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
2.4k累计阅读
2.5k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
/*每个端口包括16跟引脚，通过对相关寄存器的设置，可以实现端口之间相互独立的模式配置。
共有8种模式——
模拟输入：输入驱动器将被禁止，禁止斯密特触发器输入，实现了每个模拟IO引脚上的零消耗。斯密特触发器输出值被强制为“0”，弱上拉和弱下拉电阻被禁止，读取输入数据寄存器时数值为“0”。
浮空输入，上拉输入，下拉输入：由输出模块中上拉和下拉电阻配置而成，其中上拉零输入时，为高电平；下拉输入反之；浮空输入不确定，故一般用于ADC。
通用推挽输出：N-MOS和P-MOS都被激活，实现推挽输出模式。
通用开漏输出：N-MOS被激活，P-MOS禁止，成为开漏输出模式。
复用推挽输出：与上类似，用于复用功能。
复用开漏输出：与上类似，用于复用功能。
GPIO AFIO
其实GPIO就是类似于51单片机的IO口 有采集和控制的作用翻转指的是高电平到低电平 或者低电平到高电平1.不能接5v的电压 接了就烧2.coms端口和ttl端口的识别。
STM32基础篇：AFIO × 查表重映射 × AFIO库函数
AFIO，直译为：复用输入输出，是STM32上众多的片上外设之一；我们知道当IO引脚复用功能冲突时，可以通过重映射来解决这个问题，而AFIO就是专门用来执行"复用功能的重映射"的模块（可以看出其名字与功能并不一致）。
DQN网络代码调用env.reset()后存储观测信息报错问题ValueError: setting an array element with a sequence.解决处理
**reset()函数返回的是一个array类型以及其中数据的type！模拟游戏进行学习任务。因此需要将observation指定为真正需要的array信息即可,得到的的变量observation类型与Agent存储时的。因此怀疑返回的observation有问题，于是通过。报错信息提示为数据维度不对应，也即最初通过。在强化学习DQN网络代码实现过程中，针对。
pytest-bdd封装
简介 pytestbdd主要是用于代码封装 代码封装的好处： 1、提升代码复用率 2、提示测试效率 手工测试–使用框架0代码 流程+数据驱动 产品经理这样的不懂测试的人员–流程驱动 自动化测试–数据驱动 pytest-bdd:流程驱动封装 行为驱动–敏捷开发模式，消除开发/测试对需要了解的歧义 使用方式： 描述需求/用户场景 Feature（需求） Scenario（场景） Given（假设） When（操作步骤） Then（验证，清除） #新建feature 文件之前 需要提取安装支持featrue文.
【SOC估计】基于EKF与二阶RC等效电路模型的锂电池SOC在线估计算法实现与仿真分析
本文详细介绍了基于扩展卡尔曼滤波(EKF)与二阶RC等效电路模型的锂电池SOC在线估计算法。通过Matlab仿真分析，展示了该算法如何实现高精度SOC估计，相比传统方法显著提升估计精度与鲁棒性，适用于电动汽车、储能系统等关键领域。
dolphinscheduler集群服务一键安装启动实现流程剖析
通过以上分析我们基本梳理清楚了整个dolphinscheduler项目的一键安装启动逻辑， 我们现在在通过一张图正好理一下整个实现流程。关于一键停止集群服务及一件查询集群各个角色服务实例运行状态的实现也都在stop-all.sh/status-all.sh及dolphinscheduler-daemon.sh这三个脚本中， 有兴趣自行研究。写这篇文章的核心目的不仅在于给大家分享如何优秀的开源项目的一键安装部署的实现过程。
华为OD机试真题 新系统2026-04-08 Python&JS 实现【配置操作失败数量统计】
模拟一个系统的命令行配置，包含添加、修改、删除三项操作，详情如下: 添加操作命令:add_rulerule_id=1rule_index = 18 修改操作命令: mod_rule rule_id= 1rule_index = 100 删除操作命令:del_rulerule_id=1
Google Cameraserver 看门狗CameraServiceWatchdog超时crash原理分析
本文分析了Android CameraServiceWatchdog机制的工作原理。该看门狗用于监控相机服务关键操作（如close、flush）的超时情况，通过定时器线程检测阻塞问题。文章详细讲解了Watchdog的初始化流程、使能方式、内部定时器机制（默认周期100ms，650次循环触发超时），并提供了configureStreams阶段的客制化实现方案。当检测到超时，Watchdog会触发abort生成tombstone日志，便于调试分析。
第十六章：载板制造工艺大全
本文详细介绍了IC载板制造的完整工艺流程，主要包括开料、钻孔、孔金属化、图形形成、层压、表面处理、成型和电测8大工序。每个工序都列出了关键参数和质量控制要点，如开料尺寸精度±0.1mm、激光钻孔孔径50-150μm、孔金属化铜厚20-30μm、图形形成线宽15μm±2μm等。文中还特别说明了MSAP工艺和沉金等特殊工艺的详细流程，以及成型精度和电测标准等关键指标。全文通过流程图和参数表格直观展示了IC载板制造的核心工艺和质量要求。
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
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")
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
答案分为十章，有部分习题没有答案，全英文复分析阿尔福斯(Ahlfors) 第三版答案
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
2955
扫一扫
