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第一次搭建react项目，使用npx create-react-app my-app创建react项目时，报了如下错误
npm ERR! Unexpected end of JSON input while parsing near '...UeAD+E05SGR0joEmNFGJw'
npm ERR! A complete log of this run can be found in:
npm ERR! /Users/cdd/.npm/_logs/2018-12-05T06_19_21_061Z-debug.log
在终端执行以下命令：
npm cache clean --force
原因可能是我刚升级了Node版本和npm版本导致cache中的文件与安装的某个文件冲突了。
基于Arduino的智能垃圾桶
通过本次简单的Arduino智能垃圾桶小实验可以快速了解Arduino的基本用法 1、材料准备 Arduino UNO HC-SR04超声波模块 SG90舵机 杜邦线 电源 电脑 2、代码分析 电机的控制 #include<Servo.h> #define duoji D5 Servo myduoji; void setup() { myduoji.attach(duoji); //舵机连接 Serial.begin(115200); } void loop() { myduoji.w
NPM Error Unexpected token ＜ in JSON at position 0 while parsing near ‘＜!DOCTYP
【代码】NPM Error Unexpected token < in JSON at position 0 while parsing near '
dToF直方图之美
上面视频里如果你能看懂，那么就能理解对于dToF单点的测距问题，对于大部分的描述在AMS和ST里的手册都非常的详细，有疑问的可以后台来找我探讨，今天我们单纯从上层讨论成像的问题。作为dToF算法工程师，应该是对直方图敏感的，直方图的每个Count峰都会表征出问题，因为dToF的输出只有直方图，观察直方图能够逆推出dToF的很多东西，这也是大多数公司不愿意开放直方图和Raw data的缘故。以上是一个小面阵的分辨率，第一张是直方图，后面是基于直方图寻峰出来的成像ToF图，Peak图，以及噪声Noise图。
NPM Error Unexpected token < in JSON at position 0 while parsing near '<!DOCTYP
在 npm login 时，报错 Unexpected token < in JSON at position 0 while parsing near '<!DOCTYPE HTML PUBLI...' 原因是发包时必须是 npm 官方镜像地址: ```sh 查看当前镜像地址 $ npm config get registry 修改 npm 源地址为官方源 $ npm c...
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Windows上python的spark环境搭建
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npm 报错： npm ERR Unexpected token in JSON at position 0 while parsing near解决方案
最近使用npm install 报错，详见下图： 尝试各种解决方案，排除网络原因和仓库原因，重新安装npm,还是无法解决，最后只能强制清理npm的缓存，如下： npm cache clear --force 然后再次执行npm install ,可以正常执行。...
手把手教学｜用飞书+扣子工作流实现自动化批量图片生成，效率提升99%！体验完整操作流程
在先前的文章中，我们已经深入探讨了扣子智能体的多种应用场景，以及如何创建和使用工作流进行高效管理。本文将引领大家通过一个具体案例，将扣子的工作流功能嵌入飞书应用中，实现操作的自动化与高效化。
关于处理npm ERR! Unexpected token in JSON at position...
关于处理npm ERR! Unexpected token in JSON at position 0 while parsing near ’ npm ERR! A complete log of this run can be found in: （注册表地址） 错误如图 善用搜索得到以下解决方法：1.（强制清理npm的缓存） npm cache clean --force 2 .npm config rm proxy npm config rm https-proxy 3.重复几次–force
超强干货之---Python-数据爬取（爬虫）
定义： 深度优先搜索是一种遍历或搜索树或图的算法，从起始节点开始，一直沿着一个分支走到底，再回溯到上一个节点继续搜索下一个分支，直到遍历完所有节点。特点递归：通常用递归实现，或者使用栈来模拟递归过程。内存占用低：在有大量分支的情况下，内存占用比广度优先搜索低。适合目标较深的情况：如果目标节点距离起始节点较深，DFS能更快找到目标。适用场景需要遍历所有节点的情况，如生成树、迷宫搜索。目标节点较深，且分支较多时。在大规模数据爬取时，选择合适的存储方式取决于数据的规模、结构和访问需求。
VMware16虚拟机：下载和安装教程
VMware16 虚拟机 下载和安装教程
npm发包报错, npm ERR Unexpected token ＜ in JSON at position 0 while parsing near
npm ERR! Unexpected token < in JSON at position 0 while parsing near '<!DOCTYPE HTML P 解决办法： npm set registry https://registry.npmjs.org/ 原因：此错误意味着中间有一个代理，它拒绝您访问Internet，需要设置注册表 ...
YOLO26涨点改进 | 全网独家，注意力创新改进篇 | Arxiv2025 | YOLO26引入MSLA多尺度线性注意力模块，高效提取多尺度特征和建模长程依赖，含多种创新改进，助力有效涨点
本文提出了一种多尺度线性注意力模块(MSLA)来优化YOLO26模型。MSLA通过并行使用不同尺寸的深度可分离卷积(3×3至9×9)提取多尺度特征，并结合线性注意力机制高效建模全局依赖关系。该模块显著提升了模型对多尺度目标的检测能力，同时保持了轻量化特性。实验表明，MSLA能有效增强小目标检测和复杂场景下的分割精度。文章详细介绍了MSLA的网络结构、核心优势及实现代码，并提供了两种集成MSLA的YOLO26改进方案，为实时目标检测任务提供了高效解决方案。
使用npm install命令出现Unexpected end of JSON input while parsing near XXX 解决方法
终端执行命令npm cache clean --force 强制清除缓存 然后重新执行命令npm install 之后运行 npm run dev
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
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")
nodejs学习笔记--Unexpected end of JSON input while parsing near ‘...“解决方法
前端学习笔记–Unexpected end of JSON input while parsing near '…"解决方法 在学习nodejs时，用命令行工具执行npm install xxx，经常卡住或失败，因此想利用第三方模块nrm提高下载速度。 问题：命令行工具执行npm install nrm时，卡住或报错Unexpected end of JSON input while parsing near… 解决：参考前辈的解决思路，成功解决了该问题。 （1）npm install --registr
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npm ERR! Unexpected end of JSON input while parsing near '...{"node":">=4.2.3"},"_' 问题解决
使用npm install -g命令时报错的解决 最近在Ubuntu上安装了node.js和npm,但是使用npm install -g cnpm --registry=https://registry.npm.taobao.org 命令添加阿里云的npm时镜像时一直报如下错误： npm ERR! Unexpected end of JSON input while parsing nea...
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npm install node-sass失败
Cannot download "https://github.com/sass/node-sass/releases/download/v3.8.0/win32-x64-46_binding.node": 这句话大家都懂的哈，我就不说了，这里我也不知道为什么会下载失败，因为我的电脑全局代理的，正常的访问任何网站都是无阻碍的，百思不得解， 好吧那我就自己下载吧（为什么自己下载？因为我在google...
NPM包发布过程中报错——`npm ERR! Unexpected token ＜ in JSON at position 0 while parsing near ‘＜!DOCTYPE HTML P
1、异常报错过程：发包过程中，进行 npm login 时出现以下报错。2、报错原因：npm 的镜像源异常，且代理未进行设置。
npm ERR! Unexpected token ＜ in JSON at position 0 while parsing near ‘＜!DOCTYPE html＞＜html...‘ ，已经解决
然后修改指向为官方指向npm config set registry https://registry.npmjs.org/运行npm config get regist命令查看npm的指向，我的指向undefined。这两天电脑不知道咋了，装完公司的绿盾之后。运行npm i每次都会报这个错误，几乎都快崩溃了~再次运行npm i，已经正常拉去vuecli模块。
npm login 报错Unexpected token ＜ in JSON at position 0 while parsing near ‘＜!DOCTYPE HIML PUBLI...
npm login 之后输入邮箱，从邮箱里拿到OTP输入就可以了。
npm报错（持续更新）
npm报错报错！ npm ERR! Unexpected token < in JSON at position 0 while parsing near ' 报错！ npm ERR! Unexpected token < in JSON at position 0 while parsing near ’ 清除缓存 npm cache clear --force 换个淘宝镜像 n...
NPM安装插件报错npm ERR! Unexpected token > in JSON at position 0
npm ERR! Unexpected token > in JSON at position 0 方法一：删除根目录下的package-lock.js文件。 方法二：npm clean cache --force 方法三：我的问题是安装之前提示了rollbackFailedOptional。随后又出现上面的错误，是因为外网安装，网速太差了。用taobao的下载地址安装。如：npm in...
npm ERR! Unexpected token in JSON at position 0 while parsing near '<HTML> 解决办法
npm ERR! Unexpected token in JSON at position 0 while parsing near '<HTML> npm ERR! <!--////////...' 网上共检索出两种方法， 但是都not work for me. 我想你们也已经试过了。 1 . rm -f package-lock.json 2. npm ca...
npm install失败，数据源过期
npm install失败，更换数据源后可正常安装
在 npm login 时，报错 Unexpected token ＜ in JSON at position 0 while parsing near ‘＜!DOCTYPE HTML PUBLI..
需要将node版本切换至16.20.1版本即可。
npm ERR! Unexpected token ＜ in JSON at position 1 while parsing near ‘
根据日志（ C:\Users***\AppData\Roaming\npm-cache_logs\2023-04-14T11_41_07_273Z-debug.log），尝试从npm仓库安装vue-cli@2.9.6，但是npm在解析从服务器接收到的JSON响应时遇到了错误。响应数据似乎不是一个合法的JSON格式，而是HTML文档。仍然无法解决问题，网络环境中尝试安装，或者尝试使用其他版本的vue-cli。
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