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
2w累计阅读
2.1w+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
马上就是圣诞节了，先提前祝大家圣诞快乐！:christmas_tree::christmas_tree::christmas_tree:
有人要说了，圣诞节是耶稣诞生的日子，我又不信基督教，有啥好庆祝的。这你就有所不知了，Python 的诞生也跟圣诞节有关：1989 年，那是一个冬天，那年的第一场雪来得比以往时候来得更早一些，有一位程序员，在圣诞期间的阿姆斯特丹感觉特别闲，就决定开发一个新的脚本语言。他一边开发一边刷剧，于是新语言的命名也来自于他热衷的豆瓣9分神剧《Monty Python’s Flying Circus（巨蟒剧团之飞翔的马戏团）》。
此人便是后来的 Python 之父·仁慈的独裁者· Guido van Rossum 。
所以，临近圣诞了，那就给大家送上几棵圣诞树吧。
1、极简版
来晚了！圣诞节，用Python画一棵带音乐的雪夜圣诞树
用 Python 来画一棵带音乐效果的雪夜圣诞树，所想的思路如下： 用 Python 画一棵圣诞树作为背景图 在圣诞树背景图中添加雪落效果及音乐 下面来看一下具体实现。 首先，画一棵圣诞树，主要用到的 Python 库为 turtle，主要代码实现如下： n = 80.0 turtle.setup(700, 700, 0, 0) turtle.speed("fastest") turtle.screensize(bg='black') turtle.left(90) turtle.forward(3 *
圣诞节Python画圣诞树
Python绘制圣诞树，主要采用turtle框架。 效果图： 代码： ''' Author: TuZhou Version: 1.0 Date: 2021-12-25 14:07:36 LastEditTime: 2021-12-29 14:39:08 LastEditors: TuZhou Description: FilePath: \python_test\cheery.py ''' from turtle import * import time import turtle import ran
圣诞节怎么能缺少圣诞树呢？Python+HTML打造专属于你的圣诞树
圣诞节怎么能缺少圣诞树呢？Python+HTML打造专属于你的圣诞树
圣诞节来了，怎能还没有圣诞树呢 快来为心爱的她送上专属的圣诞礼物叭~
圣诞节来了，怎能没有圣诞树！作为我的粉丝朋友们，我不允许大家还没有专属于自己的圣诞树！我要让大家收到最特别最美丽的圣诞树！
【实战指南】DBeaver EE Windows版高效破解与配置全解析
本文详细解析了DBeaver EE Windows版的高效破解与配置方法，涵盖Java环境配置、破解包部署、配置文件修改等关键步骤，并提供了性能优化和数据库连接的最佳实践。适用于需要解锁企业版高级功能的数据库开发人员，帮助提升工作效率。
Unity URP项目里Post Processing死活没效果？别慌，这5个坑我帮你踩过了
本文详细解析了Unity URP项目中Post Processing效果失效的五大常见问题，包括渲染管线配置冲突、Volume作用域误判、摄像机参数漏配、Layer层级冲突和Profile引用异常。通过实际项目案例和代码示例，提供专业级解决方案，帮助开发者快速排查并修复后处理效果不生效的问题。
逆向选择启示录：一个失败高频策略如何意外跑赢市场
本文探讨了高频交易中的逆向选择现象，揭示了一个失败的高频策略如何通过简化意外跑赢市场。通过加密货币市场的实证分析和蒙特卡洛模拟，研究发现低预测性的简单策略反而更具鲁棒性，有效规避了逆向选择风险。文章还提供了小币种做市策略的优化框架和实践建议，为量化交易者提供了宝贵洞见。
华大HC32F460 TCP Server实验
华大HC32F460 TCP Server实验
基于OkHttp3+Retrofit2+RxJava3搭建响应式网络框架
本文介绍了一个基于OkHttp3+Retrofit3+RxJava3的响应式网络框架设计。该框架采用分层架构，OkHttp3负责底层网络请求，Retrofit3封装HTTP接口，RxJava3实现响应式流处理。核心亮点包括：1)高度解耦的分层设计；2)统一的异常处理和响应体封装；3)RxJava3实现线程切换和请求管理；4)MVVM架构适配，业务层只需关注数据逻辑。框架提供了完整的实现方案，包括全局配置、拦截器、响应转换、接口声明、仓库封装等组件，并支持生产环境扩展功能如Cookie持久化、Token刷新等
MATLAB R2023a下载链接及软件安装教程
MATLAB的基本数据单位是矩阵，它的指令表达式与数学、工程中常用的形式十分相似，故用MATLAB来解算问题要比用C，FORTRAN等语言完成相同的事情简捷得多，并且MATLAB也吸收了像Maple等软件的优点，使MATLAB成为一个强大的数学软件。a是上半年，b是下半年版本，但是有些小弱们总是觉得只要是b一定功能多，这不一定的，同年版本b比a可能稍微全点，不同年的，比如21b，22a，肯定22a要比21b版本高功能多了。10、选择目标文件夹，默认的是C盘，想修改的话可以单击【浏览】，然后单击【下一步】
《电磁兼容设计》专栏说明
本专栏自2024年1月起开始更新，仅设此一个电磁兼容相关知识专栏，并将一直持续更新下去，无期限
从 SAP GUI 走向 SAP Fiori：一套面向 SAP S/4HANA 的用户迁移实战方法论
SAP S/4HANA转型中，用户从SAP GUI迁移到Fiori的难点在于业务价值的重构而非简单的界面替换。Fiori的核心价值在于角色化、实时洞察和智能决策，而非完全替代GUI。成功迁移需分阶段推进：先展示关键角色的价值，建立统一入口，再逐步提高Fiori占比。让用户参与设计比后期培训更重要，需通过具体场景（如采购优先级管理）展示效率提升。迁移策略应平衡新旧界面优势，让Fiori成为业务价值触达的主要方式，而非强制替换。
pscad IEEE标准系统搭建说明
pscad IEEE标准系统搭建说明，包含典型的IEEE 9节点、14节点、30节点、39节点和118节点系统。内含标准测试系统的原始数据和潮流结果。
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
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
3225
扫一扫
