MATLAB深度学习工具包:掌握DeepLearnToolbox的使用

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资源摘要信息: "深度学习MATLAB工具包DeepLearnToolbox-master.zip" 深度学习是机器学习领域中的一个前沿分支,其核心目标是通过构建深层的层次化模型来学习数据的复杂表示。这种学习方式受到人类大脑层次化架构的启发,深度学习模型通常包含多个处理层,每个层都能够学习数据的不同特征。本工具包名为DeepLearnToolbox,是一个专门针对MATLAB环境开发的深度学习工具箱,提供了构建和训练深度神经网络的函数和类库。 在MATLAB中实现深度学习,需要理解深度学习的理论基础和相关的数学模型。《Learning Deep Architectures for AI》是一篇极具影响力的论文,由 Yoshua Bengio 等人撰写,它为深度学习的理论提供了详尽的介绍,并且为AI领域内的深度架构学习方法提供了深入探讨。通过阅读这篇论文,学习者可以对深度学习的基本概念、模型结构、训练技术等有一个全面的认识。 深度学习MATLAB工具包(DeepLearnToolbox)包含了多种深度神经网络模型的实现,例如卷积神经网络(CNNs)、循环神经网络(RNNs)以及深度信念网络(DBNs)。工具包中的每个文件都经过精心设计,旨在简化深度学习模型的实现过程,让研究人员和开发者能够更加专注于模型的设计和学习过程的优化,而非底层的编程细节。 在MATLAB环境下,DeepLearnToolbox提供了一系列用于数据预处理、模型设计、网络训练以及结果评估的函数。例如,数据预处理功能可以辅助用户对输入数据进行归一化、批处理等操作,以适应网络模型的输入要求。模型设计方面,工具包提供了丰富的网络层类型和激活函数,允许用户设计出各种结构复杂的深度学习模型。网络训练功能则涉及到算法选择、损失函数定义、梯度下降优化器等关键组件,这些是深度学习模型得以自我优化和迭代更新的核心。最后,结果评估功能可以用来监测训练过程中的准确率和损失值变化,进而对模型的性能进行客观评估。 使用此工具包,即使是深度学习的初学者,也可以在MATLAB平台上实现包括图像识别、语音识别、自然语言处理等在内的多种应用。例如,在图像识别领域,卷积神经网络(CNNs)已经成为主流的方法,而DeepLearnToolbox提供了丰富的CNN结构和配置选项,使得用户可以快速搭建起一个高效的图像识别系统。在自然语言处理中,循环神经网络(RNNs)及其变种如长短时记忆网络(LSTMs)被广泛应用于文本分析任务,工具包中的RNN相关模块可以帮助用户实现这些功能。 MATLAB作为一个高级数学计算和工程设计平台,因其易用性、强大的计算能力和丰富的库资源,在学术研究和工程开发领域得到广泛应用。DeepLearnToolbox在MATLAB环境下提供了深度学习的支持,使得原本需要复杂编程语言实现的深度学习模型,可以在MATLAB的友好界面中得到快速开发和验证。这种无缝集成极大地降低了深度学习应用的门槛,使得更多非专业背景的用户也能够接触和应用深度学习技术。 综上所述,深度学习MATLAB工具包DeepLearnToolbox是一个功能全面、使用便捷的深度学习开发平台,适合于从学术研究到实际工程应用的广泛需求。通过这个工具包,用户不仅能够深入理解深度学习的理论基础,还能在MATLAB环境下快速实现复杂的深度学习模型,从而推动深度学习技术在各种领域的应用和发展。
2017-10-15 上传
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 ----- This toolbox is outdated and no longer maintained.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); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.10, 'Too big error'); ``` Example: Stacked Auto-Encoders --------------------- ```matlab function test_example_SAE 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 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 FFNN nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; nn.W{1} = sae.ae{1}.W{1}; % Train the FFNN opts.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 --------------------- ```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'); %% ex3 neural net with dropout rand('state',0) nn = nnsetup([784 100 10]); nn.dropoutFraction = 0.5; % Dropout fraction 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'); %% ex4 neural net with sigmoid activation function rand('state',0) nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; % Sigmoid activation function nn.learningRate = 1; % Sigm require a lower learning rate 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'); %% ex5 plotting functionality rand('state',0) nn = nnsetup([784 20 10]); opts.numepochs = 5; % Number of full sweeps through data nn.output = 'softmax'; % use softmax output opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = 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 data vx = 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 output opts.numepochs = 5; % Number of full sweeps through data opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = 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'); ``` [![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")