecure-requests": "1",
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
                      "Chrome/87.0.4280.88 Safari/537.36"}
    # set the parameter, we can get from chrome
    kv = {"sf_url": url,
          "sf_submit": "",
          "new": "1",
          "lang": "en",
          "app": "",
          "country": "cn",
          "os": "Windows",
          "browser": "Chrome"}
    # do the POST request
    r = requests.post(url="https://en.savefrom.net/savefrom.php", headers=headers,
                      data=kv)
    r.raise_for_status()
    # get the result
    return r.text


if __name__ == '__main__':
    # target(youtube address) url
    url = "https://www.youtube.com/watch?v=YPvtz1lHRiw"
    # get the target text
    reo = gethtml(url)
    # Remove the code from the head and tail (we need the javascript part, information store with encryption in js part)
    reo = reo.split("<script type=\"text/javascript\">")[1].split("</script>")[0]
    # override the alert function, because in the code there has one place using
    # and we cannot do the alerting in execjs(it is meaningless) however, if we donnot override, the code will raise a error
    reo = reo.replace("(function(){", "(function(){\nthis.alert=function(){};")
    # split each line(help us find the decrypt function in last few line)
    reA = reo.split("\n")
    # get the depcrypt function
    name = reA[len(reA) - 3].split(";")[0] + ";"
    # add jsdom into the execjs because the code will use(maybe there is a solution without jsdom, but i have no idea)
    addition = """
    const jsdom = require("jsdom");
    const { JSDOM } = jsdom;
    const dom = new JSDOM(`<!DOCTYPE html><p>Hello world</p>`);
    window = dom.window;
    document = window.document;
    XMLHttpRequest = window.XMLHttpRequest;
    """
    # use execjs to execute the js code, and the cwd is the result of `npm root -g`(the path of npm in your computer)
    ct = execjs.compile(addition + reo, cwd=r'C:\Users\19308\AppData\Roaming\npm\node_modules')
    # do the decryption
    text = ct.eval(name.split("=")[1].replace(";", ""))
    # get the result in json
    result = re.search('show\((.*?)\);;', text, re.I | re.M).group(0).replace("show(", "").replace(");;", "")
    # use `json` to load json
    j = json.loads(result)
    # the selection of video(in this case, num=1 mean the video is
    # - 360p known from j["url"][num]["quality"]
    # - MP4 known from j["url"][num]["type"]
    # - audio known from j["url"][num]["audio"]
    num = 1
    downurl = j["url"][num]["url"]
    # do some download
    # thanks :)
    # - EOF -
  • 总计102行

 

近期有很多朋友通过私信咨询有关Python学习问题。为便于交流,点击蓝色自己加入讨论解答资源基地

  • 开发环境
# @Environment:
#           - windows 10
#           - python 3.6.2
  • 依赖
# @Dependence:
#           - jsdom in npm(windows also can use)
#           - requests, execjs, re, json in python

-end-

For 爬虫

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本文作者: https://www.cnblogs.com/Eritque-arcus/ 或 https://blog.youkuaiyun.com/qq_40832960

#感谢您访问本站#
#本文转载自互联网,若侵权,请联系删除,谢谢!

 

用python做youtube自动化下载器 思路 用python做youtube自动化下载器0. 思路1.准备i.savfrom.net2. 探索并规划获取方式i.总览ii. 获取该网页取到下载url的请求iii. 在本地获取请求iv.解析请求结果v.解析解密后的结果 根据 savefrom条例 本实例及教程只用于学习交流用,权利归savefrom.net所有 0. 思路 使用python从savefrom平台上获取指定youtube视频下载链接进行下载,不过savefrom的数据是用javascript加密的,这也是难度所在 本教程主要介绍制作思路, 阅读详情

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深度学习工具包 DeepLearningToolbox

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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WINCC7.3硬件够破解文件,有需要的下载!WINCC7.3硬件够破解文件,有需要的下载!

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