社区
下载资源悬赏专区
帖子详情
deeplearningtoolbox下载
weixin_39822095
2019-08-31 12:30:25
matlab,deeplearning toolbox,丹麦的一个人写的,具体我也不记得了
相关下载链接:
//download.csdn.net/download/u012964327/6673769?utm_source=bbsseo
...全文
167
回复
打赏
收藏
deeplearningtoolbox下载
matlab,deeplearning toolbox,丹麦的一个人写的,具体我也不记得了 相关下载链接://download.csdn.net/download/u012964327/6673769?utm_source=bbsseo
复制链接
扫一扫
分享
转发到动态
举报
写回复
配置赞助广告
用AI写文章
回复
切换为时间正序
请发表友善的回复…
发表回复
打赏红包
深度学习工具包
Deep
L
ear
ning
Tool
box
深度学习工具包 Deprecation notice. ----- This
tool
box
is outdated and no longer maintained. There are much better
tool
s available for
deep
l
ear
ning
than this
tool
box
, e.g. [Theano](http://
deep
l
ear
ning
.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/) I would suggest you use one of the
tool
s mentioned above rather than use this
tool
box
. Best, Rasmus.
Deep
L
ear
n
Tool
box
================ A Matlab
tool
box
for
Deep
L
ear
ning
.
Deep
L
ear
ning
is a new subfield of machine l
ear
ning
that focuses on l
ear
ning
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
L
ear
ning
theory is [L
ear
ning
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
L
ear
ning
](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010) * [Unsupervised Feature L
ear
ning
and
Deep
L
ear
ning
](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011) If you use this
tool
box
in your res
ear
ch please cite [Prediction as a candidate for l
ear
ning
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 l
ear
ning
deep
hierarchical models of data", y
ear
= "2012", } ``` Contact: rasmusbergpalm at gmail dot com Directories included in the
tool
box
----------------------------------- `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
tool
box
is working For references on each library check REFS.md Setup ----- 1. Download. 2. addpath(genpath('
Deep
L
ear
n
Tool
box
')); 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}.l
ear
ning
Rate = 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.l
ear
ning
Rate = 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.l
ear
ning
Rate = 1; % Sigm require a lower l
ear
ning
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 trai
ning
error % split trai
ning
data into trai
ning
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/
deep
l
ear
n
tool
box
/trend.png)](https://bitdeli.com/free "Bitdeli Badge")
Matlab-
Deep
L
ear
ning
Tool
box
Deep
L
ear
ning
Tool
box
™提供了一个框架,用于设计和实现具有算法,预训练模型和应用程序的深度神经网络。您可以使用卷积神经网络(ConvNets,CNN)和长期短期记忆(LSTM)网络对图像,时间序列和文本数据进行分类和回归。应用程序和图表可帮助您可视化激活,编辑网络体系结构以及监控培训进度。 对于小型训练集,您可以使用预训练的深层网络模型(包括SqueezeNet,Inception-v3,ResNet-101,GoogLeNet和VGG-19)以及从TensorFlow™-Keras和Caffe导入的模型执行传输学习。 了解深度学习工具箱的基础知识 深度学习图像 从头开始训练卷积神经网络或使用预训练网络快速学习新任务 使用时间序列,序列和文本进行深度学习 为时间序列分类,回归和预测任务创建和训练网络 深度学习调整和可视化 绘制培训进度,评估准确性,进行预测,调整培训选项以及可视化网络学习的功能 并行和云中的深度学习 通过本地或云中的多个GPU扩展深度学习,并以交互方式或批量作业培训多个网络 深度学习应用 通过计算机视觉,图像处理,自动驾驶,信号和音频扩展深度学习工作流程 深度学习导入,导出和自定义 导入和导出网络,定义自定义深度学习图层以及自定义数据存储 深度学习代码生成 生成MATLAB代码或CUDA ®和C ++代码和部署深学习网络 函数逼近和聚类 使用浅层神经网络执行回归,分类和聚类 时间序列和控制系统 基于浅网络的模型非线性动态系统; 使用顺序数据进行预测。
Selenium3分布式与虚拟化
Image、Container、Repository Docker DeskTop的
下载
与安装 docker
tool
box
的
下载
与安装 Selenium3与Docker的结合: 案例:FireFox与chrome运行同一脚本 案例:借助FireFox与Chrome镜像运行不同脚本
deep
l
ear
ning
tool
box
deep
l
ear
ning
tool
box
好用的
deep
l
ear
ning
工具箱
Deep
L
ear
ning
Tool
box
用户指南.pdf
Deep
L
ear
ning
Tool
box
用户指南,欢迎
下载
下载资源悬赏专区
12,891
社区成员
12,444,238
社区内容
发帖
与我相关
我的任务
下载资源悬赏专区
CSDN 下载资源悬赏专区
复制链接
扫一扫
分享
社区描述
CSDN 下载资源悬赏专区
其他
技术论坛(原bbs)
社区管理员
加入社区
获取链接或二维码
近7日
近30日
至今
加载中
查看更多榜单
社区公告
暂无公告
试试用AI创作助手写篇文章吧
+ 用AI写文章