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
200+累计阅读
270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
判断中文是否为中文数字的方法及示例代码
中文数字是指用中文字符表示的数字，例如：“一”、“二”、"三"等。在Python中，我们可以使用正则表达式或者字符编码来判断一个中文字符是否为中文数字。
方法一：使用正则表达式判断
我们可以使用正则表达式来匹配中文数字的模式，如果一个中文字符符合该模式，则可以判断它是中文数字。
示例代码：
import re
def is_chinese_number(character):
pattern = r"^[一二三四五六七八九十百千万亿零壹贰叁肆伍陆柒捌玖拾佰仟萬億〇]+$"
return re.match(<020pyqt5实现手写中文数字识别
083基于深度学习的手势识别小程序版本_含10多种模型包括alexnet、DenseNet、DLA、GoogleNet、Mobilenet、ResNet、ResNeXt、ShuffleNet、VGG、EfficientNet和Swin transformer等10多种模型。016基于CNN卷积网络的人脸识别打卡签到_resnet_mobilenet_efficientnet等。运行02文字识别程序.py可以生成一个可视化的界面，可以画图识别，也可以加载图片识别。
Python中如何判断一个字符串是否为中文数字？
通过上述的代码测试，我们可以看到，我们的函数能够正确地判断一个字符串是否为中文数字。同时，也提供了一种使用正则表达式匹配中文数字的方法，这对于其他涉及中文数字的文本处理任务也会有用处。这个函数将返回布尔值True或False，表示输入字符串是否为中文数字。首先，我们需要导入re模块，它是Python用于处理正则表达式的库。对于中文数字，我们可以使用正则表达式来进行匹配。Python中如何判断一个字符串是否为中文数字？这里使用了Unicode字符集（）来匹配所有中文数字。
NVIDIA DGX Spark 开发环境深度配置与优化指南
定期系统维护每月执行一次完整的系统更新监控存储空间使用情况，及时清理临时文件定期检查硬件健康状况开发习惯优化使用tmux或screen管理长时间运行的任务配置自动化测试和代码质量检查建立完善的项目文档体系性能调优周期每月重新评估和调整系统参数根据工作负载特点优化容器配置持续监控和记录性能指标DGX Spark作为一个强大的AI开发平台，其真正的价值需要通过精细的环境配置和优化才能充分发挥。本文提供的配置方案和建议均基于实际生产环境经验，旨在帮助开发者构建稳定、高效、可维护的开发环境。
Solidworks转URDF避坑实录：从履带机器人到ROS仿真的完整配置流程
本文详细介绍了将Solidworks履带机器人模型转换为URDF格式并配置ROS仿真的完整流程，重点解析了坐标系设置、复杂关节定义、插件配置等关键步骤中的常见问题与解决方案，帮助开发者高效实现履带机器人在RVIZ和Gazebo中的精确仿真。
从零构建FOC：手把手教你实现无刷电机磁场定向控制
本文是一份详尽的FOC（磁场定向控制）实战教程，指导开发者从零开始构建无刷电机驱动系统。文章深入解析了FOC的核心算法，包括坐标变换（Clarke/Park）、SVPWM调制和双环PI控制，并对比了有感与无感方案的实现差异。通过分步调试指南，帮助读者亲手实现稳定、高效的无刷电机控制，彻底掌握其底层原理。
ESP32-S3 + TB6600驱动42步进电机：从基础接线到AccelStepper库平滑加减速实战
本文详细介绍了如何使用ESP32-S3开发板和TB6600驱动器控制42步进电机，从基础接线到利用AccelStepper库实现平滑加减速控制。内容涵盖硬件连接、基础驱动代码、AccelStepper库的安装与参数调优，以及高级技巧与故障排除，帮助创客实现工业级精准控制。
AIDL HAL服务从零到一：构建现代Android硬件抽象层
本文详细介绍了从零开始构建现代Android AIDL HAL服务的完整流程。通过定义接口、实现服务逻辑、配置构建与VINTF清单，并最终集成到系统镜像，开发者可以掌握利用AIDL替代传统HIDL来开发硬件抽象层的核心方法。文章重点解析了AIDL HAL的创建流程，并对比了其与HIDL在语法、工具链和集成方式上的优势，帮助开发者高效构建稳定的硬件接口。
【毕设级教程】从0到1实现基于YOLOv8的xView卫星图像目标检测系统（含UI界面，助力遥感智能毕设）
本文基于YOLOv8模型设计了一个面向xView卫星图像的目标检测系统。首先对xView数据集进行了格式转换处理，将原始多边形标注转换为YOLO格式的边界框标注。随后通过配置YOLOv8模型参数完成训练，实现了对卫星图像中建筑、车辆等多类目标的准确识别。为提高系统交互性，采用PyQt开发了可视化界面，支持图像选择、实时检测和结果展示功能。整个系统兼顾学术价值和实用价值，为遥感领域的自动化目标检测提供了可行方案。
Python 爬虫实战：共享单车数据采集全流程与市场洞察深度剖析
本文介绍了如何利用Python爬虫技术获取和分析共享单车运营数据。文章从项目背景出发，详细阐述了系统架构设计，包括数据抓取、清洗存储、分析和可视化模块。通过Scrapy、BeautifulSoup等工具实现数据采集，使用Pandas进行数据清洗，并借助Matplotlib进行可视化分析。实战部分展示了爬虫代码示例和城市分布、使用频率等分析案例。最终指出数据驱动分析有助于了解行业现状和市场趋势，未来可结合机器学习等方法进行深入洞察。该技术方案为共享单车行业研究提供了有效的数据支持。
YOLOv8改进：基于EfficientNetV2均衡缩放网络的特征提取层优化【YOLOv8】
YOLO（You Only Look Once）系列模型以其高效的实时目标检测能力而闻名。YOLOv8作为该系列的最新版本，通过引入改进的网络结构和算法优化，进一步提升了目标检测的性能。然而，YOLOv8在特征提取阶段的网络结构仍有改进的空间。EfficientNetV2是一种基于均衡缩放的卷积神经网络，它通过优化网络的宽度、深度和分辨率来实现高效的特征提取。与之前的EfficientNet版本相比，EfficientNetV2在速度和精度上都有显著提升，尤其在处理复杂目标检测任务时表现尤为突出。
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
SAP B1中文培訓教材_SDK
公司内部培训资料，对开发工具 ，DIAPI， UIAPI，Add-ON有详细解说
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
2069
扫一扫
