MATLAB深度学习算法源码套件：无约束Prim算法实现

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深度学习
深度学习算法
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1. Matlab概述 Matlab（Matrix Laboratory的缩写）是一种用于算法开发、数据可视化、数据分析以及数值计算的高级编程语言和交互式环境。由MathWorks公司发布，Matlab被广泛应用于工程计算、控制系统、信号处理、图像处理、通信以及金融建模等领域。 2. 深度学习简介 深度学习是机器学习的一个分支，它基于神经网络的原理，通过模拟人脑进行分析和学习，可以处理大量的非结构化数据，如图像、声音、文本等。深度学习算法通常包括卷积神经网络（CNNs）、递归神经网络（RNNs）、长短期记忆网络（LSTMs）和深度信念网络（DBNs）等。 3. DeepLearnToolbox介绍 DeepLearnToolbox是Matlab环境下开发的一个深度学习工具箱，它提供了一系列深度神经网络的构建块，包括各种层、激活函数、损失函数、优化算法等，方便用户快速搭建和训练深度神经网络模型。工具箱内还包含了一些预训练的模型，可供用户直接使用或进行迁移学习。 4. Matlab深度学习算法实现 本资源提供的是一套完整的Matlab深度学习算法项目源码，通过这些源码，用户可以了解深度学习算法在Matlab环境下的实现原理和流程。项目源码经过了测试校正，保证了能够成功运行，对于初学者和有经验的开发者来说，这是一套非常实用的学习资源。 5. 无约束条件下普列姆(Prim)算法 在提供的文件中，包含了一个名为“Matlab实现无约束条件下普列姆(Prim)算法.docx”的文档。Prim算法是一种用于求解最小生成树问题的贪心算法。在无约束条件下，该算法能够高效地找到带权无向图的一个最小生成树，即将图中的所有顶点连接起来，使得总权重最小。Prim算法在Matlab中的实现有助于理解图算法和贪心算法的概念，同时也为深度学习算法提供了可能的输入数据预处理手段。 6. DeepLearnToolbox-master文件说明 “DeepLearnToolbox-master”表明这是一个开源的深度学习工具箱的主文件，用户可以在此基础上进行学习、修改和扩展，以满足特定的研究或开发需要。在Matlab中，可以利用其内置的工具箱和函数，进行数据预处理、网络搭建、训练、验证以及预测等操作。 7. 适合人群分析 资源中明确指出，该项目源码适合新手及有一定经验的开发人员。对于新手来说，可以通过阅读和修改源码来学习深度学习的基础知识和Matlab的编程技能。对于有经验的开发人员，可以直接使用这些源码进行项目开发或集成到更大的系统中，提高工作效率。 8. 达摩老生出品特点 达摩老生作为资源出品方，其出品保证了项目的质量，提供亲测校正服务，确保用户在遇到问题时能够得到及时的帮助和解决方案。这样的质量保证对于保证项目的成功运行以及用户学习效果具有重要意义。 总结来说，此资源提供了关于Matlab深度学习算法实现的全套项目源码，包括深度学习工具箱DeepLearnToolbox和Prim算法的Matlab实现。源码经过测试校正，适合各类开发者使用，同时达摩老生的品质保证也为用户提供了信任的保障。通过学习和应用这些资源，用户能够加深对深度学习和Matlab编程的理解，为未来在人工智能领域的深入研究和实际应用打下坚实的基础。
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深度学习工具包 DeepLearningToolbox

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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 ----- This toolbox is outdated and no longer maintained.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 com Directories 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 working For references on each library check REFS.md Setup ----- 1. Download. 2. addpath(genpath('DeepLearnToolbox')); Example: Deep Belief Network --------------------- ```matlab function test_example_DBN load 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 weights rand('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 NN rand('state',0) %train dbn dbn.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 nn nn = dbnunfoldtonn(dbn, 10); nn.activation_function = 'sigm'; %train nn opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); 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``` Example: Convolutional Neural Nets --------------------- ```matlab function test_example_CNN load 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% error rand('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 error figure; plot(cnn.rL); assert(er<0.12, 'Too big error'); ``` Example: Neural Networks --------------------- ```matlab function test_example_NN load 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 net rand('state',0) nn = nnsetup([784 100 10]); opts.numepochs = 1; % Number of full sweeps through data opts.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 decay rand('state',0) nn = nnsetup([784 100 10]); nn.weightPenaltyL2 = 1e-4; % L2 weight decay opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); 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