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有人喜欢在编程中把所有的包都建好以后再开始编程，就难免会在建包的时候出现：
Package already exists. Note: Views might filter empty parent.
这样的错误提示。错误是因为该包你已经建了，因为它是一个空包（即不包含类，并不是不包含包），所以未显示出来。要显示出来的话，你可以在【Package Exporer】中将【Package Presentation】选为“Hierarchical”，就可以看到你已经建的空包了。因为该模式下包是按树的结构显示的。
这样你就不难发现在Eclipse中当你建包时一个“.”就会帮你建一个文件夹，这里的包其实就是文件夹的概念，因此先建了子文件夹再去建父文件夹，当然会出错，因为它已经存在了！
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Package already exists with a different case.
出错的原因是包已存在，但具有不同的大小写。 在java中，包名是不区分大小写的，但包的命名规范是一律小写。
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构网型与跟网型变流器的本质区别在于控制哲学与等效模型。跟网型变流器采用"电压跟随，电流注入"策略，等效为受控电流源，依赖电网电压作为参考，在弱电网下存在稳定性问题。构网型变流器采用"自主同步，电压构建"策略，等效为受控电压源，能主动构建电压和频率，具备自主运行、黑启动和电网支撑能力。两者的核心差异体现在同步机制、电网依赖性、故障行为和系统支撑等方面。构网型变流器更适合高比例新能源、弱电网和微电网场景，是未来电力系统的重要发展方向。
Eclipse出现Package already exists. Note: Views might filter empty parent packages.错误
出现这种错误主要是因为你在之前已经建过包了，但是eclipse把空包或者相同类型的包隐藏了，com.graduation.service与com.graduation.service.impl就为同样类型. 如果都是空包，则在eclipse中不会显示com.graduation.service. 解决第一种空包 ，是将【Package Presentation】选为“Hierarchical” 。 第二种问题， 直接进入项目工作空间，在com.graduation.service文件夹下加需要的...
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有人喜欢在编程中把所有的包都建好以后再开始编程，就难免会在建包的时候出现： Package already exists. Note: Views might filter empty parent. 这样的错误提示。错误是因为该包你已经建了，因为它是一个空包（即不包含类，并不是不包含包），所以未显示出来。要显示出来的话，你可以在【Package Exporer】中将【Pack
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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; 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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")
rpmbuild时报告Package already exists: %package ...错误的可能原因
用 rpmbuild 编译一些老的 SRPM 或 tarball 时经常会报告 Package already exists: %package ... 错误而无法继续，通常其原因是这些包的 spec 文件中含有当前版本的 rpmbuild 无法识别或展开的宏定义，只要修改一下 spec 文件保证其中非标准的宏都有对应定义应该就能继续打包了。 ...
eclipse java 混淆,Eclipse被导入（“可从多个模块访问”）混淆了。
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【问题解决】Eclipse出现The package xxx is accessible from more than one module: ＜unnamed＞
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解决JSON.JAR导入项目无法使用报错：is accessible from more than one module
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