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Pytorch-Deep-Neural-Networks-master

于 2020-10-14 发布
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文件列表：

Pytorch-Deep-Neural-Networks-master, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\README.md, 6475 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\conv_module.py, 18008 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\epoch.py, 13793 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\func.py, 8011 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\layer.py, 11062 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\module.py, 13446 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\pre_module.py, 4064 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\core\run_n.py, 4202 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\processed, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\processed\test.pt, 7920462 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\processed\training.pt, 47520462 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\raw, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\raw\t10k-images-idx3-ubyte, 7840016 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\raw\t10k-labels-idx1-ubyte, 10008 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\raw\train-images-idx3-ubyte, 47040016 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\MNIST\raw\train-labels-idx1-ubyte, 60008 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\TE[fault].xlsx, 5797241 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\TE[var].xlsx, 3652841 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d00_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d01_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d02_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d03_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d04_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d05_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d06_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d07_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d08_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d09_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d10_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d11_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d12_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d13_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d14_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d15_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d16_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d17_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d18_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d19_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d20_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\test\d21_te.dat, 799680 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d00.dat, 416500 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d01.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d02.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d03.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d04.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d05.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d06.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d07.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d08.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d09.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d10.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d11.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d12.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d13.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d14.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d15.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d16.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d17.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d18.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d19.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d20.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\TE\train\d21.dat, 399840 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\dsae_dataset.py, 870 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\gene_dynamic_data.py, 10146 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\data\load.py, 3101 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\cross_entropy_loss.py, 2812 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\mnist_cls.py, 1887 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\simple_ae.py, 4379 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\te_cls.py, 1104 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\understand_gradient.py, 3670 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\example\vae_generate.py, 666 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\image, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\image\CD-K.jpg, 406898 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\image\EDBN.jpg, 747816 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\README.md, 3481 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\cnn.py, 1191 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\connect.py, 992 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\dae.py, 1878 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\dbn.py, 5336 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\dnn.py, 1063 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\dsae.py, 5043 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\mmdgm_vae.py, 2584 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\resnet.py, 5736 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\sae.py, 3829 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\svm.py, 5279 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\vae.py, 3080 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\vgg.py, 5061 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\model\vision.py, 670 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\visual, 0 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\visual\plot.py, 12886 , 2020-06-08
Pytorch-Deep-Neural-Networks-master\visual\visual_weight.py, 8599 , 2020-06-08

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  • 14596
    桌面快捷精灵源码，程序结合易语言扩展界面支持库调用API函数实现桌面快捷功能。(Desktop Shortcut Wizard source , combined with easy language program expanded interface support library call API functions to achieve the desktop shortcuts .)
    2016-08-27 14:27:32下载
    积分：1
  • 七参数坐标转换
    用布尔莎法实现了坐标系的七参数转换，大地测量学课程作业(The transformation of seven parameters of the coordinate system is realized by means of bursa method)
    2018-06-21 00:30:48下载
    积分：1
  • Prentice.Hall.PTR.C.plus.plus.GUI.Programming
    Qt 4 GUI 编程资料！ 资源格式：CHM 内容：英文(Qt 4 GUI programming information! Resources Format: CHM Content: English)
    2008-07-25 08:58:07下载
    积分：1
  • 4256074
    快捷一键通源码程序，结合易语言扩展界面支持库，调用API函数和易语言模块实现系统常用程序的快捷键启动功能。(Quick PTT source program, combined with easy language support library expansion interface , API function calls and easy language module function system commonly used shortcuts that start the program .)
    2015-10-24 10:27:43下载
    积分：1
  • api
    易语言API取设备信息源码例程程序结合易语言扩展界面支持库，调用API函数获取电脑设备信息。 (Easy language API to take the device information source routine procedures combined with easy language extensions interface support library, call the API function to obtain information on computer equipment. )
    2017-01-06 21:22:44下载
    积分：1
  • Num_clock
    VC6.0环境下实现的数字显示时间和日期的一个数字钟表，主要运用了C++的继承。(VC6.0 environment figures show the time and date of a digital watch, mainly use the C++ succession.)
    2013-03-10 02:44:39下载
    积分：1
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    百度采集源码例程程序结合易语言扩展界面支持库，实现百度信息的搜索采集。(Baidu acquisition routine program source expansion interface combined with easy language support library that implements the collection Baidu search information.)
    2016-01-05 14:58:45下载
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    一个很好的关于微软的mschart的例子，自己做的，希望可以给大家帮助(A very good on Microsoft)
    2008-08-21 13:54:10下载
    积分：1
  • tuxingxuanzhuan
    在单文档中画图，实现模拟同步卫星绕地球旋转，很不错的懂西哦(Paint in a single document, the realization of simulated synchronous satellites orbit the earth rotation, it is good to understand the West Oh)
    2008-05-11 02:50:57下载
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  • Matlab
    我完成了修图软件中的三个基本功能，人像磨皮、白平衡调节和皮肤美白。人像磨皮使用双边滤波器完成，效果良好但运行速度慢。白平衡调节使用灰度世界算法来编写程序，可以自动调节偏色照片的白平衡，运行效果良好。皮肤美白使用优化的白平衡算法来对皮肤进行提亮处理，运行效果良好。其中，磨皮力度和美白力度可以通过滑块调节，方便使用。我还为该程序设计了一个简单的界面，并切加入丰富的色彩来吸引用户使用。程序中处理的照片可以自行选择，处理后的照片也可以直接保存。(I finished retouching software three basic functions, Portrait dermabrasion, white balance adjustment, and skin whitening. Portrait microdermabrasion using bilateral filter completed with good results but runs slow. White balance adjustment to write programs using the gray world algorithm that automatically adjusts the color cast photo of white balance, running well. White skin whitening using the optimized algorithm to brighten skin treatment, running well. Among them, dermabrasion intensity and whitening efforts by the slider to adjust, easy to use. I also design for the program a simple interface and rich colors cut added to attract users. Photo processing programs can choose, after treatment photos can also be directly saved.)
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