墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
400+累计阅读
400平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
TCP代理服务器是一种可以转发客户端与目标服务器之间TCP通信的中间件。它接收客户端的请求，并将请求转发给目标服务器，然后将目标服务器的响应返回给客户端。在本文中，我们将使用Python来实现一个简单的TCP代理服务器。
首先，我们需要导入Python的socket模块，它提供了用于创建网络套接字的功能。我们还需要指定代理服务器的监听地址和端口，以及目标服务器的地址和端口。
import socket
proxy_host = '127.0.0.1' # 代理服务器监听地址
proxy_port = 8888 # 代理服务器监听端口
target_host = 'www.example.com' # 目标服务器地址
target_port = TCP、UDP 通信程序设计实验报告
1. 深入了解 TCP 和UDP 的区别与联系。 2. 掌握网络 Socket 编程的基本概念和基本编程方法。 3. 掌握 TCP 和UDP 的Client/Server 结构的程序的基本编程方法。
python实现简单的TCP代理服务器
本文实例讲述了python实现简单的TCP代理服务器的方法，分享给大家供大家参考。 具体实现代码如下： # -*- coding: utf-8 -*- ''' filename:rtcp.py @desc: 利用python的socket端口转发，用于远程维护 如果连接不到远程，会sleep 36s，最多尝试200(即两小时) @usage: ./rtcp.py stream1 stream2 stream为：l:port或c:host:port l:port表示监听指定的本地端口 c:host:port表示监听远程指定的端口 @author: watercloud, zd, knownse
FreeModbus从入门到实战：手把手教你用STM32实现工业级Modbus RTU通信
本文详细介绍了如何使用STM32和FreeModbus实现工业级Modbus RTU通信，涵盖硬件搭建、协议栈移植及优化技巧。重点解析了FreeModbus的移植过程，包括串口驱动适配、定时器配置和工业级稳定性优化，为开发者提供了一套完整的工业通信解决方案。
Python金融数据获取工具库之baostock使用详解
在金融数据分析和量化交易中，获取准确及时的市场数据是非常重要的。baostock是一个专门为中国股市数据提供支持的 Python 库，它提供了免费的股票数据接口，用户可以方便地获取股票、指数、基金等各种金融数据。本文将详细介绍baostock库，包括其安装方法、主要特性、基本和高级功能，以及实际应用场景，帮助全面了解并掌握该库的使用。
Prokka实战：10分钟搞定细菌基因组注释（附详细参数解析）
本文深入解析Prokka细菌基因组注释工具的核心参数与实战策略。通过拆解其集成化工作流程，详细解读物种分类、功能注释、输出控制等关键参数，并提供快速初筛、高质量发表、质粒注释及批量处理四种典型场景的高效参数组合方案，帮助用户精准掌控注释过程，将原始序列转化为有价值的生物学见解。
自动驾驶传感器选型实战：摄像头、激光雷达与毫米波雷达融合策略
自动驾驶感知系统的核心在于多传感器协同，而非单一技术优劣。摄像头擅长语义识别但受光照和天气制约；激光雷达提供高精度测距却面临成本、温漂与多径干扰等物理瓶颈；毫米波雷达则以全天候鲁棒性与精准测速成为安全底线的守门人。三者融合不是简单叠加，而是基于场景约束（如暴雨、强光、高速跟车）、成本边界（BOM控制）与时间窗口（量产节奏）的工程权衡。真实量产方案中，‘纯视觉’实为视觉主责+毫米波兜底，‘激光雷达上车’本质是用硬件真值加速算法迭代，而传统车企更倾向以毫米波为仲裁者协调冲突。理解这些底层原理，才能在传感器选型、
【Unity动画系统】详解Root Motion动画在Unity中的应用（一）
什么时候勾选Bake into pose，当不希望动画带动游戏对象旋转时。在旋转的动画中一般是不用勾选的，因为就需要用它来带动我们的角色对象旋转。但在比如前进、后退、待机等动画来说，我们就可以勾选这里，以避免因动画文件本身的瑕疵而给我们带来的旋转。loop match：代表了这个动画在角度上的吻合度。如果这个动画在播放中转角过大，那么就会显示红色，告诉我们如果勾选bake into pose，则会带来不好的效果。
uniapp [安卓苹果App端] - 实现获取请求手机位置权限+开启定位获取经纬度/省市区地址等，检测权限手机定位是否开启并引导用户同意授权，uniApp app端调用本机开启本机定位权限及IP属地
uni-app，手机定位权限，安卓Android，苹果ios，uniapp获取本机位置权限，uni-app获取用户定位数据，uniapp APP获取用户IP属地定位，判断权限，权限被拒绝了怎么办，苹果ios系统无法开启定位，获取定位权限报错，uniapp定位获取省市区，UNI-APP如何开启手机定位功能并获取经纬度，不依赖地图第三方插件组件，打包上架后如何获取，真机运行无法获取定位，安卓和ios权限获取，判断权限检查，正式打包，详细教程，根据获取当前经纬度,再根据经纬度去定位具体省市区，自定义基座打包能成功
【ArcGIS风暴】Win10操作系统ArcGIS许可License无法启动的解决方法汇总（绝对成功！）
文章目录1. 关闭Windows Defender2. 关闭系统防火墙3. 删除迈克菲（McAfee）杀毒软件4. 在系统服务中启动 在安装ArcGIS软件时，最重要的一步是许可服务的正常启动，通常，由于各种各样的原因，ArcGIS许可管理器License Manager十有八九会无法启动，点N多次都没反应，如果许可服务不能如愿启动的话，即使Desktop安装成功，也是无法打开的。本文汇总了Win10操作系统之上ArcGIS安装过程中可能会导致服务无法启动的原因及解决办法。 ArcGIS安装参考教程：
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
RU DOS版、EFI版32bit/64bit BIOS常用工具
DOS版、EFI版的RU工具，可用来查看IO、MEM、PCI、SMBIOS等资源。亲测在EFI SHELL下可用。
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. 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');```[](https://bitdeli.com/free "Bitdeli Badge")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
348
扫一扫
