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DeepLearnToolbox-master

于 2020-06-19 发布 文件大小:28871KB
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下载积分: 1 下载次数: 2

代码说明:

  CNN,DBN算法可以对手写体数字进行识别,准确率高(CNN and DBN algorithm can recognize handwritten numerals with high accuracy)

文件列表:

DeepLearnToolbox-master, 0 , 2014-01-12
DeepLearnToolbox-master\.travis.yml, 249 , 2014-01-12
DeepLearnToolbox-master\CAE, 0 , 2014-01-12
DeepLearnToolbox-master\CAE\caeapplygrads.m, 1219 , 2014-01-12
DeepLearnToolbox-master\CAE\caebbp.m, 917 , 2014-01-12
DeepLearnToolbox-master\CAE\caebp.m, 1011 , 2014-01-12
DeepLearnToolbox-master\CAE\caedown.m, 259 , 2014-01-12
DeepLearnToolbox-master\CAE\caeexamples.m, 754 , 2014-01-12
DeepLearnToolbox-master\CAE\caenumgradcheck.m, 3618 , 2014-01-12
DeepLearnToolbox-master\CAE\caesdlm.m, 845 , 2014-01-12
DeepLearnToolbox-master\CAE\caetrain.m, 1148 , 2014-01-12
DeepLearnToolbox-master\CAE\caeup.m, 489 , 2014-01-12
DeepLearnToolbox-master\CAE\max3d.m, 173 , 2014-01-12
DeepLearnToolbox-master\CAE\scaesetup.m, 1937 , 2014-01-12
DeepLearnToolbox-master\CAE\scaetrain.m, 270 , 2014-01-12
DeepLearnToolbox-master\CNN, 0 , 2014-01-12
DeepLearnToolbox-master\CNN\cnnapplygrads.m, 575 , 2014-01-12
DeepLearnToolbox-master\CNN\cnnbp.m, 2141 , 2014-01-12
DeepLearnToolbox-master\CNN\cnnff.m, 1774 , 2014-01-12
DeepLearnToolbox-master\CNN\cnnnumgradcheck.m, 3430 , 2014-01-12
DeepLearnToolbox-master\CNN\cnnsetup.m, 2020 , 2014-01-12
DeepLearnToolbox-master\CNN\cnntest.m, 193 , 2014-01-12
DeepLearnToolbox-master\CNN\cnntrain.m, 845 , 2014-01-12
DeepLearnToolbox-master\CONTRIBUTING.md, 544 , 2014-01-12
DeepLearnToolbox-master\create_readme.sh, 744 , 2014-01-12
DeepLearnToolbox-master\data, 0 , 2014-01-12
DeepLearnToolbox-master\data\mnist_uint8.mat, 14735220 , 2014-01-12
DeepLearnToolbox-master\DBN, 0 , 2019-08-01
DeepLearnToolbox-master\DBN\allcomb.m, 2618 , 2014-01-12
DeepLearnToolbox-master\DBN\dbnsetup.m, 557 , 2014-01-12
DeepLearnToolbox-master\DBN\dbntrain.m, 232 , 2014-01-12
DeepLearnToolbox-master\DBN\dbnunfoldtonn.m, 425 , 2014-01-12
DeepLearnToolbox-master\DBN\expand.m, 1958 , 2014-01-12
DeepLearnToolbox-master\DBN\flicker.m, 208 , 2014-01-12
DeepLearnToolbox-master\DBN\flipall.m, 80 , 2014-01-12
DeepLearnToolbox-master\DBN\fliplrf.m, 543 , 2014-01-12
DeepLearnToolbox-master\DBN\flipudf.m, 576 , 2014-01-12
DeepLearnToolbox-master\DBN\guzhangNumber.xls, 406528 , 2019-06-03
DeepLearnToolbox-master\DBN\im2patches.m, 313 , 2014-01-12
DeepLearnToolbox-master\DBN\isOctave.m, 108 , 2014-01-12
DeepLearnToolbox-master\DBN\makeLMfilters.m, 1895 , 2014-01-12
DeepLearnToolbox-master\DBN\mnist_uint8.mat, 14735220 , 2014-01-12
DeepLearnToolbox-master\DBN\myOctaveVersion.m, 169 , 2014-01-12
DeepLearnToolbox-master\DBN\nnapplygrads.m, 628 , 2014-01-12
DeepLearnToolbox-master\DBN\nnbp.m, 1638 , 2014-01-12
DeepLearnToolbox-master\DBN\nnchecknumgrad.m, 704 , 2014-01-12
DeepLearnToolbox-master\DBN\nneval.m, 772 , 2014-01-12
DeepLearnToolbox-master\DBN\nnff.m, 1849 , 2014-01-12
DeepLearnToolbox-master\DBN\nnpredict.m, 188 , 2014-01-12
DeepLearnToolbox-master\DBN\nnsetup.m, 1844 , 2014-01-12
DeepLearnToolbox-master\DBN\nntest.m, 180 , 2014-01-12
DeepLearnToolbox-master\DBN\nntrain.m, 2414 , 2014-01-12
DeepLearnToolbox-master\DBN\nnupdatefigures.m, 1858 , 2014-01-12
DeepLearnToolbox-master\DBN\normalize.m, 97 , 2014-01-12
DeepLearnToolbox-master\DBN\patches2im.m, 242 , 2014-01-12
DeepLearnToolbox-master\DBN\randcorr.m, 283 , 2014-01-12
DeepLearnToolbox-master\DBN\randp.m, 2083 , 2014-01-12
DeepLearnToolbox-master\DBN\rbmdown.m, 90 , 2014-01-12
DeepLearnToolbox-master\DBN\rbmtrain.m, 1401 , 2014-01-12
DeepLearnToolbox-master\DBN\rbmup.m, 89 , 2014-01-12
DeepLearnToolbox-master\DBN\rnd.m, 49 , 2014-01-12
DeepLearnToolbox-master\DBN\sigm.m, 48 , 2014-01-12
DeepLearnToolbox-master\DBN\sigmrnd.m, 126 , 2014-01-12
DeepLearnToolbox-master\DBN\softmax.m, 256 , 2014-01-12
DeepLearnToolbox-master\DBN\tanh_opt.m, 54 , 2014-01-12
DeepLearnToolbox-master\DBN\visualize.m, 1072 , 2014-01-12
DeepLearnToolbox-master\DBN\whiten.m, 183 , 2014-01-12
DeepLearnToolbox-master\DBN\zscore.m, 137 , 2014-01-12
DeepLearnToolbox-master\LICENSE, 1313 , 2014-01-12
DeepLearnToolbox-master\NN, 0 , 2014-01-12
DeepLearnToolbox-master\NN\nnapplygrads.m, 628 , 2014-01-12
DeepLearnToolbox-master\NN\nnbp.m, 1638 , 2014-01-12
DeepLearnToolbox-master\NN\nnchecknumgrad.m, 704 , 2014-01-12
DeepLearnToolbox-master\NN\nneval.m, 772 , 2014-01-12
DeepLearnToolbox-master\NN\nnff.m, 1849 , 2014-01-12
DeepLearnToolbox-master\NN\nnpredict.m, 188 , 2014-01-12
DeepLearnToolbox-master\NN\nnsetup.m, 1844 , 2014-01-12
DeepLearnToolbox-master\NN\nntest.m, 180 , 2014-01-12
DeepLearnToolbox-master\NN\nntrain.m, 2414 , 2014-01-12
DeepLearnToolbox-master\NN\nnupdatefigures.m, 1858 , 2014-01-12
DeepLearnToolbox-master\README.md, 8730 , 2014-01-12
DeepLearnToolbox-master\README_header.md, 2256 , 2014-01-12
DeepLearnToolbox-master\REFS.md, 950 , 2014-01-12
DeepLearnToolbox-master\SAE, 0 , 2014-01-12
DeepLearnToolbox-master\SAE\saesetup.m, 132 , 2014-01-12
DeepLearnToolbox-master\SAE\saetrain.m, 308 , 2014-01-12
DeepLearnToolbox-master\tests, 0 , 2014-01-12
DeepLearnToolbox-master\tests\runalltests.m, 165 , 2014-01-12
DeepLearnToolbox-master\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2014-01-12
DeepLearnToolbox-master\tests\test_example_CNN.m, 981 , 2014-01-12
DeepLearnToolbox-master\tests\test_example_DBN.m, 1644 , 2019-08-01
DeepLearnToolbox-master\tests\test_example_NN.m, 3247 , 2014-01-12
DeepLearnToolbox-master\tests\test_example_SAE.m, 934 , 2014-01-12
DeepLearnToolbox-master\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2014-01-12
DeepLearnToolbox-master\util, 0 , 2014-01-12
DeepLearnToolbox-master\util\allcomb.m, 2618 , 2014-01-12
DeepLearnToolbox-master\util\expand.m, 1958 , 2014-01-12
DeepLearnToolbox-master\util\flicker.m, 208 , 2014-01-12
DeepLearnToolbox-master\util\flipall.m, 80 , 2014-01-12
DeepLearnToolbox-master\util\fliplrf.m, 543 , 2014-01-12

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发表评论

0 个回复

  • ch5
    MATLAB高级讲义实例5(SIMULINK仿真基础)(MATLAB examples of senior notes 5 (SIMULINK simulation based))
    2008-08-17 17:44:16下载
    积分:1
  • YC
    在这个分支中,主要是对标准粒子群算法的惯性因子、收敛因子(约束因子)、“认知”部分的c1,“社会”部分的c2进行变化与调节,希望获得好的效果。(In this branch, mainly to the standard particle swarm algorithm of inertial factor, convergence factor (constraints), "cognitive" part of c1, "social" part of the c2 changes and adjustment, hope to get good results.)
    2014-02-04 20:32:17下载
    积分:1
  • matlabzhiling
    matlab主要指令集合表,以供大家查阅之便(matlab main instruction set table for access to the U.S. will)
    2008-04-19 17:07:03下载
    积分:1
  • 080604
    说明:  基于MATLAB的“电力电子技术"仿真实验的研究 为了方便地对这些变流电路进行仿真, 采用了MATLAB的Simulink库。同时,还采用VISUAL BASIC语言将电力电子变流电路的波形与MATLAB 仿真界面进行链接,以便更简洁地、直观地,甚至是在无人指导的情况下,让学习者独立地完成电力电子 变流电路的仿真实验。 (MATLAB based on the &quot Power Electronics&quot simulation study in order to facilitate these changes to circuit simulation using the MATLAB-Simulink library. At the same time, the use of VISUAL BASIC language of power electronic converter circuits and the MATLAB simulation waveforms link interface for a more concise and intuitive manner, even in the absence of guidance to enable learners to complete the independent power electronic converter circuit simulation.)
    2009-08-09 11:02:48下载
    积分:1
  • pplane8
    说明:  matlab中画非线性系统的各种分支图,可以进行各种非线性分支分析(Draw a branch diagram)
    2021-04-26 17:28:45下载
    积分:1
  • MATLAB
    简单叙述了M月TLAB语言的特点,并介绍了如何用材月了乙AB语言编写程序,实现示 波器和频语仪的仿真。 (Brief description of the M language features on TLAB, and describes how to use the materials on the B programming language AB, scope and frequency of language instrument to achieve the simulation.)
    2011-08-07 14:46:40下载
    积分:1
  • WAP-to-check-whether-a-given-system-H1
    WAP to check whether a given system H1
    2014-08-16 21:54:19下载
    积分:1
  • error
    双线性变换的特点是不产生频率混叠失真,是一种很好的设计方法(Bilinear transform the feature is not to produce the frequency aliasing distortion is a good design method)
    2012-10-03 18:33:29下载
    积分:1
  • Desktop
    saadat power system matlab files
    2012-01-16 16:32:43下载
    积分:1
  • knnsearch
    KNN classifiers, training is training set, testing is test set, D is the distance, D=1 is mandistance D=2 is 欧氏距离 D=3是 infinite norm K is the number of selected neighbors
    2010-05-14 18:48:54下载
    积分:1
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