登录
首页 » matlab » DeepLearnToolbox-master

DeepLearnToolbox-master

于 2021-02-27 发布
0 270
下载积分: 1 下载次数: 8
我要下载

代码说明：

说明：  深度学习工具箱，包括BP、CNN、DBN、CAE等多个网络。包含数据可直接运行。(Deep learning toolbox, including multiple networks such as BP, CNN, DBN, CAE, etc. Containing data can be run directly.)

文件列表：

DeepLearnToolbox-master\.travis.yml, 249 , 2018-03-15
DeepLearnToolbox-master\CAE\caeapplygrads.m, 1219 , 2018-03-15
DeepLearnToolbox-master\CAE\caebbp.m, 917 , 2018-03-15
DeepLearnToolbox-master\CAE\caebp.m, 1011 , 2018-03-15
DeepLearnToolbox-master\CAE\caedown.m, 259 , 2018-03-15
DeepLearnToolbox-master\CAE\caeexamples.m, 754 , 2018-03-15
DeepLearnToolbox-master\CAE\caenumgradcheck.m, 3618 , 2018-03-15
DeepLearnToolbox-master\CAE\caesdlm.m, 845 , 2018-03-15
DeepLearnToolbox-master\CAE\caetrain.m, 1148 , 2018-03-15
DeepLearnToolbox-master\CAE\caeup.m, 489 , 2018-03-15
DeepLearnToolbox-master\CAE\max3d.m, 173 , 2018-03-15
DeepLearnToolbox-master\CAE\scaesetup.m, 1937 , 2018-03-15
DeepLearnToolbox-master\CAE\scaetrain.m, 270 , 2018-03-15
DeepLearnToolbox-master\CNN\cnnapplygrads.m, 575 , 2018-03-15
DeepLearnToolbox-master\CNN\cnnbp.m, 2141 , 2018-03-15
DeepLearnToolbox-master\CNN\cnnff.m, 1774 , 2018-03-15
DeepLearnToolbox-master\CNN\cnnnumgradcheck.m, 3430 , 2018-03-15
DeepLearnToolbox-master\CNN\cnnsetup.m, 2020 , 2018-03-15
DeepLearnToolbox-master\CNN\cnntest.m, 193 , 2018-03-15
DeepLearnToolbox-master\CNN\cnntrain.m, 845 , 2018-03-15
DeepLearnToolbox-master\CONTRIBUTING.md, 544 , 2018-03-15
DeepLearnToolbox-master\create_readme.sh, 744 , 2018-03-15
DeepLearnToolbox-master\data\mnist_uint8.mat, 14735220 , 2018-03-15
DeepLearnToolbox-master\DBN\dbnsetup.m, 557 , 2018-03-15
DeepLearnToolbox-master\DBN\dbntrain.m, 232 , 2018-03-15
DeepLearnToolbox-master\DBN\dbnunfoldtonn.m, 425 , 2018-03-15
DeepLearnToolbox-master\DBN\rbmdown.m, 90 , 2018-03-15
DeepLearnToolbox-master\DBN\rbmtrain.m, 1401 , 2018-03-15
DeepLearnToolbox-master\DBN\rbmup.m, 89 , 2018-03-15
DeepLearnToolbox-master\LICENSE, 1313 , 2018-03-15
DeepLearnToolbox-master\NN\nnapplygrads.m, 628 , 2018-03-15
DeepLearnToolbox-master\NN\nnbp.m, 1638 , 2018-03-15
DeepLearnToolbox-master\NN\nnchecknumgrad.m, 704 , 2018-03-15
DeepLearnToolbox-master\NN\nneval.m, 811 , 2018-03-15
DeepLearnToolbox-master\NN\nnff.m, 1849 , 2018-03-15
DeepLearnToolbox-master\NN\nnpredict.m, 192 , 2018-03-15
DeepLearnToolbox-master\NN\nnsetup.m, 1844 , 2018-03-15
DeepLearnToolbox-master\NN\nntest.m, 184 , 2018-03-15
DeepLearnToolbox-master\NN\nntrain.m, 2414 , 2018-03-15
DeepLearnToolbox-master\NN\nnupdatefigures.m, 1858 , 2018-03-15
DeepLearnToolbox-master\README.md, 8861 , 2018-03-15
DeepLearnToolbox-master\README_header.md, 2244 , 2018-03-15
DeepLearnToolbox-master\REFS.md, 950 , 2018-03-15
DeepLearnToolbox-master\SAE\saesetup.m, 132 , 2018-03-15
DeepLearnToolbox-master\SAE\saetrain.m, 308 , 2018-03-15
DeepLearnToolbox-master\tests\runalltests.m, 165 , 2018-03-15
DeepLearnToolbox-master\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2018-03-15
DeepLearnToolbox-master\tests\test_example_CNN.m, 981 , 2018-03-15
DeepLearnToolbox-master\tests\test_example_DBN.m, 1031 , 2018-03-15
DeepLearnToolbox-master\tests\test_example_NN.m, 3247 , 2018-03-15
DeepLearnToolbox-master\tests\test_example_SAE.m, 934 , 2018-03-15
DeepLearnToolbox-master\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2018-03-15
DeepLearnToolbox-master\util\allcomb.m, 2618 , 2018-03-15
DeepLearnToolbox-master\util\expand.m, 1958 , 2018-03-15
DeepLearnToolbox-master\util\flicker.m, 208 , 2018-03-15
DeepLearnToolbox-master\util\flipall.m, 80 , 2018-03-15
DeepLearnToolbox-master\util\fliplrf.m, 543 , 2018-03-15
DeepLearnToolbox-master\util\flipudf.m, 576 , 2018-03-15
DeepLearnToolbox-master\util\im2patches.m, 313 , 2018-03-15
DeepLearnToolbox-master\util\isOctave.m, 108 , 2018-03-15
DeepLearnToolbox-master\util\makeLMfilters.m, 1895 , 2018-03-15
DeepLearnToolbox-master\util\myOctaveVersion.m, 169 , 2018-03-15
DeepLearnToolbox-master\util\normalize.m, 97 , 2018-03-15
DeepLearnToolbox-master\util\patches2im.m, 242 , 2018-03-15
DeepLearnToolbox-master\util\randcorr.m, 283 , 2018-03-15
DeepLearnToolbox-master\util\randp.m, 2083 , 2018-03-15
DeepLearnToolbox-master\util\rnd.m, 49 , 2018-03-15
DeepLearnToolbox-master\util\sigm.m, 48 , 2018-03-15
DeepLearnToolbox-master\util\sigmrnd.m, 126 , 2018-03-15
DeepLearnToolbox-master\util\softmax.m, 256 , 2018-03-15
DeepLearnToolbox-master\util\tanh_opt.m, 54 , 2018-03-15
DeepLearnToolbox-master\util\visualize.m, 1072 , 2018-03-15
DeepLearnToolbox-master\util\whiten.m, 183 , 2018-03-15
DeepLearnToolbox-master\util\zscore.m, 137 , 2018-03-15
DeepLearnToolbox-master\说明.docx, 15054 , 2018-03-15
DeepLearnToolbox-master\CAE, 0 , 2020-09-29
DeepLearnToolbox-master\CNN, 0 , 2020-09-29
DeepLearnToolbox-master\data, 0 , 2020-09-29
DeepLearnToolbox-master\DBN, 0 , 2020-09-29
DeepLearnToolbox-master\NN, 0 , 2020-09-29
DeepLearnToolbox-master\SAE, 0 , 2020-09-29
DeepLearnToolbox-master\tests, 0 , 2020-09-29
DeepLearnToolbox-master\util, 0 , 2020-09-29
DeepLearnToolbox-master, 0 , 2020-09-29

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • 8QAM
    利用Matlab仿真软件，完成如图1所示的一个基本的数字通信系统。信号源产生0、1等概分布的随机信号，映射到16QAM的星座图上，同时一路信号已经被分成了实部和虚部，后边的处理建立在这两路信号的基础上。实部、虚部信号分别经过平方根升余弦滤波器，再加入高斯白噪声，然后通过匹配滤波器（平方根升余弦滤波器）。最后经过采样，判决，得到0、1信号，同原信号进行比较，给出16QAM数字系统的误码。(Using Matlab simulation software, as shown in Figure 1 to complete a basic digital communications systems. Signal source such as arise over the distribution of 0,1 random signal, is mapped to 16QAM constellation diagram, at the same time all the way the signal has been divided into real and imaginary parts, behind the deal with the establishment of these two signals in the basis. Real part, imaginary part of the signal, respectively, after a square root raised cosine filter, then add white Gaussian noise, and then through the matched filter (square root raised cosine filter). Finally, after sampling, the verdict has been 0,1 signal, to compare with the original signal is given 16QAM digital system error.)
    2007-10-22 15:57:50下载
    积分：1
  • Skeleton-(1)
    Friction parameters identification
    2015-03-11 01:32:29下载
    积分：1
  • MIMO_OFDM
    CODE MATLAB POUR MIMO
    2015-04-18 08:36:46下载
    积分：1
  • LMS_and_VSS_LMS
    LMS和VSS_LMS算法比较其仿真源代码(Comparison of LMS and the simulation VSS_LMS algorithm source code)
    2010-12-13 19:52:15下载
    积分：1
  • yuanchenxu
    shangjiao6.m为主程序；pcode.m为产生伪码的子程序；bu.m为进行补0的子程序（为了使得进行FFT和IFFT的点数满足2的N次方个） c_b.m为数据进行串并转换的子程序。(shangjiao6.m-based procedures pcode.m for producing pseudo-code subroutine bu.m for fill 0 Subroutine (carried out in order to make the point FFT and IFFT of the N-th power to meet the 2 months) c_b.m for data for string and conversion subroutine.)
    2007-08-29 11:05:08下载
    积分：1
  • CameraDemo
    摄像功能实现，Camera function realization(Camera function realization, Camera function realization)
    2010-09-07 09:26:52下载
    积分：1
  • dwt
    说明：  对图像进行DWT变换后的图形结果，如上传所示！(DWT transform the image of the graphical results, as shown upload!)
    2011-04-12 15:34:01下载
    积分：1
  • Lp
    说明：  Algorithms for non-convex sparsity promoting analysis and synthesis priors
    2011-05-13 02:12:09下载
    积分：1
  • wlanposition
    基于WLAN的室内定位技术研究，实现wlan环境下的定位方案设计(WLAN-based indoor positioning technology research)
    2013-05-07 23:35:43下载
    积分：1
  • SAW_correlator_no_temp_diff
    surface acoustic wave (correlator no temp diff)
    2014-01-17 22:49:57下载
    积分：1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！