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多任务深度学习改进版

于 2018-11-17 发布 文件大小:14428KB
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代码说明：

  这个程序实现了多任务的深度学习，可以提高训练的收敛速度(Multi-task deep learning)

文件列表：

多任务深度学习改进版\Calculate_Bin_Num.m, 150 , 2018-07-27
多任务深度学习改进版\Check_error.m, 1810 , 2018-07-27
多任务深度学习改进版\Construct_start_end_tab.m, 593 , 2018-07-27
多任务深度学习改进版\dbnunfoldtonn1.m, 534 , 2018-07-27
多任务深度学习改进版\dbnunfoldtonn2.m, 447 , 2018-07-27
多任务深度学习改进版\dbnunfoldtonn3.m, 482 , 2018-08-04
多任务深度学习改进版\DBN_SFC.m, 2972 , 2018-09-12
多任务深度学习改进版\Decrease_zeros.m, 639 , 2018-07-27
多任务深度学习改进版\denose.m, 862 , 2018-07-27
多任务深度学习改进版\Equal.m, 1668 , 2018-07-27
多任务深度学习改进版\Load_Data_Set.m, 689 , 2018-09-10
多任务深度学习改进版\mlt_nnff.m, 3320 , 2018-09-03
多任务深度学习改进版\mlt_nntrain.m, 4073 , 2018-08-04
多任务深度学习改进版\mtl_dbntrain.m, 738 , 2018-08-04
多任务深度学习改进版\mtl_nnbp.m, 5478 , 2018-08-04
多任务深度学习改进版\mtl_nneval.m, 1447 , 2018-08-04
多任务深度学习改进版\mtl_nnpredict.m, 788 , 2018-08-04
多任务深度学习改进版\mtl_nntest.m, 536 , 2018-08-04
多任务深度学习改进版\mulitask_DBN.m, 4658 , 2018-09-06
多任务深度学习改进版\nnsetup1.m, 2622 , 2018-07-27
多任务深度学习改进版\nnsetup2.m, 1871 , 2018-08-03
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\.travis.yml, 249 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caeapplygrads.m, 1219 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caebbp.m, 917 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caebp.m, 1011 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caedown.m, 259 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caeexamples.m, 754 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caenumgradcheck.m, 3618 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caesdlm.m, 845 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caetrain.m, 1148 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\caeup.m, 489 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\max3d.m, 173 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\scaesetup.m, 1937 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CAE\scaetrain.m, 270 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnnapplygrads.m, 575 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnnbp.m, 2141 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnnff.m, 1774 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnnnumgradcheck.m, 3430 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnnsetup.m, 2020 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnntest.m, 193 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CNN\cnntrain.m, 845 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\CONTRIBUTING.md, 544 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\create_readme.sh, 744 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\data\mnist_uint8.mat, 14735220 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\dbnsetup.m, 557 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\dbntrain.m, 232 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\dbnunfoldtonn.m, 425 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\rbmdown.m, 90 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\rbmtrain.m, 1401 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\DBN\rbmup.m, 89 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\LICENSE, 1313 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnapplygrads.m, 628 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnbp.m, 1638 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnchecknumgrad.m, 704 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nneval.m, 811 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnff.m, 1904 , 2017-12-20
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnpredict.m, 357 , 2017-12-27
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnsetup.m, 1840 , 2017-12-28
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nntest.m, 202 , 2017-07-31
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nntrain.m, 2414 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\NN\nnupdatefigures.m, 1858 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\README.md, 8861 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\README_header.md, 2244 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\REFS.md, 950 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\SAE\saesetup.m, 132 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\SAE\saetrain.m, 308 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\runalltests.m, 165 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_example_CNN.m, 981 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_example_DBN.m, 1031 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_example_NN.m, 3247 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_example_SAE.m, 934 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\allcomb.m, 2618 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\expand.m, 1958 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\flicker.m, 208 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\flipall.m, 80 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\fliplrf.m, 543 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\flipudf.m, 576 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\im2patches.m, 313 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\isOctave.m, 108 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\makeLMfilters.m, 1895 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\myOctaveVersion.m, 169 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\normalize.m, 97 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\patches2im.m, 242 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\randcorr.m, 283 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\randp.m, 2083 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\rnd.m, 49 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\sigm.m, 48 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\sigmrnd.m, 126 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\softmax.m, 256 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\tanh_opt.m, 54 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\visualize.m, 1072 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\whiten.m, 183 , 2015-12-01
多任务深度学习改进版\rasmusbergpalm-DeepLearnToolbox-5df2801\rasmusbergpalm-DeepLearnToolbox-5df2801\util\zscore.m, 137 , 2015-12-01
多任务深度学习改进版\spilt_train_x.m, 377 , 2018-08-04
多任务深度学习改进版\tri_nnapplygrads.m, 2231 , 2018-07-27
多任务深度学习改进版\测试程序\ceshi_nnff.asv, 2894 , 2018-09-13
多任务深度学习改进版\测试程序\ceshi_nnff.m, 3404 , 2018-09-13
多任务深度学习改进版\测试程序\fenzushuju_andian.asv, 577 , 2018-09-12

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  • prepare themselves with a small C games, DOS interface, oh run. Float Float basi...
    自己用C编写的小游戏，DOS界面哦，可以运行。 浮点数基本运算 浮点数的基本运算主要有四则运算、符号处理、大小比较，以及浮点数分柝等。 包含头文件 "fn.hpp" #include "fn.hpp" 浮点数基本运算 浮点数的基本运算中有加、减、乘、除、取负、绝对值、相等比较等。 加减乘除 加、减、乘、除四个运算极为相似，都是需要两个参数，结果当然也是浮点数了。 例子： // 加 减 乘 除 btil::fn::plus::value // f1+f2 的结果 btil::fn::minus::value // f1-f2 的结果 btil::fn::multiplies::value // f1*f2 的结果 btil::fn::divides::value // f1/f2 的结果 plus::value::f_val // f1+f2 的结果的值 struct one { static const double f_val = 1.0 } // 两个浮点数 struct two { static const double f_val = 2.0 } minus::value::f_val == 0.5 取负 取负运算就是取一个浮点数的负数。-prepare themselves with a small C games, DOS interface, oh run. Float Float basic operations of the basic operations are four major operations, symbols, sizes, and float-watchman"s rattle so. The first document contains "fn.hpp"# include "fn.hpp" float float basic operations of the basic operations are, p
    2022-03-22 16:15:25下载
    积分：1
  • IMU_calculator_matlab
    IMU 计算器 能够计算IMU的三轴陀螺仪的飘移，三轴加速度计的偏移，俯仰、横滚、偏航角度，三轴位置信息。画出上七个数据的图像。(This function takes the IMU data as the input and returns the plot of x,y,z,theta,phi,psi,u,v,w. %The data is a 7 coloumn matrix with each row representing a reading of the %IMU. %the coloumn should contain the data in the following format: %col data %1 time %2 Ax %3 Ay %4 Az %5 p(angular rate along x) %6 q(angular rate along y) %7 r(angular rate along z) % Initial is a 3x3 matrix containing initial position and attitude % [phi theta psi % u v w % x y z])
    2020-07-24 14:48:43下载
    积分：1
  • used to achieve the Games Management System
    用c实现运动会管理系统-used to achieve the Games Management System
    2022-11-02 07:40:03下载
    积分：1
  • 该代码是基于Ｓ３Ｃ４４Ｂ０的键盘测试代码 该代码采用查询的方式来检测由外部产生的键盘信号．...
    该代码是基于Ｓ３Ｃ４４Ｂ０的键盘测试代码 该代码采用查询的方式来检测由外部产生的键盘信号．-The code is based on the keyboard S3C44B0 test code using the code the way to inquiries by external detection the keyboard signal.
    2022-01-30 16:52:15下载
    积分：1
  • underwaterCorrection
    说明：  基于暗通道先验的颜色校正算法进行仿真模拟，得到了较为自然准确的恢复图像。本文仿真的算法主要分为三个子模块：暗通道先验、折射率计算、颜色校正。暗通道先验提供了颜色校正需要的参数，颜色校正后的图片基本实现了颜色失真的校正。最后对处理得到的图片进行质量评价。实验结果表明，本文的算法能够基本实现水下图像的恢复，能够很好地校正失真的图像，改善颜色失真的情况。(The color correction algorithm based on the dark channel prior is simulated, and the more natural and accurate restoration image is obtained. In this paper, the simulation algorithm is divided into three sub modules: dark channel prior, refractive index calculation, color correction. The dark channel prior provides the parameters needed for color correction, and the color corrected image basically realizes the correction of color distortion. Finally, the quality of the processed image is evaluated. The experimental results show that the algorithm can basically achieve the restoration of underwater image, can correct the distorted image and improve the color distortion.)
    2020-05-31 15:51:06下载
    积分：1
  • graphical interface. The directory can be added for, locate, delete. There can b...
    图形界面。可以对通讯录进行添加、查找、删除。可以进行上翻、下翻和看首页、尾页等操作。没有任何BUG-graphical interface. The directory can be added for, locate, delete. There can be turned, and turned to see where Home, tail-page operational. No BUG
    2022-02-02 08:09:35下载
    积分：1
  • 一个非常好的开放
    一个很好的开源项目管理系统源代码，大家可以部署测试，看看效果如何。-A very good open-source project management system source code, we can deploy the test to see how.
    2023-06-18 03:50:04下载
    积分：1
  • ConjugateGradient
    共轭梯度法，主要用于数学中的迭代优化算法。同样可用于图像去噪(Iterative optimization)
    2018-05-23 19:57:52下载
    积分：1
  • Eviews事件研究法
    说明：  主要是采用eviews去进行事件研究法的相应计算(Eviews is mainly used to carry out the corresponding calculation of the event study method)
    2021-03-13 17:09:24下载
    积分：1
  • 我的作业,TC2.0下实现图形编辑，用堆栈实现的，还有递归算法，如果需要请留言。...
    我的作业,TC2.0下实现图形编辑，用堆栈实现的，还有递归算法，如果需要请留言。-my operation, under WITH TC 2.0 graphics editing, using the stack to achieve, recursive algorithm, please leave if necessary.
    2022-08-19 23:57:16下载
    积分：1
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