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
1.1w累计阅读
1.1w+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
django 排序通常采用如下方式：
1.使用QuerySet的order_by指定查询排序
modelname.objects.filter('updated_time') #ASC 升序
modelname.objects.filter('-updated_time') #DESC 降序
2.在model定义的class Meta添加属性：
class Meta:
ordering = ['-updated_time']
通常在需要排序的model中添加ordering属性，在特定在查询中需要改变排序再使用QuerySet order_by
deap数据集的情绪检测代码环境搭建与实现
deap数据数据集很大
Django ORM
Django ORM简单介绍
深度学习模型设计的方法和技巧
设计神经网络模型是一个迭代的过程，需要不断试验和调整各个组成部分，以达到最佳性能。同时，遵循良好的工程实践，如模块化设计、版本控制、文档记录等，也是成功的关键。
django的增删改查，排序，分组等常用的ORM操作
Django 的 ORM（对象关系映射）提供了一种方便的方式来与数据库进行交互。
立创开源桌面激光雕刻机DIY全解析：从悬臂结构、Arduino主控到Marlin固件
本文详细解析了基于立创开源平台的桌面激光雕刻机DIY全过程。重点介绍了悬臂式机械结构的设计与优化，阐述了采用Arduino Mega 2560主控与RAMPS 1.4扩展板的经典控制方案，并深入讲解了Marlin固件的关键配置步骤，包括电机步数计算与激光功能启用，为创客和电子爱好者提供了一套完整的从硬件搭建到软件调试的实战指南。
WordPress搭建“外贸独立站”完整操作教程
以往的文章，我们介绍了很多种搭建独立站的方式，有最省事的Shopify 、最专业的Magento以及最省钱的Wordpress Woocommerce。其中，通过主机托管的方式又分了免费的谷歌云vps主机、1美元Godaddy主机以及SiteGround共享主机三种。建站方法方式实在太多了，有免费的，有廉价的，有商业版的，有共享主机、有云主机主机以及各种各样的vps服务器，选择哪种方式，哪款主机，也只有真正试了才直到。 没错，上述的这些方法方式，无一例外，我都亲身折腾过。从个人实际需求和建站方式来
多表查询
多表查询 概述 基础语法： select 列名列表 from 表名列表 where… 多表查询的分类 内连接查询： 隐式内查询： 使用where消除无用的数据 显式内查询 外链接查询： 子查询：
E-Prime脑电实验打mark全攻略：从端口查询到串并口实战（附常见问题解决）
本文提供了E-Prime脑电实验打mark的完整实战指南，涵盖端口查询、并口与串口配置的核心步骤及常见问题解决。文章深入解析了mark原理，并详细介绍了通过E-Basic脚本和Serial设备对象发送信号的两种方法，帮助研究者实现E-Prime与脑电设备的高精度时间同步，确保实验数据可靠性。
django orm order_by排序
django orm order_by排序
手把手教你用示波器搞定PCIe 5.0 REFCLK时钟信号测量（附完整参数解读）
本文详细介绍了如何使用示波器进行PCIe 5.0 REFCLK时钟信号测量，包括示波器与探头的选择、关键参数的测量方法及解读，以及实战中的常见问题与解决方案。通过手把手教学和完整参数解读，帮助硬件工程师高效完成时钟信号测量，确保系统稳定性。
基于STM32设计的智能厨房环境监测报警系统_426
随着天然气、液化石油气等可燃性气体在家庭中的广泛应用，燃气泄漏所引发的安全问题日益突出。针对传统家用可燃气体报警器功能单一、信息获取受限以及缺乏远程监测能力等不足，设计了一种基于STM32单片机的家用可燃气体报警器系统。
Django ORM自定义排序的实用示例
有时候，内置的排序功能可能无法满足我们的需求。例如，假设我们希望根据某个计算得出的值进行排序，或者根据某个复杂的条件来排序。这时候，我们可以使用Django的annotate()和F()表达式。假设我们有一个Product模型，里面有price和discount字段。我们想要根据实际价格（即价格减去折扣）进行排序。可以使用annotate()在这个例子中，是一个动态生成的字段，表示折扣后的价格。通过这种方式，我们就可以根据计算出的值进行排序了。
微信小程序 - [wxapplib]] backgroundfetch privacy fail {“errno”:101,“errMsg”:“private_getBackgroundFetchDa
微信小程序报错之backgroundfetch privacy fail，微信小程序报错WAServiceMainContext.js:1 [wxapplib]] backgroundfetch privacy fail {“errno“:101,怎么解决？问题出现：调用wx.chooseMessageFile报错（开发者工具中正常）用户隐私协议中未声明小程序使用此功能，安卓真机调试报错：backgroundfetch privacy fail ，怎么解决？微信小程序[wxapplib]] backgrou
django mysql排序_django ORM排序
Fallback to SQLAFAIK, most database engines ignore order of records, until you specify an ordering column. In our case, the list is arbitrary, and does not map to any existing attribute, thus db colum...
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
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")
Django ORM 排序方法详解
Django ORM 排序方法详解
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
Django ORM QuerySet extra()方法 自定义排序
借助extra()方法使用sql语句对结果重新排序pk_list是按照指定条件、自定义序列过滤出来的主键 可以使用函数排列成我们想要的顺序 降序 第二种方法 升序降序同理 方法一的查询速度更快一下
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
django中使用orm根据某字段部分值进行排序
# 根据goods_name前两个字符排序 query_goods_list = PurchaseGoods.objects.filter(goods_cate_id=cate_id, deleted=0).extra( select={'goods_name_seq': "left(goods_name,2)"}).order_by("goods_name_seq") print(query_goods_list.query) 打印的sql语句 SELECT (left(goods_name,2
Django ORM 排序查询示例：按标题降序排列
Django ORM 排序查询示例：按标题降序排列
django orm排序 空值在后
from django.db.models import F demo.objects.filter(status=1).order_by(F(‘test_time’).asc(nulls_last=True)).values() 其中test_time即为指定字段，asc为正序 传入参数nulls_last为True，查询结果即为空值在后。 其效果等同于:select * from demo where status=1 order by test_time is NULL, test_time; se
django ORM排序
I thought it would be an easy one, but found myself lost with 34 opened tabs onstackoverflow... The problem : keep it ordered Usually, obtaining aQuerySetfrom a list is quite simple : >&g...
Django ORM排序Bug排查：NULL值处理与分页稳定性解决方案
在数据库查询中，排序是数据展示的基础操作，其稳定性直接影响分页和列表渲染的准确性。数据库对NULL值的处理存在特殊性，在默认升序排序时NULL通常被视为最小值，这可能导致排序结果与预期不符。在Web开发实践中，ORM框架如Django的查询集操作可能引入不确定性，特别是当排序字段存在重复值或NULL时，结合分页器使用容易产生“幽灵”排序问题。通过使用COALESCE函数或CASE WHEN语句统一处理NULL值，并在排序链末尾添加唯一字段（如主键id），可以确保查询结果的绝对确定性。本文以博客文章列表排序异
Django ORM 排序查询：使用 Coalesce 和 order_by 示例
Django ORM 排序查询：使用 Coalesce 和 order_by 示例
Django ORM 查询：排序和去重示例
Django ORM 查询：排序和去重示例
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
4157
扫一扫
